Somatic mutations in homologous recombination pathway predict favourable prognosis after immunotherapy across multiple cancer types

Guo, Chunguang; Li, Jing; Xu, Hui; Lu, Taoyuan; Wang, Libo; Liu, Long; Han, Xinwei

doi:10.1002/ctm2.619

Cited by 32 publications

(31 citation statements)

References 10 publications

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“…The Microenvironment Cell Populations-counter (MCPcounter) approach was utilized to quantify the absolute infiltration abundance of eight immune and two stromal cell subpopulations from the transcriptomic profiles of TCGA-CRC (25). The expression patterns of 27 immune checkpoint molecules between the two stemness clusters were further explored (26)(27)(28)(29).…”

Section: Tumor Microenvironment Profilesmentioning

confidence: 99%

Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options

Liu

Weng

et al. 2022

Front. Immunol.

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BackgroundStemness refers to the capacities of self-renewal and repopulation, which contributes to the progression, relapse, and drug resistance of colorectal cancer (CRC). Mounting evidence has established the links between cancer stemness and intratumoral heterogeneity across cancer. Currently, the intertumoral heterogeneity of cancer stemness remains elusive in CRC.MethodsThis study enrolled four CRC datasets, two immunotherapy datasets, and a clinical in-house cohort. Non-negative matrix factorization (NMF) was performed to decipher the heterogeneity of cancer stemness. Multiple machine learning algorithms were applied to develop a nine-gene stemness cluster predictor. The clinical outcomes, multi-omics landscape, potential mechanisms, and immune features of the stemness clusters were further explored.ResultsBased on 26 published stemness signatures derived by alternative approaches, we decipher two heterogeneous clusters, low stemness cluster 1 (C1) and high stemness cluster 2 (C2). C2 possessed a higher proportion of advanced tumors and displayed worse overall survival and relapse-free survival compared with C1. The MSI-H and CMS1 tumors tended to enrich in C1, and the mesenchymal subtype CMS4 was the prevalent subtype of C2. Subsequently, we developed a nine-gene stemness cluster predictor, which robustly validated and reproduced our stemness clusters in three independent datasets and an in-house cohort. C1 also displayed a generally superior mutational burden, and C2 possessed a higher burden of copy number deletion. Further investigations suggested that C1 enriched numerous proliferation-related biological processes and abundant immune infiltration, while C2 was significantly associated with mesenchyme development and differentiation. Given results derived from three algorithms and two immunotherapeutic cohorts, we observed C1 could benefit more from immunotherapy. For patients with C2, we constructed a ridge regression model and further identified nine latent therapeutic agents, which might improve their clinical outcomes.ConclusionsThis study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options. Current work not only provided new insights into the heterogeneity of cancer stemness, but also shed light on optimizing decision-making in immunotherapy and chemotherapy.

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Section: Tumor Microenvironment Profilesmentioning

confidence: 99%

Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options

Liu

Weng

et al. 2022

Front. Immunol.

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“…To estimate the stromal and immune cells in tumor tissues, the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was adopted to calculate the stromal score, immune score, and tumor purity of each patient based on ssGSEA ( Yoshihara et al, 2013 ; Liu et al, 2021a ; Liu et al, 2021b ; Liu et al, 2021c ; Liu et al, 2022a ; Liu et al, 2022b ). To evaluate the proportion of 22 immune cells, the abundance of immune cell infiltration in the low-NOP2-expressing and high-NOP2-expressing groups was estimated using the cell type identification by estimating the relative subsets of RNA transcripts (CIBERSORT) algorithm ( Newman et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Molecular Characterization Clinical and Immunotherapeutic Characteristics of m5C Regulator NOP2 Across 33 Cancer Types

Liu

Zhang

Lin

et al. 2022

Front. Cell Dev. Biol.

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Background: Recent studies have identified that RNA 5-methylcytosine (m5C) is a wide-spread epigenetic modification in tumorigenesis. However, the clinical and immunotherapeutic values of m5C regulator NOP2 in 33 cancers remain unclear.Methods: The mRNA expression data and clinical data of 33 cancers were downloaded from The Cancer Genome Atlas (TCGA) database. The immunotherapy data including GSE67501, GSE78220, GSE35640, and IMvigor210 were downloaded from the Gene Expression Omnibus (GEO) database and the website based on the Creative Commons 3.0 license (http://research-pub.Gene.com/imvigor210corebiologies). The expression, survival, clinical parameters, tumor mutation burden (TMB), microsatellite instability (MSI), and tumor microenvironment (TME) were evaluated. Finally, the relationship between NOP2 and immunotherapy response was further explored.Results: NOP2 was significantly upregulated in most cancers, and high NOP2 expression was associated with poor prognosis. TMB, MSI, and NOP2 activities were involved in the dysregulation of NOP2. NOP2 was closely associated with immune cell infiltration, immune modulators, and immunotherapeutic inactivation.Conclusions: We comprehensively explored the clinical and immunotherapeutic values of NOP2 in cancers, providing evidence regarding the function of NOP2 and its role in clinical treatment.

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“…“ClusterProfiler” ( Yu et al, 2012 ; Liu et al, 2021a ), a Bioconductor package, was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The terms with p < 0.05 were considered significant.…”

Section: Methodsmentioning

confidence: 99%

Development and Validation of Ischemic Events Related Signature After Carotid Endarterectomy

Guo

Cao

Zheng

et al. 2022

Front. Cell Dev. Biol.

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Background: Ischemic events after carotid endarterectomy (CEA) in carotid artery stenosis patients are unforeseeable and alarming. Therefore, we aimed to establish a novel model to prevent recurrent ischemic events after CEA.Methods: Ninety-eight peripheral blood mononuclear cell samples were collected from carotid artery stenosis patients. Based on weighted gene co-expression network analysis, we performed whole transcriptome correlation analysis and extracted the key module related to ischemic events. The biological functions of the 292 genes in the key module were annotated via GO and KEGG enrichment analysis, and the protein-protein interaction (PPI) network was constructed via the STRING database and Cytoscape software. The enrolled samples were divided into train (n = 66), validation (n = 28), and total sets (n = 94). In the train set, the random forest algorithm was used to identify critical genes for predicting ischemic events after CEA, and further dimension reduction was performed by LASSO logistic regression. A diagnosis model was established in the train set and verified in the validation and total sets. Furthermore, fifty peripheral venous blood samples from patients with carotid stenosis in our hospital were used as an independent cohort to validation the model by RT-qPCR. Meanwhile, GSEA, ssGSEA, CIBERSORT, and MCP-counter were used to enrichment analysis in high- and low-risk groups, which were divided by the median risk score.Results: We established an eight-gene model consisting of PLSCR1, ECRP, CASP5, SPTSSA, MSRB1, BCL6, FBP1, and LST1. The ROC-AUCs and PR-AUCs of the train, validation, total, and independent cohort were 0.891 and 0.725, 0.826 and 0.364, 0.869 and 0.654, 0.792 and 0.372, respectively. GSEA, ssGSEA, CIBERSORT, and MCP-counter analyses further revealed that high-risk patients presented enhanced immune signatures, which indicated that immunotherapy may improve clinical outcomes in these patients.Conclusion: An eight-gene model with high accuracy for predicting ischemic events after CEA was constructed. This model might be a promising tool to facilitate the clinical management and postoperative surveillance of carotid artery stenosis patients.

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Somatic mutations in homologous recombination pathway predict favourable prognosis after immunotherapy across multiple cancer types

Cited by 32 publications

References 10 publications

Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options

Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options

Molecular Characterization Clinical and Immunotherapeutic Characteristics of m5C Regulator NOP2 Across 33 Cancer Types

Development and Validation of Ischemic Events Related Signature After Carotid Endarterectomy

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