Dataset Information

Characterization of Hypoxia-associated Molecular Features to Aid Hypoxia-Targeted Therapy.

ABSTRACT: Tumor hypoxia is a major contributor to resistance to anti-cancer therapies. Given that the results of hypoxia-targeted therapy trials have been disappointing, a more personalized approach may be needed. Here we characterize multi-OMIC molecular features associated with tumor hypoxia and identify molecular alterations that correlate with both drug-resistant and drug-sensitive responses to anti-cancer drugs. Based on a well-established hypoxia gene expression signature, we classify about 10,000 tumor samples into hypoxia score-high and score-low groups across different cancer types from The Cancer Genome Atlas and demonstrate their prognostic associations. We then identify various types of molecular features associated with hypoxia status that correlate with drug resistance but, in some cases, also with drug sensitivity, contrasting the conventional view that hypoxia confers drug resistance. We further show that 110 out of 121 (90.9%) clinically actionable genes can be affected by hypoxia status and experimentally validate the predicted effects of hypoxia on the response to several drugs in cultured cells. Our study provides a comprehensive molecular-level understanding of tumor hypoxia and may have practical implications for clinical cancer therapy.

SUBMITTER: Ye Y

PROVIDER: S-EPMC6980239 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Characterization of Hypoxia-associated Molecular Features to Aid Hypoxia-Targeted Therapy.

Ye Youqiong Y Hu Qingsong Q Chen Hu H Liang Ke K Yuan Yuan Y Xiang Yu Y Ruan Hang H Zhang Zhao Z Song Anren A Zhang Huiwen H Liu Lingxiang L Diao Lixia L Lou Yanyan Y Zhou Bingying B Wang Li L Zhou Shengtao S Gao Jianjun J Jonasch Eric E Lin Steven H SH Xia Yang Y Lin Chunru C Yang Liuqing L Mills Gordon B GB Liang Han H Han Leng L

Nature metabolism 20190318 4

Tumor hypoxia is a major contributor to resistance to anti-cancer therapies. Given that the results of hypoxia-targeted therapy trials have been disappointing, a more personalized approach may be needed. Here we characterize multi-OMIC molecular features associated with tumor hypoxia and identify molecular alterations that correlate with both drug-resistant and drug-sensitive responses to anti-cancer drugs. Based on a well-established hypoxia gene expression signature, we classify about 10,000 t ...[more]

PMID: 31984309

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Project description:Objective: Tumor hypoxia is a key factor in resistance to anti-cancer treatment. Herein, this study aimed to characterize hypoxia-related molecular subtypes and assess their correlations with immunotherapy and targeted therapy in clear cell renal cell carcinoma (ccRCC). Materials: We comprehensively analyzed copy number variation (CNV), somatic mutation, transcriptome expression profile and clinical information for ccRCC from TCGA and ICGC databases. Based on 98 prognosis-related hypoxia genes, samples were clustered using unsupervized non-negative matrix factorization (NMF) analysis. We characterized the differences between subtypes concerning prognosis, CNV, somatic mutations, pathways, immune cell infiltrations, stromal/immune scores, tumor purity, immune checkpoint inhibitors (ICI), response to immunotherapy and targeted therapy and CXC chemokines. Based on differentially expressed genes (DEGs) between subtypes, a prognostic signature was built by LASSO Cox regression analysis, followed by construction of a nomogram incorporating the signature and clinical features. Results: Two hypoxia-related molecular subtypes (C1 and C2) were constructed for ccRCC. Differential CNV, somatic mutations and pathways were found between subtypes. C2 exhibited poorer prognosis, higher immune/stromal scores, and lower tumor purity than C1. Furthermore, C2 had more sensitivity to immunotherapy and targeted therapy than C1. The levels of CXCL1/2/3/5/6/8 chemokines in C2 were distinctly higher than in C1. Consistently, DEGs between subtypes were significantly enriched in cytokine-cytokine receptor interaction and immune responses. This subtype-specific signature can independently predict patients' prognosis. Following verification, the nomogram could be utilized for personalized prediction of the survival probability. Conclusion: Our findings characterized two hypoxia-related molecular subtypes for ccRCC, which can assist in identifying high-risk patients with poor clinical outcomes and patients who can benefit from immunotherapy or targeted therapy.

Project description:The worldwide prevalence of pancreatic cancer has been rising in recent decades, and its prognosis has not improved much. The imbalance of substance and energy metabolism in tumour cells is among the primary causes of tumour formation and occurrence, which is often controlled by the neuroendocrine system. We applied Cox and LASSO regression analysis to develop a neuroendocrine regulation- and metabolism-related prognostic risk score model with three genes (GSK3B, IL18 and VEGFA) for pancreatic cancer. TCGA dataset served as the training and internal validation sets, and GSE28735, GSE62452 and GSE57495 were designated as external validation sets. Patients classified as the low-risk population (category, group) exhibited considerably improved survival duration in contrast with those classified as the high-risk population, as determined by the Kaplan-Meier curve. Then, we combined all the samples, and divided them into three clusters using unsupervised clustering analysis. Unsupervised clustering, t-distributed stochastic neighbor embedding (t-SNE), and principal component analysis (PCA) were further utilized to demonstrate the reliability of the prognostic model. Moreover, the risk score was shown to independently function as a predictor of pancreatic cancer in both univariate and multivariate Cox regression analyses. The results of gene set enrichment analysis (GSEA) illustrated that the low-risk population was predominantly enriched in immune-associated pathways. "ESTIMATE" algorithm, single-sample GSEA (ssGSEA) and the Tumor Immune Estimation Resource (TIMER) database showed immune infiltration ratings were enhanced in the low-risk category in contrast with the high-risk group. Tumour immune dysfunction and exclusion (TIDE) database predicted that immunotherapy for pancreatic cancer may be more successful in the high-risk than in the low-risk population. Mutation analysis illustrated a positive link between the tumour mutation burden and risk score. Drug sensitivity analysis identified 44 sensitive drugs in the high- and low-risk population. GSK3B expression was negatively correlated with Oxaliplatin, and IL18 expression was negatively correlated with Paclitaxel. Lastly, we analyzed and verified gene expression at RNA and protein levels based on GENPIA platform, HPA database and quantitative real-time PCR. In short, we developed a neuroendocrine regulation- and metabolism-associated prognostic model for pancreatic cancer that takes into account the immunological microenvironment and drug sensitivity.

Dataset Information

Characterization of Hypoxia-associated Molecular Features to Aid Hypoxia-Targeted Therapy.

Publications

Characterization of Hypoxia-associated Molecular Features to Aid Hypoxia-Targeted Therapy.

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