Dataset Information

Multi-site tumor sampling highlights molecular intra-tumor heterogeneity in malignant pleural mesothelioma.

ABSTRACT:

Background

Malignant pleural mesothelioma (MPM) is a heterogeneous cancer. Better knowledge of molecular and cellular intra-tumor heterogeneity throughout the thoracic cavity is required to develop efficient therapies. This study focuses on molecular intra-tumor heterogeneity using the largest series to date in MPM and is the first to report on the multi-omics profiling of a substantial series of multi-site tumor samples.

Methods

Intra-tumor heterogeneity was investigated in 16 patients from whom biopsies were taken at distinct anatomical sites. The paired biopsies collected from apex, side wall, costo-diaphragmatic, or highest metabolic sites as well as 5 derived cell lines were screened using targeted sequencing. Whole exome sequencing, RNA sequencing, and DNA methylation were performed on a subset of the cohort for deep characterization. Molecular classification, recently defined histo-molecular gradients, and cell populations of the tumor microenvironment were assessed.

Results

Sequencing analysis identified heterogeneous variants notably in NF2, a key tumor suppressor gene of mesothelial carcinogenesis. Subclonal tumor populations were shared among paired biopsies, suggesting a polyclonal dissemination of the tumor. Transcriptome analysis highlighted dysregulation of cell adhesion and extracellular matrix pathways, linked to changes in histo-molecular gradient proportions between anatomic sites. Methylome analysis revealed the contribution of epigenetic mechanisms in two patients. Finally, significant changes in the expression of immune mediators and genes related to immunological synapse, as well as differential infiltration of immune populations in the tumor environment, were observed and led to a switch from a hot to a cold immune profile in three patients.

Conclusions

This comprehensive analysis reveals patient-dependent spatial intra-tumor heterogeneity at the genetic, transcriptomic, and epigenetic levels and in the immune landscape of the tumor microenvironment. Results support the need for multi-sampling for the implementation of molecular-based precision medicine.

SUBMITTER: Meiller C

PROVIDER: S-EPMC8281651 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:Malignant pleural mesothelioma (MPM) is a heterogeneous cancer. Better knowledge of molecular and cellular intra-tumor heterogeneity throughout the thoracic cavity is required to develop efficient therapies. This study focuses on molecular intra-tumor heterogeneity using the largest series to date in MPM and is the first to report on the multi-omics profiling of a substantial series of multi-site tumor samples. Methods:Intra-tumor heterogeneity was investigated in 16 patients from whom biopsies were taken at distinct anatomical sites. The paired biopsies collected from apex, side wall, costo-diaphragmatic or highest metabolic sites as well as 5 derived cell lines were screened using targeted sequencing. Whole exome sequencing, RNA sequencing, and DNA methylation were performed on a subset of the cohort for deep characterization. Molecular classification, recently defined histo-molecular gradients, and cell populations of the tumor microenvironment were assessed. Results:Sequencing analysis identified heterogeneous variants notably in NF2, a key tumor suppressor gene of mesothelial carcinogenesis. Subclonal tumor populations were shared among paired biopsies, suggesting a polyclonal dissemination of the tumor. Transcriptome analysis highlighted dysregulation of cell adhesion and extracellular matrix pathways, linked to changes in histo-molecular gradient proportions between anatomic sites. Methylome analysis revealed contribution of epigenetic mechanisms in two patients. Finally, significant changes in the expression of immune mediators and genes related to immunological synapse, as well as differential infiltration of immune populations in the tumor environment were observed and led to a switch from a hot to a cold immune profile in three patients. Conclusions:This comprehensive analysis reveals patient-dependent spatial intra-tumor heterogeneity at the genetic, transcriptomic and epigenetic levels and in the immune landscape of the tumor microenvironment. Results support

Project description:BackgroundMalignant pleural mesothelioma (MPM) is a rare and intractable disease exhibiting a remarkable intratumoral heterogeneity and dismal prognosis. Although immunotherapy has reshaped the therapeutic strategies for MPM, patients react with discrepant responsiveness.MethodsHerein, we recruited 333 MPM patients from 5 various cohorts and developed an in-silico classification system using unsupervised Non-negative Matrix Factorization and Nearest Template Prediction algorithms. The genomic alterations, immune signatures, and patient outcomes were systemically analyzed across the external TCGA-MESO samples. Machine learning-based integrated methodology was applied to identify a gene classifier for clinical application.ResultsThe gene expression profiling-based classification algorithm identified immune-related subtypes for MPMs. In comparison with the non-immune subtype, we validated the existence of abundant immunocytes in the immune subtype. Immune-suppressed MPMs were enriched with stroma fraction, myeloid components, and immunosuppressive tumor-associated macrophages (TAMs) as well exhibited increased TGF-β signature that informs worse clinical outcomes and reduced efficacy of anti-PD-1 treatment. The immune-activated MPMs harbored the highest lymphocyte infiltration, growing TCR and BCR diversity, and presented the pan-cancer immune phenotype of IFN-γ dominant, which confers these tumors with better drug response when undergoing immune checkpoint inhibitor (ICI) treatment. Genetically, BAP1 mutation was most commonly found in patients of immune-activated MPMs and was associated with a favorable outcome in a subtype-specific pattern. Finally, a robust 12-gene classifier was generated to classify MPMs with high accuracy, holding promise value in predicting patient survival.ConclusionsWe demonstrate that the novel classification system can be exploited to guide the identification of diverse immune subtypes, providing critical biological insights into the mechanisms driving tumor heterogeneity and responsible for cancer-related patient prognoses.

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