U.S. patent application number 10/917195 was filed with the patent office on 2005-04-28 for compositions and methods for prognosis and therapy of liver cancer.
Invention is credited to Cheung, Siu Tim, Fan, Sheung Tat.
Application Number | 20050089895 10/917195 |
Document ID | / |
Family ID | 34198020 |
Filed Date | 2005-04-28 |
United States Patent
Application |
20050089895 |
Kind Code |
A1 |
Cheung, Siu Tim ; et
al. |
April 28, 2005 |
Compositions and methods for prognosis and therapy of liver
cancer
Abstract
This invention provides a composition comprising the following
polynucleotide probes: IL7R (AA485865), NDRG1 (AA486403), EST1
(H50345), TRPC1 (AA017132), GFRA1 (AA512935), EST2 (AA454543),
CLDN10 (R54559), DNALI1 (R93087), RBP5 (AA453198), EST3 (AA621761),
EST4 (N63706), PCOLCE (AA670200), TDO2 (T72398), EST5 (T47454),
HIST1H2BD (N33927), PXMP2 (N70714), ACAS2 (AA455146), ANAPC7
(T68445), EST6 (AA576580), RBP5 (N92148), ANXA1 (H63077), CKB
(AA894557), ITGBL1 (N52533), KPNA2 (AA676460), EST7 (W90740) and
MEG3 (W85841). This invention further provides methods for
determining the likelihood of recurrence of hepatocellular
carcinoma (HCC) in a subject afflicted with HCC, for determining
the likelihood of death of a subject afflicted with HCC or for
determining whether to administer adjuvant therapy.
Inventors: |
Cheung, Siu Tim; (Hong Kong,
HK) ; Fan, Sheung Tat; (Hong Kong, HK) |
Correspondence
Address: |
Robert D. Katz, Esq.
Cooper & Dunham LLP
1185 Avenue of the Americas
New York
NY
10036
US
|
Family ID: |
34198020 |
Appl. No.: |
10/917195 |
Filed: |
August 12, 2004 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
60494981 |
Aug 13, 2003 |
|
|
|
60500844 |
Sep 4, 2003 |
|
|
|
Current U.S.
Class: |
435/5 ; 435/6.13;
536/25.4 |
Current CPC
Class: |
C07H 21/00 20130101;
C12Q 1/6886 20130101; C12Q 2600/118 20130101; C12Q 2600/16
20130101; C12Q 2600/158 20130101; C12Q 2600/106 20130101 |
Class at
Publication: |
435/006 ;
536/025.4 |
International
Class: |
C12Q 001/68; C07H
021/04 |
Claims
What is claimed is:
1. A composition comprising the following polynucleotide probes:
IL7R (AA485865), NDRG1 (AA486403), EST1 (H50345), TRPC1 (AA017132),
GFRA1 (AA512935), EST2 (AA454543), CLDN10 (R54559), DNALI1
(R93087), RBP5 (AA453198), EST3 (AA621761), EST4 (N63706), PCOLCE
(AA670200), TDO2 (T72398), EST5 (T47454), HIST1H2BD (N33927), PXMP2
(N70714), ACAS2 (AA455146), ANAPC7 (T68445), EST6 (AA576580), RBP5
(N92148), ANXA1 (H63077), CKB (AA894557), ITGBL1 (N52533), KPNA2
(AA676460), EST7 (W90740) and MEG3 (W85841), or any combination
thereof.
2. The composition in accordance with claim 1, wherein the
polynucleotide probes are complementary DNAs.
3. The composition in accordance with claim 1, wherein the
polynucleotide probes are clone cDNAs.
4. The composition in accordance with claim 1, wherein the
polynucleotide probes are immobilized on a substrate.
5. The composition in accordance with claim 4, wherein the
polynucleotide probes are hybridizable array elements.
6. A composition comprising the following polynucleotide probes:
IL7R (AA485865), NDRG1 (AA486403), EST1 (H50345), TRPC1 (AA017132),
GFRA1 (AA512935), EST2 (AA454543), CLDN10 (R54559), DNALI1
(R93087), RBP5 (AA453198), EST3 (AA621761), EST4 (N63706) and
PCOLCE (AA670200).
7. The composition in accordance to claim 6 further comprising one
or more of the following polynucleotide probes: TDO2 (T72398), EST5
(T47454), HIST1H2BD (N33927), PXMP2 (N70714), ACAS2 (AA455146),
ANAPC7 (T68445), EST6 (AA576580), RBP5 (N92148), ANXA1 (H63077),
CKB (AA894557), ITGBL1 (N52533), KPNA2 (AA676460), EST7 (W90740)
and MEG3 (W85841).
8. The composition in accordance with claim 6, wherein the
polynucleotide probes are complementary DNAs.
9. The composition in accordance with claim 6, wherein the
polynucleotide probes are clone cDNAs.
10. The composition in accordance with claim 6, wherein the
polynucleotide probes are immobilized on a substrate.
11. The composition in accordance with claim 4, wherein the
polynucleotide probes are hybridizable array elements.
12. A method for determining the likelihood of recurrence of
hepatocellular carcinoma (HCC) in a subject afflicted with HCC,
comprising: (a) obtaining a tumor sample from the subject; (b)
determining the gene expression pattern of the composition of claim
6 in the tumor sample; (c) calculating the prognostic gene score of
the gene expression pattern; and (d) comparing the prognostic gene
score to a prognostic gene score associated with recurrence of HCC,
thereby determining the likelihood of recurrence of HCC in the
subject.
13. The method of claim 12, wherein the gene expression pattern is
determined by microarray.
14. The method of claim 12, wherein the gene expression pattern is
determined by RT-PCR.
15. The method of claim 12, wherein a prognostic gene score of less
than 0.416 indicates a low probability of recurrence of HCC.
16. The method of claim 12, wherein a prognostic gene score of at
least 0.416 indicates a high probability of recurrence of HCC.
17. A method for determining the likelihood of hepatocellular
carcinoma (HCC) to cause death of an afflicted subject, comprising:
(a) obtaining a tumor sample from the subject; (b) determining the
gene expression pattern of the composition of claim 6 in the tumor
sample; (c) calculating the prognostic gene score of the gene
expression pattern; and (d) comparing the prognostic gene score to
a prognostic gene score associated with death caused by HCC,
thereby determining the likelihood of HCC-associated death of the
subject.
18. The method of claim 17, wherein a prognostic gene score of less
than 0.600 indicates a low probability of HCC-associated death.
19. The method of claim 17, wherein a prognostic gene score of at
least 0.600 indicates a high probability of HCC-associated
death.
20. A method of determining whether to administer adjuvant therapy
for a subject afflicted with hepatocellular carcinoma (HCC)
comprising: (a) obtaining a tumor sample from the subject; (b)
determining the expression pattern of the composition of claim 6 in
the tumor sample; and (c) calculating the prognostic gene score of
the expression pattern; and (d) comparing the prognostic gene score
to a prognostic gene score associated with recurrence of HCC,
thereby determining whether to administer adjuvant therapy.
21. The method of claim 20, wherein a prognostic gene score of at
least 0.416 indicates adjuvant therapy should be administered.
22. The method of claim 20, wherein a prognostic gene score of less
than 0.416 indicates adjuvant therapy should not be
administered.
23. A method for determining the prognosis of a subject afflicted
with hepatocellular carcinoma (HCC), comprising: (a) obtaining a
tumor sample from the subject; (b) determining the gene expression
pattern of the composition of claim 6 in the tumor sample; (c)
calculating the prognostic gene score of the gene expression
pattern, wherein a prognostic gene score of less than 0.416
indicates a good prognosis, a prognostic gene score of at least
0.416 but less than 0.600 indicates an intermediary prognosis, and
a prognostic gene score of at least 0.600 indicates a poor
prognosis.
24. A method for determining the prognosis of a subject afflicted
with hepatocellular carcinoma (HCC), comprising: (a) obtaining a
tumor sample from the subject; (b) determining the level of CLDN10
nucleic acid transcript in the tumor sample; (c) comparing the
level of CLDN10 nucleic acid transcript from step (b) with the
level of CLDN10 nucleic acid transcript in the normal tissue
sample, whereby a higher level of CLDN10 nucleic acid transcript in
step (b) indicates a poor prognosis.
25. A method for determining the prognosis of a subject afflicted
with hepatocellular carcinoma (HCC), comprising: (a) obtaining a
tumor sample from the subject; (b) determining the level of
AA454543 nucleic acid transcript in the tumor sample; (c) comparing
the level of AA454543 nucleic acid transcript from step (b) with
the level of AA454543 nucleic acid transcript in the normal tissue
sample, whereby a higher level of AA454543 nucleic acid transcript
in step (b) indicates a poor prognosis.
26. A method for determining the prognosis of a subject afflicted
with hepatocellular carcinoma (HCC), comprising: (a) obtaining a
tumor sample from the subject; (b) determining the level of DNALI1
nucleic acid transcript in the tumor sample; (c) comparing the
level of DNALI1 nucleic acid transcript from step (b) with the
level of DNALI1 nucleic acid transcript in the normal tissue
sample, whereby a higher level of DNALI1 nucleic acid transcript in
step (b) indicates a poor prognosis.
27. A method for determining the likelihood of recurrence of
hepatocellular carcinoma (HCC) in a subject afflicted with HCC,
comprising: (a) obtaining a serum sample from the subject; (b)
detecting the presence of a DNALI1 nucleic acid transcript; and (b)
determining the polymorphism present at nucleotide 194 of codon 65
of the DNALI1 nucleic acid transcript of step (c) to identify which
allele is present, whereby the presence of a T-allele indicates a
high probability of recurrence of HCC.
Description
[0001] This application claims priority of provisional application
U.S. Ser. No. 60/495,081, filed Aug. 13, 2003, and provisional
application U.S. Ser. No. 60/500,844, filed Sep. 4, 2003, the
contents of both provisional application being incorporated herein
by reference.
[0002] Throughout this application, various publications are
referenced. Full citations for these publications may be found
immediately preceding the claims. The disclosures of these
publications are hereby incorporated by reference into this
application in order to more fully describe the state of the art as
of the date of the invention described and claimed herein.
BACKGROUND OF THE INVENTION
[0003] Hepatocellular carcinoma (HCC) is a common lethal malignancy
and among the five leading causes of cancer death worldwide. The
incidence is rising in the United States, UK and Japan. Liver
cancer is the second major cause of cancer death in China.
Epidemiological studies have shown that hepatitis B and C virus
infections, alcohol-induced liver injury and consumption of
aflatoxin are closely associated with liver cancer. Extensive
studies have been performed to better understand the
clinico-pathological features to improve the clinical management
for HCC patients. However, conventional clinico-pathological
parameters have limited predictive power, and patients with the
same stage of disease can have very different disease outcomes.
Microarray technology provides a biological mean to gather large
amount of gene expression data on an unbiased basis. Molecular
portraits reviewed by the tumors' gene expression patterns have
been used to identify new molecular criteria for prognostication of
diverse cancer types including breast cancer, prostate cancer, lung
cancer and brain tumors.
[0004] Using the cDNA microarray approach, the expression profiles
of liver cancer cell lines and human samples have been reported.
Expression of alpha-fetoprotein (AFP) highlighted the molecular
subtypes of HCC cell lines. Deregulation of the cell cycle
regulators and genes associated with metabolism have been observed,
and the expression profile was associated with the tumor
differentiation status. A recent study on prediction of HCC early
recurrence by gene expression only reported the intrahepatic
recurrence within 1 year in a small patient set and used genechips
of 6000 genes. In the present study, the Cox regression and
Kaplan-Meier analyses were used on 48 HCCs to identify a set of 26
genes from microarrays printed with 23000 clones. The prognostic
gene set was then further delineated to include the top ranked 12
genes, which had an accuracy of 97.8% and 89.3% in predicting
disease recurrence and death, respectively, within 3 years after
hepatectomy. The gene expression profile thus generated can provide
a more accurate prognosis to predict disease recurrence and death
compared to the standard systems based on clinical and histological
criteria. The result also offers an approach to select patients
with poor prognosis for aggressive adjuvant therapy.
SUMMARY OF THE INVENTION
[0005] This invention provides a composition comprising the
following polynucleotide probes: IL7R (AA485865), NDRG1 (AA486403),
EST1 (H50345), TRPC1 (AA017132), GFRA1 (AA512935), EST2 (AA454543),
CLDN10 (R54559), DNALI1 (R93087), RBP5 (AA453198), EST3 (AA621761),
EST4 (N63706), PCOLCE (AA670200), TDO2 (T72398), EST5 (T47454),
HIST1H2BD (N33927), PXMP2 (N70714), ACAS2 (AA455146), ANAPC7
(T68445), EST6 (AA576580), RBP5 (N92148), ANXA1 (H63077), CKB
(AA894557), ITGBL1 (N52533), KPNA2 (AA676460), EST7 (W90740) and
MEG3 (W85841), or any combination thereof.
[0006] This invention further provides a composition comprising the
following polynucleotide probes: IL7R (AA485865), NDRG1 (AA486403),
EST1 (H50345), TRPC1 (AA017132), GFRA1 (AA512935), EST2 (AA454543),
CLDN10 (R54559), DNALI1 (R93087), RBP5 (AA453198), EST3 (AA621761),
EST4 (N63706) and PCOLCE (AA670200).
[0007] This invention provides a method for determining the
likelihood of recurrence of hepatocellular carcinoma (HCC) in a
subject afflicted with HCC, comprising: (a) obtaining a tumor
sample from the subject; (b) determining the gene expression
pattern of a set of prognostic genes in the tumor sample; (c)
calculating the prognostic gene score of the gene expression
pattern; and (d) comparing the prognostic gene score to a
prognostic gene score associated with recurrence of HCC, thereby
determining the likelihood of recurrence of HCC in the subject.
[0008] This invention provides a method for determining the
likelihood of hepatocellular carcinoma (HCC) to cause the death of
an afflicted subject, comprising: (a) obtaining a tumor sample from
the subject; (b) determining the gene expression pattern of a set
of prognostic genes in the tumor sample; (c) calculating the
prognostic gene score of the gene expression pattern; and (d)
comparing the prognostic gene score to a prognostic gene score
associated with HCC-associated death, thereby determining the
likelihood of death of the subject.
[0009] This invention also provides a method of determining whether
to administer adjuvant therapy for a subject afflicted with
hepatocellular carcinoma (HCC) comprising: (a) obtaining a tumor
sample from the subject; (b) determining the gene expression
pattern of a set of prognostic genes in the tumor sample; and (c)
calculating the prognostic gene score of the gene expression
pattern; and (d) comparing the prognostic gene score to a
prognostic gene score associated with recurrence of HCC, thereby
determining whether to administer adjuvant therapy.
[0010] This invention further provides a method for determining the
prognosis of a subject afflicted with hepatocellular carcinoma
(HCC), comprising: (a) obtaining a tumor sample from the subject;
(b) determining the level of CLDN10 nucleic acid transcript in the
tumor sample; (c) comparing the level of CLDN10 nucleic acid
transcript from step (b) with the level of CLDN10 nucleic acid
transcript in the normal tissue sample, whereby a higher level of
CLDN10 nucleic acid transcript in step (b) indicates a poor
prognosis.
[0011] This invention also provides a method for determining the
prognosis of a subject afflicted with hepatocellular carcinoma
(HCC), comprising: (a) obtaining a tumor sample from the subject;
(b) determining the level of AA454543 nucleic acid transcript in
the tumor sample; (c) comparing the level of AA454543 nucleic acid
transcript from step (b) with the level of AA454543 nucleic acid
transcript in the normal tissue sample, whereby a higher level of
AA454543 nucleic acid transcript in step (b) indicates a poor
prognosis.
[0012] This invention further provides a method for determining the
prognosis of a subject afflicted with hepatocellular carcinoma
(HCC), comprising: (a) obtaining a tumor sample from the subject;
(b) determining the level of DNALI1 nucleic acid transcript in the
tumor sample; (c) comparing the level of DNALI1 nucleic acid
transcript from step (b) with the level of DNALI1 nucleic acid
transcript in the normal tissue sample, whereby a higher level of
DNALI1 nucleic acid transcript in step (b) indicates a poor
prognosis.
[0013] Finally, this invention provides a method for determining
the likelihood of recurrence of hepatocellular carcinoma (HCC) in a
subject afflicted with HCC, comprising: (a) obtaining a serum
sample from the subject; (b) detecting the presence of a DNALI1
nucleic acid transcript; and (c) determining the polymorphism
present at nucleotide 194 of codon 65 of the DNALI1 nucleic acid
transcript of step (b) to identify which allele is present, whereby
the presence of a T-allele indicates a high probability of
recurrence of HCC.
BRIEF DESCRIPTION OF THE FIGURES
[0014] FIG. 1
[0015] Gene expression and patients outcome. (A) The global
expression data matrix for the 48 HCCs. There were 1404 cDNA clones
with significant expression difference among the samples. Each
column represented a tumor and each row a single gene. The genes
were clustered based on their expression pattern similarities
measured over the samples using hierarchical clustering algorithm.
Similarly, the samples were clustered based on their similarities
over the gene expression pattern. (B) Optimal gene set
determination: maximum standardized effect was plotted against the
number of genes used to include in the prognostic gene score. (C)
Expression data matrix of the 12 prognostic genes for 48 HCCs. The
gene name was labeled at the right end of each row. All the "good"
genes, with relative risk less than one and expression in high
level associated with longer disease-free period, clustered into
one branch at the upper panel. The "bad" genes, with relative risk
greater than 1 and high level of expression associated with shorter
disease-free period, were all clustered into another branch at the
lower panel. Similarly, the HCCs were clustered based on their
similarities over the expression level of these genes, and were
segregated into two major groups. The HCCs at the left side of the
plot showed up-regulation of good genes and down-regulation of bad
genes, and they were considered to demonstrate the "good prognosis
signature". The HCCs at the right side of the plot showed
up-regulation of bad genes and down-regulation of good genes, and
they were considered to exhibit the "bad prognosis signature".
Black box at the bottom of the data matrix indicated the event of
recurrence. Solid line, gene prognosis classifier. Dashed line,
patient prognosis classifier.
[0016] FIG. 2
[0017] Validation analysis of CLDN10 gene expression in an
independent sample set. Scatter plot of the CLDN10 expression level
by quantitative RT-PCR. The expression level of each sample was
relative to the median expression value of the sample set. Patients
with CLDN10 expression level higher than the median value were
indicated in the upper portion of the plot with relative
fold-change greater than 1. Patients with gene expression lower
than the median value were indicated in the lower portion of the
plot with relative fold-change less than 1.
[0018] FIG. 3
[0019] Prognostication by gene expression. (A) Prognostic gene
score based on the 12 top-ranked genes. The optimal cut-off value
for prediction of disease recurrence and death was 0.416 (dashed
line) and 0.600 (solid line), respectively, as determined by the
Youden Index. (B) Receiver operating characteristic (ROC) curve for
prediction of recurrence. (C) ROC curve for prediction of
death.
[0020] FIG. 4
[0021] Comparison between prognostic gene score and pTNM system.
Kaplan-Meier disease-free and overall survival curves for the HCC
patients according to prognostic gene score in (A) and (C), and
pTNM staging system in (B) and (D). In each case, P values were
calculated using the log rank test.
[0022] FIG. 5
[0023] Kaplan-Meier disease-free survival plot. (A) All patients
were categorized into low or high claudin-10 expression groups. (B)
Early stage (Stages I and II) patients were further segregated
according to claudin-10 expression level. (C) Late stage (Stages
III and IVa) patients were further segregated according to
claudin-10 expression level.
[0024] FIG. 6
[0025] The accuracy of prediction for overall survival was measured
by the area under the receiver operating characteristic curve. The
`sensitivity` (true positive fraction) against `1-specificity`
(false positive fraction) was plotted for transcript AA454543
expression level (range 0-11.50) and pTNM stage (I, II, III and
IVa), respectively.
[0026] FIG. 7
[0027] Kaplan-Meier overall survival plot. (A) All patients were
categorized into low or high transcript AA454543 expression groups.
(B) Early stage (Stages I and II) patients were further segregated
according to transcript AA454543 expression level. (C) Late stage
(Stages III and IVa) patients were further segregated according to
the transcript AA454543 expression level.
[0028] FIG. 8
[0029] Transcript AA454543 expression in human liver samples, and
transcript level was quantitated by real-time RT-PCR.
[0030] FIG. 9
[0031] Validation analysis of DNALI1 gene expression in an
independent sample set using quantitative RT-PCR. The prognostic
significance of DNALI1 level on disease-free survival was evaluated
between patients with high and low tumor DNALI1 levels, stratified
using 75 percentile as the cut-off value.
[0032] FIG. 10
[0033] DNALI1 expression level in tumor was quantitated by
real-time RT-PCR. Polymorphism at nucleotide 194 (nt194) was
examined by direct sequencing of the blood DNA. Boxplot shows a
significantly higher DNALI1 level in patients with T-allele
compared to patients with C-allele.
DETAILED DESCRIPTION OF THE INVENTION
[0034] Definitions
[0035] As used in this application, except as otherwise expressly
provided herein, each of the following terms shall have the meaning
set forth below.
[0036] As used herein, "subject" shall mean any animal, such as a
primate, mouse, rat, guinea pig or rabbit. In the preferred
embodiment, the subject is a human.
[0037] As used herein, "composition" shall mean a set of prognostic
genes.
[0038] As used herein, "hybridizable array elements" shall mean any
strand of nucleic acid capable of binding with a complimentary
strand of nucleic acid through base pairing.
[0039] As used herein, a "gene expression pattern" shall mean a set
of values representing nucleic acid levels of a set of prognostic
genes.
[0040] As used herein, a "prognostic gene score" is a statistical
means of evaluating a gene expression pattern. The prognostic gene
score is generated based on the proportion of genes in the gene set
that demonstrated expression levels associated with poor prognosis.
For genes that high level of expression was associated with poor
prognosis (bad gene, relative risk greater than 1), the expression
level higher than the mean expression value was assigned with 1
point (expression level lower than the mean value had 0 point
score). For genes that high level of expression was associated with
good prognosis (good gene, relative risk less than 1), the
expression level lower than the mean expression value was assigned
with 1 point (expression level higher than the mean value scored 0
point). The prognostic gene score for each individual was therefore
the average score of all the genes (total points earned/total
number of genes investigated). The prognostic gene score of 1, high
level of expression for all the bad genes and low level of
expression for all the good genes, is suggestive of poor prognosis.
Similarly, the prognostic gene score of 0 is indicating good
prognosis.
[0041] Embodiments of the Invention
[0042] This invention provides a composition comprising the
following polynucleotide probes: IL7R (AA485865), NDRG1 (AA486403),
EST1 (H50345.), TRPC1 (AA017132), GFRA1 (AA512935), EST2
(AA454543), CLDN10 (R54559), DNALI1 (R93087), RBP5 (AA453198), EST3
(AA621761), EST4 (N63706), PCOLCE (AA670200), TDO2 (T72398), EST5
(T47454), HIST1H2BD (N33927), PXMP2 (N70714), ACAS2 (AA455146),
ANAPC7 (T68445), EST6 (AA576580), RBP5 (N92148), ANXA1 (H63077),
CKB (AA894557), ITGBL1 (N52533), KPNA2 (AA676460), EST7 (W90740)
and MEG3 (W85841), or any combination thereof.
[0043] In one embodiment, the polynucleotide probes are
complementary DNAs. In another embodiment, the polynucleotide
probes are clone cDNAs. The polynucleotide probes may be
immobilized on a substrate and may be hybridizable array
elements.
[0044] This invention provides a composition comprising the
following polynucleotide probes: IL7R (AA485865), NDRG1 (AA486403),
EST1 (H50345), TRPC1 (AA017132), GFRA1 (AA512935), EST2 (AA454543),
CLDN10 (R54559), DNALI1 (R93087), RBP5 (AA453198), EST3 (AA621761),
EST4 (N63706) and PCOLCE (AA670200).
[0045] In a preferred embodiment, this invention further provides a
composition comprising the following polynucleotide probes: IL7R
(AA485865), NDRG1 (AA486403), EST1 (H50345), TRPC1 (AA017132),
GFRA1 (AA512935), EST2 (AA454543), CLDN10 (R54559), DNALI1
(R93087), RBP5 (AA453198), EST3 (AA621761), EST4 (N63706), PCOLCE
(AA670200) and one or more of the following polynucleotide probes:
TDO2 (T72398), EST5 (T47454), HIST1H2BD (N33927), PXMP2 (N70714),
ACAS2 (AA455146), ANAPC7 (T68445), EST6 (AA576580), RBP5 (N92148),
ANXA1 (H63077), CKB (AA894557), ITGBL1 (N52533), KPNA2 (AA676460),
EST7 (W90740) and MEG3 (W85841).
[0046] In one embodiment, the polynucleotide probes are
complementary DNAs. In another embodiment, the polynucleotide
probes are clone cDNAs. The polynucleotide probes may be
immobilized on a substrate and may be hybridizable array
elements.
[0047] This invention further provides a method for determining the
likelihood of recurrence of hepatocellular carcinoma (HCC) in a
subject afflicted with HCC, comprising: (a) obtaining a tumor
sample from the subject; (b) determining the gene expression
pattern of a set of prognostic genes in the tumor sample; (c)
calculating the prognostic gene score of the gene expression
pattern; and (d) comparing the prognostic gene score to a
prognostic gene score associated with recurrence of HCC, thereby
determining the likelihood of recurrence of HCC in the subject.
[0048] In a preferred embodiment of the instant method, the gene
expression pattern is determined by microarray. In another
embodiment, the gene expression pattern is determined by
RT-PCR.
[0049] In a preferred embodiment of the instant method, a
prognostic gene score of less than 0.416 indicates a low
probability of recurrence of HCC, and a prognostic gene score of at
least 0.416 indicates a high probability of recurrence of HCC.
[0050] This invention further provides a method for determining the
likelihood of hepatocellular carcinoma (HCC) to cause the death of
an afflicted subject, comprising: (a) obtaining a tumor sample from
the subject; (b) determining the gene expression pattern of a set
of prognostic genes in the tumor sample; (c) calculating the
prognostic gene score of the gene expression pattern; and (d)
comparing the prognostic gene score to a prognostic gene score
associated with death caused by HCC, thereby determining the
likelihood of HCC-associated death of the subject.
[0051] In a preferred embodiment of the instant method, a
prognostic gene score of less than 0.600 indicates a low
probability of HCC-associated death, and a prognostic gene score of
at least 0.600 indicates a high probability of HCC-associated
death.
[0052] This invention further provides a method of determining
whether to administer adjuvant therapy for a subject afflicted with
hepatocellular carcinoma (HCC) comprising: (a) obtaining a tumor
sample from the subject; (b) determining the gene expression
pattern of a set of prognostic genes the tumor sample; and (c)
calculating the prognostic gene score of the gene expression
pattern; and (d) comparing the prognostic gene score to a
prognostic gene score associated with recurrence of HCC, thereby
determining whether to administer adjuvant therapy.
[0053] In a preferred embodiment of the instant method, a
prognostic gene score of less than 0.416 indicates a low
probability of recurrence of HCC, and a prognostic gene score of at
least 0.416 indicates a high probability of recurrence of HCC.
[0054] This invention further provides a method for determining the
prognosis of a subject afflicted with hepatocellular carcinoma
(HCC), comprising: (a) obtaining a tumor sample from the subject;
(b) determining the level of CLDN10 nucleic acid transcript in the
tumor sample; (c) comparing the level of CLDN10 nucleic acid
transcript from step (b) with the level of CLDN10 nucleic acid
transcript in the normal tissue sample, whereby a higher level of
CLDN10 nucleic acid transcript in step (b) indicates a poor
prognosis.
[0055] This invention also provides a method for determining the
prognosis of a subject afflicted with hepatocellular carcinoma
(HCC), comprising: (a) obtaining a tumor sample from the subject;
(b) determining the level of AA454543 nucleic acid transcript in
the tumor sample; (c) comparing the level of AA454543 nucleic acid
transcript from step (b) with the level of AA454543 nucleic acid
transcript in the normal tissue sample, whereby a higher level of
AA454543 nucleic acid transcript in step (b) indicates a poor
prognosis.
[0056] This invention further provides a method for determining the
prognosis of a subject afflicted with hepatocellular carcinoma
(HCC), comprising: (a) obtaining a tumor sample from the subject;
(b) determining the level of DNALI1 nucleic acid transcript in the
tumor sample; (c) comparing the level of DNALI1 nucleic acid
transcript from step (b) with the level of DNALI1 nucleic acid
transcript in the normal tissue sample, whereby a higher level of
DNALI1 nucleic acid transcript in step (b) indicates a poor
prognosis.
[0057] Finally, this invention provides a method for determining
the likelihood of recurrence of hepatocellular carcinoma (HCC) in a
subject afflicted with HCC, comprising: (a) obtaining a serum
sample from the subject; (b) detecting the presence of a DNALI1
nucleic acid transcript; and (c) determining the polymorphism
present at nucleotide 194 of codon 65 of the DNALI1 nucleic acid
transcript of step (b) to identify which allele is present, whereby
the presence of a T-allele indicates a high probability of
recurrence of HCC.
EXAMPLE I
[0058] Synopsis
[0059] Hepatocellular carcinoma (HCC) patients with the same stage
of disease can have remarkable differences in disease outcome. The
microarray gene expression profiles of the present study were
evaluated by Cox regression and Kaplan-Meier analyses, and
identified a set of 12 genes that can provide a more accurate
prognostication compared to the conventional clinico-pathological
systems. The prognostic gene score for each patient was generated
based on the proportion of genes in the optimal gene set that
demonstrated expression level associated with poor prognosis.
Patients with good and poor prognostic gene score differed
significantly, and the prognostic gene score was the independent
factor compared with PTNM stage to predict disease recurrence. The
set of prognostic genes can help to select patients with poor
prognosis for aggressive adjuvant therapy.
[0060] Materials and Methods
[0061] Patients and Samples
[0062] In the present study, the gene expression profiles from 48
patients undergoing curative partial hepatectomy for HCC were
included for patient outcome analysis. The patients were excluded
from the present disease outcome analysis if pathological
examination of the resected specimen showed positive resection
margin or mixture of other tumor cell types (e.g.
cholangiocarcinoma), if they had received chemotherapy before or
after resection, received liver transplantation instead of partial
hepatectomy, the resection was for recurrence, or the resection was
followed by hospital mortality. Diagnosis of recurrence was based
on typical imaging findings in a contrast-enhanced CT scan and an
increased serum AFP level. In cases of uncertainty, hepatic
arteriography and a post-Lipiodol CT scan were performed, and if
necessary, fine-needle aspiration cytology was used for
confirmation. Up to the date of analysis (May 2003), 27 patients
developed recurrence and the median disease-free period was 4.5
months (range, 0.9-32.7 months), and 17 of them succumbed to
disease with median survival period of 12.4 months (rang, 4.5-34.1
months). For the 21 patients who were recurrence-free, the median
duration of follow-up was 40.9 months (range, 29.8-48.8 months).
Another 47 HCCs were later tested independently by quantitative
RT-PCR. In this second sample set, 26 of the patients developed
recurrence and median disease-free period was 5.5 months (range,
2.2-19.3 months); for the 21 patients that were disease-free, the
median duration of follow-up was 23.3 months (range, 11.5-31.1
months).
[0063] Microarray Expression Study
[0064] The cDNA microarray slides were printed with about 23,000
cDNA clones. Samples and RNA preparations, and hybridization
protocols have been established. A total of 1404 cDNA clones with
expression levels that differed by at least four-fold from the mean
in at least two samples were selected for further analysis. The
hierarchical clustering algorithm was applied both to the genes and
arrays using the Pearson correlation coefficient as the measure of
similarity. The results were further analyzed with TreeView (Eisen;
http://rana.lbl.gov).
[0065] Quantitative RT-PCR
[0066] Quantitative RT-PCR was performed. Human 18s rRNA primer and
probe reagents (Pre-Developed TaqMan Assay Reagents, Applied
Biosystems, Foster City, Calif.) were used as the normalization
control for the subsequent multiplexed reactions. Transcript
quantification was performed in triplicates for every sample.
Quantification was performed using the ABI Prism 7700 sequence
detection system (Applied Biosystems). The primers and probe for
the CLDN10 are CLDN10-F, 5'-CTGTGGAAGGCGTGCGTTA-3'; CLDN10-R,
5'-CAAAGAAGCCCAGGCTGACA-3'; and CLDN10-P, 5'-6FAM CCTCCATGCTGGCGC
MGBNFQ-3'.
[0067] Prognostic Gene Score
[0068] A prognostic gene score for each patient was generated based
on the proportion of genes in the gene set that demonstrated
expression level associated with poor prognosis. For genes that
high level of expression was associated with poor prognosis (bad
gene, relative risk greater than 1), the expression level higher
than the mean expression value was assigned with 1 point
(expression level lower than the mean value had 0 point score). For
genes that high level of expression was associated with good
prognosis (good gene, relative risk less than 1), the expression
level lower than the mean expression value was assigned with 1
point (expression level higher than the mean value scored 0 point).
The prognostic gene score for each individual was therefore the
average score of all the genes (total points earned/total number of
genes investigated). The prognostic gene score of 1, high level of
expression for all the bad genes and low level of expression for
all the good genes, is suggestive of poor prognosis. Similarly, the
prognostic gene score of 0 is indicating good prognosis.
[0069] Statistical Methods
[0070] To determine the gene set for predicting disease recurrence,
the examination of the effect of expression level on each of the
1404 clones on recurrence was performed using Cox regression
analysis. Genes with P values less than 0.05 were selected. In the
second step, the gene set was further delineated by inclusion of
genes whose P values were less than 0.05 when examined by
Kaplan-Meier log rank test. To perform the test, the patients were
categorized into two groups for each gene datum. The grouping was
according to the gene expression level with cut-off at the mean
expression value. In the third step, a "step-down" approach was
used to determine the optimal gene set with minimal number of genes
that could provide the best prediction of recurrence. One gene in
the gene set was temporarily removed at a time and a Cox regression
analysis was performed on the resulting gene score. The gene was
removed from the set when its removal had the maximum standardized
effect (i.e. log relative risk/standard error). The process
continued until one gene was left in the set. The number of genes
at which the corresponding gene score yielded the highest
standardized effect was taken as the optimum. The analysis was
programmed by using the macro language in the Statistical Analysis
System (SAS) Version 8.2. The accuracy of using a gene score for
prediction of recurrence was measured by the area under the
receiver operating characteristics (ROC) curve. The prediction
power for 3 years was analyzed. Patients who were disease-free but
with less than 3 years follow-up were excluded in the prediction
study, analyzing 45 patients with 27 of them developed recurrences.
Similarly for the survival prediction, analyzing 44 patients with
17 deaths. The Youden index, i.e. the sum of sensitivity and
(1-specificity), was used to determine the best cut-off point. The
SAS was used for the analysis. The association of
clinico-pathological parameters with patient outcome was examined
by Cox proportional hazards regression with the forward stepwise
selection procedure aided by SPSS version 11.0 software package
(SPSS Inc. Chicago, Ill.).
1TABLE 1 Disease-free survival univariate analysis for the 26 genes
Gene name Accession Relative Risk P Gene rank.sup.a IL7R AA485865
0.6 (0.4-0.9) 0.011 1 NDRG1 AA486403 1.5 (1.1-2.0) 0.006 2 EST1
H50345 1.7 (1.1-2.6) 0.011 3 TRPC1 AA017132 0.6 (0.4-0.9) 0.016 4
GFRA1 AA512935 0.5 (0.3-0.9) 0.014 5 EST2 AA454543 1.7 (1.2-2.6)
0.008 6 CLDN10 R54559 1.7 (1.1-2.7) 0.014 7 DNALI1 R93087 1.9
(1.2-3.0) 0.006 8 RBP5 AA453198 1.4 (1.0-2.0) 0.033 9 EST3 AA621761
1.7 (1.0-3.0) 0.049 10 EST4 N63706 1.8 (1.1-2.9) 0.020 11 PCOLCE
AA670200 0.7 (0.5-0.9) 0.010 12 TDO2 T72398 0.8 (0.6-1.0) 0.038 13
EST5 T47454 0.7 (0.5-1.0) 0.040 14 HIST1H2BD N33927 1.7 (1.1-2.5)
0.012 15 PXMP2 N70714 1.7 (1.1-2.6) 0.031 16 ACAS2 AA455146 1.8
(1.2-2.6) 0.004 17 ANAPC7 T68445 0.7 (0.5-1.0) 0.024 18 EST6
AA576580 1.9 (1.1-3.2) 0.021 19 RBP5 N92148 1.4 (1.0-1.9) 0.049 20
ANXA1 H63077 0.5 (0.3-0.9) 0.020 21 CKB AA894557 1.3 (1.1-1.7)
0.010 22 ITGBL1 N52533 0.7 (0.4-1.0) 0.048 23 KPNA2 AA676460 1.6
(1.0-2.6) 0.048 24 EST7 W90740 0.6 (0.4-0.9) 0.012 25 MEG3 W85841
1.2 (1.0-1.5) 0.038 26 .sup.aThe relative importance of the genes
for predicting recurrence was ranked by step-down approach.
[0071] Results
[0072] Gene Expression Profile
[0073] Fluorescence intensities of the scanned images were
quantified, normalized and corrected to yield the transcript
abundance of a gene as an intensity ratio with respect to that of
the mean value of the sample pool. A total of 1404 cDNA clones were
significantly regulated across the group of 48 HCC samples with at
least four-fold difference in two samples. Using hierarchical
clustering algorithm, the 48 HCCs were clustered based on their
similarities over the 1404 significant clones. The HCC samples were
segregated into two distinct branches (25 and 23 HCCs,
respectively) and correlated with the clinico-pathological
parameters, such as serum AFP level, size of the tumor, presence of
venous infiltration, pTNM stage and recurrence. Twenty-six patients
developed recurrence and the median disease-free period was 4.5
months (range, 0.9-32.7 months). For the 22 patients who were
recurrence-free, the median duration of follow-up was 37.2 months
(range, 26.1-45.4 months). However, none of these
clinico-pathological parameters correlated with the global
expression signatures. The result was expected as the global gene
expression profiles of HCC were associated with the proliferation
and metabolic rate of the tumor, and the status of
dedifferentiation of the tumor cells.
[0074] The 1404 clones were then specifically searched for their
association of tumor recurrence among the 48 patients. Cox
regression analysis on the gene expression level in association
with disease recurrence was evaluated and 54 genes were found to be
significantly associated with tumor recurrence (P<0.05, 3.8% of
the 1404 significant clones). In the second step to further
minimize the number of genes for recurrence prediction, the 54
genes were examined by Kaplan-Meier analysis. Twenty-six genes with
P values less than 0.05 by log rank test were identified (Table
1).
[0075] A prognostic gene score for each individual patient was
generated as described in the method section. The score was based
on the proportion of genes in the gene set that demonstrated
expression level associated with poor prognosis. A step-down
approach was adopted to determine the minimal set of genes that
could provide a prognostic gene score with the best prediction of
recurrence. The relative importance of the genes for the prediction
of recurrence was ranked and the last gene in the order of removal
was the most important gene to predict recurrence (Table 1). A
graph of the standardized effect against the number of genes
considered was plotted (FIG. 1B). The maximum standardized effect
was achieved when the number of genes was optimized to the top
ranked 12 genes that predict recurrence.
[0076] The expression pattern of the 12 genes in the 48 HCC samples
was shown (FIG. 1C). The genes were clustered on the basis of their
similarities measured over the samples by hierarchical clustering
algorithm. In the gene dendrogram two distinct groups of genes were
revealed. Notably, the top panel contained the "good" genes with
relative risk (RR) less than 1 by Cox analysis. High level of
expression of these "good" genes was associated with longer
disease-free period by Kaplan-Meier analysis. Genes at the bottom
panel were the "bad" genes with RR greater than 1, and expression
in high level associated with shorter disease-free period.
Similarly, the HCCs were segregated on the basis of their
similarities measured over these 12 prognostic genes into two
groups with the event of recurrence indicated at the bottom of the
data matrix. The HCCs clustered at the left side showed a good gene
expression signature with up-regulation of good genes and
down-regulation of bad genes. On the contrary, HCCs clustered at
the right side exhibited a bad gene expression signature with
up-regulation of bad genes and down-regulation of good genes. The
majority of patients with bad prognosis signature developed
recurrence (24/28, 85.7%) compared to low incidence of recurrence
in patients with good prognosis signature (3/20, 15%); Fisher's
exact test, P<0.001.
[0077] Confirmation Using an Independent Set of HCCs.
[0078] To validate the genes for prognosis, a gene was arbitrarily
selected from the set of 12 genes to verify the microarray
expression data using an additional independent set of primary
HCCs. A different experimental method, quantitative RT-PCR, was
employed to examine the expression level of claudin 10 (CLDN10).
Patients were categorized into two groups by using their median
expression value as cut-off. For patients with high level of CLDN10
expression, 14 of 18 patients (77.8%) developed recurrence; whereas
12 of 29 patients (41.4%) with low level of expression developed
recurrence (Fisher's exact test, P=0.015) (FIG. 2). High level of
CLDN10 expression was associated with increased risk of disease
recurrence; the RR was 3-fold (95% confidence interval (CI),
1.4-6.6; P=0.006). By Kaplan-Meier analysis, the median
disease-free survival period was 5.5 months in patients with high
CLDN10 level compared to >17.5 months in patients with low level
of expression (log rank test, P=0.004). Thus, the microarray and
RT-PCR showed comparable results on CLDN10 in the validation sample
set.
[0079] Gene Expressions and Clinico-Pathological Features
[0080] The prognostic gene score based on the optimal set of 12
genes was ranked and compared with patient outcome (FIG. 3A). The
accuracy of patient outcome prediction by prognostic gene score was
measured by the area under the receiver operating characteristic
(ROC) curve. The accuracy for recurrence prediction within 3 years
was 97.8% (CI 95%, 94.8-100%) (FIG. 3B). The best cut-off value for
recurrence prediction was 0.416 as determined by the Youden Index.
The specificity and sensitivity of predicting recurrence within 3
years was 94.4% (95% CI, 72.7-99.9%) and 92.6% (95% CI,
75.7-99.1%), respectively. The estimated RR for the development of
recurrence in 3 years was 57.7-fold. The prediction accuracy for
patients succumbed to disease was 89.3% (CI 95%, 79.4-99.2%) by ROC
curve (FIG. 3C). The optimal cut-off value for survival prediction
was 0.600 by the Youden Index. The specificity and sensitivity of
predicting death within 3 years was 88.9% (95% CI, 70.8-97.7%) and
82.4% (95% CI, 56.6-96.2%), respectively. The estimated RR for
death in 3 years was 16.9-fold.
[0081] The correlation of clinico-pathological characteristics with
HCC recurrence was analyzed (Table 2). The presence of venous
invasion, tumor size larger than 5 cm, and late pTNM stages were
all significantly associated with disease recurrence. These 3
features and the presence of microsatellite nodules were
significantly associated with disease death. Gender, age, HBV
infection history, serum level of AFP, cirrhosis of liver, tumor
encapsulation, and Edmondson grade were not significantly
associated with recurrence nor death. As suggested by the RR, the
prognostic gene score outperformed all the clinico-pathological
parameters.
2TABLE 2 Disease-free and overall survival univariate analysis for
gene score and clinico-pathological parameters Disease-free
survival Overall survival Variables.sup.a Relative Risk P Relative
Risk P Gene score 57.7 (7.6-435.9) <0.001 16.9 (4.8-60.2)
<0.001 Venous 2.2 (1.0-4.8) 0.039 2.9 (1.1-7.9) 0.035
infiltration Tumor size 2.7 (1.2-6.0) 0.013 6.9 (2.0-24.2) 0.002
pTNM stage 2.4 (1.1-5.4) 0.032 5.4 (1.5-18.7) 0.008 microsatellite
0.285 2.8 (1.0-7.7) 0.043 .sup.aFor each variables, the patients
were categorized into two groups. The cut-off for tumor size was 5
cm. The insignificant variables with P > 0.05 were not listed in
the table including gender, age (cut-off at 60 years old), HBV
infection history, serum AFP level (cut-off at 20 ng/ml), cirrhosis
liver, tumor encapsulation and Edmondson grade.
[0082] Prognosis by Gene Score and pTNM Stage.
[0083] The best cut-off value for recurrence and death prediction
was different. For overall patient outcome assessment, therefore,
we recommended to use prognostic gene score to categorize patients
into 3 groups: Gene score A (<0.416) patients with good
prognosis, where majority were disease-free and alive in 3 years,
with {fraction (1/21)} (4.8%) recurrence and death; Gene score B
(0.416-0.600) patients with intermediary prognosis, where majority
developed late recurrence but were still alive in 3 years, with
{fraction (9/10)} (90%) recurrence (median disease-free period was
16.1 months) and {fraction (2/10)} (20%) death; Gene score C
(>0.600) patients with poor prognosis, where majority developed
early recurrence and die within 3 years, with {fraction (17/17)}
(100%) recurrence (median disease-free period was 2.5 months) and
{fraction (14/17)} (82.4%) deaths (median overall survival period
was 13.7 months).
[0084] The prognostic gene score (3 category: score A, B and C) and
pTNM stage were compared (4 stage: I, II, III and IVa) by Cox
regression analysis of these two factors with the forward stepwise
selection procedure. Both prognostic gene score and pTNM stage were
independent indicators of poor prognosis. The relative risk for
disease-free survival for the prognostic gene score and pTNM stage
were 5.7 (95% CI 3.2-10.4, P<0.001) and 1.7 (95% CI 1.0-2.8,
P=0.036), respectively. The relative risk for overall survival for
the prognostic gene score and pTNM stage were 5.4 (95% CI 2.1-14.0,
P<0.001) and 2.0 (95% CI 1.1-3.4, P=0.020), respectively. The
prognostic gene score and PTNM stage were further examined by
Kaplan-Meier analysis (FIG. 4). Patients with different prognostic
gene score differed significantly in disease-free and overall
survival (log rank test P<0.05). In the overall survival
analysis between score A and B patients where majority of them were
still alive, no significant difference was observed between the 2
groups; nonetheless, as majority of score B patients had developed
recurrence within 3 years, the overall survival outcome will be
expected to be inferior than the score A patients with longer
follow-up. However, patients with different pTNM stage did not
differed significantly in disease-free and overall survival.
Comparing stage I against II, or stage II against III, no
significant difference was observed. Only stage III patients were
significantly different from the stage IVa patients. Therefore,
prognostic gene score can provide a more accurate prognosis
segregation compared to the pTNM staging system.
[0085] Discussion
[0086] These results indicate that prognosis for HCC patients can
be derived from the gene expression profile of the primary tumors.
The optimal gene set to predict recurrence was delineated to be the
top ranked 12 genes of the 26 genes that were significantly
associated with recurrence. Although the prognostic gene set was
determined by the association with recurrence event, the result was
also applicable for overall survival prediction. The prognostic
gene score thus generated had accuracy of 97.8% and 89.3%,
respectively, for predicting recurrence and death within 3 years.
The prediction power of prognostic gene score outperformed all the
clinico-pathological parameters as suggested by the relative risk.
Multivariate analysis indicated that prognosis by gene score was
independent of pTNM stages. Therefore, gene expression data
together with clinical and pathology data will definitely provide a
more accurate prediction for disease outcome.
[0087] This is the first report on gene expression profile for
prediction of disease-free and overall survival in HCC patients
after hepatectomy. The current study accounted for both intra- and
extra-hepatic recurrence within 3 years, as recurrence outside the
liver was also important for disease management and the longer
follow-up period would have included majority of recurrence after
curative surgery. The fundamental difference in clinical endpoint
consideration may account for the prognostic gene list difference
between the two reports. However, the discrepancy may also due to
the different microarrays used in the two centers, which had
included different gene sets in the genechips. Furthermore,
patients were mostly HCV-related in Iizuka et al study, whereas
majority of our patients were HBV-related, and therefore disease
progression may actually involve different genes.
[0088] The functional annotation of the genes provides insight into
the underlying biological mechanism leading to rapid recurrence.
Genes potentially involved in cell invasion and metastasis are
significantly up-regulated in the poor prognosis group. For
example, CLDN10 family members have been shown to facilitate
invasion and migration; dynein, axonemal, light intermediate
polypeptide 1 (DNALI1) is a motor protein and may regulate cell
migration/motility.
[0089] Recent reviews showed that neoadjuvant and adjuvant therapy
for localized HCC after curative surgery have modest improvement on
overall or disease-free survival. The frustration is expected
because about half of the patients would not have developed disease
recurrence (FIG. 4A) even without adjuvant treatment. These good
prognosis patients may not benefit from the adjuvant treatment but
may potentially succumb to the side effects of the adjuvant
treatment. Therefore, the prognostic gene score can help to select
those high-risk patients who would benefit from adjuvant therapy,
and significantly reduce the number of patients who do not require
the treatments at all. Furthermore, genes that are deregulated in
cancer with poor prognosis are potential targets for the rational
development of new cancer drugs and therapeutic targets. In this
study, RBP5 was down-regulated in a subset of HCC patients (FIG.
1C) and therefore they could be candidates for chemoprevention by
retinoic acid. Patients showing a high level of RBP5 may imply
non-responsiveness to retinoic acid, or measures have to be taken
to bring down the level of RBP5 for treatment. Identification of
these targets may improve the efficacy of developing treatments for
other cancers as well.
[0090] These results indicate that the prognostic gene score based
on expression pattern of 12 genes can accurately predict disease
recurrence and survival of HCC patients after curative surgery, and
implies that the invasive and metastasis behavior is the biological
nature initiated in the primary tumor.
EXAMPLE II
[0091] Synopsis
[0092] Hepatocellular carcinoma (HCC) patients with the same
clinico-pathological features can have remarkably different disease
outcomes after curative hepatectomy. To address this issue, the
cDNA microarray gene expression profiles of HCCs were evaluated and
identified that claudin-10 expression level was associated with
disease recurrence. The aim of this study was to validate the above
microarray data by alternative research method applicable for
routine practice. Quantitative RT-PCR was employed to validate the
microarray data on claudin-10 expression level. The assay was
repeated on a separate HCC sample set, to consolidate the
prognostic significance of claudin-10. Claudin-10 expression level
by quantitative RT-PCR and by microarray measurement showed a high
concordance (r=0.602, P<0.001). Quantitative RT-PCR was repeated
on a separate HCC sample set and the association of claudin-10
expression with recurrence was again confirmed (hazard ratio 1.2,
95% CI 1.0-1.4, P=0.011). By multivariable Cox regression analysis,
claudin-10 expression and pTNM stage were independent factors for
prediction of disease recurrence. Claudin-10 expression of HCC can
therefore be used as a molecular marker for disease recurrence
after curative hepatectomy.
[0093] Materials and Methods:
[0094] Patients and Samples
[0095] Gene expression profiles from 48 patients undergoing
curative partial hepatectomy for HCC during the period March 1999
to April 2000 at Queen Mary Hospital, Hong Kong, were included for
patient outcome analysis. Patients were excluded from the present
disease outcome analysis if the pathological examination of the
resected specimen showed positive resection margin or mixture of
other tumor cell types (e.g. cholangiocarcinoma); if they had
received chemotherapy before or after resection; if they had
undergone liver transplantation instead of partial hepatectomy; if
the resection was for recurrence or palliative intent; or if the
resection was followed by hospital death. Another 53 HCCs operated
during the period April 2000 to March 2002 in the same institute
with the same exclusion criteria were recruited for validation
study. Informed consents had been obtained for specimen collection.
The study protocol was approved by the Ethics Committee of the
University of Hong Kong.
[0096] Diagnosis of HCC recurrence was based on typical imaging
findings in a contrast-enhanced computed tomography scan and an
increased serum AFP level. In case of uncertainty, hepatic
arteriography and a post-Lipiodol computed tomography scan were
performed, and if necessary, fine-needle aspiration cytology was
used for confirmation. Up to the date of analysis, 59 out of the
total 101 patients developed recurrence and the median disease-free
period was 5.7 months (range, 0.9-32.7 months). For the remaining
42 patients who were disease-free, the median follow-up period was
34.0 months (range, 14.9-48.8 months). The age of the patients
ranged from 13 to 79, with a median age of 52 years. There were 81
men and 20 women. Serum hepatitis B surface antigen (HBsAg) was
positive in 92 patients (91.1%). Tumors were staged according to
the UICC pTNM tumor classification 1997 version (18), because the
2002 version did not clearly stratify the patients into different
stages in terms of survival rate (19). The clinico-pathological
features were prospectively collected into the HCC clinical
database.
[0097] Microarray Expression Study.
[0098] The cDNA microarray slides were printed with about 23,000
cDNA clones including 17,400 genes. Samples, RNA preparations, and
hybridization protocols had been established and described in
detail previously (14,20). Data were deposited into the Stanford
Microarray Database
(http://genome-www5.stanford.edu/MicroArray/SMD/) (21). The
fluorescence signals were normalized by mean-centering genes for
each array, and then mean-centering each gene across all arrays.
Only well measured genes were included in subsequent analyses, and
defined as genes that had a ratio of signal intensity to background
noise of more than 1.5 fold and net signal intensity to background
of more than 50 unit, for either the Cy5-labeled sample or the
Cy3-labeled reference, in at least 50 percent of the tested
samples. A total of 1,404 cDNA clones with expression levels
different by at least four-fold from the mean in at least two
samples were selected for further Cox regression analyses.
[0099] Quantitative RT-PCR.
[0100] Quantitative RT-PCR was performed. Human 18s rRNA primer and
probe reagents (Pre-Developed TaqMan Assay Reagents, Applied
Biosystems, Foster City, Calif.) were used as the normalization
control for subsequent multiplexed reactions. The relative amount
of claudin-10, which had been normalized with control 18s for RNA
amount variation and calibrator for plate-to-plate variation, was
presented as the relative fold change in log 2 base. Transcript
quantification was performed in at least triplicates for every
sample. Quantification was performed using the ABI Prism 7700
sequence detection system (Applied Biosystems). Primers and probe
for claudin-10 were CLDN10-F, 5'-CTGTG GAAGG CGTGC GTTA-3';
CLDN10-R, 5'-CAAAG AAGCC CAGGC TGACA-3'; and CLDN10-P, 5'-6FAM
CCTCC ATGCT GGCGC MGBNFQ-3'.
[0101] Statistical Methods.
[0102] Cox regression analyses with gene expression data as
continuous variables were computed to examine gene expression that
was associated with disease recurrence after curative resection.
The technical concern of microarray data reproducibility was
addressed by using quantitative RT-PCR for validation. Expression
data by microarray and quantitative RT-PCR data were continuous
variables assessed by Pearson's correlation coefficient (r). The
association of claudin-10 expression and disease-free survival was
validated in another independent sample set, and we employed
quantitative RT-PCR as a different assay technique for the
transcript quantitation in the independent sample set.
[0103] The claudin-10 expression data was modeled as categorical
variable only in the Kaplan-Meier analyses. The Youden index
(sensitivity+specificity-1) (23) was used to determine the optimal
cut-off point of claudin-10 expression for the prediction of 3-year
disease-free survival. Other cut-off values including the mean,
median and 75th percentile had also been considered and examined,
and they were all able to segregate the patients with clinical
implications. The Youden index was employed to maximize the
sensitivity and specificity of the prediction simultaneously.
[0104] The association of gene expression and clinico-pathological
parameters with patient outcome was examined by a multivariable Cox
proportional hazards regression with the forward stepwise selection
procedure. The claudin-10 expression data was modeled as continuous
variable, and all the clinico-pathological parameters were modeled
as categorical variables in the Cox regression analyses. The
associations of claudin-10 expression level with
clinico-pathological features were assessed by Spearman correlation
and Mann-Whitney U test where appropriate. Differences were
considered significant when P value was less than 0.05. The
statistical analyses were aided by SPSS version 11.0 software
package (SPSS Inc., Chicago, Ill.).
[0105] Additional Microarray Information.
[0106] The microarray study was carried out following the MIAME
guidelines issued by the Microarray Gene Expression Data Group
(24). The original data are available in the Stanford Microarray
Database (http://genome-www5.stanford.edu). Information is also
available from the authors on request.
[0107] Results:
[0108] Claudin-10 Expression and Recurrence.
[0109] Cox regression analyses with gene expression modeled as a
continuous variable were computed to identify gene expression that
predicts disease recurrence after curative resection (HCCs n=48).
Claudin-10 ranks high in prognosis prediction and is membrane bound
protein with potential therapeutic value. Claudin-10 encodes a
member of the claudin family in which claudins are integral
membrane proteins and components of tight junction strands. The
claudin-10 level by cDNA microarray was significantly associated
with recurrence (hazard ratio [HR] 1.7, 95% confidence interval
[CI] 1.1-2.6, P=0.014). To verify the technical concern on cDNA
microarray reproducibility, quantitative RT-PCR was performed on
the same HCC sample set. Results derived from the two research
methods demonstrated a high concordance (Pearson correlation
coefficient, r=0.602, P<0.001). To provide an independent test
of the association between claudin-10 expression and disease
recurrence, a second set of primary HCCs was used (n=53).
Quantitative RT-PCR was employed to measure the abundance of the
claudin-10 transcript. The claudin-10 level was treated as a
continuous variable, and Cox regression analysis was used to
examine the relationship of the transcript level with disease
recurrence of the patients after curative HCC surgery. Results
indicated that the transcript level of claudin-10 was significantly
associated with recurrence (HR 1.2, 95% CI 1.0-1.4, P=0.011). Thus,
the two sample sets examined by different techniques both indicated
that a higher expression level of claudin-10 in HCC was associated
with disease recurrence after curative surgery. Prognosis by
Claudin-10 Expression and Clinico-pathological Features. All the
101 patients in the two sample sets were included into the disease
recurrence analyses. The claudin-10 expression data was based on
quantitative RT-PCR, and was modeled as continuous variable in the
analyses. For clinico-pathological parameters, patients were
dichotomized accordingly (Table 3).
3TABLE 3 Cox regression analyses for disease-free survival on gene
expression and clinico-pathological parameters Multivariable
Analysis Univariable Analysis Adjusted Variables.sup.a n Hazard
ratio (95% CI) P Hazard ratio (95% P pTNM stage Stage I and II 43 1
1 Stage III and 58 3.0 (1.7-5.4) <0.001 2.6 (1.4- 0.002 Tumor
size .ltoreq.5 cm 39 1 1 >5 cm 62 2.2 (1.2-3.8) 0.006 2.7 (1.5-
0.001 Venous Absence 48 1 Presence 53 2.6 (1.5-4.5) 0.001 -- -- --
Tumor nodule Single 77 1 Multiple 24 1.9 (1.1-3.3) 0.025 -- -- --
Microsatellite Absence 52 1 Presence 49 1.7 (1.0-2.9) 0.037 -- --
-- Serum AFP level .ltoreq.20 ng/ml 34 1 1 >20 ng/ml 67 1.6
(0.9-2.8) 0.112 2.2 (1.2- 0.010 Claudin-10.sup.b 101 1.2 (1.1-1.3)
0.002 1.2 (1.1-1.3) <0.001 .sup.aInsignificant variables with P
> 0.05 were not listed in the table including gender (male
versus female), age (.ltoreq.60 versus >60 years old), hepatitis
B virus association absence versus presence of serum hepatitis B
surface antigen), chronic liver disease (normal and hepatitis
versus cirrhosis of the liver remnant), tumor encapsulation
(absence versus presence of tumor capsule), and Edmondson-Steiner
histological grade (Grade 1 and 2 versus 3 and 4). .sup.bThe
claudin-10 expression level (relative fold change in log 2 base)
examined by quantitative RT-PCR was modeled as continuous variable
in the analyses.
[0110] By univariable Cox regression analysis, claudin-10
expression (HR 1.2, 95% CI 1.1-1.3, P=0.002), late pTNM stages (HR
3.0, 95% CI 1.7-5.4, P<0.001), venous invasion (HR 2.6, 95% CI
1.5-4.5, P<0.001), large tumor size (HR 2.2, 95% CI 1.2-3.8,
P=0.006), multiple tumor nodules (HR 1.9, 95% CI 1.1-3.3, P=0.025),
and microsatellite nodules (HR 1.7, 95% CI 1.0-2.9, P=0.037) were
all significantly associated with disease recurrence. Gender, age,
HBV association, serum AFP level, cirrhosis in the remnant liver,
tumor encapsulation, and Edmondson-Steiner histological grade were
not significantly associated with recurrence.
[0111] By multivariable Cox regression analysis, claudin-10
expression (HR 1.2, 95% CI 1.1.1-1.3, P<0.001), late pTNM stage
(HR 2.6, 95% CI 1.4-4.7, P=0.002), large tumor size (HR 2.7, 95% CI
1.5-4.9, P=0.001) and high serum AFP level (HR 2.2, 95% CI 1.2-4.0,
P=0.010) were independent prognostic factors for disease
recurrence. The other clinico-pathological features did not add
independent prognostic information.
[0112] The Kaplan-Meier plot was used to further examine the
prediction power by using the claudin-10 expression level alone or
together with the pTNM stage system because these two factors were
independent prognostic indicators by Cox regression analysis. By
Youden index, the optimal cut-off value of claudin-10 expression
was 1.23 (relative fold change in log 2 base) to segregate patients
into low or high claudin-10 expression group. Using this cut off
value, there were 60 patients in the low claudin-10 expression
group (range 0-1.15), and 41 patients in the high claudin-10
expression group (range 1.30-11.21). By using the claudin-10 factor
alone to segregate the patients, the cumulative 3-year disease-free
survivals for patients with low and high claudin-10 levels were
53.3% (32/60) and 24.4% (10/41), respectively (log-rank test,
P<0.001) (FIG. 1). The analysis was repeated based on the
claudin-10 level and pTNM stages of the patients. The cumulative
3-year disease-free survival was 75% (21/28) for early stage
(Stages I and II) patients with low claudin-10 level, 40.0% (6/15)
for early stage patients with high claudin-10, 34.4% (11/32) for
late stage (Stages III and IVa) patients with low claudin-10, and
15.4% (4/26) for late stage patients with high claudin-10 (log-rank
test, P<0.001).
[0113] Decreased Claudin-10 Expression was Associated with Older
Patients, Presence of Tumor Capsule and Non-cirrhotic Liver.
[0114] To better understand the significance of claudin-10
expression, the association of claudin-10 expression level with the
clinico-pathological parameters of the HCC patients was analyzed.
The down-regulation of claudin-10 expression in tumor was
significantly associated with older patients (r=-0.223, P=0.025),
presence of tumor capsule (P=0.011), and non-cirrhotic liver
remnant (r=0.257, P=0.009). The claudin-10 expression level in
tumor was not significantly associated with the pTNM stages, venous
infiltration, tumor size, multiple tumor nodules, microsatellite
nodules, gender, HBV association, serum AFP level, or
Edmondson-Steiner histological grade.
[0115] Discussion:
[0116] In this study, the claudin-10 expression level and its
prognostic value as a novel molecular marker for HBV-related HCC
was presented. The claudin-10 gene was annotated by the Ensembl
automatic analysis pipeline (http://www.ensembl.org). The
claudin-10 gene locates at chromosome 13q31-q34 spanning 25.51 Kb
with 5 exons, and the predicted protein contains four potential
transmembrane domains. This gene encodes a member of the claudin
family in which claudins are integral membrane proteins and
components of tight junction strands (refer to ref 16 for review).
Tight junction strands serve as a physical barrier to prevent
solutes and water from passing freely through the paracellular
space between epithelial or endothelial cell sheets. The exact
function of claudin-10 is unknown, and its role in cancer
development and progression is mysterious. Interestingly, the
claudin family members have been shown to facilitate cell invasion
and migration (16). Two alternatively spliced transcript variants
that encode different isoforms have been reported for the
claudin-10 gene (NM.sub.--006984 and NM.sub.--182848). The two
transcripts are identical at the C-terminal and encode 155 amino
acids alike. In the databases (GenAtlas, GeneCard, and SwissProt),
the claudin-10 mostly refers to claudin-10b or claudin-10
transcript variant 2 (NM.sub.--006984, encodes 228 amino acids),
and this transcript is also reported to be overexpressed in lung
cancer cell lines (17). Nevertheless, claudin-10 variant refers to
claudin-10a or claudin-10 transcript variant 1 (NM.sub.--182848,
encodes 226 amino acids). In this report, the claudin-10
(NM.sub.--006984) was characterized for its clinical significance,
as it is the predominant isoform observed in various tissue organs
(NCBI GenBank) and in liver (unpublished data).
[0117] Identification of patients with different risk of disease
recurrence will become more important for patient benefit. Here,
the microarray data was validated in another independent sample
set, and employed quantitative RT-PCR for transcript quantitation
in the independent sample set. Both data sets examined by different
assay techniques demonstrated that down-regulation of claudin-10
expression was associated with prolonged disease-free period after
curative surgery. Our results indicated that prognosis for HCC
patients can be derived from the gene expression of primary tumors.
The use of quantitative RT-PCR to assess the claudin-10 level is
particularly feasible for the clinical setting, as the test is
sensitive and the assay facilities are commonly available in
routine laboratories for practical application. Cox regression
multivariate analysis indicated that claudin-10 expression was
independent of pTNM stage in predicting prognosis, and gene
expression data used together with pTNM stage can have added power
to provide more accurate prediction for disease outcome (FIG.
5).
[0118] This is the first report on claudin-10 expression associated
with disease-free survival in HCC patients after hepatectomy. There
have been reports on the expression profiles of HCCs with the
microarray approach (14,20,25-30), though there have been few
reports on the association of gene expressions with HCC patient
outcomes. Notably, a recent report by Iizuka and colleagues
demonstrated a correlation of gene expression with early
post-hepatectomy intrahepatic recurrence within 1 year (31).
Claudin-10 did not revealed prognostic significance in that report.
The discrepancy may be due to a number of reasons. Firstly, in the
study by Iizuka et al., the patients were mostly HCV-related
(22/33, 66.7%), whereas the majority of our patients were
HBV-related (92/101, 91.1%). Different HCC etiologies may actually
involve different genes and thus recurrence-associated genes in
HBV- and HCV-related HCC may be different. Secondly, the
fundamental difference in clinical endpoint consideration (only
intra-hepatic recurrence within the first year after surgery in the
report of Iizuka et al.; both intra- and/or extra-hepatic
recurrence within 3 years in our report) may account for the
differences, as different genes may be responsible for early
recurrence (within the first year) or late recurrence (after the
first year). Furthermore, we considered both intra- and
extra-hepatic recurrence within 3 years as clinical end-point
assessments, because recurrence outside the liver was also
important for disease management and the longer follow-up period
would have included the majority of recurrence after curative
surgery. It would thus be important to evaluate if claudin-10
expression level can predict 3-year disease recurrence in
HCV-related HCCs.
[0119] The functional annotation of genes provides an insight into
the underlying biological mechanism leading to cancer recurrence.
The biological function of claudin-10 is unknown. Particularly,
claudin family members have been shown to associate with cell
invasion and migration (16). Over-expression of claudin-2
transforms a `tight` tight junction into a `leaky` tight junction
in epithelial cells (32). Over-expression of claudin-11 induces
proliferation and enhances migration in an oligodendrocyte cell
line (33). Nonetheless, the role of claudins in human cancer is
still controversial. Over-expression of claudin-4/-3 has been
reported in pancreatic (34,35), colorectal (36), and ovarian (37)
cancer. Notably, claudin-4 expression decreases cell invasion and
metastatic potential of pancreatic cancer (38). On the other hand,
down-regulation of claudin-7/-1 has been reported in head and neck
squamous cell carcinomas (39) and breast cancer (40,41). Claudin-10
has not been well characterized (16). Notably, claudin-10 is
reported to be highly expressed in lung cancer cell lines (17). Low
claudin-10 expression in HCC was associated with the more favorable
features including older age of patients, presence of tumor capsule
and non-cirrhotic liver remnant. More advanced stages of the HCCs
were observed in young patients (9,42). Absence of tumor capsule
was an aggressive HCC feature and associated with early recurrence
(7,10). Operative mortality was higher in patients with cirrhotic
liver, which was related to hepatic function reserve (11,43). The
biological role of the decreased claudin-10 level in contribution
to favorable HCC prognosis is not clear. Preliminary
immunohistochemistry analysis on the cell origin of claudin-10
indicated that in the HCCs with high level of claudin-10
transcript, strong membranous signal and granular cytoplasmic
staining was observed in the neoplastic hepatocytes. Nonetheless,
further investigation is required to define the role of the
prognostic gene claudin-10 in carcinogenesis so as to delineate the
exact molecular pathways leading to disease recurrence. These
results indicate that claudin-10 expression can predict disease
recurrence after curative surgery.
EXAMPLE III
[0120] Synopsis
[0121] Among the genes that show prognostic significance and
overexpressed in tumor compared with adjacent non-tumorous liver
tissues, transcript AA454543 has potential for practical use. The
aim of this study was to validate the prognostic significance of
transcript AA454543 by alternative research method and in a
separate group of HCC patients. The data of transcript AA454543
derived from microarray analysis of the 48 patients having curative
partial hepatectomy (Group 1) was verified by quantitative RT-PCR
(r=0.618, p<0.001). A separate sample set of HCCs obtained from
53 patients (Group 2) was examined and the association of AA454543
expression level with overall survival was again validated
(p=0.027). By Cox regression analysis, transcript AA454543 (hazard
ratio 3.0, p=0.017) and pTNM stage (hazard ratio 3.3, p=0.010) were
independent prognostic factors for overall survival. The accuracy
of prediction for 3-year overall survival for transcript AA454543
(74.2%, p=0.001) and pTNM stage (76.4%, p=0.001) was comparable as
measured by the area under the receiver operating characteristic
curve. Transcript AA454543 is a potentially useful molecular
prognostic marker for overall survival after curative partial
hepatectomy for HCC.
[0122] Materials and Methods:
[0123] Patients and Samples
[0124] Forty-eight patients who underwent curative partial
hepatectomy during the period March 1999 to April 2000 at Queen
Mary Hospital, Hong Kong were selected for the initial study (Group
1). The gene expression profile of these 48 patients had been
studied by cDNA microarray [10]. To validate the data obtained from
cDNA microarray, in this study, quantitative RT-PCR was performed
in HCCs of this group for the AA454543 expression. Another 53 HCC
patients (Group 2) operated during the period April 2000 to March
2002 in the same institute with the same inclusion criteria were
recruited for further validation study by RT-PCR for transcript
AA454543. This independent cohort of patients (Group 2) was used to
confirm that the prognostic marker works in general, and not only
on the group of patients from whom the data are derived (Group 1)
[11]. Patients were included in this study if the pathological
examination of the resected specimen showed a clear resection
margin. Patients were not selected if the pathological examination
showed mixture of other tumor cell types (e.g. cholangiocarcinoma);
if they had received chemotherapy before or after resection; if
they had undergone liver transplantation instead of partial
hepatectomy; if the resection was for recurrence or palliative
intent; or if the resection was followed by hospital death. The
clinico-pathological data of the 2 groups of patients were listed
in Table 4. The age of the patients ranged from 13 to 79, with a
median age of 52 years. There were 81 men and 20 women. Serum
hepatitis B surface antigen was positive in 92 patients (91.1%).
Tumors were staged according to the International Union Against
Cancer pathological tumor lymph node metastasis (pTNM) tumor
classification 1997 version [12], because the 2002 version did not
clearly stratify survival of our patients with advanced stages
[13]. The patients were prospectively follow-up for recurrence of
HCC. Recurrence was diagnosed based on typical imaging findings in
a contrast-enhanced computed tomography scan and an increased serum
AFP level. In case of uncertainty, hepatic arteriography and a
post-Lipiodol computed tomography scan were performed, and if
necessary, fine-needle aspiration cytology was used for
confirmation. Up to the date of analysis, 31 out of the total 101
patients succumbed to disease and the median survival period was
12.5 months (range, 4.5-34.1 months). For the remaining 70
patients, the median follow-up period was 33.4 months (range,
14.9-48.8 months).
4TABLE 4 Clinico-pathological features of HCCs. Group 1 Group 2
Total HCC features n = 48 n = 53 n = 101 Age Median 51 53 52
(Range) (13-73) (16-79) (13-79) Gender Male 36 45 81 Female 12 8 20
pTNM stage Stage I and II 22 21 43 Stage III and 26 32 58 Tumor
size .ltoreq.5 cm 24 15 39 >5 cm 24 38 62 Venous infiltration
Absence 27 21 48 Presence 21 32 53 Microsatellite Absence 26 25 51
Presence 22 28 50 Edmondson-Steiner Grade 1 and 2 20 23 43 Grade 3
and 4 28 30 58 Serum AFP level .ltoreq.20 ng/ml 15 19 34 >20
ng/ml 33 34 67 HBsAg Positive 43 49 92 Negative 5 4 9 Disease
mortality Death 17 14 31 Alive 31 39 70
[0125] Normal liver specimens from 30 organ donors (8 cadaveric and
22 live donors) were collected in transplant operations performed
at the same institution from April 2000 to December 2001 for cDNA
microarray study and quantitative RT-PCR assay for transcript
AA454543. The organ donors had no underlying liver diseases and
were negative for hepatitis B serology. The liver specimens were
obtained immediately upon laparotomy to minimize the chance of
DNA/RNA alteration as a result of physiological changes or physical
manipulation. Informed consents had been obtained for specimen
collection. The study protocol was approved by the Ethics Committee
of the University of Hong Kong.
[0126] Microarray Expression Study
[0127] The cDNA microarray slides were printed with about 23,000
cDNA clones including 17,400 genes. Samples, RNA preparations, and
hybridization protocols had been established and described in
detail previously [10,14]. Data were deposited into the Stanford
Microarray Database
(http://genome-www5.stanford.edu/MicroArray/SMD/) [15]. The
fluorescence signals were normalized by mean-centering genes for
each array, and then mean-centering each gene across all arrays.
Only well measured genes were included in subsequent analyses, and
defined as genes that had a ratio of signal intensity to background
noise of more than 1.5 fold and net signal intensity to background
of more than 50 unit, for either the Cy5-labeled sample or the
Cy3-labeled reference, in at least 50 percent of the tested
samples. A total of 1,404 cDNA clones with expression levels
different by at least four-fold from the mean in at least two
samples were selected for further analyses by Cox regression.
[0128] Quantitative RT-PCR for Transcript AA454543
[0129] Quantitative RT-PCR was performed as described [16].
Briefly, the first strand cDNA was synthesized from 0.5 .mu.g of
total RNA using the High Capacity cDNA Archive kit (Applied
Biosystems, Foster City, Calif.) following the manufacturer's
instruction. Each 25 .mu.l PCR reaction contained 1.times.PCR
buffer II, 5.5 mM MgCl.sub.2, 0.2 mM each of dATP, dCTP and dGTP,
0.4 mM dUTP, 0.625 unit AmpliTaq Gold and 5 .mu.l first strand
cDNA. Primer and probe reagents for 18s rRNA (Pre-Developed TaqMan
Assay Reagents, Applied Biosystems) were used as the endogenous
normalization control. Primers and probe for transcript AA454543
were AA454543-F (5'-ACC CAC ACA CAG CGC TCA C-3'), AA454543-R
(5'-CAA GCC GTA AAA CTT CTG CAT G-3') and AA454543-P (5'-6FAM AGT
CAC TCT CAG CGG CCA TCG CCC A-3'). Quantification was performed
using the ABI Prism 7700 sequence detection system (Applied
Biosystems). Transcript quantification was performed in at least
triplicates for every sample. The relative amount of transcript
AA454543, which had been normalized with control 18s for RNA amount
variation and calibrator for plate-to-plate variation, was
log-transformed (on a base 2 scale) and presented as the relative
fold difference similar to the microarray-based data.
[0130] Statistical Methods
[0131] Cox regression analyses with gene expression data as
continuous variables were computed to examine gene expression that
was associated with the overall survival after curative resection.
The technical concern of microarray data reproducibility was
addressed by using quantitative RT-PCR for validation. Correlation
of expression data by microarray and quantitative RT-PCR data were
assessed by Spearman correlation test. The association of
transcript AA454543 expression and overall survival was validated
in Group 2 patients by quantitative RT-PCR.
[0132] The overall accuracy of using transcript AA454543 expression
level for prognosis prediction was measured by the area under the
receiver operating characteristic curve, as there could be
limitations of using hazard ratio in gauging the performance of a
prognostic marker [17]. The prediction power for 3 years was
analyzed. Patients who were alive but with less than 3 years of
follow-up were excluded from the prediction study. Thus, 59
patients with 31 of them succumbed to disease were included in this
part of analysis. The Youden index (sensitivity+specificity-1) [18]
was used to determine the optimal cut-off point of transcript
AA454543 expression for the prediction of 3-year overall survival.
The Youden index was employed to maximize the sensitivity (true
positive fraction) and specificity (1--false positive fraction) of
the prediction simultaneously.
[0133] The association of gene expression and pTNM stage with
patient outcome was examined by univariable and multivariable Cox
proportional hazards regression with the forward stepwise selection
procedure. The pTNM stage information was categorical data. To ease
interpretation, the gene expression data was modeled as categorical
variable only in the multivariable Cox regression to comprehend the
hazard ratios into a more interpretable scale for direct comparison
with pTNM stage. The transcript AA454543 expression data was also
modeled as categorical variable in the Kaplan-Meier analyses.
[0134] The associations of transcript AA454543 expression level
with clinico-pathological features were assessed by the Spearman
correlation and Mann-Whitney U test where appropriate. Differences
were considered significant when p value is less than 0.05. The
statistical analyses were aided by the SPSS version 11.0 software
package (SPSS Inc., Chicago, Ill., USA).
[0135] Additional Microarray Information
[0136] The microarray study was carried out following the MIAME
guidelines issued by the Microarray Gene Expression Data Group
[19]. The original data are available in the Stanford Microarray
Database (http://genome-www5.stanford.edu). Information is also
available from the authors on request.
[0137] Results:
[0138] Transcript AA454543 Expression and Overall Survival
[0139] In the cDNA microarray data, the transcript AA454543 ranks
high in prognosis prediction and in expression level relative to
non-tumors. Higher transcript AA454543 level by cDNA microarray was
significantly associated with shorter overall survival (hazard
ratio [HR] 1.8, 95% confidence interval [CI] 1.1-3.1, p=0.024)
(Table 5). Quantitative RT-PCR was performed on the HCC samples of
the Group 1 patients to verify the cDNA microarray data. The two
research methods demonstrated a high concordance (Spearman
correlation, r=0.618, p<0.001). In Group 2 patients, transcript
AA454543 expression level as measured by quantitative RT-PCR showed
a significant association with the overall survival (HR 1.4, 95% CI
1.0-2.0, p=0.027) (Table 5).
[0140] The two independent sample sets examined by two different
techniques both indicated that a higher expression level of
transcript AA454543 in HCC was associated with poor overall
survival after curative surgery. The two sample sets were then
included into Cox regression analyses with transcript AA454543
expression level based on quantitative RT-PCR data. The transcript
AA454543 level was significantly associated with overall survival
in the combined dataset (HR 1.3, 95% CI 1.1-1.6, p=0.008) (Table
5).
5TABLE 5 Cox regression analyses for overall survival on transcript
AA454543 expression..sup.a Univariable Analysis Hazard Patients n
ratio (95% CI) P Group 1 48 1.8 (1.1-3.1) 0.024 Group 2 53 1.4
(1.0-2.0) 0.027 Group 1 and 2 101 1.3 (1.1-1.6) 0.008 .sup.aThe
transcript AA454543 expression data was modeled as continuous
variable. The expression data was based on the microarray data in
the Group 1 patients, and quantitative RT-PCR in the Group 2
patients and the combined groups of patients.
[0141] Prognosis by Transcript AA454543 Expression and pTNM
Stage
[0142] All the patients in the two groups were included into the
overall survival analyses. The transcript AA454543 expression data
was based on quantitative RT-PCR method, and the prediction power
for overall survival was compared with pTNM stage. The accuracy of
using transcript AA454543 expression for predicting the 3-year
overall survival rate was 74.2% (95% CI 61.2-87.2%, p=0.001)
measured by the area under the receiver operating characteristic
curve (FIG. 6). For comparison, the accuracy of using pTNM stage
for survival prediction was 76.4% (95% CI 64.2-88.5%, p=0.001). By
Youden index, the optimal cut-off value of transcript AA454543
expression to segregate patients into low or high transcript
AA454543 expression group was 7.05 (relative fold change in log 2
base). Using this cut-off value for predicting patient outcome by
transcript AA454543 expression, the sensitivity and specificity was
80.6% and 67.9%, respectively. When patients were dichotomized as
early stage (stages I and II) or late stage (stages III and IVa)
groups, the sensitivity and specificity of prognosis prediction by
pTNM stage was 80.6% and 57.1%, respectively.
[0143] The Kaplan-Meier plot was used to further examine the
prediction power by using the transcript AA454543 expression level
alone or together with the pTNM stage system in the total of 101
patients. Using the Youden index as cut-off, there were 43 patients
in the low transcript AA454543 expression group (range, 0-7.02),
and 58 patients in the high transcript AA454543 expression group
(range, 7.08-11.50). By using the transcript AA454543 level alone
to segregate the patients, the cumulative 3-year overall survivals
for patients with low and high transcript AA454543 levels were
86.0% (37/43) and 56.9% (33/58), respectively (log-rank test,
p=0.001) (FIG. 7). The analysis was repeated based on the
transcript AA454543 level and PTNM stages of the patients. The
cumulative 3-year overall survivals was 96% (24/25) for early stage
(Stages I and II) patients with a low transcript AA454543 level,
72.2% (13/18) for early stage patients with high transcript
AA454543, 72.2% (13/18) for late stage (Stages III and IVa)
patients with low transcript AA454543, and 50.0% (20/40) for late
stage patients with high transcript AA454543 (log-rank test,
p=0.014).
[0144] By Cox regression analysis, transcript AA454543 expression
data modeled as continuous variable was significantly associated
with the overall survival (Table 5). However, the hazard ratios
expressed in their natural scale illustrated only the change in the
risk of disease related mortality associated with a change of 1
unit on the expression scale, a change too small to be understood
easily. To assist interpretation, the gene expression data was
modeled as categorical variable to comprehend the hazard ratios
into a more interpretable scale (Table 6). The patients were
segregated into low or high transcript AA454543 expression groups
similarly as in the Kaplan-Meier analyses, using the Youden index
to determine the optimal cut-off value. By univariable Cox
regression analysis, transcript AA454543 expression (HR 3.9, 95% CI
1.6-9.6, p=0.003) and late pTNM stage (HR 4.2, 95% CI 1.7-10.3,
p=0.002) were significantly associated with the overall survival.
By multivariable Cox regression analysis, transcript AA454543
expression (HR 3.0, 95% CI 1.2-7.5, p=0.017) and late pTNM stage
(HR 3.3, 95% CI 1.3-8.2, p=0.010) were independent prognostic
factors for overall survival.
6TABLE 6 Cox regression analyses for overall survival on transcript
AA454543 expression and pTNM stage. Multivariable Analysis
Univariable Analysis Adjusted Variables n Hazard ratio (95% CI) P
Hazard ratio (95% P Transcript AA454543.sup.a Low level (0- 43 1 1
High level (7.08- 58 3.9 (1.6-9.6) 0.003 3.0 (1.2-7.5) 0.017 pTNM
stage.sup.b Early Stage (I 43 1 1 Late Stage (III 58 4.2 (1.7-10.3)
0.002 3.3 (1.3-8.2) 0.010 and IVa) .sup.aThe transcript AA454543
expression data was modeled as categorical variable. The optimal
cut-off value to segregate patients into low or high transcript
AA454543 expression group was determined by Youden index, which was
7.05. .sup.bThe pTNM stage was modeled as categorical variable.
[0145] Transcript AA454543 Level in Liver Tissues
[0146] Transcript AA454543 expression was higher in the HCC tissues
compared to the non-tumor liver tissues adjacent to HCCs in the
earlier observation based on the cDNA microarray approach. To
validate the observation, we randomly examined 93 (out of a total
of 101) liver tissues adjacent to HCCs using real-time quantitative
RT-PCR to measure the transcript levels. The results indicated that
the HCCs demonstrated a significantly higher transcript AA454543
level (median 7.21, range 0-11.50) compared to that of liver
tissues adjacent to HCCs (median 5.54, range 1.26-10.13)
(p<0.001).
[0147] The higher expression level in HCCs than the liver tissues
adjacent to HCCs could be interpreted as either transcript AA454543
up-regulation in HCCs or transcript AA454543 down-regulation in
liver tissues adjacent to HCCs. To distinguish the two situations,
30 normal liver tissues were examined. In normal livers, the
transcript AA454543 transcript was found to express at a low level
(median 5.31, range 0-7.36), which was significantly lower than the
HCCs (p<0.001) but not significantly different from the liver
tissues adjacent to HCCs (p=0.382) (FIG. 8).
[0148] Transcript AA454543 Expression and Clinico-Pathological
Features
[0149] To better understand the significance of transcript AA454543
expression, we analysed the association of transcript AA454543
expression level with the clinico-pathological parameters of the
HCC patients. The up-regulation of transcript AA454543 expression
in tumor was significantly associated with late pTNM stage
(r=0.299, p=0.002), venous infiltration (p<0.001),
microsatellite nodules (p=0.016) and high Edmondson-Steiner
histological grade (r=0.276, p=0.005). The transcript AA454543
expression level in tumor was not significantly associated with the
tumor size, gender, age, HBsAg positivity, or serum AFP level.
[0150] Discussion:
[0151] The transcript AA454543 sequence (clone ID IMAGE:838048;
UniGene Cluster Hs.437039; accession BC043195) is 1703 bp mRNA with
partial codons and originally cloned from hypothalamus of the human
brain. By sequence homologue search with National Center for
Biotechnology Information (NCBI) BLAST, the transcript AA454543
shows 95% identities over 1686 bp with AL035705, which is the human
DNA sequence from clone RP4-758N20 on chromosome lp31.3-32.2.
Compared with the mouse genome, the transcript AA454543 shows 85%
identities over 327 bp with AL929466, which is the DNA sequence on
mouse chromosome 4. No known gene in the genomes of human, mouse
and model organism shows a high sequence homology with the
transcript AA454543 sequence.
[0152] Identification of patients with different risk after
curative treatment will be more and more important in disease
management for patient benefit. The conventional pTNM stage system
has been proven informative for identifying patient with different
prognosis, and the current study demonstrated that the molecular
characteristics of HCC could further add on the prediction power.
Thus far, we evaluated the prognostic significance of transcript
AA454543 that was chosen based on the analysis of our earlier
microarray data. In addition, its transcript level in tumor is
significantly higher than the liver tissue adjacent to tumor, which
would be important consideration for clinical application and thus
was chosen for subsequent validation as molecular prognostic
marker. The aim of the study is to consolidate the significance of
the prognostic genes, with the assay method quantitative RT-PCR
which is a technique readily available in routine laboratories for
practical use. In the current study, we reported the prognostic
significance of transcript AA454543 whose expression level can
predict survival for HCC patients after curative hepatectomy.
Transcript AA454543 expression and PTNM stage were independent
prognostic factors for overall survival by multivariable Cox
regression analyses. Gene expression data together with pTNM stage
can help to provide a more accurate overall survival prediction as
illustrated in the Kaplan-Meier analyses (FIG. 7). Remarkably, the
transcript AA454543 expression (single gene data) and PTNM stage
have similar accuracy on prognosis prediction (74.2% and 76.4%,
respectively). Our ultimate target is to recruit more genes to
increase the accuracy for prognosis prediction.
[0153] Expression of alpha-fetoprotein (AFP), cell cycle
regulators, genes associated with metabolism, and tumor
dedifferentiation status were associated with the molecular
subtypes of HCCs [10,14,20-25]. However, there have been few HCC
reports on the association of gene expressions with patient
outcomes. Notably, Iizuka et al. reported the correlation of gene
expression profile with early intrahepatic recurrence [26]. There
are fundamental differences between the present study and that of
the Iizuka et al. report, in that most of the patients in Iizuka's
study were HCV-related (22/33, 66.7%) whereas the patients in the
present study were mostly HBV-related (92/101, 91.1% in the present
cohort). Different etiological agents may have involved different
carcinogenesis pathways, resulting in different molecular
composition and behavior. Furthermore, we used the overall survival
of 3 years as end-point while Iizuka et al. used intrahepatic
recurrence in the first year as the clinical end-point for
prognosis prediction. The prognostic genes may be different for
prognosis of disease recurrence and overall survival. Nonetheless,
we had explored the original data set by Iizuka et al.
(http://surgery2.med.yama- guchi-u.ac.jp/research/DNAchip/) and
transcript AA454543 was not on the probe set list. It would thus be
important to evaluate if the transcript AA454543 expression level
can predict overall survival in HCV-related HCCs.
[0154] The transcript AA454543 has not been well characterized and
the biological function is unknown. In the clinical samples, the
transcript AA454543 level was significantly higher in HCCs compared
to their paralleled liver tissues adjacent to HCCs, and to normal
livers. The transcript AA454543 level is informative to
differentiate if the liver tissue is neoplastic tissue, in addition
to providing prognostic information. Preliminary in situ
hybridization analysis on the cell origin of transcript AA454543
indicated that cytoplasmic signal was observed in the neoplastic
hepatocytes in HCC tissue. Notably, a higher transcript AA454543
expression level in HCC was associated with poor prognostic
features including late PTNM stage, venous infiltration,
microsatellite nodules and high Edmondson-Steiner Grade. The
association study of the transcript AA454543 level with the
clinico-pathological features is exploratory in nature and further
experiments are needed to examine their causal relationship, for
example, if an increased transcript AA454543 level will result in
enhancing the invasive ability of the tumor cells thus resulting in
venous infiltration and formation of microsatellite nodules. In the
hierarchical clustering analysis, transcript AA454543 was found to
cluster closely with the proliferation cluster, tightly with G
protein-coupled receptor and zinc finger protein, which play an
important role in coordinating cell cycle progression. These genes
that co-expressed with transcript AA454543 will help to provide a
hint of the transcript AA454543 function.
[0155] The present study indicates that the transcript AA454543
expression level can predict overall survival of the patients after
curative partial hepatectomy. The current approach demonstrates the
power of expression profiles to identify prognostic markers
feasible for clinical application. It also opens the prospect for
considering unknown genes and not only focus on well-known genes
with recognized biological contribution in carcinogenesis. This
molecular marker provides prognostic information in general (two
independent cohort of patients) and the prediction is independent
of assay method (microarray or quantitative RT-PCR). By
quantitative RT-PCR, this gene is feasible for routine laboratory
assay. And together with pTNM stage, it could help to improve
prognosis prediction and disease management for patient
benefit.
EXAMPLE IV
[0156] Synopsis:
[0157] Dynein, axonemal, light intermediate polypeptide 1 (DNALI1)
expression was found to significantly associate with disease
recurrence (hazard ratio [HR] 1.7, 95% confidence interval [CI]
1.1-2.6, P=0.014) as shown in our earlier genome-wide expression
study by cDNA microarray approach on hepatocellular carcinoma
(HCC). This study was performed on an independent sample set (n=50)
and employed quantitative RT-PCR to examine the DNALI1 transcript
level. The association of higher DNALI1 expression level with early
disease recurrence was again confirmed. Our preliminary sequencing
study indicated that DNALI1 had a polymorphism at nucleotide 194
(codon 65), which either harbored the C-allele (GCA, alanine) or
T-allele (GTA, valine). We then further examined the blood samples
of the patients for the nucleotide 194 polymorphism (n=50,
paralleled samples where the HCCs had quantitative RT-PCR data).
The tumor DNALI1 transcript level was significantly higher in
patients with T-allele compared to patients with C-allele (median
67.2 and 27.6, respectively; P=0.029). The investigation on
clinical samples confirmed that DNALI1 expression level was
associated with early disease recurrence, and the DNALI1 level was
higher in patients with the T-allele at nucleotide 194.
REFERENCES FOR EXAMPLE I
[0158] 1. Pisani, P., Parkin, D. M., Bray, F. & Ferlay, J.
"Estimates of the Worldwide Mortality from 25 Cancers in 1990",
Int. J. Cancer 83, 18-29 (1999).
[0159] 2. El-Serag, H. B. & Mason, A. C., "Rising Incidence of
Hepatocellular Carcinoma in the United States", N. Engl. J. Med.
340, 745-750 (1999).
[0160] 3. Taylor-Robinson, S. D., Foster, G. R., Arora, S.,
Hargreaves, S. & Thomas, H. C., "Increase in Primary Liver
Cancer in the UK, 1979-94", Lancet 350:1142-1143 (1997).
[0161] 4. Okuda, K., Fujimoto, I., Hanai, A. & Urano, Y.,
"Changing Incidence of Hepatocellular Carcinoma in Japan," Cancer
Res. 47, 4967-4972 (1987).
[0162] 5. Tang, Z. Y., "Hepatocellular Carcinoma", J.
Gastroenterol. Hepatol. 15 Suppl, G1-7 (2000).
[0163] 6. Ng, I. O., Lai, E. C., Fan, S. T., Ng, M. M. & So, M.
K., "Prognostic Significance of Pathologic Features of
Hepatocellular Carcinoma. A Multivariate Analysis of 278 Patients",
Cancer 76, 2443-2448 (1995).
[0164] 7. Ng, I. O., Poon, R. T., Shek, T. W. & Fan, S. T.,
"Clinicopathologic and Prognostic Significance of the Histologic
Activity of Noncancerous Liver Tissue in Hepatitis B
Virus-Associated Hepatocellular Carcinoma", Am. J. Clin. Pathol.
117, 411-418 (2002).
[0165] 8. Poon, R. T. et al., "Different Risk Factors and Prognosis
for Early and Late Intrahepatic Recurrence After Resection of
Hepatocellular Carcinoma", Cancer 89, 500-507 (2000).
[0166] 9. Poon, R. T. et al., "Improving Survival Results After
Resection of Hepatocellular Carcinoma: A Prospective Study of 377
Patients over 10 Years", Ann. Surg. 234, 63-70 (2001).
[0167] 10. Poon, R. T. et al., "Clinicopathologic Features of
Long-Term Survivors and Disease-Free Survivors After Resection of
Hepatocellular Carcinoma: A Study of a Prospective Cohort", J.
Clin. Oncol. 19, 3037-3044 (2001).
[0168] 11. Vauthey, J. N. et al., "Simplified Staging for
Hepatocellular Carcinoma", J. Clin. Oncol. 20, 1527-1536
(2002).
[0169] 12. Villa, E. et al., "Estrogen Receptor Classification for
Hepatocellular Carcinoma: Comparison With Clinical Staging
Systems", J. Clin. Oncol. 21, 441-446 (2003).
[0170] 13. Yeatman, T. J., "The Future of Cancer Management:
Translating the Genome, Transcriptome, and Proteome", Ann. Surg.
Oncol. 10, 7-14 (2003).
[0171] 14. Beer, D. G. et al., "Gene-Expression Profiles Predict
Survival of Patients with Lung Adenocarcinoma," Nat. Med. 8,
816-824 (2002).
[0172] 15. Dhanasekaran, S. M. et al., "Delineation of Prognostic
Biomarkers in Prostate Cancer", Nature 412, 822-826 (2001).
[0173] 16. Garber, M. E. et al., "Diversity of Gene Expression in
Adenocarcinoma of the Lung", Proc. Natl. Acad. Sci. USA 98,
13784-13789 (2001).
[0174] 17. Pomeroy, S. L. et al., "Prediction of Central Nervous
System Embryonal Tumour Outcome Based on Gene Expression", Nature
415, 436-442 (2002).
[0175] 18. Singh, D. et al., "Gene Expression Correlates of
Clinical Prostate Cancer Behavior", Cancer Cell 1, 203-209
(2002).
[0176] 19. Sorlie, T. et al., "Gene Expression Patterns of Breast
Carcinomas Distinguish Tumor Subclasses with Clinical
Implications", Proc. Natl. Acad. Sci. USA 98, 10869-10874
(2001).
[0177] 20. van de Vijver, M. J. et al., "A Gene-Expression
Signature as a Predictor of Survival in Breast Cancer", N. Engl. J.
Med. 347, 1999-2009 (2002).
[0178] 21. van't Veer, L. J. et al., "Gene Expression Profiling
Predicts Clinical Outcome of Breast Cancer", Nature 415, 530-536
(2002).
[0179] 22. Kawai, H. F., Kaneko, S., Honda, M., Shirota, Y. &
Kobayashi, K., "Alpha-Fetoprotein-Producing Hepatoma Cell Lines
Share Common Expression Profiles of Genes in Various Categories
Demonstrated by cDNA Microarray Analysis", Hepatology 33, 676-691
(2001).
[0180] 23. Lee, J. & Thorgeirsson, S. S., "Functional and
Genomic Implications of Global Gene Expression Profiles in Cell
Lines from Human Hepatocellular Cancer", Hepatology 35, 1134-1143
(2002).
[0181] 24. Okabe, H. et al., "Genome-Wide Analysis of Gene
Expression in Human Hepatocellular Carcinomas Using cDNA
Microarray: Identification of Genes Involved in Viral
Carcinogenesis and Tumor Progression", Cancer Res. 61, 2129-2137
(2001).
[0182] 25. Shirota, Y., Kaneko, S., Honda, M., Kawai, H. F. &
Kobayashi, K., "Identification of Differentially Expressed Genes in
Hepatocellular Carcinoma with cDNA Microarrays", Hepatology 33,
832-840 (2001).
[0183] 26. Xu, X. R. et al., "Insight into Hepatocellular
Carcinogenesis at Transcriptome Level by Comparing Gene Expression
Profiles of Hepatocellular Carcinoma with Those of Corresponding
Noncancerous Liver" Proc. Natl. Acad. Sci. USA 98, 15089-15094
(2001).
[0184] 27. Chen, X. et al., "Gene Expression Profiles in Human
Liver Cancers", Mol. Biol. Cell 13, 1929-1939 (2002).
[0185] 28. Cheung, S. T. et al., "Identify Metastasis-Associated
Genes in Hepatocellular Carcinoma Through Clonality Delineation for
Multi-Nodular Tumor", Cancer Res. 62, 4711-4721 (2002).
[0186] 29. Iizuka, N. et al., "Oligonucleotide Microarray for
Prediction of Early Intrahepatic Recurrence of Hepatocellular
Carcinoma After Curative Resection", Lancet 361, 923-929
(2003).
[0187] 30. Sobin, L. H. & Whitekind, C., "In TNM Classification
of Malignant Tumours," John Wiley, New York, 5.sup.th ed.
1997).
[0188] 31. Youden, W. J., "Index for Rating Diagnostic Tests",
Cancer 3, 32-35 (1950).
[0189] 32. Edmondson, H. A. & Steiner, P. E., "Primary
Carcinoma of the Liver: A Study of 100 Cases Among 48,900
Necropsies", Cancer 7, 462-503 (1954).
[0190] 33. Gonzalez-Mariscal, L., Betanzos, A., Nava, P. &
Jaramillo, B. E., "Tight Junction Proteins", Prog. Biophys. Mol.
Biol. 81, 1-44 (2003).
[0191] 34. Kastury, K. et al., "Complementary Deoxyribonucleic Acid
Cloning and Characterization of a Putative Human Axonemal Dynein
Light Chain Gene", J. Clin. Endocrinol. Metab. 82, 3047-3053
(1997).
[0192] 35. Schwartz, J. D., Schwartz, M., Mandeli, J. & Sung,
M., "Neoadjuvant and Adjuvant Therapy for Resectable Hepatocellular
Carcinoma: Review of the Randomised Clinical Trials", Lancet Oncol.
3, 593-603 (2002).
[0193] 36. Poon, R. T., Fan, S. T. & Wong, J., "Risk Factors,
Prevention, and Management of Postoperative Recurrence After
Resection of Hepatocellular Carcinoma", Ann. Surg. 232, 10-24
(2000).
[0194] 37. Muto, Y. et al., "Prevention of Second Primary Tumors by
an Acyclic Retinoid, Polyprenoic Acid, in Patients with
Hepatocellular Carcinoma", N. Engl. J. Med. 334, 1561-1567
(1996).
[0195] 38. Marill, J., Idres, N., Capron, C. C., Nguyen, E. &
Chabot, G. G., "Retinoic Acid Metabolism and Mechanism of Action: A
Review" Curr. Drug Metab. 4, 1-10 (2003).
[0196] 39. DeRisi, J. et al., "Use of a cDNA Microarray to Analyse
Gene Expression Patterns in Human Cancer", Nat. Genet. 14, 457-460
(1996).
[0197] 40. Perou, C. M. et al., "Molecular Portraits of Human
Breast Tumours", Nature 406, 747-752 (2000).
[0198] 41. Sherlock, G. et al., "The Stanford Microarray Database",
Nucleic Acids Res. 29, 152-155 (2001).
[0199] 42. Eisen, M. B., Spellman, P. T., Brown, P. O. &
Botstein, D., "Cluster Analysis and Display of Genome-Wide
Expression Patterns", Proc. Natl. Acad. Sci. USA 95, 14863-14868
(1998).
[0200] 43. Bustin, S. A., "Absolute Quantification of mRNA Using
Real-Time Reverse Transcription Polymerase Chain Reaction Assays",
J. Mol. Endocrinol. 25, 169-193 (2000).
[0201] 44. SAS Institute Inc., "In SAS Macro Language: Reference",
First Edition. SAS Institute Inc., 1st ed. 1997).
[0202] 45. Brazma, A. et al., "Minimum Information About a
Microarray Experiment (MIAME)-Toward Standards for Microarray
Data", Nat. Genet. 29, 365-371 (2001).
REFERENCES FOR EXAMPLE II
[0203] 1. Pisani, P., Parkin, D. M., Bray, F., and Ferlay, J.
Estimates of the worldwide mortality from 25 cancers in 1990. Int.
J. Cancer, 83:18-29, 1999.
[0204] 2. El-Serag, H. B., and Mason, A. C. Rising incidence of
hepatocellular carcinoma in the United States. N. Engl. J. Med.
340:745-750, 1999.
[0205] 3. Taylor-Robinson, S. D., Foster, G. R., Arora, S.,
Hargreaves, S., and Thomas, H. C. Increase in primary liver cancer
in the UK, 1979-94. Lancet, 350:1142-1143, 1997.
[0206] 4. Okuda, K., Fujimoto, I., Hanai, A., and Urano, Y.
Changing incidence of hepatocellular carcinoma in Japan. Cancer
Res., 47:4967-4972, 1987.
[0207] 5. Tang Z Y. Hepatocellular carcinoma. J. Gastroenterol.
Hepatol., 15 Suppl:G1-G7, 2000.
[0208] 6. Ng, I. O., Lai, E. C., Fan, S. T., Ng, M. M., and So, M.
K. Prognostic significance of pathologic features of hepatocellular
carcinoma. A multivariate analysis of 278 patients. Cancer,
76:2443-2448, 1995.
[0209] 7. Poon, R. T., Fan, S. T., Ng, I. O., Lo, C. M., Liu, C.
L., and Wong, J. Different risk factors and prognosis for early and
late intrahepatic recurrence after resection of hepatocellular
carcinoma. Cancer, 89:500-507, 2000.
[0210] 8. Poon, R. T., Ng, I. O., Fan, S. T., et al.
Clinicopathologic features of long-term survivors and disease-free
survivors after resection of hepatocellular carcinoma: a study of a
prospective cohort. J. Clin. Oncol., 19:3037-3044, 2001.
[0211] 9. Ng, I. O., Ng, M. M., Lai, E. C., and Fan, S. T.
Pathologic features and patient survival in hepatocellular
carcinoma in relation to age. J. Surg. Oncol., 61:134-137,
1996.
[0212] 10. Ng, I. O., Lai, E. C., Ng, M. M., and Fan, S. T. Tumor
encapsulation in hepatocellular carcinoma. A pathologic study of
189 cases. Cancer, 70:45-49, 1992.
[0213] 11. Fan, S. T. Methods and related drawbacks in the
estimation of surgical risks in cirrhotic patients undergoing
hepatectomy. Hepatogastroenterology, 49:17-20, 2002.
[0214] 12. Vauthey, J. N., Lauwers, G. Y., Esnaola, N. F., et al.
Simplified staging for hepatocellular carcinoma. J. Clin. Oncol.,
20:1527-1536, 2002.
[0215] 13. Villa, E., Colantoni, A., Camma, C., et al. Estrogen
receptor classification for hepatocellular carcinoma: comparison
with clinical staging systems. J. Clin. Oncol., 21:441-446,
2003.
[0216] 14. Chen, X., Cheung, S. T., So, S., et al. Gene expression
patterns in human liver cancers. Mol. Biol. Cell, 13:1929-1939,
2002.
[0217] 15. Simon R, Radmacher M D, Dobbin K, et al. Pitfalls in the
use of DNA microarray data for diagnostic and prognostic
classification. J Natl Cancer Inst 2003;95:14-18.
[0218] 16. Gonzalez-Mariscal, L., Betanzos, A., Nava, P., and
Jaramillo, B. E. Tight junction proteins. Prog. Biophys. Mol.
Biol., 81:1-44, 2003.
[0219] 17. Sugita M, Geraci M, Gao B, Powell R L, Hirsch F R,
Johnson G, Lapadat R, Gabrielson E, Bremnes R, Bunn P A, Franklin W
A. Combined use of oligonucleotide and tissue microarrays
identifies cancer/testis antigens as biomarkers in lung carcinoma.
Cancer Res 2002;62(14):3971-3979.
[0220] 18. Sobin, L. H., and Whitekind, C. TNM classification of
malignant tumours. 5th ed. New York, John Wiley, 1997.
[0221] 19. Poon, R. T., and Fan, S. T. Evaluation of the new
AJCC/UICC staging system for hepatocellular carcinoma after hepatic
resection in Chinese patients. Surg. Oncol. Clin. N. Am., 12:35-50,
2003.
[0222] 20. Cheung, S. T., Chen, X., Guan, X. Y., et al. Identify
metastasis-associated genes in hepatocellular carcinoma through
clonality delineation for multi-nodular tumor. Cancer Res.,
62:4711-4721, 2002.
[0223] 21. Sherlock, G., Hernandez-Boussard, T., Kasarskis, A., et
al. The Stanford Microarray Database. Nucleic Acids Res.,
29:152-155, 2001.
[0224] 22. Bustin, S. A. Absolute quantification of mRNA using
real-time reverse transcription polymerase chain reaction assays.
J. Mol. Endocrinol., 25:169-193, 2000.
[0225] 23. Youden, W. J. Index for rating diagnostic tests. Cancer,
3:32-35, 1950.
[0226] 24. Brazma, A., Hingamp, P., Quackenbush, J., et al. Minimum
information about a microarray experiment (MIAME)-toward standards
for microarray data. Nat. Genet., 29:365-371, 2001.
[0227] 25. Kawai, H. F., Kaneko, S., Honda, M., Shirota, Y., and
Kobayashi, K. Alpha-fetoprotein-producing hepatoma cell lines share
common expression profiles of genes in various categories
demonstrated by cDNA microarray analysis. Hepatology, 33:676-691,
2001.
[0228] 26. Lee, J., and Thorgeirsson, S. S. Functional and genomic
implications of global gene expression profiles in cell lines from
human hepatocellular cancer. Hepatology, 35:1134-1143, 2002.
[0229] 27. Neo S Y, Leow C K, Vega V B, Long P M, Islam A F, Lai P
B, Liu E T, et al. Identification of discriminators of hepatoma by
gene expression profiling using a minimal dataset approach.
Hepatology 2004;39:944-953.
[0230] 28. Okabe, H., Satoh, S., Kato, T., et al. Genome-wide
analysis of gene expression in human hepatocellular carcinomas
using cDNA microarray: identification of genes involved in viral
carcinogenesis and tumor progression. Cancer Res., 61:2129-2137,
2001.
[0231] 29. Shirota, Y., Kaneko, S., Honda, M., Kawai, H. F., and
Kobayashi, K. Identification of differentially expressed genes in
hepatocellular carcinoma with cDNA microarrays. Hepatology,
33:832-840, 2001.
[0232] 30. Xu, X. R., Huang, J., Xu, Z. G., et al. Insight into
hepatocellular carcinogenesis at transcriptome level by comparing
gene expression profiles of hepatocellular carcinoma with those of
corresponding noncancerous liver. Proc. Natl. Acad. Sci. U.S.A.,
98:15089-15094, 2001.
[0233] 31. Iizuka, N., Oka, M., Yamada-Okabe, H., et al.
Oligonucleotide microarray for prediction of early intrahepatic
recurrence of hepatocellular carcinoma after curative resection.
Lancet, 361:923-929, 2003.
[0234] 32. Amasheh, S., Meiri, N., Gitter, A. H., et al. Claudin-2
expression induces cation-selective channels in tight junctions of
epithelial cells. J. Cell Sci., 115:4969-4976, 2003.
[0235] 33. Tiwari-Woodruff, S. K., Buznikov, A. G., Vu, T. Q., et
al. OSP/claudin-11 forms a complex with a novel member of the
tetraspanin superfamily and betal integrin and regulates
proliferation and migration of oligodendrocytes. J. Cell Biol.,
153:295-305, 2001.
[0236] 34. Nichols, L. S., Ashfaq, R., and Iacobuzio-Donahue, C. A.
Claudin 4 protein expression in primary and metastatic pancreatic
cancer: support for use as a therapeutic target. Am. J. Clin.
Pathol., 121:226-230, 2004.
[0237] 35. Michl, P., Buchholz, M., Rolke, M., et al. Claudin-4: a
new target for pancreatic cancer treatment using Clostridium
perfringens enterotoxin. Gastroenterology, 121:678-684, 2001.
[0238] 36. Miwa, N., Furuse, M., Tsukita, S., Niikawa, N.,
Nakamura, Y., Furukawa, Y. Involvement of claudin-1 in the
beta-catenin/Tcf signaling pathway and its frequent upregulation in
human colorectal cancers. Oncol. Res. 12:469-476, 2000.
[0239] 37. Rangel, L. B., Agarwal, R., D'Souza, T., et al. Tight
junction proteins claudin-3 and claudin-4 are frequently
overexpressed in ovarian cancer but not in ovarian cystadenomas.
Clin. Cancer Res., 9:2567-2575, 2003.
[0240] 38. Michl, P., Barth, C., Buchholz, M., et al. Claudin-4
expression decreases invasiveness and metastatic potential of
pancreatic cancer. Cancer Res., 63:6265-6271, 2003.
[0241] 39. Al Moustafa, A. E., Alaoui-Jamali, M. A., Batist, G., et
al. Identification of genes associated with head and neck
carcinogenesis by cDNA microarray comparison between matched
primary normal epithelial and squamous carcinoma cells. Oncogene,
21:2634-2640, 2002.
[0242] 40. Kominsky, S. L., Argani, P., Korz, D., et al. Loss of
the tight junction protein claudin-7 correlates with histological
grade in both ductal carcinoma in situ and invasive ductal
carcinoma of the breast. Oncogene, 22:2021-2033, 2003.
[0243] 41. Kramer, F., White, K., Kubbies, M., Swisshelm, K., and
Weber, B. H. Genomic organization of claudin-1 and its assessment
in hereditary and sporadic breast cancer. Hum. Genet., 107:249-256,
2000.
[0244] 42. Furuta, T., Kanematsu, T., Matsumata, T., et al.
Clinicopathologic features of hepatocellular carcinoma in young
patients. Cancer 66: 2395-2398, 1990.
[0245] 43. Vauthey, J. N., Klimstra, D., Franceschi, D., et al.
Factors affecting long-term outcome after hepatic resection for
hepatocellular carcinoma. Am. J. Surg., 169:28-34, 1995.
REFERENCES FOR EXAMPLE III
[0246] 1. Pisani P, Parkin D M, Bray F, and Ferlay J (1999).
Estimates of the worldwide mortality from 25 cancers in 1990. Int J
Cancer 83, 18-29.
[0247] 2. Bruix J, Boix L, Sala M, and Llovet J M. Focus on
hepatocellular carcinoma (2004). Cancer Cell 5, 215-219.
[0248] 3. Fan S T, Lo C M, Liu C L, Lam C M, Yuen W K, Yeung C, and
Wong J (1999). Hepatectomy for hepatocellular carcinoma: toward
zero hospital deaths. Ann Surg 229, 322-330.
[0249] 4. Fong Y, Sun R L, Jarnagin W, and Blumgart L H (1999). An
analysis of 412 cases of hepatocellular carcinoma at a Western
center. Ann Surg 229, 790-799.
[0250] 5. Neuhaus P, Jonas S, and Bechstein W O (2000). Hepatoma of
the liver--resection or transplantation? Langenbecks Arch Surg 385,
171-178.
[0251] 6. Poon R T, Fan S T, Lo C M, Ng I O, Liu C L, Lam C M, and
Wong J (2001). Improving survival results after resection of
hepatocellular carcinoma: a prospective study of 377 patients over
10 years. Ann Surg 234, 63-70.
[0252] 7. Nagasue N, Kohno H, Chang Y C, Taniura H, Yamanoi A,
Uchida M, Kimoto T, Takemoto Y, Nakamura T, and Yukaya H (1993).
Liver resection for hepatocellular carcinoma. Results of 229
consecutive patients during 11 years. Ann Surg 217, 375-384.
[0253] 8. Lise M, Bacchetti S, Da Pian P, Nitti D, Pilati P L, and
Pigato P (1998). Prognostic factors affecting long term outcome
after liver resection for hepatocellular carcinoma: results in a
series of 100 Italian patients. Cancer 82, 1028-1036.
[0254] 9. Vauthey J N, Klimstra D, Franceschi D, Tao Y, Fortner J,
Blumgart L, and Brennan M (1995). Factors affecting long-term
outcome after hepatic resection for hepatocellular carcinoma. Am J
Surg 169, 28-35.
[0255] 10. Chen X, Cheung S T, So S, Fan S T, Barry C, Higgins J,
Lai K M, Ji J, Dudoit S, Ng I O, et al. (2002). Gene expression
patterns in human liver cancers. Mol Biol Cell 13, 1929-1939.
[0256] 11. Simon R, Radmacher M D, Dobbin K, and McShane L M
(2003). Pitfalls in the use of DNA microarray data for diagnostic
and prognostic classification. J Natl Cancer Inst 95, 14-18.
[0257] 12. Sobin L H, and Whitekind C (1997). TNM classification of
malignant tumours. 5th ed. John Wiley, New York, USA.
[0258] 13. Poon R T, and Fan S T (2003). Evaluation of the new
AJCC/UICC staging system for hepatocellular carcinoma after hepatic
resection in Chinese patients. Surg Oncol Clin N Am 12, 35-50.
[0259] 14. Cheung S T, Chen X, Guan X Y, Wong S Y, Tai L S, Ng I O,
So S, and Fan S T (2002). Identify metastasis-associated genes in
hepatocellular carcinoma through clonality delineation for
multi-nodular tumor. Cancer Res 62, 4711-4721.
[0260] 15. Sherlock G, Hernandez-Boussard T, Kasarskis A, Binkley
G, Matese J C, Dwight S S, Kaloper M, Weng S, Jin H, Ball C A, et
al. (2001). The Stanford Microarray Database. Nucleic Acids Res 29,
152-155.
[0261] 16. Bustin S A (2000). Absolute quantification of mRNA using
real-time reverse transcription polymerase chain reaction assays. J
Mol Endocrinol 25, 169-193.
[0262] 17. Pepe M S, Janes H. Longton G, Leisenring W, and Newcomb
P (2004). Limitations of the odds ratio in gauging the performance
of a diagnostic, prognostic, or screening marker. Am J Epidemiol
159, 882-890.
[0263] 18. Youden W J (1950). Index for rating diagnostic tests.
Cancer 3, 32-35.
[0264] 19. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman
P, Stoeckert C, Aach J, Ansorge W, Ball C A, Causton H C, et al.
(2001). Minimum information about a microarray experiment
(MIAME)-toward standards for microarray data. Nat Genet 29,
365-371.
[0265] 20. Kawai H F, Kaneko S, Honda M, Shirota Y, and Kobayashi K
(2001). Alpha-fetoprotein-producing hepatoma cell lines share
common expression profiles of genes in various categories
demonstrated by cDNA microarray analysis. Hepatology 33,
676-691.
[0266] 21. Lee J, and Thorgeirsson S S (2002). Functional and
genomic implications of global gene expression profiles in cell
lines from human hepatocellular cancer. Hepatology 35,
1134-1143.
[0267] 22. Okabe H, Satoh S, Kato T, Kitahara O, Yanagawa R,
Yamaoka Y, Tsunoda T, Furukawa Y, and Nakamura Y (2001).
Genome-wide analysis of gene expression in human hepatocellular
carcinomas using cDNA microarray: identification of genes involved
in viral carcinogenesis and tumor progression. Cancer Res 61,
2129-2137.
[0268] 23. Shirota Y, Kaneko S, Honda M, Kawai H F, and Kobayashi K
(2001). Identification of differentially expressed genes in
hepatocellular carcinoma with cDNA microarrays. Hepatology 33,
832-840.
[0269] 24. Xu X R, Huang J, Xu Z G, Qian B Z, Zhu Z D, Yan Q, Cai
T, Zhang X, Xiao H S, Qu J, et al. (2001). Insight into
hepatocellular carcinogenesis at transcriptome level by comparing
gene expression profiles of hepatocellular carcinoma with those of
corresponding noncancerous liver. Proc Natl Acad Sci USA 98,
15089-15094.
[0270] 25. Neo S Y, Leow C K, Vega V B, Long P M, Islam A F, Lai P
B, Liu E T, and Ren E C (2004). Identification of discriminators of
hepatoma by gene expression profiling using a minimal dataset
approach. Hepatology 39, 944-953.
[0271] 26. Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y,
Mori N, Takao T, Tamesa T, Tangoku A, Tabuchi H, et al. (2003).
Oligonucleotide microarray for prediction of early intrahepatic
recurrence of hepatocellular carcinoma after curative resection.
Lancet 361, 923-929.
[0272] 27. Collins F S, Green E D, Guttmacher A E, Guyer M S; and
US National Human Genome Research (2003). A vision for the future
of genomics research. Nature 422, 835-847.
[0273] 28. Mattick J S (2003). The human genome and the future of
medicine. Med J Aust 179, 212-216.
[0274] 29. Ayoubi P, Jin X, Leite S, Liu X, Martajaja J, Abduraham
A, Wan Q, Yan W, Misawa E, Prade R A (2002). PipeOnline 2.0:
automated EST processing and functional data sorting. Nucleic Acids
Res 30, 4761-4769.
Sequence CWU 1
1
6 1 19 DNA Artificial Sequence PCR primer 1 ctgtggaagg cgtgcgtta 19
2 20 DNA Artificial Sequence PCR primer 2 caaagaagcc caggctgaca 20
3 15 DNA Artificial Sequence PCR primer 3 cctccatgct ggcgc 15 4 19
DNA Artificial Sequence PCR primer 4 acccacacac agcgctcac 19 5 22
DNA Artificial Sequence PCR primer 5 caagccgtaa aacttctgca tg 22 6
25 DNA Artificial Sequence PCR primer 6 agtcactctc agcggccatc gccca
25
* * * * *
References