U.S. patent application number 17/301187 was filed with the patent office on 2022-07-28 for biomarkers for assessing idiopathic pulmonary fibrosis.
This patent application is currently assigned to The University of Chicago. The applicant listed for this patent is The Board of Trustees of the University of Illinois, The University of Chicago, University of Pittsburgh - Of the Commonwealth System of Higher Education. Invention is credited to Joe G.N. Garcia, Kevin Gibson, Jose David Herazo-Maya, Yong Huang, Naftali Kaminski, Imre Noth.
Application Number | 20220235417 17/301187 |
Document ID | / |
Family ID | 1000006256048 |
Filed Date | 2022-07-28 |
United States Patent
Application |
20220235417 |
Kind Code |
A1 |
Noth; Imre ; et al. |
July 28, 2022 |
BIOMARKERS FOR ASSESSING IDIOPATHIC PULMONARY FIBROSIS
Abstract
Disclosed are methods and kits for evaluating predicting whether
an individual IPF has slowly or rapidly progressive IPF.
Inventors: |
Noth; Imre; (Chicago,
IL) ; Huang; Yong; (Dyer, IN) ; Herazo-Maya;
Jose David; (North Haven, CT) ; Kaminski;
Naftali; (New Haven, CT) ; Gibson; Kevin;
(Gibsonia, PA) ; Garcia; Joe G.N.; (Tucson,
AZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The University of Chicago
The Board of Trustees of the University of Illinois
University of Pittsburgh - Of the Commonwealth System of Higher
Education |
Chicago
Urbana
Pittsburgh |
IL
IL
PA |
US
US
US |
|
|
Assignee: |
The University of Chicago
Chicago
IL
The Board of Trustees of the University of Illinois
Urbana
IL
University of Pittsburgh - Of the Commonwealth System of Higher
Education
Pittsburgh
PA
|
Family ID: |
1000006256048 |
Appl. No.: |
17/301187 |
Filed: |
March 29, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16031384 |
Jul 10, 2018 |
10961582 |
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17301187 |
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14358945 |
May 16, 2014 |
10036069 |
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PCT/US2012/065540 |
Nov 16, 2012 |
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16031384 |
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61561543 |
Nov 18, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 2600/118 20130101;
G01N 33/56966 20130101; C12Q 1/6883 20130101 |
International
Class: |
C12Q 1/6883 20060101
C12Q001/6883; G01N 33/569 20060101 G01N033/569 |
Goverment Interests
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
[0002] This invention was made with government support under grant
numbers HL080513, HL101740, and HL089493 awarded by the National
Institutes of Health. The government has certain rights in the
invention.
Claims
1.-24. (canceled)
25. A method for determining whether a subject has or is at risk of
having rapid idiopathic pulmonary fibrosis (IPF) or slow
progressive IPF, comprising: (a) obtaining a biological sample from
said subject, wherein said subject has been identified as having an
interstitial lung disease (ILD) based on one or more of a pulmonary
function test, a high resolution CT scan (HRCT), or a measure of
blood oxygenation; (b) measuring an expression level of one or more
biomarkers associated with said rapid IPF or said slow progressive
IPF in said biological sample, wherein one or more biomarkers
comprises messenger RNA (mRNA); and (c) processing said expression
level to identify that said subject has or is at risk of having
said rapid IPF or said slow progressive IPF.
26. The method of claim 25, wherein said ILD is IPF.
27. The method of claim 25, wherein in (c), said subject is
identified as having or being at risk of having said rapid IPF.
28. The method of claim 27, further comprising treating said
subject based at least in part on said subject being identified as
having or being at risk of having said rapid IPF.
29. The method of claim 28, wherein said treating comprises the
subject getting a transplant.
30. The method of claim 25, wherein in (c), said subject is
identified as having or being at risk of having said slow
progressive IPF.
31. The method of claim 30, further comprising treating said
subject based at least in part on said subject being identified as
having or being at risk of having said slow IPF.
32. The method of claim 25, wherein said biological sample
comprises nucleated cells.
33. The method of claim 32, wherein said nucleated cells are lung
cells.
34. The method of claim 25, wherein said one or more biomarkers are
associated with a co-stimulator signal during T cell activation
pathway.
35. The method of claim 25, wherein said one or more biomarkers are
associated with a bystander B cell activation pathway.
36. The method of claim 25, wherein said one or more biomarkers are
associated with a T helper cell surface molecules pathway.
37. The method of claim 25, wherein said one or more biomarkers are
associated with a T cytotoxic cell surface molecules pathway.
38. The method of claim 25, wherein said measuring comprises use of
a microarray or polymerase chain reaction (PCR).
39. The method of claim 25, wherein said rapid IPF indicates a high
risk of mortality within 18 months of analysis.
40. The method of claim 25, wherein said slow progressive IPF
indicates that said subject will likely live more than 18 months
after analysis.
41. The method of claim 25, wherein said processing of said
expression level comprises processing said expression level with
expression levels obtained from a plurality of samples from
individuals with said rapid IPF and from individuals with said slow
progressive IPF.
42. The method of claim 25, wherein (b) comprises measuring
expression levels of a plurality of biomarkers associated with said
rapid IPF or said slow progressive IPF in said biological sample,
the plurality of biomarkers comprising said one or more
biomarkers.
43. The method of claim 25, wherein said biological sample
comprises peripheral blood mononuclear cells (PBMCs).
44. The method of claim 25, wherein said biological sample
comprises blood serum or plasma.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 16/031,384 filed Jul. 10, 2018, which is a
divisional of U.S. patent application Ser. No. 14/358,945 filed May
16, 2014, which is a national phase application under 35 U.S.C.
.sctn. 371 of International Application No. PCT/US2012/065540 filed
Nov. 16, 2012, which claims priority to U.S. Provisional
Application Ser. No. 61/561,543, filed Nov. 18, 2011. The entire
contents of each of the above-referenced disclosures are
specifically incorporated herein by reference.
INTRODUCTION
[0003] Idiopathic Pulmonary Fibrosis (IPF) is a chronic and fatal
lung disease for which the only therapy currently available is lung
transplantation. The course of IPF is variable and unpredictable.
There is thus a need in the art for methods of predicting prognosis
of the disease in individuals affected by IPF.
BACKGROUND OF THE INVENTION
[0004] Idiopathic Pulmonary Fibrosis (IPF) is a chronic and
progressive fibrosing interstitial lung disease with an unknown
etiology. Diagnosis of IPF is based on clinical and radiological
features and, when available, findings of usual interstitial
pneumonia on lung biopsy. IPF patients have an overall median
survival of 3-3.5 years. The disease is more prevalent and probably
more lethal among males..sup.3, 4 With the exception of lung
transplantation, no therapy has been proven beneficial for IPF.
[0005] The course of IPF is highly variable and largely
unpredictable among individual patients. Disease progression in
current clinical practice is monitored by pulmonary function tests
[e.g., forced vital capacity (FVC), diffusion capacity for carbon
monoxide (DLCO)]; high resolution CT scans (HRCT) and, measures of
oxygenation. Previous studies have shown associations of serial
measures of these clinical variables with disease extent and poor
outcomes. The available evidence suggests that plasma protein
concentrations and other blood cells may be of diagnostic use and
to be indicative of disease severity and outcome prediction in IPF
patients.
[0006] Identification of biomarkers may facilitate the diagnosis
and follow-up of patients with IPF as well as the implementation of
new therapeutic interventions. Currently, establishing a diagnosis
of IPF may require surgical lung biopsy in patients with atypical
clinical presentations or high-resolution computed tomography
(HRCT) scans. Patients with IPF are often evaluated by serial
pulmonary physiology measurements and repeated radiographic
examinations. These studies provide a general assessment of the
extent of disease, but do not provide information about disease
activity on a molecular level. Higher plasma concentrations of
surfactant proteins KL-6, FASL, CCL-2, .alpha.-defensins, and most
recently SPPl have been reported in patients with IPF and other
ILDs but most of these studies were modest in size and assayed only
a single or a few protein markers simultaneously. Matrix
metalloproteinase-8 ("MMP8") has been implicated as playing a role
in tissue remodeling in IPF, but also in sarcoidosis, making it a
nonspecific marker of IPF. Similarly, matrix metalloproteinase-7
("MMP7") was reported to be elevated in bronchoalveolar fluid from
both IPF patients as well as patients suffering from cryptogenic
organizing pneumonia ("COP").
[0007] More recently, certain panels of markers for diagnosing and
evaluating the severity and/or progression of IPF have been
proposed. Those panels are based, at least in part, on the
discovery that identifying increases in the plasma levels of MMP7,
MMP1 and MMP8, as well as IGFBP1 and/or TNFRSF1A, indicates a
diagnosis of IPF with a high degree of sensitivity and
specificity.
SUMMARY OF THE INVENTION
[0008] In one aspect, the present invention includes methods for
assessing survival or poor outcome of the diseased individual with
IPF. In some embodiments, the method involves measuring expression
levels of a set of at least three of the markers listed in Table 1
and Table 2 in a sample from the individual and using the
expression levels to predict survival in individual with IPF for
the effective lung transplant therapy.
[0009] In another aspect, the present invention includes kits for
performing the methods of the invention. The kits may comprise
antibodies specific for each of the markers used in the methods or
various primer pairs, each primer pair capable of amplifying one of
the markers or a portion of the marker.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1A-C. (A) Heatmap depicting the hierarchical clustering
of IPF subjects from the derivation cohort using the 38-gene
signature found to be significantly associated with survival in the
derivation cohort. Every row represents a gene and every column a
patient. Color scale is shown adjacent to heatmap in log based 2
scale--generally, gray denotes increase over the geometric mean of
samples and black decrease. Two major clusters of IPF patients were
identified by this clustering technique. (B) Comparison of survival
between the patients in the two major clusters. Kaplan-Meier
survival estimate depicts a statistically significant difference in
survival (P=0.006 by the Logrank test) between the 2 major
clusters. (C) A diagram of the co-stimulatory signal during T cell
activation pathway, genes colored in black are decreased, genes in
gray are increased and genes in white are unchanged. The T cell
receptor beta locus (TRB.beta.) gene is not colored since it was
not included in the analyses as this probe was not common to both
microarray platforms.
[0011] FIG. 2A-C. SmartChip qRT-PCR survival analysis based on
.DELTA.Ct expression values (using actin B as the endogenous
control) of CD28, ITK, ICOS, and LCK in a cohort of 139 IPF
subjects (FIG. 2A). Gray line--patients with expression levels
above the threshold .DELTA.Ct (representing a decrease in gene
expression). Black line--patients with expression levels below the
threshold .DELTA.Ct (representing an increase in gene expression).
Kaplan-Meier plots are shown for each gene in the unified cohort as
well as age and FVC % adjusted in males and females separately.
FIG. 2B and FIG. 2C depict the ROC curve of a CoxPH model that fits
the dichotomized .DELTA.Ct thresholds of CD28 along with age,
gender and FVC % for estimating survival and transplant free
survival predictions at various time points (different line colors)
after blood draw. The AUC for each plot is shown in
parentheses.
[0012] FIG. 3. The graphics represent the paired percentage of
CD4+CD28+ and CD4+CD28null cells expression of T-cell
co-stimulatory proteins ITK, ICOS, LCK, and CD3E in IPF subjects
evaluated at the University of Pittsburgh. Gray dot--median, gray
lines--error bars (95% CI for a median), black lines--connecting
lines between the paired samples.
DETAILED DESCRIPTION OF THE INVENTION
[0013] The present invention relates to methods and kits for
diagnosing or evaluating the progression of IPF. As described
below, two independent studies including individuals with IPF were
conducted to identify genes the expression of which is correlated
with overall survival. A matched gene expression dataset containing
17417 unique gene probes in each cohort was used for statistical
analyses (see supplementary methods). A set of 217 gene-sets were
analyzed by the gene set analysis (GSA) method. Of those genes, 38
(Table 1) were found to be significantly associated with survival
in IPF patients and provided clues about potential biological
process associated with survival in IPF with the co-stimulatory
signal during T-cell activation pathway (Table 2). The expression
products, i.e., RNA or protein products, of these 38 genes are also
referred to herein as "biomarkers".
TABLE-US-00001 TABLE 1 Genes Associated with Survival Gene
designation Gene symbol Cox Peroxisome proliferator-activated
receptor gamma PPARG 4.06 Tyrosylprotein sulfotransferase 1 TPST1
3.97 Chromosome 19 open reading frame 59 C19orf59 3.93 (mast
cell-expressed Monoamine oxidase A MAOA 3.84 Phospholipase B domain
containing 1 PLBD1 3.81 Interleukin 1 receptor, type II IL1R2 3.72
ADAM metallopeptidase with thrombospondin ADAMTS2 3.71 type 1
motif, 2 Jun dimerization protein 2 JDP2 3.70 Fms-related tyrosine
kinase 3 FLT3 3.64 Nucleosome assembly protein 1-like 2 NAP1L2
-4.23 Interleukin 7 receptor IL7R -4.04 Kruppel-like factor 12
KLF12 -3.95 Sphingosine-1-phosphate receptor 1 S1PR1 -3.84 Small
nucleolar RNA host gene 1 SNHG1 -3.83 Guanylate binding protein 4
GBP4 -3.73 CD96 molecule CD96 -3.71 Coiled-coil domain containing
127 CCDC127 -3.65 Chemokine (C-X-C motif) receptor 6 CXCR6 -3.64
IL2 inducible T cell kinase ITK -3.63 Butyrophilin, subfamily 3,
member A3 BTN3A3 -3.60 Major histocompatibility complex, class II,
HLA-DPB1 -3.55 DP beta 1 Dedicator of cytokinesis 10 DOCK10 -3.53
CD28 molecule CD28 -3.50 Cadherin-like and PC-esterase domain
containing 1 CPED1 -3.49 ADP-ribosylation factor-like 4C ARL4C
-3.46 Rho GTPase activating protein 5 ARHGAP5 -3.46 Glucosaminyl
(N-acetyl) transferase 4, core 2 GCNT4 -3.43 Tandem C2 domains,
nuclear TC2N -3.42 Butyrophilin, subfamily 3, member A1 BTN3A1
-3.41 V-ets erythroblastosis virus E26 oncogene ETS1 -3.40 homolog
1 CD47 molecule CD47 -3.40 Baculoviral IAP repeat containing 3
BIRC3 -3.39 Chromosome 2 open reading frame 27A C2orf27A -3.39
Leucine rich repeat containing 8 family, member C LRRC8C -3.38
Nucleoporin 43 kDa NUP43 -3.36 G protein-coupled receptor 174
GPR174 -3.36 Inducible T cell co-stimulator ICOS -3.35 La
ribonucleoprotein domain family, member 4 LARP4 -3.34
[0014] It is envisioned that assessing expression of any subset of
the 38 biomarkers in an individual with IPF will allow one to
predict whether the disease is likely to have poor outcome. For
example, as described below, a decrease in expression of CD28, ITK,
ICOS, or LCK was found to be predictive of earlier mortality in
patients with IPF.
TABLE-US-00002 TABLE 2 Signature Associated with Survival Gene
designation Gene symbol Cox IL2 inducible T cell kinase ITK -3.56
CD28 molecule CD28 -3.42 Inducible T cell co-stimulator ICOS -3.32
Lymphocyte-specific protein tyrosine kinase LCK -3.17 CD86 molecule
CD86 -3.09 CD3g molecule, gamma (CD3-TCR complex) CD3G -2.92 CD247
molecule CD247 -2.87 Phosphoinositide-3-kinase, regulatory subunit
1 PIK3R1 -2.74 (alpha) Major histocompatibility complex, class II,
HLA-DRB1 -2.73 DR beta 1 Major histocompatibility complex, class
II, HLA-DRA -2.60 DR alpha T cell receptor alpha locus TRA.alpha.
-2.55 CD3d molecule, delta (CD3-TCR complex) CD3D -2.23
Phosphatidylinositol-4,5-bisphosphate 3-kinase, PIK3CA -2.20
catalytic subunit CD3e molecule, epsilon (CD3-TCR complex) CD3E
-2.18 Cytotoxic T-lymphocyte-associated protein 4 CTLA4 -2.00
Protein tyrosine phosphatase, non-receptor type 11 PTPN11 -1.03
Interleukin 2 IL2 -0.93 CD80 molecule CD80 0.13 Inducible T-cell
co-stimulator ligand ICOSLG 0.40 Growth factor receptor-bound
protein 2 GRB2 1.30
[0015] As used herein, "patients with poor prognostic IPF" is used
interchangeably with "patients with rapidly progressive IPF". These
patients are expected to have early mortality, relative to patients
with good prognostic IPF or slowly progressive IPF. In general,
this subpopulation of IPF patients can be expected to die within 18
months of analysis assuming that they do not receive lung
transplants. Patients with good prognostic IPF or slowly
progressive IPF are expected to have relatively late mortality, and
can be expected to live more than 18-30 months after testing.
[0016] In the examples below, differentially expressed genes were
identified in peripheral blood mononuclear cells (PBMCs) at the
level of RNA expression by hybridization of cRNA to microarrays or
indirectly by quantitative reverse transcription polymerase chain
reaction ((qRT-PCR). Any suitable method of assaying relative or
absolute RNA expression levels may be used in the methods of the
invention. Additionally, it is envisioned that the relative or
absolute levels of expression of the protein products of the
differentially expressed genes may be used in the methods of the
invention, using any suitable method of detection, including, for
example, ELISA or Western blots.
[0017] Further, whether expression of a biomarker in a sample is
"increased" or "decreased" may be determined by comparing the
expression level in that sample to expression levels obtained from
a plurality of samples taken from a population of individuals with
IPF that includes both individuals with rapidly progressive IPF and
individuals with slowly progressive IPF, for example, by comparing
the expression level in a sample to the median value for that gene
in a population of individuals with IPF. Additionally, or
alternatively, increased or decreased expression of a particular
biomarker may be evaluated using gene normalization. Although
relative expression levels were used in the examples, one could
readily establish ranges of expression levels for each of the
tested biomarkers correlated with slowly or rapidly progressive
IPF.
[0018] Although PBMCs may be conveniently used in the methods of
the invention, it is expected that one or more of the biomarkers
may be assayed in plasma or serum samples.
[0019] In one set of non-limiting embodiments, the panel comprises
of a set of at least three of the markers listed in Table 1 and
Table 2. In particular non-limiting embodiments, markers in the
panel include CD28, ITK, ICOS, or LCK. In another particular
non-limiting embodiment, markers in the panel include PPARG, TPST1,
or MCEMP1. In another set of non-limiting embodiments, the panel
comprises of a set of at least five of the markers listed in Table
1 and Table 2. In particular non-limiting embodiments, markers in
the panel include CD28, ITK, ICOS, or LCK. In another particular
non-limiting embodiment, markers in the panel include PPARG, TPST1,
or MCEMP1.
[0020] In a first set of non-limiting embodiments, the present
invention provides for a method of determining IPF prognosis in a
subject, comprising measuring the levels of a set of at least three
of the markers listed in Table 1 and Table 2. For those markers
with a positive Cox score, increased expression (relative to
control values) correlates with shorter survival while decreased
expression indicates longer survival. For those markers with a
negative Cox score, increased expression (relative to control
values) correlates with longer survival while decreased expression
indicates shorter survival.
[0021] In particular non-limiting embodiments, increases (relative
to control values) in the levels of CD28, ITK, ICOS, or LCK
indicate slowly progressing IPF and longer expected survival. In a
first set of non-limiting embodiments, the present invention
provides for a method of determining IPF prognosis in a subject,
comprising measuring the levels of a set of at least three of the
markers listed in Table 1 and Table 2, wherein increases (relative
to control values) in the levels of CD28, ITK, ICOS, or LCK
indicate slowly progressing IPF and longer expected survival.
[0022] An "increase" as that term is used herein means an increase
of at least about 25% or of at least about 50% relative to control
(normal plasma/plasma) values or to the mean of a plurality of
normal values. A "decrease" as that term is used herein means a
decrease of at least about 25% or of at least about 50% relative to
control (normal plasma) values or to the mean of a plurality of
normal values.
[0023] After determining the prognosis, the patient may then be
advised regarding the prognosis and options for treatment (e.g.
transplant), and may optionally receive one or more further
diagnostic step (e.g., broncheolar lavage or biopsy) and/or
treatment (e.g., lung transplant).
[0024] In one set of non-limiting embodiments, the present
invention provides for a kit comprising one or more probes, such as
those used in assays including, but not limited to, microarray
analysis, qRT-PCR, ELISA, or Western blots, e.g., labeled or
unlabeled antibody or nucleic acid probes, for determining the
expression level of at least three of the markers listed in Table 1
and Table 2. In another set of non-limiting embodiments, the
present invention provides for a kit comprising a one or more
probes for determining the plasma levels of a panel of markers
comprising CD28, ITK, ICOS, and LCK.
[0025] In one set of non-limiting embodiments, the present
invention provides for a kit comprising one or more probes for
determining the expression level of at least three of the markers
listed in Table 1. In another set of non-limiting embodiments, the
present invention provides for a kit comprising one or more probes
for determining the expression level of at least three of the
markers listed in Table 2. In a further set of non-limiting
embodiments, the present invention provides for a kit comprising
one or more probes for determining the expression level of markers
comprising CD28, ITK, ICOS, and LCK.
[0026] In an alternative set of non-limiting embodiments, the
present invention provides for a kit comprising one or more probes
for determining the expression level of at least five of the
markers listed in Table 1. In another set of non-limiting
embodiments, the present invention provides for a kit comprising
one or more probes for determining the expression level of at least
five of the markers listed in Table 2. In a further set of
non-limiting embodiments, the present invention provides for a kit
comprising one or more probes for determining the expression level
of markers comprising CD28, ITK, ICOS, and LCK and at least two
additional markers from Table 2.
[0027] In another set of non-limiting embodiments, the present
invention provides for a kit comprising one or more probes for
determining the plasma levels of a panel of markers comprising
CD28, ITK, ICOS, or LCK and at least two, at least three, at least
four, at least five, at least six, at least seven, at least eight,
at least nine, or at least ten, or at least fifteen, or at least
twenty, markers selected from the Table 1 and Table 2.
[0028] As used herein the specification, "a" or "an" may mean one
or more. As used herein in the claim(s), when used in conjunction
with the word "comprising", the words "a" or "an" may mean one or
more than one.
[0029] The use of the term "or" in the claims is used to mean
"and/or" unless explicitly indicated to refer to alternatives only
or the alternatives are mutually exclusive, although the disclosure
supports a definition that refers to only alternatives and
"and/or." As used herein "another" may mean at least a second or
more.
[0030] Throughout this application, the term "about" is used to
indicate that a value includes the inherent variation of error for
the device, the method being employed to determine the value, or
the variation that exists among the study subjects.
[0031] Other objects, features and advantages of the present
invention will become apparent from the description herein. It
should be understood, however, that the detailed description and
the specific examples, while indicating preferred embodiments of
the invention, are given by way of illustration only, since various
changes and modifications within the spirit and scope of the
invention will become apparent to those skilled in the art from
this detailed description.
Examples
Methods
Population.
[0032] Microarray studies were performed in a derivation cohort of
IPF subjects evaluated at the University of Chicago (N=45) and
results were validated in a replication cohort of IPF subjects
evaluated at the University of Pittsburgh (N=75). qRT-PCR studies
were performed in a cohort (N=139) including 43 and 74 IPF subjects
from the derivation and replication cohorts respectively, and 22
additional IPF subjects evaluated at the University of Chicago.
Flow cytometry studies were performed in IPF subjects (N=20)
evaluated at the University of Pittsburgh. IPF diagnosis was
established by a multidisciplinary group at each institution using
ATS/ERS criteria.sup.26 and it was consistent with recent
guidelines (see supplementary methods)..sup.1
[0033] The primary time-to-event outcome analyzed was overall
survival (survival); subjects were followed in clinics (at 3 to
4-month intervals) from blood draw until death, or censoring on
Jun. 30, 2010; the patients who had a lung transplant during follow
up were censored at transplant date. For transplant free survival,
the secondary time-to-event outcome, transplants and deaths were
both counted as events.
[0034] IPF diagnosis was established by a multidisciplinary group
of pulmonologist, radiologist, pathologist, and rheumatologist at
both institutions using ATS/ERS criteria.sup.20 and it was
consistent with recent guidelines..sup.1 Subjects were excluded of
the study if they had evidence of autoimmune syndromes,
malignancies, infections, drugs, or occupational exposures known to
cause lung fibrosis. The studies were approved by the institutional
review boards, and informed consent was obtained from all
subjects.
Microarray Experiments, Derivation Cohort (University of
Chicago)
[0035] PBMC were obtained by density centrifugation. RNA was
extracted using TRIzol (Invitrogen, Carlsbad, Calif.) and labeling
reactions were performed using a GeneChip WT cDNA Synthesis and
Amplification Kit, followed by hybridization using GeneChip Human
1.0 exon ST arrays (Affymetrix, Santa Clara, Calif.) following the
manufacturer's protocol. Data was processed using dChip software
(http://www.bioinformatics.org/dchip) and normalized by Robust
Multi-array Analysis..sup.27
[0036] More particularly, PBMC were obtained by Ficoll density
centrifugation from blood obtained by venous phlebotomy from each
subject. After washing, the PBMC were suspended on TRIzol
(Invitrogen, Carlsbad, Calif.) and RNA extracted following the
manufacturer's protocol. RNA yield and quality was evaluated using
NanoDrop at 260 nm and the 2100 Bioanalyzer (Agilent Technologies,
Santa Clara, Calif.) and specimens were stored at -70.degree. C.
for later use. Labeling reactions for microarray generation were
performed using a GeneChip WT cDNA Synthesis and Amplification Kit
(Affymetrix, Santa Clara, Calif.). In brief, a ribosomal RNA
reduction step was performed on 1 .mu.g of total RNA followed by
cDNA synthesis with random hexamers tagged with a T7 promoter
sequence. The double stranded cDNA product was then used as a
template for T7 RNA polymerase amplification to produce copies of
antisense cRNA. Random hexamers were used to prime reverse
transcription of the cRNA from the first cycle to produce
single-stranded DNA in the sense orientation using dUTP
incorporation to increase the reproducibility of the fragmentation.
The single-stranded DNA was then treated with a combination of
uracil DNA glycosylase and apurinic/apyrimidinic endonuclease 1 to
break the DNA strand. In turn, these strands were biotinylated by
use of terminal deoxynucleotidyl transferase (TdT) with the
Affymetrix proprietary DNA Labeling Reagent. Labeling efficiency
was evaluated using the Gel-Shift Assay. The labeled single
stranded DNA was hybridized using GeneChip Human 1.0 exon ST arrays
(Affymetrix, Santa Clara, Calif.) and scanned using the Affymetrix
GeneChip Scanner 3000, following the manufacturer's protocol. Data
were processed using dChip software
(http://www.bioinformatics.org/dchip). Briefly, after whole array
quintile normalization using Robust Multi-array Analysis,.sup.27
the exon intensities were summarized into gene expression levels by
mapping exon probe sets into U133 Plus 2 consensus or exemplar
sequences based on Affymetrix annotation U133 Plus Vs Hu Ex
(03/09). Microarray experiments, replication cohort (University of
Pittsburgh)
[0037] PBMC were obtained by density centrifugation. Total RNA was
extracted using QIAzol (Qiagen, Valencia, Calif.) and labeling
reactions were performed using Agilent Low RNA Input Linear
Amplification Kit PLUS, One-Color, followed by hybridization using
Whole Human Genome Oligo Microarray, 4.times.44K (G4112F, Agilent
Technologies) following the manufacturers protocol. To normalize
the gProcessed signal, cyclic-LOESS was performed using the
bioconductor package as described previously..sup.28 Microarray
experiments were compliant with MIAME guidelines. The complete
datasets are available in the Gene Expression Omnibus database
(http://www.ncbi.nlm.nih.gov/geo/; accession number GSE28221)
[0038] More particularly, peripheral blood was collected in a cell
preparation tube (CPT), followed by centrifugation to isolate PBMC,
these cells were suspended in QIAzol (Qiagen, Valencia, Calif.) and
stored at -80.degree. C. Total RNA was extracted and purified using
the miRNeasy Mini Kit (Qiagen), and QIAcube device (Qiagen),
following the manufacturers protocol. After extraction, total RNA
yield and quality were evaluated using NanoDrop at 260 nm and the
2100 Bioanalyzer (Agilent Technologies, Santa Clara, Calif.).
Labeling reactions were performed using Agilent Low RNA Input
Linear Amplification Kit PLUS, One-Color (Agilent Technologies).
Briefly, an initial cDNA strand was synthesized using 400 nanograms
of total RNA and an oligo(dT)24 primer containing T7 RNA
polymerase. This cDNA was then used as a template to generate Cy3
labeled cRNA by a reverse transcriptase enzyme. The cRNA was
fragmented, hybridized to Whole Human Genome Oligo Microarray,
4.times.44K (G4112F, Agilent Technologies), and scanned using an
Agilent Microarray Scanner. For array readout, Agilent Feature
Extraction software version 10.7 was used..sup.41 To normalize the
gProcessed signal, cyclic-LOESS was performed using the
bioconductor package as described previously..sup.28 The average of
the gene expression signal was used in the case of replicated
probes for the same gene with different expression values.
qRT-PCR Experiments
[0039] A multi-sample high-throughput qRT-PCR assay was designed
based on the Wafergen 5K SmartChip (Wafergen, Freemont, Calif.)
which consist of a 5K SmartChip (5184 wells) preloaded with PCR
primers for the genes CD28, ITK, ICOS and the endogenous control
ACTB. The starting total RNA concentration was 150 ng per sample to
generate cDNA that was combined with LightCycler SYBR I green
master mix (Roche Applied Science, Indianapolis, Ind.) and
dispensed in each well using the customized IDEX/Innovadyne
Nanodrop system (Rohnert Park, Calif.). Thermocycler conditions for
the PCR were: 95 degrees for 180 sec, for 40 cycles: 95 degrees for
60 sec, 60 degrees for 70 sec, Melt: 0.4 degrees/step to 95
degrees. ACT values were calculated by subtracting the threshold
cycles of each gene target and replicates, to the threshold cycles
of ACTB.
[0040] More particularly, a custom multi-sample high-throughput
qRT-PCR assay was designed based on the Wafergen 5K SmartChip
(Wafergen, Freemont, Calif.). In brief, this system consisted of a
5K SmartChip (5184 wells) that was preloaded with PCR primers for
the genes CD28, ITK, ICOS, and ACTB. Forward and reverse primers
for these genes were designed using the Universal ProbeLibrary
System (Roche Applied Science, Indianapolis, Ind.) based on the
Primer3 software program. Each chip was set to have 46 samples, one
positive control (universal cDNA) and one negative control (NTC)
with the primers in four replicates printed for each one of the
samples by the manufacturing group. All primers preloaded on
SmartChips were verified using human universal reference total RNA
in conjunction with a two-step qPCR assay. The starting total RNA
concentration was 150 ng per sample to generate cDNA using the High
Capacity cDNA Reverse Transcription Kit (Applied Biosystems,
Carlsbad, Calif.) following the manufacturer's protocol. The cDNA
(100 pg) was combined with LightCycler SYBR I green master mix
(Roche Applied Science, Indianapolis, Ind.) and dispensed in each
well using the customized IDEX/Innovadyne Nanodrop system (Rohnert
Park, Calif.). SmartChip thermocycler conditions for the PCR were:
95 degrees for 180 sec, for 40 cycles: 95 degrees for 60 sec, 60
degrees for 70 sec, Melt: 0.4 degrees/step to 95 degrees. qRT-PCR
was not performed in two IPF subjects from the derivation and one
IPF subject from the replication cohort because RNA samples were
exhausted for these individuals.
Flow Cytometry Experiments
[0041] Flow cytometry methods have been previously detailed..sup.12
In brief, freshly isolated PBMC were stained with anti-human
CD4-allophycocyanin and anti-human CD28-fluorescein isothiocyanate
MAb. Individual aliquots of these cells were also stained with
phycoerythrin conjugated MAb against other cell surface epitopes of
interest (ICOS and CD3E in 20 IPF subjects) and these expressions
were immediately determined by flow cytometery. Transcription
factors (ITK and LCK in 16 IPF subjects) were determined among
identical PBMC aliquots that were similarly stained with anti-human
CD4 and CD28 MAb.
[0042] More particularly, freshly isolated PBMC were stained with
anti-human CD4-allophycocyanin (APC) and anti-human
CD28-fluorescein isothiocyanate (FITC) MAb. Individual aliquots of
these cells were also stained with phycoerythrin (PE)-conjugated
MAb against other cell surface epitopes of interest (ICOS and CD3E
in 20 consecutive IPF individuals) and these expressions were
immediately determined by flow cytometery. Transcription factors
(ITK and LCK in 16 IPF subjects) were determined among identical
PBMC aliquots that were similarly stained with anti-human CD4 and
CD28 MAb, and fixed and permeabilized using reagents and products
supplied in a kit (Cytofix/Cytoperm, BD Bioscience, San Jose,
Calif.), prior to incubation with MAb having specificities for
these intracellular molecules. MAb, including isotype control
antibodies, were purchased from BD Bioscience (San Jose,
Calif.).
[0043] Flow cytometry characterizations were performed on
>10,000 live cells using a BD FACSCalibur (BD Bioscience). Gates
for quantitative analyses were set using control fluorochrome
positive and negative PBMC, including isotype controls. Unless
otherwise denoted, data are delineated as percentages of cells
within the respective autologous CD4.sup.+CD28.sup.+ and
CD4.sup.+CD28.sup.null subpopulations that express the phenotypic
characteristic(s) of interest. Because absolute values of mean
fluorescent intensities (MFI) can vary somewhat from day to day,
even using the same flow cytometer and despite appropriate
calibration, cell surface CD3E MFI expression is depicted as a
ratio of autologous, concurrent
CD4.sup.+CD28.sup.null/CD4.sup.+CD28.sup.+ values.
Statistical Analyses
[0044] A matched gene expression dataset containing 17417 unique
gene probes in each cohort was used for statistical analyses (see
supplementary methods). Significance analysis of microarrays (SAM)
with censored survival data.sup.29 was used to test the association
between PBMC microarray gene expression and censored survival data
in IPF subjects from the derivation cohort. A stringent cutoff for
gene selection (FDR<0.01) was used. For validation, hierarchical
clustering using cluster 3.sup.30 was performed in the replication
cohort dataset by using the survival associated genes with
FDR<0.01 identified in the derivation cohort.
[0045] Gene-set information was collected using Biocarta. 217
gene-sets containing at least six but not more than 87 genes were
tested. The gene set analysis (GSA) method with censored survival
data.sup.31 was used to evaluate the association between gene-sets
and censored survival data in IPF subjects in the derivation cohort
and results were validated in the replication cohort. Gene-set
significance was defined as P<0.05 and FDR<0.5
[0046] The SmartChip qRT-PCR .DELTA.Ct values obtained from each
gene were analyzed using the survival package.sup.32 of the R
environment..sup.33 .DELTA.Ct values obtained from each gene were
dichotomized into high- and low-risk ranges using profile
likelihood..sup.34 The profile likelihood was maximized for each
threshold and the threshold yielding the highest maximized profile
likelihood was chosen for each gene separately for survival and
transplant free survival. The step AIC.sup.35 approach was applied
for variable selection to fit a multivariate Cox Proportional
Hazard model (CoxPH) including .DELTA.Ct risk thresholds of CD28,
ITK and ICOS along with known clinical and demographic predictors
of outcome in IPF such as gender, age (dichotomized at 62 years)
and baseline FVC % (dichotomized at 68% predicted). By using
receiver operating characteristics (ROC) curves, the area under the
curve (AUC) of this model was tested at different time points
(0.25, 0.5, 0.9, 1.25, 1.5 and 2 years). The comparison of the
studied T cell co-stimulatory proteins ICOS, ITK, LCK, and CD3E
between CD4+CD28+ and CD4+CD28null cells was performed using the
Wilcoxon test for paired samples.
[0047] Differences in age and pulmonary function tests between IPF
subjects were evaluated with an unpaired, two tailed, T-test.
Differences in gender, smoking status, diagnostic strategy, and use
of immunosuppressive therapy were evaluated using Fisher's exact
test.
[0048] Given the differences in microarray technologies between the
studied cohorts (derivation cohort--Affymetrix and replication
cohort--Agilent) a matched gene expression dataset was generated
for statistical analyses containing 17417 unique gene probes. After
each microarray platform normalization, the Affymetrix microarray
gene expression dataset (N=44280 probes) was matched with the
Agilent microarray gene expression dataset (N=29807 probes) by
their corresponding gene ID's (http://www.ncbi.nlm.nih.gov/gene).
Since there are multiple replicated probes for the same gene in the
platforms studied, after microarray normalization and probe
matching, the probes were selected with the highest Inter Quartile
Range (IQR) variation across the arrays (N=17417 unique gene
probes) and each independent dataset was used for analyses.
[0049] We used significance analysis of microarrays (SAM) with
censored survival data.sup.29 to test the association between PBMC
microarray gene expression values and censored survival data in IPF
subjects from the derivation cohort. SAM computes a Cox score test
for each gene; a positive score indicates that higher expression
correlates with higher risk (i.e shorter survival) while lower
expression indicates lower risk (i.e longer survival) and a
negative score indicates that higher expression correlates with
lower risk (i.e. longer survival) while lower expression correlates
with higher risk (i.e shorter survival). SAM also performs
permutations of the censored survival data of each individual to
calculate the false discovery rate (FDR); 100 permutations were
used to identify a survival gene signature. Significance was
defined as a FDR<0.05, although a stringent cutoff of
FDR<0.01 was used for gene selection. Hierarchical clustering
using cluster 3.sup.30 was performed in the replication cohort
dataset by using the survival associated genes with FDR<0.01
previously identified in the derivation cohort. The genes were
centered by the median and genes and arrays were clustered using
complete linkage and centered correlation.
[0050] We also collected gene-set information using Biocarta. 217
gene-sets containing at least six but not more than 87 genes were
tested in the derivation and replication cohorts. The gene set
analysis (GSA) method with censored survival data.sup.31 was used
to evaluate the association between gene-sets and censored survival
data in IPF subjects in each independent microarray cohort. GSA
calculates a Cox score test for each gene and then uses the Maxmean
summary statistic; this is the mean of the positive or negative
part of gene scores in the gene-set, whichever is large in absolute
values. A negative gene-set is one in which lower expression of
most genes in the gene set correlates with higher risk (i.e shorter
survival) and a positive gene-set is one in which lower expression
of most genes in the gene set correlates with higher risk (i.e
shorter survival). GSA also performs permutations of the censored
survival data of each individual to calculate the false discovery
rate (FDR); 1000 permutations were used to identify statistically
significant gene-sets. Gene-set significance was defined as
P<0.05 and FDR<0.5
[0051] The SmartChip qRT-PCR .DELTA.Ct values obtained from each
gene were analyzed using the survival package.sup.32 of the R
environment..sup.33 .DELTA.Ct values obtained from each gene were
dichotomized into high- and low-risk ranges using profile
likelihood..sup.34 The profile likelihood was maximized for each
threshold and the threshold yielding the highest maximized profile
likelihood was chosen for each gene separately for survival and
transplant free survival. The .DELTA.Ct risk thresholds of CD28,
ITK, ICOS, and LCK, obtained from profile likelihood (there being
only finitely many possible thresholds based on the observed data),
were used as a parameter in a Cox proportional hazards (CoxPH)
model to adjust each gene to known clinical and demographic
predictors of outcome in IPF such as gender, age (dichotomized at
62 years) and baseline FVC % (dichotomized at 68% predicted).
Differences in survival were evaluated using the logrank test and
results were shown using Kaplan Meier curves. Lastly, the
stepAIC.sup.35 approach was applied for variable selection in a
multivariate CoxPH model to fit CD28, ITK, ICOS, age, gender, and
FVC % in an attempt to identify the best prediction model for
survival and transplant free survival. By using receiver operating
characteristics (ROC) curves, the Area Under the Curve (AUC) of
this model was tested, which estimates the probability that between
two randomly selected patients, the patient with the higher
predicted risk of dying (in the case of survival) or dying and
having a lung transplant (in the case of transplant free survival)
will be the first to have the studied outcome and this probability
was computed at different time points (0.25, 0.5, 0.9, 1.25, 1.5
and 2 years).
[0052] The comparison of the studied T-cell co-stimulatory proteins
ICOS, ITK, LCK, and CD3E between CD4+CD28+ and CD4+CD28null cells
was performed using the Wilcoxon test for paired samples.
[0053] Differences in age and pulmonary function tests between IPF
subjects were evaluated with an unpaired, two tailed, T-test.
Differences in gender, smoking status, diagnostic strategy, and use
of immunosuppressive therapy were evaluated using Fisher's exact
test.
[0054] Given the differences in microarray technologies between the
studied cohorts (derivation cohort--Affymetrix and replication
cohort--Agilent) a matched gene expression dataset was generated
for statistical analyses containing 17417 unique gene probes. After
each microarray platform normalization, the Affymetrix microarray
gene expression dataset (N=44280 probes) was matched with the
Agilent microarray gene expression dataset (N=29807 probes) by
their corresponding gene ID's (http://www.ncbi.nlm.nih.gov/gene).
Since there are multiple replicated probes for the same gene in the
platforms studied, after microarray normalization and probe
matching, the probes with the highest Inter Quartile Range (IQR)
variation across the arrays (N=17417 unique gene probes) were
selected and each independent dataset was used for analyses.
[0055] We used significance analysis of microarrays (SAM) with
censored survival data 29 to test the association between PBMC
microarray gene expression values and censored survival data in IPF
subjects from the derivation cohort. SAM computes a Cox score test
for each gene; a positive score indicates that higher expression
correlates with higher risk (i.e shorter survival) while lower
expression indicates lower risk (i.e longer survival) and a
negative score indicates that higher expression correlates with
lower risk (i.e. longer survival) while lower expression correlates
with higher risk (i.e shorter survival). SAM also performs
permutations of the censored survival data of each individual to
calculate the false discovery rate (FDR); 100 permutations were
used to identify a survival gene signature. Significance was
defined as a FDR<0.05, although a stringent cutoff of
FDR<0.01 was used for gene selection. Hierarchical clustering
using cluster 3 30 was performed in the replication cohort dataset
by using the survival associated genes with FDR<0.01 previously
identified in the derivation cohort. The genes were centered by the
median and genes and arrays were clustered using complete linkage
and centered correlation.
[0056] Gene-set information was also collected using Biocarta. 217
gene-sets containing at least six but not more than 87 genes were
tested in the derivation and replication cohorts. The gene set
analysis (GSA) method with censored survival data 31 was used to
evaluate the association between gene-sets and censored survival
data in IPF subjects in each independent microarray cohort. GSA
calculates a Cox score test for each gene and then uses the Maxmean
summary statistic; this is the mean of the positive or negative
part of gene scores in the gene-set, whichever is large in absolute
values. A negative gene-set is one in which lower expression of
most genes in the gene set correlates with higher risk (i.e shorter
survival) and a positive gene-set is one in which lower expression
of most genes in the gene set correlates with higher risk (i.e
shorter survival). GSA also performs permutations of the censored
survival data of each individual to calculate the false discovery
rate (FDR); 1000 permutations were used to identify statistically
significant gene-sets. Gene-set significance was defined as
P<0.05 and FDR<0.5
[0057] The SmartChip qRT-PCR .DELTA.Ct values obtained from each
gene were analyzed using the survival package 32 of the R
environment. 33 .DELTA.Ct values obtained from each gene were
dichotomized into high- and low-risk ranges using profile
likelihood. 34 The profile likelihood was maximized for each
threshold and the threshold yielding the highest maximized profile
likelihood was chosen for each gene separately for survival and
transplant free survival. The .DELTA.Ct risk thresholds of CD28,
ITK, ICOS, and LCK, obtained from profile likelihood (there being
only finitely many possible thresholds based on the observed data),
were used as a parameter in a Cox proportional hazards (CoxPH)
model to adjust each gene to known clinical and demographic
predictors of outcome in IPF such as gender, age (dichotomized at
62 years) and baseline FVC % (dichotomized at 68% predicted).
Differences in survival were evaluated using the logrank test and
results were shown using Kaplan Meier curves. Lastly, the stepAIC
35 approach was applied for variable selection in a multivariate
CoxPH model to fit CD28, ITK, ICOS, age, gender, and FVC % in an
attempt to identify the best prediction model for survival and
transplant free survival. By using receiver operating
characteristics (ROC) curves, the Area Under the Curve (AUC) of
this model was tested, which estimates the probability that between
two randomly selected patients, the patient with the higher
predicted risk of dying (in the case of survival) or dying and
having a lung transplant (in the case of transplant free survival)
will be the first to have the studied outcome and this probability
was computed at different time points (0.25, 0.5, 0.9, 1.25, 1.5
and 2 years).
[0058] The comparison of the studied T-cell co-stimulatory proteins
ICOS, ITK, LCK, and CD3E between CD4+CD28+ and CD4+CD28null cells
was performed using the Wilcoxon test for paired samples.
Results
Characteristics of the Patients
[0059] The microarray derivation and replication cohort were
comparable with the exception of gender, race, and lung
transplants. Lung biopsy confirmed the finding of usual
interstitial pneumonia (UIP) in 63.3% of the cases and only a small
proportion of patients (9.3%) were on immunosuppressant agents at
blood draw.
Microarray Analyses Demonstrate a 38-Gene Signature and the T-Cell
Co-Stimulatory Pathway Associated with Survival in the Derivation
Cohort.
[0060] Thirty eight genes (N=38) were significantly associated with
survival in the derivation cohort (FDR<0.01). The majority of
these genes (N=29) had a negative score, with the lowest score
indicating shorter survival while nine genes (N=9) had a positive
score, with the highest score indicating shorter survival (Table
1). This 38-gene signature provided clues about potential
biological process associated with survival in IPF given the
presence of many T-cell related genes (especially in the negative
score group); this led us to study more deeply the potential
association of pathways with survival by using a survival gene-set
analysis in the derivation cohort, which identified the
co-stimulatory signal during T-cell activation pathway (Tables 4
and S2) as the gene-set with the lowest score and P value (score
-1.74, P=0.004, FDR=0.45), indicating that lower expression of most
genes in this gene-set were correlated with shorter survival. ITK,
CD28, and ICOS had the lowest score within the T-cell
co-stimulatory signaling pathway (Table 2) and were also part of
the 38-gene survival signature.
Microarray Analyses Validate the 38-Gene Signature and T-Cell
Co-Stimulatory Pathway Association with Survival in the Replication
Cohort.
[0061] To validate the 38-gene signature identified in the
derivation cohort, hierarchical clustering of these genes and
arrays in the replication cohort was performed, demonstrating two
major clusters of IPF subjects (FIG. 1, panel A) with significant
differences in survival (hazard ratio 3.16, 95% CI 1.23-8.07,
P=0.006) (FIG. 1, panel B) and without significant clinical and
epidemiological differences (Table 51). The median survival of
subjects in cluster two was 2.01 years while survival in cluster
one subjects was much greater and median levels were not reached.
To confirm the presence of pathways associated with survival in the
replication cohort the survival gene-set analysis was performed and
again, the co-stimulatory signal during T-cell activation pathway
(FIG. 1, panel C) had the lowest score and P value (score -1.34,
P=0.008, FDR=0.42) from the negative gene-set group (Table S3). As
evidenced in the derivation cohort CD28, ITK, ICOS, and LCK were
also the genes with the lowest score within the pathway (FIG. 1,
panel C).
[0062] SmartChip qRT-PCR confirms that decreases in PBMC CD28, ITK,
ICOS, and LCK expression are associated with poor outcomes in
IPF.
[0063] A custom SmartChip qRT-PCR assay was designed to evaluate
the performance and prognostic significance of the T-cell
co-stimulatory signaling pathway genes CD28, ITK, ICOS, and LCK in
a more clinically feasible platform. .DELTA.Ct values for CD28,
ITK, ICOS, and LCK above the calculated threshold (split at 6.112,
5.600, and 6.939 cycles respectively, for survival) and thereby
indicative of reduced gene expression, were significantly
associated with lesser median survival (2.3, 2.9, and 2.9 years,
respectively). Conversely, lower .DELTA.Ct values (denoting greater
gene expression) were predictive of longer median survival in the
qRT-PCR cohort (N=139) (FIG. 2, panel A). This effect was much more
evident in males older than 62 years with a FVC % below 68%.
Decrease in CD28, ITK, ICOS, and LCK expression in this subgroup
was associated with significantly lower median survival (1.12-1.42
years) and increased expression was predictive of significantly
longer median survival (2.31-3.46 years). The shortest median
survival in older males with low FVC % was seen with low CD28 (1.12
years, .DELTA.Ct above 6.112) and low ICOS (1.38 years, .DELTA.Ct
above 6.939) and the longest median survival (3.46 years) was seen
with increased ICOS (.DELTA.Ct below 6.939). The adjusted hazard
ratios to age, gender, and FVC % for all subjects were generally
similar for CD28, ITK, ICOS, and LCK (2.57, 2.00, and 2.88
respectively) meaning that low level of expression of these genes
at evaluation, was associated with at least two-fold higher risk of
death. A multivariate CoxPH model including .DELTA.Ct expression of
CD28 (split at 6.112 for survival and 4.673 for transplant free
survival), age (split at 62 years), gender, and FVC % (split at
68%) showed an accurate prediction of survival (AUC range:
77.7%-86.2%) and transplant free survival (AUC range: 68.4%-82.2%)
with the highest AUC being for predicting death and transplant free
survival within 3 months after blood draw (86.2% and 82.2%
respectively) (FIG. 3, panels B and C).
The Down Regulation of Co-Stimulatory Molecules can be Explained by
CD4 T-Cell End-Differentiation.
[0064] Recent reports implicate adaptive immune processes in IPF,
12, 36, 37 in particular, repetitive CD4 T-cell activation, clonal
expansion, and end-differentiation, the latter characterized by
phenotypic changes including the loss of cell surface CD28, have
also been associated with decreased survival of IPF
patients..sup.12 In order to examine the possibility that gene
down-regulations of the T-cell co-stimulatory signaling pathway
found here may be related to T-cell activation and differentiation,
protein levels of the T-cell co-stimulatory signaling molecules in
CD4+CD28.sup.null T-cells using flow cytometry were measured. The
levels of members of the prognosis signature ITK, ICOS as well as
the T-cell co-stimulatory members LCK, and CD3E were significantly
decreased in the end-differentiated CD4+CD28.sup.null cells of IPF
patients, compared to autologous CD4+CD28+ cells (P<0.001,
P=0.004, P<0.001, and P<0.001 respectively).
DISCUSSION
[0065] Microarray analysis of PBMC gene expression in two cohorts
of IPF subjects were evaluated and found to be concordant at two
different academic institutions using two different microarray
platforms. A signature of 38 genes was identified, as well as
down-regulation of the T-cell co-stimulatory signaling pathway
significantly associated with shorter survival in the derivation
cohort. Significant differences in survival based on clustering of
the 38-gene signature as well as down-regulation of the T-cell
co-stimulatory signaling pathway associated with shorter survival
in the replication cohort, provided validation of the findings.
qRT-PCR, confirmed microarray results and indicated that IPF
subjects with decreased expression of CD28, ITK, and ICOS had
shorter survival, a finding that was impressively more evident
among males. A combined genomic and clinical prediction model
including .DELTA.Ct expression of CD28, age, gender, and FVC %
provided a prediction above 80% for survival and transplant free
survival within 3 months after blood draw. Finally, the T-cell
co-stimulatory proteins ITK, ICOS, LCK, and CD3E were found to be
decreased in CD4+CD28.sup.null T-cells suggesting that the gene
expression findings described herein may be indicative of T-cell
end-differentiation that occurs in IPF..sup.12, 37
[0066] Increases in peripheral blood protein concentrations such as
KL-6, surfactant protein A, CCL18, MMP7, ICAM, and IL8.sup.8, 11,
13, 38 have all been associated with decreased survival in IPF
patients. Most recently a prognostic score derived from the
integration of clinical information and MMP7 concentrations has
been identified and confirmed in two cohorts..sup.13 While previous
reports based their initial search for markers on prior hypotheses
or on a limited list of proteins, the largest number being
95,.sup.13 the biomarkers described herein were derived from an
unbiased genome scale screening of gene transcripts.
[0067] Another aspect that distinguishes this study from previous
peripheral blood molecular marker studies in IPF is that this study
focuses on PBMC, not on serum or plasma. PBMC gene expression
patterns have been shown to be different from healthy controls in
multiple disease classes.sup.17, 18, 20-25 but the studies only
rarely find outcome indicative signatures and their potential
cellular origin. Given our previous reports of the existence of
T-cell end-differentiation (transition of T cells from CD4+CD28+ to
CD4+CD28.sup.null) in IPF.sup.37 and particularly, the increase in
CD4+CD28.sup.null T cells inversely associated with poor IPF
outcomes,.sup.12 we studied and confirmed diminished expression of
T-cell co-stimulatory proteins among CD4+CD28.sup.null lymphocytes
providing protein confirmation of the gene expression findings
suggesting that mortality in IPF is probably not merely associated
with decreases in T-cell co-stimulatory signaling pathway genes but
is potentially determined by the T-cell end-differentiation process
that is potentially important in disease pathogenesis.
[0068] The implications of predicting survival in IPF are
significant. The only effective therapy currently available for IPF
patients is lung transplantation. The timing of transplantation is
determined by the clinical evaluation, as well as the lung
allocation score..sup.39 The pre-transplant evaluation is
cost-intensive and not invariably accurate enough to establish
optimal timing..sup.40 Furthermore, shortage of organs is still a
significant limitation. Hence, risk stratification based on the
PBMC expression of T-cell co-stimulatory signaling pathway genes
and specifically the genomic and clinical predictor model described
herein could have valuable applications in determining who should
be referred for pre transplantation assessments and specifically,
given the ability of the model to predict early mortality, to
prioritize organ allocations to those who have been evaluated. The
ability to predict survival is also important for drug studies in
IPF. In a relatively uncommon disease, to show an effect of a drug
on mortality, investigators need to recruit patients who are likely
to progress during the course of the study. The relative accurate
prediction of early death by our markers may help to recruit such
patients. Additionally, because of the difficulty in clinically
predicting the disease course it is possible that patients from a
certain risk strata end up randomly and disproportionately assigned
to one of the experimental groups leading to spurious results.
Molecular based patient risk stratification will address this
challenge. Finally, the use of the markers described herein is
highly feasible since qRT-PCR is commonly used, easy to interpret
and highly reproducible and PBMC isolation is easy to obtain and
does not require sophisticated immunological methods.
[0069] Genomic biomarker studies often cannot be replicated. In our
case while the cohorts were relatively similar clinically, there
were some significant differences that could have prevented the
replication. Gene expression analysis was performed on two
different platforms, the practice patterns are very different in
the two institutions and most critically the rate of lung
transplantations (Table 1) was overtly varied. Despite these
limitations we were able to replicate our results and demonstrate
that both the 38-gene signature and the T-cell co-stimulatory
signaling pathway predicted survival in both cohorts. In this
context it is important to note that it is possible that these
differences may have affected our ability to detect signals for
more granular disease subphenotypes, such as pulmonary
hypertension, disease progression or impending acute exacerbations.
Larger studies will be required to determine whether PBMC gene
expression patterns are also predictive of these phenotypes.
[0070] CD28, ITK, and ICOS gene expression was sufficient to
identify IPF patients destined for poor outcomes and thus could
have considerable value in clinical evaluations, and management of
patients with this morbid lung disease; naturally, despite the
marked reproducibility of our findings across two cohorts,
additional studies focused on validating our results will be
required before PBMC gene expression can be used clinically for
prognosis determination.
[0071] Each of the publications cited herein is incorporated by
reference in its entirety. [0072] 1. Raghu G, Collard H R, Egan J
J, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic
pulmonary fibrosis: evidence-based guidelines for diagnosis and
management. American journal of respiratory and critical care
medicine 2011; 183(6): 788-824. [0073] 2. Fernandez Perez E R,
Daniels C E, Schroeder D R, et al. Incidence, prevalence, and
clinical course of idiopathic pulmonary fibrosis: a
population-based study. Chest 2010; 137(1): 129-37. [0074] 3.
Schwartz D A, Helmers R A, Galvin J R, et al. Determinants of
survival in idiopathic pulmonary fibrosis. American journal of
respiratory and critical care medicine 1994; 149(2 Pt 1): 450-4.
[0075] 4. King T E, Jr., Tooze J A, Schwarz M I, Brown K R,
Cherniack R M. Predicting survival in idiopathic pulmonary
fibrosis: scoring system and survival model. American journal of
respiratory and critical care medicine 2001; 164(7): 1171-81.
[0076] 5. Zappala C J, Latsi P I, Nicholson A G, et al. Marginal
decline in forced vital capacity is associated with a poor outcome
in idiopathic pulmonary fibrosis. Eur Respir J 2010; 35(4): 830-6.
[0077] 6. Ley B, Collard H R, King T E, Jr. Clinical course and
prediction of survival in idiopathic pulmonary fibrosis. American
journal of respiratory and critical care medicine 2011; 183(4):
431-40. [0078] 7. du Bois R M, Weycker D, Albera C, et al. Forced
vital capacity in patients with idiopathic pulmonary fibrosis: test
properties and minimal clinically important difference. American
journal of respiratory and critical care medicine 2011; 184(12):
1382-9. [0079] 8. Rosas I O, Richards T J, Konishi K, et al. MMP1
and MMP7 as potential peripheral blood biomarkers in idiopathic
pulmonary fibrosis. PLoS medicine 2008; 5(4): e93. [0080] 9.
Moeller A, Gilpin S E, Ask K, et al. Circulating fibrocytes are an
indicator of poor prognosis in idiopathic pulmonary fibrosis.
American journal of respiratory and critical care medicine 2009;
179(7): 588-94. [0081] 10. Prasse A, Probst C, Bargagli E, et al.
Serum CC-chemokine ligand 18 concentration predicts outcome in
idiopathic pulmonary fibrosis. American journal of respiratory and
critical care medicine 2009; 179(8): 717-23. [0082] 11. Kinder B W,
Brown K K, McCormack F X, et al. Serum surfactant protein-A is a
strong predictor of early mortality in idiopathic pulmonary
fibrosis. Chest 2009; 135(6): 1557-63. [0083] 12. Gilani S R, Vuga
L J, Lindell K O, et al. CD28 down-regulation on circulating CD4
T-cells is associated with poor prognoses of patients with
idiopathic pulmonary fibrosis. PloS one 2010; 5(1): e8959. [0084]
13. Richards T J, Kaminski N, Baribaud F, et al. Peripheral blood
proteins predict mortality in idiopathic pulmonary fibrosis.
American journal of respiratory and critical care medicine 2012;
185(1): 67-76. [0085] 14. Yang I V, Luna L G, Cotter J, et al. The
peripheral blood transcriptome identifies the presence and extent
of disease in idiopathic pulmonary fibrosis. PloS one 2012; 7(6):
e37708. [0086] 15. Herazo-Maya J D, Kaminski N. Personalized
medicine: applying `omics` to lung fibrosis. Biomarkers in medicine
2012; 6(4): 529-40. [0087] 16. Achiron A, Gurevich M, Friedman N,
Kaminski N, Mandel M. Blood transcriptional signatures of multiple
sclerosis: unique gene expression of disease activity. Annals of
neurology 2004; 55(3): 410-7. [0088] 17. Bull T M, Coldren C D,
Nana-Sinkam P, et al. Microarray analysis of peripheral blood cells
in pulmonary arterial hypertension, surrogate to biopsy. Chest
2005; 128(6 Suppl): 584S. [0089] 18. Moore D F, Li H, Jeffries N,
et al. Using peripheral blood mononuclear cells to determine a gene
expression profile of acute ischemic stroke: a pilot investigation.
Circulation 2005; 111(2): 212-21. [0090] 19. Bluth M, Lin Y Y,
Zhang H, Viterbo D, Zenilman M. Use of gene expression profiles in
cells of peripheral blood to identify new molecular markers of
acute pancreatitis. Arch Surg 2008; 143(3): 227-33; discussion
33-4. [0091] 20. Showe M K, Vachani A, Kossenkov A V, et al. Gene
expression profiles in peripheral blood mononuclear cells can
distinguish patients with non-small cell lung cancer from patients
with nonmalignant lung disease. Cancer research 2009; 69(24):
9202-10. [0092] 21. Pham M X, Teuteberg J J, Kfoury A G, et al.
Gene-expression profiling for rejection surveillance after cardiac
transplantation. The New England journal of medicine 2010; 362(20):
1890-900. [0093] 22. Risbano M G, Meadows C A, Coldren C D, et al.
Altered immune phenotype in peripheral blood cells of patients with
scleroderma-associated pulmonary hypertension. Clin Transl Sci
2010; 3(5): 210-8. [0094] 23. Baine M J, Chakraborty S, Smith L M,
et al. Transcriptional profiling of peripheral blood mononuclear
cells in pancreatic cancer patients identifies novel genes with
potential diagnostic utility. PloS one 2011; 6(2): e17014. [0095]
24. Ottoboni L, Keenan B T, Tamayo P, et al. An RNA profile
identifies two subsets of multiple sclerosis patients differing in
disease activity. Science translational medicine 2012; 4(153): 153
ra31. [0096] 25. Segman R H, Shefi N, Goltser-Dubner T, Friedman N,
Kaminski N, Shalev A Y. Peripheral blood mononuclear cell gene
expression profiles identify emergent post-traumatic stress
disorder among trauma survivors. Molecular psychiatry 2005; 10(5):
500-13, 425. [0097] 26. American Thoracic S, European Respiratory
S. American Thoracic Society/European Respiratory Society
International Multidisciplinary Consensus Classification of the
Idiopathic Interstitial Pneumonias. This joint statement of the
American Thoracic Society (ATS), and the European Respiratory
Society (ERS) was adopted by the ATS board of directors, June 2001
and by the ERS Executive Committee, June 2001. American journal of
respiratory and critical care medicine 2002; 165(2): 277-304.
[0098] 27. Irizarry R A, Hobbs B, Collin F, et al. Exploration,
normalization, and summaries of high density oligonucleotide array
probe level data. Biostatistics (Oxford, England) 2003; 4(2):
249-64. [0099] 28. Wu W, Dave N, Tseng G C, Richards T, Xing E P,
Kaminski N. Comparison of normalization methods for CodeLink
Bioarray data. BMC bioinformatics 2005; 6: 309. [0100] 29. Tusher V
G, Tibshirani R, Chu G. Significance analysis of microarrays
applied to the ionizing radiation response. Proceedings of the
National Academy of Sciences of the United States of America 2001;
98(9): 5116-21. [0101] 30.
http://bonsai.hgc.jp/.about.mdehoon/software/cluster/manual. [0102]
31. Efron B, Tibshirani R. On testing the significance of set of
genes. Ann App Stat 2007; 1(1): 107-29. [0103] 32. Therneau T M G
P. Modeling survival data: extending the Cox model. New York:
Springer; 2000. [0104] 33. Ihaka R G R. A language for data
analysis and graphics. J Comput Graph Statist 1996; 5: 299-314.
[0105] 34. Murphy S A, Van Der Vart A W. On Profile Likelihood.
Journal of the American Statistical Association 2000; 95: 449-85.
[0106] 35. Venables W N R, B. D. Modern applied statistics with S.
New York: Springer; 2002. [0107] 36. Xue J, Gochuico B R, Alawad A
S, et al. The HLA class II Allele DRB1*1501 is over-represented in
patients with idiopathic pulmonary fibrosis. PloS one 2011; 6(2):
e14715. [0108] 37. Feghali-Bostwick C A, Tsai C G, Valentine V G,
et al. Cellular and humoral autoreactivity in idiopathic pulmonary
fibrosis. J Immunol 2007; 179(4): 2592-9. [0109] 38. Yokoyama A,
Kondo K, Nakajima M, et al. Prognostic value of circulating KL-6 in
idiopathic pulmonary fibrosis. Respirology (Carlton, Vic 2006;
11(2): 164-8. [0110] 39. Egan T M, Murray S, Bustami R T, et al.
Development of the new lung allocation system in the United States.
Am J Transplant 2006; 6(5 Pt 2): 1212-27. [0111] 40. Trulock E P,
Edwards L B, Taylor D O, Boucek M M, Keck B M, Hertz M I. Registry
of the International Society for Heart and Lung Transplantation:
twenty-second official adult lung and heart-lung transplant
report--2005. J Heart Lung Transplant 2005; 24(8): 956-67. [0112]
41. Zahurak M, Parmigiani G, Yu W, et al. Pre-processing Agilent
microarray data. BMC bioinformatics 2007; 8: 142.
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References