Prognostic Model for Stage II/III Colon Cancer Based on Gene Expression Subtypes and KRAS Mutation Status
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE92921 | GEO | 2018/03/30
REPOSITORIES: GEO
Similar Datasets
Project description:Purpose: The present study aimed to develop a classification model to predict recurrence in stage II and III colon cancer, using a previously published 128-gene signature on external and independent material. Experimental Design: Microarray gene expression data from 148 patients (37 Danish patients and 111 patients retrieved from the Gene Expression Omnibus, GSE17536) with stage II and III colon cancer were analyzed using Affymetrix Arrays (Affymetrix, Santa Clara, USA). Based on a known 128-gene signature, a classification model was created with the random forest method, using a training set consisting of stage I colon cancers (with localized disease and a good prognosis) and stage IV colon cancers (with metastasis and a poor prognosis). The classifier were built to predict stage II and III colon cancers as either stage I-like (good prognosis) or stage IV-like (poor prognosis). Results: The 3-year relapse-free survival probability (RFS) of stage III patients predicted to have a good prognosis was 79% compared to 55% of patients with a poor prognosis (P = 0.177, log-rank test). The classification model could not stratify stage II colon cancer. The complete dataset representing: (1) the 37 Danish patients (2) the 111 patients retrieved from Series GSE17536 (re-used data), is linked below as a supplementary file. Tumor samples were obtained from 37 patients with stage II and III colon cancer, who underwent colon resection at the Department of Surgery, Roskilde Hospital, Denmark and the Department of Surgery, Bispebjerg Hospital, Denmark between 2001 and 2008. Purified tumor RNA was reverse-transcribed, labelled and hybridized to Affymetrix Human Genome U133 Plus 2.0 GeneChip Array (Affymetrix, Santa Clara, USA) according to the manufacturers instructions and scanned at the RH Microarray Center, Rigshospitalet, University of Copenhagen.
2011-08-31 | E-GEOD-31595 | biostudies-arrayexpress
Project description:Purpose: A 128-gene signature has been proposed to predict poor outcomes in patients with stage II and III colorectal cancer. In the present study we aimed to validate this previously published 128-gene signature on external and independent data from patients with stage II and III colon cancer.
2011-09-01 | GSE31595 | GEO
Project description:Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Keywords: Functional genomics, metastatic colon cancer, mouse model, human colon cancer
2009-11-14 | GSE17537 | GEO
Project description:Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Keywords: Functional genomics, metastatic colon cancer, mouse model, human colon cancer
2009-11-14 | GSE17536 | GEO
Project description:Purpose: The goal of this experiment was to identify differentially expressed miRNAs between colon cancer patients with and without metachronous metastases. Background: Colon cancer prognosis and treatment are currently based on a classification system still showing large heterogeneity in clinical outcome, especially in TNM-stages II-III. Prognostic biomarkers for metastasis risk are warranted, as development of distant recurrent disease mainly accounts for the high lethality rates of colon cancer. MicroRNAs have been proposed as potential biomarkers for cancer. Furthermore, a verified standard for normalization of the amount of input material in PCR-based relative quantification of miRNA expression is lacking. Methods: A selection of frozen tumor specimens from two independent patient cohorts with TNM-stage II-III microsatellite stable primary adenocarcinomas were used for laser capture microdissection. Next-generation sequencing was performed on small RNAs isolated from colorectal tumors from the Dutch cohort (N=50). Differential expression analysis, comparing in metastasized- and non-metastasized tumors, identified prognostic microRNAs. Validation was performed on colon tumors from the German cohort (N=43) using qPCR. Results: MiR-25-3p and miR-339-5p were identified and validated as independent prognostic markers and used to construct a multivariate nomogram for metastasis risk prediction. The nomogram showed good probability prediction in validation. Additionally, we recommend combination of miR-16-5p and miR-26a-5p as standard for normalization in qPCR of colon cancer tissue-derived microRNA expression. Conclusions: In this international study, we identified and validated an miRNA classifier in primary cancers, and propose a nomogram capable of predicting metastasis risk in microsatellite stable TNM-stage II-III colon cancer.
2014-11-08 | GSE63119 | GEO
Project description:Background: Around 30% of all stage II colon cancer patients will relapse and die of their disease. At present no objective parameters for identification of high-risk stage II colon cancer patients, who will benefit from adjuvant chemotherapy, are established. With traditional histopathological features definition of high-risk stage II colon cancer patients is inaccurate. Therefore more objective and robust markers for prediction of relapse are needed. DNA copy number aberrations have proven to be robust prognostic markers, but have not been investigated for this specific group of patients. The aim of the present study is to identify chromosomal aberrations that can predict relapse of tumor in patients with stage II colon cancer. Materials and Methods: DNA was isolated from 40 formaldehyde fixed paraffin embedded stage II colon cancer samples with extensive clinicopathological data. Samples where hybridized using Comparative Genomic Hybridization (CGH) arrays to determine DNA copy number changes and microsatellite stability was determined by PCR. To analyze differences between stage II colon cancer patients with and without relapse of tumor a Wilcoxon rank-sum test was implemented with multiple testing correction Results: Patients with stage II colon cancer who had relapse of disease showed significant more losses on chromosome 4, 5, 15q, 17q and 18q. When microsatellite stable (MSS) patients were analyzed separately, only losses on chromosome 4q22.1-4q35.2 predicted worse outcome in stage II colon cancer patients. No differences in clinicopathological characteristics between patients with and without relapse were observed. Conclusion: Losses on 4q22.1-4q35.2 predict worse outcome in MSS stage II colon cancer patients and may aid in the selection of patients for adjuvant therapy.
2010-07-13 | GSE17181 | GEO
Project description:Prognostic Model for Stage II/III Colon Cancer Based on Gene Expression Subtypes and KRAS Mutation Status
| PRJNA358866 | ENA
Project description:Background: Around 30% of all stage II colon cancer patients will relapse and die of their disease. At present no objective parameters for identification of high-risk stage II colon cancer patients, who will benefit from adjuvant chemotherapy, are established. With traditional histopathological features definition of high-risk stage II colon cancer patients is inaccurate. Therefore more objective and robust markers for prediction of relapse are needed. DNA copy number aberrations have proven to be robust prognostic markers, but have not been investigated for this specific group of patients. The aim of the present study is to identify chromosomal aberrations that can predict relapse of tumor in patients with stage II colon cancer. Materials and Methods: DNA was isolated from 40 formaldehyde fixed paraffin embedded stage II colon cancer samples with extensive clinicopathological data. Samples where hybridized using Comparative Genomic Hybridization (CGH) arrays to determine DNA copy number changes and microsatellite stability was determined by PCR. To analyze differences between stage II colon cancer patients with and without relapse of tumor a Wilcoxon rank-sum test was implemented with multiple testing correction Results: Patients with stage II colon cancer who had relapse of disease showed significant more losses on chromosome 4, 5, 15q, 17q and 18q. When microsatellite stable (MSS) patients were analyzed separately, only losses on chromosome 4q22.1-4q35.2 predicted worse outcome in stage II colon cancer patients. No differences in clinicopathological characteristics between patients with and without relapse were observed. Conclusion: Losses on 4q22.1-4q35.2 predict worse outcome in MSS stage II colon cancer patients and may aid in the selection of patients for adjuvant therapy. 40 Stage II colorectal cancer (CRC) tissue samples (FFPE), 16 with and 24 without relapse of tumor
2010-07-13 | E-GEOD-17181 | biostudies-arrayexpress
Project description:Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Experiment Overall Design: Gene expression array differences between highly invasive mouse colon cancer cells and non-invasive colon cancer cells were used to develop a metastasis gene expression profile. It was refined using gene expression data from 55 patient (VMC) samples and trained using 177 patient (Moffitt) samples.
2009-12-02 | E-GEOD-17537 | biostudies-arrayexpress
Project description:Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Experiment Overall Design: Gene expression array differences between highly invasive mouse colon cancer cells and non-invasive colon cancer cells were used to develop a metastasis gene expression profile. It was refined using gene expression data from 55 patient (VMC) samples and trained using 177 patient (Moffitt) samples.
2009-12-02 | E-GEOD-17536 | biostudies-arrayexpress