Abstract 4847: Purifying tumor gene expression profiles leads to predictive models of patient outcome

Abstract

Abstract INTRODUCTION: Molecular-based diagnostics tools such as gene expression signatures for predicting patient outcome or response to treatment are cornerstones of personalized medicine. However, they have a reputation for poor validation performance on patient cohorts independent of those on which they were developed, a combined result of small patient cohorts and tumor heterogeneity. We quantitatively assess the impact of tumor heterogeneity on gene-expression based predictive models of patient outcome for non small cell lung cancer. EXPERIMENTS: We developed a fully automated pipeline to computationally purify tumor gene expression profiles using a tool called ISOLATE (Quon et al., 2009), identify a prognostic gene signature on a training cohort (Friedman et al., 2010), and validate the signature on independent cohorts. ISOLATE accepts heterogeneous tumor profiles and healthy tissue profiles, and outputs purified tumor profiles and percentage tumor content for each heterogeneous sample. We used 1 training cohort of 86 patients (Beer et al., 2002), and 4 independent validation cohorts totaling 443 patients (Shedden et al., 2008). The computational pipeline was run on the training and validation cohorts in two identical scenarios with the exception of either performing computational purification on all samples or not. RESULTS: We first validated the estimates of % tumor content of ISOLATE on 32 lung adenocarcinomas on which two pathologists independently estimated % content. For the 23 samples on which the two pathologists agreed strongly, ISOLATE estimates had 0.51 correlation with their average, compared with an overall correlation of 0.59 between the two pathologists. Our computational pipeline identified an 82-gene signature (‘unpurified-sig’) on the unpurified tumor profiles, and a 110-gene signature (‘ISOLATE-sig’) on the purified tumor profiles. We used these signatures to identify low and high risk patient groups within the validation cohorts of 443 patients. ISOLATE-sig achieved a very significant difference in survival between the identified low and high risk patients (Hazard Ratio (HR)=1.92, p=10⁁(−6)), compared to unpurified-sig (HR=1.53, p=10⁁(−3)). We separately examined all 227 Stage I patients, and found consistent results: ISOLATE-sig performance (HR=1.80, p=10⁁(−3)) compared very favorably to the original profiles (HR=1.45, p=10⁁(−2)). Here, only ISOLATE-sig achieves a hazard ratio whose 95% CI strictly exceeds 1. CONCLUSION: This is the first study to date that quantitatively assesses the effect of tumor heterogeneity on models for predicting patient outcome. We also present a fully automated computational pipeline for purifying, identifying, and validating gene expression based signatures. We showed purification lead to much better prognostic models of lung cancer, and anticipate similar results for other solid cancers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4847. doi:10.1158/1538-7445.AM2011-4847