仿形(计算机编程)
组学
计算生物学
白血病
淋巴瘤
生物
医学
癌症研究
生物信息学
免疫学
计算机科学
操作系统
作者
Aleksandr Ianevski,Kristen Nader,Julia Nguyen,Helena Sorger,Sanna Timonen,Edith Julia,Daniel Pölöske,Katrin Spirk,Christina Wagner,Dennis Jungherz,Minoru Nakano,Sisira Kadambat Nair,Philipp Ianevski,Matti Kankainen,Diogo Dias,Anna Cichońska,Tea Pemovska,Christine Pirker,Walter Berger,Till Braun
出处
期刊:Cancer Research
[American Association for Cancer Research]
日期:2025-10-30
标识
DOI:10.1158/0008-5472.can-25-0881
摘要
T-cell leukemias and lymphomas (TCLs) form a heterogeneous group of rare and often aggressive malignancies. Due to 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 multi-omics 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 multi-omics 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.
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