# Patent

## Title
Diagnostic methods for colorectal cancer

## Abstract

The present application discloses a diagnostic method and a kit for prognosis assessment of colorectal cancer (CRC) and a novel tumor suppressor gene to be used for diagnosis of colorectal cancer (CRC), the method comprising the steps of: (a) identifying recurrently altered regions (RAR) on a chromosome; and (b) detecting genomic alterations in the RAR. The present method makes it possible to perform early diagnosis as well as prognosis assessment for various cancers and tumors including colorectal cancer (CRC).

## Background
RELATED APPLICATIONS/PATENTS & INCORPORATION BY REFERENCE
The present application claims the benefit of Korean Patent Application No. 10-2006-0061741 filed Jul. 3, 2006 and U.S. Provisional Application No. 60/864,413 filed Nov. 6, 2006, the entire contents of which are hereby incorporated by reference.


Also, documents or references are cited in this text, either in a Reference List before the claims, or in the text itself; and, each of these documents or references (“herein cited references”), as well as each document or reference cited in each of the herein cited references (including any manufacturer's specifications, instructions, etc.), is hereby expressly incorporated herein by reference.
BACKGROUND
1. Technical Field
The present invention relates to novel diagnostic methods and kits for colorectal cancer (CRC). More specifically, the present invention relates to diagnostic methods, comprising the steps of; a) identifying recurrently altered regions (RAR) on chromosome; and (b) detecting genomic alterations in RAR.
Also the present invention relates to kits for prognosis assessment of colorectal cancer (CRC) and novel tumor suppressor genes for diagnosis of colorectal cancer (CRC).
2. Background Art
Colorectal cancer (CRC) accounted for about 1 million new cases in 2002 worldwide (9.4% of the world total). In terms of incidence, CRC ranks fourth in men and third in women. Mortality is about one half of incidence (about 529,000 deaths in 2002), while prevalence is second only to that of breast cancer worldwide, with an estimated 2.8 million persons alive with CRC diagnosed within 5 years of diagnosis. There is at least a 25-fold variation in occurrence of CRC worldwide. The incidence rates are highest in developed countries, while they tend to be low in Africa and Asia. In Korea, CRC cancer became the fourth leading cause of cancer death in 2004 and the age-standardized incidence rates of CRC in both sexes are higher than world average rates. These geographic differences are probably due to genetic background as well as environmental factors since CRC is one of multifactorial diseases; environmental and genetic factors interact and may work synergistically to develop a disease.
It is known that multiple mutations accumulate during the pathogenesis of CRC. Two major forms of genetic instability in CRC have been classified as either microsatellite instability (MIN) or chromosomal instability (CIN). In about 13% of CRC, mismatch repair deficiency leads to MIN, whereas in the remaining 87%, CIN appears to result in gains and losses of genetic materials. So, characterization of CIN may help to identify potential oncogenes and/or tumor suppressor genes and furthermore elucidate the pathogenesis of CRC.
To characterize CIN, conventional comparative genomic hybridization (CGH) has been used to identify multiple chromosomal imbalances in a sample from a single experiment. However, resolution of the conventional CGH is insufficient for precise identification of sub-microscopic changes. As accumulated evidence suggests that changes in genomic dosage contribute to tumorigenesis by altering the expression levels of cancer-related genes, more detailed analyses with high resolution are necessary.
There is thus a need for an improved diagnostic method.
The information disclosed in this Background section is only for enhancement of understanding of the background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art that is already known to a person skilled in the art.
SUMMARY OF THE INVENTION
The present invention has been made in an effort to provide a new diagnostic method and a kit for prognostic assessment of colorectal cancer. The present invention has been made based, at least in part, on the following discovery. To see genomic alterations and their clinicopatholigical implications in CRC, the present inventor applied genome-wide array CGH to the genomic DNAs extracted from microdissected tissues of 59 colorectal cancer cases. Using this strategy, various genomic copy number changes related to CRC including novel recurrently altered regions (RAR) were identified and associations between genetic alterations detected by array CGH and clinicopathological variables were examined.
As a result, twenty-seven RARs were identified in CRC and RAR-L1 and RAR-L20 found to be independent indicators of poor prognosis. Expression of CAMTA1, located in RAR-L1, was frequently reduced in CRCs and low CAMTA1 expression was significantly associated with poor prognosis, which indicates CAMTA1 plays as a tumor suppressor in CRC.
The present invention is broadly directed to a method for prognosis assessment of colorectal cancer (CRC) by identifying recurrently altered genomic regions (RAR) in colorectal cancer with high resolution (one Mb-resolution) microarray based comparative genomic hybridization (array CGH), and using the specific recurrently altered genomic regions in colorectal cancer as a prognostic marker for colorectal cancer progress.
In one aspect, the present invention provides a diagnostic method for prognostic assessment of colorectal cancer, comprising the steps of: (a) obtaining a nucleic acid sample from a subject; (b) identifying recurrently altered regions (RAR) on chromosome by array CGH; (C) detecting variation of expression of a specific gene in the RAR; and (d) performing prognostic assessment based on the detected variation.
In a preferred embodiment, the RAR in the step (b) may be one or more region selected from group consisting of RAR-L1 (loss of chromosome 1p36) and RAR-L20 (loss of chromosome 21q22). Preferably, the specific gene in the step (c) may be a cancer suppressor gene located in the RAR. More preferably, the cancer suppressor gene may be CAMTA1. When reduced gene expression level of CAMTA1 is detected, it may be assessed as poor prognosis.
In another aspect, the present invention provides a diagnostic kit for prognostic assessment of colorectal cancer, which comprises: (a) an array CGH instrument for identifying recurrently altered regions (RAR) on a chromosome; and (b) an image analysis device for detecting variation of expression of a specific gene in the RAR. The kit may further include a container for holding the instrument and device.
In still another aspect, the present invention provides a use of cancer suppressor gene CAMTA1 for prognostic assessment of colorectal cancer.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 shows an analysis result for genome of patients with colorectal cancer. A: Genome-wide profiles of patients with colorectal cancer, B: Frequencies of all significant gains and losses on chromosome
FIG. 2 shows an analysis result for verification of array-CGH copy number profiles. A: normal tissue DNA, B: tumor tissue DNA of CCRC80, C: tumor versus normal peak ratio plot
FIG. 3 shows an analysis result for examples of recurrently altered regions (RAR) and survival curves. A: Stage, B: RAR-L1 on 1p36, C: RAR-L4 on 1p31, D: RAR-L20 on 21q22, E: Graph of RAR-L1 (loss of chromosome 1p36), F: Graph of RAR-L20 (loss of chromosome 21q22).
FIG. 4 shows an analysis result for expression profiles of cancer suppressor genes. A: Plots of tumor/normal intensity ratios, B: Kaplan-Meier survival curves, C: Examples of missense mutation (SEQ ID NOS 35 and 36, respectively, in order of appearance).



DETAILED DESCRIPTION
Hereinafter, the present invention will be described in further detail with reference to examples. It is to be understood, however, that these examples are for illustrative purposes only and are not to be construed to limit the scope of the present invention.
Example 1
General Characteristics of Genomic Alterations in Colorectal Cancer
(1) Collection of Tissue Samples from Patients with Colorectal Cancer
Surgical specimens from 59 CRC patients, who underwent surgical resection during 1995 and 1997 at Dankook University Hospital, Cheonan, Korea, were examined in this invention. This examination was performed under the approval of Institutional Review Boards of Kangnam St. Mary's Hospital, The Catholic University of Korea, Korea. After surgical resection, tumor and adjacent normal tissues from each patient were collected separately and snap frozen in deep freezer. Frozen sections were prepared of 10 μm thickness on a gelatin coated slide using cryotom (Reighert-Jung, Germany). After H&E staining of frozen section, tumor cell rich area (more than 60% of tumor cells) and normal cell area were selected under the microscope and dissected manually. Microdissected tissues were transferred into the cell lysis buffer (1% proteinase-K in TE buffer) and genomic DNA was extracted by incubating at 50° C. for 12 hours. DNA from normal tissue was used as reference for array CGH. Extracted DNA was purified using a DNA purification Kit (Solgent, Daejon, Korea) and quantified using NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Delaware USA). Histopathological review of the tumors was performed by experienced pathologist according to the standard TNM classification in the American Joint Committee on Cancer guidelines.
(2) Array Comparative Genomic Hybridization and Data Processing
We used human large-insert clone arrays with 1 Mb resolution across the whole genome printed by the Sanger Institute Microarray Facility (Fiegler et al, Genes Chromosomes Cancer 2003; 36:361-374; Kim T M et al, Clin Cancer Res 2005; 11:8235-8242.) Details of DNA labeling, pre-hybridization, hybridization, and post-hybridization processes are described below. Genomic DNA from cancer tissue was labeled with Cy3-dCTP and DNA from normal tissue of the same patient was labeled with Cy5-dCTP. Open-well hybridization was done as described previously. Arrays were scanned using GenePix 4100A scanner (Axon Instruments, USA) and the image was processed using GenePix Pro 6.0. Normalization and re-aligning of raw array CGH data were performed using the web-based array CGH analysis interface, ArrayCyGHt (URL: genomics.catholic.ac.kr/arrayCGH/). In brief, we used print-tip loess normalization method for analysis. Mapping of large insert clones was done according to the genomic location in the Ensembl and UCSC genome browser. In sum, 2,981 BAC clones out of initial 3,014 clones were processed. Information of whole clone set is available in the Ensembl human genome browser (URL: ensembl.org/Homo_sapiens/index.html).
(3) Data Analysis for Chromosomal Alterations
To set the cutoff value for chromosomal alterations of individual clones, we performed four independent series of normal hybridization (three self to self and one male to female hybridizations) as controls. Based on the control hybridizations, the cutoff value for copy number aberration was set to above or below 3-fold of standard deviation at individual data point. Regional copy number change was defined as DNA copy number alterations stretching across more than 2 consecutive BAC clones, but not across entire chromosomes. High-level amplification of clones was defined when their intensity ratios were higher than 1.0 in log 2 scale, and vice versa for homozygous deletion. The boundary of the copy number changes was assigned to the halfway between two neighboring clones. RAR was defined as regional copy number changes, which appear in at least 10 tumor samples.
(4) The Data of Genomic Alterations
The clinicopathological data of all 59 patients are summarized in Table 1. There were 39 men and 20 women and the mean patient age at the time of surgery was 58.7 years (range from 23 to 81). Among the 59 cases, 41 patients (69.5%) had rectosigmoid cancer. Thirty six cancers (61.0%) were categorized as early stage tumors. At the end of the follow up, 23 patients were dead.







TABLE 1







General characteristics of study subjects










Number of patients
59 (100%)







male
39 (66.1%)



female
20 (33.9%)







Age group










Male
59.2



Female
57.8



<60
31 (52.5%)



>=60
28 (47.5%)







Stage










Early (Satge I and II)
36 (61.0%)



Advanced (Stage III and IV)
23 (39.0%)







Tumor site










Rectosigmoid
41 (69.5%)



Other sites
18 (30.5%)







Note:



other sites denote ascending, transverse, descending colon and cecum






The overall genomic alterations detected in 59 colorectal cancers are illustrated in FIG. 1A. The frequency plot of the chromosomal changes shows that they are not randomly distributed, but clustered in several hot regions across the whole genome (FIG. 1B). The array CGH signal intensity ratio (log 2 scale) data of the 59 cases can be downloaded from our website (URL: lib.cuk.ac.kr/micro/CGH/colon.htm).
The mean number of altered clones per case was 764.8 out of total 2,981 clones (range from 58 to 1,540). The mean numbers of altered clones are significantly higher in males (832.6 vs. 632.6, p=0.04), advanced stage group (897.7 vs. 679.9, p=0.03), and rectosigmoid cancer (826.7 vs. 623.8, p=0.03). The most frequent changes of entire chromosomal arms were gains of 13q (31/59, 52.5%), 20q (30/59, 50.8%), 20p (23/59, 40.0%), 7p (21/59, 35.6%), 8q (20/59, 33.9%) as well as losses of 18q (29/59, 49.2%), 18p (27/59, 45.8%) and 17p (26/59, 44.1%).
Example 2
Verification of Copy Number Alterations
To verify the copy number changes identified, multiplex ligation-dependent probe amplification (MLPA) analysis was performed using MLPA-Aneuploidy test kit P095 (MRC Holland, Amsterdam, Netherlands) as described below. Briefly, genomic DNA (250 ng) was denatured for 10 minutes at 98° C. and 3 μl of probe-mix including buffer was added. Then the mixture was heated at 95° C. for 1 minute and incubated at 60° C. for 16 hours. Ligation reaction was performed using a heat stable ligase-65 enzyme at 54° C. for 15 minutes. Ten μl of ligation reaction was mixed with 40 μl of PCR reaction mix containing universal primers. One primer is unlabelled and the other is labeled with FAM [N-(3-fluoranthyl)maleimide]. The thermal cycling was as follows: 1 minute at 95° C. followed by 35 cycles of 30 sec at 95° C., 30 sec at 60° C., and 60 sec at 72° C. Analysis of the amplified fragments was performed using ABI PRISM 3730 XL DNA Analyzer (Applied Biosystems, Foster City, USA) with ROX-500 (ROX-500 Genescan, ABI, USA) as a size standard. The peak area of the PCR products was determined by Genotyper software (Applied Biosystems, Foster City, USA) and data analysis was performed using a simplified analysis method from Coffalyser macro (URL: mlpa.com).
To verify the copy number changes identified by array-CGH, we performed MLPA analysis with 13 primary CRCs showing copy number aberrations. Copy number alterations identified by array-CGH were generally consistent with MLPA results. FIG. 2 illustrates example of MLPA validation results. Twelve peaks (numbered at each peak) are the examples of copy number alterations on chromosome 13, 18, 21, and X.
Example 3
Recurrently Altered Regions
In addition to the entire chromosomal changes, a lot of regional copy number changes were identified. Among those regional changes, we defined the chromosomal region recurrently altered in at least 10 cases as RAR. In sum, 7 RAR gains (RAR-G) and 20 RAR losses (RAR-L) were detected. Table 2 lists the map position, size and cancer-related genes located in 27 RARs. Five RARs were detected in more than 40% of cases; RAR-G4 (28/59, 47.5%), RAR-L2 (27/59, 45.8%), RAR-L5 (25/59, 42.4%), RAR-L14 (28/59, 47.5%), and RAR-L17 (28/59, 47.5%) (Table 2).







TABLE 2







Recurrent genetic alteration regions in 59 colorectal cancers
















Boundary
Size

Putative cancer


Alterations
BAC clone ID
Cytoband
(Mb)
(Mb)
Frequency
related genes

















Gain
RAR-G1
RP11-440P5-RP11-373L24
2p16.1-p15 
59.90-61.92
2.01
15/59
BCL11A, REL



RAR-G2
RP11-163H6-RP11-4S4D1S
 3q26.2-q26.32
172.14-178.64
6.49
12/59
PLD1, ECT2,



RAR-G3
RP11-196O16-RP11-486P11
7p21.1
15.35-20.32
4.97
21/59
AGR2, TWIST1



RAR-G4
RP11-495D4-RP11-17E16
 8q24.13-q24.21
126.22-131.11
4.88
28/59
MYC



RAR-G5
RP11-121C18-RP11-34N19
11p15.1-p14.3
20.97-23.47
2.49
11/59




RAR-G6
RP11-31I23-RP1-68D18
11p13
34.47-35.49
1.01
19/59
CD44



RAR-G7
RP3-404F18-RP3-394F12
 Xq24-q25
117.92-125.02
7.10
21/59
BIRC4


Loss
RAR-L1
RP3-438L4-RP11-338N10
 1p36.31-p36.23
6.52-8.43
1.90
15/59
CAMTA1



RAR-L2
RP11-428D12-RP1-86A18
  1p33-p32.3
48.50-51.17
2.66
27/59
FAF1



RAR-L3
RP5-944F13-RP11-175G14
1p31.1
69.56-72.27
2.70
15/59
CTH, PTGER3



RAR-L4
RP5-963M5-RP4-739M21
1p31.1
76.26-77.47
1.20
23/59




RAR-L5
RP11-22A3-RP11-446J8
 4p15.33-p15.32
12.05-17.17
5.11
25/59




RAR-L6
RP11-100N21-RP11-415L23
4p12
46.67-48.32
1.65
19/59
TEC



RAR-L7
RP11-87F15-RP11-347K3
 4q34.1-q26.33
177.12-189.92
12.79
10/59
CLDN22, IRF2,









ING2, CASP3



RAR-L8
CTD-2011L22-RP11-20O13
5q14.3-q15 
91.52-93.36
1.83
12/59




RAR-L9
RP11-391B7-CTC-279E3
5q33.3-q34 
157.36-160.78
3.41
12/59




RAR-L10
RP3-365E2-RP1-13D10
  6p23-p22.3
13.97-17.02
3.04
19/59




RAR-L11
RP11-338B22-RP11-16H11
 8p23.3-p23.2
0.46-4.49
4.02
23/59




RAR-L12
RP11-325D15-RP11-619F23
10q22.2-q22.3
77.33-79.26
1.91
13/59




RAR-L13
RP11-381K7-RP11-426E5
10q25.2
 112.7-114.46
1.75
12/59
ACSL5



RAR-L14
RP11-164H13-RP11-76E12
14q32.13-q32.2 
95.15-97.53
2.40
28/59




RAR-L15
RP11-353B9-RP11-105D1
15q21.1-q21.2
47.35-49.35
1.99
17/59




RAR-L16
RP11-231A23-RP11-24N10
 15q22.2-q22.31
57.44-61.74
4.29
21/59
ANXA2, RORA



RAR-L17
RP11-401O9-RP11-219A15
17p13.1-p11.2
 9.83-17.02
7.18
28/59
SCO1



RAR-L18
RP5-836L9-RP11-121A13
17p11.2
19.88-22.24
0.35
23/59




RAR-L19
RP1-270M7-RP1-152M24
21q11.2-q21.1
15.21-16.83
1.61
18/59




RAR-L20
RP11-98O13-RP5-1031P17
21q22.13-q22.2 
37.50-40.67
3.16
13/59





Note:


The frequency represents the number of samples with the corresponding genomic change out of 59 colorectal cancers.






Several cancer-related genes are included in the RARs. For example, known oncogenes such as MYC and REL as well as putative oncogens such as BLC11A, PLD1, ECT2, AGR2, TWIST1, and BIRC4 are included in the RAR-Gs. Also, a number of known or putative tumor suppressor genes such as CAMTA1, FAF1, CTH, PTGER3, TEC, CLDN22, ING2, IRF2, ACSL5, ANXA2, RORA, and SCO1 are located in the RAR-Ls.
Example 4
High Copy Number Changes
All high-level amplifications and homozygous deletions along with the putative cancer-related genes located in them are summarized in Table 3.







TABLE 3







Genomic segments representing high copy number changes in 59 colorectal cancers
















Boundary
Size
Observed
Putative cancer-


Change
BAC clone ID
Cytoband
(Mb)
(Mb)
cases a
related genes
















Amp
RP11-449G3-RP4-725G10
 7p12.1-p11.2
53.47-56.26
2.78
CCRC93
EGFR



RP4-550A13-RP11-506M12
7q22.1
97.86-99.59
1.72
CCRC29
MCM7



RP11-90J7-RP11-20E23
 10q22.3-q23.1
79.26-83.53
4.27
CCRC37




RP5-1096D14-RP11-319E16
 12p13.33-p13.31
1.43-5.57
4.13
CCRC33
CCND2, FGF6, FGF23,








AKAP3



RP11-129M14-RP11-332E3
13q21.31-q22.2
64.90-75.98
11.07
CCRC59
KLF5



RP11-564N10-RP11-255P5
13q33.1
100.86-102.84
1.97
CCRC72
FGF14



RP11-265C7-RP11-245B11
13q34
112.49-113.85
1.35
CCRC19
CUL4A, TFDP1



RP11-390P24-RP11-94L15
  17q12-q21.2
34.71-35.45
0.73
CCRC17,
PPARBP, PPP1R1B,







81
STARD3, TCAP, PNMT,








ERBB2, GRB7, CDC6,








RARA



RP11-13L22-RP11-28F1
18q21.33
57.72-59.73
2
CCRC80




RP3-324O17-RP4-633O20
 20q11.21-q11.23
28.92-36.34
7.41
CCRC12,
ID1, BCL2L1, HCK,







90, 93
TPX2, MYLK2, PLAGL2,








TGIF2, SRC



RP5-1028D15-RP4-719C8
 20q13.12-q13.33
41.66-58.31
16.64
CCRC 11,
MYBL2, RAB22A







43, 72, 90,








93



HD
RP11-350K6-RP11-520K18
 18q21.31-q21.33
54.78-57.70
2.91
CCRC73
PMAIP1/NOXA



RP11-25L3-RPU-396D4
18q22.3-q23 
69.26-71.83
2.56
CCRC73
—





Note:


Amp, amplification; HD, homozygous deletion.


a In case of more than two cases observed, the boundary of high copy number change was defined as the most extended set of clones, so they were not necessarily overlapping.






In sum, 11 genomic segments of high-level amplifications and 2 homozygous deletions were identified at least in one case. Although, most high copy number changes were identified in single case, amplifications on 17q12, 20q11 and 20q13 were observed in more than two cases. There are known oncogenes such as EGFR, CCND2, ERBB2, and MYBL2 in the amplified regions. Also, there are several putative cancer-related genes in the high copy number change regions (Table 3).
Example 5
Correlation Between Genomic Alterations
Pairwise correlation analysis between the RARs was done to investigate the significant co-occurrence of them. For comparison, all possible pairs of RARs located on different chromosomal arms were considered. Five pairs of RARs were found to be significantly correlated to each other after adjusting for multiple testing. The RAR-L5 on 4p15 correlates with the RAR-L2 on 1p33 (r=0.66; padj=0.0001) and the RAR-G7 on Xq24 (r=0.51; padj=0.042). The RAR-L17 on 17p13 correlates with the RAR-L5 on 4p15 (r=0.56; padj=0.0073) and the RAR-L14 on 14q32 (r=0.59; padj=0.0022). The RAR-L6 on 4p12 correlates with the RAR-L2 on 1p33 (r=0.53, padj=0.02).
We further investigated whether significantly correlated RARs share functionally related genes using public gene database, Gene Ontology (GO). We selected genes that have the same functional annotations (e.g. signal transduction) but are separately located on two correlated RARs. Three RAR pairs were found to share functionally related genes across 12 annotations.







TABLE 4







Functionally related genes shared by significantly co-occurred RARs













Pathway
RefSeq
Symbol
RAR-G7
RAR-L5
RAR-L17
RAR-L14


















0.0428















adjusted p-value


0.0073


















0.0022













Receptor activity
NM_000623
BDKRB2



1



NM_000676
ADORA2B


1




NM_000710
BDKRB1



1



NM_001775
CD38

1





NM_006667
PGRMC1
1






NM_012452
TNFRSF13B


1



Regulation of transcription, DNA-dependent
NM_001189
BAPX1

1





NM_006777
ZBTB33
1






NM_017544
NKRF
1






NM_020653
ZNF287


1




NM_020787
ZNF624


1




NM_032498
PEPP-2
1






NM_144680
ZNF18


1




NM_153604
MYOCD


1



Transcription factor activity
NM_001189
BAPX1

1





NM_006470
TRIM16


1




NM_020653
ZNF287


1




NM_032498
PEPP-2
1






NM_144680
ZNF18


1



Sensory perception
NM_000623
BDKRB2



1



NM_006017
PROM1

1





NM_016113
TRPV2


1



Signal transduction
NM_000676
ADORA2B


1




NM_000710
BDKRB1



1



NM_001775
CD38

1





NM_003010
MAP2K4


1




NM_005130
FGFBP1

1





NM_016084
RASD1


1



Development
NM_001290
LDB2

1





NM_004334
BST1

1





NM_006978
RNF113A
1





ATP binding
NM_002470
MYH3


1




NM_002472
MYH8


1




NM_003010
MAP2K4


1




NM_003384
VRK1



1



NM_003802
MYH13


1




NM_005963
MYH1


1




NM_017533
MYH4


1




NM_017534
MYH2


1



Generation of precursor metabolites and energy
NM_001775
CD38

1





NM_004541
NDUFA1
1





Hydrolase activity
NM_001775
CD38

1





NM_004278
PIGL


1




NM_004334
BST1

1




Protein amino acid phosphorylation
NM_003010
MAP2K4


1




NM_003384
VRK1



1


G-protein coupled receptor protein signaling pathway
NM_000623
BDKRB2



1



NM_000676
ADORA2B


1




NM_000710
BDKRB1



1



NM_016084
RASD1


1



Transcription
NM_020653
ZNF287


1




NM_032632
PAPOLA



1









Example 6
Differential Distribution of Genetic Alterations According to Clinicopathologic Parameters
Four types of clinical variables (age, stage, sex, tumor site) were analysed for their associations with the genomic alterations identified. The RAR-G7, RAR-L11, RAR-L12, RAR-L13, RAR-L16, RAR-L17, RAR-L18, gains of 8q, 19p, X, loss of 14q, 15q, Xq, and Y were associated with sex. The RAR-G3, RAR-L1, RAR-L2, RAR-L5, RAR-L6, RAR-L20, loss of 1p, and 4p were found to be associated with advanced tumor stage. The RAR-G7, RAR-L4, RAR-L9, RAR-L11, RAR-L12, gains of 13q, 20p, 20q, losses of 18p, and 18q were associated with rectosigmoid tumor site.
Example 7
Survival Analysis with Genomic Alterations
Survival analysis was performed to assess the prognostic values of the clinicopathological parameters and the RARs. In univariate analysis, advanced stage (p=0.001), RAR-L1 (p=0.000), RAR-L4 (p=0.026), and RAR-L20 (p=0.031) were significantly associated with poor survival (FIG. 3). The statistically highest significance was observed for the existence of RAR-L1.
Multivariate analysis using all the significant genomic alterations identified in univariate analysis as well as clinical variables such as age, sex and stage of tumor revealed that two RARs (RAR-L1 and RAR-L20), age, and stage are independent predictors for poor outcome in CRC (Table 5). Representative diagrams of these two RARs showing significant association with patient survival are illustrated in FIGS. 3E and F.







TABLE 5







Result of Cox regression analysis










95% Confidence interval












Variable
Hazard ratio
Lower
Upper
p value





Age
9.979
2.688
37.050
0.001


Stage
5.073
1.880
13.689
0.001


RAR-L1
8.151
2.167
30.657
0.002


RAR-L20
3.528
1.098
11.339
0.034


Age
6.455
2.034
20.488
0.002


Stage
7.409
2.481
22.124
0.000


Low CAMTA1
7.089
2.121
23.688
0.001





Note:


upper table, Cox regression using all the significant genomic alterations identified in univariate analysis as well as clinical variables such as age, sex and stage; lower table, Cox regression using CAMTA1 expression status and clinical variables such as age, sex and stage.






Example 8
Expression of Putative Cancer-Related Gene in Survival-Associated RARs
(1) Real-Time Quantitative PCR Assay
The first-strand cDNA was synthesized from total RNA of 44 pairs of cancer/normal tissues and 3 cell lines (RKO, HT29 and HCT116) using M-MLV reverse transcriptase (Invitrogen, Carlsbad, Calif.). Real-time quantitative PCR for analyzing CAMTA1 expression profile was performed using Mx3000P qPCR system and MxPro Version 3.00 software (Stratagene, CA, USA). The real-time qPCR mixture of 20 μl contained 10 ng of cDNA, 1X SYBR® Green Tbr polymerase mixture (FINNZYMES, Finland), 0.5×ROX, and primers of 20 pmole. GAPDH was used as an internal control in each procedure. The thermal cycling was as follows: 10 min at 95° C. followed by 40 cycles of 10 sec at 94° C., 30 sec at 54° C. and 30 sec at 72° C. To verify specific amplification, melting curve analysis was performed (55-95° C., 0.5° C./sec). Relative quantification was performed by the ΔΔCT method. We defined 40% reduction of expression in cancer tissue as low CAMTA1 expression. All the experiments were repeated twice and mean value of intensity ratio with standard deviation was plotted for each case. Primer sequences for CAMTA1 real-time quantitative PCR were as follows: 5′-AGTGCAGAAAATGAAGAATGCG-3′ (SEQ ID NO: 1) and 5′-CAAAATTCTCCTGCTTGATTCG-3′ (SEQ ID NO: 2) for forward and reverse, respectively.
(2) CAMTA1 Mutation Analysis
Somatic mutation of CAMTA1 was screened by PCR-direct sequencing. Primer sets for amplification of specific exons were prepared as described previously with some modifications. All the amplification was performed using PhusionTM High-Fidelity DNA polymerase (FINNZYMES, Finland). PCR products were purified using MEGA-spinTM gel extraction kit (iNtRON, Korea).







TABLE 6







Primer sequences for CAMTA1 mutation analysis


(SEQ ID NOS 3-34,


respectively, in order of appearance)










Forward primer
Reverse primer





Exon1
CCACTAGGAAGCTTTGTTTAG
CTCTTACCTTCCGGCCTTGTT



GT
T


 


Exon2
TTGGCAGGAATATCACAGAAG
TTTTGCTACCCCAGAAGGATT



AG
A


 


Exon3
GGAGATTTTATCTATTATTTT
GGACTATGTGAAGCAACCTAA



CTCTA



 


Exon4
AACAGCAAAAACTTTCTTACC
CCAAATCAGGTAATCAATGCA



TCTC



 


Exon5
TTTCTTCTACTTGGTACTCTT
AATGACATTTGTGCACCAAGG



GGTA



 


Exon6
CCCTCTTTCCAACTGAATTCT
CCAGAGACAGAAGAAGAATCC



C



 


Exon7
AGTCTGCTAATATCCCACATG
TGGTTGATGCCAGCCTGGTTC



CGC



 


Exon9
CCAGCACCATGGCCTACATGC
CAGCGGCGGCAGCTTACCTCT


 


Exon10
AACTCTGTTCCCCTCTCTGTT
CAGGCCATCACACTCACCTTG



CTCT



 


Exon11
CATTAAGGAGAGCTGGACATT
ACGACCCAAGCACTGTTCTTA



A



 


Exon13
GTGGTATGCGAGAAGATGATG
CAGTGCTCAGGAAGAATGTGA


 


Exon14
TACCCAGTTGGGTTTCATCTT
ATGCCAGACTGGAAGAACAGC



GGTG
AAG


 


Exon15-1
GGTCTTGACCTCTGATTGAGA
CTCTGCTAATTTCACATGACC


 


Exon15-2
ATCTCGATTCCCGACTCTCTA
ATAACAGTGACTCCCTTGGGT



G



 


Exon19
AAGCTGACATTTCTGGTAGTT
TTTAGCCAAACCAGGATCTTC



AATC



 


Exon20
TTCTCTTCTTCCCTTCCCGGT
AAGTCAGAGTTCTCTTCCCTA



A
GGG









Among the coding genes in RAR-L1 and RAR-20, CAMTA1 was suggested as a putative tumor suppressor gene in neuronal tumor. Therefore, we examined the expression profile of this gene in three CRC cell lines and 44 pairs primary CRCs by real-time quantitative PCR. Ratio of gene expression values (cancer versus normal) was calculated. All three cell lines and 26 CRCs out of 44 (59.1%) showed low expression of CAMTA1 compared with normal tissue (FIG. 4A). Low CAMTA1 expression was significantly associated with poor survival than CAMTA1 intact cases (p=0.029) (FIG. 4B). After being adjusted for age, sex and stage by Cox regression, low CAMTA1 expression showed more significant association with poor survival as an independent predictor (HR=7.089, p=0.001) (Table 5).
Low CAMTA1 expression was observed more frequently in the CRCs with RAR-L1 (70%, 7/10) than those without RAR-L1 (55.9%, 19/34) and expression level was also lower in CRCs with RAR-L1 (mean ratio 0.74) than those without RAR-L1 (mean ratio 0.93), but not significantly. For further exploring putative mechanisms of low CAMTA1 expression, we screened somatic mutations (26 CRCs) and methylation status (38 CRCs). One missense mutation was found in a primary CRC (CCRC71), which showed low CAMTA1 expression but without RAR-L1 (FIG. 4C). However, no hypermethylation was observed in the promoter region of CAMTA1 (data not shown).
The invention has been described in detail with reference to preferred embodiments thereof. However, it will be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
All patents and publications mentioned in this specification are herein incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference.
REFERENCES


    1. Parkin D M, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin 2005; 55:74-108.
    2. National Statistical Office, Korea. Korean Statistical Information System. 2006. Available from: URL: ncc.re.kr.
    3. Shin H R, Won Y J, Jung K W, Kong H J, Yim S H, Lee J K, Noh H I, Lee J K, Park J G. Nationwide Cancer Incidence in Korea, 1999-2001; First result using the National Cancer Incidence Database. Cancer Res Treat 2006; 37:in press.
    4. Michor F, Iwasa Y, Lengauer C, Nowak M A. Dynamics of colorectal cancer. Semin Cancer Biol 2005; 15:484-493.
    5. Yim S H, Chung Y J. Current Status and Future Clinical Applications of Array based Comparative Genomic Hybridization. Genomics & Informatics 2004; 2:113-120.
    6. Pinkel D, Segraves R, Sudar D, Clark S, Poole I, Kowbel D, Collins C, Kuo W L, Chen C, Zhai Y, Dairkee S H, Ljung B M, Gray J W, Albertson D G. High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet 1998; 20:207-211.
    7. Fiegler H, Carr P, Douglas E J, Burford D C, Hunt S, Scott C E, Smith J, Vetrie D, Gorman P, Tomlinson I P, Carter N P. DNA microarrays for comparative genomic hybridization based on DOP-PCR amplification of BAC and PAC clones. Genes Chromosomes Cancer 2003; 36:361-374.
    8. Kim T M, Yim S H, Lee J S, Kwon M S, Ryu J W, Kang H M, Fiegler H, Carter N P, Chung Y J. Genome-wide screening of genomic alterations and their clinicopathologic implications in non-small cell lung cancers. Clin Cancer Res 2005; 11:8235-8242.
    9. Chung Y J, Jonkers J, Kitson H, Fiegler H, Humphray S, Scott C, Hunt S, Yu Y, Nishijima I, Velds A, Holstege H, Carter N, Bradley A. A whole-genome mouse BAC microarray with 1-Mb resolution for analysis of DNA copy number changes by array comparative genomic hybridization. Genome Res 2004; 14:188-196.
    10. Kim S Y, Nam S W, Lee S H, Park W S, Yoo N J, Lee J Y, Chung Y J. ArrayCyGHt: a web application for analysis and visualization of array-CGH data. Bioinformatics 2005; 21:2554-2555.
    11. Livak K J, Schmittgen T D. Analysis of relative gene expression data using real-time quantitative PCR and the 2 (-Delta Delta C(T)) Method. Methods 2001; 25: 402-408.
    12. Henrich K O, Fischer M, Mertens D, Benner A, Wiedemeyer R, Brors B, Oberthuer A, Berthold F, Wei J S, Khan J, Schwab M, Westermann F. Reduced expression of CAMTA1 correlates with adverse outcome in neuroblastoma patients. Clin Cancer Res 2006; 12:131-138.
    13. Barbashina V, Salazar P, Holland E C, Rosenblum M K, Ladanyi M. Allelic losses at 1p36 and 19q13 in gliomas: correlation with histologic classification, definition of a 150-kb minimal deleted region on 1p36, and evaluation of CAMTA1 as a candidate tumor suppressor gene. Clin Cancer Res 2005; 11:1119-1128.
    14. Schouten J P, McElgunn C J, Waaijer R, Zwijnenburg D, Diepvens F, Pals G. Relative quantification of 40 nucleic acid sequences by multiplex ligation-dependent probe amplification. Nucleic Acids Res 2002; 30:e57.
    15. He Q J, Zeng W F, Sham J S, Xie D, Yang X W, Lin H L, Zhan W H, Lin F, Zeng S D, Nie D, Ma L F, Li C J, Lu S, Guan X Y. Recurrent genetic alterations in 26 colorectal carcinomas and 21 adenomas from Chinese patients. Cancer Genet Cytogenet 2003; 144:112-118.
    16. Poeaim S, Rerkamnuaychoke B, Jesdapatarakul S, Campiranon A. Chromosome alterations in colorectal cancer in That patients. Cancer Genet Cytogenet 2005; 160:152-159.
    17. Ried T, Knutzen R, Steinbeck R, Blegen H, Schrock E, Heselmeyer K, du Manoir S, Auer G. Comparative genomic hybridization reveals a specific pattern of chromosomal gains and losses during the genesis of colorectal tumors. Genes Chromosomes Cancer 1996; 15:234-245.
    18. Nakao K, Mehta K R, Fridlyand J, Moore D H, Jain A N, Lafuente A, Wiencke J W, Terdiman J P, Waldman F M. High-resolution analysis of DNA copy number alterations in colorectal cancer by array-based comparative genomic hybridization. Carcinogenesis 2004; 25:1345-1357.
    19. Knosel T, Petersen S, Schwabe H, Schluns K, Stein U, Schlag P M, Dietel M, Petersen I. Incidence of chromosomal imbalances in advanced colorectal carcinomas and their metastases. Virchows Arch 2002; 440:187-194.
    20. Jones A M, Douglas E J, Halford S E, Fiegler H, Gorman P A, Roylance R R, Carter N P, Tomlinson I P. Array-CGH analysis of microsatellite-stable, near-diploid bowel cancers and comparison with other types of colorectal carcinoma. Oncogene 2005; 24:118-129.
    21. Knosel T, Schluns K, Stein U, Schwabe H, Schlag P M, Dietel M, Petersen I. Genetic imbalances with impact on survival in colorectal cancer patients. Histopathology 2003; 43:323-331.
    22. Aragane H, Sakakura C, Nakanishi M, Yasuoka R, Fujita Y, Taniguchi H, Hagiwara A, Yamaguchi T, Abe T, Inazawa J, Yamagishi H. Chromosomal aberrations in colorectal cancers and liver metastases analyzed by comparative genomic hybridization. Int J Cancer 2001; 94:623-629.
    23. Douglas E J, Fiegler H, Rowan A, Halford S, Bicknell D C, Bodmer W, Tomlinson I P, Carter N P. Array comparative genomic hybridization analysis of colorectal cancer cell lines and primary carcinomas. Cancer Res 2004; 64:4817-4825.
    24. De Angelis P M, Clausen O P, Schjolberg A, Stokke T. Chromosomal gains and losses in primary colorectal carcinomas detected by CGH and their associations with tumour DNA ploidy, genotypes and phenotypes. Br J Cancer 1999; 80:526-535.
    25. Shivapurkar N, Maitra A, Milchgrub S, Gazdar A F. Deletions of chromosome 4 occur early during the pathogenesis of colorectal carcinoma. Hum Pathol 2001; 32:169-177.
    26. Finch R, Moore H G, Lindor N, Jalal S M, Markowitz A, Suresh J, Offit K, Guillem J G. Familial adenomatous polyposis and mental retardation caused by a de novo chromosomal deletion at 5q15-q22: report of a case. Dis Colon Rectum 2005; 48:2148-2152.
    27. Flanagan J M, Healey S, Young J, Whitehall V, Trott D A, Newbold R F, Chenevix-Trench G. Mapping of a candidate colorectal cancer tumor-suppressor gene to a 900-kilobase region on the short arm of chromosome 8. Genes Chromosomes Cancer 2004; 40:247-260.
    28. Frayling I M, Bodmer W F, Tomlinson I P. Allele loss in colorectal cancer at the Cowden disease/juvenile polyposis locus on 10q. Cancer Genet Cytogenet 1997; 97:64-69.
    29. Bando T, Kato Y, Ihara Y, Yamagishi F, Tsukada K, Isobe M. Loss of heterozygosity of 14q32 in colorectal carcinoma. Cancer Genet Cytogenet 1999; 111:161-165.
    30. Park W S, Park J Y, Oh R R, Yoo N J, Lee S H, Shin M S, Lee H K, Han S, Yoon S K, Kim S Y, Choi C, Kim P J, Oh S T, Lee J Y. A distinct tumor suppressor gene locus on chromosome 15q21.1 in sporadic form of colorectal cancer. Cancer Res 2000; 60:70-73.
    31. Risio M, Casorzo L, Chiecchio L, De Rosa G, Rossini F P. Deletions of 17p are associated with transition from early to advanced colorectal cancer. Cancer Genet Cytogenet 2003; 147:44-49.
    32. Knosel T, Petersen S, Schwabe H, Schluns K, Stein U, Schlag P M, Dietel M, Petersen I. Incidence of chromosomal imbalances in advanced colorectal carcinomas and their metastases. Virchows Arch 2002; 440:187-194.
    33. Donzelli M, Bernardi R, Negri C, Prosperi E, Padovan L, Lavialle C, Brison O, Scovassi A I. Apoptosis-prone phenotype of human colon carcinoma cells with a high level amplification of the c-myc gene. Oncogene 1999; 18:439-448.
    34. Lakshman M, Subramaniam V, Rubenthiran U, Jothy S. CD44 promotes resistance to apoptosis in human colon cancer cells. Exp Mol Pathol 2004; 77:18-25.
    35. Cummins J M, Kohli M, Rago C, Kinzler K W, Vogelstein B, Bunz F. X-linked inhibitor of apoptosis protein (XIAP) is a nonredundant modulator of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-mediated apoptosis in human cancer cells. Cancer Res 2004; 64:3006-3008.
    36. Satterwhite E, Sonoki T, Willis T G, Harder L, Nowak R, Arriola E L, Liu H, Price H P, Gesk S, Steinemann D, Schlegelberger B, Oscier D G, Siebert R, Tucker P W, Dyer M J. The BCL11 gene family: involvement of BCL11A in lymphoid malignancies. Blood 2001; 98:3413-3420.
    37. Ahn B H, Kim S Y, Kim E H, Choi K S, Kwon T K, Lee Y H, Chang J S, Kim M S, Jo Y H, Min D S. Transmodulation between phospholipase D and c-Src enhances cell proliferation. Mol Cell Biol 2003; 23:3103-3115.
    38. Zhang J S, Gong A, Cheville J C, Smith D I, Young C Y. AGR2, an androgen-inducible secretory protein overexpressed in prostate cancer. Genes Chromosomes Cancer 2005; 43:249-259.
    39. Shoji Y, Takahashi M, Kitamura T, Watanabe K, Kawamori T, Maruyama T, Sugimoto Y, Negishi M, Narumiya S, Sugimura T, Wakabayashi K. Downregulation of prostaglandin E receptor subtype EP3 during colon cancer development. Gut 2004; 53:1151-1158.
    40. Bjorling-Poulsen M, Seitz G, Guerra B, Issinger O G. The pro-apoptotic FAS-associated factor 1 is specifically reduced in human gastric carcinomas. Int J Oncol 2003; 23:1015-1023.
    41. Dunn J R, Risk J M, Langan J E, Marlee D, Ellis A, Campbell F, Watson A J, Field J K. Physical and transcript map of the minimally deleted region III on 17p implicated in the early development of Barrett's oesophageal adenocarcinoma. Oncogene 2003; 22:4134-4142.
    42. Kuo T, Fisher G A. Current status of small-molecule tyrosine kinase inhibitors targeting epidermal growth factor receptor in colorectal cancer. Clin Colorectal Cancer 2005; Suppl 2:S62-70.
    43. Gunther K, Leier J, Henning G, Dimmler A, Weissbach R, Hohenberger W, Forster R. Prediction of lymph node metastasis in colorectal carcinoma by expression of chemokine receptor CCR7. Int J Cancer 2005; 116:726-733.
    44. Aligayer H, Boyd D D, Heiss M M, Abdalla E K, Curley S A, Gallick G E. Activation of Src kinase in primary colorectal carcinoma: an indicator of poor clinical prognosis. Cancer 2002; 94:344-351.
    45. Villunger A, Michalak E M, Coultas L, Mullauer F, Bock G, Ausserlechner M J, Adams J M, Strasser A. p53- and drug-induced apoptotic responses mediated by BH3-only proteins puma and noxa. Science 2003; 302:1036-1038.
    46. Ogunbiyi O A, Goodfellow P J, Gagliardi G, Swanson P E, Birnbaum E H, Fleshman J W, Kodner I J, Moley J F. Prognostic value of chromosome 1p allelic loss in colon cancer. Gastroenterology 1997; 113:761-766.
    47. Ray R, Cabal-Manzano R, Moser A R, Waldman T, Zipper L M, Aigner A, Byers S W, Riegel A T, Wellstein A. Up-regulation of fibroblast growth factor-binding protein, by beta-catenin during colon carcinogenesis. Cancer Res 2003; 63:8085-8089.

## Claims

1. A diagnostic method for prognostic assessment of colorectal cancer, comprising the steps of:
(a) obtaining a nucleic acid sample from a colon tissue sample obtained from a human subject;
(b) identifying a recurrently altered region (RAR) on a chromosome in said nucleic acid sample by array CGH;
(c) detecting variation of expression of a specific gene in the RAR in said nucleic acid sample; and
(d) performing prognostic assessment of colorectal cancer in the human subject based on the detected variation, wherein loss of a recurrently altered region (RAR) in step (b) and a reduced expression level of the specific gene compared to a reference from normal colon tissue indicates a poor prognosis of colorectal cancer; wherein the RAR in step (b) is the chromosome region 1p36.31-1p36.23; wherein the specific gene in step (c) is CAMTA1; and wherein poor prognosis of colorectal cancer is defined by poor survival time.

2. The diagnostic method according to claim 1, wherein the reference is obtained from a human subject not having colorectal cancer.