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Immune checkpoint inhibitors (ICI) in MPM have not been extremely successful, likely due to poor identification of suitable candidate patients for the therapy. We aimed to identify cellular immune fractions associated with clinical outcome and classify patients with MPM based on their immune contexture. For each defined group, we sought for molecular specificities that could help further define our MPM classification at the genomic and transcriptomic level, as well as identify differential therapeutic strategies based on transcriptional signatures predictive of drug response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The abundance of 20 immune cell fractions in 516 MPM samples from 7 gene expression datasets was inferred using gene set variation analysis. Identification of clinically relevant fractions was performed with Cox proportional-hazards models adjusted for age, stage, sex, and tumor histology. Immune-based groups were defined based on the identified fractions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      T-helper 2 (T\n                      <jats:sub>H2<\/jats:sub>\n                      ) and cytotoxic T (T\n                      <jats:sub>C<\/jats:sub>\n                      ) cells were found to be consistently associated with overall survival. Three immune clusters (IG) were subsequently defined based on T\n                      <jats:sub>H2<\/jats:sub>\n                      and T\n                      <jats:sub>C<\/jats:sub>\n                      immune infiltration levels: IG1 (54.5%) was characterized by high T\n                      <jats:sub>H2<\/jats:sub>\n                      and low T\n                      <jats:sub>C<\/jats:sub>\n                      levels, IG2 (37%) had either low or high levels of both fractions, and IG3 (8.5%) was defined by low T\n                      <jats:sub>H2<\/jats:sub>\n                      and high T\n                      <jats:sub>C<\/jats:sub>\n                      levels. IG1 and IG3 groups were associated with worse and better overall survival, respectively. While no differential genomic alterations were identified among immune groups, at the transcriptional level, IG1 samples showed upregulation of proliferation signatures, while IG3 samples presented upregulation of immune and inflammation-related pathways. Finally, the integration of gene expression with functional signatures of drug response showed that IG3 patients might be more likely to respond to ICI.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      This study identifies a novel immune-based signature with potential clinical relevance based on T\n                      <jats:sub>H2<\/jats:sub>\n                      and T\n                      <jats:sub>C<\/jats:sub>\n                      levels, unveiling a fraction of patients with MPM with better prognosis and who might benefit from immune-based therapies. Molecular specificities of the different groups might be used to tailor specific potential therapies in the future.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/jitc-2020-001601","type":"journal-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T11:52:16Z","timestamp":1614253936000},"page":"e001601","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":25,"title":["Integrative transcriptome analysis of malignant pleural mesothelioma reveals a clinically relevant immune-based classification"],"prefix":"10.1136","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5090-2913","authenticated-orcid":false,"given":"Ania","family":"Alay","sequence":"first","affiliation":[{"name":"Unit of Bioinformatics for Precision Oncology, Catalan Institute of Oncology (ICO)\u2014Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, 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