Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Cell reports. Medicine, 2023-02, Vol.4 (2), p.100938-100938, Article 100938

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. [Display omitted] •A 122-patient mesothelioma cohort with genomics, transcriptomics, and phenotypic data•48-gene signature highly predictive of patient survival•Evaluated tumor immune microenvironment using transcriptomics•Transcriptomic analysis to predict response to therapy Nair et al. present a 122-patient pleural and peritoneal mesothelioma cohort with genomics, transcriptomics, and phenotypic data. They identify a 48-gene transcriptomics-based signature that is highly predictive of mesothelioma patient survival, including the CCNB1 gene. Transcriptomics analysis is done to study the tumor immune microenvironment and predict drug response in patients.