Mesothelioma survival prediction based on a six-gene transcriptomic signature

Our plain-English summary of Mesothelioma survival prediction based on a six-gene transcriptomic signature, by K. Behrouzfar, S.E. Mutsaers, W.L. Chin, et al (iScience, 2024).

The study

• Authors: K. Behrouzfar, S.E. Mutsaers, W.L. Chin, K. Patrick, I.T. Ng, F.J. Pixley, G. Morahan, R.A. Lake, S.A. Fisher
• Journal: iScience
• Published: 2024;27(10):111011
• Cited by: 1 article, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Mesothelioma is a lethal cancer. Despite promising outcomes associated with immunotherapy, durable responses remain restricted to a minority of patients, highlighting the need for improved strategies that better predict outcome. Here, we described the development of a mesothelioma-specific gene signature that accurately predicts survival. Comprehensive gene expression analysis of asbestos exposed MexTAg Collaborative Cross mouse tumors revealed distinct tumor clusters characterized by epithelial mesenchymal transition/extracellular matrix, or immune infiltrate related gene expression profiles. Weighted gene co-expression network analysis (WGCNA) identified 20 hub genes that drove differential gene expression. Human homologues of these 20 hub genes were refined through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to identify a six-gene mesothelioma-specific prognostic signature that accurately predicted patient survival across four independent human mesothelioma datasets. Furthermore, this six-gene signature demonstrated the potential to predict treatment response, thus advancing the management of this challenging malignancy.

© 2024 The Author(s).

In plain English

This laboratory study combined mouse tumors with analysis of existing human data. The authors measured gene activity in 167 mesothelioma tumors from 119 mice of 35 genetically different strains. The mice carried a cancer-promoting gene and were given two intraperitoneal (abdominal cavity) injections of crocidolite (blue) asbestos. The tumors formed two groups: 144 marked by tissue-scaffolding (extracellular matrix) genes and 23 with more immune cells. The groups were not associated with how long the mice survived. From 20 key genes, the authors built a six-gene score using 211 human mesotheliomas (the Bueno dataset). There its accuracy scores (AUC) were 0.68, 0.70 and 0.74 for survival 1, 2 and 3 years after surgery. The score's link to survival was confirmed in the TCGA and Creaney datasets but was not significant in MATCH. In the NCI dataset it predicted survival only after excluding 6 patients who survived over 10 years. The authors concluded it predicted survival in multiple cohorts, but that limited data, including only 38 treated patients, restrict its use for predicting treatment response.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Laboratory study: gene expression (RNA sequencing) and immune staining of stored tumors from asbestos-exposed mice from the authors' earlier study, followed by building and testing a gene signature in publicly available human mesothelioma datasets. No new animals were used.
• Who was studied: Mouse part: 167 tumors from 119 MexTAg-Collaborative Cross (CCMT) mice of 35 strains, chosen from 72 strains (2562 mice) in an earlier study in Western Australia. Human part: Bueno dataset (n = 211) used for training, and NCI (n = 100), TCGA-MESO (n = 74), MATCH (n = 67) and Creaney (n = 42) datasets used for validation.
• Exposure: Mice (MexTAg mice expressing SV40 large T antigen in mesothelial cells, crossed with Collaborative Cross strains) received two intraperitoneal injections of 3 mg Wittenoom crocidolite (blue) asbestos one month apart and were monitored for up to 548 days. Asbestos exposure in the human datasets was not studied.
• Main results:
  • The mouse tumors split into two clusters: Cluster 1 with 144 tumors and Cluster 2 with 23 tumors.
  • The variation in gene expression between mouse tumors was not associated with median survival or other traits such as disease latency.
  • Cluster 2 mouse tumors had significantly more CD4+ T cells and CD19+ B cells than Cluster 1 tumors on immunofluorescence staining.
  • The six genes in the signature were IRF9, USP18, COL4A1, ITGA5, FN1 and ITGB1.
  • In the Bueno training dataset, the signature's AUC was 0.68, 0.70 and 0.74 for 1-, 2- and 3-year survival after surgery.
  • The signature's prognostic value was validated in the TCGA and Creaney datasets but was not significantly associated with prognosis in the MATCH dataset.
  • In multivariate analysis adjusted for sex, age and histological subtype, the signature was an independent prognostic factor in the Bueno, TCGA and Creaney cohorts.
  • In the NCI dataset, the signature was not significantly correlated with prognosis for all 100 patients. It predicted prognosis for 94 patients after excluding 6 who survived over 10 years, with AUCs of 0.84, 0.70 and 0.70 at 1, 2 and 3 years after diagnosis.
  • In the NCI dataset, the signature's prognostic value was absent when patients were stratified by pleural versus peritoneal disease.
  • For predicting response to immune checkpoint blockade in the NCI dataset, no significant difference was found between the six-gene signature and other mesothelioma signatures. The SELECT, CCNB1 and Bai signatures had the highest AUC values. All signatures were poor at predicting response to chemotherapy.
  • The signature did not significantly stratify patients with several other cancers (melanoma, thymoma, prostate, ovarian). It did stratify bladder cancer and lung adenocarcinoma patients, with lower predictive performance than in mesothelioma.
• The authors' conclusion: The authors concluded that the six-gene signature predicted survival in multiple human mesothelioma cohorts, but that the limited availability of large multi-omics datasets and comprehensive clinical information restricts its use for predicting response to cancer therapies.
• Limitations the authors state:
  • Only a small number of treated patients were available in the NCI dataset (38 in total; 3 of 16 responded to immune checkpoint blockade), which limits the robustness of any signature's predictive capacity.
  • RNA sequencing was done on whole tumor tissue, and cell types were estimated by a computer method rather than separating tumor cells from surrounding cells.
  • The mice were no longer available, so the hypothesis that immune-rich tumors respond better to immunotherapy could not be tested directly in them.
  • The crossbred mice were bred specifically for this project and are not a commercial colony, so repeating the experiments would require re-breeding.
  • The mice were untreated, whereas patients in the human datasets received treatment.

Topics

Medical terms in this study, explained in our Lexicon: Mesothelioma, Immunotherapy.

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