Integrated analysis of RNA and DNA from a phase III trial of trastuzumab-based neoadjuvant chemotherapy identifies response predictors in HER2-positive breast cancer

Purpose: Response to a complex trastuzumab-based regimen is affected by multiple features of the tumor and its microenvironment. Developing a predictive algorithm is key to optimizing HER2-targeting therapy. Methods: We analyzed 137 pre-treatment tumors with mRNA-seq and DNA exome sequencing from CALGB 40601, a neoadjuvant phase III trial of paclitaxel plus trastuzumab with or without lapatinib in stage II-III HER2-positive breast cancer. We adopted an Elastic Net regularized regression approach that controls for co-varying features within high-dimensional data. First, we applied 517 known gene expression signatures to develop an Elastic Net model to predict pCR, which we validated on 143 samples from 4 independent trials. Next, we performed integrative analyses incorporating clinicopathologic information with somatic mutation status, DNA copy number alterations (CNAs) and gene signatures. Results: The Elastic Net model using only gene signatures predicted pCR in the validation sets (AUC = 0.76). Integrative analyses showed that models containing gene signatures, clinical features, and DNA information were better pCR predictors than models containing a single data type. Frequently selected variables from the multi-platform models included amplifications of chromosome 6p, TP53 mutation, HER2-enriched subtype and immune signatures. Variables predicting resistance included Luminal/ER+ features. Conclusions: Models using RNA only, as well as integrated RNA and DNA models, can predict pCR with improved accuracy over clinical variables. Somatic DNA alterations (mutation, CNAs), tumor molecular subtype (HER2E, Luminal), and the microenvironment (immune cells) were independent predictors of response to trastuzumab and paclitaxel-based regimens. This highlights the complexity of predicting response in HER2-positive breast cancer.

https://ift.tt/2uIafiu