Browsing by Author "Karthikeyan, Santhosh Kumar"

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    MammOnc-DB, an integrative breast cancer data analysis platform for target discovery
    (Springer Nature, 2025-04-18) Karthikeyan, Santhosh Kumar; Chandrashekar, Darshan S.; Sahai, Snigdha; Shrestha, Sadeep; Aneja, Ritu; Singh, Rajesh; Kleer, Celina G.; Kumar, Sidharth; Qin, Zhaohui S.; Nakshatri, Harikrishna; Manne, Upender; Creighton, Chad J.; Varambally, Sooryanarayana; Surgery, School of Medicine
    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.
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    MammOnc-DB, an integrative breast cancer data analysis platform for target discovery
    (Research Square, 2024-09-26) Varambally, Sooryanarayana; Karthikeyan, Santhosh Kumar; Chandrashekar, Darshan; Sahai, Snigdha; Shrestha, Sadeep; Aneja, Ritu; Singh, Rajesh; Kleer, Celina; Kumar, Sidharth; Qin, Zhaohui; Nakshatri, Harikrishna; Manne, Upender; Creighton , Chad; Surgery, School of Medicine
    Breast cancer (BCa) is one of the most common malignancies among women worldwide. It is a complex disease that is characterized by morphological and molecular heterogeneity. In the early stages of the disease, most BCa cases are treatable, particularly hormone receptor-positive and HER2-positive tumors. Unfortunately, triple-negative BCa and metastases to distant organs are largely untreatable with current medical interventions. Recent advances in sequencing and proteomic technologies have improved our understanding of the molecular changes that occur during breast cancer initiation and progression. In this era of precision medicine, researchers and clinicians aim to identify subclass-specific BCa biomarkers and develop new targets and drugs to guide treatment. Although vast amounts of omics data including single cell sequencing data, can be accessed through public repositories, there is a lack of user-friendly platforms that integrate information from multiple studies. Thus, to meet the need for a simple yet effective and integrative BCa tool for multi-omics data analysis and visualization, 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 was developed to provide a unique resource for hypothesis generation and testing, as well as for the discovery of biomarkers and therapeutic targets. The platform also provides pre- and post-treatment data, which can help users identify treatment resistance markers and patient groups that may benefit from combination therapy.