Abstract
Purpose
HGSOC is a kind of gynecological cancer with high mortality and strong heterogeneity. The study used multi-omics and multiple algorithms to identify novel molecular subtypes, which can help patients obtain more personalized treatments.
Methods
Firstly, the consensus clustering result was obtained using a consensus ensemble of ten classical clustering algorithms, based on mRNA, lncRNA, DNA methylation, and mutation data. The difference in signaling pathways was evaluated using the single-sample gene set enrichment analysis (ssGSEA). Meanwhile, the relationship between genetic alteration, response to immunotherapy, drug sensitivity, prognosis, and subtypes was further analyzed. Finally, the reliability of the new subtype was verified in three external datasets.
Results
Three molecular subtypes were identified. Immune desert subtype (CS1) had little enrichment in the immune microenvironment and metabolic pathways. Immune/non-stromal subtype (CS2) was enriched in the immune microenvironment and metabolism of polyamines. Immune/stromal subtype (CS3) not only enriched anti-tumor immune microenvironment characteristics but also enriched pro-tumor stroma characteristics, glycosaminoglycan metabolism, and sphingolipid metabolism. The CS2 had the best overall survival and the highest response rate to immunotherapy. The CS3 had the worst prognosis and the lowest response rate to immunotherapy but was more sensitive to PARP and VEGFR molecular-targeted therapy. The similar differences among three subtypes were successfully validated in three external cohorts.
Conclusion
We used ten clustering algorithms to comprehensively analyze four types of omics data, identified three biologically significant subtypes of HGSOC patients, and provided personalized treatment recommendations for each subtype. Our findings provided novel views into the HGSOC subtypes and could provide potential clinical treatment strategies.
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All data used in this study can be downloaded from the TCGA (https://www.portal.gdc.cancer.gov/repository), MSigDB (http://www.gsea-msigdb.org/gsea/msigdb/), Xena Public Data Hubs (https://xena.ucsc.edu/public-hubs), Firehose (http://www.firehose.org/), cBioPortal (https://www.cbioportal.org/datasets), GEO (https://www.ncbi.nlm.nih.gov/geo), ICGC (https://www.daco.icgc.org/), ArrayExpress database (https://www.ebi.ac.uk/biostudies/arrayexpress).
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Funding
This work was supported by the Natural Science Foundation of Jiangsu Province (Grants No. BK20190549), the National Natural Science Foundation of China under Grant Numbers 81973145 and 82273735, and Key R&D Program of Jiangsu Province (Social Development) under Grand Number BE2020694.
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All authors contributed to the study conception and design. DC contributed to data collection, data processing, program implementation, and manuscript writing. TL provided scientific views and research suggestions. YL and JF designed and guided this study. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Heatmap of subtype-specific upregulated pathways using gene set enrichment analysis among the three subtypes (PDF 178 KB)
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Consistency heatmap using Kappa statistics and the prediction of immune therapy response. A–D Consistency heatmap using Kappa statistics between COMIC and PAM in TCGA discovery cohort, and between NTP and PAM in external validation cohorts (GSE32062, OV-AU and E-MTAB-386, respectively). E–F The bar plots of prediction by the TIDE method about immune therapy responders and non-responders in external validation cohorts (OV-AU and E-MTAB-386, respectively) (PDF 471 KB)
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Cai, D., Liu, T., Fang, J. et al. Molecular cluster mining of high-grade serous ovarian cancer via multi-omics data analysis aids precise medicine. J Cancer Res Clin Oncol 149, 9151–9165 (2023). https://doi.org/10.1007/s00432-023-04831-x
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DOI: https://doi.org/10.1007/s00432-023-04831-x