ROCplot.org: Validating predictive biomarkers of chemotherapy/hormonal therapy/anti‐HER2 therapy using transcriptomic data of 3,104 breast cancer patients

• Published in: International journal of cancer Vol. 145; no. 11; pp. 3140 - 3151
• Main Authors: Fekete, János T., Győrffy, Balázs
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01-12-2019; Wiley Subscription Services, Inc
• Subjects: Adult; Aged; Antineoplastic Agents, Hormonal - pharmacology; Antineoplastic Agents, Hormonal - therapeutic use; Antineoplastic Agents, Immunological - pharmacology; Antineoplastic Agents, Immunological - therapeutic use; Biomarkers; Biomarkers, Tumor - genetics; Breast cancer; Breast Neoplasms - drug therapy; Breast Neoplasms - genetics; Cancer; Cancer therapies; Chemotherapy; Databases, Genetic; Drug Resistance, Neoplasm; Drug Therapy; Endocrine therapy; Epidermal growth factor; ErbB-2 protein; Female; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; hormonal therapy; Humans; Medical research; Menopause; Middle Aged; molecular subtypes; Monoclonal antibodies; p53 Protein; Paclitaxel; Patients; Precision Medicine; Receptor, ErbB-2 - antagonists & inhibitors; relapse‐free survival; ROC; Survival; Targeted cancer therapy; targeted therapy; Trastuzumab; hormonal therapy; relapse-free survival; molecular subtypes; targeted therapy; chemotherapy; ROC
• ISSN: 0020-7136; 1097-0215
• DOI: 10.1002/ijc.32369

Systemic therapy of breast cancer can include chemotherapy, hormonal therapy and targeted therapy. Prognostic biomarkers are able to predict survival and predictive biomarkers are able to predict therapy response. In this report, we describe the initial release of the first available online tool able to identify gene expression‐based predictive biomarkers using transcriptomic data of a large set of breast cancer patients. Published gene expression data of 36 publicly available datasets were integrated with treatment data into a unified database. Response to therapy was determined using either author‐reported pathological complete response data (n = 1,775) or relapse‐free survival status at 5 years (n = 1,329). Treatment data includes chemotherapy (n = 2,108), endocrine therapy (n = 971) and anti‐human epidermal growth factor receptor 2 (HER2) therapy (n = 267). The transcriptomic database includes 20,089 unique genes and 54,675 probe sets. Gene expression and therapy response are compared using receiver operating characteristics and Mann–Whitney tests. We demonstrate the utility of the pipeline by cross‐validating 23 paclitaxel resistance‐associated genes in different molecular subtypes of breast cancer. An additional set of established biomarkers including TP53 for chemotherapy in Luminal breast cancer (p = 1.01E‐19, AUC = 0.769), HER2 for trastuzumab therapy (p = 8.4E‐04, AUC = 0.629) and PGR for hormonal therapy (p = 8.6E‐05, AUC = 0.7), are also endorsed. The tool is designed to validate and rank new predictive biomarker candidates in real time. By analyzing the selected genes in a large set of independent patients, one can select the most robust candidates and quickly eliminate those that are most likely to fail in a clinical setting. The analysis tool is accessible at www.rocplot.org.
What's new?
While several online tools capable of delivering a prognostic prediction for breast cancer already exist, no such genome‐wide biomarker validation tool is available to evaluate and compare predictive biomarker candidates. Here, the authors combine multiple datasets to establish a sufficiently large breast cancer cohort with transcriptomic, anticancer treatment, and clinical response data. Then, they establish a framework capable of studying new candidate genes by mining this database and demonstrate the robustness of the pipeline by cross‐validating an established set of resistance‐associated genes. The novel online platform provides an easily accessed resource for researchers to validate and rank future biomarker candidates.