Clustering-Based Method for Developing a Genomic Copy Number Alteration Signature for Predicting the Metastatic Potential of Prostate Cancer
The transition of cancer from a localized tumor to a distant metastasis is not well understood for prostate and many other cancers, partly, because of the scarcity of tumor samples, especially metastases, from cancer patients with long-term clinical follow-up. To overcome this limitation, we develo...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2012/873570 |
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| author | Alexander Pearlman Christopher Campbell Eric Brooks Alex Genshaft Shahin Shajahan Michael Ittman G. Steven Bova Jonathan Melamed Ilona Holcomb Robert J. Schneider Harry Ostrer |
| author_facet | Alexander Pearlman Christopher Campbell Eric Brooks Alex Genshaft Shahin Shajahan Michael Ittman G. Steven Bova Jonathan Melamed Ilona Holcomb Robert J. Schneider Harry Ostrer |
| author_sort | Alexander Pearlman |
| collection | DOAJ |
| description | The transition of cancer from a localized tumor to a distant metastasis is not well understood for prostate and many other cancers, partly, because of the scarcity of tumor samples, especially metastases, from cancer patients with long-term clinical follow-up. To overcome this limitation, we developed a semi-supervised clustering method using the tumor genomic DNA copy number alterations to classify each patient into inferred clinical outcome groups of metastatic potential. Our data set was comprised of 294 primary tumors and 49 metastases from 5 independent cohorts of prostate cancer patients. The alterations were modeled based on Darwin’s evolutionary selection theory and the genes overlapping these altered genomic regions were used to develop a metastatic potential score for a prostate cancer primary tumor. The function of the proteins encoded by some of the predictor genes promote escape from anoikis, a pathway of apoptosis, deregulated in metastases. We evaluated the metastatic potential score with other clinical predictors available at diagnosis using a Cox proportional hazards model and show our proposed score was the only significant predictor of metastasis free survival. The metastasis gene signature and associated score could be applied directly to copy number alteration profiles from patient biopsies positive for prostate cancer. |
| format | Article |
| id | doaj-art-b03d95ddfc4747af90aa645fa5e3cd2a |
| institution | OA Journals |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-b03d95ddfc4747af90aa645fa5e3cd2a2025-08-20T02:19:18ZengWileyJournal of Probability and Statistics1687-952X1687-95382012-01-01201210.1155/2012/873570873570Clustering-Based Method for Developing a Genomic Copy Number Alteration Signature for Predicting the Metastatic Potential of Prostate CancerAlexander Pearlman0Christopher Campbell1Eric Brooks2Alex Genshaft3Shahin Shajahan4Michael Ittman5G. Steven Bova6Jonathan Melamed7Ilona Holcomb8Robert J. Schneider9Harry Ostrer10Department of Pathology, Albert Einstein College of Medicine, Bronx, NY 10461, USADepartment of Pathology, Albert Einstein College of Medicine, Bronx, NY 10461, USAHuman Genetics Program, Department of Pediatrics, NYU Langone Medical Center, New York, NY 10016, USAHuman Genetics Program, Department of Pediatrics, NYU Langone Medical Center, New York, NY 10016, USAHuman Genetics Program, Department of Pediatrics, NYU Langone Medical Center, New York, NY 10016, USADepartment of Pathology, Baylor College of Medicine, Houston, TX 77030, USADepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USADepartment of Pathology, NYU Langone Medical Center, New York, NY 10016, USADepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USANYU Cancer Institute and Department of Microbiology, NYU Langone Medical Center, New York, NY 10016, USADepartment of Pathology, Albert Einstein College of Medicine, Bronx, NY 10461, USAThe transition of cancer from a localized tumor to a distant metastasis is not well understood for prostate and many other cancers, partly, because of the scarcity of tumor samples, especially metastases, from cancer patients with long-term clinical follow-up. To overcome this limitation, we developed a semi-supervised clustering method using the tumor genomic DNA copy number alterations to classify each patient into inferred clinical outcome groups of metastatic potential. Our data set was comprised of 294 primary tumors and 49 metastases from 5 independent cohorts of prostate cancer patients. The alterations were modeled based on Darwin’s evolutionary selection theory and the genes overlapping these altered genomic regions were used to develop a metastatic potential score for a prostate cancer primary tumor. The function of the proteins encoded by some of the predictor genes promote escape from anoikis, a pathway of apoptosis, deregulated in metastases. We evaluated the metastatic potential score with other clinical predictors available at diagnosis using a Cox proportional hazards model and show our proposed score was the only significant predictor of metastasis free survival. The metastasis gene signature and associated score could be applied directly to copy number alteration profiles from patient biopsies positive for prostate cancer.http://dx.doi.org/10.1155/2012/873570 |
| spellingShingle | Alexander Pearlman Christopher Campbell Eric Brooks Alex Genshaft Shahin Shajahan Michael Ittman G. Steven Bova Jonathan Melamed Ilona Holcomb Robert J. Schneider Harry Ostrer Clustering-Based Method for Developing a Genomic Copy Number Alteration Signature for Predicting the Metastatic Potential of Prostate Cancer Journal of Probability and Statistics |
| title | Clustering-Based Method for Developing a Genomic Copy Number Alteration Signature for Predicting the Metastatic Potential of Prostate Cancer |
| title_full | Clustering-Based Method for Developing a Genomic Copy Number Alteration Signature for Predicting the Metastatic Potential of Prostate Cancer |
| title_fullStr | Clustering-Based Method for Developing a Genomic Copy Number Alteration Signature for Predicting the Metastatic Potential of Prostate Cancer |
| title_full_unstemmed | Clustering-Based Method for Developing a Genomic Copy Number Alteration Signature for Predicting the Metastatic Potential of Prostate Cancer |
| title_short | Clustering-Based Method for Developing a Genomic Copy Number Alteration Signature for Predicting the Metastatic Potential of Prostate Cancer |
| title_sort | clustering based method for developing a genomic copy number alteration signature for predicting the metastatic potential of prostate cancer |
| url | http://dx.doi.org/10.1155/2012/873570 |
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