Modeling integration site data for safety assessment with MELISSA

Tsai-Yu Lin, Giacomo Ceoldo, Kimberley House, Matthew Welty, Thao Thi Dang, Denise Klatt, Christian Brendel, Michael P. Murphy, Kenneth Cornetta and Danilo Pellin ()
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Tsai-Yu Lin: Indiana University School of Medicine
Giacomo Ceoldo: Harvard Medical School
Kimberley House: Indiana University School of Medicine
Matthew Welty: Indiana University School of Medicine
Thao Thi Dang: Indiana University School of Medicine
Denise Klatt: Harvard Medical School
Christian Brendel: Harvard Medical School
Michael P. Murphy: Indiana University School of Medicine
Kenneth Cornetta: Indiana University School of Medicine
Danilo Pellin: Harvard Medical School

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Gene and cell therapies pose safety concerns due to potential insertional mutagenesis by viral vectors. We introduce MELISSA, a regression-based statistical framework for analyzing Integration Site (IS) data to assess insertional mutagenesis risk, by estimating and comparing gene-specific integration rates and their impact on clone fitness. We characterized the IS profile of a lentiviral vector on Mesenchymal Stem Cells (MSCs) and compared it with that of Hematopoietic Stem and Progenitor Cells (HSPCs). We applied MELISSA to published IS data from patients enrolled in gene therapy clinical trials, successfully identifying both known and novel genes that drive changes in clone growth through vector integration. MELISSA offers a quantitative tool to bridge the gap between IS data and safety and efficacy evaluation, facilitating the generation of comprehensive data packages supporting Investigational New Drug (IND) and Biologics License (BLA) applications and the development of safe and effective gene and cell therapies.

Date: 2025
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DOI: 10.1038/s41467-025-63017-w

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