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DOI 10.1016/j.xcrm.2023.100938

Медицинская библиотека · Cell reports. Medicine · 2023

Genomic and transcriptomic analyses identify a prognostic gene signature and predict response to therapy in pleural and peritoneal mesothelioma.

Nair Nishanth Ulhas, Jiang Qun, Wei Jun Stephen, Misra Vikram Alexander, Morrow Betsy, Kesserwan Chimene, Hermida Leandro C, Lee Joo Sang, Mian Idrees, Zhang Jingli, Lebensohn Alexandra, Miettinen Markku, Sengupta Manjistha, Khan Javed, Ruppin Eytan, Hassan Raffit

СтатьяResearch Support, N.I.H., IntramuralПеревод пока не подготовлен

Аннотация

Malignant mesothelioma is an aggressive cancer with limited treatment options and poor prognosis. A better understanding of mesothelioma genomics and transcriptomics could advance therapies. Here, we present a mesothelioma cohort of 122 patients along with their germline and tumor whole-exome and tumor RNA sequencing data as well as phenotypic and drug response information. We identify a 48-gene prognostic signature that is highly predictive of mesothelioma patient survival, including CCNB1, the expression of which is highly predictive of patient survival on its own. In addition, we analyze the transcriptomics data to study the tumor immune microenvironment and identify synthetic-lethality-based signatures predictive of response to therapy. This germline and somatic whole-exome sequencing as well as transcriptomics data from the same patient are a valuable resource to address important biological questions, including prognostic biomarkers and determinants of treatment response in mesothelioma.

Для цитирования: Nair Nishanth Ulhas, Jiang Qun, Wei Jun Stephen, Misra Vikram Alexander, Morrow Betsy, Kesserwan Chimene, Hermida Leandro C, Lee Joo Sang, Mian Idrees, Zhang Jingli, Lebensohn Alexandra, Miettinen Markku, Sengupta Manjistha, Khan Javed, Ruppin Eytan, Hassan Raffit. Genomic and transcriptomic analyses identify a prognostic gene signature and predict response to therapy in pleural and peritoneal mesothelioma.. Cell reports. Medicine. 2023. DOI: 10.1016/j.xcrm.2023.100938.

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