Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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BIOMARKERS AND METHODS FOR CLASSIFYING SUBJECTS FOLLOWING VIRAL
EXPOSURE
Field of the Invention
[0001] The present invention relates to biomarkers for predicting whether a
subject will develop acute
symptoms or signs of disease following exposure, or possible exposure, to a
respiratory virus such, for
example, as an influenza virus. The present invention provides methods for
predicting whether a subject
will develop a severe or complicated form of disease. As disclosed herein, the
invention includes methods
of conducting clinical trials or field studies comprising analysing the
biomarkers, but more generally, the
biomarkers of the invention may be used in any healthcare or non-healthcare
setting; for example to triage
patients infected with a respiratory virus to identify those who are
susceptible to developing acute signs or
symptoms and may therefore require medical intervention. Subjects may have
been administered a
medicinal product for treatment or prevention of respiratory disease, and the
biomarkers of the invention
may therefore be used as a companion analytical product to predict the likely
efficacy of the medicinal
product. The present invention further provides computer programmes, computer
readable media,
computer implemented-methods and classification algorithms that generate or
utilise the biomarkers of
the invention.
Background to the Invention
[0002] Acute upper and lower respiratory infections are a major public health
problem and a leading
cause of morbidity and mortality worldwide. Viruses are the predominant cause
of respiratory tract
illnesses and include RNA viruses such as respiratory syncytial virus (RSV),
influenza virus,
parainfluenza virus, metapneumovirus, rhinovirus (HRV) and coronavirus
(Hodinka, "Respiratory RNA
Viruses", Microbiol Spectr., 2016 Aug; 4(4)).
[0003] The CDC estimates that in the 2015-2016 period in the US there were 25
million influenza
illnesses, 11 million influenza-associated medical visits, 310,000 influenza-
related hospitalizations, and
12,000 pneumonia and influenza deaths (Rolfes et al 2016). In 2003 the annual
economic burden of
influenza in the US alone was estimated to be around 87 billion dollars
(Molinari et al 2007). The costs of
influenza are clearly substantial and any method to treat or diagnose
influenza would be of enormous
value.
[0004] Influenza infects all age groups and causes a range of outcomes from
asymptomatic infection and
mild respiratory disease through to severe respiratory disease and even death.
As such, different subjects
exposed to the same influenza virus may be asymptomatic, mildly symptomatic,
subclinical, exhibit acute
symptoms, or require medical attention, or even urgent hospitalization (Cox et
al 1999). Further, the
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proportion of infections that are asymptomatic or subclinical, and the degree
to which these are
contagious, as well as the proportion of shedding which occurs prior to onset
of symptoms, affect the
potential impact of control measures and decisions regarding treatment and the
administration of
medicaments (Lau et al., 2010).
[0005] Current trial designs within the human challenge model for assessing
investigational treatment
drugs and medicaments for influenza, RSV, or HRV rely on either:
a. "universal dosing" ¨ Universally treating all subjects
inoculated with virus on a given day
post inoculation (e.g. 24 hrs or 28 hrs post inoculation), irrespective of
whether the subjects
become infected or not;
b. "Triggered dosing" - Treating only those subjects when they
have either one or both of the
following:
i. their first (or confirmed) PCR positive respiratory sample (i.e.
treating only those that
are expected to be infected post inoculation);
ii. initial respiratory symptoms that are indicative of onset of viral
infection;
c. "Triggered dosing + Universal dosing" (DeVincenzo et al, NEJM 2014;
DeVincenzo et al,
NEJM 2015)- This uses the principles of triggered dosing for the primary
endpoint, however
at a certain day post inoculation e.g. Day 5 any subjects that still don't
have a positive viral
sample (or symptoms) are subsequently given the drug anyway. The ones that are
a
universally given the drug in this scenario may be included for analysis in
two sub-analysis
approaches:
i. On their own as a sub-group
ii. Combined with the triggered sub-group
[0006] In research models such as the human challenge model, knowing who will
develop significant
symptoms in advance, would allow dosing of an investigational medicament to be
triggered only in
subjects who would otherwise go on to develop acute symptoms of an influenza-
like disease. A method
capable of predicting who will develop acute symptoms of an influenza-like
disease would allow the
identification of subjects appropriate for administration of the medicament.
The benefits of this volunteer
selection method for dosing include:
a. Improved ability to detect a clinically relevant reduction in disease by
only evaluating the
medicament effects in those that would have gone on to present with acute
symptoms of an
influenza-like disease. This contrasts with trial designs where triggering of
treatment might
be based on presence of viral shedding/symptoms or administration of the
medicament to all
inoculated people. Selecting appropriate subjects for the trial in advance
avoids the problems
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associated with assessing the efficacy of the medicament in populations where
the ability to
detect a difference is more difficult (i.e. uninfected, asymptomatic infected
or people who
only have a mild infection with minimal viral loads).
b. fewer people will be exposed to the medicament unnecessarily
thereby:
i. reducing medicament requirements and thus manufacturing and cost
benefits;
ii. providing a treatment regime with an improved benefit: risk profile by
selecting to
provide treatment only to those that will develop acute symptoms;
iii. providing an improved benefit: risk profile for both the medicament
and the study
by requiring fewer people to be exposed to an investigational medicament.
[0007] Therefore, there remains a need for methods of predicting whether a
subject will develop acute
symptoms of an influenza-like disease to enable informed treatment decisions,
to administer the correct
level of care, and/or to improve the trial design for investigative
medicaments to treat influenza- like
disease.
[0008] Woods et al., "A Host Transcriptional Signature for Presymptomatic
Detection of Infection in
Humans Exposed to Influenza H1N1 or H3N2", PT.OS ONE, January 2013; 8(1)-
e52198 describe the
generation of a viral gene signature (or factor) for symptomatic influenza
that is capable of detecting 94%
of infected cases. The gene signature is detectable as early as 29 hours post-
exposure and is reported to
achieve maximum accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-
value equals 0.005,
H3N2) before peak clinical symptoms.
[0009] While Woods et al. discloses methods for identifying a subject infected
with a respiratory virus
prior to presentation of symptoms, such methods do not predict whether or not
an individual will develop
acute symptoms of an influenza-like disease.
Summary of the invention
[00010] The present invention provides a biomarker for predicting whether a
subject will develop acute
symptoms of disease after exposure, or possible exposure, to a respiratory
virus, wherein the biomarker
comprises or is derived from expression levels of one or more genes selected
from a gene panel
comprising PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP,
BST1,
TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K measured in a biological sample
obtained from the
subject after exposure, or possible exposure, to a respiratory virus.
[00011] A biomarker in the context of the present invention is a measurable
indicator of biological state
or condition, in particular, an output to predict whether a subject will
develop acute symptoms of disease.
The output may be a numerical output. In some embodiments, the biomarker of
the invention may be a
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composite biomarker comprising expression levels of two or more genes of the
gene panel or the
expression level of at least one gene of the gene panel in combination with at
least one other factor as
described herein.
[00012] The subject may be a human or non-human mammal.
11000131 The acute symptoms of disease may consist of symptoms of an influenza-
like or other respiratory
disease, as disclosed herein.
[00014] Acute symptoms of an influenza-like or other respiratory disease means
that the subject
experiences four or more of the following symptoms and these symptoms, either
individually or
combined, interfere with daily activities. The symptoms include runny nose,
stuffy nose, sore throat,
sneezing, earache, cough, shortness of breath, wheezing, chest tightness,
headache, malaise, myalgia,
muscle and/or joint aches, elevated temperature, chilliness, and feverishness.
The elevated temperature
may be a temperature of 38 C or more, optionally experienced together with a
cough, optionally with
onset within the last 8-12 (e.g. 10) days. During an average human challenge
study involving inoculation
with a respiratory virus in which the symptoms of influenza-like or other
respiratory disease are evaluated
a subset of subjects will have acute symptoms, these subject's symptoms will
score in the 85' percentile,
for example the subject's total VAS or CAT score will be in the 8511i
percentile. For example, a total VAS
score of greater than or equal to 25 units, or a CAT score greater than or
equal to 10 units, equates to
acute, symptoms.
[00015] Thus in some embodiments, a subject may be identified as susceptible
to progression to a
complicated form of an influenza-like or other respiratory disease. A
complicated respiratory disease such
as influenza is defined as disease requiring hospital admission and/or with
symptoms and signs of lower
respiratory tract infection (hypoxaemia, dyspnoea, lung infiltrate), central
nervous system involvement
and/or a significant exacerbation of an underlying medical condition.
[00016] If a subject is predicted to develop acute symptoms of an influenza-
like or other respiratory
disease, such as complicated flu or other respiratory disease, then in
accordance with the present
invention, it is predicted that the subject will go on to exhibit acute
symptoms as defined above. It may be
predicted that a subject will develop acute symptoms of an influenza-like or
other respiratory disease, but
the subject may self-resolve, or action may be taken to prevent the subject
developing acute symptoms;
for example a medicament may be administered. Thus, a subject predicted to
develop acute symptoms of
an influenza-like or other respiratory disease does not inevitably develop
acute symptoms of an influenza-
like or other respiratory disease.
[00017] Respiratory virus includes all viral infections of the respiratory
tract including respiratory
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syncytial virus (RSV), parainfluenza virus (HPIV), metapneumovirus (HMPV),
rhinovirus (HRV),
coronavirus, adenovirus (HAdV), enterovirus (EV), bocavirus (HBoV),
parechovirus (HPeV), influenza
including influenza A and influenza B.
[00018[A gene panel in the context of the present invention is a set of genes
the expression levels of
which can be analysed and used to predict the progression and/or outcome of an
influenza-like or other
respiratory disease in a subject. A gene sub-panel is a set of genes selected
from a gene panel which may
be used to predict at a certain stage in the progression of the disease, for
example early, mid or late stage
progression towards possibly developing acute symptoms of an influenza like or
other respiratory disease,
or at a certain time point following inoculation with, or exposure to, a
respiratory virus, for example up to
25 hours (for example 13-25 hours), or 37-49 hours, or 49-61 hours, in some
embodiments these time
frames may be referred to as early, mid or late stage respectively. In some
circumstances it may not be
possible to determine when a subject was exposed to a respiratory virus, in
addition some subjects
develop symptoms quicker than other subjects, and so disease progression and
stage of disease
progression may be estimated based on evaluated symptoms.
[00019] In particular the gene panel of the present invention may include one
or more genes (including
two genes, three genes, four genes, five genes, six genes, etc.) selected from
PHF20, ABCA1, APBA2,
MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6,
EPHA4
and BMP2K.
[00020[1n some embodiments, the gene panel may consist of up to 16 genes
(typically up to 10 genes,
and more typically up to 6 genes) including one or more genes (including two
genes, three genes, four
genes, five genes, six genes, etc.) selected from PHF20, ABCA1, APBA2, MORC2,
SNU13, DCUN1D2,
MAX, NOL9, MPRIP, HP. BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
[00021] In some embodiments, the gene panel may comprise PHF20. In some
embodiments, the gene
panel may comprise NOL9. In some embodiments, the gene panel may comprise both
PHF20 and NOL9.
[00022] The gene panel of the present invention may consist of one, two,
three, four, five or six genes
selected from PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP,
HP,
BST1, T1\'I9SF2, HOMER3, NSUN6, EPHA4 and BMP2K. Thus, for example, the gene
panel of the
present invention may consist of one, two, three, four, five or six genes,
including PHF20 and optionally
NOL9.
[00023] Unless indicated otherwise, the present invention does not exclude the
possibility of including
within the gene panel one or more further genes not specifically disclosed
herein, which may be found to
improve further the accuracy, sensitivity or specificity of the methods of the
invention.
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[00024] A first gene sub-panel may comprise PHF20, ABCA1, APBA2, MORC2, SNU13
and
DCUN1D2.
[00025] Thus, the first gene sub-panel may comprise the expression level of
PHF20. In addition to the
expression level of PHF20, the first gene sub-panel may comprise the
expression level of one or both of
APBA2 and A BCAl. Where the first gene sub-panel comprises the expression
level of PHF20 and the
expression level of one or both of APBA2 and ABCA1, the first gene sub-panel
may additionally
comprise the expression level of one, two or three of MORC2, SNU13 and
DCUN1D2.
[00026] The first gene sub-panel may consist of one, two, three, four, five,
or six of PHF20, ABCA1,
APBA2, MORC2, SNU13 and DCUN1D2.
[00027] The first gene sub-panel may consist of one gene, which is PHF20. The
first gene sub-panel may
consist of two genes, one of which is PHF20. The first gene sub-panel may
consist of three genes, one of
which is PHF20. The first gene sub-panel may consist of four genes, one of
which is PHF20. The first
gene sub-panel may consist of five genes, one of which is PHF20. The first
gene sub-panel may consist of
six genes, one of which is PHF20.
[00028] The first gene sub-panel may consist of two genes, one of which is
PHF20, and the other of
which is APBA2 or A BCAl. The first gene sub-panel may consist of three genes,
including PHF20,
accompanied by one or both of APBA2 and ABCAl. The first gene sub-panel may
consist of four genes,
including PHF20, accompanied by one or both of APBA2 and ABCAl. The first gene
sub-panel may
consist of five genes, including PHF20, accompanied by one or both of APBA2
and ABCA1 . The first
gene sub-panel may consist of six genes, including PHF20, accompanied by one
or both of APBA2 and
ABCA 1.
[00029] A second gene sub-panel may comprise MAX, NOL9, MPRIP, HP, BST1 and
TM9SF2.
[00030] Thus, the second gene sub-panel may comprise the expression level of
one or more of NOL9, HP
and MAX (particularly NOL9). In addition to the expression level of one or
more of HP, MAX and
NOL9 (particularly NOL9) the second gene sub-panel may comprise the expression
level of one or both
of BST1 and MPRIP. Where the second gene sub-panel comprises the expression
level of one or more of
HP, MAX and NOL9 (particularly NOL9), and the expression level of one or both
of BST1 and MPRIP,
the second gene sub-panel may additionally comprise the expression level of
TM9SF2.
[00031] The second gene sub-panel may consist of one, two, three, four, five,
or six of MAX, NOL9,
MPRIP, HP, BST1 and TM9SF2.
[00032] The second gene sub-panel may consist of one gene, which is NOL9, HP
or MAX (particularly
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NOL9). The second gene sub-panel may consist of two genes, one or both of
which are selected from
NOL9, HP and MAX (particularly NOL9). The second gene sub-panel may consist of
three genes, one,
two or all of which are selected from NOL9, HP and MAX (particularly NOL9).
The second gene sub-
panel may consist of four genes, one, two or three of which are selected from
NOL9, HP and MAX
(particularly NOL9). The second gene sub-panel may consist of five genes, one,
two or three of which are
selected from NOL9, HP and MAX (particularly NOL9). The second gene sub-panel
may consist of six
genes, one, two or three of which are selected from NOL9, HP and MAX
(particularly NOL9).
[00033] The second gene sub-panel may consist of two genes, one of which is
NOL9, HP or MAX
(particularly NOL9) and the other of which is BST1 or MPRIP. The second gene
sub-panel may consist
of three genes, including one or more of NOL9, HP or MAX (particularly NOL9),
accompanied by one or
both of BST1 and MPRIP. The second gene sub-panel may consist of four genes,
including one or more
of NOL9, HP or MAX (particularly NOL9), accompanied by one or both of BST1 and
MPRIP. The
second gene sub-panel may consist of five genes, including one or more of
NOL9, HP or MAX
(particularly NOL9), accompanied by one or both of BST1 and MPRIP. The second
gene sub-panel may
consist of six genes, including one or more of NOL9, HP or MAX (particularly
NOL9), accompanied by
one or both of BST1 and MPRIP.
[000341A third gene sub-panel may comprise HOMER3, NSUN6, HP, EPHA4 and BMP2K.
[00035] Thus, the third gene sub-panel may comprise the expression level of
one or both of HP and
HOMER3. In addition to the expression level of one or both of HP and HOMER3,
the third gene sub-
panel may comprise the expression level of one or both of EPHA4 and BMP2K.
Where the third gene
sub-panel comprises the expression level of one or both of HP and HOMER3, the
expression level of one
or both of EPHA4 and BMP2K, the third gene sub-panel may additionally comprise
the expression level
of NSUN6.
[00036] The third gene sub-panel may consist of one, two, three, four or five
of HOMER3, NSUN6, HP,
EPHA4 and BMP2K.
[00037] The third gene sub-panel may consist of one gene, which is HP or
HOMER3. The third gene sub-
panel may consist of two genes, one or both of which are selected from HP and
HOMER3. The third gene
sub-panel may consist of three genes, one or two of which are selected from HP
and HOMER3. The third
gene sub-panel may consist of four genes, one or two of which are selected
from HP and HOMER3. The
third gene sub-panel may consist of five genes, one or two of which arc
selected from HP and HOMER3.
[00038] The third gene sub-panel may consist of two genes, one of which is HP
or HOMER3; and the
other of which is EPHA4 or BMP2K. The third gene sub-panel may consist of
three genes, including one
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or both of HP and HOMER3, accompanied by one or both of EPHA4 and BMP2K. The
third gene sub-
panel may consist of four genes, including one or both of HP and HOMER3,
accompanied by one or both
of EPHA4 and BMP2K. The third gene sub-panel may consist of five genes,
including one or both of HP
and HOMER3, accompanied by one or both of EPHA4 and BMP2K.
[00039] In some embodiments, any of the aforementioned gene panels may further
comprise 1 to 2 genes
in addition to those listed above, without departing from the essential
character of the panels of the
present disclosure.
[00040] As shown in the Examples below, the genes that have been identified as
being predictive of a
subject developing acute symptoms of an influenza like or other respiratory
disease, in accordance with
the present invention, exhibit altered expression levels following inoculation
with a virus in subjects who
then go on to exhibit acute symptoms relative to those who do not develop
acute symptoms, as defined
above. This indicates the potential of the genes to identify subjects who are
more likely to develop acute
symptoms of an influenza-like or other respiratory disease. Since symptoms of
viral infection develop
sooner in some subjects than in others, altered expression of the one or more
genes according to the
present invention may be predictive of acute symptoms, before a subject shows
any symptoms of
infection, or an early diagnostic indicator of acute symptoms at about the
same time as the subject starts
to show the first symptoms of infection.
[00041] The expression levels of the one or more genes in the biological
sample may be measured using
any suitable method known in the art for quantifying the expression level of a
gene, particularly a
mammalian gene. In some embodiments, the expression level of the one or more
genes may be measured
by quantifying mRNA transcripts of the one or more genes according to the
invention in the biological
sample.
[00042] Preferably, a PCR-based method may be used such, for example, as RT-
qPCR. Examples of RT-
qPCR-based methods are disclosed by United States patent no. 7,101,663, the
contents of which are
incorporated herein by reference. An advantage of real-time PCR is its
relative ease and convenience of
use.
100043] Alternatively, a gene expression microarray may be used of the kind
disclosed in, for example,
United States patent no. 6,040,138, the contents of which are incorporated
herein by reference, in which a
pool of labelled target cRNA molecules, which are obtained by transcribing
double-stranded cDNA
derived from the mRNA transcripts that are isolated from the biological sample
and fragmenting the
resulting cRNA transcripts, are hybridised to oligonucleotide probes having
specific sequences that are
immobilised at specific addresses on a solid support. After incubating the
cRNA targets with the surface-
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bound probes, the arrays are washed and the labels on the targets may be used
to quantify how much
target is bound to any given feature on the array. The amount of a given
surface-bound target cRNA is
proportional to the expression level of the corresponding gene.
[00044] Alternatively, RNA-seq may be used to quantify, discover and profile
RNAs. This uses next-
generation sequencing on cDNA converted from RNA (Wang et al 2009).
[00045] Suitably, the biological sample may be a blood or a respiratory
sample. In particular, the sample
may be a sample containing immune cells.
[00046] In some embodiments, the expression level of each of the one or more
genes may be compared
with a respective reference level. The reference level may be a threshold
expression level that indicates
acute symptoms of an influenza -like or other respiratory disease or a
prediction of acute symptoms
developing. Alternatively, the reference level may be a baseline level of
expression which indicates that
the subject is unlikely to develop acute symptoms of an influenza-like or
other respiratory disease.
Significantly altered expression (increased expression or decreased
expression) of the one or more genes
relative to their respective baseline levels, for instance by at least 1.1x,
preferably at least 1.5x or 2x, or
3x, or 4x, or 5x, etc. up to 100x, may be indicative of acute symptoms of an
influenza like or other
respiratory disease or predictive that a subject will develop acute symptoms
of an influenza like or other
respiratory disease.
[00047] In some embodiments, the method may involve an individual reference
level for each gene.
Altered expression of at least one of the genes, preferably two or more of the
genes, relative to their
respective reference levels may indicate acute symptoms of an influenza like
or other respiratory disease
or predict developing acute symptoms of an influenza like or other respiratory
disease in accordance with
the present invention.
[00048] In some embodiments, the reference level for the, or each, gene may be
a previously measured
expression level for the gene in the same subject. In particular, the
reference level for the, or each, gene
may comprise a baseline expression level of the gene for the subject which is
measured at a time when the
subject is known not to be infected with a respiratory virus such, for
example, as influenza. Where
previous expression levels for the one or more genes, measured on more than
one previous occasion, are
available for a subject, the reference level for each gene may comprise an
average of multiple previous
levels.
[00049] Thus, in some circumstances, a subject may be tested once to obtain
baseline levels for the one or
more genes, which form reference levels that may be used subsequently in case
of suspected viral
infection or a routine check, for comparison with contemporaneous expression
levels to predict whether
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or not the subject is likely to develop acute symptoms of an influenza like or
other respiratory disease.
[00050] Exposure to a respiratory virus, or possible exposure to a respiratory
virus, includes any contact
or possible contact with a respiratory virus including, exposure to a
community acquired respiratory viral
infection, exposure to respiratory virus at home, within a care home, hospital
or military setting. Exposure
also includes inoculation of subjects during a human challenge model and/or
clinical trial.
[00051] As explained above, it is not always possible to ascertain when a
subject has been exposed to a
respiratory virus. Furthermore, different subjects exhibit symptoms at
different time points, with some
subjects exhibiting symptoms earlier than other subjects. Therefore the
progression of an influenza-like or
other respiratory disease may be measured on a relative scale and may be
referred to as early, mid or late
stage progression towards a possible presentation of acute symptoms of an
influenza-like or other
respiratory disease. In other circumstances, for example following inoculation
in a human challenge
model, the precise timing of the exposure to a respiratory virus is known and
it is possible to measure
time from exposure in hours, for example up to 25 hours (for example 13-25
hours) after exposure.
[00052] As explained in the Figures and the Examples below, a single gene
panel, containing one or more
genes, may be analysed using a first algorithm. Alternatively, a combination
of gene panels and gene-sub
panels may be analysed. The different gene panels and gene sub-panels may be
analysed simultaneously,
for example using the same biological sample or samples obtained within a
similar, or the same, time
frame. The different gene panels and gene sub-panels may be analysed
sequentially with a first gene panel
or gene sub-panel analysed in a sample taken at a first time point using a
first algorithm, and a second
gene panel or gene sub-panel analysed in a sample taken at a second time point
using a second algorithm,
and a third gene panel or gene sub-panel analysed in a sample taken at a third
time point using a third
algorithm etc.
[00053] As mentioned above, the biomarker of the invention may be based on a
number of input variables
or factors, including gene expression levels, for example the age of the
subject, or other underlying
conditions that a subject may suffer e.g. asthma, may be included in the
variables used to calculate the
biomarker. The biomarker may therefore be a composite biomarker. The output of
the biomarker may be
a numerical value. The numerical value may be determined using a threshold,
reference level, or baseline
level, for example a numerical value above a certain reference level predicts
that a subject will develop
acute symptoms of an influenza-like or other respiratory disease.
[00054] The biomarker may be computer-generated and comprises an output
variable of a classification
algorithm that uses as input variables the expression levels of one or more
genes in the gene panel; or one
or more genes in the first gene sub-panel; or one or more genes in the second
gene sub-panel; or one or
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more genes in the third gene sub-panel.
[00055] The classification algorithm may be configured to prioritise accuracy
such that the algorithm
produces the greatest number of correct predictions. Thus, the classification
algorithm may be configured
to prioritise Negative Predictive Value (NPV), the proportion of negative test
results that are true
negatives, the aim being to minimise the number of subjects predicted not to
develop acute symptoms of
influenza-like disease who in fact go on to develop acute symptoms of an
influenza-like or other
respiratory disease.
[00056] The classification algorithm may be derived by machine-learning from a
training data-set that
uses as input variables expression levels of one or more genes from the gene
panel measured from a
biological sample obtained from a group of subjects at a predetermined time
after exposure to the
respiratory virus, wherein the group of subjects is divided into two classes
according to whether or not
they developed acute symptoms of an influenza-like or other respiratory
disease after exposure to the
respiratory virus, and wherein the classification algorithm operates on the
expression levels to produce an
output variable that differentiates between the classes.
11000571 Numerous classification algorithms are available to those skilled in
the art for classifying subjects
into two or more classes based on their symptoms scores. Similarly, numerous
machine learning
techniques are available for using a training dataset comprising the two or
more classes and their
respective expression levels for the one or more genes to derive a
classification algorithm that is able to
classify a new subject based on their expression levels of the one or more
genes. The performance of a
classification algorithm built using a machine learning process may be
validated using one or more
known validation methods, e.g. cross-validation, and calculating statistical
parameters (e.g. accuracy,
sensitivity, specificity) so that the person skilled in the art can obtain a
classification algorithm that is best
suited for classifying subjects based on their expression levels of the one or
more genes.
[00058] The acute symptoms of an influenza-like or other respiratory disease
in the subjects of the group
in the training data set may be assessed by evaluating one or more symptoms of
influenza-like or other
respiratory disease at a series of pre-set times after exposure to the
respiratory virus. The one or more
symptoms are evaluated by the subjects using diary cards, optionally visual
analogue scare symptom
diary cards (VAS), or optionally categorical symptoms (CAT) are recorded using
a modified standardized
symptom score for example the modified Jackson Score. The symptoms evaluated
may include ninny
nose, stuffy nose, sore throat, sneezing, earache, cough, shortness of breath,
headache, malaise, myalgia,
muscle and/or joint aches, chilliness, and feverishness.
[00059] The first class of subjects may record a total VAS of greater than or
equal to 25 units and/or a
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total CAT score of 10 units or greater, or may show one or more of: greatest
variance in total VAS or
CAT up to the peak of symptoms; greatest variance in total VAS or CAT over the
duration of quarantine;
or steepest gradient (slope of regression line) of total VAS or CAT up to the
peak of symptoms.
[00060] Typically, machine learning processes and the resulting classification
algorithms may be carried
out using a computer.
[00061] The gene panels and gene sub-panels may be selected by i) analysing
expression levels in
biological samples obtained from the group of subjects in the data training
set across the whole series of
pre-set times after exposure to the virus; and ii) identifying genes that show
a nominal association with
acute symptoms of an influenza-like or other respiratory disease, and iii)
using a variable selection
process to select panels of the identified genes whose expression levels at a
predetermined time after
exposure to the virus exhibit maximal predictive value for developing acute
symptoms of an influenza-
like or other respiratory disease.
[00062] The variable selection process may comprise subjecting the expression
levels of the identified
genes at the predetermined time after exposure to the respiratory virus to a
repeated gradient boosting
process and selecting a set of 1, 2, 3, 4, 5 or 6 genes that are selected most
frequently by the gradient
boosting process. A variable selection process is illustrated in FIG. 6 of the
accompanying drawings and
is performed by a gradient boosting machine (GBM; Friedman 2001; Friedman
2002). In the context of
the present invention, differential expression analysis of genes between
subjects that developed acute
symptoms of influenza-like or other respiratory disease and subjects that did
not develop acute symptoms
of influenza-like or other respiratory disease was performed by application of
a cubic p-spline model.
Nominal associations arising from the cubic spline analysis were input into a
variable selection process
comprising gradient boosting machine, and iterative searches were conducted
using fifty starting point
(seeds), to determine the best gene predictors of developing acute symptoms of
influenza-like or other
respiratory disease.
[00063] The biomarker may be used to allocate subjects to groups in a clinical
trial or to make treatment
decisions. Subjects allocated to one subgroup are administered a medicament
while those allocated to
another subgroup are not administered a medicament or do not receive a
medicament until later in the trial
or study. The biomarker may also be used to monitor the efficacy of a
medicament by assessing whether a
subject to whom the medicament has been administered is likely to develop
acute symptoms of disease
after exposure, or possible exposure, to a respiratory virus. The medicament
may comprise a therapeutic
agent or a preventative agent such, for example, as a vaccine.
[00064] Thus, the present invention provides a method of predicting whether a
subject will develop acute
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13
or complicated symptoms of disease after exposure, or possible exposure, to a
respiratory virus which
comprises analysing a biomarker according to the invention and comparing the
biomarker to a reference
for the biomarker.
[00065] The present invention also provides a method of conducting a clinical
trial or field study in which
a group of subjects are exposed to a respiratory virus, the method comprising
analysing a biomarker
according to the invention for each subject and comparing the biomarker to a
reference for the biomarker
to predict whether the subject is likely to develop acute symptoms of disease,
and including subjects who
arc predicted to develop acute symptoms of disease in a first subgroup of the
clinical trial or field study
and including subjects who are predicted not to develop acute symptoms of
disease in a second subgroup.
[00066] As explained above, the methods of the invention may include comparing
the biomarker to a
reference for the biomarker or to a baseline for the biomarker. The baseline
for the biomarker may be
determined at a time when the subject is known not to be infected with a
respiratory virus. The disease
may be an influenza-like or other respiratory disease.
[00067] A medicament includes all substances used for medical treatment and
includes vaccines, drugs,
placebos and investigational medicaments, for example investigational
medicaments that are the subject
of a clinical trial. Therefore medicament includes licensed, unlicensed and
investigational medicaments.
Medicament also includes products that already have a marketing authorisation
but that are being tested
for a different use, or for efficacy when assembled in a different way, or
tested to gain further information
about the authorised usc.
[00068] The invention also provides a computer program for predicting whether
a subject will develop
acute symptoms of disease such, for example, as an influenza-like disease
after exposure, or possible
exposure, to a respiratory virus, which comprises instructions which, when the
program is executed by a
computer, cause the computer to generate a biomarker according to the
invention.
[00069] The invention further provides a classification algorithm for
predicting whether a subject will
develop acute symptoms of disease such for example as an influenza-like or
other respiratory disease after
exposure, or possible exposure, to a respiratory virus, wherein the
classification algorithm is derived by
analysing expression levels of one or more genes in subjects who have
developed acute symptoms of
disease and comparing with the expression levels in subjects who do not
develop acute symptoms of
disease, wherein the one or more genes are PHF20, ABCA1, APBA2, MORC2, SNU13
DCUN1D2,
MAX, NOL9, MPR1P, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
[00070] The invention further provides a computer readable medium and/or
computer program
comprising instructions which, when executed by a computer, cause the computer
to carry out the
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classification algorithm according to the invention.
[00071] The invention also provides a computer-implemented method for
predicting whether a subject
will develop acute symptoms of disease such for example as an influenza-like
disease, wherein a
biomarker is generated by analysing expression levels of one or more genes in
subjects who have
developed acute symptoms of disease following inoculation with a respiratory
virus and comparing with
the expression levels in subjects who do not develop acute symptoms of disease
following inoculation
with a respiratory virus, wherein the one or more genes are PHF20, ABCA1,
APBA2, MORC2, SNU13
DCUN1D2, MAX, NOL9, MPR1P, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
[00072] The invention also provides a computer-implemented method, wherein the
method comprises a
graphical user interface which displays the biomarker to the user. It is also
contemplated that the
computer-implemented aspects of the invention may be carried out by more than
one computer e.g. two
or more computer operating in different locations. Two or more computers may
communication via a data
channel, for example the intemet.
[00073] The invention also provides a method of predicting whether a subject
will develop acute
symptoms of disease after exposure, or possible exposure, to a respiratory
virus, which comprises
estimating time elapsed after the exposure, or possible exposure, to the
respiratory virus by analysing
expression levels of one or more genes selected from PHF20, ABCA1, APBA2,
MORC2, SNU13,
DCLTN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSLTN6, EPHA4, in a
biological
sample obtained from the subject; selecting a biomarker described herein,
which at said time exhibits
maximal predictive value for developing acute symptoms of disease; and
comparing the biomarker to a
reference for the biomarker.
[00074] For example, in some embodiments, the time elapsed may be estimated to
be about a day (i.e.
about 23-26 hours, e.g. 25 hours) after exposure, or possible exposure, to the
respiratory virus. If so, the
selected biomarker may comprise expression levels of one or more genes from
the first gene sub-panel
described herein.
[00075] The time elapsed may be estimated to be about 1.5-2 days (i.e. about
37-49 hours) after exposure,
or possible exposure, to the respiratory virus. If so, the selected biomarker
may comprise expression
levels of one or more genes from the second gene sub-panel described herein.
[00076] The time elapsed may be estimated to be he about 2-2.5 days (i.e.
about 49-61 hours) after
exposure, or possible exposure, to the respiratory virus. If so, the selected
biomarker may comprise
expression levels of one or more genes from the third gene sub-panel described
herein.
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[00077] The invention also provides a kit for use in a method according to the
invention. The kit may
comprise one or more reagents that allow detection, optionally quantitation,
of one or more nucleotides,
or one or more peptides, corresponding to one or more genes from the gene
panel or the first, second or
third gene sub-panel described herein. The kit may be for detection of one or
more analytes in, or
extracted from, a biological sample, such as, but not limited to, a blood,
serum, plasma, urine, saliva,
tissue biopsy, stool, sputum, skin, nose or throat sample. The kit may
comprise a device for conducting an
assay, such as a lateral flow assay. The device may bc configured for
autonomous usc by a patient
(without assistance from a physician), for example in the homc (as opposed to
a hospital or other medical
facility). The device may comprise a strip of porous material, which is
capable of supporting capillary
flow, wherein there is a zone for receiving a sample; a zone comprising a
reagent for detection of an
analyte; a detection zone; and a control zone. In use, a reaction between the
reagent and the analyte may
be detected in the detection zone, for example by a change in colour of
material in the detection zone. In
use, the control zone may- serve as a reference against which to benchmark the
reaction detected in the
detection zone. The device may comprise more than one test strip, for example
the device may comprise
two, three, four, five or six test strips, in communication with the same or
separate receiving zones.
[00078] Literature references to and sequence listings for the above-mentioned
genes are included at the
end of this description. It will be understood that the reference and sequence
listings necessarily disclose
specific alleles and are included by way of example only. The invention is not
limited to the use of such
specific alleles, but may also be implemented using products of expression of
different variants of the one
or more genes.
Brief description of the drawings
[00079] Following is a description by way of example only with reference to
the accompanying drawings
of embodiments of the present invention.
[00080] In the drawings:
[00081] FIG. 1 is a chart showing how a subject is processed through a typical
clinical study as described
in Example 1 below.
[00082] FIG. 2 is a graph showing peak level of Total VAS for infected (left)
and non-infected (right)
subjects.
[00083] FIG. 3 is a graph showing change in maximum variance of VAS scores
demonstrating that the
four individuals with acute symptoms of influenza-like disease at peak all
experienced a change in
variance of Total VAS greater than 30 units.
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[00084] FIG. 4 is a graph showing peak level of peak categorical scores for
infected (left) and non-
infected (right) subjects across three studies (hVIVO-1, Duke-1, Duke-2).
[00085] FIG. 5 is a principal component analysis to show greater homogeneity
after imposing adjustment
for study.
[00086] FIG. 6 is a flow chart to demonstrate the variable selection process
performed by gradient
boosting machines.
[00087] FIG. 7 is a flow diagram to demonstrate scenarios in which 1 to 3 of
the algorithms are nin in
parallel to assign subjects to groups for actioning (e.g. dosing with a
medicament, additional clinical
assessments). The scenarios include: within the human viral challenge model,
field study, or in the
community, wherein subjects are exposed to a respiratory virus.
[00088] FIG. 8 is a flow diagram to demonstrate scenarios in which each gene
algorithm is used
sequentially to assign subjects to groups for actioning (e.g. dosing with a
medicament, additional clinical
assessments) within the human viral challenge model.
Examples
Example 1
[00089] As described below, subjects from three separate studies were
determined to include a subset of
subjects that exhibited acute symptoms of an influenza-like disease based on
their peak symptom score
across the quarantine.
Methods
[00090] AffmetrixTM HG-U133 Plus 2.0 microarray across the whole quarantine
post inoculation were
used to perform transcriptomics analysis. Differential expression analysis
between subjects developing
acute symptoms of an influenza-like disease and subjects that did not develop
acute symptoms of an
influenza-like disease was performed by application of a cubic p-spline model.
Nominal associations
arising from the cubic spline analysis were input into a variable selection
process to determine the best six
gene predictors of acute symptoms of influenza-like disease at Day 1 morning,
Day 2 morning, and Day 2
evening after inoculation. These genes could be used to distinguish between
subjects that developed acute
symptoms of an influenza-like disease and subjects that did not develop acute
symptoms of an influenza-
like disease within the model at different times after exposure to virus.
[00091] Data from three separate studies was combined for this analysis, the
largest of which was run by
hVIVO (hVIVO-1) and two publicly available studies also run in the hVIVO
challenge model with Duke
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University Medical Centre (Duke-1 (Zaas et al 2009) and Duke-2 (Woods et al
2013): indexed in GEO as
GSE52428 and publicly available):
Table ii. ¨ For each study the following are demonstrated; virus used, the
number of subjects, the
microarray platform used, the time points at which PaxGene blood samples were
taken, the methods used
in diary cards to measure symptoms (VAS = Visual analogue scale, CAT =
modified Jackson
score/categorical), the number of diary cards taken a day and the method used
to confirm influenza
infection.
ESSWIllmillimmil:Duke-1 = = = = = lirillimill1111.1
Virus ILI H3N2 H3N2 H1N1
. (A/Perth/9/2009) (A/Wisconsin/67/2005)
(A/Brisbane/59/2007)
Volunteers 27 17 24
Transcriptomids: Affymetrix Affymetrix Affymetrix
U133A 2.0 Array U133A 2.0 Array U133A 2.0
Array
Transcriptomics -24h, pre-inoc., -24h, pre-inoc., -24h, pre-
inoc.,
Time points g g every 12 hours every 8 hours every 12
hours
Clinical Data VAS + CAT CAT CAT
12 Symptoms 10 Symptoms 10 Symptoms
Clinical Data Twice per day Twice per day Twice per
day
Time points
Infection Status : PCR + TCID50 TCID50 TCID50
[00092] For hVIVO-1, 60 healthy volunteers were inoculated intranasally with
influenza A H3N2
Perth/16/2009, 27 of which were used for this analysis. For Duke-1, 17 healthy
volunteers were
inoculated intranasally with influenza A H3N2 A/Wisconsin/67/2005. For Duke-2,
24 healthy volunteers
were inoculated intranasally with Influenza A H1N1 A/Brisbane/59/2007. All
volunteers provided
informed consent and underwent extensive pre-enrolment health screening (FIG.
1) and any with
significant baseline antibodies to the strain of influenza utilised were
excluded. After approximately 48
hours in quarantine (approximately mid-day on study day 0), a predetermined
dose of influenza A was
instilled into bilateral nares of subjects using standard pipetting methods.
The volunteers had clinical
measurements and samples taken until discharged from quarantine and then at
each follow up visit.
[00093] In hVIVO-1, 33 of the 60 subjects became infected after inoculation
(evidenced by confirmed
viral shedding), 25 were identified as not infected and 2 inconclusive. An
interim analysis was performed
after the first 27 were inoculated and all samples for each subject were sent
for gene microarray assays.
One of the 27 subjects did not complete the quarantine and so was excluded
from analysis. Of the 26
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subjects with viable microarray data, 13 were identified as confirmed as
infected and 11 as not infected, 2
were inconclusive.
[00094] In Duke-1, 9 of the 17 subjects became infected after inoculation
(evidenced by confirmed viral
shedding) and 8 were identified as not infected. Four dilutions used (6.41
TCID50/mL, 5.25 TCID50/mL,
4.41 TCI D50/m L and 3.08 TCI D50/m L) with four to five subjects receiving
each dose.
[00095] In Duke-2, 9 of the 24 subjects became infected after inoculation
(evidenced by confirmed viral
shedding) and 15 were identified as not infected. Four dilutions used (2.35
TCID50/mL, 1.8 TCID50/mL,
1.25 TCID50/mL and 1.4 TCID50/mL) with four to six subjects receiving each
dose. One subject was
excluded due to a secondary infection.
[00096] Subjects had influenza infection confirmed based on qualitative viral
culture and quantitative
influenza RT-PCR data from epithelial lining fluid for the hVIVO study.
Epithelial lining fluid was
collected from nasopharyngeal FLOQ swabs twice daily (starting on Day 1
morning, first sample
approximately 20 hrs post inoculation). Nasal collection continued throughout
the duration of the
quarantine. For the Duke studies infection status of the subjects were
obtained from the Woods et al,
2013.
[00097] Subjects self-assessed their symptoms three times daily throughout
quarantine on both categorical
and continuous (Visual Analogue Scale, VAS) symptom diary cards. Categorical
symptoms were
recorded using a modified standardized symptom score. The modified Jackson
Score requires subjects to
rank 10 symptoms consisting of: upper respiratory tract symptoms (runny nose,
stuffy nose, sore throat,
sneezing, and earache), lower respiratory symptoms (cough and shortness of
breath) and systemic
symptoms (headache, myalgia, and muscle and/or joint aches) on a scale of 0-3
of "no symptoms-, "just
noticeable", -bothersome but can still do activities" and "bothersome and
cannot do daily activities".
hVIVO-1 included wheeze and chilliness/feverishness in addition to the 10
symptoms. Additionally,
shortness of breath at rest and wheeze at rest were also recorded using an
additional grade for these
symptoms only (grade 4 = symptoms at rest). VAS symptoms were measure along a
10cm line,
measurements were made in mm.
[00098] To determine which subjects were considered to have significant
symptoms initially subjects
were identified in hVIVO-1 using the VAS data which was recorded alongside the
categorical score.
Total VAS score was calculated for each time-point during quarantine for all
fifty-eight evaluable
participants from hVIVO-1. Peak VAS score was determined for each participant,
and it was observed
that the range in non-infected individuals was 0-20 units. Amongst the
infected set however, four
individuals experienced Total VAS > 25 units (FIG. 2). The four individuals
were distinguishable from
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other participants in additional ways; Greatest variance in Total VAS up to
the peak of symptoms,
Greatest variance in Total VAS over the duration of quarantine and Steepest
gradient (slope of regression
line) of Total VAS up to the peak of symptoms
[00099] In order to address the question of whether the four could have been
identified early in
quarantine, piece-wise estimates of variance and change in variance were
calculated for the period of
quarantine.
[000100] It was observed that the four individuals with most
severe symptoms at peak all
experienced a change in variance of Total VAS greater than 30 units (FIG. 3).
Remaining individuals had
maximum change in variance less than 25 units. Furthermore, the change was
observed consistently at
Day 2, time-point 2, which in three of the four instances, was in advance of
symptom peak (Table 2).
Table 2
Individual Change in Variance At Day 2; TPT 2 Peak Time-
point
1 35.64 Day 3; TPT 1
2 88.14 Day 2; TPT 2
3 48.83 Day 2; TPT 3
4 70.57 Day 2; TPT 3
[000101] Upon the inclusion of Duke-I and Duke-2 to the analysis
the severity score had to be
adapted for use with the categorical symptom score due to not having VAS
available in Duke-1 and
Duke-2. A similar analysis as was conducted for VAS (above) was now performed
for categorical
symptom score, for the three studies. It was observed that across the three
studies, 11 individuals had
peak total categorical score greater than or equal to 10 units (10 subjects
with microarray data). These
included the four members of hVIVO-1 already discussed. Four individuals from
the H1N1 study, and
three individuals from the H3N2 study also passed this threshold (FIG. 4). As
with VAS, sudden changes
in the variance of categorical score was associated with acute symptoms of an
influenza-like disease.
[000102] At predetermined intervals, blood was collected into RNA
PAXGeneTM collection tubes.
This occurred once on Day -1, in the morning on the day of inoculation
(approximately 5 hours before
inoculation) followed by every 12 hours for hVIVO-1 and Duke-2 and every g
hours for Duke-1 for the
remainder of the quarantine.
[000103] All three studies used GeneChip0 Human Genome U133A 2.0
Array (Affymetrix, Santa
Clara, CA) for the microarray. Microarray data for both Duke studies was
obtained from the Liu et al
2016 covering both studies. The public data comprised 22,277 probe-sets, a
subset of the 54,675 probe-
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sets available for hVIVO-1.
[000104] Principal Components Analysis (PCA) showed that the three
transcriptomics data sets
differed systematically (FIG. 5). It was concluded that direct pooling would
lead to spurious results. An
adjustment for study was therefore applied to the transcriptomics measurements
and the PCA repeated
(FIG. 5).
[000105] In order to exclude non-informative probe-sets, two
groups namely, severe and non-
severe were considered. For each molecule in each group, filter ratios were
calculated reflecting
variability over time, and variability across individuals. Under recommended
thresholds, 13,806 probe-
sets were informative in at least one group, and were explored further.
[000106] Differential expression analysis between subjects that
developed acute symptoms of an
influenza-like disease (n=10) and those that did not develop acute symptoms of
an influenza-like disease
(n=56) was performed by application of a cubic p-spline model (Straube et al).
A test was applied for
group x time interaction, and a total of 1052 transcripts had q<0.05 after
adjustment for False Discovery
Rate (FDR) (Benjamini et al 1995).
[000107] To develop a molecular signature nominal associations
arising from cubic p-spline
analysis were input into a variable selection process to determine the best
predictors of developing acute
symptoms of an influenza-like disease at three time-points post inoculation;
Day 1, Day 2 morning, and
Day 2 evening (Approximately 13 to 25, 37 to 49 and 49 to 61 hours post
inoculation respectively).
[000108] Variable selection was performed by gradient boosting
machines (GBA-1; Friedman 2001;
Friedman 2002), and the number of molecules to be selected was limited to six
for best results. FIG. 6
shows the process that was followed.
[000109] Logistic regression was applied to produce prediction
models (signatures or gene panels
or gene sub-panels) for the variables selected. Signature performance
(sensitivity, specificity, positive
and negative predictive value) was determined at the time-point on which the
model was based, and at all
other time-points considered. Sensitivity ¨ the proportion of cases that
receive a positive test result.
Specificity ¨ the proportion of cases that receive a negative test result.
Positive predictive value (PPV) ¨
the proportion of positive test results that are true positives. Negative
predictive value (NPV) ¨ the
proportion of negative test results that are true negatives. The AUC (area
under the receiver operating
characteristic (ROC) curve) was determined.
Day 1 (AM) Signature
[000110] Table 3 shows the variables selected by gradient
boosting, Table 4 shows the signature
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arising from logistic regression and lastly, Table 5 shows the test
performance characteristics at all time-
points considered. It can be seen that the signature, or gene sub-panel,
performs well (AUC > 0.80) on
Day 1 data.
[000111] This signature, or gene sub-panel, includes the genes
PHF20, ABCA1, APBA2, MORC2,
SNU13, and DCUN1D2.
Table 3: Variables selected at Day 1 (AM)
Variable Symbol Entrez Gene Name
Relin& Times Selected
209422_at PHF20 PHD finger protein 20
23.6 49
203504_s_at ABCA1 ATP binding cassette subfamily A member 1 19.03
49
209870_s_at APBA2
amyloid beta precursor protein binding family A member 2 17.34 19
216863 s at MORC2 MORC family CW-type zinc finger 2 16.53
49
201076_at SNU13 small nuclear
ribonucleoprotein 13 14.25 48
219116_s_at DCUN1D2 defective in cullin neddylation 1 domain containing 2
9.26 37
Table 4: Day 1 (AM) signature
Estimate Std. Error z value Pr(> I z I )
(Intercept) -2.03 0.65 -3.14 0.001679
X203504_s_at -5.02 2.17 -2.32 0.020531
X209422_at -7.49 6.43 -1.16 0.244544
X216863 s at -1.48 6.33 -0.23 0.815441
X201076_at -5.42 9.01 -0.60 0.547252
X219116_s_at -6.21 4.78 -1.30 0.193459
X209870 s at -6.88 4.98 -1.38 0.167406
Table 5: Test performance of Day 1 (AM) signature
Train Test Cases N AUC Optimise
Cut Propn.Pos Sensitivity Specificity Accuracy PPV NPV
Day1AM Baseline 10 63 0.71 Accuracy NA NA 0.00 1.00
0.84 NA 0.84
NPV 0.02 0.68 0.90 0.36
0.44 0.21 0.95
Day1AM Day1AM 9 62 0.92 Accuracy 0.24 0.21
0.89 0.91 0.90 0.62 0.98
NPV 024 0.21 0.89 0.91
0.90 0.62 0.98
Day1AM Day1PM 10 62 0.63 Accuracy 0.64 0.08
0.30 0.96 0.85 0.60 0.88
NPV 0.03 0.77 0.90 0.25
0.35 0.19 0.93
Day1AM Day2AM 10 65 0.81 Accuracy 1.00 0.02
0.10 1.00 0.86 1.00 0.86
NPV 0.04 0.69 0.90 0.35
0.43 0.20 0.95
Day1AM Day2PM 10 66 0.89 Accuracy 0.81 0.21
0.80 0.89 0.88 0.57 0.96
NPV 0.81 0.21 0.80 0.89
0.88 0.57 0.96
Day 2 (AM) Signature
[000112] Table 6 shows the variables selected by gradient
boosting, Table 7 shows the signaturc
arising from logistic regression and lastly, Table 8 shows the test
performance characteristics at all time-
points considered. The signature performs well at Day 2 (AM).
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[000113] This signature, or gene sub-panel, includes the genes
MAX, NOL9, MPRIP, HP, BST1,
TM9SF2.
Table 6: Variables selected at Day 2 (AM)
Variable Symbol Entrez Gene Name
Relin& Times Selected
214108_at MAX MYC associated factor X
37.82 50
218754_at NOL9 nucleolar protein 9
19.05 50
212197 x at MPRIP myosin phosphatase Rho interacting protein
14.28 50
208470_s_at HP haptoglobin 11.48
45
205715_at BST1 bone marrow stromal cell
antigen 1 9.39 48
201078_at TM9SF2 transmembrane 9
superfamily member 2 7.97 30
Table 7: Day 2 (AM) signature
Estimate Std. Error z value Pil> I z I )
(Intercept) -3.28 0.90 -3.63 0.000280
X212197_x_at 1.07 7.98 0.13 0.893670
X214108_at 6.88 3.87 1.78 0.075326
X218754_at -0.01 7.01 0.00 0.999142
X205715_at -2.08 4.32 -0.48 0.629615
X208470_s_at 4.75 2.81 1.69 0.090620
X201078_at 7.39 10.83 0.68 0.494829
Table 8: Test performance of Day 2 (AM) si2nature
Train Test Cases N AUC Optimise
Cut Propn.Pos Sensitivity Specificity Accuracy PPV NPV
Day2AM Baseline 10 63 0.58 Accuracy 0.48 0.02
0.10 1.00 0.86 1.00 0.85
NPV 0.01 0.68 0.90 0.36
0.44 0.21 0.95
Day2AM Day1AM 9 62 0.58 Accuracy NA NA 0.00 1.00
0.85 NA 0.85
NPV 0.01 0.74 0.89 0.28
0.37 0.17 0.94
Day2AM Day1PM 10 62 071 Accuracy NA NA 000 1.00
084 NA 0.84
NPV 0.02 0.68 0.90 0.37
0.45 0.21 0.95
Day2AM Day2AM 10 65 0.91 Accuracy 0.64 0.08
0.50 1.00 0.92 1.00 0.92
NPV 0.28 0.18 0.70 0.91
0.88 0.58 0.94
Day2AM Day2PM 10 66 0.87 Accuracy 0.64 0.11
0.50 0.96 0.89 0.71 0.92
NPV 043 0.18 070 091
088 058 0.94
Day 2 (PM) Signature
[000114] Table 9 shows the variables selected by gradient
boosting, Table 10 shows the signature
arising from logistic regression and lastly, Table 11 shows the test
performance characteristics at all time-
points considered.
[000115] This signature, or gene sub-panel, includes the genes
HOMER3, NSUN6, HP, EPHA4,
BMP2K.
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Table 9: Variables selected at Day 2 (PM)
Variable Symbol Entrez Gene Name Relin&
Times Selected
215489_x_at HOMER3 homer scaffolding protein 3 30.73
50
214541_s_at OKI OKI, KH domain containing RNA binding 23.43
50
222128_at NSUN6 NOP2/Sun RNA methyltransferase family member 6
14.75 50
208470_s_at HP haptoglobin 12.64
50
206114 at EPHA4 EPH receptor A4 12.05
50
59644_at BMP2K BMP2 inducible kinase 6.4
29
Table 10: Day 2 (PM) signature
Estimate Std. Error z value Pr(>1z I )
(Intercept) -19.21 18.67 -1.03
0.303403
X206114_at -13.07 19.53 -0.67 0.50355
X208470_s_at 16.03 16.49 0.97 0.33089
X215489_x_at 57.62 66.78 0.86 0.388274
X222128_at -17.21 15.54 -1.11 0.268238
X59644_at 55.37 62.33 0.89 0.374308
Table 11: Test performance of Day 2 (PM) signature
Train Test Cases N AUC Optimise Cut
Propn.Pos sensitivity specificity Accuracy PPV NPV
Day2PM Baseline 10 63 0.58 Accuracy 0.02 0.99
0.10 1.00 0.86 1.00 0.85
NPV 0.63 0.00 0.80 0.40
0.46 0.20 0.91
Day2PM Day1AM 9 62 0.62 Accuracy NA NA 0.00 1.00
0.85 NA 0.85
NPV 0.58 0.00 0.78 0.45
0.50 0.19 0.92
Day2PM Day1PM 10 62 0.65 Accuracy NA NA 0.00 1.00
0.84 NA 0.84
NPV 0.68 0.00 0.90 0.37
0.45 0.21 0.95
Day2PM Day2AM 10 65 0.83 Accuracy 0.03 1.00
0.20 1.00 0.88 1.00 0.87
NPV 0.38 0.00 0.80 0.69
0.71 0.32 0.95
Day2PM Day2PM 10 66 1.00 Accuracy 0.17 0.45
1.00 0.98 0.98 0.91 1.00
NPV 0.17 0.45 1.00 0.98
0.98 0.91 1.00
[000116]
Once the gene sub-panels were identified, it was possible to determine the
minimum
number of genes required to provide a good prediction of whether a subject
would go on to develop acute
symptoms of an influenza-like disease. An AUC value of 0.8 or higher provides
a good prediction that a
subject will develop acute symptoms of an influenza-like disease. The
parameter with lowest relative
influence was dropped from consideration and an updated signature was derived.
Model performance
(sensitivity, specificity, PPV and NPV) was derived for the updated model. A
ROC curve was drawn and
AUC was tabulated. Further model parameters were sequentially dropped in order
of increasing relative
influence. In this way, model performance was determined for signatures based
upon 5,4,3,2 and 1 genes
at different time points (Tables 12 to 14). It can be seen that any of 1, 2,
3, 4, 5, or 6 genes can be
predictive of developing acute symptoms of an influenza-like disease.
Table 12 - Day lam algorithm performance AUC values
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No. of genes analysed
Test Sets 6 5 4 3 2 1
PHF20, PHF20, PHF20, PHF20, PHF20,
PHF20
ABCA1, ABCA1, ABCA1, ABCA1, ABCA1
APBA2, APBA2, APBA2, APBA2
MORC2, MORC2, MORC2
SNU13, SNU13
DCUN1D2
Baseline 0.71 0.70 0.69 0.66 0.54
0.52
Day 1 am 0.92 0.90 0.90 0.90 0.79
0.74
Day 1 pm 0.63 0.68 0.70 0.72 0.63
0.62
Day 2am 0.81 0.81 0.80 0.81 0.75
0.81
Day 2pm 0.89 0.90 0.91 0.91 0.80
0.86
Table 13 - Day 2am algorithm performance AUC values
No. of genes analysed
Test Sets 6 5 4 3 2 1
MAX, MAX, MAX, MAX, MAX,
MAX
NOL9, NOL9, NOL9, NOL9, NOL9
MPRIP, MPRIP, MPRIP, HP MPRIP
HP, BST1, HP, BST1
TM9SF2
Baseline 0.58 0.59 0.57 0.60 0.62
0.58
Day 1 am 0.58 0.57 0.56 0.65 0.67
0.65
Day 1 pm 0.71 0.71 0.71 0.52 0.53
0.56
Day 2am 0.91 0.90 0.90 0.91 0.91
0.90
Day 2pm 0.87 0.86 0.85 0.84 0.85
0.83
Table 14 - Day 2pm algorithm performance AUC values
No. of genes analysed
Test Sets 5 4 3 2 1
HOMER3 HOMER3 HOMER3 HOMER3 HOMER3
NSUN6 NSUN6 NSUN6 NSUN6
HP HP HP
EPHA4 EPHA4
BMP2K
Baseline 0.58 0.54 0.62 0.59 0.58
Day 1 am 0.62 0.70 0.71 0.75 0.68
Day 1 pm 0.65 0.65 0.66 0.66 0.61
Day 2am 0.83 0.92 0.89 0.87 0.81
Day 2pm 1.00 0.98 0.97 0.97 0.95
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Example 2
[000117] A group of individuals are recruited into a human viral
challenge model and inoculated
with a respiratory virus. It is beneficial to identify, in advance, subjects
who will progress to develop
acute symptoms of an influenza like disease, allowing selective dosing of
these subjects with an
investigational or licensed medicament (drug/vaccine/ placebo) at the earliest
opportunity. This will
improve the ability to detect a clinically relevant reduction in disease in
response to the medicament by
only evaluating the medicament effects in individuals that will/would have
developed acute symptoms of
an influenza-like disease. This will also reduce unnecessary exposure of
subjects to an investigational
medicament. This will also reduce the amount of medicament required.
[000118] Volunteers are screened for eligibility for the
evaluation of efficacy of an investigational
medicament in a human challenge study with a respiratory virus, in particular
with influenza.
[000119] Eligible volunteers arrive at the clinic and baseline
samples and clinical measures are
taken, before they are exposed to virus (e.g. inoculation). Baseline values
are obtained pre-inoculation
using one or more blood samples over varying time-points.
[000120] Blood samples are taken regularly before and after virus
exposure (e.g. paxgene RNA
samples twice, three times a day, or more) alongside clinical measures of
their disease.
[000121] Expression levels of specific gene panels and sub-panels
are measured in real-time from
the blood paxgenes. As in Example 1, blood is assessed for gene expression
utilising Affymetrix HG-
U133 Plus 2.0 microarray chips, which were used to measure the transcripts'
expression. Microarray data
was pre-processed using RMA background correction and quantiles normalization.
One can use the
absolute value of each gene at a given time-point or alternatively where a
baseline gene level was
obtained and available, the gene levels post exposure to virus can be baseline
normalised for each subject
(i.e. compared to the subject's baseline expression level for that gene or
gene panel or gene sub-panel).
[000122] Three separate gene subpanels can be used (i.e. 3
algorithms) to identify which
individuals will develop acute symptoms of an influenza-like disease
a. The 3 gene sub panels are:
i. Subpanel A: PHF20, ABCA1, APBA2, MORC2, SNU13 and
DCUN1D2
Subpanel B: MAX, NOL9, MPRIP, HP, BST1 and TM9SF2
Subpanel C: HOMER3, NSUN6, HP, EPHA4 and BMP2K
[000123] In one instance the different gene subpanels are used at
different time points (FIG. 8, sub
panel A, followed by sub panel B, followed by subpanel C). Alternatively,
instead of using the subpanels
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sequentially 1, 2, or 3 gene sub panels are used at the same time (FIG. 7),
and may be repeated at several
points following inoculation.
a. For both approaches, a positive result for any test immediately triggers
dosing of the subject
with the investigational medicament (drug/vaccine/placebo).
b. Alternatively, two positive results, or three positive results, arc
required to trigger dosing.
[000124] For each gene sub-panel the stringency can be varied by
modifying the threshold at
which a positive result is obtained. For example, the threshold for gene sub-
panel 1 could be set to be
more stringent avoiding false positives. Gene sub-panel 2 is then set with
lower stringency and gene sub-
panel 3 with even lower stringency, thus increasing the chances of identifying
and dosing all subjects who
will develop acute symptoms of influenza-like disease as early as possible.
[000125] In addition to the use of the gene sub-panels, the
results may be combined with a
diagnostic test that confirms the subject has the respiratory viral infection
relevant to the trial (e.g. a viral
test).
[000126] In addition to the use of the gene sub-panels, the
results may be combined with
measurements of the change in variance/gradient of symptoms.
[000127] Other actions that can be triggered alongside dosing with
a medicament include
increasing the observations/samples/measurements in those predicted to develop
acute symptoms of an
influenza-like disease or reducing observations/samples/measurements in those
who are predicted not to
develop acute symptoms of an influenza-like disease.
[000128] By using this invention as part of the trial design
decision making, only those subjects
most likely to develop acute symptoms of influenza like disease are included
in the statistical analysis of
efficacy for the investigational medicament, which has the benefits as
previously described.
[000129] In some instances, where the algorithms do not report a
positive test result, the subjects
may be dosed at a predetermined time point post exposure or inoculation (e.g.
Day 4). These subjects
form a further subgroup for analysis.
Example 3
[0001 301 A group of individuals are recruited into an efficacy
field study and become infected in
the community with a respiratory virus, in particular influenza. Following
exposure, it would be
beneficial to identify, in advance, subjects who will develop acute symptoms
of an influenza like disease,
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allowing selective dosing of these subjects with an investigational or
licensed medicament (drug/vaccine/
placebo) at the earliest opportunity. This would improve ability to detect
clinically relevant reduction in
disease by only evaluating the medicament effects in individuals that would
have gone on to present with
acute symptoms of an influenza-like disease. This will reduce unnecessary
exposure of subjects to an
investigational medicament as well as reducing the amount of medicament
required.
[000131] In this example, volunteers have been screened for
eligibility for the evaluation of the
efficacy of an investigational medicament in a clinical field trial against a
respiratory virus, in particular
influenza.
[000132] Eligible volunteers arrive at the clinic and baseline
samples and clinical measures are
taken as they are enrolled in the study. Baseline values would be obtained
using one or more blood
samples over varying time-points post enrolment and prior to contracting a
respiratory virus infection in
the community.
[000133] Blood samples (e.g. paxgene RNA samples twice, three
times a day, or more) are taken
after virus exposure from a household contact that has a respiratory
infection, or after showing initial
symptoms of respiratory disease (the trial subjects may or may not be using a
study
questionnaire/symptom diary card that captures these symptoms).
[000134] Specific gene sub-panels are measured real-time in the
blood paxgenes using the methods
described in Examples 1 and 2. One can use the absolute value of each gene at
a given time-point or
alternatively where a baseline gene level was obtained and available, the gene
levels post exposure to
virus can be baseline normalised for each subject (i.e. compared to baseline).
[000135] Three separate gene sub-panels can be used (i.e. 3
algorithms) to identify which
individuals will progress to have acute symptoms of an influenza-like disease
a. The 3 subpanels of genes are:
i. Subpanel A: PHF20, ABCA1, APBA2, MORC2, SNU13 and
DCUNID2
Subpanel B: MAX, NOL9, MPRIP, HP, BST1 and TM9SF2
Subpanel C: HOMER3, NSUN6, HP, EPHA4 and BMP2K
[000136] In one instance 1, 2, or 3 gene sub-panels are used at
the same (FIG. 7), and may be
repeated at several points over time.
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a. a positive result for any test immediately triggers dosing
of the subject with the
investigational medicament (drug/vaccine/placebo).
a. Alternatively, two positive results or three positive
results are required to trigger dosing.
[000137] For each gene sub-panel the stringency can vary by
modifying the threshold at which a
positive result is obtained. For example, the threshold for gene sub-panel 1
could be set to be more
stringent avoiding false positives. Gene sub-panel 2 is then set with lower
stringency and gene sub-panel
3 with even lower stringency, thus increasing the chances of identifying and
dosing all subjects who will
develop acute symptoms of an influenza-like disease as early as possible.
[000138] In addition to the use of the gene sub-panels, the
results may be combined with a
diagnostic test that confirms the subject has the respiratory viral infection
relevant to the trial (e.g. viral
test).
[0001391 In addition to the use of the gene sub-panels, where a
symptom diary card is used the
results may be combined with measurements of the change in variance/gradient
of symptoms.
[000140] Other actions that can be triggered alongside dosing with
a medicament include
increasing the observations/samples/measurements in those predicted to go on
develop acute symptoms of
influenza-like disease or reducing observations/samples/measurements in those
predicted to not develop
acute symptoms of an influenza-like disease.
[000141] By using this invention as part of the trial design
decision making, only those most likely
to develop acute symptoms of influenza-like disease are included in the
statistical analysis of efficacy for
the investigational medicament, which has the benefits as previously
described.
[000142] In some instances, where the algorithms do not report a
positive, the subjects may be
dosed at a predetermined time points post exposure (e.g. Day 4, Day 5), with
these subjects forming a
secondary subgroup analysis.
Example 4
[000143] Subjects may become infected with a respiratory virus in
the community or exposed to an
infected person for an extended period in the community. Community settings
can include at home
(family member, household contact), at work, in transit (e.g. on a train,
coach, plane, ship), within a care
home (fellow resident, family visitor, carer), as an inpatient in hospital
(fellow inpatient, healthcare
worker, visitor), within military setting (fellow personnel). Following
exposure, it would be beneficial to
identify, in advance, people who will progress to have acute symptoms of an
influenza-like disease,
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allowing an intervention at the earliest opportunity. Interventions include
assisting referral to healthcare
professionals, enabling earlier treatment with an antiviral than would
otherwise be possible (for example
Tamiflu), administration of an immunomodulator drug or combination of
antiviral and
immunomodulator, separation from others (quarantine), inclusion in a study
(IMP trial, transmission
study), initiate sampling for disease/biomarker monitoring.
[000144] A subject becomes infected with a respiratory virus in
the community or is exposed to an
infected person for a prolonged period in the community.
[000145] Post viral exposure blood samples may be collected and
gene levels quantified following
one or more triggers:
a. a positive diagnostic test (e.g. based on viral replication, diagnostic
biomarker)
b. initial symptoms of respiratory viral disease
c. prolonged exposure to an infected contact
[000146] Specific gene panels or gene sub-panels are measured real-
time in the blood sample (e.g.
using a Point of Care test). The absolute value of each gene at a given time-
point can be used or
alternatively where a baseline gene level was obtained or is available, the
gene levels post exposure to
virus can be baseline normalised for each subject (i.e. compared to baseline).
[000147] In one scenario, three separate gene sub-panels arc used
(i.e. three algorithms) to idcntify
which individuals are likely to develop acute symptoms of an influenza-like
disease
a. The three subpanels of genes are:
i. Subpanel A: PHF20, ABCA1, APBA2, MORC2, SNU13 and
DCUN1D2
Subpanel B: MAX, NOL9, MPRIP, HP, BST1 and TM9SF2
iii Subpanel C: HOMER3, NSUN6, HP, EPHA4 and BMP2K
[00014g] In another scenario one, two, or three gene sub-panels
are used at the same (FIG. 7), and
may be repeated at several time points.
a. a positive result for any test may trigger one or more of
the following:
i. assisting referral to healthcare professionals,
enabling earlier treatment with an antiviral than would otherwise be possible
(e.g.Tamiflu),
administration of an immunomodulator drug or combination of antiviral and
immunomodulator,
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iv. separation from others (quarantine, using barriers to transmission e.g.
masks),
v. inclusion in a study (e.g. IMP trial, disease study, transmission
study),
vi. initiate sampling for disease/biomarker monitoring
h. Alternatively, two positive results or three positive results
are required to trigger the actions,
as listed above.
[000149] For each gene .. sub-panel the stringency can vary by
modifying the threshold at which a
positive result is obtained. For example, the threshold for gene sub-panel 1
could be set to be more
stringent avoiding false positives. Gene sub-panel 2 is then set with lower
stringency and gene sub-
panel 3 with even lower stringency, thus increasing the chances of identifying
and intervening as early as
possible.
[000150] In addition to the use of the gene sub-panels, the
results may be combined with a
diagnostic test that confirms the subject has the respiratory viral infection
(if not already performed).
Gene sequences
00015 11 Included below for each gene identified are the probe
name specific to the GeneChip0
Human Genome U133A 2.0 Array (Affymetrix, Santa Clara, CA), the gene ID and
the full sequence of
the entire gene. It will be understood that the present invention is not
limited to use of the GeneChip
Human Genome U133A 2.0 Array; the expression levels of the one or more genes
disclosed herein may
be measured using any suitable method known to those skilled in the art,
employing any suitable probe or
probes which are capable of binding uniquely to respective nucleic acid
sequences that represent the one
or more genes.
Probe Name: 209422 at
Gene ID: PHF20 (SEQ ID No: 1)
Full gene sequence:
10 20 30 40 50
MTKHPPNRRG ISFEVGAQLE ARDRIKNWYP AHIEDIDYEE GKVIIHFKRW
60 70 80 90 100
NHRYDEWFCW DSPYLRPLEK IQLRIKEGLHE EDGSSEFQIN EQVLACWSDC
110 120 130 140 150
RFYPAKVTAV NKDGTYTVKF YDGVVQTVKH IHVKAFSKDQ NIVGNARPKE
160 170 180 190 200
TDHKSLSSSP DKREKFKEQR KLTVNVKKDK EDKPLKTEKR PKQPDKEGKL
210 220 230 240 250
ICSEKGKVSE KSLPKNEKED KENISENDRE YSGDAQVDKK PENDIVKSPQ
260 270 280 290 300
ENLREPKRKR GRPPSIAPTA VDSNSQTLQP ITLELRRRKI SKGCEVPLKR
310 320 330 340 350
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PRLDKNSSQE KSKNYSENTD KDLSRRRSSR LSTNGTHEIL DPDLVVSDLV
360 370 380 390 400
DTDPLQDTLS STKESEEGQL ESALEAGOVS SALTCHSFGD GSGAAGLELN
410 420 430 440 450
CPSMGENTMK TEPTSPLVEL QEISTVEVTN TFKKTDDFGS SNAPAVDLDH
460 470 480 490 500
KFRCKVVDCL KFFRKAKLLH YHMKYFHGME KSLEPEESPG KREVQTRGPS
510 520 530 540 550
ASDKPSQETL TRKRVSASSP TTKDKEKNKE KKEKEFVRVK PKKKKKKKKK
560 570 580 590 600
TKPECPCSEE ISDTSQEPSP PKAFAVTRCG SSHKPGVHMS PQLHGPESGH
610 620 630 640 650
HKGKVKALEE DNLSESSSES FLWSDDEYGQ DVDVTTNPDE ELDGDDRYDF
660 670 680 690 700
EVVRCICEVQ EENDFMIQCE ECQCWQHGVC MGLLEENVPE KYTCYVCQDP
710 720 730 740 750
PGQRPGFKYW YDKEWLSRGH MHGLAFLEEN YSHQNAKKIV ATHQLLGDVQ
760 770 780 790 800
RVIEVIHGLQ LKMSILQSRE HPDLPLWCQP WKQHSGEGRS HFRNIPVTDT
810 820 830 840 850
RSKEEAPSYR TLNGAVEKPR PLAIPLPRSV EESYITSEHC YQKPRAYYPA
860 870 880 890 900
VEQKLVVETR GSALDDAVNP LHENGDDSLS PRLGWPLDQD RSKGDSDPKP
910 920 930 940 950
GSPKVKEYVS KKALPEEAPA RKLLDRGGEG LLSSQHQWQF NLLTHVESLQ
960 970 980 990 1000
DEVTHRMDSI EKELDVLESW LDYTGELEPP EPLARLPQLK HCIKQLLMDL
1010
GKVQQIALCC ST
Probe Name: 203504...s. at
Gene ID: ABCA1 (SEQ ID No: 2)
Full gene sequence:
10 20 30 40 50
MACWPQLRLL LWKNLTFRRR QTCQLLLEVA WPLFIFLILI SVRLSYPPYE
60 70 80 90 100
QHECHFPNKA MPSAGTLPWV QGIICNANNP CFRYPTPGEA PGVVGNFNKS
110 120 130 140 150
TVARLFSDAR RLLLYSQKDT SMKDMRKVLR TLQQIKKSSS NLKLQDFLVD
160 170 180 190 200
NETFSGFLYH NLSLPKSTVD KMLRADVILH KVFLQGYQLH LTSLCNGSKS
210 220 230 240 250
EEM1QLGDQE VSELCGLPRE KLAAAERVLR SNMDILEPIL RTLNSTSPFP
260 270 280 290 300
SKELAEATKT LLHSLGTLAQ ELFSMRSWSD MRQEVMFLTN VNSSSSSTQI
310 320 330 340 350
YQAVSRIVCG HPEGGGLKIK SLNWYEDNNY KALFGGNGTE EDAETFYDNS
360 370 380 390 400
TTPYCNDLMK NLESSPLSRI IWKALKPLLV GKILYTPDTP ATRQVMAEVN
410 420 430 440 450
KTFULAVFH DLEGMWEELS PKIWTFMENS QEMDLVRMLL DSRDNDHFWE
460 470 480 490 500
QQLDGLDWTA QDIVAFLAKH PEDVQSSNGS VYTWREAFNE TNQAIRTISR
510 520 530 540 550
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FMECVNLRKL EPIATEVWLI NKSMELLDER KFWAGIVFTG ITPGSIELPH
560 570 580 590 600
HVKYKIRMDI DNVERTNRIK DGYWDPGPRA DPFEDMRYVW GGFAYLQDVV
610 620 630 640 650
EQAIIRVLTG TEKKTGVYMQ QMPYPCYVDD IFLRVMSRSM PLFMTLAWIY
660 670 680 690 700
SVAVIIKGIV YEKEARLKET MRIMGLDNSI LWFSWFISSL IPLLVSAGLL
710 720 730 740 750
VVILKLGNLL PYSDPSVVFV FLSVFAVVTI LQCFLISTLF SRANLAAACG
760 770 780 790 800
GIIYFTLYLP YVLCVAWQDY VGFTLKIFAS LLSPVAFGFG CEYFALFEEQ
810 820 830 840 850
GIGVQWDNLF ESPVEEDGFN LTTSVSMMLF DTFLYGVMTW YIEAVFPGQY
860 870 880 890 900
GIPRPWYFPC TKSYWFGEES DEKSHPCSNQ KRISEICMEE EPTHLKLGVS
910 920 930 940 950
IQNLVKVYRD GMKVAVDGLA LNFYEGQITS FLGHNGAGKT TTMSILTGLF
960 970 980 990 1000
PPTSGTAYIL GKDIRSEMST IRQNLGVCPQ HNVLFDMLTV EEHIWFYARL
1010 1020 1030 1040 1050
KGLSENHVKA EMEQMAIDVG LPSSKLKSKT SQLSGGMQRK LSVALAFVGG
1060 1070 1080 1090 1100
SKVVILDEPT AGVDPYSRRG IWELLLKYRQ GRTIILSTHH MDEADVLGDR
1110 1120 1130 1140 1150
IAIISHGKLC CVGSSLFLKN QLGTGYYLTL VKKDVESSLS SCRNSSSTVS
1160 1170 1180 1190 1200
YLKKEDSVSQ SSSDAGLGSD HESDTLTIDV SAISNLIRKH VSEARLVEDI
1210 1220 1230 1240 1250
GHELTYVLPY EAAKEGAFVE LFHEIDDRLS DLGISSYGIS ETTLEEIFLK
1260 1270 1280 1290 1300
VAEESGVDAE TSDGTLPARR NRRAFGDKQS CLRPFTEDDA ADPNDSDIDP
1310 1320 1330 1340 1350
ESRETDLLSC MDCKGSYQVK CWKLTQQQFV ALLWKRLLIA RRSRKGFFAQ
1360 1370 1380 1390 1400
IVLPAVFVCI ALVFSLIVPP FGKYPSLELQ PWMYNEQYTF VSNDAPEDTG
1410 1420 1430 1440 1450
TLELLNALTK DPGFGTRCME GNPIPDTPCQ AGEEEWTTAP VPQTIMDLFQ
1460 1470 1480 1490 1500
NGNWTMQNPS PACQCSSDKI KKMLPVCPPG AGGLPPPQRK QNTADILQDL
1510 1520 1530 1540 1550
TGRNISDYLV KTYVQIIAKS LKNKIWVNEF RYGGFSLGVS NTQALPPSQE
1560 1570 1580 1590 1600
VNDAIKQMKK HLKLAKDSSA DRFLNSLGRF MTGLDTKNNV KVWFNNKGWH
1610 1620 1630 1640 1650
AISSFLNVIN NAILRANLQK GENPSHYGIT ATNHPLNLTK QQLSEVALMT
1660 1670 1680 1690 1700
TSVDVLVSIC VIFAMSFVPA SFVVFLIQER VSKAKHLQFI SGVKPVIYWL
1710 1720 1730 1740 1750
SNFVWDMCNY VVPATLVIII FICFQQKSYV SSTNLPVLAI LLLLYGWSIT
1760 1770 1780 1790 1800
PLMYPASFVF KIPSTAYVVL TSVNLFIGIN GSVATFVLEL FTDNKLNNIN
1810 1820 1830 1840 1850
DILKSVFLIF PHFCLGRGLI DMVKNQAMAD ALERFGENRF VSPLSWDLVG
1860 1870 1880 1890 1900
RNLFAMAVEG VVFFLITVLI QYRFFIRPRP VNAKLSPLND EDEDVRRERQ
CA 03220934 2023- 11-30
WO 2022/254221
PCT/GB2022/051405
33 =
1910 1920 1930 1940 1950
RILDGGGQND ILEIKELTKI YRRKRKPAVD RICVGIPPGE CFGLLGVNGA
1960 1970 1980 1990 2000
GKSSTFKMLT GDTTVTRGDA FLNKNSILSN IHEVHQNMGY CPQFDAITEL
2010 2020 2030 2040 2050
LTGREHVEFF ALLRGVPEKE VGKVGEWAIR KLGLVKYGEK YAGNYSGGNK
2060 2070 2080 2090 2100
RKLSTAMAII GGPPVVFLDE PTT GMDPKAR RFLWNCALSV VKEGRSVVLT
2110 2120 2130 2140 2130
SHSMEECEAL CTRMAIMVNG RFRCLGSVQH LKNRFGDGYT IVVRIAGSNP
2160 2170 2180 2190 2200
DLKPVQDFFG LAFPGSVLKE KHRNMLQYQL PSSLSSLARI FSILSQSKKR
2210 2220 2230 2240 2250
LHIEDYSVSQ TTLDQVFVNF AKDQSDDDHL KDLSLHKNQT VVDVAVLTSF
2260
LQDEKVKESY V
Probe Mame: 209870_s_at
Gene ID: APBA2 (SEQ ID No: 3)
Full gene sequence:
10 20 30 40 50
MAHRKLESVG SGMLDHRVRP GPVPHSQEPE SEDMELPLEG YVPEGLELAA
60 70 80 90 100
LRPESPAPEE QECHNHSPDG DSSSDYVNNT SEEEDYDEGL PEEEEGITYY
110 120 130 140 150
IRYCPEDDSY LEGMDCNGEE YLAHSAHPVD TDECQEAVEE WTDSAGPHPH
160 170 180 190 200
GHEAEGSQDY PDGQLPIPED EPSVIEAHDQ EEDGHYCASK EGYQDYYPEE
210 220 230 240 250
ANGNTGASPY RLRRGDGDLE DQEEDIDQIV AEIKMSLSMT SITSASEASP
260 270 280 290 300
EHGPEPGPED SVEACPPIKA SCSPSRHEAR PKSLNLLPEA KHPGDPQRGF
310 320 330 340 350
KPKTRTPEER LKWPHEQVCN GLEQPRKQQR SDLNGYVDNN NIPETKKVAS
360 370 380 390 400
FPSFVAVPGP CEPEDLIDGI IFAANYLGST QLLSERNPSK NIRMMQAQEA
410 420 430 440 450
VSRVKRMQKA AKIKKKANSE GDAQTLTEVD LFISTQRIKV LNADTQETMM
460 470 480 490 500
DHALRTISYI ADIGNIVVLM ARRRMPRSAS QDCIETTPGA QEGKEQYKMI
510 520 530 540 550
CHVFESEDAQ LIAQS1GQAF SVAYQEFLRA NGINPEDLSQ KEYSDIINTQ
560 570 580 590 600
EMYNDDLIHF SNSENCKELQ LEKHKGEILG VVVVESGWGS ILPTVILANM
610 620 630 640 650
MNGGPAARSG KLSIGDQIMS INGTSLVGLP LATCQGIIKG LKNQTQVKLN
660 670 680 690 700
IVSCPPVTTV LIKRPDLKYQ LGFSVQNGII CSLMRGGIAE RGGVRVGHRI
710 720 730 740
IEINGQSVVA TAHEKIVQAL SNSVGEIHMK TMPAAMERLL TGQETPLYI
Probe Name: 216863at
CA 03220934 2023- 11-30
WO 2022/254221
PCT/GB2022/051405
34
Gene ID: MORC2 (SEQ ID No: 4)
Full gene sequence:
10 20 30 40 50
MAFTNYSSLN RAQLTFEYLH TNSTTHEFLF GALAELVDNA RDADATRIDI
60 70 80 90 100
YAERREDLRG GFMLCFLDDG AGMDPSDAAS VIQFGKSAKR TPESTQIGQY
110 120 130 140 150
GNGLKSGSMR IGKDFILFTK KEDTMTCLFL SRTFHEEEGI DEVIVPLPTW
160 170 180 190 200
NARTPEPVTD NVEKFAIETE LIYKYSPFRT EEEVMTQFMK IPGDSCTLVT
210 220 230 240 250
1FNLKLMDNG EPELDIISNP RDIOMAETSP EGTKPERRSF RAYAAVLYID
260 270 280 290 300
PRMRIF1HGH KVQTKRLSCC LYKPRMYKYT SSREKTRAEQ EVKKAEHVAR
310 320 330 340 350
1AEEKAREAE SKARTLEVRL GGDLTRDSRV MLRQVQNRAI TLRREADVKK
360 370 380 390 400
RIKEAKQRAL KEPKELNFVF GVNIEHRDLD GMFIYNCSRL IKMYEKVGPQ
410 420 430 440 450
LEGGMACGGV VGVVDVPYLV LEPTHNKQDF ADAKEYRHLL RAMGEHLAQY
460 470 480 490 500
WKDIAIAQRG IIKEWDEFGY LSANWNQPPS SELRYKRRRA MEIPTLIQCD
510 520 530 540 550
LCLKWRTLPF QLSSVEKDYP DTWVCSMNPD PEQDRCEASE QKQKVPLGTF
560 570 580 590 600
RKDMKTQEEK QKQLTEKIRQ QQEKLEALQK TTPIRSQADL KKLPLEV1TR
610 620 630 640 650
PSTEEPVRRP QRPRSPPLPA VIRNAPSRPP SLPTPRPASQ PRKAPVISST
660 670 680 690 700
PKLPALAARE EASTSRLLQP PEAPRKPANT LVKTASRPAP LVQQLSPSLL
710 720 730 740 750
PNSKSPREVP SPKV1KTPVV KKTESPIKLS PATPSRKRSV AVSDEEEVEE
760 770 780 790 800
EAERRKERCK RGRFVVKEEK KDSNELSDSA GEEDSADLKR AQKDKGLHVE
810 820 830 840 850
VRVNREWYTG RVTAVEVGKH VVRWKVKFDY VPTDTTPRDR WVEKGSEDVR
860 870 880 890 900
LMKPPSPEHQ SLDTQQEGGE EEVGPVAQQA IAVAEPSTSE CLRIEPDTTA
910 920 930 940 950
LSTNHETIDL LVQILRNCLR YFLPPSFPIS KKQLSAMNSD ELISFPLKEY
960 970 980 990 1000
FKQYEVGLQN LCNSYQSRAD SRAKASEESL RTSERKLRET EEKLQKLRTN
1010 1020 1030
IVALLQKVQE DIDINTDDEL DAYIEDLITK GD
Probe Name: 201076_at
Gene ID: SNU13 (SEQ ID No: 5)
Full gene sequence:
10 20 30 40 50
MTEADVNPKA YPLADAHLTK KLLDLVQQSC NYKQLRKGAN EATKTLNRGI
60 70 80 90 100
SEFIVMAADA EPLEIILHLP LLCEDKNVPY VFVRSKQALG RACGVSRPVI
CA 03220934 2023- 11-30
WO 2022/254221
PCT/GB2022/051405
110 120
ACSVTIKEGS QLKQQIQSIQ QSIERLLV
Probe Name: 219116_s_at
Gene ID: DCUN1D2 (SEQ ID No: 6)
Full gene sequence:
10 20 30 40 50
MNKLESSQKD KVRQFMACTQ AGERTATYCL TQNEWRLDEA TDSFFQNPDS
60 70 80 90 100
LHRESMRNAV DKKKLERLYG RYKDPQDENK IGVDGIQQFC DDLSLDPASI
110 120 130 140 150
SVLVIAWKFR AATQCEFSRK EFLDGMTELG CDSMEKLEAL LPRIEQELKD
160 170 180 190 200
TAKFKDFYQF TFTFAKNPGQ KGLDIEMAVA YWKLVLSGRF KFLDLWNTFL
210 220 230 240 250
MEHHKRSIPR DTWNLLLDFG NMIADDMSNY DEEGAWPVLI DDFVEYARPV
VTGGKRSLF
Probe Name: 214:1.08 at
Gene ID: MAX (SEQ ID No: 7)
Full gene sequence:
10 20 30 40 50
MSDNDDI EVE SDEEQPREQS AADKRAHHNA LERKRRDHIK DSFHSLRDSV
60 70 80 90 100
PSLQGEKASR AQILDKATEY IQYMRRKNHT HQQDIDDLKR QNALLEQQVR
110 120 130 140 150
ALEKARSSAQ LQTNYPSSDN SLYTNAKGST ISAFDGGSDS SSESEPEEPQ
160
SRKKLRMEAS
Probe Name: 210754 at
Gene ID: NOL9 (SEQ ID No: 8)
Full gene sequence:
10 20 30 40 50
MADSGLLLKR GSCRSTWLRV RKARPQLILS RRPRRRLGSL RWCGRPRLRW
60 70 80 90 100
RLLQAQASGV DWREGARQVS RAAAARRPNT ATPSPIPSPT PASEPESEPE
110 120 130 140 150
LFSASSCHRP LLTRPVPPVG PGRATLLLPV EQGFTFSGTC RVTCLYGQVQ
160 170 180 190 200
VEGFTISQGQ PAQDIFSVYT HSCLSIHALH YSQPEKSKKE LKREARNLLK
210 220 230 240 250
SHLNLDDRRW SMQNFSPQCS IVLLEHLKTA TVNFITSYPG SOYIEVQESP
260 270 280 290 300
TPQIKPEYLA LRSVGIPREK KRKGLQLTES TLSALEELVN VSCEEVDGCP
310 320 330 340 350
VILVCGSQDV GKSTFNRYLI NHLLNSLPCV D1LECDLGQT EFTPPGCISL
360 370 380 390 400
LNTTEPVLGP PFTHLPTPOK MVYYGKPSCK NNYENYIDIV KYVFSAYKRE
410 420 430 440 450
CA 03220934 2023- 11-30
WO 2022/254221
PCT/GB2022/051405
36
SPLIVNTMGW VSDQGLLLLI DLIRLLSPSH VVQFRSDHSK YMPDLTPQYV
460 470 480 490 500
DDMDGLYTKS KTKMRNPRFR LAAFADALEF ADEEKESPVE FTGBKLIGVY
510 520 530 540 550
TDFAFRITPR NRESHNKILR DLSIISYLSQ LQPPMPKPLS PLHSLTPYQV
560 570 580 590 600
PFNAVALRIT HSDVAPTHIL YAVNASWVGL CKIQDDVRGY TNGPILLAQT
610 620 630 640 650
PICDCLGFGI CRGTDMEKRL YHTLTPVPPE ELRTVNCLLV GATAIPHCVL
660 670 680 690 700
KCQRGIEGTV PYVTTDYNFK LPGASEKIGA REPEEAHKEK PYRRPKFCRK
MK
Probe Name: 212197 x at
Gene ID: MPRIP (SE-6 ID No: 9)
Full gene sequence:
10 20 10 40 50
MSAAKENPCR KFQANIFNKS KCQNCFKPRE SHLLNDEDLT QAKPIYGGWL
60 70 80 90 100
LLAPDGTDFD NPVHRSRKWQ RRFFILYEHG LLRYALDEMP TTLPQGTINM
110 120 130 140 150
NQCTDVVDGE GRTGQKFSLC ILTPEKEHF1 RAETKEIVSG WLEM1MVYPR
160 170 180 190 200
TNKQNQKKKR KVEPPTPQEP GPAKVAVTSS SSSSSSSSSI PSAEKVPTTK
210 220 230 240 250
STLWQEEMRT KDQPDGSSLS PAQSPSQSQP PAASSLREPG LESKEEESAM
260 270 280 290 300
SSDRMDCGRK VRVESGYFSL EKTKQDLKAE EQQLPPPLSP PSPSTPNHRR
310 320 330 340 350
SQVIEKFEAL DIEKAEHMET NAVGPSPSSD TROGRSEKRA FPRKRDFTNE
360 370 380 390 400
APPAPLPDAS ASPLSPHRRA ESLDRRSTEP SVTPDLLNFK KGWLTKQYED
410 420 430 440 450
GQWKKHWFVL ADQSLRYYRD SVAEEAADLD GEIDLSACYD VTEYPVQPNY
460 470 480 490 500
GFQIHTKEGE FTLSAMTSGI RRNWIQTIMK HVHPTTAPDV TSSLPEEKNK
510 520 530 540 550
SSCSFETCPR PTEKQEAELG EPDPEQKRSR ARERRREGRS KTFDWAEFRP
560 570 580 590 600
IQQALAQERV GGVGPADTHE PLRPEAEPGE LERERARRRE ERRKREGMLD
610 620 630 640 650
ATDGPGTEDA ALRMEVDRSP GLPMSDLKTH NVHVE1EQRW HQVETTPLRE
660 670 680 690 700
EKQVPIAPVH LSSEDGGDRL STHEITSLLE KELEQSQKEA SDLLEQNPLL
710 720 730 740 750
QDQLRVALGR EQSAREGYVL QATCERGFAA MEETHQKKIE DLQRQHQREL
760 770 780 790 800
EKLREEKDRL LAEETAATIS AIEAMKNAHR EEMERELEKS QRSQISSVNS
810 820 830 840 850
DVEALRRQYL EELQSVQREL EVLSEQYSQK CLENAHLAQA LEAERQALRQ
860 870 880 890 900
CQRENQELNA HNQELNNRLA AEITRLRTLL TGDGGGEATG SPLAQGKDAY
910 920 930 940 950
CA 03220934 2023- 11-30
WO 2022/254221
PCT/GB2022/051405
37
ELEVLLRVKE SEIQYLKQEI SSLKDELQTA LRDKKYASDK YKDIYTELSI
960 970 900 990 1000
AKAKADCDIS RLKEQLKAAT EALGEKSPDS ATVSGYDIMK SKSNPDFLKK
1010 1020
DRSCVTRQLR NIRSKSVIEQ VSWDT
Probe Name: 208470 s at
Gene ID: HP (SEQ IT) No: 10)
Full gene sequence:
10 20 30 40 50
MSALGAVIAL LLWGQLFAVD SGNDVTDIAD DGCPKPPEIA HGYVEHSVRY
60 70 80 90 100
QCKNYYKLRT EGDGVYTLND RKQWINKAVG DKLPECEADD GCPKPPEIAH
110 120 130 140 150
GYVEHSVRYQ CKNYYKLRTE GDGVYTLNNE KQW1NKAVGD KLPECEAVCG
160 170 180 190 200
KPKNPANPVQ RILGGHLDAK GSFPWQAKMV SHHNLTTGAT LINEQWLLTT
210 220 230 240 250
AKNLFLNHSE NATAKDIAPT LTLYVGKKQL VEIEKVVLHP NYSQVDIGLI
260 270 280 290 300
KLKQKVSVNE RVMPICLPSK DYAEVGRVGY VSGWGRNANF KFTDHLKYVM
310 320 330 340 350
LPVADQDQCI RHYEGSTVPE KKTPKSPVGV QP1LNEHTFC AGMSKYQEDT
360 370 380 390 400
CYGDAGSARA VHDLEEDTWY ATGIISFDKS CAVAEYGVYV KVTSIQDWVQ
KTIAEN
Probe Name: 205715_at
Gene ID: BST1 (SEQ ID No: 11)
Full gene sequence:
10 20 30 40 50
MAAQGCAASR LLQLLLQLLL LLLLLAAGGA RARWRGEGTS AHLRDIFLGR
60 70 80 90 100
CAEYRALLSP EQRNKNCTAI WEAFKVALDK DPCSVLPSDY DLFINLSRHS
110 120 130 140 150
IPRDKSLFWE NSHLLVNSFA DNTRRFMPLS DVLYGRVADF LSWCRQKNDS
160 170 180 190 200
GLDYQSCPTS EDCENNPVDS FWKRASIQYS KDSSGVIHVM LNGSEPTGAY
210 220 230 240 250
PIKGFFADYE IPNLQKEKIT RIEIWVMHEI GGPNVESCGE GSMKVLEKRL
260 270 280 290 300
KDMGFQYSCI NDYPPVKLLQ CVDHSTHPDC ALKSAAAATQ RKAPSLYTEQ
310
RAGLIIPLFL VLASRTQL
Probe Name: 201076 at
Gene ID: TM9SF2 (SEC? ID No: 12)
Full gene sequence:
10 20 30 40 50
MSARLPVLSP PRWPRLLLLS LLLLGAVPGP RRSGAFYLPG LAPVNFCDEE
60 70 80 90 100
CA 03220934 2023- 11-30
WO 2022/254221
PCT/GB2022/051405
38
KKSDECKAEI ELFVNRLDSV ESVLPYEYTA FDFCQASEGK RPSENLGQVL
110 120 130 140 150
FGERIEPSPY KFTFNKKETC KLVCTKTYHT EKAEDKQKLE FLKKSMLLNY
160 170 180 190 200
QHHWIVDNMP VTWCYDVEDG QRFCNPGFPI GCYITDKGHA KDACVISSDF
210 220 230 240 250
HERDTFYIFN HVDIKIYYHV VETGSMGARL VAAKLEPKSF KHTHIDKPDC
260 270 280 290 300
SGPPMD1SNK ASGEIKTAYT YSVSFEEDDK IRWASRWDYT LESMPHTH1Q
310 320 330 340 350
WFSIMNSLVI VLFLSGMVAM IMLRTLHKDI ARYNQMDSTE DAQEEFGWKL
360 370 380 390 400
VHGDIFRPPR KGMLLSVFLG SGTQILIMTF VTLFFACLGF LSPANRGALM
410 420 430 440 450
TCAVVIWVIL GTPAGYVAAR FYKSFGGEKW KTNVILTSFL CPGIVFADFF
460 470 480 490 500
IMNLILWGEG SSAAIPFGTL VAILALWFCI SVPLTFIGAY FGFKKNA1EH
510 520 530 540 550
PVRTNQ1PRQ IPEQSFYTKP LPGIIMGGIL PFGCIFTQLF FTLNSTWSHQ
560 570 580 590 600
MYYMFGFLFL VFIILVITCS EATILLCYFH LCAEDYHWQW RSFLTSGFTA
610 620 630 640 650
VYFLIYAVHY FFSKLQITGT ASTILYFGYT MIMVLIFFLF TGTIGFFACF
660
WFVTKIYSVV KVD
Probe Name: 215489 x_at
Gene ID: HOMER3 (SEQ ID No: 13)
Full gene sequence:
10 20 30 40 50
MSTAREQPIF STRAHVFQID PATKRNWIPA GEHALTVSYF YDATRNVYRI
60 70 80 90 100
ISIGGAKAII NSTVTPNMTF TKTSQKFGQW ADSRANTVYG LGFASEQHLT
110 120 130 140 150
QFAEKFQEVK EAARLAREKS QDGGELTSPA LGLASHQVPP SPLVSANGPG
160 170 180 190 200
EEKLFRSQSA DAPGPTERER LKKMLSEGSV GEVQWEAEFF ALQDSNNKLA
210 220 230 240 250
GALREANAAA AQWRQQLEAQ RAEAERLRQR VAELEAQAAS EVTPTGEKEG
260 270 280 290 300
LGQGQSLEQL EALVQTKDQE IQTLKSQTGG PREALEAAER EETQQKVQDL
310 320 330 340 350
ETRNAELEHQ LRAMERSLEE ARAERERARA EVGRAAQLLD VSLFELSELR
360
EGLARLAEAA P
Probe Name: 222128 at
Gene ID: NSUN6 (SEZ ID No: 15)
Full gene sequence:
10 20 30 40 50
MSIFPKISLR PEVENYLKEG FMNKEIVTAL GKQEAERKFE TLLKHLSHPP
CA 03220934 2023- 11-30
WO 2022/254221
PCT/GB2022/051405
39
60 70 80 90 100
SETTVRVNTH LASVQHVKNL LLDELQKQFN GLSVPILQHP DLQDVLLIPV
110 120 130 140 150
IGPRKFIEKQ QCEAIVGAQC GNAVLRGAHV YAPGIVSASQ FMEAGDVISV
160 170 180 190 200
YSDIEGKCKK GAKEFDGTKV FLGNGISELS RKEIFSGLPE LKGMGIRMTE
210 220 230 240 250
PVYLSPSFDS VLPRYLFLQN LPSALVSHVL NPQPGEKILD LCAAPGGETT
260 270 280 290 300
HIAALMHDQG EVIALDEIFN KVEKIKONAL LLGLWSIRAF CFDGTKAVKL
310 320 330 340 350
DMVEDTEGEP PFLPESFDRI LLDAPCSGMG QRPNMACTWS VKEVASYQPL
360 370 380 390 400
QRKLETAAVQ LLKPEGVLVY STCTITLAEN EEQVAWALTK FPCLQLQPQE
410 420 430 440 450
PQIGGEGMRG AGLSCEQLKQ LQRFDPSAVP LPDTDMDSLR EARREDMIRL
460
ANKDSIGFFI AKFVKCKST
Probe Name: 206114_at
Gene ID: EPHA4 (SEQ ID No: 16)
Full gene sequence:
10 20 30 40 50
MAGIFYFALF SCLFGICDAV TGSRVYPANE VTLLDSRSVQ GELGWIASPL
60 70 80 90 100
EGGWEEVSIM DEKNTPIRTY QVCNVMEPSQ NNWLRTDWIT REGAQRVYIE
110 120 130 140 150
IKFTIRDCNS LPGVMGTCKE TFNLYYYESD NDKERFIREN QFVKIDTIAA
160 170 180 190 200
DESFTQVDIG DRIMKLNTEI RDVGPLSKKG FYLAFQDVGA CIALVSVRVF
210 220 230 240 250
YKKCPLTVRN LAQFPDTITG ADTSSLVEVR GSCVNNSEEK DVPKMYCGAD
260 270 280 290 300
GEWLVPIGNC LCNAGHEERS GECQACKIGY YKALSTDATC AKCPPHSYSV
310 320 330 340 350
WEGATSCTCD RGFFRADNDA ASMPCTRPPS APLNLISNVN ETSVNLEWSS
360 370 380 390 400
PQNTGGRQDI SYNVVCKKCG AGDPSKCRPC GSGVHYTPQQ NGLETTKVSI
410 420 430 440 450
TDLLAHTNYT FEIWAVNGVS KYNPNPDQSV SVTVTTNQAA PSSIALVQAK
460 470 480 490 500
EVTRYSVALA WLEPDRPNGV ILEYEVKYYE KDQNERSYRI VRTAARNTDI
510 520 530 540 550
EGLNPLTSYV FHVRARTAAG YGDFSEPLEV TTNTVPSRII GDGANSTVLL
560 570 580 590 600
VSVSGSVVLV VILIAAFVIS RRRSKYSKAK QEADEEKHLN QGVRTYVDPF
610 620 630 640 650
TYEDPNQAVR EFAKEIDASC IKIEKVIGVG EFGEVCSGRL KVPGKRETCV
660 670 680 690 700
AIKTLKAGYT DKQRRDELSE ASIMGQFDHP NIIHLEGVVT KCKPVMIITE
710 720 730 740 750
YMENGSLDAF LRENDGRFTV IQLVGMLRGI GSGMKYLSDM SYVHRDLAAR
CA 03220934 2023- 11-30
WO 2022/254221
PCT/GB2022/051405
40 .
760 770 780 790 800
NILVNSNIVC KVSDFGMSRV LEDDPEAAYT TRGGKIPIRW TAPEAIAYRK
810 820 830 840 850
FTSASDVWSY GIVMWEVMSY GERPYWDMSN QDVIKAIEEG YRLPPPMDCP
860 870 880 890 900
1ALHQLMLDC WQKERSDRPK FGQ1VNMLDK LIRNPNSLKR TGTESSRPNT
910 920 930 940 950
ALLDPSSPEF SAVVSVGDWL QAIKMDRYKD NFTAAGYTTL EAVVHVNQED
960 970 980
LARIGITAIT HQNKILSSVQ AMRTQMQQMH GRMVPV
Probe Name: 59644 at
Gene ID: BMP2K (SEQ ID No: 17)
Full gene sequence:
10 20 30 40 50
MKKFSRMPKS EGGSGGGAAG GGAGGAGAGA GCGSGGSSVG VRVFAVGRHQ
60 70 80 90 100
VTLEESLAEG GESTVELVRT HGGIRCALKR MYVNNMPDLN VCKREITIME
110 120 130 140 150
ELSGHNNIVG YLDCAVNSIS DNVWEVLILM EYCRAGQVVN QMNKELQTGF
160 170 180 190 200
TEPEVIQIFC DTCEAVARLH QCKTPIIHRD LKVENILLND GGNYVLCDFG
210 220 230 240 250
SATNKFLNPQ KDGVNVVEEE IKKYTTLSYR APEMINLYGG KPITTKADIW
260 270 280 290 300
ALGCLLYKLC FFTLPFGESQ VAICDGNFTI PDNSRYSRNI HCLIRFMLEP
310 320 330 340 350
DPEHRPDIFQ VSYFAFKFAK KDCPVSNINN SSIPSALPEP MTASEAAARK
360 370 380 390 400
SQIKARITDT IGPTETSIAP RQRPKANSAT TATPSVITIQ SSATPVKVLA
410 420 430 440 450
PGEFGNHRPK GALRPCNGPE ILLGQCPPQQ PPQQHRVLQQ LQQGDWRLQQ
460 470 480 490 500
LHLQHRHPHQ QQQQQQQQQQ QQQQQQQQQQ QQQQQQHHHH HHHHLLQDAY
510 520 530 540 550
MQQYQHATQQ QQMLQQQFLM HSVYQPQPSA SQYPTMMPQY QQAFFQQQML
560 570 580 590 600
AQHQPSQQQA SPEYLTSPQE FSPALVSYTS SLPAQVGTIM DSSYSANRSV
610 620 630 640 650
ADKEAIANFT NQKNISNPPD MSGWNPFGED NFSKLTEEEL LDREFDLLRS
660 670 680 690 700
NRLEERASSD KNVDSLSAPH NHPPEDPFGS VPFISHSGSP EKKAEHSSIN
710 720 730 740 750
QENGTANPIK NGKTSPASKD QRTGKKTSVQ GQVQKGNDES ESDFESDPPS
760 770 780 790 800
PKSSEEEEQD DEEVLQGEQG DFNDDDTEPE NLGHRPLLMD SEDEEEEEKH
810 820 830 840 850
SSDSDYEQAK AKYSDMSSVY RDRSGSGPTQ DLNTILLTSA QLSSDVAVET
860 870 880 890 900
PKQEFDVFGA VPFFAVRAQQ PQQEKNEKYL PQHRFPAAGL EQEEFDVETK
910 920 930 940 950
APFSKKVNVQ ECHAVGPEAH TIPGYPKSVD VEGSTPFQPF LTSTSKSESN
960 970 980 990 1000
EDLFGLVPFD EITGSQQQKV KQRSLQKLSS RQRRTKQDMS KSNGKRHHGT
CA 03220934 2023- 11-30
WO 2022/254221
PCT/GB2022/051405
41
1010 1020 1030 1040 1050
PTSTKKTLKP TYRTPERARR FIKKVGRRDSQ SSNEFLTISD SKENISVALT
1060 1070 1080 1090 1100
DGKDRGNVIQ PEESLLDPFG AKPFHSPDLS WHPVHQGLSD IRADHNTVLP
1110 1120 1130 1140 1150
GRPRQNSLHG SFHSADVLKM DDFGAVPFTE LVVQSITPHQ SQQSQPVELD
1160
PFGAAPFPSK
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