Independent validation of a model using cell line chemosensitivity to predict response to therapy

Wenting Wang, Keith A. Baggerly, Steen Knudsen, Jon Askaa, Wiktor Mazin, Kevin R. Coombes

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Background Methods using cell line microarray and drug sensitivity data to predict patients' chemotherapy response are appealing, but groups may be reluctant to release details to preserve intellectual property. Here we describe a case study to validate predictions while treating the methods as a "black box." Methods Medical Prognosis Institute (MPI) constructed cell-line-derived sensitivity scores (SSs) and combined scores (CSs) that incorporate clinical variables. MD Anderson researchers evaluated their predictions. We searched the Gene Expression Omnibus (GEO) to identify validation datasets, and we performed statistical evaluation of the agreement between prediction and clinical observation. Results We identified 3 suitable datasets: GSE16446 (n = 120; binary outcome), GSE17920 (n = 130; binary outcome), and GSE10255 (n = 161; continuous and time-to-event outcomes). The SS was statistically significantly associated with primary treatment responses for all studies (GSE16446: P = .02; GSE17920: P = .02; GSE10255: P = .02). Dichotomized SSs performed no better than chance for GSE16446 and GSE17920, and categorized SSs did not predict disease-free survival (GSE10255). SSs sometimes improved on predictions using clinical variables (GSE16446: P = .05; GSE17920: P = .31; GSE10255: P = .045), but gains were limited (95% confidence intervals for GSE16446 and GSE17920 include 0). The CS did not predict treatment response for GSE16446 (P = .55), but it did for GSE17920 (P < .001). Coefficients of clinical variables provided by MPI for CSs agree with estimates for GSE17920 better than estimates for GSE16446. Conclusions Model predictions were better than chance in all three datasets. However, these scores added little to existing clinical predictors; statistically significant contributions were likely to be too small to change clinical practice. These findings suggest that discovering better predictors will require both cell line data and a clinical training dataset of patient samples.

Original languageEnglish (US)
Pages (from-to)1284-1291
Number of pages8
JournalJournal of the National Cancer Institute
Issue number17
StatePublished - Sep 4 2013
Externally publishedYes

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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