Affymetrix OncoScan SNP arrays for profiling copy-number alterations (CNAs) and loss-of-heterozygosity (LOH) in colon cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE171777 | GEO | 2021/12/01
REPOSITORIES: GEO
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Project description:Intratumor heterogeneity fosters the evolution of the genome leading to metastatic progress and therapy resistance. Here, we investigate the relative contribution of genomic heterogeneity involving mutations and CNAs as prognostic and predictive determinants for disease recurrence in early-stage colon cancer. We combined targeted next-generation sequencing (NGS) and SNP arrays on a retrospective cohort of untreated stage II colon cancer patients to assess the association of genomic subclonality with time to recurrence (TTR).
2021-12-01 | GSE172190 | GEO
Project description:Affymetrix OncoScan SNP arrays for profiling copy-number alterations (CNAs) and loss-of-heterozygosity (LOH) in colon cancer
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Project description:Dermal neurofibromas (dNFs) are benign tumors of the peripheral nervous system typically associated with Neurofibromatosis type 1 (NF1) patients. Genes controlling the integrity of the DNA are likely to influence the number of neurofibromas developed because dNFs are caused by somatic mutational inactivation of the NF1 gene, frequently evidenced by loss of heterozygosity (LOH). We performed a comprehensive analysis of the prevalence and mechanisms of LOH in dNFs. Our study included 518 dNFs from 113 patients. LOH was detected in 25% of the dNFs (N = 129). The most frequent mechanism causing LOH was mitotic recombination, which was observed in 62% of LOH-tumors (N = 80), and which does not reduce the number of NF1 gene copies. All events were generated by a single crossover located between the centromere and the NF1 gene, resulting in isodisomy of 17q. LOH due to the loss of the NF1 gene accounted for a 38% of dNFs with LOH (N = 49), with deletions ranging in size from ?80?kb to ?8?Mb within 17q. In one tumor we identified the first example of a neurofibroma-associated second-hit type-2 NF1 deletion. Analysis of the prevalence of mechanisms causing LOH in dNFs in individual patients (possibly under genetic control) will elucidate whether there exist interindividual variation.
| S-EPMC3151547 | biostudies-literature
Project description:Gene expression profiles reflect unique aspects of individual biologic phenotypes and may characterize the heterogeneity of solid tumors. Using previously-described methodologies that employ DNA microarray data, a 50-gene expression profile (metagene) that predicts risk of recurrence in early stage colon carcinoma was identified. This analysis used an initial discovery cohort of 52 patients. The performance of the 50-gene predictor was evaluated in an independent validation cohort of 73 patients. Using a connectivity map analysis of the 50-gene model, we identified candidate agents and then tested the in vitro efficacy of these compounds in colon cancer cell lines. 73 samples that had patient recurrence data with stage information were used in the analysis. Keywords: Disease state analysis A total of 73 samples were spotted on microarray slides. No replicates are included in the study.
2010-05-15 | E-GEOD-10402 | biostudies-arrayexpress
Project description:Gene expression profiles reflect unique aspects of individual biologic phenotypes and may characterize the heterogeneity of solid tumors. Using previously-described methodologies that employ DNA microarray data, a 50-gene expression profile (metagene) that predicts risk of recurrence in early stage colon carcinoma was identified. This analysis used an initial discovery cohort of 52 patients. The performance of the 50-gene predictor was evaluated in an independent validation cohort of 73 patients. Using a connectivity map analysis of the 50-gene model, we identified candidate agents and then tested the in vitro efficacy of these compounds in colon cancer cell lines. 73 samples that had patient recurrence data with stage information were used in the analysis. Keywords: Disease state analysis
2008-12-03 | GSE10402 | 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: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
Project description:A major barrier to oral cancer prevention has been the lack of validated risk predictors for oral premalignant lesions (OPL). In 2000, we proposed a loss of heterozygosity (LOH) risk model in a retrospective study. This paper validated the previously reported LOH profiles as risk predictors and developed refined models via the largest longitudinal study to date of low-grade OPLs from a population-based patient group. Analysis involved a prospective cohort of 296 patients with primary mild/moderate oral dysplasia enrolled in the Oral Cancer Prediction Longitudinal Study. LOH status was determined in these OPLs. Patients were classified into high-risk or low-risk profiles to validate the 2000 model. Risk models were refined using recursive partitioning and Cox regression analyses. The prospective cohort validated that the high-risk lesions (3p and/or 9p LOH) had a 22.6-fold increase in risk (P = 0.002) compared with low-risk lesions (3p and 9p retention). Addition of another 2 markers (loci on 4q/17p) further improved the risk prediction, with five-year progression rates of 3.1%, 16.3%, and 63.1% for the low-, intermediate-, and high-risk lesions, respectively. Compared with the low-risk group, intermediate- and high-risk groups had 11.6-fold and 52.1-fold increase in risk (P < 0.001). LOH profiles as risk predictors in the refined model were validated in the retrospective cohort. Multicovariate analysis with clinical features showed LOH models to be the most significant predictors of progression. LOH profiles can reliably differentiate progression risk for OPLs. Potential uses include increasing surveillance for patients with elevated risk, improving target intervention for high-risk patients while sparing a large number of low-risk patients from needless screening and treatment.
| S-EPMC3793638 | biostudies-other