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Translating Cancer Complexity to Clinical Decisions

Translating Cancer Complexity to Clinical Decisions EDITORIAL David P. Carbone, MD, PhD ells are complex machines that sometimes get out of control and take over their host, Ci.e., become cancer cells. It goes without saying that to control or repair them, a thorough understanding of how they work and a specific understanding of what has gone awry is required. The study by Chen et al., led by one of the pioneers in this field, is a technically rigorous step toward sorting these errant machines into broad categories that may require different therapeutic approaches. In this study, Chen et al. defined a predictor of survival based on data from 680 tumors and defined a clinically feasible reverse transcriptase polymerase chain reaction classifier that significantly classified a test set of tumors for survival. It is thus a significant study for the size of the training set, the rigor of the analysis, and the translation to a reverse transcriptase polymerase chain reaction assay in a 101-patient test set. Such classification attempts, and in particular attempting to understand what these classifications imply for patient management, are clearly the next frontier in oncology. The last sentence of the abstract looks toward clinical application and states that these data should be http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Thoracic Oncology Wolters Kluwer Health

Translating Cancer Complexity to Clinical Decisions

Carbone, David P.
Journal of Thoracic Oncology , Volume 6 (9) – Sep 1, 2011
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References (1)

ISSN
1556-0864
DOI
10.1097/JTO.0b013e3182291953
pmid
21849850
Publisher site
See Article on Publisher Site

Abstract

EDITORIAL David P. Carbone, MD, PhD ells are complex machines that sometimes get out of control and take over their host, Ci.e., become cancer cells. It goes without saying that to control or repair them, a thorough understanding of how they work and a specific understanding of what has gone awry is required. The study by Chen et al., led by one of the pioneers in this field, is a technically rigorous step toward sorting these errant machines into broad categories that may require different therapeutic approaches. In this study, Chen et al. defined a predictor of survival based on data from 680 tumors and defined a clinically feasible reverse transcriptase polymerase chain reaction classifier that significantly classified a test set of tumors for survival. It is thus a significant study for the size of the training set, the rigor of the analysis, and the translation to a reverse transcriptase polymerase chain reaction assay in a 101-patient test set. Such classification attempts, and in particular attempting to understand what these classifications imply for patient management, are clearly the next frontier in oncology. The last sentence of the abstract looks toward clinical application and states that these data should be

Journal

Journal of Thoracic Oncology – Wolters Kluwer Health

Published: Sep 1, 2011

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