Whole genome sequence and RNA-seq data from paired tumour and germline samples from mesothelioma patients.
Malignant pleural mesothelioma (MPM) has a poor overall survival with few treatment options. Whole genome sequencing (WGS) combined with RNA sequencing (RNA-seq) analysis of the immune features of MPM offers the prospect of identifying changes that could inform future clinical trials. We analysed somatic mutation and RNA-seq data from 229 MPM samples, including 58 MPM samples that had undergone WGS from our own institutions, together with other published data. This combined analysis identified somatic driver genes, including newly identified candidate genes. Whole genome doubling was a frequent event that correlated with shorter survival. Mutational signature analysis revealed dominant signatures and showed that defects in homologous recombination repair were infrequent in our cohort. Within the tumour immune environment we identified high M2 macrophage infiltrate linked with MMP2, MMP14, TGFB1 and CCL2 expression, representing an immunosuppressive environment. A small subset of samples had a higher proportion of CD8 T cells and a high cytolytic score, suggesting a ‘hot’ immune environment which is independent of the somatic mutations. We propose that our findings on genomic changes and subtypes of immune microenvironments may influence therapeutic planning in the future.
- Type: Other
- Archiver: European Genome-Phenome Archive (EGA)
Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data
Dataset ID | Description | Technology | Samples |
---|---|---|---|
EGAD00001007874 | Illumina HiSeq 2500 Illumina NovaSeq 6000 | 42 | |
EGAD00001008341 | HiSeq X Ten | 74 | |
EGAD00001008447 | HiSeq X Ten | 42 |
Publications | Citations |
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Comprehensive genomic and tumour immune profiling reveals potential therapeutic targets in malignant pleural mesothelioma.
Genome Med 14: 2022 58 |
24 |
Mesothelioma survival prediction based on a six-gene transcriptomic signature.
iScience 27: 2024 111011 |
0 |