The Gasdermin E gene Potential as a Pan-Cancer Biomarker, While Discriminating between Different Tumor Types

Due to the elevated rates of incidence and mortality of cancer, early and accurate detection is crucial for achieving optimal treatment. Molecular biomarkers remain important screening and detection tools, especially in light of novel blood-based assays. DNA methylation in cancer has been linked to tumorigenesis, but its value as a biomarker has not been fully explored. In this study, we have investigated the methylation patterns of the Gasdermin E gene across 14 different tumor types using The Cancer Genome Atlas (TCGA) methylation data (N = 6502). We were able to identify six CpG sites that could effectively distinguish tumors from normal samples in a pan-cancer setting (AUC = 0.86). This combination of pan-cancer biomarkers was validated in six independent datasets (AUC = 0.84–0.97). Moreover, we tested 74,613 different combinations of six CpG probes, where we identified tumor-specific signatures that could differentiate one tumor type versus all the others (AUC = 0.79–0.98). In all, methylation patterns exhibited great variation between cancer and normal tissues, but were also tumor specific. Our analyses highlight that a Gasdermin E methylation biomarker assay, not only has the potential for being a methylation-specific pan-cancer detection marker, but it also possesses the capacity to discriminate between different types of tumors.

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Inferring Homologous Recombination Deficiency of Ovarian Cancer From the Landscape of Copy Number Variation at Subchromosomal and Genetic Resolutions

Frontiers in Oncology ◽

10.3389/fonc.2021.772604 ◽

2021 ◽

Vol 11 ◽

Author(s):

Meng Zhang ◽

Si-Cong Ma ◽

Jia-Le Tan ◽

Jian Wang ◽

Xue Bai ◽

...

Keyword(s):

Ovarian Cancer ◽

Homologous Recombination ◽

Copy Number Variation ◽

Copy Number ◽

The Cancer Genome Atlas ◽

Homologous Recombination Deficiency ◽

Number Variation ◽

Cancer Genome Atlas ◽

Tumor Types ◽

Pan Cancer

BackgroundHomologous recombination deficiency (HRD) is characterized by overall genomic instability and has emerged as an indispensable therapeutic target across various tumor types, particularly in ovarian cancer (OV). Unfortunately, current detection assays are far from perfect for identifying every HRD patient. The purpose of this study was to infer HRD from the landscape of copy number variation (CNV).MethodsGenome-wide CNV landscape was measured in OV patients from the Australian Ovarian Cancer Study (AOCS) clinical cohort and >10,000 patients across 33 tumor types from The Cancer Genome Atlas (TCGA). HRD-predictive CNVs at subchromosomal resolution were identified through exploratory analysis depicting the CNV landscape of HRD versus non-HRD OV patients and independently validated using TCGA and AOCS cohorts. Gene-level CNVs were further analyzed to explore their potential predictive significance for HRD across tumor types at genetic resolution.ResultsAt subchromosomal resolution, 8q24.2 amplification and 5q13.2 deletion were predominantly witnessed in HRD patients (both p < 0.0001), whereas 19q12 amplification occurred mainly in non-HRD patients (p < 0.0001), compared with their corresponding counterparts within TCGA-OV. The predictive significance of 8q24.2 amplification (p < 0.0001), 5q13.2 deletion (p = 0.0056), and 19q12 amplification (p = 0.0034) was externally validated within AOCS. Remarkably, pan-cancer analysis confirmed a cross-tumor predictive role of 8q24.2 amplification for HRD (p < 0.0001). Further analysis of CNV in 8q24.2 at genetic resolution revealed that amplifications of the oncogenes, MYC (p = 0.0001) and NDRG1 (p = 0.0004), located on this fragment were also associated with HRD in a pan-cancer manner.ConclusionsThe CNV landscape serves as a generalized predictor of HRD in cancer patients not limited to OV. The detection of CNV at subchromosomal or genetic resolution could aid in the personalized treatment of HRD patients.

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Association of PD-1/PD-L axis expression with cytolytic activity, mutational load, and prognosis in melanoma and other solid tumors

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1607836113 ◽

2016 ◽

Vol 113 (48) ◽

pp. E7769-E7777 ◽

Cited By ~ 85

Author(s):

Ludmila Danilova ◽

Hao Wang ◽

Joel Sunshine ◽

Genevieve J. Kaunitz ◽

Tricia R. Cottrell ◽

...

Keyword(s):

Predictive Biomarkers ◽

Gene Signature ◽

Cytolytic Activity ◽

The Cancer Genome Atlas ◽

Conditional Inference ◽

Tumor Type ◽

Mutational Load ◽

Prognostic Features ◽

Cancer Genome Atlas ◽

Tumor Types

Programmed cell death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) checkpoint blockade has led to remarkable and durable objective responses in a number of different tumor types. A better understanding of factors associated with the PD-1/PD-L axis expression is desirable, as it informs their potential role as prognostic and predictive biomarkers and may suggest rational treatment combinations. In the current study, we analyzedPD-L1,PD-L2,PD-1, and cytolytic activity (CYT) expression, as well as mutational density from melanoma and eight other solid tumor types using The Cancer Genome Atlas database. We found that in some tumor types,PD-L2expression is more closely linked toTh1/IFNGexpression and PD-1 and CD8 signaling thanPD-L1. In contrast, mutational load was not correlated with aTh1/IFNGgene signature in any tumor type.PD-L1,PD-L2,PD-1,CYTexpression, and mutational density are all positive prognostic features in melanoma, and conditional inference modeling revealedPD-1/CYTexpression (i.e., an inflamed tumor microenvironment) as the most impactful feature, followed by mutational density. This study elucidates the highly interdependent nature of these parameters, and also indicates that future biomarkers for anti–PD-1/PD-L1 will benefit from tumor-type–specific, integrated, mRNA, protein, and genomic approaches.

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Pan-cancer characterization of metabolism-related biomarkers identifies potential therapeutic targets

Journal of Translational Medicine ◽

10.1186/s12967-021-02889-0 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Guoshu Bi ◽

Yunyi Bian ◽

Jiaqi Liang ◽

Jiacheng Yin ◽

Runmei Li ◽

...

Keyword(s):

Metabolic Reprogramming ◽

The Cancer Genome Atlas ◽

Score System ◽

Molecular Features ◽

Cancer Genome Atlas ◽

Metabolic Heterogeneity ◽

Tumor Types ◽

Pan Cancer ◽

Worse Prognosis

Abstract Background Generally, cancer cells undergo metabolic reprogramming to adapt to energetic and biosynthetic requirements that support their uncontrolled proliferation. However, the mutual relationship between two critical metabolic pathways, glycolysis and oxidative phosphorylation (OXPHOS), remains poorly defined. Methods We developed a “double-score” system to quantify glycolysis and OXPHOS in 9668 patients across 33 tumor types from The Cancer Genome Atlas and classified them into four metabolic subtypes. Multi-omics bioinformatical analyses was conducted to detect metabolism-related molecular features. Results Compared with patients with low glycolysis and high OXPHOS (LGHO), those with high glycolysis and low OXPHOS (HGLO) were consistently associated with worse prognosis. We identified common dysregulated molecular features between different metabolic subgroups across multiple cancers, including gene, miRNA, transcription factor, methylation, and somatic alteration, as well as investigated their mutual interfering relationships. Conclusion Overall, this work provides a comprehensive atlas of metabolic heterogeneity on a pan-cancer scale and identified several potential drivers of metabolic rewiring, suggesting corresponding prognostic and therapeutic utility.

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Exploring epigenetic biomarkers of universal specificities and commonalities among pan-cancer cohorts in The Cancer Genome Atlas

Epigenomics ◽

10.2217/epi-2021-0050 ◽

2021 ◽

Vol 13 (8) ◽

pp. 599-612

Author(s):

Jie Wu ◽

Deng Lin ◽

Liandi Jiu ◽

Qi Liu ◽

Zhenglong Gu ◽

...

Keyword(s):

Cancer Genome ◽

The Cancer Genome Atlas ◽

Common Features ◽

Machine Learning Methods ◽

Epigenetic Biomarkers ◽

Cancer Genome Atlas ◽

Methylation Patterns ◽

Pan Cancer ◽

High Instability ◽

Genome Atlas

Aim: To explore the mechanism of cancer by employing a comprehensive analysis of DNA methylation patterns and variations among pan-cancer cohorts. Materials & methods: This research focused on the discovery of universally specific or common biomarkers by mathematical statistics and machine learning methods in The Cancer Genome Atlas. Results: We found 138 differently methylated CpGs (DMCs) with a common methylation trend and eight common differently methylated regions in different cancer cohorts. Additionally, we found 99 DMCs to distinguish 32 different cancer cohorts in random forest analysis because of the specificity mechanism, but each DMC still had high instability. Conclusion: Our results could facilitate the development of biomarkers that are universally specific and common features across pan-cancer cohorts.

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Abnormal Expression of NOS3 and Association with Clinical Outcome are Highly Cancer Dependent as Revealed through Pan-Cancer Analysis

10.21203/rs.2.19006/v1 ◽

2019 ◽

Author(s):

Dan Zou ◽

Fei Lv ◽

Yi Yang ◽

Chunjiao Yang ◽

Yang Chen ◽

...

Keyword(s):

Nitric Oxide ◽

Cancer Cell Line ◽

Cancer Therapeutics ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Normal Tissues ◽

Cancer Genome Atlas ◽

Cancer Tissues ◽

Oxide Synthase ◽

Pan Cancer

Abstract Background: NOS3 (endothelial NOS, eNOS) is a member of the nitric oxide synthase (NOS) enzyme family, mainly participating in nitric oxide (NO) generation. NOS3 has been reported to inhibit apoptosis and promote angiogenesis, proliferation, and invasiveness. However, the expression pattern of NOS3 and its diagnostic and prognostic potential has not been investigated in a pan-cancer perspective. Methods: In this research, data from the Genotype-Tissue Expression (GTEx), the Cancer Genome Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE) and the Cancer Therapeutics Response Portal (CTRP) were employed and NOS3 expression was comprehensively analysed in normal tissues, cancer tissues and cell lines. Its relationship with clinical phenotypes and drug responses was also analysed. Results: In normal tissues, NOS3 was expressed at the highest levels in the spleen and the lowest levels in the blood. Compared with the corresponding normal tissues, its expression in tumour was significantly increased in seven tumour types but decreased in eight tumour types. And NOS3 expression was positively or negatively related to tumour stage and overall survival of patients depending on the tumour types. The expression of NOS3 was related to the response to ‘SR8278’. Conclusions: NOS3 was differentially expressed in tumour tissues, and had prognosis value in some tumour types. And its correlation to drug response warrants further investigation.

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A Comprehensive Survey of Immune Cytolytic Activity-Associated Gene Co-Expression Networks across 17 Tumor and Normal Tissue Types

Cancers ◽

10.3390/cancers10090307 ◽

2018 ◽

Vol 10 (9) ◽

pp. 307

Author(s):

Tian Tian ◽

Ji Wan ◽

Yan Han ◽

Haoran Liu ◽

Feng Gao ◽

...

Keyword(s):

Cytolytic Activity ◽

The Cancer Genome Atlas ◽

Immune Activity ◽

Multiple Tumor ◽

Normal Tissues ◽

Tumor Tissues ◽

Comprehensive Survey ◽

Cancer Genome Atlas ◽

Normal Networks ◽

Tumor Types

Cytolytic immune activity in solid tissue can be quantified by transcript levels of two genes, GZMA and PRF1, which is named the CYT score. A previous study has investigated the molecular and genetic properties of tumors associated CYT, but a systematic exploration of how co-expression networks across different tumors are shaped by anti-tumor immunity is lacking. Here, we examined the connectivity and biological themes of CYT-associated modules in gene co-expression networks of 14 tumor and 3 matched normal tissues constructed from the RNA-Seq data of the “The Cancer Genome Atlas” project. We first found that tumors networks have more diverse CYT-correlated modules than normal networks. We next identified and investigated tissue-specific CYT-associated modules across 14 tumor types. Finally, a common CYT-associated network across 14 tumor types was constructed. Two common modules have mixed signs of correlation with CYT in different tumors. Given the tumors and normal tissues surveyed, our study presents a systematic view of the regulation of cytolytic immune activity across multiple tumor tissues.

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The Burden of Post-Translational Modification (PTM)—Disrupting Mutations in the Tumor Matrisome

Cancers ◽

10.3390/cancers13051081 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1081

Author(s):

Elisa Holstein ◽

Annalena Dittmann ◽

Anni Kääriäinen ◽

Vilma Pesola ◽

Jarkko Koivunen ◽

...

Keyword(s):

The Cancer Genome Atlas ◽

Cell Of Origin ◽

Post Translational Modification ◽

Functional Protein ◽

Distinctive Features ◽

Coding Regions ◽

Heterotypic Interactions ◽

Cancer Genome Atlas ◽

Tumor Types ◽

Pan Cancer

Background: To evaluate the occurrence of mutations affecting post-translational modification (PTM) sites in matrisome genes across different tumor types, in light of their genomic and functional contexts and in comparison with the rest of the genome. Methods: This study spans 9075 tumor samples and 32 tumor types from The Cancer Genome Atlas (TCGA) Pan-Cancer cohort and identifies 151,088 non-silent mutations in the coding regions of the matrisome, of which 1811 affecting known sites of hydroxylation, phosphorylation, N- and O-glycosylation, acetylation, ubiquitylation, sumoylation and methylation PTM. Results: PTM-disruptive mutations (PTMmut) in the matrisome are less frequent than in the rest of the genome, seem independent of cell-of-origin patterns but show dependence on the nature of the matrisome protein affected and the background PTM types it generally harbors. Also, matrisome PTMmut are often found among structural and functional protein regions and in proteins involved in homo- and heterotypic interactions, suggesting potential disruption of matrisome functions. Conclusions: Though quantitatively minoritarian in the spectrum of matrisome mutations, PTMmut show distinctive features and damaging potential which might concur to deregulated structural, functional, and signaling networks in the tumor microenvironment.

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Scaling concepts in 'omics: nuclear lamin-B scales with tumor growth and predicts poor prognosis, whereas fibrosis can be pro-survival

10.1101/2021.02.25.432860 ◽

2021 ◽

Author(s):

Manasvita Vashisth ◽

Dennis Discher ◽

Sangkyun Cho ◽

Jerome Irianto ◽

Yuntao Xia ◽

...

Keyword(s):

Single Cell ◽

Power Laws ◽

The Cancer Genome Atlas ◽

Structural Factors ◽

Rna Seq ◽

Lamin B ◽

Nuclear Lamin ◽

Cancer Genome Atlas ◽

Tumor Types ◽

Pan Cancer

Spatiotemporal relationships between genes expressed in tissues likely reflect physicochemical principles that range from stoichiometric interactions to co-organized fractals with characteristic scaling. For key structural factors within the nucleus and extracellular matrix (ECM), gene-gene power laws are found to be characteristic across several tumor types in The Cancer Genome Atlas (TCGA) and across single-cell RNA-seq data. The nuclear filament LMNB1 scales with many tumor-elevated proliferation genes that predict poor survival in liver cancer, and cell line experiments show LMNB1 regulates cancer cell cycle. Also high in the liver, lung, and breast tumors studied here are the main fibrosis-associated collagens, COL1A1 and COL1A2, that scale stoichiometrically with each other and superstoichiometrically with a pan-cancer fibrosis gene set. However, high fibrosis predicts prolonged survival of patients undergoing therapy and does not correlate with LMNB1. Single-cell RNA-seq data also reveal scaling consistent with the pan-cancer power laws obtained from bulk tissue, allowing new power law relations to be predicted. Lastly, although noisy data frustrate weak scaling, concepts such as stoichiometric scaling highlight a simple, internal consistency check to qualify expression data.

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Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images

Cancers ◽

10.3390/cancers13153811 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3811

Author(s):

Hyun-Jong Jang ◽

In-Hye Song ◽

Sung-Hak Lee

Keyword(s):

Deep Learning ◽

Relative Proportion ◽

The Cancer Genome Atlas ◽

Cancer Tissue ◽

Mucinous Tumor ◽

Receiver Operating Characteristic Curves ◽

Tumor Tissues ◽

Cancer Genome Atlas ◽

Stomach Adenocarcinoma ◽

Tumor Types

Histomorphologic types of gastric cancer (GC) have significant prognostic values that should be considered during treatment planning. Because the thorough quantitative review of a tissue slide is a laborious task for pathologists, deep learning (DL) can be a useful tool to support pathologic workflow. In the present study, a fully automated approach was applied to distinguish differentiated/undifferentiated and non-mucinous/mucinous tumor types in GC tissue whole-slide images from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset (TCGA-STAD). By classifying small patches of tissue images into differentiated/undifferentiated and non-mucinous/mucinous tumor tissues, the relative proportion of GC tissue subtypes can be easily quantified. Furthermore, the distribution of different tissue subtypes can be clearly visualized. The patch-level areas under the curves for the receiver operating characteristic curves for the differentiated/undifferentiated and non-mucinous/mucinous classifiers were 0.932 and 0.979, respectively. We also validated the classifiers on our own GC datasets and confirmed that the generalizability of the classifiers is excellent. The results indicate that the DL-based tissue classifier could be a useful tool for the quantitative analysis of cancer tissue slides. By combining DL-based classifiers for various molecular and morphologic variations in tissue slides, the heterogeneity of tumor tissues can be unveiled more efficiently.

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