Dataset Information

A combination of intrinsic and extrinsic features improves prognostic prediction in malignant pleural mesothelioma.

ABSTRACT:

Background

Malignant pleural mesothelioma (MPM) is a lung pleural cancer with very poor disease outcome. With limited curative MPM treatment available, it is vital to study prognostic biomarkers to categorise different patient risk groups.

Methods

We defined gene signatures to separately characterise intrinsic and extrinsic features, and investigated their interactions in MPM tumour samples. Specifically, we calculated gene signature scores to capture the downstream pathways of major mutated driver genes (BAP1, NF2, SETD2 and TP53) as tumour-intrinsic features. Similarly, we inferred the infiltration levels for major immune cells in the tumour microenvironment to characterise tumour-extrinsic features. Lastly, we integrated these features with clinical factors to predict prognosis in MPM.

Results

The gene signature scores were more prognostic than the corresponding genomic mutations, mRNA and protein expression. High immune infiltration levels were associated with prolonged survival. The integrative model indicated that tumour features provided independent prognostic values than clinical factors and were complementary with each other in survival prediction.

Conclusions

By using an integrative model that combines intrinsic and extrinsic features, we can more correctly predict the clinical outcomes of patients with MPM.

SUBMITTER: Nguyen TT

PROVIDER: S-EPMC9596423 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

A combination of intrinsic and extrinsic features improves prognostic prediction in malignant pleural mesothelioma.

Nguyen Thinh T TT Lee Hyun-Sung HS Burt Bryan M BM Amos Christopher I CI Cheng Chao C

British journal of cancer 20220823 9

<h4>Background</h4>Malignant pleural mesothelioma (MPM) is a lung pleural cancer with very poor disease outcome. With limited curative MPM treatment available, it is vital to study prognostic biomarkers to categorise different patient risk groups.<h4>Methods</h4>We defined gene signatures to separately characterise intrinsic and extrinsic features, and investigated their interactions in MPM tumour samples. Specifically, we calculated gene signature scores to capture the downstream pathways of ma ...[more]

PMID: 35999269

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Project description:Malignant pleural mesothelioma (MPM) is a highly lethal, poorly understood neoplasm that is typically associated with asbestos exposure. We performed transcriptional profiling using high-density oligonucleotide microarrays containing approximately 22,000 genes to elucidate potential molecular and pathobiological pathways in MPM using discarded human MPM tumor specimens (n=40), normal lung specimens (n=4), normal pleura specimens (n=5), and MPM and SV40-immortalized mesothelial cell lines (n=5). In global expression analysis using unsupervised clustering techniques, we found two potential subclasses of mesothelioma which correlate loosely with tumor histology. We also identified sets of genes with expression levels that distinguish between multiple tumor subclasses, normal and tumor tissues, and tumors with different morphologies. Microarray gene expression data were confirmed using quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) and protein analysis for three novel candidate oncogenes (NME2, CRI1, and PDGFC) and one candidate tumor suppressor (GSN). Finally, we used bioinformatics tools (i.e. software) to create and explore complex physiological pathways that may be relevant in mesothelioma tumorigenesis, pathobiology, or both. Tissues and cell lines profiled using microarrays. Discarded MPM surgical specimens (n=40), normal pleura specimens (n=5), and normal lung specimens (n=4) were freshly collected (and snap frozen) from patients who underwent surgery at Boston's Brigham and Women's Hospital (BWH) between October 1998 and August 2000. All of these patients underwent extrapleural pneumonectomy with heated intra-pleural cisplatin chemotherapy delivered after the specimens were removed. All normal specimens were obtained from patients who were never diagnosed with MPM. Two human MPM cell lines (MS589 and MS428) were kindly provided by Jonathan A. Fletcher, M.D., Department of Pathology, BWH. The JMN1B MPM cell line19,20 has been described previously. The SV40-immortalized, non-tumorigenic mesothelial cell line (Met-5A)21 and the MPM cell line MSTO-211H22 were purchased from the American Type Culture Collection. Normal tissues were obtained from additional consented patients undergoing treatment for diseases other than MPM. All MPM samples used in these studies contained relatively pure tumor (greater than 50% tumor cells per high power field examined in a section adjacent to the tissue used). The microscopic slides from the patients' resection specimens were reviewed by one of the authors (J.G.), and the diagnosis and histologic subclassification of MPM confirmed in all cases. Linked clinical and pathological data were obtained for all patients who contributed tumor specimens. Specimens and data were rendered anonymous to protect patient confidentiality. Studies utilizing human tissues were approved by and conducted in accordance with the policies of the Institutional Review Board at BWH.

Project description:ObjectivesPrognostic models for malignant pleural mesothelioma (MPM) are needed to prevent potentially futile outcomes. We combined MPM plasma biomarkers with validated clinical prognostic indices to determine whether stratification of risk for death in 194 patients with MPM improved.MethodsIndividuals were recruited from three different centers: a discovery cohort (83 patients with MPM) created by combining patients from two U.S. centers and a separate, independent cohort from Canada (111 patients with MPM). Univariable and multivariable analyses were performed on the initial discovery and independent cohorts separately. In the multivariable analyses, prognostic factors were adjusted for the European Organisation for Research and Treatment of Cancer (EORTC) prognostic index (PI) of mesothelioma. The prognostic significance of adding plasma biomarker data to the PI was determined by using the likelihood ratio test, comparing models with and without the addition of biomarker to the clinical PI. The predictive ability of the biomarker was then assessed formally using Harrell's C-index by applying the fitted model variables of the discovery cohort to the second, independent cohort, including and not including the biomarker with the PI.ResultsHigher levels of osteopontin and mesothelin were individually associated with worse prognosis after adjusting for the PI. In the independent cohort, incorporating either plasma osteopontin or mesothelin into the baseline predictive PI model substantively and statistically significantly improved Harrell's C-statistic. In the final prognostic model, log-osteopontin, EORTC clinical prognostic index, and hemoglobin remained as independently significant predictors and the entire prognostic model improved the optimism-corrected Harrell's C-index significantly, from 0.718 (0.67-0.77) to 0.801 (0.77-0.84).ConclusionsThese data suggest a possible role for preoperative plasma biomarkers to improve the prognostic capability of the EORTC PI of MPM.

Project description:Introduction: Dysregulated genes play a critical role in the development and progression of cancer, suggesting their potential as novel independent biomarkers for cancer diagnosis and prognosis. Prognostic model-based gene expression profiles are not widely utilized in clinical medicine. We investigated the prognostic significance of an expression profile-based gene signature for outcome prediction in patients with malignant pleural mesothelioma (MPM). Methods: The gene expression profiles of a large cohort of patients with MPM were obtained and analyzed by repurposing publicly available microarray data. A gene-based risk score model was developed with the training dataset and then validated with the TCGA-MESO (mesothelioma) dataset. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance of survival prediction. The biological function of the prognostic genes was predicted using bioinformatics analysis. Results: Three genes in the training dataset (GSE2549) were identified as significantly associated with the overall survival (OS) of patients with MPM and were combined to develop a three-gene prognostic signature to stratify patients into low-risk and high-risk groups. The MPM patients of the training dataset in the low-risk group exhibited longer OS than those in the high-risk group (HR = 0.25, 95% CI = 0.11-0.56, P < 0.001). Similar prognostic values for the three-gene signature were observed in the validated TCGA-MESO cohort (HR = 0.53 95% CI = 0.33-0.85, P = 0.008). ROC analysis also demonstrated the good performance in predicting 3-year OS in the GEO and TCGA cohorts (KM-AUC for GEO = 0.989, KM-AUC for TCGA = 0.618). The C-statistic for the 3-gene model was 0.761. Validation with TCGA-MESO confirmed the model's ability to discriminate between risk groups in an alternative data set with fair performance (C-statistic: 0.68). Functional enrichment analysis suggested that these three genes may be involved in genetic and epigenetic events with known links to MPM. Conclusions: This study has identified and validated a novel 3-gene model to reliably discriminate patients at high and low risk of death in unselected populations of patients with MPM. Further larger, prospective multi-institutional cohort studies are necessary to validate this model.

Dataset Information

A combination of intrinsic and extrinsic features improves prognostic prediction in malignant pleural mesothelioma.

Background

Methods

Results

Conclusions

Publications

A combination of intrinsic and extrinsic features improves prognostic prediction in malignant pleural mesothelioma.

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