MammOnc-DB, an integrative breast cancer data analysis platform for target discovery
Santhosh Kumar Karthikeyan, Darshan S. Chandrashekar, Snigdha Sahai, Sadeep Shrestha, Ritu Aneja, Raj Singh, Celina G. Kleer, Sidharth Kumar, Zhaohui Qin, Harikrishna Nakshatri, Upender Manne, Chad J. Creighton, Sooryanarayana Varambally
Abstract
Breast cancer (BCa), a leading malignancy among women, is characterized by morphological and molecular heterogeneity. While early-stage, hormone receptor, and HER2-positive BCa are treatable, triple-negative BCa and metastatic BCa remains largely untreatable. Advances in sequencing and proteomic technologies have improved our understanding of the molecular alterations that occur during BCa initiation and progression and enabled identification of subclass-specific biomarkers and therapeutic targets. Despite the availability of abundant omics data in public repositories, user-friendly tools for multi-omics data analysis and integration are scarce. To address this, we developed a comprehensive BCa data analysis platform called MammOnc-DB ( http://resource.path.uab.edu/MammOnc-Home.html ), comprising data from more than 20,000 BCa samples. MammOnc-DB facilitates hypothesis generation and testing, biomarker discovery, and therapeutic targets identification. The platform also includes pre- and post-treatment data, which can help users identify treatment resistance markers and support combination therapy strategies, offering researchers and clinicians a comprehensive tool for BCa data analysis and visualization.