scholarly journals Integrative analysis of common genes and driver mutations implicated in hormone stimulation for four cancers in women

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6872 ◽  
Author(s):  
Salma Begum Bhyan ◽  
YongKiat Wee ◽  
Yining Liu ◽  
Scott Cummins ◽  
Min Zhao
Keyword(s):  
Hormonal Regulation ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Diagnostic Tools ◽  
Genomic Alteration ◽  
Management Decision ◽  
Hormone Regulation ◽  
Driver Genes ◽  
Hormonal Stimulation ◽  
Hormone Stimulation

Cancer is one of the leading cause of death of women worldwide, and breast, ovarian, endometrial and cervical cancers contribute significantly to this every year. Developing early genetic-based diagnostic tools may be an effective approach to increase the chances of survival and provide more treatment opportunities. However, the current cancer genetic studies are mainly conducted independently and, hence lack of common driver genes involved in cancers in women. To explore the potential common molecular mechanism, we integrated four comprehensive literature-based databases to explore the shared implicated genetic effects. Using a total of 460 endometrial, 2,068 ovarian, 2,308 breast and 537 cervical cancer-implicated genes, we identified 52 genes which are common in all four types of cancers in women. Furthermore, we defined their potential functional role in endogenous hormonal regulation pathways within the context of four cancers in women. For example, these genes are strongly associated with hormonal stimulation, which may facilitate rapid diagnosis and treatment management decision making. Additional mutational analyses on combined the cancer genome atlas datasets consisting of 5,919 gynaecological and breast tumor samples were conducted to identify the frequently mutated genes across cancer types. For those common implicated genes for hormonal stimulants, we found that three quarter of 5,919 samples had genomic alteration with the highest frequency in MYC (22%), followed by NDRG1 (19%), ERBB2 (14%), PTEN (13%), PTGS2 (13%) and CDH1 (11%). We also identified 38 hormone related genes, eight of which are associated with the ovulation cycle. Further systems biology approach of the shared genes identified 20 novel genes, of which 12 were involved in the hormone regulation in these four cancers in women. Identification of common driver genes for hormone stimulation provided an unique angle of involving the potential of the hormone stimulants-related genes for cancer diagnosis and prognosis.

scholarly journals Identifying Mutually Exclusive Gene Sets with Prognostic Value and Novel Potential Driver Genes in Patients with Glioblastoma

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Qian Gao ◽  
Yan Cui ◽  
Yanan Shen ◽  
Yanyan Li ◽  
Xue Gao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Prognostic Value ◽  
Risk Prediction Model ◽  
The Cancer Genome Atlas ◽  
Genomic Alteration ◽  
Driver Genes ◽  
Gene Sets ◽  
Rb Pathway ◽  
Prognostic Stratification

The pathogenesis and prognosis of glioblastoma (GBM) remain poorly understood. Mutual exclusivity analysis can distinguish driver genes and pathways from passenger ones. The purpose of this study was to identify mutually exclusive gene sets (MEGSs) that have prognostic value and to detect novel driver genes in GBM. The genomic alteration profile and clinical information were derived from The Cancer Genome Atlas, and the MEGSA method was used to identify the MEGS. Next, we performed survival analysis and constructed a risk prediction model for prognostic stratification. Leave-one-out cross-validation and permutation test were used to evaluate its performance. Finally, we identified 21 statistically significant MEGSs. We found that the MEGS in the RB pathway was significantly associated with poor prognosis, after adjusting for age and gender (HR = 1.837, 95% CI: 1.192–2.831). Based on the risk prediction model, 208 (80.9%) and 49 (19.1%) patients were assigned to high- and low-risk groups, respectively (log-rank: p<0.001, adjusted p=0.001). Additionally, we found that SPTA1, a novel gene involved in the MEGS, was mutually exclusive with members of cell cycle, P53, and RB pathways. In conclusion, the MEGS in the RB pathway had considerable clinical value for GBM prognostic stratification. Mutated SPTA1 may be involved in GBM development.

scholarly journals OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

2020 ◽  
Vol 49 (D1) ◽  
pp. D1289-D1301 ◽  
Author(s):  
Tao Wang ◽  
Shasha Ruan ◽  
Xiaolu Zhao ◽  
Xiaohui Shi ◽  
Huajing Teng ◽  
...  
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Cell Population ◽  
Cancer Types ◽  
Neutral Mutations ◽  
Analysis Platform

Abstract The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

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

2016 ◽  
Vol 14 (06) ◽  
pp. 1650031 ◽  
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.

scholarly journals MEGSA: A powerful and flexible framework for analyzing mutual exclusivity of tumor mutations

2015 ◽  
Author(s):  
Xing Hua ◽  
Paula L. Hyland ◽  
Jing Huang ◽  
Bin Zhu ◽  
Neil E. Caporaso ◽  
...  
Keyword(s):  
Statistical Power ◽  
De Novo ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Ratio Test ◽  
Mutual Exclusivity ◽  
Driver Genes ◽  
Functional Relationships ◽  
Cancer Types ◽  
Tumor Sequencing

The central challenge in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when patients carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, pathway-guided searches or for expanding established small MEGS. We applied our method to the whole exome sequencing data for fourteen cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a novel MEGS with five genes (FLT3, IDH2, NRAS, KIT and TP53) and a MEGS (NPM1, TP53 and RUX1) whose mutation status was strongly associated with survival (P=6.7×10-4). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23 and TBL1XR1), providing support for their role as cancer drivers. Keywords: Mutual exclusivity, oncogenic pathways, driver genes, tumor sequencing

Circulating tumor DNA-based mutational landscape of advanced colorectal cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15034-e15034
Author(s):  
Fang Yan ◽  
Wei Gao ◽  
Lifei Zhu ◽  
Wenzhuan Xie ◽  
Chan Gao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Copy Number ◽  
Circulating Tumor Dna ◽  
Copy Number Variations ◽  
The Cancer Genome Atlas ◽  
Genomic Alteration ◽  
Driver Genes ◽  
Tissue Samples ◽  
Mutational Landscape ◽  
Tumor Dna

e15034 Background: Alterations in oncogenic driver genes are extensively tested to guide anticancer treatment in colorectal cancer (CRC). As the repertoire of therapeutic targets continue to grow, we are interested to interrogate the possibility of resorting to circulating tumor DNA (ctDNA) where tissue samples are not readily available. Methods: Targeted next generation sequencing (NGS) was performed on ctDNA obtained from patients with advanced CRC using a 150-gene panel. Mutational profiles of ctDNA were compared with those detected in tissue samples from the 3D Medicine database as well as The Cancer Genome Atlas (TCGA) database. Genomic aberrations such as single nucleotide variations (SNVs), insertions/deletions, copy number variations, gene rearrangement and fusions were analyzed. Results: Notably, 244 of 256 samples (95.3%) were ctDNA-positive, as indicated by a maximum somatic allele frequency (MSAF) of > 0%. At least one reportable genomic alteration was detected in 96.5% of all cases, with an average of 8.12 mutations per case. The most frequently mutated genes were TP53 (47%), APC (42%), KRAS (32%) in ctDNA. A similar trend was also observed for SNVs, where TP53 (46.9% vs. 77.6% vs. 53.8%), APC (41.8% vs. 66.0% vs. 73.0%), and KRAS (30.5% vs. 48.4% vs. 43.5%) had the highest mutation frequencies across three data sets (ctDNA, tissues and the TCGA database). The SNV profiles of therapeutically relevant genes for CRC were also largely consistent between ctDNA and the tissue samples included in the 3D Medicine database: BRAF (7.0% vs. 8.7%), KRAS (30.5% vs. 48.4%), NRAS (2.7 vs. 3.1%), ERBB2 (5.1% vs. 5.2%), MLH1 (0.8% vs. 2.9%), MSH2 (2.7% vs. 3.1%), and MSH6 (3.1% vs. 3.7%). Patients harboring copy number gains had a significantly higher level of MSAF than those free of gene amplifications (p < 0.0001). Conclusions: Mutational landscape of ctDNA in general correlated with that detected in prior tissue samples as well as the TCGA database, supporting a role for ctDNA testing as a complementary approach to tissue testing in CRC.

scholarly journals Mutational Signature and Transcriptomic Classification Analyses as the Decisive Diagnostic Tools for a Cancer of Unknown Primary

2018 ◽  
pp. 1-25 ◽  
Author(s):  
Roger Olofsson Bagge ◽  
Akif Demir ◽  
Joakim Karlsson ◽  
Babak Alaei-Mahabadi ◽  
Berglind O. Einarsdottir ◽  
...  
Keyword(s):  
Correct Diagnosis ◽  
Cancer Of Unknown Primary ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Diagnostic Tools ◽  
Unknown Primary ◽  
Precision Oncology ◽  
Primary Tumor Site ◽  
Mutational Signature ◽  
Genomic Analyses

Purpose Cancer of unknown primary is a group of metastatic tumors in which the standard diagnostic workup fails to identify the site of origin of the tumor. The potential impact of precision oncology on this group of patients is large, because actionable driver mutations and a correct diagnosis could provide treatment options otherwise not available for patients with these fatal cancers. This study investigated if comprehensive genomic analyses could provide information on the origin of the tumor. Patients and Methods Here we describe a patient whose tumor was misdiagnosed at least three times. Next-generation sequencing, a patient-derived xenograft mouse model, and bioinformatics were used to identify an actionable mutation, predict resistance development to the targeted therapy, and correctly diagnose the origin of the tumor. Transcriptomic classification was benchmarked using The Cancer Genome Atlas (TCGA). Results Despite the lack of a known primary tumor site and the absence of diagnostic immunohistochemical markers, the origin of the patient’s tumor was established using the novel bioinformatic workflow. This included a mutational signature analysis of the sequenced metastases and comparison of their transcriptomic profiles to a pan-cancer panel of tumors from TCGA. We further discuss the strengths and limitations of the latter approaches in the context of three potentially incorrectly diagnosed TCGA lung tumors. Conclusion Comprehensive genomic analyses can provide information on the origin of tumors in patients with cancer of unknown primary.

scholarly journals CHASMplus reveals the scope of somatic missense mutations driving human cancers

2018 ◽  
Author(s):  
Collin Tokheim ◽  
Rachel Karchin
Keyword(s):  
Missense Mutation ◽  
Large Scale ◽  
Computational Method ◽  
The Cancer Genome Atlas ◽  
Superior Performance ◽  
Driver Mutations ◽  
Cancer Type ◽  
Missense Mutations ◽  
Driver Genes ◽  
Cancer Types

SummaryLarge-scale cancer sequencing studies of patient cohorts have statistically implicated many genes driving cancer growth and progression, and their identification has yielded substantial translational impact. However, a remaining challenge is to increase the resolution of driver prediction from the gene level to the mutation level, because mutation-level predictions are more closely aligned with the goal of precision cancer medicine. Here we present CHASMplus, a computational method, that is uniquely capable of identifying driver missense mutations, including those specific to a cancer type, as evidenced by significantly superior performance on diverse benchmarks. Applied to 8,657 tumor samples across 32 cancer types in The Cancer Genome Atlas, CHASMplus identifies over 4,000 unique driver missense mutations in 240 genes, supporting a prominent role for rare driver mutations. We show which TCGA cancer types are likely to yield discovery of new driver missense mutations by additional sequencing, which has important implications for public policy.SignificanceMissense mutations are the most frequent mutation type in cancers and the most difficult to interpret. While many computational methods have been developed to predict whether genes are cancer drivers or whether missense mutations are generally deleterious or pathogenic, there has not previously been a method to score the oncogenic impact of a missense mutation specifically by cancer type, limiting adoption of computational missense mutation predictors in the clinic. Cancer patients are routinely sequenced with targeted panels of cancer driver genes, but such genes contain a mixture of driver and passenger missense mutations which differ by cancer type. A patient’s therapeutic response to drugs and optimal assignment to a clinical trial depends on both the specific mutation in the gene of interest and cancer type. We present a new machine learning method honed for each TCGA cancer type, and a resource for fast lookup of the cancer-specific driver propensity of every possible missense mutation in the human exome.

Multigene Combined Detection by RT-qPCR Using Cytological Specimens

2021 ◽  
pp. 1-10
Author(s):  
Yang Ma ◽  
Jingxia Zhao ◽  
Yun Du ◽  
Rui Wang ◽  
Xiaokun Ji ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Smoking Status ◽  
Single Gene ◽  
Rna Extraction ◽  
Pcr Amplification ◽  
Driver Mutations ◽  
Amplification Refractory Mutation System ◽  
Driver Genes ◽  
Mutation Status ◽  
Pik3ca Mutations

<b><i>Objective:</i></b> The aim of the study was to investigate the mutation status of multiple driver genes by RT-qPCR and their significance in advanced lung adenocarcinoma using cytological specimens. <b><i>Materials and Methods:</i></b> 155 cytological specimens that had been diagnosed with lung adenocarcinoma in the Fourth Hospital of Hebei Medical University were selected from April to November 2019. The cytological specimens included serous cavity effusion and fine-needle aspiration biopsies. Among cytological specimens, 108 cases were processed by using the cell block method (CBM), and 47 cases were processed by the disposable membrane cell collector method (MCM) before DNA/RNA extraction. Ten drive genes of EGFR, ALK, ROS1, BRAF, KRAS, NRAS, HER2, RET, PIK3CA, and MET were combined detected at one step by the amplification refractory mutation system and ABI 7500 RT-qPCR. <b><i>Results:</i></b> The purity of RNA (<i>p</i> = 0.005) and DNA (<i>p</i> = 0.001) extracted by using the MCM was both significantly higher than that extracted by using the CBM. Forty-seven cases of fresh cell specimens processed by the MCM all succeeded in multigene detections, while of 108 specimens processed by the CBM, 6 cases failed in multigene detections. Among 149 specimens, single-gene mutation rates of EGFR, ALK, ROS1, RET, HER2, MET, KRAS, NRAS, BRAF, and PIK3CA mutations were 57.71%, 6.04%, 3.36%, 2.68%, 2.01%, 2.01%, 1.34%, 0.67%, 0% and 0% respectively, and 6 cases including 2 coexistence mutations. We found that mutation status was correlated with gender (<i>p</i> = 0.047), but not correlated with age (<i>p</i> = 0.141) and smoking status (<i>p</i> = 0.083). We found that the EGFR mutation status was correlated with gender (<i>p</i> = 0.003), age (<i>p</i> = 0.015) and smoking habits (<i>p</i> = 0.007), and ALK mutation status was correlated with age (<i>p</i> = 0.002). <b><i>Conclusion:</i></b> Compared with the CBM, the MCM can improve the efficiency of DNA/RNA extraction and PCR amplification by removing impurities and enriching tumor cells. And we speculate that the successful detection rate of fresh cytological specimens was higher than that of paraffin-embedded specimens. EGFR, ALK, and ROS1 mutations were the main driver mutations in patients with advanced lung adenocarcinoma. We speculate that EGFR and ALK are more prone to concomitant mutations, respectively. Targeted therapies for patients with coexisting mutations need further study.

scholarly journals Inferring perturbation profiles of cancer samples

2021 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Indirect Evidence ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Patient Specific ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Incomplete Coverage ◽  
Gene Perturbations

Abstract Motivation Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. Results We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas. Availability and implementation The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MBCL-08. INTEGRATIVE MOLECULAR ANALYSIS OF PATIENT-MATCHED DIAGNOSTIC AND RELAPSED MEDULLOBLASTOMAS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii389-iii389
Author(s):  
Rahul Kumar ◽  
Maximilian Deng ◽  
Kyle Smith ◽  
Anthony Liu ◽  
Girish Dhall ◽  
...  
Keyword(s):  
Molecular Analysis ◽  
Copy Number ◽  
Driver Mutations ◽  
P Value ◽  
Next Generation ◽  
Driver Genes ◽  
Methylation Array ◽  
Primary Tumors ◽  
Group 4 ◽  
Group 3

Abstract INTRODUCTION The next generation of clinical trials for relapsed medulloblastoma demands a thorough understanding of the clinical behavior of relapsed tumors as well as the molecular relationship to their diagnostic counterparts. METHODS A multi-institutional molecular cohort of patient-matched (n=126 patients) diagnostic MBs and relapses/subsequent malignancies was profiled by DNA methylation array. Entity, subgroup classification, and genome-wide copy-number aberrations were assigned while parallel next-generation (whole-exome or targeted panel) sequencing on the majority of the cohort facilitated inference of somatic driver mutations. RESULTS Comprised of WNT (2%), SHH (41%), Group 3 (18%), Group 4 (39%), primary tumors retained subgroup affiliation at relapse with the notable exception of 10% of cases. The majority (8/13) of discrepant classifications were determined to be secondary glioblastomas. Additionally, rare (n=3) subgroup-switching events of Group 4 primary tumors to Group 3 relapses were identified coincident with MYC/MYCN pathway alterations. Amongst truly relapsing MBs, copy-number analyses suggest somatic clonal divergence between primary MBs and their respective relapses with Group 3 (55% of alterations shared) and Group 4 tumors (63% alterations shared) sharing a larger proportion of cytogenetic alterations compared to SHH tumors (42% alterations shared; Chi-square p-value &lt; 0.001). Subgroup- and gene-specific patterns of conservation and divergence amongst putative driver genes were also observed. CONCLUSION Integrated molecular analysis of relapsed MB discloses potential mechanisms underlying treatment failure and disease recurrence while motivating rational implementation of relapse-specific therapies. The degree of genetic divergence between primary and relapsed MBs varied by subgroup but suggested considerably higher conservation than prior estimates.

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