scholarly journals Observed Survival Interval: A Supplement to TCGA Pan-Cancer Clinical Data Resource

Cancers ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 280 ◽  
Author(s):  
Jie Xiong ◽  
Zhitong Bing ◽  
Shengyu Guo
Keyword(s):  
Clinical Data ◽  
The Cancer Genome Atlas ◽  
Time Interval ◽  
Omics Data ◽  
Pathological Stage ◽  
Sequencing Data ◽  
Patient Death ◽  
Data Resource ◽  
Pan Cancer ◽  
Survival Interval

To drive high-quality omics translational research using The Cancer Genome Atlas (TCGA) data, a TCGA Pan-Cancer Clinical Data Resource was proposed. However, there is an out-of-step issue between clinical outcomes and the omics data of TCGA for skin cutaneous melanoma (SKCM), due to the majority of metastatic samples. In clinical cases, the survival time started from the initial SKCM diagnosis, while the omics data were characterized at TCGA sampling. This study aimed to address this issue by proposing an observed survival interval (OBS), which was defined as the time interval from TCGA sampling to patient death or last follow-up. We compared the OBS with the usual recommended overall survival (OS) by associating them with both clinical data and microRNA sequencing data of TCGA-SKCM. We found that the OS of primary SKCM was significantly shorter than that of metastatic SKCM, while the opposite happened if OBS was compared. OS was associated with the pathological stage of both primary and metastatic SKCM, while OBS was associated with the pathological stage of primary SKCM but not that of metastatic SKCM. Five previously cross-validated survival-associated microRNAs were found to be associated with the OBS rather than OS in metastatic SKCM. Thus, the OBS was more appropriate for associating microRNA-omics data of TCGA-SKCM than OS, and it is a timely supplement to TCGA Pan-Cancer Clinical Data Resource.

Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations

2021 ◽  
pp. 1-10
Author(s):  
Zoe Guan ◽  
Ronglai Shen ◽  
Colin B. Begg
Keyword(s):  
Rare Variant ◽  
Rare Variants ◽  
Association Studies ◽  
The Cancer Genome Atlas ◽  
Considerable Proportion ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Risk Variants ◽  
Cancer Types ◽  
Pan Cancer

<b><i>Background:</i></b> Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The “rare variant hypothesis” proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale. <b><i>Objectives:</i></b> In this study, we investigated associations between rare variants and 14 cancer types. <b><i>Methods:</i></b> We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG). <b><i>Results:</i></b> We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer). <b><i>Conclusions:</i></b> Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

scholarly journals Ferroptosis-Related Long Non-Coding RNAs and the Roles of LASTR in Stomach Adenocarcinoma

2021 ◽  
Author(s):  
Gongjun Wang ◽  
Libin Sun ◽  
Shasha Wang ◽  
Jing Guo ◽  
Hui Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Comprehensive Evaluation ◽  
Progression Free Survival ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Regulatory Relationship ◽  
Data Resource ◽  
Stomach Adenocarcinoma

Abstract Background: Ferroptosis is a form of cell death involved in diverse physiological context. Increasing evidence suggests that there is a closely regulatory relationship between ferroptosis and long noncoding RNAs (lncRNAs).Method: RNA-sequencing data from The Cancer Genome Atlas (TCGA) data resource and ferroptosis-related genes from FerrDb (http://www.zhounan.org/ferrdb/) data resource were employed to select differentially expressed lncRNAs. We performed Univariate Cox regression and multivariate Cox analyses analysis on these differentially expressed lncRNAs to screen independent predictive factors. Subsequently, we established two signatures for predicting overall survival (OS) and progression-free survival (PFS). Finally, experiments were conducted to verify the roles of LASTR in gastric cancer (GC).Results: We identified 12 differentially expressed lncRNAs linked with OS and 13 associated with PFS. Kaplan-Meier(K-M) analyses exhibited that the high-risk group was related to a poor prognosis of stomach adenocarcinoma (STAD). The AUCs of the OS, as well as PFS signatures of lncRNAs were 0.734 and 0.771, respectively, indicating their excellent efficacy in predicting STAD prognosis. Our experimental results illustrated that the inhibition of LASTR inhibited tumor proliferation and migration in GC.Conclusion: This comprehensive evaluation of the ferroptosis-related lncRNA landscape in STAD unearthed novel lncRNAs related to carcinogenesis. In addition, we also experimentally confirmed the effects of LASTR on proliferation, migration and ferroptosis. These results provide potential novel targets for tumor treatment and promote personalized medicine.

scholarly journals An R Package for Divergence Analysis of Omics Data

2019 ◽  
Author(s):  
Wikum Dinalankara ◽  
Qian Ke ◽  
Donald Geman ◽  
Luigi Marchionni
Keyword(s):  
High Throughput Sequencing ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
High Dimensional ◽  
Omics Data ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Ternary Code ◽  
Cancer Genome Atlas ◽  
Level Analysis

AbstractGiven the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with sample high throughput sequencing data from the Cancer Genome Atlas.

Correlation between JAK1/2 expression and immune-related genes and JAK2 gene variants: A pan-cancer analysis.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15057-e15057
Author(s):  
Lichao Xu ◽  
Ding Zhang ◽  
Guoqiang Wang ◽  
Chao Chen ◽  
Ying Wang ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Copy Number ◽  
Checkpoint Inhibitors ◽  
The Cancer Genome Atlas ◽  
Gene Variants ◽  
Missense Mutations ◽  
Sequencing Data ◽  
Primary Resistance ◽  
Immune Related Genes ◽  
Pan Cancer

e15057 Background: Loss of function mutations for Janus kinases 1/2 (JAK1/2) have shown to be the underling mechanism of primary resistance to immune checkpoint inhibitors (ICIs). However, the correlation between JAK1/2 expression and immune-related genes have not been studied. Methods: Survival, mRNA expression and whole-exome sequencing data from 32 pan-cancer atlas studies were obtained from The Cancer Genome Atlas (TCGA). Correlations between JAK1/2 expression and immune-related genes were depicted in heatmaps. We also analyzed the association between JAK2 gene variants and JAK2 expression. Results: In total, 10071 samples with mRNA expression data were included for analysis. Expression of 46 immune-related genes were positively correlated with JAK2 expression in 25 tumors instead of JAK1 expression. Patients with higher expression of JAK2 had better prognosis than patients with lower expression of JAK2 in 13 tumors. Among 10071 patients, 363 (3.60%) patients harbored JAK2 variants, including 8 with frame shift mutations, 44 with nonsense mutations, 142 with missense mutations, 11 with splices, 8 with fusions, 90 with copy-number reduction and 116 with copy-number amplification. There was no difference in JAK2 expression between patients with JAK2 variants and those without JAK2 variants. However, JAK2 fusion (2.20%, 8/363) and amplification (31.96%, 116/363) were associated with higher JAK2 expression. Conclusions: Our pan-cancer analysis found that JAK2 expression was correlated with immune-related genes and the prognosis of cancer patients. JAK2 fusion and amplification increased the expression of JAK2. Altogether, patients with high JAK2 expression may benefit from ICIs.

scholarly journals Pan-cancer analysis of non-coding recurrent mutations and their possible involvement in cancer pathogenesis

NAR Cancer ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Chie Kikutake ◽  
Minako Yoshihara ◽  
Mikita Suyama
Keyword(s):  
The Cancer Genome Atlas ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Huge Number ◽  
Protein Coding ◽  
Coding Regions ◽  
Cancer Pathogenesis ◽  
Recurrent Mutations ◽  
Cancer Genome Atlas ◽  
Pan Cancer

Abstract Cancer-related mutations have been mainly identified in protein-coding regions. Recent studies have demonstrated that mutations in non-coding regions of the genome could also be a risk factor for cancer. However, the non-coding regions comprise 98% of the total length of the human genome and contain a huge number of mutations, making it difficult to interpret their impacts on pathogenesis of cancer. To comprehensively identify cancer-related non-coding mutations, we focused on recurrent mutations in non-coding regions using somatic mutation data from COSMIC and whole-genome sequencing data from The Cancer Genome Atlas (TCGA). We identified 21 574 recurrent mutations in non-coding regions that were shared by at least two different samples from both COSMIC and TCGA databases. Among them, 580 candidate cancer-related non-coding recurrent mutations were identified based on epigenomic and chromatin structure datasets. One of such mutation was located in RREB1 binding site that is thought to interact with TEAD1 promoter. Our results suggest that mutations may disrupt the binding of RREB1 to the candidate enhancer region and increase TEAD1 expression levels. Our findings demonstrate that non-coding recurrent mutations and coding mutations may contribute to the pathogenesis of cancer.

scholarly journals Reprocessing of RNA-sequencing samples from publicly-available data to yield normalized and comparable expression results.

2019 ◽  
Author(s):  
William C. Wright ◽  
Taosheng Chen
Keyword(s):  
Rna Sequencing ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Linear Modeling ◽  
Gene Count ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

Abstract Here we obtained RNA-sequencing data from the publicly-available Pan-Cancer analysis project performed by The Cancer Genome Atlas (TCGA). Data within this project were processed the same experimentally, and analyzed downstream by the UCSC Toil recompute project. We reprocessed the resulting gene count files in batch to obtain normalized expression, which is a step critical for proper and comparable interpretation. We describe the linear modeling and normalization protocol, and provide an example of plotting the results using a gene of interest. We perform the entire protocol using freely available packages within the R framework.

scholarly journals Supervised Hierarchical Autoencoders for Multi-Omics Integration in Cancer Survival Models

2021 ◽  
Author(s):  
David Wissel ◽  
Daniel Rowson ◽  
Valentina Boeva
Keyword(s):  
Clinical Data ◽  
High Throughput Sequencing ◽  
Cancer Survival ◽  
Linear Models ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Survival Models ◽  
Sequencing Data ◽  
Omics Integration

With the increasing amount of high-throughput sequencing data becoming available, the proper integration of differently sized and heterogeneous molecular and clinical groups of variables has become crucial in cancer survival models. Due to the difficulty of multi-omics integration, the Cox Proportional-Hazards (Cox PH) model using clinical data has remained one of the best-performing methods [Herrmann et al., 2021]. This motivates the need for new models which can successfully perform multi-omics integration in survival models and outperform the Cox PH model. Furthermore, there is a strong need to make multi-omics models more sparse and interpretable to encourage their usage in clinical settings. We developed a neural architecture, termed Supervised Hierarchical Autoencoder (SHAE), based on supervised autoencoders and Sparse-Group-Lasso regularization. Our new method performed competitively with the best performing statistical models used for multi-omics survival analysis. Moreover, it outperformed the Cox PH model using clinical data across all 17 cancers from The Cancer Genome Atlas (TCGA) considered in our work. We further showed that surrogate linear models for SHAE trained on a subset of multi-omics groups achieved competitive performance at consistently high sparsity levels, enabling usage within clinics. Alternatively, surrogate models can act as a feature selection step, permitting improved performance in arbitrary downstream survival models. Code for the reproduction of our results is available on Github.

scholarly journals The B7H4-PDL1 classifier stratifies immuno-phenotype in cervical cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Lingyan Chen ◽  
Jianfeng Dong ◽  
Zeying Li ◽  
Yu Chen ◽  
Yan Zhang
Keyword(s):  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Sequencing Data ◽  
T Cell Infiltration ◽  
Quantitative Immunofluorescence ◽  
Ki67 Antigen ◽  
Cancer Genome Atlas ◽  
Pan Cancer

Abstract Background It has been revealed that B7H4 is negatively correlated with PDL1 and identifies immuno-cold tumors in glioma. However, the application of the B7H4-PDL1 classifier in cancers has not been well testified. Methods A pan-cancer analysis was conducted to evaluate the immunological role of B7H4 using the RNA-sequencing data downloaded from the Cancer Genome Atlas (TCGA). Immunohistochemistry (IHC) and multiplexed quantitative immunofluorescence (QIF) were performed to validate the primary results revealed by bioinformatics analysis. Results The pan-cancer analysis revealed that B7H4 was negatively correlated with PDL1 expression and immune cell infiltration in CeCa. In addition, patients with high B7H4 exhibited the shortest overall survival (OS) and relapse-free survival (RFS) while those with high PDL1 exhibited a better prognosis. Multiplexed QIF showed that B7H4 was mutually exclusive with PDL1 expression and the B7H4-high group exhibited the lowest CD8 + T cell infiltration. Besides, B7H4-high predicted highly proliferative subtypes, which expressed the highest Ki67 antigen. Moreover, B7H4-high also indicated a lower response to multiple therapies. Conclusions Totally, the B7H4-PDL1 classifier identifies the immunogenicity and predicts proliferative subtypes and limited therapeutic options in CeCa, which may be a convenient and feasible biomarker in clinical practice.

scholarly journals CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Judith Abécassis ◽  
Fabien Reyal ◽  
Jean-Philippe Vert
Keyword(s):  
Tumor Heterogeneity ◽  
Cancer Genomics ◽  
Computational Method ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Cancer Dataset ◽  
Whole Exome Sequencing Data ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Mutational Processes

AbstractSystematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.

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