A pan-cancer analysis of HER2 index revealed transcriptional pattern for precise selection of HER2-targeted therapy

Ziteng Li; Siyuan Chen; Wanjing Feng; Yixiao Luo; Hongyan Lai; Qin Li; Bingqiu Xiu; Yuchen Li; Yan Li; Shenglin Huang; Xiaodong Zhu

doi:10.1016/j.ebiom.2020.103074

A pan-cancer analysis of HER2 index revealed transcriptional pattern for precise selection of HER2-targeted therapy

EBioMedicine. 2020 Dec:62:103074. doi: 10.1016/j.ebiom.2020.103074. Epub 2020 Nov 9.

Authors

Ziteng Li¹, Siyuan Chen¹, Wanjing Feng¹, Yixiao Luo¹, Hongyan Lai², Qin Li², Bingqiu Xiu³, Yuchen Li², Yan Li¹, Shenglin Huang⁴, Xiaodong Zhu⁵

Affiliations

¹ Department of Medical Oncology, Fudan University Shanghai Cancer Center, and the Shanghai Key Laboratory of Medical Epigenetics, the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
² Department of Medical Oncology, Fudan University Shanghai Cancer Center, and the Shanghai Key Laboratory of Medical Epigenetics, the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
³ Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
⁴ Department of Medical Oncology, Fudan University Shanghai Cancer Center, and the Shanghai Key Laboratory of Medical Epigenetics, the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. Electronic address: slhuang@fudan.edu.cn.
⁵ Department of Medical Oncology, Fudan University Shanghai Cancer Center, and the Shanghai Key Laboratory of Medical Epigenetics, the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. Electronic address: xddr001@163.com.

Abstract

Background: The prevalence of HER2 alterations in pan-cancer indicates a broader range of application of HER2-targeted therapies; however, biomarkers for such therapies are still insufficient and limited to breast cancer and gastric cancer.

Methods: Using multi-omics data from The Cancer Genome Atlas (TCGA), the landscape of HER2 alterations was exhibited across 33 tumor types. A HER2 index was constructed using one-class logistic regression (OCLR). With the predictive value validated in GEO cohorts and pan-cancer cell lines, the index was then applied to evaluate the HER2-enriched expression pattern across TCGA pan-cancer types.

Findings: Increased HER2 somatic copy number alterations (SCNAs) could be divided into two patterns, focal- or arm-level. The expression-based HER2 index successfully distinguished the HER2-enriched subtype from the others and provided a stable and superior performance in predicting the response to HER2-targeted therapies both in breast tumor tissue and pan-cancer cell lines. With frequencies varying from 12.0% to 0.9%, tumors including head and neck squamous tumors, gastrointestinal tumors, bladder cancer, lung cancer and uterine tumors exhibited high HER2 indices together with HER2 amplification or overexpression, which may be more suitable for HER2-targeted therapies. The BLCA.3 and HNSC.Basal were the most distinguishable subtypes within bladder cancer and head and neck cancer respectively by HER2 index, implying their potential benefits from HER2-targeted therapies.

Interpretation: As a pan-cancer predictive biomarker of HER2-targeted therapies, the HER2 index could help identify potential candidates for such treatment in multiple tumor types by combining with HER2 multi-omics features. The discoveries of our study highlight the importance of incorporating transcriptional pattern into the assessment of HER2 status for better patient selection.

Funding: The National Key Research and Development Program of China; Clinical Research and Cultivation Project of Shanghai ShenKang Hospital Development Center.

Keywords: Biomarker; HER2-amplification; HER2-enriched subtype; Machine learning; Multi-omics analysis; Pan-cancer.

MeSH terms

Biomarkers, Tumor / genetics*
Clinical Decision-Making
Computational Biology / methods
DNA Copy Number Variations
Databases, Genetic
Disease Management
Disease Susceptibility
Gene Amplification
Gene Expression Profiling
Gene Expression Regulation*
Humans
Machine Learning
Molecular Targeted Therapy / methods
Neoplasms / drug therapy
Neoplasms / genetics*
Neoplasms / metabolism
Polymorphism, Single Nucleotide
Proteomics / methods
Receptor, ErbB-2 / genetics*
Receptor, ErbB-2 / metabolism
Transcription, Genetic*

Substances

Biomarkers, Tumor
Receptor, ErbB-2