Burden of unique and low prevalence somatic mutations correlates with cancer survival

Klebanov, Nikolai; Artomov, Mykyta; Goggins, William B.; Daly, Eoin; Daly, Mark J.; Tsao, Hensin

doi:10.1038/s41598-019-41015-5

Cited by 77 publications

(56 citation statements)

References 32 publications

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“…TMB represents the overall amount of somatic mutations that could generate neoantigens to elicit immune response [32][33][34]. Consistently, tumors with high TMB scores contain more TILs [35] and are associated with better response to immunotherapy [36], such as melanoma [37] and NSCLC [38], with high TMB have shown better response and treatment results in immune checkpoint blockade treatment. In our analysis, we found that late-stage OCs tend to have higher TMB, which has not been described before.…”

Section: Discussionmentioning

confidence: 98%

Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

Fan

Lei

Lin

et al. 2020

Preprint

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Background: Checkpoint block-mediated tumor immunotherapy has obtained huge success in treating various malignancies. However, the efficacy of immunotherapies against ovarian cancer (OC) was low largely due to limited information on the tumor immune microenvironment (TIME) of OC. This results in lacking reliable biomarkers to select the right patients for immunotherapy and prognosis prediction. Published studies have reported that high tumor mutation burden (TMB) can generate many neoantigens resulting in a higher degree of an anti-tumor immune response. However, the correlation between TMB and TIME of OC remains controversial let along a collection of TMB information is time and resource consuming. In this study, we considered the TMB and TIME of OC comprehensively to categorize patients based on their tumor local immune status and provide information that could guide further OC immunotherapy trials as well as the prognosis prediction.Method: OC RNAseq data were downloaded from The Cancer Genome Atlas (TCGA) and divided into TMBhigh and TMBlow subgroups according to TMB scores for comparison. OC cases were also grouped into immunityhigh or immunitylow based on their profiles of tumor-infiltrating leukocytes (TILs) analyzed by ssGSEA (Single Sample Gene Set Enrichment Analysis). Besides, the function of genes enriched in both TMBhigh and immunityhigh was analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), and protein-protein interaction (PPI) network. The correlation of each gene with overall survival was also analyzed.Results: Positive association was observed between the OC grade and the TMB. Both TMBhigh and immunityhigh were significantly correlated with a better prognosis. However, different from the findings of other studies, TMB of OC didn’t correlated with preferable TIME in our analysis. By comparing the up-regulated signature genes in TMBhigh and immunityhigh cases, we found 14 overlapped genes that were mainly involved in immune response-related pathways. We further analyzed the prognostic value of these 14 genes and found the upregulation of 4 of them, CXCL13, FCRLA, PLA2G2D, and MS4A1 are significantly associated with better survival. With available data collected form a melanoma cohort, we also found that these four genes are positively associated with better response to immune checkpoint blockade-based immunotherapy.Conclusion: By comparing signature genes enriched in TMBhigh and immunityhigh subgroups, four genes, CXCL13, FCRLA, PLA2G2D, and MS4A1, were found positively correlated with both OC immune infiltration and better prognosis. Additionally, these four genes predicted the response to checkpoint blockade-based immunotherapy in a melanoma cohort indicating that they can also be used as biomarkers for further immunotherapy clinical trials of OC.

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Section: Discussionmentioning

confidence: 98%

Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

Fan

Lei

Lin

et al. 2020

Preprint

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“…And new research suggests high TMB and neoantigen load in tumors have been associated with an enhanced response to immune checkpoint blockade therapy [36] . And some researches have also shown that high TMB was inclined to confer a survival benefit in diverse cancers with more non-recurrent mutations, and higher TMB also has been reported to have higher immune infiltration in tumors [37] . Such as melanoma [38] and NSCLC [39] , with high TMB have shown better response and treatment results in immune checkpoint blockade treatment.…”

Section: Discussionmentioning

confidence: 99%

Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

Fan¹,

Liu²,

Li³

et al. 2020

Preprint

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Background : Ovarian cancer (OC) is a serious tumor disease in gynecology. Many papers have reported that high tumor mutational burden (TMB) can generate many neoantigens to result in a higher degree of tumor immune infiltration, so our study aims to predict the key molecules in OC immunotherapy by combined TMB with immunoactivity-related gene. Method: We divided OC cases into two groups: the low & high TMB group hinged on the somatic mutation data from the Cancer Genome Atlas (TCGA). We also used single-sample gene set enrichment analysis (ssGSEA) scores of immune cell types to conduct unsupervised clustering of OC patients in the TCGA cohort and some of them were defined as the low & high immunity group. Besides, to further understand the function of these genes, we conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, protein-protein interaction network, survival prognosis analysis and immune infiltration analysis. Finally, the effects on prognosis and immunotherapy in OC patients were explored by the Group on Earth Observations verification the patients' responses to immunotherapy. Results: We found that the higher the TMB was associated with the higher OC grades. Moreover, both high TMB and high immunity were significantly correlated with a good prognosis of OC. Then, 14 up-regulated differential expression genes (Up-DEGs) that were closely related to the prognosis of OC patients were screened according to the high TMB group and the high immunity group. Next, pathway analysis revealed that Up-DGEs were mainly involved in immune response and T cell proliferation. Finally, four genes had a good prognosis and were validated in the GEO dataset which included CXCL13, FCRLA, PLA2G2D, and MS4A1. We also identified that four genes had a good prognosis in melanoma patients treated with anti-PD-L1 and anti-CTLA-4 in the TIDE database. Conclusion: High TMB can promote immune cell infiltration and increases immune activity. And our analysis also demonstrated that the higher the TMB, the higher the immune activity, the better the prognosis of OC. Altogether, we found that CXCL13, FCRLA, PLA2G2D, and MS4A1 may be biomarkers for OC immunotherapy. Keywords: ovarian cancer, TMB, immune cells infiltration, survival prognosis.

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“…TMB represents the overall amount of somatic mutations that could generate neoantigens to elicit immune response [31][32][33][34]. Consistently, tumors with high TMB scores contain more TILs and are associated with better response to immunotherapy response [35], such as melanoma [36] and nonsmallcell lung cancer (NSCLC) [37], with high TMB have shown better response and treatment results in immune checkpoint blockade treatment. However, the clinical application of TMB as a prognostic marker was both time and nancial consuming, and the predictive signi cance of TMB has been questioned by some other studies [15,16].…”

Section: Discussionmentioning

confidence: 99%

Identification of a Gene Set Correlated with Immune Status in Ovarian Cancer by Transcriptome-Wide Data Mining

Fan

Lei

Lin

et al. 2020

Preprint

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Background: Immune checkpoint blocking (ICB) immunotherapy has achieved great success in the treatment of various malignancies. Although not have been approved for the treatment of ovarian cancer (OC), it has been actively tested for the treatment of OC. Response to immunotherapy closely relies on the immune profile of tumors. However, biomarkers that could indicate the immune status of OC and predict the response to ICB are rare.Method: We downloaded RNAseq and clinical data of ovarian cancer from The Cancer Genome Atlas (TCGA). Up-regulated differentially expressed genes (Up-DEGs) were identified by analyzing the gene expression levels using the “limma, version=3.8” package in R software. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the “GSVA” and “limma” package in R software. Furthermore, gene set enrichment analysis (GSEA) and investigation of protein-protein interaction (PPI) networks were performed to clarify the function of Up-DEGs in both TMBhigh group and immunityhigh group. The correlation of genes with overall survival was also analyzed by conducted Kaplan-Meier survival analysis. Finally, TIMER and TIDE were used to predict gene immune function and its association with immunotherapy.Results: Data analysis revealed both TMBhigh and immunityhigh were significantly related to better survival of OC. Four genes, CXCL13, FCRLA, MS4A1, and PLA2G2D were found positively correlated with better prognosis of OC and mainly involved in immune response-related pathways. We also found that these four genes were positively correlated with better response to immune checkpoint blockade-based immunotherapy according to available data of a melanoma cohort.Conclusion: CXCL13, FCRLA, MS4A1, and PLA2G2D may be used as potential therapeutic biomarkers for reflecting OC immune status and predicting response to immunotherapy.

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Burden of unique and low prevalence somatic mutations correlates with cancer survival

Cited by 77 publications

References 32 publications

Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

Identification of a Gene Set Correlated with Immune Status in Ovarian Cancer by Transcriptome-Wide Data Mining

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