Whole-transcriptome analysis links trastuzumab sensitivity of breast tumors to both HER2 dependence and immune cell infiltration

While results thus far demonstrate the clinical benefit of trastuzumab, some patients do not respond to this therapy. To identify a molecular predictor of trastuzumab benefit, we conducted whole-transcriptome analysis of primary HER2+ breast carcinomas obtained from patients treated with trastuzumab-containing therapies and correlated the molecular portrait with treatment benefit. The estimated association between gene expression and relapse-free survival allowed development of a trastuzumab risk model (TRAR), with ERBB2 and ESR1 expression as core elements, able to identify patients with high and low risk of relapse. Application of the TRAR model to 24 HER2+ core biopsies from patients treated with neo-adjuvant trastuzumab indicated that it is predictive of trastuzumab response. Examination of TRAR in available whole-transcriptome datasets indicated that this model stratifies patients according to response to trastuzumab-based neo-adjuvant treatment but not to chemotherapy alone. Pathway analysis revealed that TRAR-low tumors expressed genes of the immune response, with higher numbers of CD8-positive cells detected immunohistochemically compared to TRAR-high tumors. The TRAR model identifies tumors that benefit from trastuzumab-based treatment as those most enriched in CD8-positive immune infiltrating cells and with high ERBB2 and low ESR1 mRNA levels, indicating the requirement for both features in achieving trastuzumab response.

[1]  Krishna R. Kalari,et al.  Genomic analysis reveals that immune function genes are strongly linked to clinical outcome in the North Central Cancer Treatment Group n9831 Adjuvant Trastuzumab Trial. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  Jong-Hyeon Jeong,et al.  Trastuzumab plus adjuvant chemotherapy for human epidermal growth factor receptor 2-positive breast cancer: planned joint analysis of overall survival from NSABP B-31 and NCCTG N9831. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  L. De Cecco,et al.  Comprehensive gene expression meta-analysis of head and neck squamous cell carcinoma microarray data defines a robust survival predictor. , 2014, Annals of oncology : official journal of the European Society for Medical Oncology.

[4]  S Michiels,et al.  Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial. , 2014, Annals of oncology : official journal of the European Society for Medical Oncology.

[5]  A. Harris,et al.  Prospective neoadjuvant analysis of PET imaging and mechanisms of resistance to Trastuzumab shows role of HIF1 and autophagy , 2014, British Journal of Cancer.

[6]  Anton Belousov,et al.  Research-Based PAM50 Subtype Predictor Identifies Higher Responses and Improved Survival Outcomes in HER2-Positive Breast Cancer in the NOAH Study , 2014, Clinical Cancer Research.

[7]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[8]  Jong-Hyeon Jeong,et al.  Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31. , 2013, Journal of the National Cancer Institute.

[9]  Guillem Rigaill,et al.  Identifying subgroup markers in heterogeneous populations , 2013, Nucleic acids research.

[10]  M. Campiglio,et al.  Effect of adjuvant trastuzumab treatment in conventional clinical setting: an observational retrospective multicenter Italian study , 2013, Breast Cancer Research and Treatment.

[11]  R. Wirtz,et al.  Identification and Validation of a Multigene Predictor of Recurrence in Primary Laryngeal Cancer , 2013, PloS one.

[12]  M. Piccart-Gebhart,et al.  Trastuzumab for patients with HER2 positive breast cancer: delivery, duration and combination therapies. , 2013, Breast.

[13]  D. Felsher,et al.  Noncanonical roles of the immune system in eliciting oncogene addiction. , 2013, Current opinion in immunology.

[14]  Stefan Michiels,et al.  Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[15]  R. Kay The Analysis of Survival Data , 2012 .

[16]  R. Nahta,et al.  Therapeutic implications of estrogen receptor signaling in HER2-positive breast cancers , 2012, Breast Cancer Research and Treatment.

[17]  L. Moja,et al.  Trastuzumab containing regimens for early breast cancer. , 2012, The Cochrane database of systematic reviews.

[18]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.

[19]  C. Perou,et al.  TP53 genomics predict higher clinical and pathologic tumor response in operable early-stage breast cancer treated with docetaxel-capecitabine ± trastuzumab , 2012, Breast Cancer Research and Treatment.

[20]  Haleh Yasrebi,et al.  SurvJamda: an R package to predict patients' survival and risk assessment using joint analysis of microarray gene expression data , 2011, Bioinform..

[21]  S. Loi,et al.  Anti–ErbB-2 mAb therapy requires type I and II interferons and synergizes with anti–PD-1 or anti-CD137 mAb therapy , 2011, Proceedings of the National Academy of Sciences.

[22]  M. Greene,et al.  The therapeutic effect of anti-HER2/neu antibody depends on both innate and adaptive immunity. , 2010, Cancer cell.

[23]  Achim Rody,et al.  T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers , 2009, Breast Cancer Research.

[24]  Pan Du,et al.  lumi: a pipeline for processing Illumina microarray , 2008, Bioinform..

[25]  D. Cimino,et al.  Quantitative expression profiling of highly degraded RNA from formalin-fixed, paraffin-embedded breast tumor biopsies by oligonucleotide microarrays , 2008, Laboratory Investigation.

[26]  Joachim Selbig,et al.  pcaMethods - a bioconductor package providing PCA methods for incomplete data , 2007, Bioinform..

[27]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[28]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[30]  R. Tibshirani,et al.  Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data , 2004, PLoS biology.

[31]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[32]  K. Calman,et al.  Immunological Aspects of Cancer Chemotherapy , 1980 .