Transcription profiling of human colon cancer patients reveals a gene expression profile which predicts recurrence and death
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ABSTRACT: Functional genomics approach to metastatic colon cancer; Mouse model translated to human colon cancer Experiment Overall Design: Gene expression array differences between highly invasive mouse colon cancer cells and non-invasive colon cancer cells
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.
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.
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
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
Project description:This SuperSeries is composed of the following subset Series:; GSE17536: Metastasis Gene Expression Profile Predicts Recurrence and Death in Colon Cancer Patients (Moffitt Samples); GSE17537: Metastasis Gene Expression Profile Predicts Recurrence and Death in Colon Cancer Patients (VMC Samples) Experiment Overall Design: Refer to individual Series
Project description:Cancer stem cells (CSCs) are profoundly associated with refractory nature of cancer. A quiescent population of CSCs is responsible for tumorigenesis and chemoresistance in leukemia, whereas neither the presence nor clinical importance of the quiescent CSCs is clearly established in solid tumors. In colon cancer, LGR5 is regarded as a functional marker of CSCs, but heterogeneity among LGR5+ cells was not clearly defined. Here we stratified LGR5+ cells by single-cell gene expression analyses and revealed that a tumorigenic population of mouse LGR5+ cells resides in a quiescent state. We identified 23 signature genes that were uniquely expressed in the quiescent CSCs of mouse tumors, and found that 7 of them were also specifically expressed in a quiescent population of LGR5+ cells in human colon xenograft tumors. Among them, PROX1 was expressed in invasive fronts of colon tumors. PROX1 was induced by TCF1 (TCF7), and the TCF1-mediated PROX1 expression was responsible for not only the maintenance of quiescence but also chemoresistance of colon cancer organoids. Knockout of PROX1 in patient-derived xenograft tumors resulted in inhibition of tumor recurrence after chemotherapeutic treatment. Our data underscore the therapeutic importance of a quiescent CSC population in colon cancer.
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.