Integrative Analysis of Cancer Omics Data for Prognosis Modeling

Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensively and more accurately describe prognosis. In this study, we conduct marginal and joint integrative analysis of multiple cancer types, effectively introducing integration in the discovery process. For accommodating high dimensionality and identifying relevant markers, we adopt the advanced penalization technique which has a solid statistical ground. Gene expression data on nine cancer types from The Cancer Genome Atlas (TCGA) are analyzed, leading to biologically sensible findings that are different from the alternatives. Overall, this study provides a novel venue for cancer prognosis modeling by integrating multiple cancer types.

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Pan-cancer analysis reveals complex tumor-specific alternative polyadenylation

10.1101/160960 ◽

2017 ◽

Author(s):

Zhuyi Xue ◽

René L Warren ◽

Ewan A Gibb ◽

Daniel MacMillan ◽

Johnathan Wong ◽

...

Keyword(s):

Alternative Polyadenylation ◽

Length Change ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Rna Seq ◽

Multiple Cancer ◽

Tissue Samples ◽

Cancer Genome Atlas ◽

Specific Alternative ◽

Cancer Types

AbstractAlternative polyadenylation (APA) of 3’ untranslated regions (3’ UTRs) has been implicated in cancer development. Earlier reports on APA in cancer primarily focused on 3’ UTR length modifications, and the conventional wisdom is that tumor cells preferentially express transcripts with shorter 3’ UTRs. Here, we analyzed the APA patterns of 114 genes, a select list of oncogenes and tumor suppressors, in 9,939 tumor and 729 normal tissue samples across 33 cancer types using RNA-Seq data from The Cancer Genome Atlas, and we found that the APA regulation machinery is much more complicated than what was previously thought. We report 77 cases (gene-cancer type pairs) of differential 3’ UTR cleavage patterns between normal and tumor tissues, involving 33 genes in 13 cancer types. For 15 genes, the tumor-specific cleavage patterns are recurrent across multiple cancer types. While the cleavage patterns in certain genes indicate apparent trends of 3’ UTR shortening in tumor samples, over half of the 77 cases imply 3’ UTR length change trends in cancer that are more complex than simple shortening or lengthening. This work extends the current understanding of APA regulation in cancer, and demonstrates how large volumes of RNA-seq data generated for characterizing cancer cohorts can be mined to investigate this process.

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Cancer Type-Dependent Correlations betweenTP53Mutations and Antitumor Immunity

10.1101/692715 ◽

2019 ◽

Author(s):

Lin Li ◽

Mengyuan Li ◽

Xiaosheng Wang

Keyword(s):

Antitumor Immunity ◽

Colon Adenocarcinoma ◽

Joint Effect ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Mutation Burden ◽

Cancer Genome Atlas ◽

Negative Role ◽

Stomach Adenocarcinoma ◽

Cancer Types

AbstractMany studies have shown thatTP53mutations play a negative role in antitumor immunity. However, a few studies reported thatTP53mutations could promote antitumor immunity. To explain these contradictory findings, we analyzed five cancer cohorts from The Cancer Genome Atlas (TCGA) project. We found thatTP53-mutated cancers had significantly higher levels of antitumor immune signatures thanTP53-wildtype cancers in breast invasive carcinoma (BRCA) and lung adenocarcinoma (LUAD). In contrast,TP53-mutated cancers had significantly lower antitumor immune signature levels thanTP53-wildtype cancers in stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and head and neck squamous cell carcinoma (HNSC). Moreover,TP53-mutated cancers likely had higher tumor mutation burden (TMB) and tumor aneuploidy level (TAL) thanTP53-wildtype cancers. However, the TMB differences were more marked betweenTP53-mutated andTP53-wildtype cancers than the TAL differences in BRCA and LUAD, and the TAL differences were more significant in STAD and COAD. Furthermore, we showed that TMB and TAL had a positive and a negative correlation with antitumor immunity and that TMB affected antitumor immunity more greatly than TAL did in BRCA and LUAD while TAL affected antitumor immunity more strongly than TMB in STAD and HNSC. These findings indicate that the distinct correlations betweenTP53mutations and antitumor immunity in different cancer types are a consequence of the joint effect of the altered TMB and TAL caused byTP53mutations on tumor immunity. Our data suggest that theTP53mutation status could be a useful biomarker for cancer immunotherapy response depending on cancer types.

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Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

Scientific Reports ◽

10.1038/s41598-020-74276-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Gaojianyong Wang ◽

Dimitris Anastassiou

Keyword(s):

Gene Expression ◽

Copy Number ◽

The Cancer Genome Atlas ◽

Copy Number Alterations ◽

Multiple Cancer ◽

Cancer Driver ◽

Large Gene ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Pan Cancer

Abstract Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.

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Identifying cancer type specific oncogenes and tumor suppressors using limited size data

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500311 ◽

2016 ◽

Vol 14 (06) ◽

pp. 1650031 ◽

Cited By ~ 4

Author(s):

Ana B. Pavel ◽

Cristian I. Vasile

Keyword(s):

Tumor Suppressors ◽

Molecular Mechanisms ◽

Lung Squamous Cell Carcinoma ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Type ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Subtypes ◽

Cancer Types

Cancer is a complex and heterogeneous genetic disease. Different mutations and dysregulated molecular mechanisms alter the pathways that lead to cell proliferation. In this paper, we explore a method which classifies genes into oncogenes (ONGs) and tumor suppressors. We optimize this method to identify specific (ONGs) and tumor suppressors for breast cancer, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and colon adenocarcinoma (COAD), using data from the cancer genome atlas (TCGA). A set of genes were previously classified as ONGs and tumor suppressors across multiple cancer types (Science 2013). Each gene was assigned an ONG score and a tumor suppressor score based on the frequency of its driver mutations across all variants from the catalogue of somatic mutations in cancer (COSMIC). We evaluate and optimize this approach within different cancer types from TCGA. We are able to determine known driver genes for each of the four cancer types. After establishing the baseline parameters for each cancer type, we identify new driver genes for each cancer type, and the molecular pathways that are highly affected by them. Our methodology is general and can be applied to different cancer subtypes to identify specific driver genes and improve personalized therapy.

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Identification and Characterization of MicroRNAs Associated with Somatic Copy Number Alterations in Cancer

Cancers ◽

10.3390/cancers10120475 ◽

2018 ◽

Vol 10 (12) ◽

pp. 475 ◽

Cited By ~ 1

Author(s):

Jihee Soh ◽

Hyejin Cho ◽

Chan-Hun Choi ◽

Hyunju Lee

Keyword(s):

Copy Number ◽

The Cancer Genome Atlas ◽

Cancer Development ◽

Cancer Type ◽

Biological Processes ◽

Copy Number Alterations ◽

Coding Regions ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Somatic Copy Number Alterations

MicroRNAs (miRNAs) are key molecules that regulate biological processes such as cell proliferation, differentiation, and apoptosis in cancer. Somatic copy number alterations (SCNAs) are common genetic mutations that play essential roles in cancer development. Here, we investigated the association between miRNAs and SCNAs in cancer. We collected 2538 tumor samples for seven cancer types from The Cancer Genome Atlas. We found that 32−84% of miRNAs are in SCNA regions, with the rate depending on the cancer type. In these regions, we identified 80 SCNA-miRNAs whose expression was mainly associated with SCNAs in at least one cancer type and showed that these SCNA-miRNAs are related to cancer by survival analysis and literature searching. We also identified 58 SCNA-miRNAs common in the seven cancer types (CC-SCNA-miRNAs) and showed that these CC-SCNA-miRNAs are more likely to be related with protein and gene expression than other miRNAs. Furthermore, we experimentally validated the oncogenic role of miR-589. In conclusion, our results suggest that SCNA-miRNAs significantly alter biological processes related to cancer development, confirming the importance of SCNAs in non-coding regions in cancer.

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Chromoanagenesis Landscape in 10,000 TCGA Patients

Cancers ◽

10.3390/cancers13164197 ◽

2021 ◽

Vol 13 (16) ◽

pp. 4197

Author(s):

Roni Rasnic ◽

Michal Linial

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Learning Algorithm ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Whole Genome ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Tumor Biopsies ◽

Pan Cancer

During the past decade, whole-genome sequencing of tumor biopsies and individuals with congenital disorders highlighted the phenomenon of chromoanagenesis, a single chaotic event of chromosomal rearrangement. Chromoanagenesis was shown to be frequent in many types of cancers, to occur in early stages of cancer development, and significantly impact the tumor’s nature. However, an in-depth, cancer-type dependent analysis has been somewhat incomplete due to the shortage in whole genome sequencing of cancerous samples. In this study, we extracted data from The Pan-Cancer Analysis of Whole Genome (PCAWG) and The Cancer Genome Atlas (TCGA) to construct and test a machine learning algorithm that can detect chromoanagenesis with high accuracy (86%). The algorithm was applied to ~10,000 unlabeled TCGA cancer patients. We utilize the chromoanagenesis assignment results, to analyze cancer-type specific chromoanagenesis characteristics in 20 TCGA cancer types. Our results unveil prominent genes affected in either chromoanagenesis or non-chromoanagenesis tumorigenesis. The analysis reveals a mutual exclusivity relationship between the genes impaired in chromoanagenesis versus non-chromoanagenesis cases. We offer the discovered characteristics as possible targets for cancer diagnostic and therapeutic purposes.

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Transfer Learning via Optimal Transportation for Integrative Cancer Patient Stratification

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/380 ◽

2021 ◽

Author(s):

Ziyu Liu ◽

Wei Shao ◽

Jie Zhang ◽

Min Zhang ◽

Kun Huang

Keyword(s):

Cancer Patients ◽

Transfer Learning ◽

Transport Theory ◽

Optimal Transport ◽

Early Stage ◽

The Cancer Genome Atlas ◽

Sufficient Information ◽

Cancer Type ◽

Early Stage Cancer ◽

Cancer Types

The Stratification of early-stage cancer patients for the prediction of clinical outcome is a challenging task since cancer is associated with various molecular aberrations. A single biomarker often cannot provide sufficient information to stratify early-stage patients effectively. Understanding the complex mechanism behind cancer development calls for exploiting biomarkers from multiple modalities of data such as histopathology images and genomic data. The integrative analysis of these biomarkers sheds light on cancer diagnosis, subtyping, and prognosis. Another difficulty is that labels for early-stage cancer patients are scarce and not reliable enough for predicting survival times. Given the fact that different cancer types share some commonalities, we explore if the knowledge learned from one cancer type can be utilized to improve prognosis accuracy for another cancer type. We propose a novel unsupervised multi-view transfer learning algorithm to simultaneously analyze multiple biomarkers in different cancer types. We integrate multiple views using non-negative matrix factorization and formulate the transfer learning model based on the Optimal Transport theory to align features of different cancer types. We evaluate the stratification performance on three early-stage cancers from the Cancer Genome Atlas (TCGA) project. Comparing with other benchmark methods, our framework achieves superior accuracy for patient outcome prediction.

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Prognostic Roles of N6-Methyladenosine METTL3 in Different Cancers: A System Review and Meta-Analysis

Cancer Control ◽

10.1177/1073274821997455 ◽

2021 ◽

Vol 28 ◽

pp. 107327482199745

Author(s):

KuangZheng Liu ◽

Yue Gao ◽

Kai Gan ◽

YuQing Wu ◽

Bin Xu ◽

...

Keyword(s):

Confidence Interval ◽

Odds Ratio ◽

Meta Analysis ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Tumor Biomarker ◽

Cancer Breast ◽

Cancer Genome Atlas ◽

Cancer Types ◽

And Gender

Background: Recent studies have shown that methyltransferase-like 3, a catalytic enzyme that is predominant in the N6-methyladenosine methyltransferase system, is abnormally expressed in various types of carcinoma and is correlated with poorer prognosis. However, the clinical functions of methyltransferase-like 3 in the prognosis of tumors are not fully understood. Methods: We identified studies by searching PubMed, Web of Science, and MedRvix for literature (up to June 30, 2020), and collected a total of 9 studies with 1257 patients for this meta-analysis. The cancer types included gastric cancer, breast cancer, non-small cell lung cancer, bladder cancer, colorectal cancer and ovarian. We further used The Cancer Genome Atlas dataset to validate the results. Results: High methyltransferase-like 3 expression clearly predicted a worse outcome (high vs. low methyltransferase-like 3 expression group; hazard ratio = 2.09, 95% confidence interval 1.53–2.89, P = 0.0001). Moreover, methyltransferase-like 3 expression was associated with differentiation (moderate + poor vs. well, pooled odds ratio = 1.76, 95% confidence interval 1.32–2.35, P = 0.0001), and gender (male vs. female, pooled odds ratio = 0.73, 95% confidence interval 0.55-0.97, P = 0.029). Conclusion: Our results suggest that methyltransferase-like 3 upregulation is significantly associated with poor prognosis and could potentially function as a tumor biomarker in cancer prognosis.

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Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments

Frontiers in Genetics ◽

10.3389/fgene.2021.747931 ◽

2022 ◽

Vol 12 ◽

Author(s):

Yiran Zhou ◽

Qinghua Cui ◽

Yuan Zhou

Keyword(s):

Signaling Pathway ◽

Cancer Progression ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Composition Analysis ◽

Multiple Cancer ◽

Systematic Analysis ◽

Cancer Subtypes ◽

Cancer Types

tRNA-derived fragments (tRFs) constitute a novel class of small non-coding RNA cleaved from tRNAs. In recent years, researches have shown the regulatory roles of a few tRFs in cancers, illuminating a new direction for tRF-centric cancer researches. Nonetheless, more specific screening of tRFs related to oncogenesis pathways, cancer progression stages and cancer prognosis is continuously demanded to reveal the landscape of the cancer-associated tRFs. In this work, by combining the clinical information recorded in The Cancer Genome Atlas (TCGA) and the tRF expression profiles curated by MINTbase v2.0, we systematically screened 1,516 cancer-associated tRFs (ca-tRFs) across seven cancer types. The ca-tRF set collectively combined the differentially expressed tRFs between cancer samples and control samples, the tRFs significantly correlated with tumor stage and the tRFs significantly correlated with patient survival. By incorporating our previous tRF-target dataset, we found the ca-tRFs tend to target cancer-associated genes and onco-pathways like ATF6-mediated unfolded protein response, angiogenesis, cell cycle process regulation, focal adhesion, PI3K-Akt signaling pathway, cellular senescence and FoxO signaling pathway across multiple cancer types. And cell composition analysis implies that the expressions of ca-tRFs are more likely to be correlated with T-cell infiltration. We also found the ca-tRF expression pattern is informative to prognosis, suggesting plausible tRF-based cancer subtypes. Together, our systematic analysis demonstrates the potentially extensive involvements of tRFs in cancers, and provides a reasonable list of cancer-associated tRFs for further investigations.

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Somatic Copy Number Alterations in Human Cancers: An Analysis of Publicly Available Data From The Cancer Genome Atlas

Frontiers in Oncology ◽

10.3389/fonc.2021.700568 ◽

2021 ◽

Vol 11 ◽

Author(s):

Luuk Harbers ◽

Federico Agostini ◽

Marcin Nicos ◽

Dimitri Poddighe ◽

Magda Bienko ◽

...

Keyword(s):

Copy Number ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Copy Number Alterations ◽

Multiple Cancer ◽

Human Cancers ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Somatic Copy Number Alterations ◽

Genome Atlas

Somatic copy number alterations (SCNAs) are a pervasive trait of human cancers that contributes to tumorigenesis by affecting the dosage of multiple genes at the same time. In the past decade, The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) initiatives have generated and made publicly available SCNA genomic profiles from thousands of tumor samples across multiple cancer types. Here, we present a comprehensive analysis of 853,218 SCNAs across 10,729 tumor samples belonging to 32 cancer types using TCGA data. We then discuss current models for how SCNAs likely arise during carcinogenesis and how genomic SCNA profiles can inform clinical practice. Lastly, we highlight open questions in the field of cancer-associated SCNAs.

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