This paper identifies nuclear cathepsin L as a regulator of replication stress in ovarian cancer, showing that its activation creates a therapeutic vulnerability through replication machinery remodeling and DNA repair inhibition.

bioRxiv · Sep 3, 2026 · PreprintCancer Biology

Drug-induced nuclear cathepsin L (nCTSL) defines a targetable DNA damage response axis and PARP inhibitor sensitivity in ovarian cancer

Prabhu Thirusangu, Ling Jin, Shruti Rao, Upasana Ray, Anya Zhao, Julie K. Staub, Xinyan Wu, Xiaonan Hou, Jamison L. VanBlaricom, Syed Mohammed Musheer Aalam, Sun-Hee Lee, Ann L. Oberg, Nagarajan Kannan, John Weroha, Jeremy Chien, , Viji Shridhar

This study identifies nuclear cathepsin L (nCTSL) as a biomarker for PARP inhibitor sensitivity in ovarian cancer and shows that combining clofarabine with PARP inhibitors enhances therapeutic response.

Cell Communication and Signaling · Aug 28, 2026Peer-reviewedCancer Biology

Evolution of the open-access CIViC knowledgebase is driven by the needsof the cancer variant interpretation community

Kilannin Krysiak, Arpad M. Danos, Susanna Kiwala, Joshua F. McMichael, Adam C. Coffman, Erica Kay Barnell, Lana M. Sheta, Jason Saliba, Cameron J. Grisdale, Lynzey Kujan, Shahil P. Pema, Jake Lever, Nicholas C. Spies, Andreea Chiorean, Damian Tobias Rieke, , Malachi Griffith

The paper describes the evolution of the CIViC knowledgebase to meet the needs of the cancer variant interpretation community by expanding its functionality and data types.

bioRxiv · Jun 13, 2021 · PreprintGenomics

MACE2K: A Text-Mining Tool to Extract Literature-based Evidence for Variant Interpretation using Machine Learning

Samir Gupta, Shruti Rao, Trisha Miglani, Yasaswini Iyer, Junxia Lin, Ahson M. Saiyed, Ifeoma Ikwuemesi, Shannon M. McNulty, Courtney Thaxton, Subha Madhavan

MACE2K extracts variant-disease association evidence from biomedical literature using machine learning models, achieving high recall and precision in identifying relevant sentences.

bioRxiv · Dec 3, 2020 · PreprintBioinformatics

An evidence-based network approach to recommending targeted cancer therapies

Jayaram Kancherla, Shruti Rao, Krithika Bhuvaneshwar, Rebecca Baab Riggins, Robert A. Beckman, Subha Madhavan, Héctor Corrada Bravo, Simina Maria Boca

CDGnet recommends targeted cancer therapies using a network approach that integrates biological pathway information and patient-specific molecular data.

JCO Clinical Cancer Informatics · Jan 28, 2020Peer-reviewedBioinformatics

The CIViC knowledge model and standard operating procedures for curation and clinical interpretation of variants in cancer

Arpad M. Danos, Kilannin Krysiak, Erica Kay Barnell, Adam C. Coffman, Joshua F. McMichael, Susanna Kiwala, Nicholas C. Spies, Lana M. Sheta, Shahil P. Pema, Lynzey Kujan, Kaitlin A. Clark, Amber Z. Wollam, Shruti Rao, Deborah Ritter, Dmitriy Sonkin, , Obi L. Griffith

The paper describes the CIViC knowledge model and curation procedures for systematically organizing and interpreting cancer-related genetic variants.

Genome Medicine · Nov 28, 2019Peer-reviewedBioinformatics

eGARD: Extracting associations between genomic anomalies and drug responses from text

A. S. M. Ashique Mahmood, Shruti Rao, Peter B. McGarvey, Cathy H. Wu, Subha Madhavan, K. Vijay‐Shanker

eGARD extracts associations between genomic anomalies and drug responses from text with high precision and recall.

PLoS ONE · Dec 20, 2017Peer-reviewedBioinformatics

Clingen Cancer Somatic Working Group: standardizing and democratizing access to cancer molecular diagnostic data to drive translational research

Subha Madhavan, Deborah Ritter, Christine Micheel, Shruti Rao, Angshumoy Roy, Dmitriy Sonkin, Matthew McCoy, Malachi Griffith, Obi L. Griffith, Peter B. McGarvey, Shashikant Kulkarni, On Behalf Of The Clingen Somatic Working Group

The paper describes the development of standards for cancer variant interpretation and sharing through the ClinGen Cancer Somatic Working Group.

Pacific Symposium on Biocomputing (PSB) · Nov 17, 2017Peer-reviewedBioinformatics