Aleksandr Ianevski Aleksandr Ianevski Kristen Nader Kristen Nader Julia Nguyen Julia Nguyen Helena Sorger Helena Sorger Sanna Timonen Sanna Timonen Edith Julia Edith Julia Daniel Pölöske Daniel Pölöske Katrin Spirk Katrin Spirk Christina Wagner Christina Wagner Dennis Jungherz Dennis Jungherz Minoru Nakano Minoru Nakano Sisira Kadambat Nair Sisira Kadambat Nair Philipp Ianevski Philipp Ianevski Matti Kankainen Matti Kankainen Diogo Dias Diogo Dias Anna Cichońska Anna Cichońska Tea Pemovska Tea Pemovska Christine Pirker Christine Pirker Walter Berger Walter Berger Till Braun Till Braun Richard Moriggl Richard Moriggl Emmanuel Bachy Emmanuel Bachy Satu Mustjoki Satu Mustjoki Marco Herling Marco Herling Heidi A. Neubauer Heidi A. Neubauer Benjamin Haibe-Kains Benjamin Haibe-Kains Tero Aittokallio Tero Aittokallio American Association for Cancer Research 2026 T-cell leukemias and lymphomas (TCL) form a heterogeneous group of rare and often aggressive malignancies. Because of the rarity and heterogeneity of TCL subtypes, clinical trials are challenging to conduct, making pharmacogenomic studies in cell line panels critical for the discovery of targeted therapeutics. The scarcity of data repositories with integrated multiomics and drug screening data hinders the preclinical evaluation of drug vulnerabilities and the identification of molecular markers predictive of responses to monotherapies and combinations. To address this gap, we conducted comprehensive pharmacogenomic profiling on a panel of 38 TCL cell lines, representing major clinical TCL subtypes to capture the molecular and phenotypic diversity. The TCL-38 multiomics data resource includes harmonized genetic, molecular, and epigenetic profiles, with comprehensive annotations and standardized drug response assessment of each cell line. This resource, together with machine learning predictions, was leveraged to identify TCL subtype–specific therapeutic vulnerabilities, including single-agent sensitivities and synergistic drug combinations, which were linked to genetic or epigenetic features as potential predictive biomarkers. This integrated and openly available resource (https://aittokallio.group/tcl38) could help advance the currently limited treatment options for patients with TCL. PDFDocument eng 2026-07-08T09:02:23.005828942Z 2026 5089570 b application/pdf http://creativecommons.org/licenses/by-nc-nd/4.0/