Prognostic and Chemotherapy Predictive Value of Gene-Expression Phenotypes in Primary Lung Adenocarcinoma.

Purpose: Primary lung adenocarcinoma remains a deadly disease. Gene-expression phenotypes (GEPs) in adenocarcinoma have potential to provide clinically relevant disease stratification for improved prognosis and treatment prediction, given appropriate clinical and methodologic validation.

Experimental Design: 2,395 transcriptional adenocarcinoma profiles were assembled from 17 public cohorts and classified by a nearest centroid GEP classifier into three subtypes: terminal respiratory unit (TRU), proximal-proliferative, and proximal-inflammatory, and additionally scored by five transcriptional metagenes representing different biologic processes, including proliferation. Prognostic- and chemotherapy-predictive associations of the subtypes were analyzed by univariate and multivariate analysis using overall survival or distant metastasis-free survival as endpoints.

Results: Overall, GEPs were associated with patient outcome in both univariate and multivariate analyses, although not in all individual cohorts. The prognostically relevant division was between TRU- and non-TRU-classified cases, with expression of proliferation-associated genes as a key prognostic component. In contrast, GEP classification was not predictive of adjuvant chemotherapy response. GEP classification showed stability to random perturbations of genes or samples and alterations to classification procedures (typically <10% of cases/cohort switching subtype). High classification variability (>20% of cases switching subtype) was observed when removing larger or entire fractions of a single subtype, due to gene-centering shifts not addressable by the classifier.

Conclusions: In a large-scale evaluation, we show that GEPs add prognostic value to standard clinicopathologic variables in lung adenocarcinoma. Subject to classifier refinement and confirmation in prospective cohorts, GEPs have potential to affect the prognostication of adenocarcinoma patients through a molecularly driven disease stratification.