Note: Descriptions are shown in the official language in which they were submitted.
WO 2022/197968
PCT/US2022/020815
METHODS OF CLASSIFYING AND TREATING PATIENTS
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims the benefit of U.S. Provisional App. No.
63/163,414, filed
March 19, 2021, and U.S. Provisional App. No. 63/306,054, filed February 2,
2022, each of which
is incorporated by reference herein in its entirety.
BACKGROUND
[0002] Autoimmune diseases such as rheumatoid arthritis (RA) affect millions
of patients, and
their treatments represent a significant component of overall healthcare
expenditure. Autoimmune
diseases can be divided into two groups ¨ organ-specific and systemic
autoimmunity. Rheumatoid
diseases including RA belong to the systemic autoimmune diseases which
primarily manifests in
synovial joints and eventually causes irreversible destruction of tendons,
cartilage, and bone.
Although there is no current cure for RA, significant improvements have been
made to manage the
treatment of these patients mainly through the development of anti-TNF (tumor
necrosis factor)
agents, which act to neutralize the pro-inflammatory signaling of this
cytokine. Such biologic
therapies (e.g., Humira , Enbrel , Remicade , Simponi , and Cimzia ) have
significantly
improved the treatment outcome of some RA patients.
[0003] Roughly 34% of RA patients (a low percentage) show a clinical response
to anti-TNF
therapies, achieving low disease activity (LDA) and sometimes achieving
remission. Disease
progression in these so called "responder" patients, is likely a result of
inappropriate 'INF-driven
pro-inflammatory responses. For patients failing to respond to anti-TNFs,
there are alternative
approved therapies available such as anti-CD20, co-stimulation blockade, JAK
and anti- IL6
therapy. However, patients may be switched onto such alternative therapy after
first cycling
through different anti-TNFs, which can take over a year, while symptoms
persist and the disease
progresses further, making it more difficult to reach treatment targets. In
addition to the problem
of delay in treatment, risks of serious infection and malignancy associated
with anti-TNF therapy
are so significant that product approvals may require so-called "black box
warnings' be included
on the label. Other potential side effects of such therapy include, for
example, congestive heart
failure, demyelinating disease, and other systemic side effects.
SUMMARY
[0004] A significant problem with anti-TNF therapies is that response rates
are inconsistent.
Regardless of the measure used to define response, a subset of RA patients may
an adequate
response to TNFi treatment: 50-70% achieve ACR20, 30-40% achieve ACR50, 15-25%
achieve
ACR70, and 10-25% achieve remission. Many studies have attempted to identify
biomarkers and
1
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
develop models to predict response to TNFi therapy before the initiation of
treatment. Failure to
validate and reproduce the performance of these predictive biomarkers in new
patient populations
and clinical trials was a typical outcome Differing characteristics between
patient populations,
laboratory methods and procedures in generating molecular data and other
biases inherent to
single-cohort retrospective blood studies have hindered precision medicine
progress not only in
rheumatology but in other medical specialties as well.
[0005] In some aspects, the methods and compositions described herein permit
care providers to
distinguish between or among categories of subjects ¨ e.g., subjects likely to
benefit from a
particular therapy (e.g., anti-TNF therapy) from those who are not, those who
are more likely to
achieve or suffer a particular outcome or side effect, etc. In some
embodiments, such provided
technologies thus reduce risks to patients, increase timing and quality of
care for non-responder
patient populations, increase efficiency of drug development, or avoid costs
associated with
administering ineffective therapy to non-responder patients or with treating
side effects such
patients experience upon receiving the relevant therapy (e.g., anti-TNF
therapy).
[0006] In some aspects, the present disclosure provides methods of treating
subjects with particular
therapy (e g , anti-TNF therapy), in some embodiments, a method comprising:
administering a
therapy to subjects who have been determined to be responsive via a classifier
established to
distinguish between subjects expected to be responsive vs non-responsive to
the therapy. In some
embodiments, a classifier identifies 60% or greater of non-responders within a
treatment-naive
cohort. In some embodiments, a classifier identifies 60% or greater of non-
responders within a
treatment-naive cohort of at least 350 subjects.
[0007] A classifier can be a molecular signature response classifier derived
from differences in
gene expression between known responders and non-responders within a cohort.
In some
embodiments, one or more genes having statistically significant differences in
expression between
responders and non-responders are included as part of a molecular signature
response classifier.
In some embodiments, proteins associated with genes having statistically
significant differences in
expression between responders and non-responders are mapped onto a human
interactome to
validate relationship between selected genes and disease biology.
[0008] Provided classifiers further incorporate additional elements, e.g.,
clinical characteristics or
single nucleotide polymorphisms useful for classifying response or non-
response in a given patient.
[0009] In some embodiments, the present disclosure provides methods of
treating subjects
suffering from an autoimmune disorder, in some embodiments, a method
comprising:
administering an anti-TNF therapy to subjects who have been determined to be
responsive via a
classifier established to distinguish between responsive and non-responsive
prior subjects in a
cohort who have received the anti-TNF therapy; wherein a classifier is
developed by assessing:
2
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
one or more genes whose expression levels significantly correlate (e.g., in a
linear or non-linear
manner) to clinical responsiveness or non-responsiveness; at least one of:
presence of one or more
single nucleotide polymorphisms (SNPs) in an expressed sequence of the one or
more genes; or at
least one clinical characteristic of the responsive and non-responsive prior
subjects; and wherein
the classifier is validated by an independent cohort than the cohort who have
received the anti-TNF
therapy.
[0010] In some embodiments, the subject has been previously administered the
anti-TNF therapy.
In some embodiments, the subject has been administered the anti -TNF therapy
at least one, at least
two, at least three, at least four, at least five, or at least six months
prior to said administering.
[0011] In some embodiments, a classifier identifies 60% or greater of non-
responders within a
treatment-naive cohort. In some embodiments, a classifier identifies 60% or
greater of non-
responders within a treatment-naive cohort of at least 350 subjects.
[0012] In some embodiments, one or more genes are characterized by their
topological properties
when mapped on a human interactome map. In some embodiments, SNPs are
identified in
reference to a human genome. In some embodiments, a classifier is developed by
assessing each
of: the one or more genes whose expression levels significantly correlate
(e.g., in a linear or non-
linear manner) to clinical responsiveness or non-responsiveness; presence of
the one or more SNPs;
and the at least one clinical characteristic.
[0013] In some embodiments, one or more genes comprise: ALPL, ATRAID, BCL6,
CDK11A,
CFLAR, CO1VIMD5, GOLGA1, 1L1B, IMPDH2, JAK3, KLHDC3, LEVIK2, NOD2, NOTCH1,
SPINT2, SPON2, STOML2, TRIM25, or ZFP36.
[0014] In some embodiments, one or more genes comprise: ALPL, BCL6, CDK11A,
CFLAR,
IL1B, JAK3, LIMK2, NOD2, NOTCH1, TRI1\425, or ZFP36.
[0015] In some embodiments, at least one clinical characteristic is selected
from: body-mass index
(BMI), gender, age, race, previous therapy treatment, disease duration, C-
reactive protein level,
presence of anti-cyclic citrullinated peptide, presence of rheumatoid factor,
patient global
assessment, treatment response rate (e.g., ACR20, ACR50, ACR70), and
combinations thereof.
[0016] In some embodiments, anti-TNF therapy comprises administration of
infliximab,
adalimumab, etanercept, cirtolizumab pegol, golilumab, or biosimilars thereof.
In some
embodiments, a disease, disorder, or condition is selected from rheumatoid
arthritis, psoriatic
arthritis, ankylosing spondylitis, Crohn's disease, ulcerative colitis,
chronic psoriasis, hidradenitis
suppurativa, multiple sclerosis, and juvenile idiopathic arthritis. In some
embodiments, a classifier
is established using microarray analysis derived from responsive and non-
responsive prior
subjects. In some embodiments, a classifier is validated using RNAseq data
derived from the
independent cohort. In some embodiments, the SNPs are selected from Table 3.
3
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0017] In some embodiments, the present disclosure provides a system for
classifying a subject
suffering from an autoimmune disease as likely responsive or likely non-
responsive to an anti-TNF
therapy prior to any administration of said anti-TNF therapy to said subject,
the system comprising:
a processor; and a memory having instructions thereon, the instructions, when
executed by the
processor, causing the processor to: (a) receive a set of data, said set of
data comprising an
expression level for the subject of each of one or more genes comprising:
ALPL, ATRAID, BCL6,
CDK11A, CFLAR, COMMD5, GOLGA1, IL1B, IMPDH2, JAK3, KLHDC3, LIM1K2, NOD2,
NOTCH], SPINT2, SPON2, STOML2, TRIM25, or ZFP36.
[0018] Additional aspects and advantages of the present disclosure will become
readily apparent
to those skilled in this art from the following detailed description, wherein
only illustrative
embodiments of the present disclosure are shown and described. As will be
realized, the present
disclosure is capable of other and different embodiments, and its several
details are capable of
modifications in various obvious respects, all without departing from the
disclosure. Accordingly,
the drawings and description are to be regarded as illustrative in nature, and
not as restrictive.
INCORPORATION BY REFERENCE
[0019] All publications, patents, and patent applications mentioned in this
specification are herein
incorporated by reference to the same extent as if each individual
publication, patent, or patent
application was specifically and individually indicated to be incorporated by
reference. To the
extent publications and patents or patent applications incorporated by
reference contradict the
disclosure contained in the specification, the specification is intended to
supersede or take
precedence over any such contradictory material.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] FIG. 1 is an example embodiment of proteins encoded by transcripts
predictive of response
were mapped onto the human interactome. Proteins are shown in circles and pair-
wise physical
protein-protein interactions are indicated as lines. The RA disease module is
composed of seed
genes (red) and DIAMOnD genes (teal). The proteins encoded by eleven
transcript features
(squares) were significantly connected to the RA disease module (p-value
<0.05).
[0021] FIG. 2A, FIG. 2B, FIG. 2C, AND FIG. 2D illustrate cross-validation of
the molecular
signature response classifier ("MSRC") among 245 patients from the Corrona
CERTAIN study.
FIG. 2A illustrates a receiver operator curve for stratification of patients
based on CDAI, DAS28-
CRP, ACR70 and ACR50 clinical outcomes. FIG. 2B illustrates a comparison of
model scores for
patients with or without a molecular signature of non-response. Boxes and
intersecting line depict
interquartile range and median, respectively. Bisecting colored lines indicate
change in mean.
Ratio of the percentage of patients with or without a molecular signature of
non-response in CDAI
4
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
remission, LDA, moderate or high disease activity illustrated in FIG 2C (CDAI)
and FIG. 2D
(DAS28-CRP). Bars indicate a greater proportion of patients with a molecular
signature when
above 1.0 or without a molecular signature when below 1Ø NA: not applicable,
no patients in
category.
[0022] FIG. 3A, FIG. 3B, FIG. 3C, FIG. 3D, FIG. 3E, and FIG. 3F illustrate
validation of the
MSRC to identify patients naive to targeted therapies who are unlikely to
respond to TNFi therapy.
Receiver operator curve for stratification of patients based on CDAI, DAS28-
CRP, ACR70 and
ACR50 clinical outcomes at 3 months (FIG. 3A) and 6 months (FIG. 3B)
Comparison of model
scores at 3 months (FIG. 3C) and 6 months (FIG. 3D) for patients with or
without a molecular
signature of non-response. Boxes and intersecting line depict interquartile
range and median,
respectively. Bisecting colored lines indicate change in mean Ratio of the
percentage of patients
with or without a molecular signature of non-response in CDAI remission, LDA,
moderate or high
disease activity per CDAI (FIG. 3E) and DAS28-CRP (FIG. 3F). Bars indicate a
greater proportion
of patients with a molecular signature when above 1.0 or without a molecular
signature when below
1Ø NA: not applicable, no patients in category; NS: not significant.
[0023] FIG. 4A and FIG. 4B illustrate validation of the MSRC to identify TNFi-
exposed patients
who are unlikely to respond to TNFi therapy. FIG. 4A illustrates receiver
operator curve for
stratification of patients who are receiving a TNFi therapy based on
achievement of CDAI
remission or DAS28-CRP remission 3 months after test results. FIG. 4B
illustrates comparison of
model scores for patients with or without a molecular signature of non-
response. Boxes and
intersecting line depict interquartile range and median, respectively.
Bisecting colored lines
indicate change in mean.
[0024] FIG. 5 illustrates biology of inadequate response to TNFi therapies.
The MSRC includes
transcript that encode proteins involved in many aspects of RA
pathophysiology: innate immune
response, cytokine biosynthesis, T and B cell homeostasis, bone homeostasis,
the unfolded protein
response, autophagy, apoptosis and pro-inflammatory signaling.
[0025] FIG. 6 is a flow chart of study design. A subset of 345 patients from
the CERTAIN study
were analyzed: 100 for identification of transcript biomarkers of non-response
to TNFi therapies
and 245 for cross-validation. 273 patients enrolled in the NETWORK-004
prospective
observational study; 244 passed initial enrollment screening, 194 completed
the 3-month follow-
up visit and 168 completed the 6-month follow-up visit. 87% (146/168) of
patients who completed
the study had complete molecular and clinical data required to perform
validation analyses.
[0026] FIG. 7 is a Venn diagram showing breakdown of patients who provided
samples at 3-
months, 6-months, and at both 3-months and 6-months exposure to TNF therapy.
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0027] FIG. 8A, FIG. 8B, FIG. 8C, and FIG. 813 provide ROC curves showing
PrismRA
performance among patient samples collected 3-month and 6-month after TNF
initiation. FIG. 8A
shows 3-month samples using +3-month outcome. FIG 8B shows 3-month samples
using +6-
month outcome. FIG 8C shows 6-month samples using +3-month outcome. FIG 8D
shows 6-
month samples using +6-month outcome.
[0028] FIG. 9A and FIG. 9B provide ROC curves showing model performance among
122
patients that provide both 3-month and 6-month samples. FIG 9A shows 3-month
samples using
+6-month endpoints. FIG. 9B shows 6-month samples using +3-month endpoints.
[0029] FIG. 10 provides an example computer system for executing methods
according to some
aspects or embodiments of the disclosure.
DETAILED DESCRIPTION
[0030] A significant problem with various therapies (e.g., anti-TNF) therapies
is that response rates
are inconsistent. Indeed, recent international conferences designed to bring
together leading
scientists and clinicians in the fields of immunology and rheumatology to
identify unmet needs in
these fields almost universally identify uncertainty in response rates as an
ongoing challenge. For
example, the 19th annual International Targeted Therapies meeting, which held
break-out sessions
relating to challenges in treatment of a variety of diseases, including
rheumatoid arthritis, psoriatic
arthritis, axial spondyloarthritis, systemic lupus erythematous, and
connective tissue diseases (e.g.
Sjogren' s syndrome, systemic sclerosis, vasculitis including Bechet's and
IgG4 related disease),
identified certain issues common to all of these diseases, specifically, "the
need for better
understanding the heterogeneity within each disease . . . so that predictive
tools for therapeutic
responses can be developed. See Winthrop, et al., "The unmet need in
rheumatology: Reports from
the targeted therapies meeting 2017," Cl/n. Immunol. pii: S1521-6616(17)30543-
0, Aug. 12,2017,
which is incorporated herein by reference for all purposes. Similarly,
extensive literature relating
to treatment of Crohn's Disease with anti-TNF therapy consistently bemoans
erratic response rates
and inability to predict which patients will benefit. See, e.g., MT. Abreu,
"Anti-TNF Failures in
Crohn's Disease," Gastroenterol Hepatol (NY), 7(1):37-39 (Jan. 2011); see also
Ding et al.,
"Systematic review: predicting and optimising response to anti-TNF therapy in
Crohn's disease ¨
algorithm for practical management," Aliment Pharmacol. Ther., 43(1):30-51
(Jan. 2016), which
is incorporated herein by reference for all purposes, (reporting that
"[p]rimary nonresponse to anti-
TNF treatment affects 13-40% of patients.").
[0031] Thus, a significant number of patients to whom anti-TNF therapy is
currently being
administered do not benefit from the treatment, and can even be harmed. Risks
of serious infection
and malignancy associated with anti-TNF therapy are so significant that
product approvals may
require so-called "black box warnings" be included on the label. Other
potential side effects of
6
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
such therapy include, for example, congestive heart failure, demyelinating
disease, and other
systemic side effects. Furthermore, given that several weeks to months of
treatment are required
before a patient is identified as not responding to anti-TNF therapy (e.g., is
a non-responder to anti-
TNF therapy), proper treatment of such patients can be significantly delayed
as a result of the
current inability to identify responder vs non-responder subjects. See, e.g.,
Roda et aL, "Loss of
Response to Anti-TNFs: Definition, Epidemiology, and Management," Clin. Trani.
Gastroenterol.,7 (1):e135 (Jan. 2016), which is incorporated herein by
reference for all purposes,
(citing Hanauer et al.," ACCENT I Study group. Maintenance Infliximab for
Crohn's disease: the
ACCENT I randomized trial," Lancet 59:1541-1549 (2002); Sands et al.,
"Infliximab maintenance
therapy for fistulizing Crohn' s disease," N. Engl. J. Med. 350:876-885
(20004)).
[0032] Accordingly, in some embodiments, the present disclosure provides
methods of treating
subjects with anti-TNF therapy, the method comprising: administering the anti-
TNF therapy to
subjects who have been determined to be responsive via a classifier
established to distinguish
between responsive and non-responsive prior subjects who have received the
anti-TNF therapy
wherein the classifier that is developed by assessing: one or more genes whose
expression levels
significantly correlate (e.g, in a linear or non-linear mariner) to clinical
responsiveness or non-
responsiveness; and at least one of: presence of one or more single nucleotide
polymorphisms
(SNPs) in an expressed sequence of the one or more genes; or at least one
clinical characteristic of
the responsive and non-responsive prior subjects.
[0033] Presented herein are systems and methods for the automated prediction
of subject response
to anti -TNF therapies. Also presented herein are modular systems for
automated interpretation of
genomic or multi-omic data.
[0034] As used herein, the term "administration" generally refers to the
administration of a
composition to a subject or system, for example to achieve delivery of an
agent that is, or is
included in or otherwise delivered by, the composition
[0035] As used herein, the term "agent" generally refers to an entity (e.g.,
for example, a lipid,
metal, nucleic acid, polypeptide, polysaccharide, small molecule, etc., or
complex, combination,
mixture or system [e.g., cell, tissue, organism] thereof), or phenomenon
(e.g., heat, electric current
or field, magnetic force or field, etc.).
[0036] As used herein, the term "amino acid" generally refers to any compound
or substance that
can he incorporated into a polypepti de chain, e.g., through formation of one
or more peptide bonds.
In some embodiments, an amino acid has the general structure H2N¨C(H)(R)¨COOH.
In some
embodiments, an amino acid is a naturally-occurring amino acid. In some
embodiments, an amino
acid is a non-natural amino acid; in some embodiments, an amino acid is a D-
amino acid; in some
embodiments, an amino acid is an L-amino acid. As used herein, the term
"standard amino acid"
7
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
refers to any of the twenty L-amino acids commonly found in naturally
occurring peptides.
"Nonstandard amino acid" refers to any amino acid, other than the standard
amino acids, regardless
of whether it is or can be found in a natural source. In some embodiments, an
amino acid, including
a carboxy- or amino-terminal amino acid in a polypeptide, can contain a
structural modification as
compared to the general structure above. For example, in some embodiments, an
amino acid may
be modified by methylation, amidation, acetylation, pegylation, glycosylation,
phosphorylation, or
substitution (e.g., of the amino group, the carboxylic acid group, one or more
protons, or the
hydroxyl group) as compared to the general structure In some embodiments, such
modification
may, for example, alter the stability or the circulating half-life of a
polypeptide containing the
modified amino acid as compared to one containing an otherwise identical
unmodified amino acid.
In some embodiments, such modification does not significantly alter a relevant
activity of a
polypeptide containing the modified amino acid, as compared to one containing
an otherwise
identical unmodified amino acid. As will be clear from context, in some
embodiments, the term
"amino acid" may be used to refer to a free amino acid; in some embodiments it
may be used to
refer to an amino acid residue of a polypeptide, e.g., an amino acid residue
within a polypeptide.
As used herein, the term "analog" generally refers to a substance that shares
one or more particular
structural features, elements, components, or moieties with a reference
substance. Generally, an
"analog" shows significant structural similarity with the reference substance,
for example sharing
a core or consensus structure, but also differs in certain discrete ways. In
some embodiments, an
analog is a substance that can be generated from the reference substance,
e.g., by chemical
manipulation of the reference substance. In some embodiments, an analog is a
substance that can
be generated through performance of a synthetic process substantially similar
to (e.g., sharing a
plurality of steps with) one that generates the reference substance. In some
embodiments, an
analog is or can be generated through performance of a synthetic process
different from that used
to generate the reference substance.
[0037] As used herein, the term "antagonist" generally may refer to an agent,
or condition whose
presence, level, degree, type, or form is associated with a decreased level or
activity of a target.
An antagonist may include an agent of any chemical class including, for
example, small molecules,
polypeptides, nucleic acids, carbohydrates, lipids, metals, or any other
entity that shows the
relevant inhibitory activity. In some embodiments, an antagonist may be a
"direct antagonist" in
that it binds directly to its target; in some embodiments, an antagonist may
be an "indirect
antagonist" in that it exerts its influence by mechanisms other than binding
directly to its target;
e.g., by interacting with a regulator of the target, so that the level or
activity of the target is altered).
In some embodiments, an "antagonist" may be referred to as an "inhibitor".
8
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0038] As used herein, the term "antibody" generally refers to a polypeptide
that includes
canonical immunoglobulin sequence elements sufficient to confer specific
binding to a particular
target antigen. Intact antibodies as produced in nature are, in some
embodiments, approximately
150 kD tetrameric agents comprised of two identical heavy chain polypeptides
(about 50 kD each)
and two identical light chain polypeptides (about 25 kD each) that associate
with each other into
what is commonly referred to as a "Y-shaped" structure. In some embodiments,
each heavy chain
is comprised of at least four domains (each about 110 amino acids long) - an
amino-terminal
variable (VH) domain (located at the tips of the Y structure), followed by
three constant domains:
CH1, CH2, and the carboxy-terminal CH3 (located at the base of the Y's stem).
In some
embodiments, a short region, referred to as the "switch", connects the heavy
chain variable and
constant regions. The "hinge" connects CH2 and CH3 domains to the rest of the
antibody. In
some embodiments, two disulfide bonds in this hinge region connect the two
heavy chain
polypeptides to one another in an intact antibody. In some embodiments, each
light chain is
comprised of two domains - an amino-terminal variable (VL) domain, followed by
a carboxy-
terminal constant (CL) domain, separated from one another by another "switch".
In some
embodiments, intact antibody tetramers are comprised of two heavy chain-light
chain dim ers in
which the heavy and light chains are linked to one another by a single
disulfide bond; two other
disulfide bonds connect the heavy chain hinge regions to one another, so that
the dimers are
connected to one another and the tetramer is formed. In some embodiments,
naturally-produced
antibodies are also glycosylated, such as on the CH2 domain. Each domain in a
natural antibody
has, in some embodiments, a structure characterized by an "immunoglobulin
fold" formed from
two beta sheets (e.g., 3-, 4-, or 5-stranded sheets) packed against each other
in a compressed
antiparallel beta barrel. Each variable domain contains, in some embodiments,
three hypervariable
loops referred to as "complement determining regions- (CDR1, CDR2, and CDR3)
and four
somewhat invariant "framework" regions (FRI, FR2, FR3, and FR4). In some
embodiments, when
natural antibodies fold, the FR regions form the beta sheets that provide the
structural framework
for the domains, and the CDR loop regions from both the heavy and light chains
are brought
together in three-dimensional space so that they create a single hypervariable
antigen binding site
located at the tip of the Y structure. In some embodiments, the Fe region of
naturally-occurring
antibodies binds to elements of the complement system, and also to receptors
on effector cells,
including for example effector cells that mediate cytotoxicity. In some
embodiments, affinity or
other binding attributes of Fe regions for Fc receptors can be modulated
through glycosylation or
other modification. In some embodiments, antibodies produced or utilized in
accordance with the
present disclosure include glycosylated Fe domains, including Fe domains with
modified or
engineered such glycosylation. For purposes of the present disclosure, in
certain embodiments,
9
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
any polypeptide or complex of polypeptides that includes sufficient
immunoglobulin domain
sequences as found in natural antibodies can be referred to or used as an
"antibody", whether such
polypeptide is naturally produced (e.g., generated by an organism reacting to
an antigen), or
produced by recombinant engineering, chemical synthesis, or other artificial
system or
methodology. In some embodiments, an antibody is polyclonal; in some
embodiments, an
antibody is monoclonal. In some embodiments, an antibody has constant region
sequences that
are characteristic of mouse, rabbit, primate, or human antibodies. In some
embodiments, antibody
sequence elements are humanized, primatized, chimeric, etc.. Moreover, the
term "antibody" as
used herein, can refer in appropriate embodiments (unless otherwise stated or
clear from context)
to any constructs or formats for utilizing antibody structural and functional
features in alternative
presentation. For example, in some embodimentsõ an antibody utilized in
accordance with the
present disclosure is in a format selected from, but not limited to, intact
IgA, IgG, IgE or IgM
antibodies; bi- or multi-specific antibodies (e.g., Zybodiest, etc); antibody
fragments such as Fab
fragments, Fab' fragments, F(ab')2 fragments, Fd' fragments, Fd fragments, and
isolated CDRs or
sets thereof; single chain Fvs; polypeptide-Fc fusions; single domain
antibodies (e.g., shark single
domain antibodies such as IgNAR or fragments thereof), camelcid antibodies,
masked antibodies
(e.g., Probodiesc), Small Modular ImmunoPharmaceuticals ("SMIPsTm"); single
chain or Tandem
diabodies (TandAb"); VI-11-1s; Anticalins ; Nanobodies minibodies; BiTE s;
ankyrin repeat
proteins or DARPINs ; Avimers ; DARTs; TCR-like antibodies;, Adnectins ;
Affilins; Trans-
bodies ; Affibodies ; TrimerX ; MicroProteins; Fynomers , Centyrins ; and
KALBITOR . In
some embodiments, an antibody may lack a covalent modification (e g ,
attachment of a glycan)
that it can have if produced naturally. In some embodiments, an antibody may
contain a covalent
modification (e.g., attachment of a glycan, a payload [e.g., a detectable
moiety, a therapeutic
moiety, a catalytic moiety, etc], or other pendant group [e.g., poly-ethylene
glycol, etc.]).
[0039] Two events or entities are "associated" generally with one another, as
that term is used
herein, if the presence, level, degree, type or form of one is correlated with
that of the other. For
example, a particular entity (e.g., polypeptide, genetic signature,
metabolite, microbe, etc) is
considered to be associated with a particular disease, disorder, or condition,
if its presence, level
or form correlates with incidence of or susceptibility to the disease,
disorder, or condition (e.g.,
across a relevant population). In some embodiments, two or more entities are
physically
"associated" with one another if they interact, directly or indirectly, so
that they are or remain in
physical proximity with one another. In some embodiments, two or more entities
that are
physically associated with one another are covalently linked to one another;
in some embodiments,
two or more entities that are physically associated with one another are not
covalently linked to
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
one another but are non-covalently associated, for example by mechanisms of
hydrogen bonds,
van der Waals interaction, hydrophobic interactions, magnetism, and
combinations thereof.
[0040] As used herein, the term "biological sample" generally refers to a
sample obtained or
derived from a biological source (e.g., a tissue or organism or cell culture)
of interest, as described
herein. In some embodiments, a source of interest comprises an organism, such
as an animal or
human. In some embodiments, a biological sample is or comprises biological
tissue or fluid. In
some embodiments, a biological sample may be or comprise bone marrow; blood;
blood cells;
ascites; tissue or fine needle biopsy samples; cell-containing body fluids;
free floating nucleic
acids; sputum; saliva; urine; cerebrospinal fluid, peritoneal fluid; pleural
fluid; feces; lymph;
gynecological fluids; skin swabs; vaginal swabs; oral swabs; nasal swabs;
washings or lavages
such as a ductal lavages or broncheoalveolar lavages; aspirates; scrapings;
bone marrow
specimens, tissue biopsy specimens; surgical specimens; feces, other body
fluids, secretions, or
excretions; or cells therefrom, etc. In some embodiments, a biological sample
is or comprises cells
obtained from an individual. In some embodiments, obtained cells are or
include cells from an
individual from whom the sample is obtained. In some embodiments, a sample is
a -primary
sample" obtained directly from a source of interest by any appropriate method
For example, in
some embodiments, a primary biological sample is obtained by methods selected
from the group
consisting of biopsy (e.g., fine needle aspiration or tissue biopsy), surgery,
collection of body fluid
(e.g, blood, lymph, feces etc.), etc. In some embodiments, as will be clear
from context, the term
-sample" refers to a preparation that is obtained by processing (e.g., by
removing one or more
components of or by adding one or more agents to) a primary sample For
example, filtering using
a semi-permeable membrane Such a "processed sample" may comprise, for example
nucleic acids
or proteins extracted from a sample or obtained by subjecting a primary sample
to techniques such
as amplification or reverse transcription of mRNA, isolation or purification
of certain components,
etc.
[0041] As used herein, the term "combination therapy" generally refers to a
clinical intervention
in which a subject is simultaneously exposed to two or more therapeutic
regimens (e.g. two or
more therapeutic agents). In some embodiments, the two or more therapeutic
regimens may be
administered simultaneously. In some embodiments, the two or more therapeutic
regimens may
be administered sequentially (e.g., a first regimen administered prior to
administration of any doses
of a second regimen) In some embodiments, the two or more therapeutic regimens
are
administered in overlapping dosing regimens. In some embodiments,
administration of
combination therapy may involve administration of one or more therapeutic
agents or modalities
to a subject receiving the other agent(s) or modality. In some embodiments,
combination therapy
does not necessarily require that individual agents be administered together
in a single composition
11
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
(or even necessarily at the same time). In some embodiments, two or more
therapeutic agents or
modalities of a combination therapy are administered to a subject separately,
e.g., in separate
compositions, via separate administration routes (e.g., one agent orally and
another agent
intravenously), or at different time points. In some embodiments, two or more
therapeutic agents
may be administered together in a combination composition, or even in a
combination compound
(e.g., as part of a single chemical complex or covalent entity), via the same
administration route,
or at the same time.
[0042] As used herein, the term "comparable" generally refers to two or more
agents, entities,
situations, sets of conditions, etc., that may not be identical to one another
but that are sufficiently
similar to permit comparison there between so that conclusions may reasonably
be drawn based on
differences or similarities observed. In some embodiments, comparable sets of
conditions,
circumstances, individuals, or populations are characterized by a plurality of
substantially identical
features and one or a small number of varied features. It may be understood,
in context, what
degree of identity is required in any given circumstance for two or more such
agents, entities,
situations, sets of conditions, etc. to be considered comparable. For example,
sets of
circumstances, individuals, or populations may be comparable to one another
when characterized
by a sufficient number and type of substantially identical features to warrant
a reasonable
conclusion that differences in results obtained or phenomena observed under or
with different sets
of circumstances, individuals, or populations are caused by or indicative of
the variation in those
features that are varied.
[0043] As used herein, the phrase "corresponding to" generally refers to a
relationship between
two entities, events, or phenomena that share sufficient features to be
reasonably comparable such
that "corresponding" attributes are apparent. For example, in some
embodiments, the term may be
used in reference to a compound or composition, to designate the position or
identity of a structural
element in the compound or composition through comparison with an appropriate
reference
compound or composition. For example, in some embodiments, a monomeric residue
in a polymer
(e.g., an amino acid residue in a polypeptide or a nucleic acid residue in a
polynucleotide) may be
identified as "corresponding to" a residue in an appropriate reference
polymer. For example, those
of ordinary skill will appreciate that, for purposes of simplicity, residues
in a polypeptide are often
designated using a canonical numbering system based on a reference related
polypeptide, so that
an amino acid "corresponding to" a residue at position 190, for example, may
not actually be the
190t11 amino acid in a particular amino acid chain but rather corresponds to
the residue found at 190
in the reference polypeptide; various approaches may be used to identify
"corresponding" amino
acids For example, there are various sequence alignment strategies, including
software programs
such as, for example, BLAST, CS-BLAST, CUSASW++, DIAMOND, FASTA,
12
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
GGSEARCH/GLSEARCH, Genoogle, EIMMER, RHpred/HHsearch, IDF, Infernal, KLAST,
USEARCH, parasail, PSI-BLAST, PSI-Search, ScalaBLAST, Sequilab, SAM, S SEARCH,
SWAPHI, SWAPHI-LS, SWI1VIM, or SWIPE that can be utilized, for example, to
identify
"corresponding" residues in polypeptides or nucleic acids in accordance with
the present
disclosure.
[0044] As used herein, the term "dosing regimen" generally refers to a set of
unit doses (e.g., more
than one) that are administered individually to a subject, e.g., separated by
periods of time. In
some embodiments, a given therapeutic agent has a recommended dosing regimen,
which may
involve one or more doses. In some embodiments, a dosing regimen comprises a
plurality of doses
each of which is separated in time from other doses. In some embodiments,
individual doses are
separated from one another by a time period of the same length; in some
embodiments, a dosing
regimen comprises a plurality of doses and at least two different time periods
separating individual
doses. In some embodiments, all doses within a dosing regimen are of the same
unit dose amount.
In some embodiments, different doses within a dosing regimen are of different
amounts. In some
embodiments, a dosing regimen comprises a first dose in a first dose amount,
followed by one or
more additional doses in a second dose amount different from the first dose
amount In some
embodiments, a dosing regimen comprises a first dose in a first dose amount,
followed by one or
more additional doses in a second dose amount same as the first dose amount.
In some
embodiments, a dosing regimen is correlated with a desired or beneficial
outcome when
administered across a relevant population (e.g., is a therapeutic dosing
regimen).
[0045] As used herein, the terms "improved," "increased," or "reduced," or
grammatically
comparable comparative terms thereof, generally indicate values that are
relative to a comparable
reference measurement. For example, in some embodiments, an assessed value
achieved with an
agent of interest may be "improved" relative to that obtained with a
comparable reference agent.
Alternatively or additionally, in some embodiments, an assessed value achieved
in a subject or
system of interest may be "improved" relative to that obtained in the same
subject or system under
different conditions (e.g., prior to or after an event such as administration
of an agent of interest),
or in a different, comparable subject (e.g., in a comparable subject or system
that differs from the
subject or system of interest in presence of one or more indicators of a
particular disease, disorder
or condition of interest, or in prior exposure to a condition or agent, etc.).
[0046] As used herein, the term "pharmaceutical composition" generally refers
to an active agent,
formulated together with one or more pharmaceutically acceptable carriers. In
some embodiments,
the active agent is present in unit dose amounts appropriate for
administration in a therapeutic
regimen to a relevant subject (e g , in amounts that have been demonstrated to
show a statistically
significant probability of achieving a predetermined therapeutic effect when
administered), or in a
13
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
different, comparable subject (e.g., in a comparable subject or system that
differs from the subject
or system of interest in presence of one or more indicators of a particular
disease, disorder or
condition of interest, or in prior exposure to a condition or agent, etc.). In
some embodiments,
comparative terms refer to statistically relevant differences (e.g., that are
of a prevalence or
magnitude sufficient to achieve statistical relevance).
[0047] As used herein, the phrase "pharmaceutically acceptable" generally
refers to those
compounds, materials, compositions, or dosage forms which are, within the
scope of sound medical
judgment, suitable for use in contact with the tissues of human beings and
animals with out
excessive toxicity, irritation, allergic response, or other problem or
complication, commensurate
with a reasonable benefit/risk ratio.
[0048] As used herein, the term "reference" generally describes a standard or
control relative to
which a comparison is performed. For example, in some embodiments, an agent,
animal,
individual, population, sample, sequence or value of interest is compared with
a reference or
control agent, animal, individual, population, sample, sequence or value. In
some embodiments, a
reference or control is tested or determined substantially simultaneously with
the testing or
determination of interest In some embodiments, a reference or control is a
historical reference or
control, optionally embodied in a tangible medium. In some embodiments, a
reference or control
is determined or characterized under comparable conditions or circumstances to
those under
assessment. It may be determined when sufficient similarities are present to
justify reliance on or
comparison to a particular possible reference or control.
[0049] As used herein, the term "therapeutically effective amount" generally
refers to an amount
of a substance (e.g., a therapeutic agent, composition, or formulation) that
elicits an intended
biological response when administered as part of a therapeutic regimen. In
some embodiments, a
therapeutically effective amount of a substance is an amount that is
sufficient, when administered
to a subject suffering from or susceptible to a disease, disorder, or
condition, to treat, diagnose,
prevent, or delay the onset of the disease, disorder, or condition. As will be
appreciated by those
of ordinary skill in this art, the effective amount of a substance may vary
depending on such factors
as the intended biological endpoint, the substance to be delivered, the target
cell or tissue, etc. For
example, the effective amount of compound in a formulation to treat a disease,
disorder, or
condition is the amount that alleviates, ameliorates, relieves, inhibits,
prevents, delays onset of,
reduces severity of or reduces incidence of one or more symptoms or features
of the disease,
disorder or condition. In some embodiments, a therapeutically effective amount
is administered in
a single dose; in some embodiments, multiple unit doses are required to
deliver a therapeutically
effective amount
14
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0050] As used herein, the term "variant" generally refers to an entity that
shows significant
structural identity with a reference entity but differs structurally from the
reference entity in the
presence or level of one or more chemical moieties as compared with the
reference entity. In many
embodiments, a variant also differs functionally from its reference entity. In
general, whether a
particular entity is properly considered to be a "variant" of a reference
entity is based on its degree
of structural identity with the reference entity. Any biological or chemical
reference entity has
certain characteristic structural elements. A variant, by definition, is a
distinct chemical entity that
shares one or more such characteristic structural elements To give but a few
examples, a small
molecule may have a characteristic core structural element (e.g., a macrocycle
core) or one or more
characteristic pendent moieties so that a variant of the small molecule is one
that shares the core
structural element and the characteristic pendent moieties but differs in
other pendent moieties or
in types of bonds present (single vs double, E vs Z, etc.) within the core, a
polypeptide may have
a characteristic sequence element comprised of a plurality of amino acids
having designated
positions relative to one another in linear or three-dimensional space or
contributing to a particular
biological function, a nucleic acid may have a characteristic sequence element
comprised of a
plurality of nucleotide residues having designated positions relative to on
another in linear or three-
dimensional space. For example, a variant polypeptide may differ from a
reference polypeptide as
a result of one or more differences in amino acid sequence or one or more
differences in chemical
moieties (e.g., carbohydrates, lipids, etc.) covalently attached to the
polypeptide backbone. In
some embodiments, a variant polypeptide shows an overall sequence identity
with a reference
polypeptide that is at least 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%,
95%, 96%,
97%, or 99%. Alternatively or additionally, in some embodiments, a variant
polypeptide does not
share at least one characteristic sequence element with a reference
polypeptide. In some
embodiments, the reference polypeptide has one or more biological activities.
In some
embodiments, a variant polypeptide shares one or more of the biological
activities of the reference
polypeptide. In some embodiments, a variant polypeptide lacks one or more of
the biological
activities of the reference polypeptide. In some embodiments, a variant
polypeptide shows a
reduced level of one or more biological activities as compared with the
reference polypeptide. In
many embodiments, a polypeptide of interest is considered to be a "variant" of
a parent or reference
polypeptide if the polypeptide of interest has an amino acid sequence that is
identical to that of the
parent hut for a small number of sequence alterations at particular positions.
For example, fewer
than 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2% of the residues in the
variant are
substituted as compared with the parent. In some embodiments, a variant has
10, 9, 8, 7, 6, 5, 4,
3, 2, or 1 substituted residue as compared with a parent. Often, a variant has
a very small number
(e.g., fewer than 5, 4, 3, 2, or 1) number of substituted functional residues
(e.g., residues that
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
participate in a particular biological activity). Furthermore, a variant may
have not more than 5,
4, 3, 2, or 1 additions or deletions, and often has no additions or deletions,
as compared with the
parent. Moreover, any additions or deletions may be fewer than about 25, about
20, about 19,
about 18, about 17, about 16, about 15, about 14, about 13, about 10, about 9,
about 8, about 7,
about 6, and commonly are fewer than about 5, about 4, about 3, or about 2
residues. In some
embodiments, the parent or reference polypeptide is one found in nature.
A. Provided Classifier(s)
[0051] The present disclosure provides a classifier and development of such a
classifier that can
identify (e.g., predict) which patients will or will not respond to a
particular therapy. In some
embodiments, a classifier is established to distinguish between responsive and
non-responsive
prior subjects who have received an anti-TNF therapy (e g , a particular anti-
TNF agent or
regimen).
[0052] Among other things, the present disclosure encompasses an insight that
expression level(s)
for a certain set of genes, alone and in combination with one another,
optionally coupled with
certain clinical characteristics or with presence or absence of certain single
nucleotide
polymorphi sm (s), are useful for predicting response (e g , one or more
features of response) to anti-
TNF therapy.
[0053] In some embodiments, the present disclosure provides a classifier that
is or includes such
gene expression level(s), clinical characteristic(s) or SNP(s), and
demonstrates that it has been
established to distinguish between subjects who do and who do not respond to
anti-TNF therapy.
In some embodiments, a provided classifier is established to distinguish,
through retrospective
analysis of historical (e.g., prior) subject population(s) who received anti-
TNF therapy and whose
responsiveness is known (e.g., was previously determined), between subjects
(e.g., anti-TNT
therapy naive subjects) who are responsive or non-responsive to anti-TNF
therapy. In some
embodiments, a classifier that, when applied to such historical (e g , prior)
population(s) identifies
at least 50% of non-responders within a cohort with at least 70% accuracy is
considered
"validated." In some embodiments, a classifier that, when applied to such
historical (e.g., prior)
population(s) identifies at least 60% of non-responders within a cohort with
at least 70% accuracy
is considered "validated." In some embodiments, a classifier that, when
applied to such historical
(e.g., prior) population(s) identifies at least 70% of non-responders within a
cohort with at least
70% accuracy is considered "validated." In some embodiments, a classifier
that, when applied to
such historical (e.g., prior) population(s) identifies at least 80% of non-
responders within a cohort
with at least 70% accuracy is considered "validated." In some embodiments, a
classifier that, when
applied to such historical (e g , prior) population(s) identifies at least 90%
of non-responders within
a cohort with at least 70% accuracy is considered "validated." In some
embodiments, a classifier
16
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
that, when applied to such historical (e.g., prior) population(s) identifies
at least 99% of non-
responders within a cohort with at least 70% accuracy is considered
"validated."
[0054] In some embodiments, a classifier that, when applied to such historical
(e.g., prior)
population(s) identifies at least 50% of non-responders within a cohort with
at least 80% accuracy
is considered "validated." In some embodiments, a classifier that, when
applied to such historical
(e.g., prior) population(s) identifies at least 50% of non-responders within a
cohort with at least
90% accuracy is considered 'validated." In some embodiments, a classifier
that, when applied to
such historical (e.g., prior) population(s) identifies at least 50% of non-
responders within a cohort
with at least 99% accuracy is considered "validated."
[0055] In some embodiments, the present disclosure provides methods of
treating subjects
suffering from a disease, disorder, or condition, comprising administering an
anti-TNF therapy to
a subject(s) that has been determined through application of a provided
classifier to be likely to
respond to such anti-TNF therapy; alternatively or additionally, in some
embodiments, the present
disclosure provides methods of treating subjects suffering from a disease,
disorder or condition,
comprising withholding anti-TNF therapy, or administering an alternative to
anti-TNF therapy to
a subject(s) determined through application of a provided classifier to be
unlikely to respond to
such anti-TNF therapy.
[0056] In some embodiments, a provided classifier may be or comprise gene
expression
information for one or more genes. Alternatively or additionally, in some
embodiments, a provided
classifier may be or comprise presence or absence of one or more single
nucleotide polymorphisms
(SNP) or one or more clinical features or characteristics of a relevant
subject
[0057] In some embodiments, a classifier is developed by assessing each of the
one or more genes
whose expression levels significantly correlate (e.g., in a linear or non-
linear manner) to clinical
responsiveness or non-responsiveness; presence of the one or more SNPs; and at
least one clinical
characteristic
[0058] In some embodiments, as described herein, a classifier is developed by
retrospective
analysis of one or more features (e.g., gene expression levels, presence or
absence of one or more
SNP s, etc.) of biological samples from patients (e.g., prior subjects) who
have received anti-TNF
therapy and have been determined to respond (e.g., are responders) or not to
respond (e.g., are non-
responders); alternatively or additionally, in some embodiments, a classifier
is developed by
retrospective analysis of one or more clinical characteristics of such
patients, which may or may
not involve assessment of any biological samples (and may be accomplished, for
example, by
reference to medical records). In some embodiments, all such patients have
received the same
anti-TNF therapy (optionally for the same or different periods of time);
alternatively or
additionally, in some embodiments, all such patients have been diagnosed with
the same disease,
17
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
disorder or condition. In some embodiments, patients whose biological samples
are analyzed in
the retrospective analysis had received different anti-TNF therapy (e.g., with
a different anti-TNT'
agent or according to a different regimen); alternatively or additionally, in
some embodiments,
patients whose biological samples are analyzed in the retrospective analysis
have been diagnosed
with different diseases, disorders, or conditions.
[0059] Many statistical classification techniques are suitable as approaches
to perform the
classification described above (e.g. distinguish between subjects who do and
who do not respond
to anti -TNF therapy) Such methods include but are not limited to supervised
learning approaches.
[0060] In supervised learning approaches, a group of samples from two or more
groups (e.g. those
do and do not respond to anti-TNF therapy) are analyzed or processed with a
statistical
classification method. Absence/presence of genes or particular SNPs or
variants, or expression
level of genes or biomarkers described herein can be used as a basis for
classifier that differentiates
between the two or more groups. A new sample can then be analyzed or processed
so that the
classifier can associate the new sample with one of the two or more groups.
[0061] Commonly used supervised classifiers include without limitation the
neural network (e.g.
artificial neural network, multi-layer perceptron), support vector machines, k-
nearest neighbours,
Gaussian mixture model, Gaussian, naive Bayes, decision tree and radial basis
function (RBF)
classifiers. Linear classification methods include Fisher's linear
discriminant, logistic regression,
naive Bayes classifier, perceptron, and support vector machines (SVMs). Other
classifiers for use
with methods according to the disclosure include quadratic classifiers, k-
nearest neighbor,
boosting, decision trees, random forests, neural networks, pattern
recognition, Bayesian networks
and Hidden Markov models. Other classifiers, including improvements or
combinations thereof,
commonly used for supervised learning, can also be suitable for use with the
methods described
herein.
[0062] Classification using supervised methods can generally be performed by
the following
methodology:
[0063] 1. Gather a training set. These can include, for example, expression
levels of one or more
genes or biomarkers described herein from a sample from a patient responding
or not responding
to anti-TNF therapy. The training samples are used to "train" the classifier.
[0064] 2. Determine the input "feature" representation of the learned
function. The accuracy of
the learned function depends on how the input object is represented. For
example, the input object
is transformed into a feature vector, which contains a number of features that
are descriptive of the
object. The features might include a set of genes detected in a sample from a
patient or subject.
18
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
100651 3. Determine the structure of the learned function and corresponding
learning algorithm. A
learning algorithm is chosen, e.g., artificial neural networks, decision
trees, Bayes classifiers or
support vector machines. The learning algorithm is used to build the
classifier.
[0066] 4. Build the classifier (e.g., classification model). The learning
algorithm is run on the
gathered training set. Parameters of the learning algorithm may be adjusted by
optimizing
performance on a subset (called a validation set) of the training set, or via
cross-validation. After
parameter adjustment and learning, the performance of the algorithm may be
measured on a test
set of' naive samples that is separate from the training set The built model
can involve feature
coefficients or importance measures assigned to individual features.
[0067] In some cases, the individual features are individual genes or levels
of individual genes. In
some cases, the level of the gene is a normalized value, an average value, a
median value, a mean
value, an adjusted average, or other adjusted level or value. The individual
features may comprise
or consist of sets or panels of genes, such as the sets provided herein.
[0068] Once the classifier (e.g., classification model) is determined as
described above ("trained"),
it can be used to classify a sample, e.g., a patient sample comprising
expressed genes that is
analyzed or processed according to methods described herein.
1. Gene Expression
[0069] In some embodiments, a gene expression aspect of a classifier as
described herein is
determined by assessing one or more genes whose expression levels
significantly correlate (e.g.,
in a linear or non-linear manner) to clinical responsiveness or non-
responsiveness; and at least one
of: presence of one or more single nucleotide polymorphi sms (SNPs) in an
expressed sequence of
the one or more genes; or at least one clinical characteristic of the
responsive and non-responsive
prior subjects. Genes whose expression levels show statistically significant
differences between
the responder and non-responder populations may be included in the gene
response signature.
[0070] In some embodiments, the present disclosure embodies an insight that
the source of a
problem with certain prior efforts to identify or provide a classifier between
responsive and non-
responsive subj ects is through comparison of gene expression levels in
responder vs non-responder
populations have emphasized or focused on (often solely on) genes that show
the largest difference
(e.g., greater than 2-fold change) in expression levels between the
populations. The present
disclosure appreciates that even genes those expression level differences are
relatively small (e.g.,
less than 2-fold change in expression) provide useful information and are
valuably included in a
classifier in embodiments described herein.
[0071] Moreover, in some embodiments, the present disclosure embodies an
insight that analysis
of interaction patterns of genes whose expression levels show statistically
significant differences
(optionally including small differences) between responder and non-responder
populations as
19
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
described herein provides new and valuable information that materially
improves the quality and
predictive power of a classifier.
[0072] In some embodiments a provided classifier is or comprises a gene or set
of genes that can
be used to determine (e.g., whose expression level correlates with) whether a
subject will or will
not respond to a particular therapy (e.g., anti-TNF therapy). In some
embodiments, a classifier is
developed by assessing one or more genes whose expression levels significantly
correlate (e.g., in
a linear or non-linear manner) to clinical responsiveness or non-
responsiveness; and at least one
of: presence of one or more single nucleotide polymorphisms (SNPs); and at
least one clinical
characteristic of the responsive and non-responsive prior subjects.
[0073] In some embodiments, one or more genes for use in a classifier and/or
for measuring gene
expression are selected from genes in Table 1, and combinations thereof:
Table 1
ALPL
ATRAID
BCL6
CDK1 1A
CFLAR
C OMNID5
GOL GA1
1L113
IMPDH2
JAK3
KT J---TDC3
LIIVIK2
NOD2
NOTCH1
SPINT2
SP ON2
STOML2
TRIM25
ZFP3 6
[0074] In some embodiments, genes for use in a classifier or for measuring
gene expression are
selected from two or more genes from Table 1. In some embodiments, genes for
use in a classifier
or for measuring gene expression are selected from two or more, three or more,
four or more, five
or more, six or more, seven or more, eight or more, nine or more, ten or more,
eleven or more,
twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen
or more, seventeen or
more, eighteen or more or all nineteen genes from Table 1.
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
100751 In some embodiments, genes for use in a classifier or for measuring
gene expression are
selected from one or more genes from Table 2, and combinations thereof:
Table 2
ALPL
BCL6
CDK11A
CFLAR
IL 1B
JAK3
LIMK2
NOD2
NOTCH'
TRI1\425
ZFP36
[0076] In some embodiments, genes for use in a classifier or for measuring
gene expression are
selected from two or more genes from Table 2. In some embodiments, genes for
use in a classifier
or for measuring gene expression are selected from two or more, three or more,
four or more, five
or more, six or more, seven or more, eight or more, nine or more, ten or more
or all eleven genes
from Table 2.
[0077] In some embodiments, a gene expression pattern in a classifier can be
identified or detected
using mRNA or protein expression datasets, for example as may be or have been
prepared from
validated biological data (e.g., biological data derived from publicly
available databases such as
Gene Expression Omnibus ("GEO")). In some embodiments, a classifier may be
derived by
comparing gene expression levels of known responsive and known non-responsive
prior subjects
to a specific therapy (e.g., anti-TNF therapy). In some embodiments, certain
genes (e.g., signature
genes) are selected from this cohort of gene expression data to be used in
developing the classifier.
[0078] In some embodiments, signature genes or expression patterns are
identified by methods
analogous to those reported by Santolini, "A personalized, multiomics approach
identifies genes
involved in cardiac hypertrophy and heart failure," Systems Biology and
Applications, (2018)4:12;
doi:10.1038/s41540-018-0046-3, which is incorporated herein by reference for
all purposes. in
some embodiments, signature genes or expression patterns are identified by
comparing gene
expression levels of known responsive and non-responsive prior subjects and
identifying
significant changes between the two groups, wherein the significant changes
can be large
differences in expression (e.g., greater than 2-fold change), small
differences in expression (e.g.,
less than 2-fold change), or both. In some embodiments, genes are ranked by
significance of
difference in expression. In some embodiments, significance is measured by
Pearson correlation
21
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
between gene expression and response outcome. In some embodiments, signature
genes are
selected from the ranking by significance of difference in expression. In some
embodiments, the
number of signature genes selected is less than the total number of genes
analyzed. In some
embodiments, 200 signature genes or less are selected. In some embodiments 100
genes or less
are selected.
[0079] In some embodiments, signature genes are selected in conjunction with
or are characterized
by their location on a human interactome (HI), a map of protein-protein
interactions. Use of the
HI in this way encompasses a recognition that mRNA activity is dynamic and
determines the actual
over and under expression of proteins critical to understanding certain
diseases. In some
embodiments, genes associated with response to certain therapies (e.g., anti-
TNF therapy) may
cluster (e.g., form a cluster of genes) in discrete modules on the HI map. The
existence of such
clusters is associated with the existence of fundamental underlying disease
biology. In some
embodiments, a classifier is derived from signature genes selected from the
cluster of genes on the
HI map. Accordingly, in some embodiments, a classifier is derived from a
cluster of genes
associated with response to anti-TNF therapy on a human interactome map.
[0080] In some embodiments, genes associated with response to certain
therapies exhibit certain
topological properties when mapped onto a human interactome map. For example,
in some
embodiments, a plurality of genes associated with response to anti-TNF therapy
and characterized
by their position (e.g., topological properties, e.g., their proximity to one
another) on a human
interactome map.
[0081] In some embodiments, genes associated with response to certain
therapies (e g , anti-TNF
therapy) may exist within close proximity to one another on the HI map. Said
proximal genes, do
not necessarily share fundamental underlying disease biology. That is, in some
embodiments,
proximal genes do not share significant protein interaction. Accordingly, in
some embodiments,
the classifier is derived from genes that are proximal on a human interactome
map. In some
embodiments, the classifier is derived from certain other topological features
on a human
interactome map.
[0082] In some embodiments, genes associated with response to certain
therapies (e.g., anti-TNF
therapy) may be determined by Diffusion State Distance (DSD) (see Cao, et al.,
PLOS One, 8(10):
e76339 (Oct. 23, 2013), which is incorporated herein by reference for all
purposes) when used in
combination with the HI map.
[0083] In some embodiments, signature genes are selected by (1) ranking genes
based on the
significance of difference of expression of genes as compared to known
responders and known
non-responders; (2) selecting genes from the ranked genes and mapping the
selected genes onto a
human interactome map; and (3) selecting signature genes from the genes mapped
onto the human
22
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
interactome map. Thus, in some embodiments, signature genes are characterized
by the relative
ranking of their expression difference in responder vs non-responder subjects
or populations.
[0084] In some embodiments, signature genes (e.g., selected from the Santolini
method, or using
various network topological properties including, but not limited to,
clustering, proximity and
diffusion-based methods) are provided to a probabilistic neural network or
other classifier
described herein to thereby provide (e.g., "train") the classifier. In some
embodiments, the
probabilistic neural network implements the algorithm proposed by D. F. Specht
in "Probabilistic
Neural Networks," Neural Networks, 3(1 ): 1 09-1 1 8 (1990), which is
incorporated herein by
reference. In some embodiments, the probabilistic neural network is written in
the R-statistical
language, and knowing a set of observations described by a vector of
quantitative variables,
classifies observations into a given number of groups (e.g., responders and
non-responders). The
algorithm is trained with the data set of signature genes taken from known
responders and non-
responders provides new observations. In some embodiments, the probabilistic
neural network is
one derived from pnn: Probabilistic neural networks v1Ø1 at The
Comprehensive R Archive
Network. In some embodiments, signature genes are analyzed according to a
Random Forest
Model to provide a classifier.
2. Single Nucleotide Polymorphisms
[0085] The present disclosure further encompasses an insight that single
nucleotide
polymorphisms (SNPs) can be identified via RNA sequence data. That is, by
comparison of RNA
sequence data to a reference human genome, e.g., by mapping RNA sequence data
to the GRCh3 8
human genome. Without wishing to be bound by theory, it is believed that the
presence of SNPs
that correlate to RNA sequences used in a classifier can facilitate
identifying a subpopulation of
subjects who respond or do not respond to certain therapies (e.g., anti-TNF
therapies). That is,
protein products of discriminatory genes or SNP-containing RNAs can be
analyzed using network
medicine and pathway enrichment analyses. Proteins encoded by discriminatory
genes or SNP-
containing RNAs included in the classifier can be overlaid on, for example, a
map of the human
interactome to help identify certain subpopulations of subjects by identifying
certain sets of
discriminatory genes.
[0086] In some embodiments, provided classifiers and methods of using such
classifiers,
incorporate an assessment related to single nucleotide polymorphisms (SNPs).
In some
embodiments, the present disclosure provides methods of developing a
classifier for stratifying
subjects with respect to one or more therapeutic attributes comprising:
analyzing sequence data of
RNA expressed in subjects representing at least two different categories with
respect to at least one
of the therapeutic attributes; assessing the presence of one or more single
nucleotide
polymorphisms (SNPs) from the sequence data; determining the presence of the
one or more SNPs
23
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
correlates with the at least one therapeutic attribute; and including the one
or more SNPs in the
classifier.
[0087] In some embodiments, the present disclosure provides, in a method of
developing a
classifier for stratifying subjects with respect to one or more therapeutic
attributes by analyzing
sequence data of RNA expressed in subjects representing at least two different
categories with
respect to at least one of the therapeutic attributes, the improvement that
comprises: assessing
presence of one or more single nucleotide polymorphisms (SNPs) from the
sequence data; and
determining the presence of the one or more SNPs correlates with the at least
one therapeutic
attribute; and including presence of the one or more SNPs in the classifier.
[0088] In some embodiments, one or more SNPs are selected from Table 3.
Table 3
chr1.161644258
chr1.2523811
chr11.10796735
0
chr17.38031857
chr7. 128580004
2
rs10774624
rs10985070
rs11889341
rs1571878
rs1633360
rs17668708
rs 1 877030
rs 1 893592
rs1980422
rs2228145
rs2233424
rs2236668
rs2301888
rs2476601
rs3 087243
rs3218251
rs331463
rs34536443
rs34695944
rs4239702
rs4272
rs45475795
rs508970
rs5987194
24
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
rs657075
rs67 15284
rs706778
rs72634030
rs73013527
rs73 194058
rs773125
rs7752903
rs8083786
rs9653442
[0089] In some embodiments, SNPs are selected from two or more SNPs from Table
3. In some
embodiments, SNPs are selected two or more, three or more, four or more, five
or more, six or
more, seven or more, eight or more, nine or more, ten or more, eleven or more,
twelve or more,
thirteen or more, fourteen or more, fifteen or more, sixteen or more,
seventeen or more, eighteen
or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or
more, twenty-three
or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-
seven or more,
twenty-eight or more, twenty-nine or more, thirty or more, thirty-one or more,
thirty-two or more,
thirty-three or more, thirty-four or more, thirty-five or more, thirty-six or
more, thirty-seven or
more, thirty-eight or more or all 39 SNPs from Table 3.
3. Clinical Characteristics
[0090] In some embodiments, a classifier can also incorporate additional
information, for example
in order to further improve predictive ability of the classifier to identify
between responders and
non-responders. For example, in some embodiments, a classifier is developed or
assessed (e.g.,
detected) by assessing one or more genes whose expression levels significantly
correlate (e g , in
a linear or non-linear manner) to clinical responsiveness or non-
responsiveness; and at least one of
presence of one or more single nucleotide polymorphisms (SNPs) in an expressed
sequence of the
one or more genes; or at least one clinical characteristic of the responsive
and nonresponsive prior
subjects. That is, in some embodiments, a classifier is developed or assessed
(e.g., detected) by
assessing one or more genes whose expression levels significantly correlate
(e.g., in a linear or
non-linear manner) to clinical responsiveness or non-responsiveness and the
presence of one or
more single nucleotide polymorphisms (SNPs) in an expressed sequence of the
one or more genes.
In some embodiments, a classifier is developed or assessed (e.g., detected) by
assessing one or
more genes whose expression levels significantly correlate (e.g., in a linear
or non-linear manner)
to clinical responsiveness or non-responsiveness and at least one clinical
characteristic of the
responsive and non-responsive prior subjects.
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0091] The present disclosure further encompasses an insight that certain
clinical characteristics
(e.g., BMI, gender, age, and the like), can be incorporated into classifiers
provided herein. In some
embodiments, provided classifiers and methods of using such classifiers,
incorporate an
assessment related to clinical characteristics. In some embodiments, the
present disclosure
provides methods of developing a classifier for stratifying subj ects with
respect to one or more
therapeutic attributes comprising: analyzing sequence data of RNA expressed in
subjects
representing at least two different categories with respect to at least one of
the therapeutic
attributes; assessing the presence of one or more clinical characteristics;
determining that
expression related to said clinical characteristics correlate with the at
least one therapeutic attribute;
and including the one or more clinical characteristics in the classifier.
[0092] In some embodiments, at least one clinical characteristic is selected
from: body-mass index
(BMI), gender, age, race, previous therapy treatment, disease duration, C-
reactive protein (CRP)
level, presence of anti-cyclic citrullinated peptide, presence of rheumatoid
factor, patient global
assessment, treatment response rate (e.g., ACR20, ACR50, ACR70), and
combinations thereof.
[0093] In some embodiments, a clinical characteristic is selected from Table
4.
Table 4
Age
Gender at birth
Duration of disease (in years)
Race (included white, Asian, black, mixed race, Native American, Pacific
Islander, and other)
History of fibromyalgia
History of chronic vascular disease (includes acute coronary syndrome,
coronary artery disease,
congestive heart failure, hypertension, myocardial infarction, peripheral
arterial disease, stroke,
unstable angina, cardiac arrest, revascularization procedure, and ventricular
arrhythmia)
History of serious infection that led to hospitalization (includes infections
of bursa or joint,
cellulitis, sinusitis, diverticulitis, sepsis, pneumonia bronchitis gastro
meningitis, urinary tract
infection, upper respiratory infection, and tuberculosis)
History of cancer (includes breast, lung, skin, lymphoma but excludes non-
melanoma skin)
BMI
Smoking status (includes never, previous or current)
Predni sone dose
DMARD dose
C-reactive protein level at baseline
DAS28-CRP at baseline
Swollen 28-joint count at baseline
Tender 28-joint count at baseline
26
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Patient global assessment at baseline
Physician global assessment at baseline
CDAI at baseline
Modified health assessment questionnaire score at baseline
Patient pain assessment at baseline
EULAR response at baseline using DAS28-CRP (includes poor, moderate or good)
Anti-CCP status (positive or negative)
Anti-CCP titer at baseline
Rheumatoid factor status (positive or negative)
Rheumatoid factor titer at baseline
[0094] In some embodiments, clinical characteristics are selected from two or
more clinical
characteristics from Table 4. In some embodiments, clinical characteristics
are selected from two
or more, three or more, four or more, five or more, six or more, seven or
more, eight or more, nine
or more, ten or more, eleven or more, twelve or more, thirteen or more,
fourteen or more, fifteen
or more, sixteen or more, seventeen or more, eighteen or more, nineteen or
more, twenty or more,
twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or
more, twenty-five
or more or all twenty-six clinical characteristics from Table 4.
4. Validating Classifiers
[0095] Alternatively or additionally, in some embodiments, a classifier can be
trained in the
probabilistic neural network using a cohort of known responders and non-
responders using leave-
one-out cross or k-fold cross validation. In some embodiments, such a process
leaves one sample
out (e.g., leave-one-out) of the analysis and trains the classifier based on
the remaining samples.
In some embodiments, the updated classifier is then used to predict a
probability of response for
the sample that's left out. In some embodiments, such a process can be
repeated iteratively, for
example, until all samples have been left out once. In some embodiments, such
a process randomly
partitions a cohort of known responders and non-responders into k equal sizes
groups. Of the k
groups, a single group is retained as validation data for testing the model,
and the remaining groups
are used as training data. Such a process can be repeated k times, with each
of the k groups being
used exactly once as the validation data. In some embodiments, the outcome is
a probability score
for each sample in the training set. Such probability scores can correlate
with actual response
outcome. A Recursive Operating Curves (ROC) can be used to estimate the
performance of the
classifier. In some embodiments, an Area Under Curve (AUC) of' about 0.6 or
higher reflects a
suitable validated classifier. In some embodiments, a Negative Predictive
Value (NPV) of 0.9
reflects a suitable validated classifier. In some embodiments, a classifier
can be tested in a
27
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
completely independent (e g , blinded) cohort to, for example, confirm the
suitability (e.g., using
leave-one-out or k-fold cross validation). Accordingly, in some embodiments,
provided methods
further comprise validating a classifier, for example, by assigning
probability of response to a
group of known responders and non-responders; and checking the classifier
against a blinded group
of responders and non-responders. The output of these processes is a trained
classifier useful for
establishing whether a subject will or will not respond to a particular
therapy (e.g., anti-TNT
therapy).
[0096] In some embodiments, a classifier is established to distinguish between
responsive and non-
responsive prior subjects who have received a type of therapy, e.g., anti-TNF
therapy. This
classifier can predict whether a subj ect will or will not respond to a given
therapy. In some
embodiments, the response and non-responsive prior subjects suffered from the
same disease,
disorder, or condition.
[0097] In some embodiments, validation of treatment is assessed by monitoring
particular clinical
characteristics. For example, in some embodiments, treatment response is
validated in subjects by
statistical analysis of clinical features. In certain embodiments,
development, validation, or use of
a relevant classifier may involve or have involved assessments of one or more
clinical parameters
(e.g., of a patient's presentation or status of disease). The present
disclosure appreciates that
variation may occur in such clinical assessments that may, for example,
represent inputs external
to the patient (e.g., differences in application of an assessment or
interpretation of a patient
characteristic or response). The present disclosure provides a solution to
this identified problem
in providing for patient self-assessment of one or more relevant parameters.
[0098] In some embodiments, validation of a classifier comprises statistical
analysis of clinical
features to analyze changes in clinical characteristics in a patient who has
been so classifier by the
classifier and received anti-TNT therapy. Such validation methods recognizes
that certain
subjective measurements of clinical change cannot be quantified compared to
methods described
herein and involve self-assessment. The present disclosure encompasses an
insight that patient
self-assessment is not necessarily consistent, but can provide valuable
information on treatment
response over time. Such self-assessment response can be used to confirm
whether a patient is a
true responder or non-responder. For example, statistical analysis of certain
clinical characteristics
of a cohort of patients can validate the accuracy of the classifier. In some
embodiments, statistical
analysis of clinical features analyzes changes of one or more of ACR50, ACR70,
CDAI LDA,
CDAI remission, DAS28-CRP LDA, and DAS28-CRP remission and combinations
thereof. In
some embodiments, statistical analysis is performed via a Monte Carlo
simulation.
[0099] In some embodiments, a classifier is validated using a cohort of subj
ects having previously
been treated with anti-TNT therapy, but is independent from the cohort of
subjects used to prepare
28
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
the classifier. In some embodiments, the classifier is updated using gene
expression data, SNP
data, or clinical characteristics. In some embodiments, a classifier is
considered "validated" when
90% or greater of non-responding subjects are predicted with 60% or greater
accuracy within the
validating cohort.
[0100] In some embodiments, the classifier predicts non-responsiveness of
subjects with at least
60% accuracy predicting non-responsiveness across a population of' at least
100 subjects. In some
embodiments, the classifier predicts non-responsiveness of subjects with at
least 60% accuracy
across a population of at least 150 subjects. In some embodiments, the
classifier predicts non-
responsiveness of subjects with at least 60% accuracy across a population of
at least 170 subjects.
In some embodiments, the classifier predicts non-responsiveness of subjects
with at least 60%
accuracy across a population of at least 200 or more subjects
[0101] In some embodiments, the classifier predicts non-responsiveness of
subjects with at least
80% accuracy across a population of at least 100 subjects. In some
embodiments, the classifier
predicts non-responsiveness of subjects with at least 80% accuracy across a
population of at least
150 subjects. In some embodiments, the classifier predicts non-responsiveness
of subjects with at
least SO% accuracy across a population of at least 170 subjects. In some
embodiments, the
classifier predicts non-responsiveness of subjects with at least 80% accuracy
across a population
of at least 200 or more subjects. In some embodiments, the classifier predicts
non-responsiveness
of subjects with at least 80% accuracy across a population of at least 300 or
more subjects. In
some embodiments, the classifier predicts non-responsiveness of subjects with
at least 80%
accuracy across a population of at least 350 or more subjects
B. Detecting Gene Signature(s) or SNPs
[0102] Detecting gene signatures in a subject using a trained classifier may
be performed. In other
words, by first defining the gene signatures (from the classifier), a variety
of methods can be used
to determine whether a subject or group of subjects express the established
gene signatures. For
example, in some embodiments, a practitioner can obtain a blood or tissue
sample from the subject
prior to administering of therapy, and extract and analyze mRNA profiles from
said blood or tissue
sample. The analysis of mRNA profiles can be performed by various approaches,
including, but
not limited to gene arrays, RNA-sequencing, nanostring sequencing, real-time
quantitative reverse
transcription PCR (qRT-PCR), bead arrays, or enzyme-linked immunosorbent assay
(ELISA) and
combinations thereof. Accordingly, in some embodiments, the present disclosure
provides
methods of determining whether a subject is classified as a responder or non-
responder, comprising
measuring gene expression by at least one of a microarray, RNA sequencing,
real-time quantitative
reverse transcription PCR (qRT-PCR), bead array, and ELISA and combinations
thereof. In some
embodiments, the present disclosure provides methods of determining whether a
subject is
29
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
classified as a responder or non-responder comprising measuring gene
expression of a subject by
RNA sequencing (e.g., RNAseq).
[0103] The present disclosure further encompasses an insight that single
nucleotide
polymorphisms (SNPs) can be identified via RNA sequence data. That is, by
comparison of RNA
sequence data to a reference human genome, e.g., by mapping RNA sequence data
to the GRCh38
human genome. Without wishing to be bound by theory, it is believed that the
presence of SNPs
that correlate to RNA sequences used in the classifier can facilitate
identifying a subpopulation of
subjects who respond or do not respond to certain therapies (e.g., anti -TNF
therapies). That is,
protein products of the discriminatory genes and SNP-containing RNAs can be
analyzed using
network medicine and pathway enrichment analyses. The proteins encoded by the
discriminatory
genes and SNP-containing RNAs included in the classifier can be overlaid on,
for example, a map
of the human interactome to help identify certain subpopulations of subjects
by identifying certain
sets of discriminatory genes.
[0104] In some embodiments, gene expression is measured by subtracting
background data,
correcting for batch effects, and dividing by mean expression of housekeeping
genes. See
Eisenberg & I-evanon, "Human housekeeping genes, revisited," Trends in
Genetics, 29(10):569-
574 (October 2013), which is incorporated herein by reference for all
purposes. In the context of
microarray data analysis, background subtraction refers to subtracting the
average fluorescent
signal arising from probe features on a chip not complimentary to any mRNA
sequence, e.g. signals
that arise from non-specific binding, from the fluorescence signal intensity
of each probe feature.
The background subtraction can be performed with different software packages,
such as
Affymetrix Gene Expression Console. Housekeeping genes are involved in basic
cell maintenance
and, therefore, are expected to maintain constant expression levels in all
cells and conditions. The
expression level of genes of interest, e.g., those in the response signature,
can be normalized by
dividing the expression level by the average expression level across a group
of selected
housekeeping genes. This housekeeping gene normalization procedure calibrates
the gene
expression level for experimental variability. Further, normalization methods
such as robust multi-
array average ("RMA") correct for variability across different batches of
microarrays, are available
in R packages recommended by either Illumina or Affymetrix platforms. The
normalized data is
log transformed, and probes with low detection rates across samples are
removed. Furthermore,
probes with no available genes symbol or Entrez ID are removed from the
analysis.
[0105] In some embodiments, the present disclosure provides a kit comprising a
classifier
established to distinguish between responsive and non-responsive prior
subjects who have received
anti-TINT therapy.
C. Using Classifiers
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
1. Patient Stratification
[0106] Among other things, the present disclosure provides technologies for
predicting
responsiveness to anti-TNF therapies. In some embodiments, provided
technologies exhibit
consistency or accuracy across cohorts superior to other methodologies.
[0107] Thus, the present disclosure provides technologies for patient
stratification, defining or
distinguishing between responder and non-responder populations. For example,
in some
embodiments, the present disclosure provides methods for treating subjects
with anti-TNF therapy,
which methods, in some embodiments, comprise: administering the anti - TNF
therapy to subjects
who have been determined to be responsive via a classifier established to
distinguish between
responsive and non-responsive prior subjects who have received the anti-TNF
therapy.
[0108] In some embodiments, the present disclosure provides methods of
developing a classifier
for stratifying subjects with respect to one or more therapeutic attributes
comprising analyzing
sequence data of RNA expressed in subjects representing at least two different
categories with
respect to at least one of the therapeutic attributes; assessing the presence
of one or more single
nucleotide polymorphisms (SNPs) from the sequence data; determining the
presence of the one or
more SNPs correlates with the at least one therapeutic attribute, and
including the one or more
SNPs in the classifier.
[0109] Classifiers described herein can be used by analyzing gene expression
of subjects. In some
embodiments, genes of the subject are measured by at least one of a
microarray, RNA sequencing,
real-time quantitative reverse transcription PCR (qRT-PCR), bead array, ELISA,
and protein
expression and combinations thereof
2. Therapy Monitoring
[0110] Further, the present disclosure provides technologies for monitoring
therapy for a given
subject or cohort of subjects. As a subject's gene expression level can change
over time, it maybe
desirable to evaluate a subject at one or more points in time, for example, at
specified and or
periodic intervals.
[0111] In some embodiments, validation of treatment is assessed by monitoring
particular clinical
characteristics. For example, in some embodiments, treatment response is
validated in subjects by
statistical analysis of clinical features. In certain embodiments,
development, validation, or use of
a relevant classifier may involve or have involved assessments of one or more
clinical parameters
(e g , of a patient's presentation or status of disease) The present
disclosure appreciates that
variation may occur in such clinical assessments that may, for example,
represent inputs external
to the patient (e.g., differences in application of an assessment or
interpretation of a patient
characteristic or response) The present disclosure provides a solution to this
identified problem
in providing for patient self-assessment of one or more relevant parameters.
31
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0112] In some embodiments, validation of a classifier comprises statistical
analysis of clinical
features to analyze changes in clinical characteristics in a patient who has
been so classifier by the
classifier and received anti-TNF therapy. Such validation methods recognizes
that certain
subjective measurements of clinical change cannot be quantified compared to
methods described
herein and involve self-assessment. The present disclosure encompasses an
insight that patient
self-assessment is not necessarily consistent but can provide valuable
information on treatment
response over time. Such self-assessment response can be used to confirm
whether a patient is a
true responder or non-responder. For example, statistical analysis of certain
clinical characteristics
of a cohort of patients can validate the accuracy of the classifier. In some
embodiments, statistical
analysis of clinical features analyzes changes of one or more of ACR50, ACR70,
CDAI LDA,
CDAI remission, DAS28-CRP LDA, and DAS28-CRP remission and combinations
thereof In
some embodiments, statistical analysis is performed via a Monte Carlo
simulation.
[0113] In some embodiments, repeated monitoring under time permits or achieves
detection of one
or more changes in a subject's gene expression profile or characteristics that
may impact ongoing
treatment regimens. In some embodiments, a change is detected in response to
which particular
therapy administered to the subject is continued, is altered, or is suspended
In some embodiments,
therapy may be altered, for example, by increasing or decreasing frequency or
amount of
administration of one or more agents or treatments with which the subject is
already being treated.
Alternatively or additionally, in some embodiments, therapy may be altered by
addition of therapy
with one or more new agents or treatments. In some embodiments, therapy may be
altered by
suspension or cessation of one or more particular agents or treatments
[0114] To give but one example, if a subject is initially classified as
responsive (because the
subject's gene expression was determined, via classifier, to be associated
with a disease, disorder,
or condition), a given anti-TNF therapy can then be administered. At a given
interval (e.g., every
six months, every year, etc.), the subject can be tested again to ensure that
they still qualify as
"responsive" to a given anti-TNF therapy. In the event the gene expression
levels for a given
subject change over time, and the subject no longer expresses genes associated
with the disease,
disorder, or condition, or now expresses genes associated with non-
responsiveness, the subject's
therapy can be altered to suit the change in gene expression.
[0115] Accordingly, in some embodiments, the present disclosure provides
methods of
administering therapy to a subject previously established via classifier as
responsive with anti-TNF
therapy.
[0116] In some embodiments, the present disclosure provides methods further
comprising
determining, prior to the administering, that a subject is not a responder via
a classifier; and
administering a therapy alternative to anti -TNF therapy.
32
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0117] In some embodiments, genes of the subject are measured by at least one
of a microarray,
RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR),
bead array, ELISA,
and protein expression and combinations thereof.
[0118] In some embodiments, the subject suffers from a disease, disorder, or
condition selected
from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis,
Crohn's disease, ulcerative
colitis, chronic psoriasis, hidradenitis suppurativa, multiple sclerosis, and
juvenile idiopathic
arthritis and combinations thereof.
[0119] In some embodiments, the anti -TNF therapy is or comprises
administration of i nfl i xi m ab,
adalimumab, etanercept, cirtolizumab pegol, golilumab, or biosimilars thereof
and combinations
thereof. In some embodiments, the anti-TNF therapy is or comprises
administration of intliximab
or adalimumab.
[0120] In some embodiments, the responsive and non-responsive prior subjects
suffered from the
same disease, disorder, or condition.
[0121] In some embodiments, the subjects to whom the anti-TNF therapy is
administered are
suffering from the same disease, disorder or condition as the prior responsive
and non-responsive
prior subjects.
[0122] In some embodiments, the disease, disorder, or condition is selected
from rheumatoid
arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn's disease,
ulcerative colitis, chronic
psoriasis, hidradenitis suppurativa, multiple sclerosis, and juvenile
idiopathic arthritis and
combinations thereof.
[0123] In some embodiments, the disease, disorder, or condition is rheumatoid
arthritis
[0124] In some embodiments, the disease, disorder, or condition is ulcerative
colitis.
D. Methods of Treatment
[0125] In some embodiments, a subject or population with respect to which anti-
TNF therapy is
administered, or from which anti-TNF therapy is withheld (or alternative
therapy is administered)
is one that is determined to exhibit a particular expression level one or more
genes, and in some
cases for a plurality of genes. In some embodiments, one or more genes is
determined to have an
expression level below a particular threshold; alternatively or additionally,
in some embodiments,
one or more genes is determined to have an expression level below a particular
threshold. In some
embodiments, a particular set of genes is determined to have a pattern of
expression in which each
is assessed relative to a particular threshold (and, e.g., is determined to he
above, below, or
comparable with such threshold).
[0126] In some embodiments, the present disclosure provides methods of
treating subjects
suffering from a disease, disorder, or condition comprising administering an
alternative to anti-
33
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
TNF therapy to a subject that has been determined to exhibit less than a
particular expression level
of one or more genes.
[0127] In some embodiments, the present disclosure provides methods of
administering the anti-
TNF therapy to subjects who have been determined to be responsive via a
classifier established to
distinguish between responsive and non-responsive prior subj ects who have
received the anti-TNF
therapy (e.g., wherein the classifier has been established, through
retrospective analysis, to
distinguish between those who did vs those who did not respond to anti-TNF
therapy that they
received); wherein the classifier that is developed by assessing: one or more
genes whose
expression levels significantly correlate (e.g., in a linear or non-linear
manner) to clinical
responsiveness or non-responsiveness; and at least one of: presence of one or
more single
nucleotide polymorphisms (SNPs) in an expressed sequence; and at least one
clinical characteristic
of the responsive and non-responsive prior subjects.
[0128] TNF-mediated disorders are currently treated by inhibition of TNF, and
in particular by
administration of an anti-TNF agent (e.g., by anti-TNF therapy). Examples of
anti-TNF agents
approved for use in the United States include monoclonal antibodies such as
adalimumab
(Hurniraw), certolizumab pegol (Cimzia'), infliximab (Remicade'), and decoy
circulating receptor
fusion proteins such as etanercept (Enbrelc)). These agents are currently
approved for use in
treatment of indications, according to dosing regimens, as set forth in Table
5.
34
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Table 5
Indicat Adalimumab Certolizum a Infliximab2 Etanerceptl
Golimumabl Golimumab2
ion b Pegoli
Juvenil = 10 kg (22 N/A N/A 0.8 mg/kg N/A N/A
lbs) to weekly, with a
Idiopat <15 kg maximum of
hic (33 lbs): 50 mg per
Arthriti 10 mg week
every
other
week
= 15 kg (33
lbs) to <
30 kg (66
lbs): 20
mg every
other
week
= > 30 kg
(66 lbs):
40 mg
every
other
week
Psoriati 40 mg every 400 mg 5 mg/kg at 0, 50 mg once
50 mg N/A
other week initially and at 2 and 6 weekly with
administered
Arthriti week 2 and 4, weeks, then or without
by
followed by every 8 weeks methotrexate
subcutaneous
200 mg every injection once
other week; a month
for
maintenance
dosing, 400
mg every 4
weeks
Rheum 40 mg every 400 mg In conjunction 50 mg once 50 mg
once a 2 mg/kg
atoid other week initially and at with
weekly with month intravenous
Arthriti Weeks 2 and methotrexate, or
without infusion over
4, followed by 3 mg/kg at 0. mcthotrcxatc 30
minutes at
200 mg every 2 and 6 weeks
0 and
other week; weeks, then 4,
then every
for every 8 weeks 8
weeks
maintenance
dosing, 400
mg every 4
weeks
Ankylo 40 mg every 400 mg (given 5 mg/kg at 0, 50 mg once 50 mg
N/A
sing other week as 2 2 and 6 weekly
administered
Spondy subcutaneous weeks, then by
litis injections of every 6 weeks
subcutaneous
200 mg each) injection once
initially and at a month
weeks 2 and
4, followed by
200 mg every
other week or
400 mg every
4 weeks
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Indic at Adalimumab Certolizuma Infliximab2 Etanercepti
Golimumabl Golimumab2
ion 1 b Pego11
Adult = Initial = 400 mg 5 mg/kg at 0; N/A N/A
N/A
Crohn's dose initially 2 and 6
Disease (Day 1): and at weeks, then
160 mg Weeks 2 every 8
= Second and 4 weeks.
dose two = Continue
weeks with 400
later (Day mg every
15): 80 four
mg weeks
= Two
weeks
later (Day
29):
Begin a
maintena
nce dose
of 40 mg
every
other
week
Pediatri 17 kg (37 lbs) N/A 5 mg/kg at 0, N/A N/A
N/A
to < 40 kg (88 2 and 6
Crohn's lbs): weeks, then
Disease = Initial every 8
dose weeks.
(Day 1):
80 mg
= Second
dose two
weeks
later (Day
15): 40
mg
= Two
weeks
later (Day
29):
Begin a
maintena
nce dose
of 20 mg
every
other
week
> 40 kg (88
lbs):
= Initial
dose
(Day 1):
160 mg
= Second
dose two
weeks
later (Day
15): 80
mg
36
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Indic at Adalimumab Certolizum a Infliximab2 Etanercepti
Golimumabl Golimumab2
ion b Pego11
= Two
weeks
later (Day
29):
Begin a
maintena
nce dose
of 40 mg
every
other
week
Ulccrati = Initial N/A 5 mg/kg at 0, N/A N/A
N/A
ye dose 2 and 6
Colitis (Day 1): weeks, then
160 mg every 8
= Second weeks.
dose two
weeks
later (Day
15): 80
mg
= Two
weeks
later (Day
29):
Begin a
maintena
nce dose
of 40 mg
every
other
week
Plaque 80 mg initial N/A N/A 50 mg twice N/A N/A
Psoriasi dose; 40 mg weekly for 3
evely other months,
week followed by
beginning one 50 mg once
week after weekly
initial close
Hidrade = Initial N/A N/A N/A N/A N/A
nitis dose
Suppur (Day 1):
ativa 160 mg
= Second
dose two
weeks
later (Day
15): 80
mg
= Third
dose
(Day 29)
and
subseque
nt doses:
40 mg
37
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Indic at Adalimumab Certolizum a Infliximab2 Etanercepti
Golimumabl Golimumab2
ion b Pegoll
every
week
Uveitis 80 mg initial N/A N/A N/A N/A N/A
close: 40 mg
every other
week
beginning onc
week after
initial dose
1. Administered by subcutaneous injection.
2. Administered by intravenous infusion.
38
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0129] The present disclosure provides technologies relevant to anti-TNF
therapy, including those
therapeutic regimens as set forth in Table 5. In some embodiments, the anti-
TNF therapy is or
comprises administration of infliximab (Remicade'), adalimumab (Humire),
certolizumab pegol
(Cimzia"), etanercept (Enbrel"), or biosimilars thereof. In some embodiments,
the anti-TNF
therapy is or comprises administration of infliximab (Remicade ) or adalimumab
(Humira ) and
combinations thereof. In some embodiments, the anti-TNF therapy is or
comprises administration
of infliximab (Remicade-). In some embodiments, the anti-TNF therapy is or
comprises
admini strati on of adali mum ab (Hum i ra").
[0130] In some embodiments, the anti-TNF therapy is or comprises
administration of a biosimilar
anti-TNF agent. In some embodiments, the anti-TNF agent is selected from
infliximab biosimilars
such as CT-P13, BOW015, SB2, Inflectra , Renflexis , and IxifliM, adalimumab
biosimilars such
as ABP 501 (AMGEVITATm), Adfrar, and HulioTM and etanercept biosimilars such
as HD203,
SB4 (Benepali"), GP2015, Erelzi", and Intacept and combinations thereof.
[0131] In some embodiments, treatment of, for example, juvenile idiopathic
arthritis, psoriatic
arthritis, rheumatoid arthritis, ankylosing spondylitis, pediatric Crohn's
Disease, ulcerative colitis,
plaque psoriasis, hidradenitis suppurativa, and uveitis comprises a dosing
regimen of an anti-TNF
agent in Table 5. In some embodiments, the anti-TNF agent comprises, for
example, adalimumab
in Table 5. In some embodiments, dosing regimen for adalimumab comprises, for
example, an
initial dose of up to 160 mg or more. In some embodiments, dosing regimen for
adalimumab
comprises, for example, a second dose of up to 80 mg or more. In some
embodiments, dosing
regimen for adalimumab comprises, for example, a maintenance dose of up to 40
mg or more every
other week. In some embodiments, the anti-TNF agent comprises, for example,
certolizumab pegol
in Table 5. In some embodiments, dosing regimen for certolizumab pegol
comprises, for example,
a first initial dose up to 400 mg or more. In some embodiments, dosing regimen
for certolizumab
pegol comprises, for example, a second initial dose up to 400 mg or more at
week 2. In some
embodiments, dosing regimen for certolizumab pegol comprises, for example, a
third initial dose
up to 400 mg or more at week 4. In some embodiments, dosing regimen for
certolizumab pegol
comprises, for example, a maintenance dose of up to 200 mg or more every other
week or a
maintenance dose of up to 400 mg or more every four weeks. In some
embodiments, the anti-TNF
agent comprises, for example, infliximab in Table 5. In some embodiments,
dosing regimen for
infliximab comprises, for example, a first initial dose of up to 5 mg/kg or
more. In some
embodiments, dosing regimen for infliximab comprises, for example, a second
initial dose of up
to 5 mg/kg or more at week 2. In some embodiments, dosing regimen for
infliximab comprises,
for example, a third initial dose of up to 5 mg/kg or more at week 6 In some
embodiments, dosing
regimen for infliximab comprises, for example, a maintenance dose of up to 5
mg/kg or more every
39
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
6 weeks or every 8 weeks. In some embodiments, the anti-TNT agent comprises,
for example,
etanercept in Table 5. In some embodiments, dosing regimen for etanercept
comprises, for
example, initial doses of up to 50 mg or more twice weekly for three months.
In some
embodiments, dosing regimen for etanercept comprises, for example, a
maintenance dose of up to
50 mg or more every week. In some embodiments, the anti-TNF agent comprises,
for example,
golimumab in Table 5. In some embodiments, dosing regimen for golimumab
comprises, for
example, a dose of up to 50 mg or more every month. In some embodiments,
dosing regimen for
golimumab comprises, for example, a first initial dose of up to 2 mg/kg. In
some embodiments,
dosing regimen for golimumab comprises, for example, a second initial dose of
up to 2 mg/kg or
more at week 2. In some embodiments, dosing regimen for golimumab comprises,
for example, a
maintenance dose of up to 2 mg/kg or more every 8 weeks.
[0132] In some embodiments, the present disclosure provides methods of
treating subjects
suffering from an autoimmune disorder, the method comprising: administering an
anti-TNF
therapy to subjects who have been determined to be responsive via a classifier
established to
distinguish between responsive and non-responsive prior subjects in a cohort
who have received
the anti-TNF therapy; wherein the classifier is developed by assessing: one or
more genes whose
expression levels significantly correlate (e.g., in a linear or non-linear
manner) to clinical
responsiveness or non-responsiveness; at least one of: presence of one or more
single nucleotide
polymorphisms (SNPs) in an expressed sequence of the one or more genes; or at
least one clinical
characteristic of the responsive and non-responsive prior subjects; and
wherein the classifier is
validated by an independent cohort than the cohort who have received the anti -
TNF therapy.
[0133] In some embodiments, the subject has been previously administered the
anti-TNF therapy.
In some embodiments, the subject has been administered the anti-TNF therapy at
least one, at least
two, at least three, at least four, at least five, or at least six months
prior to said administering.
[0134] In some embodiments, data derived from subjects in the cohort who have
received the anti-
TNF therapy is of one type (e.g., microarray, RNAseq, etc.), and the data used
to validate the
classifier in the independent cohort is derived from a different type (e.g.,
microarray, RNAseq).
Accordingly, some embodiments, the classifier is established using microarray
analysis derived
from the responsive and non-responsive prior subjects In some embodiments, the
classifier is
validated using RNAseq data derived from the independent cohort.
E. Diseases, Disorders or Conditions
[0135] In general, provided disclosures are useful in any context in which
administration of anti-
TNF therapy is contemplated or implemented. In some embodiments, provided
technologies are
useful in the diagnosis or treatment of subjects suffering from a disease,
disorder, or condition
associated with aberrant (e.g., elevated) TNF expression or activity. In some
embodiments,
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
provided technologies are useful in monitoring subj ects who are receiving or
have received anti-
TNF therapy. In some embodiments, provided technologies identify whether a
subject will or will
not respond to a given anti-TNF therapy. In some embodiments, the provided
technologies identify
whether a subject will develop resistance to a given anti-TNF therapy.
[0136] Accordingly, the present disclosure provides technologies relevant to
treatment of the
various disorders related to TNF, including those listed in Table 5. In some
embodiments, a subject
is suffering from a disease, disorder, or condition selected from rheumatoid
arthritis, psoriatic
arthritis, ankylosing spondylitis, Crohn's disease (adult or pediatric),
ulcerative colitis,
inflammatory bowel disease, chronic psoriasis, plaque psoriasis, hidradenitis
suppurativa, asthma,
uveitis, and juvenile idiopathic arthritis and combinations thereof. In some
embodiments, the
disease, disorder, or condition is rheumatoid arthritis. In some embodiments,
the disease, disorder,
or condition is psoriatic arthritis. In some embodiments, the disease,
disorder, or condition is
ankylosing spondylitis. In some embodiments, the disease, disorder, or
condition is Crohn's
disease. In some embodiments, the disease, disorder, or condition is adult
Crohn' s disease. In
some embodiments, the disease, disorder, or condition is pediatric Crohn's
disease. In some
embodiments, the disease, disorder, or condition is inflammatory bowel
disease. In some
embodiments, the disease, disorder, or condition is ulcerative colitis. In
some embodiments, the
disease, disorder, or condition is chronic psoriasis. In some embodiments, the
disease, disorder,
or condition is plaque psoriasis. In some embodiments, the disease, disorder,
or condition is
hidradenitis suppurativa. In some embodiments, the disease, disorder, or
condition is asthma. In
some embodiments, the disease, disorder, or condition is uveiti s In some
embodiments, the
disease, disorder, or condition is juvenile idiopathic arthritis.
[0137] In some embodiments, the disease, disorder or condition is granuloma
annulare, necrobiosis
lipoidica, hiradenitis suppurativa, pyoderma gangrenossum, Sweet's syndrome,
subcorneal
pustular dermatosis, systemic lupus erythema tosus, scleroderma,
dermatomyositis, Behcet's
disease, acute/chronic graft versus host disease, pityriasi s rubra pilaris,
Sjorgren's syndrome,
Wegener's granulomatosis, polymyalgia rheumatic, dermatomyositis, and pyoderma
gangrenosum
and combinations thereof.
[0138] Further, as noted, the present disclosure provides technologies that
allow practitioners to
reliably and consistently predict response in a cohort of subj ects. In
particular, for example, the
rate of response for some anti-TNF therapies is less than 35% within a given
cohort of subjects.
The provided technologies allow for prediction of greater than 65% accuracy
within a cohort of
subjects a response rate (e.g., whether certain subjects will or will not
respond to a given therapy).
In some embodiments, the methods and systems described herein predict 65% or
greater the
subjects that are non-responders (e.g., will not respond to anti-TNF therapy)
within a given cohort.
41
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
In some embodiments, the methods and systems described herein predict 70% or
greater the
subjects that are non-responders (e.g., will not respond to anti-TNF therapy)
within a given cohort.
In some embodiments, the methods and systems described herein predict 80% or
greater the
subjects that are non-responders (e.g., will not respond to anti-TNF therapy)
within a given cohort.
In some embodiments, the methods and systems described herein predict 90% or
greater the
subjects that are non-responders (e.g., will not respond to anti-TNF therapy)
within a given cohort.
In some embodiments, the methods and systems described herein predict 100% the
subj ects that
are non-responders (e.g., will not respond to anti-TNF therapy) within a given
cohort.
Computer control systems
[0139] The present disclosure provides computer control systems that are
programmed to
implement methods of the disclosure. FIG. 10 shows a computer system 1001 that
is programmed
or otherwise configured to generate or develop autoantibody profile or compare
autoantibodies
with the profile of the specific immune response. The computer system 1001 can
regulate various
aspects of the present disclosure, such as, for example, receive or generate
sequence reads, correlate
sequences to specific epitopes or autoantibodies, output a result for the user
as to the presence of
an autoantibody or profile, or an expected progression of a disease The
computer system 1001 can
be an electronic device of a user or a computer system that is remotely
located with respect to the
electronic device. The electronic device can be a mobile electronic device.
[0140] The computer system 1001 includes a central processing unit (CPU, also
"processor" and
-computer processor" herein) 1005, which can be a single core or multi core
processor, or a
plurality of processors for parallel processing The computer system 1001 also
includes memory
or memory location 1010 (e.g., random-access memory, read-only memory, flash
memory),
electronic storage unit 1015 (e.g., hard disk), communication interface 1020
(e.g., network adapter)
for communicating with one or more other systems, and peripheral devices 1025,
such as cache,
other memory, data storage or electronic display adapters The memory 1010,
storage unit 1015,
interface 1020 and peripheral devices 1025 are in communication with the CPU
1005 through a
communication bus (solid lines), such as a motherboard. The storage unit 1015
can be a data
storage unit (or data repository) for storing data. The computer system 1001
can be operatively
coupled to a computer network ("network") 1030 with the aid of the
communication interface
1020. The network 1030 can be the Internet, an internet or extranet, or an
intranet or extranet that
is in communication with the Internet. The network 1030 in some cases is a
telecommunication or
data network. The network 1030 can include one or more computer servers, which
can enable
distributed computing, such as cloud computing. The network 1030, in some
cases with the aid of
the computer system 1001, can implement a peer-to-peer network, which may
enable devices
coupled to the computer system 1001 to behave as a client or a server.
42
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0141] The CPU 1005 can execute a sequence of machine-readable instructions,
which can be
embodied in a program or software. The instructions may be stored in a memory
location, such as
the memory 1010. The instructions can be directed to the CPU 1005, which can
subsequently
program or otherwise configure the CPU 1005 to implement methods of the
present disclosure.
Examples of operations performed by the CPU 1005 can include fetch, decode,
execute, and
writeback.
[0142] The CPU 1005 can be part of a circuit, such as an integrated circuit.
One or more other
components of the system 1001 can be included in the circuit In some cases,
the circuit is an
application specific integrated circuit (ASIC).
[0143] The storage unit 1015 can store files, such as drivers, libraries and
saved programs. The
storage unit 1015 can store user data, e.g., user preferences and user
programs The computer
system 1001 in some cases can include one or more additional data storage
units that are external
to the computer system 1001, such as located on a remote server that is in
communication with the
computer system 1001 through an intranet or the Internet.
[0144] The computer system 1001 can communicate with one or more remote
computer systems
through the network 1030. For instance, the computer system 1001 can
communicate with a
remote computer system of a user. Examples of remote computer systems include
personal
computers (e.g., portable PC), slate or tablet PC's (e.g., Apple iPad,
Samsung Galaxy Tab),
telephones, Smart phones (e.g., Apple iPhone, Android-enabled device,
Blackberry ), or personal
digital assistants. The user can access the computer system 1001 via the
network 1030.
[0145] Methods as described herein can be implemented by way of machine (e.g.,
computer
processor) executable code stored on an electronic storage location of the
computer system 1001,
such as, for example, on the memory 1010 or electronic storage unit 1015. The
machine executable
or machine-readable code can be provided in the form of software. During use,
the code can be
executed by the processor 1005 In some cases, the code can be retrieved from
the storage unit
1015 and stored on the memory 1010 for ready access by the processor 1005. In
some situations,
the electronic storage unit 1015 can be precluded, and machine-executable
instructions are stored
on memory 1010.
[0146] The code can be pre-compiled and configured for use with a machine
haying a processer
adapted to execute the code or can be compiled during runtime. The code can be
supplied in a
programming language that can be selected to enable the code to execute in a
pre-compiled or as-
compiled fashion.
[0147] Aspects of the systems and methods provided herein, such as the
computer system 1001,
can be embodied in programming Various aspects of the technology may be
thought of as
"products" or "articles of manufacture" typically in the form of machine (or
processor) executable
43
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
code or associated data that is carried on or embodied in a type of machine
readable medium.
Machine-executable code can be stored on an electronic storage unit, such as
memory (e.g., read-
only memory, random-access memory, flash memory) or a hard disk. "Storage"
type media can
include any or all of the tangible memory of the computers, processors or the
like, or associated
modules thereof, such as various semiconductor memories, tape drives, disk
drives and the like,
which may provide non-transitory storage at any time for the software
programming. All or
portions of the software may at times be communicated through the Internet or
various other
telecommunication networks. Such communications, for example, may enable
loading of the
software from one computer or processor into another, for example, from a
management server or
host computer into the computer platform of an application server. Thus,
another type of media
that may bear the software elements includes optical, electrical and
electromagnetic waves, such
as used across physical interfaces between local devices, through wired and
optical landline
networks and over various air-links. The physical elements that carry such
waves, such as wired
or wireless links, optical links or the like, also may be considered as media
bearing the
software. As used herein, unless restricted to non-transitory, tangible
"storage" media, terms such
as computer or machine "readable medium" refer to any medium that participates
in providing
instructions to a processor for execution.
[0148] Hence, a machine readable medium, such as computer-executable code, may
take many
forms, including but not limited to, a tangible storage medium, a carrier wave
medium or physical
transmission medium. Non-volatile storage media include, for example, optical
or magnetic disks,
such as any of the storage devices in any computer(s) or the like, such as may
he used to implement
the databases, etc. shown in the drawings. Volatile storage media include
dynamic memory, such
as main memory of such a computer platform. Tangible transmission media
include coaxial cables;
copper wire and fiber optics, including the wires that comprise a bus within a
computer
system. Carrier-wave transmission media may take the form of electric or
electromagnetic signals,
or acoustic or light waves such as those generated during radio frequency (RF)
and infrared (IR)
data communications. Common forms of computer-readable media therefore include
for example:
a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic
medium, a CD-ROM,
DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other
physical storage
medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM,
any
other memory chip or cartridge, a carrier wave transporting data or
instructions, cables or links
transporting such a carrier wave, or any other medium from which a computer
may read
programming code or data. Many of these forms of computer readable media may
be involved in
carrying one or more sequences of one or more instructions to a processor for
execution.
44
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0149] The computer system 1001 can include or be in communication with an
electronic display
1035 that comprises a user interface (UI) 1040 for providing, for example,
selecting autoantibodies
for analysis, interacting with graphs correlating autoantibodies to specific
generated profiles.
Examples of UI' s include, without limitation, a graphical user interface
(GUI) and web-based user
interface
[0150] Methods and systems of the present disclosure can be implemented by way
of one or more
algorithms. An algorithm can be implemented by way of software upon execution
by the central
processing unit 1005. The algorithm can, for example, calculate statistics
measurements to identify
autoantibodies and generate profiles or predict efficacy and toxicity of a
treatment.
EXAMPLES
Example 1 - A molecular signature response classifier to predict inadequate
response to tumor
necrosis factor-alpha inhibitors in rheumatoid arthritis.
[0151] Rheumatoid arthritis (RA) is an autoimmune disease characterized by
chronic
inflammation that causes joint destruction. Following inadequate response to
synthetic disease
modifying anti-rheumatic drugs (csDMARDs) such as methotrexate, clinical
guidelines suggest
one of many targeted therapies with comparable efficacies and safety profiles
including tumor
necrosis factor-a inhibitors (TNFi), 1L-6 inhibitors, Janus kinase (JAK)
inhibitors, and B or T cell
modulators. The abundance of treatment options underscores the need for
precision medicine in
rheumatology. Because clinical guidelines do not recommend one treatment over
another, therapy
selection is often driven by administrative directives and TNFi therapies
remain the prevailing
treatment in nearly 90% of RA patients. Matching each patient with the right
targeted therapy to
reach treat-to-target goals of low disease activity (LDA) or remission is a
critical unmet medical
need in RA.
[0152] A subset of RA patients have an adequate response to TNFi treatment: 50-
70% achieve
ACR20, 30-40% achieve ACR50, and 15-25% achieve ACR70 response and 10-25%
achieve
remission. Many studies have attempted to identify biomarkers and develop
models to predict
response to TNFi therapy before the initiation of treatment. Failure to
validate and reproduce the
performance of these predictive biomarkers in new patient populations and
clinical trials was a
typical outcome. Differing characteristics between patient populations,
laboratory methods,
procedures in generating molecular data, and other biases inherent to single-
cohort retrospective
blood studies have hindered precision medicine progress not only in
rheumatology but in other
medical specialties as well.
[0153] A blood-based molecular signature test that integrated next generation
RNA sequencing
data with clinical features to predict the likelihood of an RA patient having
an inadequate response
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
to TNFi therapy was developed with a novel network medicine approach to
biomarker discovery.
Clinical validation of this molecular signature test in a subset of patients
from the CERTAIN study
revealed that patients with a molecular signature of non-response were
unlikely to reach ACR50
at 6 months.
[0154] Mapping of disease-related proteins onto the human interactome, a
network map of
pairwise protein-protein interactions that occur in human cells, has yielded
new insights into
human disease biology and response to therapy. With the identification of
molecular biomarkers
involved in RA biology discovered from human interactome analyses, this study
demonstrated the
TNFi inadequate response prediction performance of a molecular signature
response classifier
(MRSC) in two prospective observational clinical studies: the CERTAIN study,
and NETWORK-
004. Validation of the MSRC was performed in a total of 391 blood samples from
targeted therapy-
naive patients and 113 blood samples from TNFi therapy-exposed patients.
Methods
[0155] Patients
[0156] An outline of the study design, Corrona, is described in FIG. 6. The
CERTAIN study
included 345 RA patient PAXgene blood samples and clinical measurements, a
comparative
effectiveness study for RA patients initiating a biologic. The CERTAIN study
was nested within
the Corrona registry. Institutional Review Board or Ethics Committee approvals
were obtained
prior to sample collection and study participation, and patients provided
informed consent.
CERTAIN was a comparative effectiveness study investigating initiators of
biologics. For these
analyses, samples selected were from patients who were naive to targeted
therapies at the time of
sample collection and initiated a TNFi therapy. 92% (318/345) of these
patients were included in
previous classifier training and validation analyses. Consistent with the
inclusion criteria of the
CERTAIN study, all patients had a Clinical Disease Activity Index (CDAI)
greater than ten at the
time of biologic therapy initiation_ Clinical and molecular data were used for
biomarker feature
selection (100 patients) and in-cohort cross-validation (245 patients).
Patients were randomly
allocated to these two separate analyses, irrespective of how each sample was
used in previous
studies.
[0157] The NETWORK-004. Patients were determined by the treating
rheumatologist to be
candidates for TNFi therapy prior to enrollment. Eligible patients were >18
years of age, had
active RA (CDAI >10, swollen joint count >4) and were receiving a stable dose
of methotrexate
(2'15 mg/week) for >10 weeks prior to baseline. Doses of hydroxychloroquine
not exceeding 400
mg per day or leflunomide not exceeding 20 mg per day were permitted so long
as the dose was
stable for at least 4 weeks prior to the baseline visit Prednisone doses of
<10 mg per day were
allowable as long as the dose was stable for at least 2 weeks prior to
baseline. Use of intra-articular
46
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
or parenteral corticosteroids <2 weeks prior to the first study procedure was
prohibited. The study
was approved by the Copernicus Group Independent Review Board (approval it
20191082) and
local review boards where required. All patients provided written informed
consent. Dosage and
treatment of TNFi therapies were at the rheumatologist's discretion. At the 3-
month follow-up,
rheumatologists were permitted to make dosing adjustments if deemed
appropriate clinical care.
Initiation of a second TNFi therapy resulted in subject withdrawal. The COVID-
19 pandemic
contributed to a higher level of attrition than initially projected. Of the
273 RA patients enrolled,
168 completed the 24-week study. 146 patients had complete clinical and
molecular data and were
included in analyses. Information about patients who left the study are
available in Supplementary
Table Si. PAXgene blood samples collected at the 3-month visits from 113
patients were analyzed
as TNFi-exposed samples.
[0158] Clinical evaluation and response to TNFi therapies
[0159] Feature selection response definition: Clinical outcome metrics such as
swollen and tender
joint counts, patient and physician disease assessments have inherent
variability. To identify a
subset of patients in the training cohort who have been assigned the responder
and non-responder
labels with high confidence, a Monte-Carlo simulation approach was implemented
to calculate a
confidence outcome score for each patient. The clinical outcomes data for
patients with at least
70% concordance between the simulations and the actual reported outcome were
considered high
confidence. High confidence clinical outcomes for both ACR and EULAR metrics
were used for
the feature selection.
[0160] The CERTAIN study examined baseline RNA sequencing data and clinical
assessments to
predict response to TNFi therapy at the 3- and 6-month follow-up visits
according to ACR, CDAI
and DAS28-CRP criteria.
[0161] The NETWORK-004 study examined clinical assessments were collected at
baseline, 3-
month and 6-month visits: 28-joint count for tenderness and swelling, patient
global assessment of
pain, patient global assessment of disease activity, CDAI score, Health
Assessment Questionnaire
and C-reactive protein (CRP). Rheumatoid factor (RF) and anti-cyclic
citrullinated protein (anti-
CCP) antibody serostatus was recorded at baseline. PAXgene RNA blood tubes
were collected at
all visits. The 3-month follow-up visit RNA sequencing data was used to
predict response to TNFi
therapy at the 6-month follow-up visit according to the ACR, CDAI and DAS28-
CRP criteria.
[0162] RNA preparation and sequencing analysis
[0163] RNA was extracted from whole blood in PAXgene RNA tubes using MagMaxTm
for
Stabilized Blood PAXgene Tubes RNA Isolation Kit (Thermo Fisher Scientific)
per the
manufacturer's instructions; 100-1000 ng of RNA was processed using the KAPA
RNA HyperPrep
Kit with RiboErase (HMIR) Globin. Samples were quantified using Agilent D1000
reagents.
47
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Libraries were sequenced to high uniform depth targeting > 7 million protein
coding reads. The
CERTAIN cohort was sequenced using the Illumina NextSeq DX 500 and NovaSeq
6000
instruments. The NETWORK-004 samples were sequenced using the Illumina NovaSeq
6000
instrument using a validated diagnostic assay under Clinical Laboratory
Improvement
Amendments (CLIA). Sequence data was processed to determine gene expression
across the
whole genome. To be included in analyses, samples had to have a TapeStation
RIN > 4, RNA
concentration > 10 ng/uL, sequencing library yield > 10 n1V1, % perfect
basepair index > 85, %
bases over Phred score 30 > 75, the mean quality Phred score > 30, the median
Phred score > 25
and a lower quartile Phred score > 10 for all bases.
[0164] Human interactome analysis and feature selection
[0165] For selection of transcript biomarker features, 100 samples were
randomly selected out of
the cohort of 345 patients (CERTAIN study). The random forest algorithm was
used to rank
protein coding transcripts through 96 rounds of 20% cross validation in-silico
experiments.
Features that were ranked in the top 100 in 70/96 iterations were further
analyzed by the human
interactome analysis to identify biologically relevant biomarkers. Biomarkers
overlapping with
the RA disease module on the human interactome35 or possessing a significant
number of
connections to the disease module were used in the final model. Significance
of connections was
assessed using the hypergeometric test.
[0166] Predictive classification model training and validation
[0167] Samples not included in biomarker feature selection were evaluated.
Transcripts identified
in this study were integrated with previously described biomarkers using
machine learning to re-
train a response classification model. To assess model performance, 10-fold
cross-validation was
conducted using a feedforward artificial neural network. Model building was
done using the
MLPClassifier package available in Python's machine learning library sklearn.
[0168] Statistical analysis
[0169] Statistical analyses were performed using Python 3.7.6 and R version
3.6.1. Continuous
data were summarized with mean, standard deviation, median, minimum, maximum,
and number
of evaluable observations. Categorical variables were summarized with
frequency counts and
percentages. Confidence intervals (CI) were determined, where appropriate,
using the t-
distribution for continuous data an exact method for categorical variables.
All tests were done in
a two-sided setting Unless otherwise specified, hypothesis testing was
performed at the two-sided
0.05 significance level. All attempts were made to limit missing data. No
attempt was made to
impute missing data.
[0170] Results
48
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0171] Identification of a molecular signature of non-response to TNFi
therapies using the human
interactome
[0172] Transcripts predictive of inadequate response to TNFi therapies
according to ACR50 and
EULAR response definitions (see Methods) at 6 months were determined using
machine learning
from baseline blood sample data of 100 RA targeted-therapy naive patients
randomly selected from
the Corrona CERTAIN study. To ensure that transcripts reflected RA disease
biology, the proteins
encoded by selected transcripts were mapped onto the human interactome map of
pairwise protein-
protein interactions to identify transcripts that were significantly connected
(p-value <0.05) to the
RA disease module (FIG. 1). The TNFi therapy response features overlap with to
the same network
neighborhood of the human interactome consisting of RA disease-associated
proteins. These
features included proteins with relevance to RA pathobiology, including JAK3
and interleukin-1
beta (IL-1B). The molecular signature of non-response to TNFi therapy included
23 features: 19
RNA transcripts and 4 clinical features (Table 6):
Table 6
ALPL
ATRAID
BCL6
CDK 1 1 A
CFLAR
COMMD5
GOL GA1
'LIB
IMPDH2
JAK3
KLEIDC3
LIMK2
NOD2
NOTCH1
SPINT2
SPON2
STOML2
TRIM25
ZFP3 6
BMI
Sex
Patient Global
Assessment
Anti-CCP
[0173] In-cohort validation of the MSRC
49
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0174] The MSRC was tested through in-cohort cross-validation among baseline
blood samples of
an independent cohort of 245 patients from the Corrona CERTAIN study who were
naive to
targeted therapies (Table 7). This resulted in AUC values of 0.63 to 0.67 for
ACR50, ACR70,
CDAI and DAS28 responses at six months post treatment initiation (FIG. 2A, and
Table 8).
Significant differences in the log-likelihood ratio of model scores (p<0.001)
were observed
between patients who did and did not have the molecular signature of non-
response (FIG. 2B).
Furthermore, the proportion of patients who achieved LDA or remission at 6
months per CDAI
and DA S28-CRP was greater among those patients who lacked a molecular
signature of non-
response (FIG. 2C).
Table 7
Characteristic Corrona Corrona NETWORK- NETWORK-
CERTAIN CERTAIN 004 study
004 study
study feature study cross- targeted TNFi-
exposed
selection validation therapy-naïve
(N =113)
(N =100) (N = 245) (N-146)
Age (year), mean (SD) 54 (12.4) 55 (12.3) 58 (14.1) 57
(14.9)
Female, n (%) 72 (72.0%) 179 (73.1%) 115 (78.8%) 87
(77.0%)
Duration of disease 1(1,5) 2(1, 6) 1(0, 4.25) 1(0,4)
(year), median (1QR)
Race, n (%)
White 83(83.0%) 213 (86.9%) 113 (77.4%)
92(81.4%)
Black 9(9.0%) 13 (5.3%) 16 (11.0%) 12
(10.6%)
Other 8 (8.0%) 19 (7.8%) 13 (8.9%) 9
(8.0%)
CCP positive, n (%) 62 (62.0%) 154 (62.9%) 72 (49.3%) 54
(47.8%)
RF positive, n (%) 76 (76.0%) 172 (70.2%) 55 (37.7%) 48
(54.5%)
Prednisone at baseline, 30 (30.0%) 64 (26.1%) 33 (22.6%) 23
(20.4%)
n(%)
Prednisone dosage, 5 (5, 10) 5 (5, 10) 5 (5, 9.38) 5 (5,
5)
median (IQR)
Current csDMARD, n
(%)
Methotrexate 56(56.0%) 138 (56.3%) 113 (77.4%)
81(71.7%)
>2 csDMARDs 7(7.0%) 42 (17.1%) 11(7.5%) 8(7.1%)
None 15 (15.0%) 37 (15.1%) 32 (22.6%) 32
(28.3%)
TNFi use, n (%)
Adalimumab 36 (36.0%) 98 (40.0%) 48 (32.9%) 40
(35.4%)
Etanercept 35(35.0%) 76(31.0%) 31(21.2%)
25(22.1%)
Infliximab 15(15.0%) 48(19.6%) 18(12.3%
16(14.2%)
Certolizumab pegol 10 (10.0%) 17 (6.9%) 13 (8.9%) 7
(6.2%)
Golimumab 4 (4.0%) 6 (2.4%) 36 (24.7%) 25
(22.1%)
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Table 8
Cross-validation, naive AUC
Odds ratio (95% CI; p-value)
ACR50, 6 months 0.66 3.0
(1.6-5.5; 0.0002)
ACR70, 6 months 0.66 3.4
(1.6-7.1; 0.0008)
CDAI LDA, 6 months 0.67 3.7
(2.2-6.4; <0.0001)
CDAI remission, 6 months 0.67 3.4
(1.6-7.6; 0.0014)
DAS28-CRP LDA, 6 months 0.64 2.5
(1.5-4.3; 0.0005)
DAS28-CRP remission, 6 months 0.65 2.7
(1.6-4.7; 0.0003)
[0175] Validation of the MSRC in a prospective observational clinical study
among targeted
treatment naive patient samples
[0176] To further validate the ability of the MSRC to predict the likelihood
of inadequate response
to TNFi therapies, patient samples were prospectively collected in a multi-
center observational
clinical study. Following clinical and molecular data quality review, 146
patients completed the
24-week study and were included in analyses. These patients were predominantly
female (78.8%)
and white (80.1%), with a median age of 58 years (Table 7). TNFi therapy
choice was at the
discretion of the prescribing physician and all five therapeutic options
within the class were
represented (adalimumab 32.9%, certolizumab pegol 8.9%, etanercept 21.2%,
infliximab 12.3%
and golimumab 24.7%). A molecular signature of non-response was detected at
baseline for 44.5%
(65/146) of patients.
[0177] According to the primary endpoint of ACR50 response to TNFi therapy at
6 months, the
MSRC stratified patients according to their likelihood of inadequate response
to TNFi therapy with
an AUC of 0.64 (FIG. 3A) and an odds ratio of 4.1 (95% CI: 2.0-8.3; p-value
0.0001) (Table 9).
[0178] Additional endpoints included assessment of ACR50 response at 3 months
and response to
treatment at 3 and 6 months according to ACR70, DAS28-CRP remission (<2.4) or
LDA (<2.9)
and CDAI remission (< 2.8) or LDA (< 10). The MSRC stratified patients
according to their
likelihood of inadequate response at both timepoints and response criteria
with AUC values
ranging from 0.59-0.74 (FIGs. 3A-3B) and significant odds ratios of 3.0-9.1 (p-
value <0.01) (Table
7). Odds ratios describing whether patients with a molecular signature of non-
response failed to
achieve ACR70 or DAS28-CRP remission were significant at 6 months (p-value
<0.0001), but not
3 months (p-values 0.07 and 0.34, respectively). Significant differences (p-
value <0.002) in model
scores between patients who did and did not have a molecular signature of non-
response were
observed for all response criteria except DAS28-CRP remission at 3 months
(FIGs. 3B-3C).
51
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Furthermore, the fraction of patients who achieved remission and LDA at 6
months per CDAI and
DAS28-CRP definitions was greater among those patients who did not have a
molecular signature
of non-response (FIGs. 3E-3F).
Table 9
AUC Odds ratio (95%
CI; p-value)
Prospective observational, naive
ACR50, 3 months 0.67 3.5 (1.6-7.7;
0.002)
ACR50, 6 months 0.64 4.1(2.0-8.3;
0.0001)
ACR70, 3 months 0.67 2.6 (1.0-6.7;
0.07)
ACR70, 6 months 0.72 9.1 (3.5-24.2;
<0.0001)
CDAI LDA, 3 months 0.65 3.0 (1.4-6.1;
0.005)
CDAI LDA, 6 months 0.68 3.6 (1.8-7.2;
0.0002)
CDAI remission, 3 months 0.70 3.4 (1.3-8.7;
0.01)
CDAI remission, 6 months 0.74 8.4 (3.0-23.9;
<0.0001)
DAS28-CRP LDA, 3 months 0.69 3.3 (1.5-7.1,
0.002)
DAS28-CRP LDA, 6 months 0.67 3.4 (1.7-6.8;
0.0007)
DAS28-CRP remission, 3 months 0.59 1.5 (0.7-3.2;
0.34)
DAS28-CRP remission, 6 months 0.74 5.2 (2.5-11.1;
<0.0001)
Prospective observational, TNFi-exposed
ACR50, 6 months 0.67 3.3 (1.5-7.4;
<0.0001)
ACR70, 6 months 0.76 7.3 (2.3-23.3;
0.00004)
CDAI LDA, 6 months 0.74 7.5 (3.2-17.6;
<0.0001)
CDAI remission, 6 months 0.84 25.4 (3.2-200.6;
<0.0001)
DAS28-CRP LDA, 6 months 0.71 6.2 (2.6-14.9;
<0.0001)
DAS28-CRP remission, 6 months 0.65 2.0 (0.9-4.6; 0.1)
[0179] Validation of the MSRC in a prospective observational clinical study
among TNFi-exposed
patient samples
[0180] Among patients who completed the 24-week study, RNA blood samples at 3
months were
available for 113 patients. Using the same MSRC as in the targeted therapy-
naive analyses, 3-
month patient samples were used to predict inadequate response to TNFi
therapy. The molecular
signature in these TNFi-exposed samples stratified patients according to
inadequate response to
treatment with AUC values of 0.65 to 0.84 (FIG. 4A). A molecular signature of
non-response was
detected for 40.7% (46/113) of TNFi-exposed patients and a significant
difference in model scores
(p-values <0.012) was observed between patients who did and did not have a
molecular signature
of non-response (FIG. 4B). This corresponded to significant odds ratios of 3.3-
25.4 among patients
with a molecular signature failing to have a response to treatment according
to all criteria except
for DAS28-CRP remission (Table 9).
Discussion
52
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0181] Many targeted therapeutic options are available in RA, but therapy
selection is a challenge
because these options have similar treatment outcomes. Precision medicine
tools are greatly
needed to identify which patients have the appropriate disease biology for
each targeted therapy.
The blood-based MSRC described analyzes RNA sequencing data along with
clinical features to
accurately identify targeted-therapy naïve and TNFi-exposed patients who were
unlikely to have
an adequate response to TNFi therapy. Among patients initiating their first
targeted therapy, those
with a molecular signature of non-response were three to nine times less
likely to have an adequate
response to a TNFi therapy (Table 7). When testing was performed after the
patient had been
receiving TNFi therapy for at least three months, patients with a molecular
signature of non-
response were as much as 25 times less likely to achieve remission.
Furthermore, the molecular
signature of non-response was predictive of inadequate response to TNFi
therapy according to
multiple clinically validated measures including ACR50, ACR70, DAS28-CRP and
CDAI. The
MSRC can inform provider decision-making at multiple occasions in the care
pathway, such as
before initial therapy selection or when targeted TNFi therapy does not result
in treatment goals
and a second therapy or dose escalation is being considered. By validating
multiple response target
definitions, the MSRC fits within multiple practice protocols, making it easy
to understand, act
upon and operationalize within clinical settings.
[0182] Precision medicine has improved patient outcomes in oncology and
hematology by
matching therapy selection to patients' unique biology. Yet even in these
fields, predicting drug
response, particularly from blood, has remained a challenging technical
problem. In addition,
machine learning and statistical approaches are prone to over-fitting to
characteristics and
attributes of the study cohort population. Unlike in oncology, the reliance on
DNA analyses and
biopsies of disease tissue is not readily amenable in RA patient care because
synovial biopsies
outside of clinical studies are rare and DNA sequence variations provide
limited actionable
information in RA. Studies of the AMPLE, AVERT, GO-BEFORE and GO-FORWARD
trials
have used baseline disease assessments such as DAS28, RAPID3, CDAI or SDAI to
predict
radiographic progression or magnetic resonance imaging-detected synovitis in
response to
treatment with a targeted therapy. Comparable AUC values were reported (0.54-
0.72) but the odds
ratios (1.01-1.65) were lower than those observed in this study (3.0-25.4).
Additionally, the odds
ratios in this study were consistent between the cross-validation CERTAIN
cohort and the
prospective NETWORK-004 cohort indicating that the MSRC is reproducible and
generalizable
across studies and patient populations. The technical challenges surrounding
development of
precision medicine tools underscores the importance of evaluating biomarkers
relevant to disease
biology and development of new approaches, such as network-based methods, as
evidenced by the
results of this study.
53
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0183] The network-based methods used in this study uncovered novel
connections within disease
biology. A survey of 248 US-based rheumatologists demonstrated that
rheumatologists may
welcome precision medicine advances in autoimmune diseases and may find value
in this
predictive drug response test Selection of TNFi therapy declined by more than
80% (from 79.8%
to as low as 11.3%) when rheumatologists were presented with a sample MSRC
result indicating
non-response. Furthermore, the majority of rheumatologists surveyed reported
that test results can
increase their confidence in prescribing decisions, improve medical decision-
making and alter their
treatment choices. Treatment selection guided by a precision medicine tool
that predicts response
to TNFi therapy was modeled to improve response rates to targeted therapies
and result in
healthcare cost savings.
[0184] The RNA transcripts in the MSRC evaluate seemingly disparate aspects of
disease biology
that are nonetheless unified in the same network neighborhood on the human
interactome and
capture the diverse biology of RA and response to TNFi therapy. The proteins
encoded by these
transcripts influence biological processes including cellular homeostasis for
adaptive and innate
immune cells, production of TNF-a and other secreted signaling molecules,
synovitis, and bone
destruction (FIG 5) TNF-a biology is robustly captured in the MSRC and
features are involved
in the production and release of TNF-a (e.g., COMMD5), and upstream or
downstream TNF-a
signaling events (eg., NOTCH1). Identification of molecular characteristics
expressed in
circulating blood cells suggests that direct evaluation of j oint physiology
or biochemistry may not
be essential to evaluating synovial phenotypes or response to treatment. The
MSRC is rooted in
RA disease biology and readily generalizes to the molecular phenotypes of an
independent cohort
of patients in the blinded study.
Conclusions
[0185] Validation of the MSRC involved analysis of RNA sequencing data derived
from blood
samples of 391 RA patients who were treated with a TNFi therapy from two
independent studies
and patient populations, reproducing for the first time the predictive ability
of molecular
biomarkers using seemingly disparate RA biology. These findings demonstrate
that direct
evaluation of joint physiology or biochemistry may not be essential to predict
response. Among
patients who are naive to targeted therapy and those who are TNFi-exposed,
patients with a
molecular signature of non-response are unlikely to respond to TNFi therapy at
3 or 6 months as
assessed by ACR50, ACR70, DAS28-CRP and CDAI. When providers use MSRC test
results to
stratify patients to treatment, patients who have a molecular signature of non-
response to TNFi
therapies can be directed to an alternative therapy to avoid expenses and
potential toxicities without
possible benefit. Those who lack this signature can proceed with TNFi therapy
and possibly
achieve an increased response rate relative to the unstratified population.
54
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Example 2 - Clinical lonkevitv of an R1VA siznature panel for prediction of
non-response to
tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients
101861 As a potentially debilitating autoimmune disease, rheumatoid arthritis
(RA) is the telltale
clinical presentation involving joint deterioration and chronic inflammation.
Although no cure
exists for the disease, RA patients do have a wide variety of therapies
available that can mitigate
symptoms and forestall joint destruction. Treatment guidelines indicate that
early therapeutic
intervention is important for delaying permanent loss of joint function
accompanying structural
damage to tissues Once patients are diagnosed with RA, the first course of
treatment applied can
be a synthetic disease modifying antirheumatic drug (csDMARD), with
methotrexate as an
example first line option. RA patients whose symptoms are not sufficiently
controlled with
csDMARDs have a wide range of other therapies according to treatment
guidelines, including
targeted drugs for inhibiting interleukin-6 (IL-6), Janus kinase (JAK), and
tumor necrosis factor-a
(TNF). While targeted therapies indicated as the next step in treatment beyond
csDMARDs, no
one therapy is recommended over other targeted therapies in these
circumstances and choice of
therapy may be dependent upon non-clinical selection factors. This is
demonstrated by the >80%
of biologic-naive RA patients with symptoms insufficiently controlled by
csDMARDs who are
then directed toward anti-TNF therapies.
[0187] Within the large subset of patients that respond inadequately to
csDMARDs and
subsequently initiate anti-TNF medications, roughly 75-90% of these RA
patients do not reach the
intended therapeutic targets of low disease activity (LDA) or remission in
guidelines from the
American College of Rheumatology (ACR). Of the patients with RA just starting
TNF inhibitors
(TNFi), a 20% improvement in symptoms (ACR20) was seen in 50-70% of patients,
a 50% ACR
score improvement (ACR50) was observed in 30-40% of patients, and 15-25% of
patients reached
a 70% improvement (ACR70). No more than roughly 10-25% of biologic-naive RA
patients who
initiated TNFi treatment are reported as being able to achieve remission of RA
symptoms,
indicating that the broad application of TNFi therapy has an unmet need for
precision medicine to
reduce the likelihood of treatment cycling. Given the degenerative nature of
RA over time,
mitigating delays in reaching treatment targets can bring quality of life
benefits to RA patients who
are non-responders to anti-TNF treatments
101881 Under the present circumstances of TNFi use, previous studies indicate
that RA patients
who initiate such treatment are receiving a therapeutic regimen that is sub-
optimal for their specific
biology. This results in a substantial amount of monetary waste within the
healthcare system in
paying for 'TNFi therapy in patients who will not benefit from the treatment,
and this also delays
those RA patients from being directed toward therapies with an alternative
mechanism of action
that may be better suited to their specific circumstances. Given this current
scenario is unhelpful
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
for most RA patients and healthcare resource efficiency alike, the
introduction of a validated
biomarker panel with the capacity to predict patient non-responsiveness to
TNFi can be highly
beneficial and well-received by rheumatologists. A proprietary biomarker panel
and predictive
algorithm analyzing 19 RNA transcripts, laboratory tests for anti-cyclic
citrullinated peptide (anti-
CCP), and 3 clinical metrics (BMI, gender, and patient global assessment)
developed by Scipher
Medicine, known as PrismRA, has been demonstrated to be predictive of biologic-
naive RA patient
response to anti-TNF therapies. At present, the length of time for which
PrismRA results remain
valid after patients begin TNFi treatment is unknown This current study was
crafted to evaluate
the stability of PrismRA predictions throughout a time course of TNFi therapy
with the intention
of determining the long-term clinical meaningfulness of PrismRA scores on both
the population
and individual levels.
[0189] Study Populations
[0190] The demographics of the patient population assessed herein are outlined
in Table 10. A
total of 452 whole-blood samples and accompanying clinical measurements were
obtained from
330 patients with rheumatoid arthritis. Samples were collected from the RA
patients after the
patients had initiated anti-TNF therapy. All patients included in the study
were naive to RA
biologics prior to starting on a TNFi course of treatment RA patient blood
samples were collected
at 3 months or 6 months following the start of TNFi initiation, with a cross-
section of patients who
provided samples at both time points. Within the patient populace, 94 patients
provided samples at
3-months post TNFi initiation only, 114 patients provided samples at the 6-
month time point only,
and 122 provided samples at both the 3-month and 6-month time points Overlap
amongst the three
patient groups and two sample collection time points is shown in FIG. 7. All
patients involved in
this study provided informed consent, with approvals from an institutional
review board obtained
before any sample collection or study participation by patients took place.
Selection of TNFi
therapies and associated dosages were at the rheumatologists' discretion for
all patients_
[0191] Clinical Evaluation and Response to Anti-TNF Therapy
[0192] Clinical response to anti-TNF therapies were assessed at baseline, 3-
month, and 6-month
visits according to criteria defined for ACR, Clinical Disease Activity Index
(CDAI), and Disease
Activity Score 28 with C-reactive protein (DAS28-CRP). The ACR measurements of
ACR50, and
ACR70 were defined as when an individual demonstrated >50%, or >70%
improvement in the 28
tender joint count, the 28 swollen joint count, and in a minimum of three of
the five clinical values
used in evaluating an RA patient's disease state. Whole blood samples in
PAXgene RNA blood
tubes were collected at each visit. Rheumatoid factor (RF) and anti-cyclic
citrullinated protein
(anti-CCP) antibody serostatus measurements were established at patients'
baseline sampling
points. The variables used in evaluating an RA patient's disease state
included the Health
56
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Assessment Questionnaire disability index, patient global assessment, provider
global assessment,
CRP and anti-CCP levels, and patient-reported pain. Clinical assessment data
were also used to
determine if patients met the clinical thresholds for CDAI low disease
activity (CDAI-LDA),
CDAI remission (CDAI-R), DAS28-CRP low disease activity (DAS28-CRP-LDA), and
DAS28-
CRP remission (DAS28-CRP-R).
[0193] RNA Isolation, Preparation, and Sequencing Analysis
[0194] PAX-gene Blood RNA Tubes were used to collect blood samples for total
RNA isolation.
MagMaxTM for Stabilized Blood PAXgene Tubes RNA Isolation Kit from Thermo
Fisher
Scientific was used for RNA sample preparation according to protocols from the
manufacturer.
RNA within a mass range of 100-1000 ng were processed using a KAPA RNA
HyperPrep Kit with
RiboErase (HIVIR) Globin. An Agilent Bioanalyzer automated electrophoresis
platform was used
to evaluate the quality of collected RNA, while a NanoDrop ND-8000
spectrophotometer was used
for RNA quantification. RNA samples were sequenced using an Illumina NovaSeq
6000 platform
with a Clinical Laboratory Improvement Amendments (CLIA)-validated diagnostic
assay. Gene
expression across entire genomes was determined from processed sequence data.
For inclusion in
sample analysis, RNA samples were required to have a TapeStati on RIN >4, an
RNA concentration
>10 ng/pI, a sequencing library yield >10 nM, a perfect base-pair index
percentage >85, a
percentage of bases over Phred score 30 >75, a mean quality Phred score >30, a
median Phred
score >25, and a lower quartile Phred score >10 for all bases in the RNA
molecules.
[0195] TNFi Response Predictive Model
[0196] A TNF'i therapy response classification model was trained using 245
samples collected
from RA patients using panel of 23 selected biomarkers. Model building was
done using
MLPClassifier package available in Python's machine learning library sklearn.
[0197] Statistical Analysis
[0198] Performance of the PrismRA biomarker panel was evaluated using the area
under (AUC)
receiver operating characteristic (ROC) curves. MSRS model cutoff used for
odds ratio calculation
was selected based on previous validation results. Odds ratio was calculated.
Python 3.7.6 was
used to perform all statistical analyses and data processing procedures. All
values classifiable as
continuous data were represented with mean, standard deviation, median,
minimum, maximum,
and observation count as appropriate. For categorical variables, values were
summarized using
frequency counts and percentages. For determination of confidence intervals
(CI), continuous data
CI were obtained using a t distribution, while CI for categorical variables
were determined using
an exact method. Two-sided tests were applied in all circumstances at the 0.05
significance level
unless otherwise stated.
[0199] PrismRA Maintains Performance 1hroughout Anti-JIVE Exposure lime Course
57
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
[0200] Table 10 shows demographics of the patient population evaluated in this
study. In total,
452 samples collected from 330 RA patients who had recently initiated TNF
therapy were
evaluated. Samples were collected at 3-months after TNF initiation or at 6-
months after TNF
initiation. FIG. 7 shows the overlap of patients who provided samples at the 3-
month and 6-month
time points. Out of the 330 patients in the study, 94 provided samples at only
the 3-month time
point, 122 provided samples at both the 3-month and 6-month time points, and
114 provided
samples at only the 6-month time point.
Table 10
Characteristic n=330
age, mean (SD) 55.5 (12.5)
gender, female/male 238/92
CCP, positive/negative/unknown 211/104/15
RF, positive/negative/unknown 233/83/14
Methotrexate, yes/no 235/95
Predni sone, yes/no 75/255
Plaquenil, yes/no 50/280
Azulfidine, yes/no 10/320
Arava, yes/no 22/308
TNF,
Humira /Enbrel /Remi cade/S imp oni t'/Cimzi a* 124/107/65/9/25
102011 A molecular signature response classifier (MSRC) was used to predict
therapeutic response
to TNF therapy using patient data collected at the two timepoints. Patient
response to TNF therapy
was assessed at +3 months and +6 months after the time of sample collection
using seven different
clinically accepted response definitions (ACR20, ACR50, ACR70, CDAI-R, CDAI-
LDA, DAS28-
CRP-R, and DAS28-CRP-LDA). See materials and methods for more details. FIG. 8
shows a ROC
curve which was generated by comparing the MSRC scores to the +3 month and +6
month
therapeutic outcomes.
[0202] Comparable performance was observed when using 3-month data (FIG. 8
a/b) as compared
to using 6-month data (FIG. 8 c/d) to make the prediction. Across the various
response definitions,
AUC's ranged from 0.66-0.73 when using 3-month data and 0.67-0.75 when using 6-
month data.
Similar performance was observed when comparing model predictions to
therapeutic response
outcomes at +3 months (FIG. 8 a/c) and +6 months (FIG. 8 b/d) after the time
of data collection,
with AUC's ranging from 0.67-0.79 and 0.66-0.76, respectively. Table 11
provides a summary of
the odds ratios observed among each of the samples and endpoint definitions.
For all models
evaluated, statistically significant differences in score distribution between
responders and
nonresponders was observed (p<0.001).
58
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
Table 11
Time point Endpoint AUC OR [CI]
3-month ACR50+3 0.73 4.83 [2.42-
9.62]
3-month das28crp r+3 0.71 4.65 [2.36-
9.15]
3-month das28crp lda+6 0.67 3.53 [1.87-
6.64]
3-month das28crp lda+3 0.72 5.73 [2.99-
10.99]
3-month cdai r+6 0.68 3.12 [1.35-
7.18]
3-month cdai r+3 0.72 5.03 [1.86-
13.54]
3-month das28crp_r+6 0.66 4.42 [2.1-9.29]
3-month cdai lda+3 0.72 5.49 [2.98-
10.11]
3-month cdai lda+6 0.67 3.04 [1.67-
5.53]
3-month ACR70+6 0.68 3.65 [1.52-
8.76]
3-month ACR70+3 0.73 4.63 [1.85-
11.61]
3-month ACR50+6 0.72 3.63 [1.88-
6.99]
6-month cdai lda+3 0.7 4.32 [2.21-
8.43]
6-month cdai lda+6 0.71 5.98 [2.91-
12.26]
6-month ACR70+3 0.75 7.71 [2.64-
22.48]
6-month cdai r+3 0.79 7.19 [2.46-
21.0]
6-month cdai r+6 0.76 7.14 [2.45-
20.87]
6-month das28crp lda+3 0.71 4.69 [2.16-
10.18]
6-month ACR50+6 0.68 3.34 [1.69-
6.59]
6-month das28crp Jda+6 0.67 4.8 [2.07-
11.13]
6-month das28crp_r+3 0.67 5.22 [2.14-
12.72]
6-month ACR50+3 0.75 6.36 [2.92-
13.87]
6-month ACR70+6 0.74 5.18 [2.08-
12.88]
6-month das28crp r+6 0.71 8.5 [2.82-
25.58]
[0203] Stability of MSRC predictions were further evaluated by comparing model
performance
among the 122 patients who provided both 3-month and 6-month samples (FIG. 9).
In order to
ensure outcomes were consistent, response was evaluated at +9 months after TNF
therapy was
initiated (+6-month from the 3 month sample collection timepoint and +3 months
from the 6 month
sample collection timepoint). Across the various response definitions, AUC's
ranged from 0.66-
0.74 when using data collected at 3 months after TNF initiation and 0.65-0.73
when using data
collected at 6 months after TNF initiation. In both cases, statistically
significant differences in
MSRC score distribution between responders and nonresponders was observed
(p<0.001).
[0204] Prisml-M Predictions Demonstrate Stability Throughout Anti-JIVE
Exposure Timecourse
[0205] In order to assess the longitudinal stability of PrismRA response
predictions on an
individual basis, we first investigated the stability of the response outcome
labels among the 122
patients for which 3 month and 6 month data was available. Table 12 details
the degree of
agreement between outcomes when considering data collected at 3 months and 6
months after TNF
initiation. On average, +3 month outcomes were consistent 73.9% of the time
across the various
59
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
endpoint definitions while +6 month outcomes were consistent 80.9% of the
time. Among the
patients who were not consistent, similar proportions changed from
nonresponders to responders
as nonresponders to responders. For +3 month outcomes, on average 14% change
from
nonresponder to responder and 12% changed from responder to nonresponder. For
+6 month
outcomes, 8.5% changed from nonresponder to responder and 10.5% changed from
responder to
nonresponder.
Table 12
Endpoint NR-NR R-R agreement NR-R R-NR
disagreement
ACR50+3 52 (44.1%) 33 (28.0%)
72.0% 18 (15.3%) 15 (12.7%) 28.0%
ACR50+6 55 (49.1%) 37 (33.0%) 82.1% 8(7.1%)
12(10.7%) 17.9%
ACR70+3 79 (66.9%) 17(14.4%) 81.4% 11(9.3%)
11(9.3%) 18.6%
ACR70+6 77 (68.1%) 20 (17.7%) 85.8% 8 (7.1%)
8 (7.1%) 14.2%
cdai lda+3 27 (22.7%) 55 (46.2%)
68.9% 19(16.0%) 18 (15.1%) 31.1%
cdai_lda-F6 31(27.2%) 59(51.8%) 78.9% 11(9.6%)
13 (11.4%) 21.1%
cdai_r+3 85 (71.4%) 17(14.3%) 85.7% 11(9.2%)
6 (5.0%) 14.3%
cdai_r+6 80 (70.2%) 19 (16.7%) 86.8% 6(5.3%)
9 (7.9%) 13.2%
da s2Scrp_r+3 54 (47.4%) 29 (25 4%)
72.8% 15 (13 2%) 16 (14 0%) 27.2%
das28crp r+6 53(50.0%) 31(29.2%)
79.2% 11(10.4%) 11(10.4%) 20.8%
das28crp_1da+3 33 (28.9%) 43 (37.7%)
66.7% 23 (20.2%) 15 (3.2%) 33.3%
das28crp_1da+6 34 (32.1%) 46(43.4%)
75.5% 12(11.3%) 14(13.2%) 24.5%
[0206] Stability of MSRC predictions throughout the TNF therapy time course
was evaluated by
comparing predictions made using data collected three months after 'TNF
therapy initiation to those
made using data collected six months after TNF therapy initiation. Out of the
122 patients for
which both 3-month and 6-month data was available, 97 (81.5%) had consistent
class predictions
between the two time points, while 22 (18.5%) had different predictions
between the two time
points. Among the 18.5% that changed, 9 changed from nonresponder to responder
and 12 changed
from responder to nonresponder.
[0207] Discussion
[0208] Biologic therapies for rheumatoid arthritis can be aimed at a range of
different targets (TNF,
IL-6, and JAK) and have roughly equivalent benefits when patients respond to
the therapies, yet
the most frequent choice of biologic treatment by rheumatologists is a TNFi.
Without the presence
of additional clinical guidance, the predominant use of TNFi is sure to
continue, which means that
the 90% of patients not responding sufficiently to csDMARDs will be put on a
medication that has
a 70% chance of failing to meet treat-to-target thresholds for RA. However,
these negative
outcomes can be mitigated through implementation of a clinical panel that can
determine if a
patient will respond to TNFi therapy. The PrismRA predictive biomarker panel
has been clinically
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
validated in previous studies to successfully classify RA patients as TNFi
responders or non-
responders.
[0209] One goal of this study was to determine the efficacy of the PrismRA
MSRC throughout a
timecourse of TNF therapy. On a population level, the model performance is
good and reflects
previous validation results. The MSRC was able to distinguish nonresponders
from responders
with AUCs ranging from 0.61-0.69 when using 3-month data and 0.64-0.73 when 6-
month data.
This corresponded well with observations by Mellors et al., where the
biomarker response panel
successfully identified 'TNFi non-responders with an odds ratio of 6.57.
Additionally, prior
validation of the MSRC by Cohen et al. successfully stratified a patient's
likelihood of being a
TNFi non-responder with an odds ratio of 4.1 according to an ACR50 response
end point at 6
months.
[0210] The predictive performance for the +3-month and +6-month outcomes
remained consistent
regardless of when samples were collected throughout an anti-TNF therapy
timeline. Stability of
PrismRA outcomes indicate that the biomarker panel may be employed at any time
during a TNFi
treatment course while still offering an effective prediction about how a
patient will respond to the
therapy in another 3 to 6 months_ Rather than offering valid treatment
guidance merely at the
beginning of treatment and changing over time, this study shows the long-term
validity of this
biomarker panel during a TNFi therapy time course.
[0211] Outcomes from this study do have a certain degree of variability, which
can be inferred
from the consistency rates of roughly 74% for +3-month outcomes and 81% for +6-
month
outcomes. The occurrence of incorrect predictions or patients switching
between responder and
non-responder in consecutive measurements both highlight the importance to
understand the
natural outcome variability for properly characterizing model performance.
[0212] Among outcomes that disagree, roughly the same proportion of patients
will change from
responder to non-responder as will change from non-responder to responder. On
average, the
proportion of non-responders changing to responders was 14% at the +3-month
mark and 8.5% at
the +6-month timepoint, while responders were found to switch to non-
responders 12% of the time
at +3-months and 10.5% at +6-months. Since there is a small chance that a
patient on anti- TNT
therapy who tests as a non-responder may flip status to become a responder,
some clinicians may
be inclined to keep non-responding patients on a TNFi in the event that they
eventually respond.
However, the time-sensitive and debilitating nature of RA disease progression
can warrant a
clinical decision with a higher probability of success than waiting out an
ineffective therapy for
the chance of eventual response. In their 2021 guidelines, the American
College of Rheumatology
adjusted the recommendation for bDMARDs like anti-TNF therapies, where
patients who are not
at target improvement should be switched to a bDMARD of a different drug class
rather than a
61
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
different bDMARD in the same class. Among the alternatives to TNF inhibitors,
options such as
inhibitors of IL-6, 1L-6 receptors, and JAK have all been reported to be
roughly as effective as
TNF inhibitors (29-36). Moreover, the other classes of bDMARDs have been
reported to remain
effective in RA patients even after use of and non-response to anti-TNF
therapies, so patients who
may switch from responder to non-responder after an initial TNFi treatment
still have biologics as
an effective therapeutic option (37-39). The updated ACR recommendations and
availability of
effective alternative to TNFi therapies substantiate the motivation for
stratifying patients as
responders or non-responders to TNFi treatment so that no time is wasted
either trialing a therapy
with a poor population response rate or continuing on a medication to which a
patient no longer
responds. In a study regarding perceived clinical utility of the PrismRA
panel, Pappas et al. found
that an MSRC capable of classifying patients' TNFi response may be well
received by
rheumatologists. Of the 248 clinicians surveyed, 92% felt that the test
results can raise their
confidence when deciding on RA patients' treatment, and roughly 80% of the
surveyed
rheumatologists agreed that this type of biomarker panel can improve medical
decision-making.
[0213] The MSRC showed a high level of consistency when comparing predictions
made using 3-
month data as compared to the 6-month data Results compared between
measurements taken at
these two time points remained consistent >81% of all instances. These data
suggest that even if
the MSRC is given at various points through a TNFi time course, the panel will
still yield consistent
results. With results establishing the validity and utility of using an MSRC
to identify non-
responders to TNFi treatment at the point of therapy selection (24,26,40-43),
this longitudinal
study shows that physicians may or may not retest their patients after the
initial test
Correspondingly, patients are recommended to receive the PrismRA test when a
prescription
change is warranted secondary to disease progression or side effects from
current therapy. Further
studies measuring the PrismRA testing interval are planned to be conducted to
better define the
patient testing recommendations_
[0214] Knowing that there is sustained accuracy of the PrismRA test, further
waste and
inefficiencies indicative of repeat testing or indeterminate results can be
avoided and the cost
savings of placing patients on the most effective treatment based on their
molecular profile can be
realized. An approximation of the cost savings from predicting non-response to
TNF inhibitors by
PrismRA in clinical decision-making was described by Bergman et al. When
modeling standard-
of-care biologic pharmacy treatment costs compared to using PrismRA
stratification in 12 months
of RA patient treatment, a 22% decrease in costs spent on ineffective
treatments and 5% cost of
RA treatment overall can be obtained. Among the Medicare-eligible population,
these savings were
equated to an annual per-patient $6668 reduction in spending on ineffective
treatments When
considering that PrismRA stratification causes both direct reduction in
ineffective treatments as
62
CA 03212448 2023- 9- 15
WO 2022/197968
PCT/US2022/020815
well as bringing indirect value by mitigating disease progression, the
multifold benefits possible
in RA reinforce the value of advancing precision medicine in the clinical
space.
[0215] The foregoing has been a description of certain non-limiting
embodiments of the subject
matter described within. Accordingly, it is to be understood that the
embodiments described in
this specification are merely illustrative of the subject matter reported
within. Reference to details
of the illustrated embodiments is not intended to limit the scope of the
claims, which themselves
recite those features regarded as essential.
[0216] It is contemplated that systems and methods of the claimed subject
matter encompass
variations and adaptations developed using information from the embodiments
described within.
Adaptation, modification, or both, of the systems and methods described within
may be performed
by those of ordinary skill in the relevant art.
[0217] Throughout the description, where systems are described as having,
including, or
comprising specific components, or where methods are described as having,
including, or
comprising specific steps, it is contemplated that, additionally, there are
systems encompassed by
the present subject matter that consist essentially of, or consist of, the
recited components, and that
there are methods encompassed by the present subject matter that consist
essentially of, or consist
of, the recited processing steps.
[0218] It should be understood that the order of steps or order for performing
certain action is
immaterial so long as any embodiment of the subject matter described within
remains operable.
Moreover, two or more steps or actions may be conducted simultaneously.
[0219] While preferred embodiments of the present invention have
been shown and described
herein, it will be obvious to those skilled in the art that such embodiments
are provided by way of
example only. Numerous variations, changes, and substitutions will now occur
to those skilled in
the art without departing from the invention. It should be understood that
various alternatives to
the embodiments of the invention described herein may be employed in
practicing the invention
It is intended that the following claims define the scope of the invention and
that methods and
structures within the scope of these claims and their equivalents be covered
thereby.
63
CA 03212448 2023- 9- 15
