scholarly journals S5-KL-1 CANCER GENOMIC MEDICINE: FROM BENCH TO CLINIC

2019 ◽  
Vol 1 (Supplement_2) ◽  
pp. ii4-ii4
Author(s):  
Hiroyuki Aburatani
Keyword(s):  
Cancer Biology ◽  
Alkylating Agents ◽  
Genomic Medicine ◽  
Cellular Transformation ◽  
Cancer Genome ◽  
Driver Genes ◽  
Cancer Driver ◽  
Personalized Oncology ◽  
The Individual ◽  
Next Generation Sequencing Ngs

Abstract Over the last two decades, genomic technology such as microarray and next generation sequencing (NGS) enabled comprehensive analysis of cancer genome. International cancer genome consortium, established in 2007, completed the analysis of 25,000 cases and has brought discovery of novel cancer driver genes and improved our understanding cancer biology. For example, discovery of IDH1/2 mutation in various cancers created a new concept, 2-hydroxyglutarate as Oncometabolite. The mutational signature patterns allow us to predict how the individual cancer was developed. Anti-cancer drugs, such as alkylating agents, occasionally modify the bases and introduce mutations through mispairing in replication. Currently we are aware that cancer is a genetic disease, where accumulation of genetic and epigenetic alterations in the genome leads to cellular transformation, and that mutation in each patient is unique. To realize the personalized oncology, clinical sequencing test was developed. This year a couple of NGS-based cancer panel tests have been approved for reimbursement by nation-wide healthcare system in Japan. In this seminar I will discuss the future improvement in genomic oncology.

scholarly journals Targeting STAT3 and STAT5 in Cancer

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2002 ◽  
Author(s):  
Elvin D. de Araujo ◽  
György M. Keserű ◽  
Patrick T. Gunning ◽  
Richard Moriggl
Keyword(s):  
Cancer Biology ◽  
Human Cancer ◽  
Cancer Genome ◽  
Driver Genes ◽  
Cancer Driver ◽  
Mutational Landscape ◽  
Coding Regions ◽  
Cancer Driver Genes

Insights into the mutational landscape of the human cancer genome coding regions defined about 140 distinct cancer driver genes in 2013, which approximately doubled to 300 in 2018 following advances in systems cancer biology studies [...]

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 DriverDBv3: a multi-omics database for cancer driver gene research

2019 ◽  
Author(s):  
Shu-Hsuan Liu ◽  
Pei-Chun Shen ◽  
Chen-Yang Chen ◽  
An-Ni Hsu ◽  
Yi-Chun Cho ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Synergistic Effects ◽  
Driver Gene ◽  
Driver Genes ◽  
Specific Patient ◽  
Mutation Status ◽  
Cancer Driver ◽  
Cancer Driver Gene ◽  
Significant Survival ◽  
Gene Database

Abstract An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics’ sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the ‘Cancer’ and ‘Gene’ sections. The ‘Survival’ panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, ‘Survival Analysis’ in ‘Customized-analysis,’ allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics’ sophisticated information, and also constructed a Summary panel in the ‘Cancer’ and ‘Gene’ sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.

scholarly journals Model-based analysis of positive selection significantly expands the list of cancer driver genes, including RNA methyltransferases

2018 ◽  
Author(s):  
Siming Zhao ◽  
Jun Liu ◽  
Pranav Nanga ◽  
Yuwen Liu ◽  
A. Ercument Cicek ◽  
...  
Keyword(s):  
Positive Selection ◽  
Cancer Biology ◽  
Spatial Clustering ◽  
Strong Support ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Model Based ◽  
Cancer Driver Genes ◽  
Model Based Analysis

AbstractIdentifying driver genes is a central problem in cancer biology, and many methods have been developed to identify driver genes from somatic mutation data. However, existing methods either lack explicit statistical models, or rely on very simple models that do not capture complex features in somatic mutations of driver genes. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a more comprehensive model-based approach to driver gene identification. This new method explicitly models, at the single-base level, the effects of positive selection in cancer driver genes as well as highly heterogeneous background mutational process. Its selection model captures elevated mutation rates in functionally important sites using multiple external annotations, as well as spatial clustering of mutations. Its background mutation model accounts for both known covariates and unexplained local variation. Simulations under realistic evolutionary models demonstrate that driverMAPS greatly improves the power of driver gene detection over state-of-the-art approaches. Applying driverMAPS to TCGA data across 20 tumor types identified 159 new potential driver genes. Cross-referencing this list with data from external sources strongly supports these findings. The novel genes include the mRNA methytransferases METTL3-METTL14, and we experimentally validated METTL3 as a potential tumor suppressor gene in bladder cancer. Our results thus provide strong support to the emerging hypothesis that mRNA modification is an important biological process underlying tumorigenesis.

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 239-239
Keyword(s):  
Symptom Severity ◽  
Exploratory Study ◽  
Cancer Genome ◽  
Research Network ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Genome Atlas ◽  
Comprehensive Characterization ◽  
Genome Atlas

Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, Ng PK, Jeong KJ, Cao S, Wang Z, Gao J, Gao Q, Wang F, Liu EM, Mularoni L, Rubio-Perez C, Nagarajan N, Cortes- Ciriano I, Zhou DC, Liang WW, Hess JM, Yellapantula VD, Tamborero D, Gonzalez- Perez A, Suphavilai C, Ko JY, Khurana E, Park PJ, Van Allen EM, Liang H; MC3 Working Group; Cancer Genome Atlas Research Network, Lawrence MS, Godzik A, Lopez-Bigas N, Stuart J, Wheeler D, Getz G, Chen K, Lazar AJ, Mills GB, Karchin R, Ding L. Comprehensive characterization of cancer driver genes and mutations. Cell 2018 Apr 5;173(2):371-385.el8 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029450/ Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A, Bussink J, Gillies RJ, Mak RH, Aerts HJWL. Deep learning for lung cancer prognostication: A retrospective multicohort radiomics study. PLoS Med 2018 Nov 30;15(11):e1002711 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6269088/ Low CA, Dey AK, Ferreira D, Kamarck T, Sun W, Bae S, Doryab A. Estimation of symptom severity during chemotherapy from passively sensed data: Exploratory study. J Med Internet Res 2017 Dec 19;19(12):e420 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750420/ Tamborero D, Rubio-Perez C, Deu-Pons J, Schroeder MP, Vivancos A, Rovira A, Tusquets I, Albanell J, Rodon J, Tabernero J, de Torres C, Dienstmann R, Gonzalez-Perez A, Lopez-Bigas N. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med 2018 Mar 28;10(1):25 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875005/

Individualized discovery of rare cancer drivers in global network context

2021 ◽  
Author(s):  
Iurii Petrov ◽  
Andrey Alexeyenko
Keyword(s):  
Protein Function ◽  
Cancer Biology ◽  
Gene Interaction ◽  
Error Rates ◽  
Global Network ◽  
Genomic Context ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Phenotypes ◽  
Personalized Cancer

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become "hard currency" of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of gene interaction networks and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations' co-occurrence in tumour genomes. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to ten largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. We demonstrate that the individualized discovery revealed driver events which were individually rare, not detectable by other computational approaches, and related to cancer biology domains poorly covered by previous analyses. Considering the novel driver candidates and their constellations in individual tumor genomes opens a novel avenue for personalized cancer medicine.

scholarly journals The landscape of genomic alterations across childhood cancers

Nature ◽  
2018 ◽  
Vol 555 (7696) ◽  
pp. 321-327 ◽  
Author(s):  
Susanne N. Gröbner ◽  
◽  
Barbara C. Worst ◽  
Joachim Weischenfeldt ◽  
Ivo Buchhalter ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Copy Number Variant ◽  
Genetic Alterations ◽  
Childhood Cancers ◽  
Mutational Signatures ◽  
Driver Genes ◽  
Mutation Status ◽  
Cancer Driver ◽  
Cancer Types ◽  
Pan Cancer

Abstract Pan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7–8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.

scholarly journals Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Gao Li ◽  
Xiaowei Du ◽  
Xiaoxiong Wu ◽  
Shen Wu ◽  
Yufei Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Critical Role ◽  
Risk Groups ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and heterogeneity. Genetic mutations caused by driver genes are important contributors to the formation of the tumor microenvironment. The purpose of this study is to discuss the expression of cancer driver genes in tumor tissues and their clinical value in predicting the prognosis of HCC.Methods: All data were sourced from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) public databases. Differentially expressed and prognostic genes were screened by the expression distribution of the cancer driver genes and their relationship with survival. Candidate genes were subjected to functional enrichment and transcription factor regulatory network. We further constructed a prognostic signature and analyzed the survival outcomes and immune status between different risk groups.Results: Most cancer driver genes are specifically expressed in cancer tissues. Driver genes may influence HCC progression through processes such as transcription, cell cycle, and T-cell receptor-related pathways. Patients in different risk groups had significant survival differences (p < 0.05), and risk scores showed high predictive efficacy (AUC>0.69). Besides, risk subgroups were also associated with multiple immune functions and immune cell content.Conclusion: We confirmed the critical role of cancer driver genes in mediating HCC progression and the immune microenvironment. Risk subgroups contribute to the assessment of prognostic value in different patients and explain the heterogeneity of HCC.

scholarly journals Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gabriel A. Colozza-Gama ◽  
Fabiano Callegari ◽  
Nikola Bešič ◽  
Ana C. de J. Paviza ◽  
Janete M. Cerutti
Keyword(s):  
Machine Learning ◽  
Sanger Sequencing ◽  
Learning Algorithm ◽  
Absolute Quantification ◽  
Braf V600e Mutation ◽  
Braf V600e ◽  
Driver Genes ◽  
Quantitative Classification ◽  
Cancer Driver

AbstractSomatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

Sign in / Sign up

Export Citation Format

Share Document