Predicting clinical endocrine response in advanced breast cancers using a reproducible low-dimensional biomarker

In the absence of reliable and effective prognostic biomarkers, endocrine therapy remains the standard of care for all advanced and metastatic estrogen receptor-positive (ER+) breast cancers. Attempts to develop biomarkers using the baseline tumor transcriptome or genome of advanced ER+ breast cancers have so far been unsuccessful due to predictive models with poor reproducibility in independent studies. Here we present an approach to develop a low-dimensional biomarker that estimates the risk of adverse events on endocrine therapy using the baseline tumor transcriptome of patients. Using a framework for supervised dimensionality reduction of the gene expression feature space, we constructed an endocrine response signature (ENDORSE) modeled on the survival outcomes of ER+ breast cancers from METABRIC. ENDORSE outperformed transcriptome-wide and knowledge-based signature models while significantly improving upon routine histopathological and genomic classifiers in cross-validation analyses. The ENDORSE risk estimate accurately predicted the outcomes for endocrine therapy in three independent clinical trials for ER+ breast cancers. Further, analysis of the phenotypes enriched in high-risk categories show endocrine resistance was not associated with rates of proliferation, but instead with a potential loss of DNA damage repair and cell-matrix interaction pathways.