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A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types
Cancer research continues to highlight the extensive genetic diversity that exists both between and within tumors. This intrinsic heterogeneity poses one of the central challenges to predicting patient clinical outcome and the personalization of treatments. Despite progress in some individual tumor...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5356843/ https://www.ncbi.nlm.nih.gov/pubmed/27833072 http://dx.doi.org/10.18632/oncotarget.13203 |
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author | Patrick, Ellis Schramm, Sarah-Jane Ormerod, John T Scolyer, Richard A Mann, Graham J Mueller, Samuel Yang, Jean Y.H. |
author_facet | Patrick, Ellis Schramm, Sarah-Jane Ormerod, John T Scolyer, Richard A Mann, Graham J Mueller, Samuel Yang, Jean Y.H. |
author_sort | Patrick, Ellis |
collection | PubMed |
description | Cancer research continues to highlight the extensive genetic diversity that exists both between and within tumors. This intrinsic heterogeneity poses one of the central challenges to predicting patient clinical outcome and the personalization of treatments. Despite progress in some individual tumor types, it is not yet possible to prospectively, accurately classify patients by expected survival. One hypothesis proposed to explain this is that the prognostic classifiers developed to date are insufficiently sensitive and specific; however it is also possible that patients are not equally easy to classify by any given biomarker. We demonstrate in a cohort of 45 AJCC stage III melanoma patients that clinico-pathologic biomarkers can identify those patients that are most likely to be misclassified by a molecular biomarker. The process of modelling the classifiability of patients was then replicated in a cohort of 49 stage II breast cancer patients and 53 stage III colon cancer patients. A multi-step procedure incorporating this information not only improved classification accuracy but also indicated the specific clinical attributes that had made classification problematic in each cohort. These findings show that, even when cohorts are of moderate size, including features that explain the patient-specific performance of a prognostic biomarker in a classification framework can improve the modelling and estimation of survival. |
format | Online Article Text |
id | pubmed-5356843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-53568432017-04-20 A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types Patrick, Ellis Schramm, Sarah-Jane Ormerod, John T Scolyer, Richard A Mann, Graham J Mueller, Samuel Yang, Jean Y.H. Oncotarget Research Paper Cancer research continues to highlight the extensive genetic diversity that exists both between and within tumors. This intrinsic heterogeneity poses one of the central challenges to predicting patient clinical outcome and the personalization of treatments. Despite progress in some individual tumor types, it is not yet possible to prospectively, accurately classify patients by expected survival. One hypothesis proposed to explain this is that the prognostic classifiers developed to date are insufficiently sensitive and specific; however it is also possible that patients are not equally easy to classify by any given biomarker. We demonstrate in a cohort of 45 AJCC stage III melanoma patients that clinico-pathologic biomarkers can identify those patients that are most likely to be misclassified by a molecular biomarker. The process of modelling the classifiability of patients was then replicated in a cohort of 49 stage II breast cancer patients and 53 stage III colon cancer patients. A multi-step procedure incorporating this information not only improved classification accuracy but also indicated the specific clinical attributes that had made classification problematic in each cohort. These findings show that, even when cohorts are of moderate size, including features that explain the patient-specific performance of a prognostic biomarker in a classification framework can improve the modelling and estimation of survival. Impact Journals LLC 2016-08-11 /pmc/articles/PMC5356843/ /pubmed/27833072 http://dx.doi.org/10.18632/oncotarget.13203 Text en Copyright: © 2017 Patrick et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Patrick, Ellis Schramm, Sarah-Jane Ormerod, John T Scolyer, Richard A Mann, Graham J Mueller, Samuel Yang, Jean Y.H. A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types |
title | A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types |
title_full | A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types |
title_fullStr | A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types |
title_full_unstemmed | A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types |
title_short | A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types |
title_sort | multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5356843/ https://www.ncbi.nlm.nih.gov/pubmed/27833072 http://dx.doi.org/10.18632/oncotarget.13203 |
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