Characterization of hypoxia-associated molecular features to aid hypoxia-targeted therapy

Ye, Youqiong; Hu, Qingsong; Hu, Chen; Liang, Ke; Yuan, Yuan; Yu, Xiang; Ruan, Hang; Zhao, Zhongwei; Song, Anren; Zhang, Huiwen; Liu, Lingxiang; Diao, Lixia; Lou, Yanyan; Zhou, Bingying; Wang, Li; Zhou, Shengtao; Gao, Jianjun; Jonasch, Eric; Lin, Steven H.; Yang, Xia; Lin, Chunru; Yang, Liuqing; Mills, Gordon B.; Liang, Han; Han, Leng

doi:10.1038/s42255-019-0045-8

Cited by 148 publications

(139 citation statements)

References 68 publications

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“…3a and Supplementary Table 3). To reduce potential confounding effects, we employed a propensity score algorithm, which is an important statistical tool for controlling confounding in observational studies and has been widely used in clinical research 26,27 , to reweight potential confounding effect in a multivariate manner 26 (e.g., age at diagnosis, race, smoking status, tumor stage, histological type and tumor purity; see Methods; Supplementary Fig. 4b).…”

Section: Resultsmentioning

confidence: 99%

“…Immune checkpoint genes with known costimulatory or co-inhibitory effects in T cells were obtained from Auslander et al 40 We used gene set variation analysis 41 (GSVA) to compute the relative abundance of the immune cell population and GEP level in each sample based on the gene signatures of six immune cell populations from Charoentong et al 42 and the GEP gene signature from Ayers et al 14 CYT was calculated as the geometric mean of the gene expression of two cytolytic markers (GZMA and PRF1) 43 . To balance potential confounding factors, including age at diagnosis, race, smoking status, tumor stage, histological type and tumor purity, between female and male patients, we used the propensity score algorithm 26 . The patient's age and tumor purity are continuous variables, and remained confounding factors are categorical variables.…”

Section: Methodsmentioning

confidence: 99%

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Sex-associated molecular differences for cancer immunotherapy

et al. 2020

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Immune checkpoint blockade therapies have extended patient survival across multiple cancer lineages, but there is a heated debate on whether cancer immunotherapy efficacy is different between male and female patients. We summarize the existing meta-analysis to show inconsistent conclusions for whether gender is associated with the immunotherapy response. We analyze molecular profiling from ICB-treated patients to identify molecular differences for immunotherapy responsiveness. We perform comprehensive analyses for patients from The Cancer Genome Atlas (TCGA) and reveal divergent patterns for sex bias in immune features across multiple cancer types. We further validate our observations in multiple independent data sets. Considering that the majority of clinical trials are in melanoma and lung cancer, meta-analyses that pool multiple cancer types have limitations to discern whether cancer immunotherapy efficacy is different between male and female patients. Future studies should include omics profiling to investigate sex-associated molecular differences in immunotherapy.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Sex-associated molecular differences for cancer immunotherapy

et al. 2020

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“…Hypoxia induces a series of biological changes that contribute to tumorigenesis and metastasis 24,64 , and are associated with resistance to chemotherapy, radiation therapy, drug therapy and immunotherapy. Therefore, understanding the effect of hypoxia on multi-omics signatures is crucial to improving the outcomes of cancer therapy 65 .…”

Section: Discussionmentioning

confidence: 99%

Metastatic signaling of hypoxia-related genes across TCGA Pan-Cancer types

López‐Cortés

Guevara-Ramírez

Guerrero

et al. 2020

Preprint

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Many primary-tumor subregions have low levels of molecular oxygen, termed hypoxia.Hypoxic tumors are at elevated risk for local failure and distant metastasis. Metastatic disease is the leading cause of cancer-related deaths and involves critical interactions between tumor cells and the microenvironment. Here we focused on elucidating the molecular hallmarks of tumor hypoxia that remains poorly defined. To fill this gap, we analyzed the genomic alterations and hypoxia score of 233 hypoxia-related genes of 6,343 individuals across 17 TCGA Pan-Cancer types. In addition, we analyzed a protein-protein interactome (PPi) network and the shortest paths from hypoxic proteins to metastasis. As results, mRNA high alteration was prevalent in all cancer types. Genomic alterations and hypoxia score presented a highest frequency in tumor stage 4 and positive metastasis status in all cancer types. The most significant signaling pathways were HIF-1, ErbB, PI3K-Akt, FoxO, mTOR, Ras and VEGF. The PPi network revealed a strong association among hypoxic proteins, cancer driver proteins and metastasis driver proteins. The analysis of shortest paths revealed 99 ways to spread metastasis signaling from hypoxic proteins. Additionally, we proposed 62 hypoxic genes strongly associated with metastasis and 27 of them with high amount of genomic alterations. Overall, tumor hypoxia may drive aggressive molecular features across cancer types. Hence, we identified potential biomarkers and therapeutic targets regulated by hypoxia that could be incorporated into strategies aimed at improving novel drug development and treating metastasis.

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“…Based on cellular response to hypoxia signatures was significantly active in glycolysis-high tumors shown in the above result ( Fig.3A), we sought to determine whether there is a relationship between them through big data research. Since direct measurements of environment status of the cells are not available, the hypoxia status was defined by an established gene expression signature and was widely applied in previous researches [28][29][30] .…”

Section: The Association Between Glycolysis and Hypoxiamentioning

confidence: 99%

Persistent cell proliferation signals correlates with increased glycolysis in tumor hypoxia microenvironment across cancer types

Wei

Huang

et al. 2020

Preprint

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Background：Altered metabolism is a hallmark of cancer and glycolysis is one of the important factors promoting tumor development. Given that the absence of multi-sample big data research about glycolysis, the molecular mechanisms involved in glycolysis or the relationships between glycolysis and tumor microenvironment are not fully studied. Thus, a more comprehensive approach in a pan-cancer landscape may be needed.Methods: Here, we develop a computational pipeline to study multi-omics molecular features defining glycolysis activity and identify molecular alterations that correlate with glycolysis.We apply a 22-gene expression signature to define the glycolysis activity landscape and verify the robustness using clinically defined glycolysis samples from several previous studies. Based on gene expression signature, we classify about 5552 of 9229 tumor samples into glycolysis score-high and score-low groups across 25 cancer types from The Cancer Genome Atlas (TCGA) and demonstrate their prognostic associations. Moreover, using genomes and transcriptome data, we characterize the association of copy-number aberrations (CNAs), somatic single-nucleotide variants (SNVs) and hypoxia signature with glycolysis activity.Findings: Gene set variation analysis (GSVA) score by gene set expression was verified robustly to represent glycolytic activity and highly glycolytic tumors presented a poor overall survival in some cancer types. Then, we identified various types of molecular features promoting tumor cell proliferation were associated with glycolysis activity. Our study showed that TCA cycle and respiration electron transport were active in glycolysis-high tumors, indicating glycolysis was not a symptom of impaired oxidative metabolism. The glycolytic score significantly correlated with hypoxia score across all 3 cancer types. Glycolysis score was also associated with elevated genomic instability. In all tumor types, high glycolysis tumors exhibited characteristic driver genes altered by CNAs identified multiple oncogenes and tumor suppressors. We observed widespread glycolysis-associated dysregulation of mRNA across cancers and screened out HSPA8 and P4HA1 as the potential modulating factor to glycolysis. Besides, the expression of genes encoding glycolytic enzymes positively correlated with genes in cell cycle.Interpretation: This is the first study to identify gene expression signatures that reflect glycolysis activity, which can be easily applied to large numbers of patient samples. Our analysis establishes a computational framework for characterizing glycolysis activity using gene expression data and defines correlation of glycolysis with the hypoxia microenvironment, tumor cell cycle and proliferation at a pan-cancer landscape. The findings suggest that the mechanisms whereby hypoxia influence glycolysis are likely multifactorial.Our finding is significant not just in demonstrating definition value for glycolysis but also in providing a comprehensive molecular-level understanding of glycolysis and suggesting a framewo...

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Characterization of hypoxia-associated molecular features to aid hypoxia-targeted therapy

Cited by 148 publications

References 68 publications

Sex-associated molecular differences for cancer immunotherapy

Sex-associated molecular differences for cancer immunotherapy

Metastatic signaling of hypoxia-related genes across TCGA Pan-Cancer types

Persistent cell proliferation signals correlates with increased glycolysis in tumor hypoxia microenvironment across cancer types

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