Integrated analysis of RNA and DNA from the phase III trial CALGB 40601 identifies predictors of response to trastuzumab-based neoadjuvant chemotherapy in HER2-positive breast cancer Journal Article


Authors: Tanioka, M.; Fan, C.; Parker, J. S.; Hoadley, K. A.; Hu, Z.; Li, Y.; Hyslop, T. M.; Pitcher, B. N.; Soloway, M. G.; Spears, P. A.; Henry, L. N.; Tolaney, S.; Dang, C. T.; Krop, I. E.; Harris, L. N.; Berry, D. A.; Mardis, E. R.; Winer, E. P.; Hudis, C. A.; Carey, L. A.; Perou, C. M.
Article Title: Integrated analysis of RNA and DNA from the phase III trial CALGB 40601 identifies predictors of response to trastuzumab-based neoadjuvant chemotherapy in HER2-positive breast cancer
Abstract: 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. Experimental Design: We analyzed 137 pretreatment 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 to III HER2-positive breast cancer. We adopted an Elastic Net regularized regression approach that controls for covarying 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 four independent trials. Next, we performed integrative analyses incorporating clinicopathologic information with somatic mutation status, DNA copy number alterations (CNA), and gene signatures. Results: The Elastic Net model using only gene signatures predicted pCR in the validation sets (AUC 1⁄4 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 multiplatform models included amplifications of chromosome 6p, TP53 mutation, HER2-enriched subtype, and immune signatures. Variables predicting resistance included Luminal/ERþ 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. © 2018 American Association for Cancer Research.
Journal Title: Clinical Cancer Research
Volume: 24
Issue: 21
ISSN: 1078-0432
Publisher: American Association for Cancer Research  
Date Published: 2018-11-01
Start Page: 5292
End Page: 5304
Language: English
DOI: 10.1158/1078-0432.ccr-17-3431
PUBMED: 30037817
PROVIDER: scopus
PMCID: PMC6214737
DOI/URL:
Notes: Article -- Export Date: 3 December 2018 -- Source: Scopus
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MSK Authors
  1. Clifford Hudis
    838 Hudis
  2. Chau Dang
    156 Dang