scholarly journals Only three driver gene mutations are required for the development of lung and colorectal cancers

2014 ◽  
Vol 112 (1) ◽  
pp. 118-123 ◽  
Author(s):  
Cristian Tomasetti ◽  
Luigi Marchionni ◽  
Martin A. Nowak ◽  
Giovanni Parmigiani ◽  
Bert Vogelstein
Keyword(s):  
Gene Mutations ◽  
Epidemiologic Studies ◽  
Driver Gene ◽  
Cancer Type ◽  
Sequencing Data ◽  
Incidence Data ◽  
Cancer Genome Sequencing ◽  
Genome Wide ◽  
Normal Human Cell ◽  
Normal Human

Cancer arises through the sequential accumulation of mutations in oncogenes and tumor suppressor genes. However, how many such mutations are required for a normal human cell to progress to an advanced cancer? The best estimates for this number have been provided by mathematical models based on the relation between age and incidence. For example, the classic studies of Nordling [Nordling CO (1953) Br J Cancer 7(1):68–72] and Armitage and Doll [Armitage P, Doll R (1954) Br J Cancer 8(1):1–12] suggest that six or seven sequential mutations are required. Here, we describe a different approach to derive this estimate that combines conventional epidemiologic studies with genome-wide sequencing data: incidence data for different groups of patients with the same cancer type were compared with respect to their somatic mutation rates. In two well-documented cancer types (lung and colon adenocarcinomas), we find that only three sequential mutations are required to develop cancer. This conclusion deepens our understanding of the process of carcinogenesis and has important implications for the design of future cancer genome-sequencing efforts.

scholarly journals Significant associations between driver gene mutations and DNA methylation alterations across many cancer types

2017 ◽  
Author(s):  
Yun-Ching Chen ◽  
Valer Gotea ◽  
Gennady Margolin ◽  
Laura Elnitski
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Thyroid Cancer ◽  
Gene Mutations ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Genome Wide ◽  
Cancer Types ◽  
Methylation Patterns

AbstractRecent evidence shows that mutations in several driver genes can cause aberrant methylation patterns, a hallmark of cancer. In light of these findings, we hypothesized that the landscapes of tumor genomes and epigenomes are tightly interconnected. We measured this relationship using principal component analyses and methylation-mutation associations applied at the nucleotide level and with respect to genome-wide trends. We found a few mutated driver genes were associated with genome-wide patterns of aberrant hypomethylation or CpG island hypermethylation in specific cancer types. We identified associations between 737 mutated driver genes and site-specific methylation changes. Moreover, using these mutation-methylation associations, we were able to distinguish between two uterine and two thyroid cancer subtypes. The driver gene mutation-associated methylation differences between the thyroid cancer subtypes were linked to differential gene expression in JAK-STAT signaling, NADPH oxidation, and other cancer-related pathways. These results establish that driver-gene mutations are associated with methylation alterations capable of shaping regulatory network functions. In addition, the methodology presented here can be used to subdivide tumors into more homogeneous subsets corresponding to their underlying molecular characteristics, which could improve treatment efficacy.Author summaryMutations that alter the function of driver genes by changing DNA nucleotides have been recognized as a key player in cancer progression. Recent evidence showed that DNA methylation, a molecular signature that is used for controlling gene expression and that consists of cytosine residues with attached methyl groups in the context of CG dinucleotides, is also highly dysregulated in cancer and contributes to carcinogenesis. However, whether those methylation alterations correspond to mutated driver genes in cancer remains unclear. In this study, we analyzed 4,302 tumors from 18 cancer types and demonstrated that driver gene mutations are inherently connected with the aberrant DNA methylation landscape in cancer. We showed that those driver gene-associated methylation patterns can classify heterogeneous tumors in a cancer type into homogeneous subtypes and have the potential to influence the genes that contribute to tumor growth. This finding could help us to better understand the fundamental connection between driver gene mutations and DNA methylation alterations in cancer and to further improve the cancer treatment.

scholarly journals Integrated Whole-Exome and Transcriptome Sequencing of Sporadic Parathyroid Adenoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Ya Hu ◽  
Xiang Zhang ◽  
Ou Wang ◽  
Ming Cui ◽  
Xiaobin Li ◽  
...  
Keyword(s):  
Parathyroid Adenoma ◽  
Molecular Mechanism ◽  
Clustering Analysis ◽  
Transcriptome Sequencing ◽  
Gene Mutations ◽  
Driver Gene ◽  
Consensus Clustering ◽  
Rna Seq ◽  
Sequencing Data ◽  
Whole Exome

PurposeHyperparathyroidism is the third most common endocrine disease. Parathyroid adenoma (PA) accounts for approximately 85% of cases of primary hyperparathyroidism, but the molecular mechanism is not fully understood. Herein, we aimed to investigate the genetic and transcriptomic profiles of sporadic PA.MethodsWhole-exome sequencing (WES) and transcriptome sequencing (RNA-seq) of 41 patients with PA and RNA-seq of 5 normal parathyroid tissues were performed. Gene mutations and characterized expression changes were identified. To elucidate the molecular mechanism underlying PA, unsupervised consensus clustering of RNA-seq data was performed. The correlations between the sequencing data and clinicopathological features of these patients were analyzed.ResultsPreviously reported PA driver gene mutations, such as MEN1 (9/41), mTOR (4/41), ZFX (3/41), CASR (3/41), EZH2 (2/41) and FAT1 (2/41), were also identified in our cohort. Furthermore, somatic mutation of EZH1, which had not been reported in PA, was found in 4 samples. RNA-seq showed that the expression levels of 84 genes were upregulated and 646 were downregulated in PA samples compared with normal samples. Unsupervised clustering analysis of RNA-seq data clustered these patients into 10 subgroups related to mutation or abnormal expression of a group of potential pathogenic genes.ConclusionMEN1, EZH2, CASR, EZH1, ZFX, mTOR and FAT1 mutations in PA were revealed. According to the RNA-seq data clustering analysis, cyclin D1, β-catenin, VDR, CASR and GCM2 may be important factors contributing to the PA gene expression profile.

scholarly journals Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

2018 ◽  
Author(s):  
Stefan C. Dentro ◽  
Ignaty Leshchiner ◽  
Kerstin Haase ◽  
Maxime Tarabichi ◽  
Jeff Wintersinger ◽  
...  
Keyword(s):  
Tumor Heterogeneity ◽  
Human Cancer ◽  
Driver Mutations ◽  
Whole Genome Sequencing Data ◽  
Tumor Evolution ◽  
Driver Gene ◽  
Cancer Type ◽  
Whole Genome ◽  
Sequencing Data ◽  
Cancer Types

SUMMARYIntra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin and drivers of ITH across cancer types are poorly understood. To address this question, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples, spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions, with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types, and identify cancer type specific subclonal patterns of driver gene mutations, fusions, structural variants and copy-number alterations, as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution, and provide an unprecedented pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

scholarly journals Deep learning for cancer type classification and driver gene identification

2021 ◽  
Vol 22 (S4) ◽  
Author(s):  
Zexian Zeng ◽  
Chengsheng Mao ◽  
Andy Vo ◽  
Xiaoyu Li ◽  
Janna Ore Nugent ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Somatic Mutations ◽  
Disease Classification ◽  
Driver Gene ◽  
Cancer Type ◽  
Sequencing Data ◽  
Germline Variants ◽  
Insertions And Deletions ◽  
Novel Method

Abstract Background Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction. Results We proposed DeepCues, a deep learning model that utilizes convolutional neural networks to unbiasedly derive features from raw cancer DNA sequencing data for disease classification and relevant gene discovery. Using raw whole-exome sequencing as features, germline variants and somatic mutations, including insertions and deletions, were interactively amalgamated for feature generation and cancer prediction. We applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p < 0.001). Strikingly, using DeepCues, the top 20 breast cancer relevant genes we have identified, had a 40% overlap with the top 20 known breast cancer driver genes. Conclusion Our results support DeepCues as a novel method to improve the representational resolution of DNA sequencings and its power in deriving features from raw sequences for cancer type prediction, as well as discovering new cancer relevant genes.

scholarly journals Genomic Analysis Reveals Heterogeneity Between Lesions in Synchronous Primary Right-Sided and Left-Sided Colon Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Hanqing Hu ◽  
Qian Zhang ◽  
Rui Huang ◽  
Zhifeng Gao ◽  
Ziming Yuan ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Histological Grade ◽  
Copy Number Variants ◽  
Gene Mutations ◽  
Genomic Analysis ◽  
Normal Mucosa ◽  
Driver Gene ◽  
Sequencing Data ◽  
Whole Exome ◽  
Medical University

Background: The synchronous primary right-sided and left-sided colon cancer (sRL-CC) is a peculiar subtype of colorectal cancer. However, the genomic landscape of sRL-CC remains elusive.Methods: Twenty-eight paired tumor samples and their corresponding normal mucosa samples from 14 patients were collected from the Second Affiliated Hospital of Harbin Medical University from 2011 to 2018. The clinical–pathological data were obtained, and whole-exome sequencing was performed based on formalin-fixed and paraffin-embedded samples of these patients, and then, comprehensive bioinformatic analyses were conducted.Results: Both the lesions of sRL-CC presented dissimilar histological grade and differentiation. Based on sequencing data, few overlapping SNV signatures, onco-driver gene mutations, and SMGs were identified. Moreover, the paired lesions harbored a different distribution of copy number variants (CNVs) and loss of heterozygosity. The clonal architecture analysis demonstrated the polyclonal origin of sRL-CC and inter-cancerous heterogeneity between two lesions.Conclusion: Our work provides evidence that lesions of sRL-CC share few overlapping mutational signatures and CNVs, and may originate from different clones.

scholarly journals Minimal functional driver gene heterogeneity among untreated metastases

Science ◽  
2018 ◽  
Vol 361 (6406) ◽  
pp. 1033-1037 ◽  
Author(s):  
Johannes G. Reiter ◽  
Alvin P. Makohon-Moore ◽  
Jeffrey M. Gerold ◽  
Alexander Heyde ◽  
Marc A. Attiyeh ◽  
...  
Keyword(s):  
Gene Mutations ◽  
Tumor Evolution ◽  
Driver Gene ◽  
Sequencing Data ◽  
Primary Tumors ◽  
Essential Information ◽  
Treatment Naïve ◽  
Functional Consequences ◽  
Prostate Cancers ◽  
Therapeutic Decision Making

Metastases are responsible for the majority of cancer-related deaths. Although genomic heterogeneity within primary tumors is associated with relapse, heterogeneity among treatment-naïve metastases has not been comprehensively assessed. We analyzed sequencing data for 76 untreated metastases from 20 patients and inferred cancer phylogenies for breast, colorectal, endometrial, gastric, lung, melanoma, pancreatic, and prostate cancers. We found that within individual patients, a large majority of driver gene mutations are common to all metastases. Further analysis revealed that the driver gene mutations that were not shared by all metastases are unlikely to have functional consequences. A mathematical model of tumor evolution and metastasis formation provides an explanation for the observed driver gene homogeneity. Thus, single biopsies capture most of the functionally important mutations in metastases and therefore provide essential information for therapeutic decision-making.

scholarly journals Integrated molecular characterisation of endometrioid ovarian carcinoma identifies opportunities for stratification

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Robert L. Hollis ◽  
Barbara Stanley ◽  
John P. Thomson ◽  
Michael Churchman ◽  
Ian Croy ◽  
...  
Keyword(s):  
High Risk ◽  
Ovarian Carcinoma ◽  
Expression Patterns ◽  
Parp Inhibitors ◽  
Receptor Expression ◽  
Cancer Type ◽  
Sequencing Data ◽  
Molecular Features ◽  
Endometrioid Ovarian Carcinoma ◽  
Molecular Landscape

AbstractEndometrioid ovarian carcinoma (EnOC) is an under-investigated ovarian cancer type. Recent studies have described disease subtypes defined by genomics and hormone receptor expression patterns; here, we determine the relationship between these subtyping layers to define the molecular landscape of EnOC with high granularity and identify therapeutic vulnerabilities in high-risk cases. Whole exome sequencing data were integrated with progesterone and oestrogen receptor (PR and ER) expression-defined subtypes in 90 EnOC cases following robust pathological assessment, revealing dominant clinical and molecular features in the resulting integrated subtypes. We demonstrate significant correlation between subtyping approaches: PR-high (PR + /ER + , PR + /ER−) cases were predominantly CTNNB1-mutant (73.2% vs 18.4%, P < 0.001), while PR-low (PR−/ER + , PR−/ER−) cases displayed higher TP53 mutation frequency (38.8% vs 7.3%, P = 0.001), greater genomic complexity (P = 0.007) and more frequent copy number alterations (P = 0.001). PR-high EnOC patients experience favourable disease-specific survival independent of clinicopathological and genomic features (HR = 0.16, 95% CI 0.04–0.71). TP53 mutation further delineates the outcome of patients with PR-low tumours (HR = 2.56, 95% CI 1.14–5.75). A simple, routinely applicable, classification algorithm utilising immunohistochemistry for PR and p53 recapitulated these subtypes and their survival profiles. The genomic profile of high-risk EnOC subtypes suggests that inhibitors of the MAPK and PI3K-AKT pathways, alongside PARP inhibitors, represent promising candidate agents for improving patient survival. Patients with PR-low TP53-mutant EnOC have the greatest unmet clinical need, while PR-high tumours—which are typically CTNNB1-mutant and TP53 wild-type—experience excellent survival and may represent candidates for trials investigating de-escalation of adjuvant chemotherapy to agents such as endocrine therapy.

scholarly journals Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mariana G. Ferrarini ◽  
Avantika Lal ◽  
Rita Rebollo ◽  
Andreas J. Gruber ◽  
Andrea Guarracino ◽  
...  
Keyword(s):  
Transposable Element ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Initiation Factor ◽  
Sequence Variant ◽  
The Novel ◽  
Sequencing Data ◽  
Genome Wide ◽  
Respiratory Cells ◽  
Nuclear Ribonucleoprotein

AbstractThe novel betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after emerging in Wuhan, China. Here we analyzed public host and viral RNA sequencing data to better understand how SARS-CoV-2 interacts with human respiratory cells. We identified genes, isoforms and transposable element families that are specifically altered in SARS-CoV-2-infected respiratory cells. Well-known immunoregulatory genes including CSF2, IL32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were upregulated. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) and eukaryotic initiation factor 4 (eIF4b). We also identified a viral sequence variant with a statistically significant skew associated with age of infection, that may contribute to intracellular host–pathogen interactions. These findings can help identify host mechanisms that can be targeted by prophylactics and/or therapeutics to reduce the severity of COVID-19.

scholarly journals Prediction of genome-wide effects of single nucleotide variants on transcription factor binding

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sebastian Carrasco Pro ◽  
Katia Bulekova ◽  
Brian Gregor ◽  
Adam Labadorf ◽  
Juan Ignacio Fuxman Bass
Keyword(s):  
Binding Sites ◽  
Cancer Type ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Regulatory Regions ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
The Impact ◽  
The Relationship

Abstract Single nucleotide variants (SNVs) located in transcriptional regulatory regions can result in gene expression changes that lead to adaptive or detrimental phenotypic outcomes. Here, we predict gain or loss of binding sites for 741 transcription factors (TFs) across the human genome. We calculated ‘gainability’ and ‘disruptability’ scores for each TF that represent the likelihood of binding sites being created or disrupted, respectively. We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare SNVs in the human population. In addition, we show that cancer somatic mutations have different effects on TF binding sites from different TF families on a cancer-type basis. Finally, we discuss the relationship between these results and cancer mutational signatures. Altogether, we provide a blueprint to study the impact of SNVs derived from genetic variation or disease association on TF binding to gene regulatory regions.

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