Dataset Information

Evolutionary genetic algorithm identifies IL2RB as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.

ABSTRACT: Identifying robust predictive biomarkers to stratify colorectal cancer (CRC) patients based on their response to immune-checkpoint therapy is an area of unmet clinical need. Our evolutionary algorithm Atlas Correlation Explorer (ACE) represents a novel approach for mining The Cancer Genome Atlas (TCGA) data for clinically relevant associations. We deployed ACE to identify candidate predictive biomarkers of response to immune-checkpoint therapy in CRC. We interrogated the colon adenocarcinoma (COAD) gene expression data across nine immune-checkpoints (PDL1, PDCD1, CTLA4, LAG3, TIM3, TIGIT, ICOS, IDO1 and BTLA). IL2RB was identified as the most common gene associated with immune-checkpoint genes in CRC. Using human/murine single-cell RNA-seq data, we demonstrated that IL2RB was expressed predominantly in a subset of T-cells associated with increased immune-checkpoint expression (P < 0.0001). Confirmatory IL2RB immunohistochemistry (IHC) analysis in a large MSI-H colon cancer tissue microarray (TMA; n = 115) revealed sensitive, specific staining of a subset of lymphocytes and a strong association with FOXP3+ lymphocytes (P < 0.0001). IL2RB mRNA positively correlated with three previously-published gene signatures of response to immune-checkpoint therapy (P < 0.0001). Our evolutionary algorithm has identified IL2RB to be extensively linked to immune-checkpoints in CRC; its expression should be investigated for clinical utility as a potential predictive biomarker for CRC patients receiving immune-checkpoint blockade.

SUBMITTER: Alderdice M

PROVIDER: S-EPMC8057496 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Evolutionary genetic algorithm identifies <i>IL2RB</i> as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.

Alderdice Matthew M Craig Stephanie G SG Humphries Matthew P MP Gilmore Alan A Johnston Nicole N Bingham Victoria V Coyle Vicky V Senevirathne Seedevi S Longley Daniel B DB Loughrey Maurice B MB McQuaid Stephen S James Jacqueline A JA Salto-Tellez Manuel M Lawler Mark M McArt Darragh G DG

NAR genomics and bioinformatics 20210420 2

Identifying robust predictive biomarkers to stratify colorectal cancer (CRC) patients based on their response to immune-checkpoint therapy is an area of unmet clinical need. Our evolutionary algorithm Atlas Correlation Explorer (ACE) represents a novel approach for mining The Cancer Genome Atlas (TCGA) data for clinically relevant associations. We deployed ACE to identify candidate predictive biomarkers of response to immune-checkpoint therapy in CRC. We interrogated the colon adenocarcinoma (CO ...[more]

PMID: 33928242

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Project description:Somatostatin receptor 2 (SSTR2), the most abundant receptor of somatostatin (SST), possesses immunoreactivity and is altered in many cancers. However, the association between SSTR2 and efficacy of immune checkpoint inhibitors (ICIs) has not yet been reported. Immunohistochemistry (IHC) information across 20 cancers was collected from the Human Protein Atlas (HPA) and used to analyze the expression of SSTR2. Immune signatures collected from public databases, such as BioCarta or Reactome, were used to investigate the association between SSTR2 and the tumor microenviroment in the Cancer Genome Atlas (TCGA). Data from cohorts treated with ICIs were collected to assess whether SSTR2 is associated with benefits from ICIs treatment. In the HPA, we found the SSTR2 IHC-positive rate of 13 cancers to be above 50%. Five types of cancer express SSTR2 mildly (positive rate: 25%-50%), while the remaining two types of cancer barely stained SSTR2-positive (positive rate: 0%-24%). In TCGA analysis, immune cell signatures and immune function pathways were enriched in high SSTR2 expression groups in most cancers. In each ICIs treated cohort, patients with high SSTR2 expression experienced numerically superior objective response rate (Braun: 14.8% vs 13.4%, p = 0.85; Gide: 69.4% vs 40.5%, p = 0.025; Mariathasan: 22.4% vs 16.7%, p = 0.233; Miao: 37.5% vs 11.8%; Riaz: 32.0% vs 7.7%, p = 0.067) and overall survival (Braun: HR (95%CI): 0.80 [0.62-1.04], p = 0.80; Gide: HR (95%CI): 0.61 [0.29-1.30], p = 0.20; Mariathasan: HR (95%CI): 0.83 [0.64-1.08], p = 0.16; Miao: HR (95%CI): 0.24 [0.086-0.65], p = 0.0028; Nathanson cohort: HR (95%CI): 0 [0-inf], p = 0.18; Riaz: HR (95%CI): 0.24 [0.086-0.65], p = 0.028) than patients with low SSTR2 expression. In pooled cohort, we found these differences were significant (Pool: 24.6% vs 16.7%, p = 0.0077; HR (95% CI): 0.77 [0.65-0.91], p = 0.0018). Our results suggest that SSTR2 is a potential predictive biomarker for response to ICIs.

Project description:Background: Nuclear factor interleukin 3 (NFIL3) mainly focuses on the regulation of the circadian rhythm and immune system. However, the potential role of NFIL3 in human cancers has not been studied extensively. Methods: We retrieved original data from the TCGA, TARGET, and GTEx datasets via the UCSC Xena browser (http://genome.ucsc.edu/) and integrated them using R version 3.6.4. NFIL3 expression was assessed using resources such as UCSC, GEPIA (http://gepia.cancer-pku.cn/), Kaplan-Meier Plotter (KM Plotter; https://kmplot.com/), and the Human Protein Atlas (HPA; https://www.proteinatlas.org/) databases. To investigate the prognostic implications of NFIL3, we utilized GEPIA, Kaplan-Meier Plotter, and PrognoScan (http://www.abren.net/PrognoScan/) datasets. For a comprehensive analysis across multiple cancer types, we employed pan-cancer data from UCSC, examining associations between NFIL3 expression and genomic heterogeneity, tumor mutational burden (TMB), microsatellite instability (MSI), tumor purity, and neoantigens. Furthermore, we explored the relationships between NFIL3 expression and the infiltration of immune cells and the expression of immune checkpoint genes. In the context of ovarian cancer, we validated the expression and functional relevance of NFIL3. Cell Counting Kit 8 (CCK8) assays were conducted to assess cell proliferation, while scratch and transwell assays were employed to evaluate cell migration capabilities. We further examined the interaction between NFIL3 and the p53 signaling pathway through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot analysis, immunofluorescence confocal, and Coimmunoprecipitation (Co-IP) assays. Results: In general, NFIL3 expression in cancerous tissues exhibited diminished levels when compared to normal tissue samples. Notably, NFIL3 expression demonstrated a robust correlation with several pivotal aspects, including prognosis, immune cell infiltration, immune checkpoint-related genes, TMB, MSI, tumor purity, and the presence of neoantigens. Experimental investigations involving scratch assays, transwell assays, and assessments of cell proliferation in ovarian cancer cells have provided indications that NFIL3 may exert influence over cell migration and proliferation processes. Moreover, a substantial association between NFIL3 and the p53 signaling pathway was discerned through Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, with subsequent validation through qRT-PCR, Western blot analysis, immunofluorescence confocal, and co-immunoprecipitation (Co-IP) assays. Conclusions: Therefore, we concluded NFIL3 may serve as a possible prognostic and immunological pan-cancer biomarker.

Project description:Immune checkpoint inhibitors (ICIs) therapy elicits admirable anti-tumor responses across many types of cancer. Growing evidence point to a link to Mediator complex subunit 12 (MED12) and DNA damage repair (DDR) and TGF-β signing, while the clinical data on the association of MED12 and ICIs response are lacking. In this study, clinical and whole-exome sequencing (WES) data from published studies were merged as a WES cohort to explore the association between MED12 mutation (MED12-Mut) and ICIs efficiency across cancers. Then, Memorial Sloan Kettering Cancer Center (MSKCC) cohort was used for validating our findings. The Cancer Genome Atlas (TCGA) cohort was used to perform anti-tumor immunity and prognosis analysis. In the WES cohort (n = 474), significant differences were detected between MED12-Mut and MED12-wildtype (MED12-Wt) patients regarding durable clinical benefit (DCB, 80.00% vs. 53.67%, P = 0.022). In addition, significantly prolonged PFS was observed in MED12-Mut patients (mPFS: not reached, NR vs. 5.87 months, HR: 0.38, 95% CI 0.17-0.85, log-rank P = 0.015), After taking into account age, gender, metastasis, treatment and TMB status, the result of multivariable Cox proportional hazards regression showed significantly better PFS (HR:0.40, 95% CI 0.18-0.92; P = 0.031). In the MSKCC cohort (n = 1513), overall survival advantage was achieved in MED12-Mut patients (mOS: 41 vs. 19 months, HR:0.54, 95%CI 0.34-0.85; log-rank P = 0.007), after taking into account same factors in WES cohort, this link still existed (HR: 0.60, 95% CI: 0.38-0.96, P = 0.033), Notably, TMB was also found significantly higher in MED12-Mut patients in both WES and MSKCC cohort. Further tumor-infiltrating lymphocytes and DDR-related gene analysis revealed anti-tumor immunity in MED12-Mut patients. Totally, MED12-Mut successfully predicted better clinical outcomes in ICIs-treated pan-cancer cohort, indicating that MED12-Mut could serve as a potential predictive biomarker for immune checkpoint inhibitors in pan-cancer.

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Evolutionary genetic algorithm identifies IL2RB as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.

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Evolutionary genetic algorithm identifies <i>IL2RB</i> as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.

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Evolutionary genetic algorithm identifies IL2RB as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.

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Evolutionary genetic algorithm identifies &lt;i&gt;IL2RB&lt;/i&gt; as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.

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Evolutionary genetic algorithm identifies <i>IL2RB</i> as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.