scholarly journals Recurrent alternative splicing isoform switches in tumor samples provide novel signatures of cancer

2014 ◽  
Author(s):  
Endre Sebestyén ◽  
Michał Zawisza ◽  
Eduardo Eyras
Keyword(s):  
Alternative Splicing ◽  
Somatic Mutations ◽  
Cancer Genomics ◽  
Genetic Alterations ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Low Frequencies ◽  
Cancer Subtypes ◽  
Alternatively Spliced

Cancer genomics has been instrumental to determine the genetic alterations that are predictive of various tumor conditions. However, the majority of these alterations occur at low frequencies, motivating the need to expand the catalogue of cancer signatures. Alternative pre-mRNA splicing alterations, which bear major importance for the understanding of cancer, have not been exhaustively studied yet in the context of recent cancer genome projects. In this article we analyze RNA sequencing data for more than 4000 samples from The Cancer Genome Atlas (TCGA) project, including paired normal samples, to detect recurrent alternative splicing isoform switches in 9 different cancer types. We first investigate whether alternative splicing isoform changes are predictive of tumors by applying a rank-based algorithm based on the reversal of the relative expression of transcript isoforms. We find that consistent alternative splicing isoform changes can separate with high accuracy tumor and normal samples, as well as some cancer subtypes. We then searched for those changes that occur in the most abundant isoform, i.e isoform switches, and are therefore more likely to have a functional impact. In total we detected 244 isoform switches, which are associated to functional pathways that are frequently altered in cancer and also separate tumor and normal samples accurately. We further assessed whether these isoform changes are associated to somatic mutations. Surprisingly, only a few cases appear to have association, including the putative tumor suppressor FBLN2 and the tumor driver MYH11, which show association of an isoform switch to mutations and indels on the alternatively spliced exon. However, the number of observed mutations is in general not sufficient to explain the frequency of the found isoform switches, suggesting that recurrent isoform switching in cancer is mostly independent of somatic mutations. In summary, we present an effective approach to detect novel alternative splicing signatures that are predictive of tumors. Moreover, the same methodology has led to uncover recurrent isoform switches in tumors, which may provide novel prognostic and therapeutic targets. Software and data are available at: https://bitbucket.org/regulatorygenomicsupf/iso-ktsp and http://dx.doi.org/10.6084/m9.figshare.1061917

CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model

2021 ◽  
Author(s):  
Wenyi Zhao ◽  
Jingwen Yang ◽  
Jingcheng Wu ◽  
Guoxing Cai ◽  
Yao Zhang ◽  
...  
Keyword(s):  
Somatic Mutations ◽  
Cancer Genomics ◽  
Evolutionary Model ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Component Mixture ◽  
Cancer Driver ◽  
Two Component ◽  
Potential Cancer

Abstract Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).

scholarly journals A Statistical Framework for Evolutionary Analysis of Recurrent Somatic Mutations in Cancers

2020 ◽  
Author(s):  
Xun Gu
Keyword(s):  
Somatic Mutations ◽  
Cancer Genomics ◽  
Computational Procedure ◽  
The Cancer Genome Atlas ◽  
Component Model ◽  
Evolutionary Analysis ◽  
Cancer Genes ◽  
Component Mixture ◽  
Two Component ◽  
Empirical Bayesian Method

AbstractCurrent cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored, faciltating enormous high throuput analyses to explore the underlying mechanisms that may contribute to malignant initiation or progression. In the context of over-dominant passenger mutations (unrelated to cancers), the challenge is to identify somatic mutations that are cancer-driving. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model that enables to accomplish the following analyses. (i) We formulated a quasi-likelihood approach to test whether the two-component model is significantly better than a single-component model, which can be used for new cancer gene predicting. (ii) We implemented an empirical Bayesian method to calculate the posterior probabilities of a site to be cancer-driving for all sites of a gene, which can be used for new driving site predicting. (iii) We developed a computational procedure to calculate the somatic selection intensity at driver sites and passenger sites, respectively, as well as site-specific profiles for all sites. Using these newly-developed methods, we comprehensively analyzed 294 known cancer genes based on The Cancer Genome Atlas (TCGA) database.

scholarly journals A comprehensive analysis of the angiogenesis-related genes in glioblastoma multiforme vs. brain lower grade glioma

2020 ◽  
Vol 78 (1) ◽  
pp. 34-38
Author(s):  
Burcu BITERGE-SUT
Keyword(s):  
Glioblastoma Multiforme ◽  
Malignant Gliomas ◽  
Genetic Alterations ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Lower Grade ◽  
Nonsense Mutations ◽  
Lower Grade Glioma ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Brain tumors are one of the most common causes of cancer-related deaths around the world. Angiogenesis is critical in high-grade malignant gliomas, such as glioblastoma multiforme. Objective: The aim of this study is to comparatively analyze the angiogenesis-related genes, namely VEGFA, VEGFB, KDR, CXCL8, CXCR1 and CXCR2 in LGG vs. GBM to identify molecular distinctions using datasets available on The Cancer Genome Atlas (TCGA). Methods: DNA sequencing and mRNA expression data for 514 brain lower grade glioma (LGG) and 592 glioblastoma multiforme (GBM) patients were acquired from The Cancer Genome Atlas (TCGA), and the genetic alterations and expression levels of the selected genes were analyzed. Results: We identified six distinct KDR mutations in the LGG patients and 18 distinct KDR mutations in the GBM patients, including missense and nonsense mutations, frame shift deletion and altered splice region. Furthermore, VEGFA and CXCL8 were significantly overexpressed within GBM patients. Conclusions: VEGFA and CXCL8 are important factors for angiogenesis, which are suggested to have significant roles during tumorigenesis. Our results provide further evidence that VEGFA and CXCL8 could induce angiogenesis and promote LGG to progress into GBM. These findings could be useful in developing novel targeted therapeutics approaches in the future.

scholarly journals Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Dafeng Xu ◽  
Yu Wang ◽  
Kailun Zhou ◽  
Jincai Wu ◽  
Zhensheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Immune Cells ◽  
Prognostic Value ◽  
Tumor Tissue ◽  
Clinical Stage ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Clinical Prognosis

Abstract Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.

scholarly journals Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qidong Cai ◽  
Boxue He ◽  
Pengfei Zhang ◽  
Zhenyu Zhao ◽  
Xiong Peng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alternative Splicing ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Machine Learning Algorithms ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. Method RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). Results Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. Conclusion In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

scholarly journals Toolbox for Mobile-Element Insertion Detection on Cancer Genomes

2015 ◽  
Vol 14s1 ◽  
pp. CIN.S24657
Author(s):  
Wan-Ping Lee ◽  
Jiantao Wu ◽  
Gabor T. Marth
Keyword(s):  
Human Genome ◽  
Mobile Element ◽  
Mobile Elements ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Element Insertion ◽  
Human Genome Evolution ◽  
Mobile Element Insertion

Mobile elements constitute greater than 45% of the human genome as a result of repeated insertion events during human genome evolution. Although most of mobile elements are fixed within the human population, some elements (including ALU, long interspersed elements (LINE) 1 (L1), and SVA) are still actively duplicating and may result in life-threatening human diseases such as cancer, motivating the need for accurate mobile-element insertion (MEI) detection tools. We developed a software package, TANGRAM, for MEI detection in next-generation sequencing data, currently serving as the primary MEI detection tool in the 1000 Genomes Project. TANGRAM takes advantage of valuable mapping information provided by our own MOSAIK mapper, and until recently required MOSAIK mappings as its input. In this study, we report a new feature that enables TANGRAM to be used on alignments generated by any mainstream short-read mapper, making it accessible for many genomic users. To demonstrate its utility for cancer genome analysis, we have applied TANGRAM to the TCGA (The Cancer Genome Atlas) mutation calling benchmark 4 dataset. TANGRAM is fast, accurate, easy to use, and open source on https://github.com/jiantao/Tangram .

scholarly journals ENDOCRINE TUMOURS: Advances in the molecular pathogenesis of thyroid cancer: lessons from the cancer genome

2016 ◽  
Vol 175 (5) ◽  
pp. R203-R217 ◽  
Author(s):  
Garcilaso Riesco-Eizaguirre ◽  
Pilar Santisteban
Keyword(s):  
Thyroid Cancer ◽  
Cancer Genomics ◽  
Treatment Strategies ◽  
Genetic Alterations ◽  
Cancer Genome ◽  
Molecular Pathogenesis ◽  
Cancer Genes ◽  
New Information ◽  
Novel Treatment Strategies ◽  
And Behavior

Thyroid cancer is the most common endocrine malignancy giving rise to one of the most indolent solid cancers, but also one of the most lethal. In recent years, systematic studies of the cancer genome, most importantly those derived from The Cancer Genome Altas (TCGA), have catalogued aberrations in the DNA, chromatin, and RNA of the genomes of thousands of tumors relative to matched normal cellular genomes and have analyzed their epigenetic and protein consequences. Cancer genomics is therefore providing new information on cancer development and behavior, as well as new insights into genetic alterations and molecular pathways. From this genomic perspective, we will review the main advances concerning some essential aspects of the molecular pathogenesis of thyroid cancer such as mutational mechanisms, new cancer genes implicated in tumor initiation and progression, the role of non-coding RNA, and the advent of new susceptibility genes in thyroid cancer predisposition. This look across these genomic and cellular alterations results in the reshaping of the multistep development of thyroid tumors and offers new tools and opportunities for further research and clinical development of novel treatment strategies.

A heuristic platform for clinical interpretation of cancer genome sequencing data.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10502-10502
Author(s):  
Eliezer Mendel Van Allen ◽  
Nikhil Wagle ◽  
Gregory Kryukov ◽  
Alexis Ramos ◽  
Gad Getz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Genome Sequencing ◽  
Heuristic Algorithm ◽  
Genetic Alterations ◽  
Cancer Genome ◽  
Clinical Samples ◽  
Whole Genome ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Clinical Interpretation

10502 Background: The ability to identify and effectively sort the full spectrum of biologically and therapeutically relevant genetic alterations identified by massively parallel sequencing may improve cancer care. A major challenge involves rapid and rational categorization of data-intensive output, including somatic mutations, insertions/deletions, copy number alterations, and rearrangements into ranked categories for clinician review. Methods: A database of clinically actionable alterations was created, consisting of over 100 annotated genes known to undergo somatic genomic alterations in cancer that may impact clinical decision-making. A heuristic algorithm was developed, which selectively identifies somatic alterations based on the clinically actionable alterations database. Remaining variants are sorted based on additional heuristics, including high priority alterations based on presence in the Cancer Gene Census, biologically significant cancer genes based on presence in COSMIC or MSigDB, and low priority alterations in the same gene family as biologically significant cancer genes. The heuristic algorithm was applied to whole exome sequencing data of clinical samples and whole genome sequencing data from a cohort of prostate cancer samples processed using established Broad Institute pipelines. Results: Application of the heuristic algorithm to the prostate cancer whole genome rearrangement data identified 172 (out of 5978) rearrangements involving actionable genes (averaging 2-3 events per tumor). Furthermore, two clinical samples processed prospectively were analyzed, yielding three potentially actionable alterations for clinical review. Conclusions: The heuristic model for clinical interpretation of next generation sequencing data may facilitate rapid analysis of tumor genomic information for clinician review by identifying and prioritizing alterations that can directly impact care. Our platform can also be applied to research data to prospectively explore clinically relevant findings from existing cohorts. Future analytical approaches using heuristic or probabilistic algorithms should underpin a robust prospective assessment of clinical cancer genome data.

Are prostate cancer patients with BRCA1 and BRCA2 mutations safe for active surveillance?

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 19-19
Author(s):  
Marc Dall'era ◽  
Christopher P. Evans ◽  
Chong-Xian Pan ◽  
Mamta Parikh ◽  
Primo Lara ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Genetic Alterations ◽  
The Cancer Genome Atlas ◽  
Secondary Outcome ◽  
Sequencing Data ◽  
Brca1 And Brca2 ◽  
Data Set ◽  
Entire Cohort ◽  
Cancer Pathogenesis

19 Background: Active surveillance (AS) is recommended as a treatment option for men presenting with low risk (Gleason 3+3) and some intermediate risk (Gleason 3+4) prostate cancer. BRCA1 or 2 germline mutations have been implicated in prostate cancer pathogenesis. It is unknown if germline BRCA1/2 mutations in AS candidacy are associated with more aggressive histologic grade, higher stage or other worse genetic alterations, such as RB1 and p53 deletions. Methods: We analyzed sequencing data from 498 men who underwent radical prostatectomy from The Cancer Genome Atlas (TCGA) data set. The primary outcome was the difference in the proportions of AS candidates among subjects with and without BRCA homodeletions. Tests for differences in the proportions were conducted using Fisher’s Exact Test. Equivalence tests for proportions of AS candidates were conducted using the two-one sided tests (TOST) method. As a secondary outcome we studied the associated coincident mutations in the men with BRCA1 and BRCA2 homodeletions. Results: Forty-one men (8%) of the cohort had homodeletion of BRCA1 or BRCA2. Ten men (2%) had complete loss of BRCA1 while 31 (6%) had loss of BRCA2. Rates of candidacy for AS based on histology and stage (defined as stage T2, Gleason 6) are not different between subjects with and without BRCA 1 or 2 homodeletions, within an equivalence margin of 10 percentage points. These findings are similar when the AS criteria are modified to add Gleason 3+4 subjects. Fifty percent of men with organ confined (pT2), 3+3 and 3+4 prostate cancer with BRCA1 or BRCA2 homodeletions had concomitant RB1 deletions compared with 16.5% of the entire cohort (p = 0.002). This was primarily driven by BRCA2 deletions co-occurrent with RB1 deletions (log OR: 2.4, p < 0.001). Twenty-nine percent of men from this group had concomitant p53 deletions compared to 7.5% of the entire cohort (p = 0.004). Conclusions: Men with prostate cancer and BRCA1 or BRCA2 homodeletions present with similar stage and grade tumors than men without these deletions. Despite having low or low intermediate grade histology, BRCA1 and BRCA2 deleted tumors are enriched with deletions in RB1 and TP53, both of which are associated with more aggressive phenotypes and treatment resistance.

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