scholarly journals Mutational analysis of field cancerization in bladder cancer

2019 ◽  
Author(s):  
Trine Strandgaard ◽  
Iver Nordentoft ◽  
Philippe Lamy ◽  
Emil Christensen ◽  
Mathilde Borg Houlberg Thomsen ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Mutational Analysis ◽  
Read Depth ◽  
Field Cancerization ◽  
Driver Mutations ◽  
Driver Genes ◽  
Bladder Tissue ◽  
Cancer Driver ◽  
Bladder Urothelium ◽  
Multifocal Disease

AbstractThe multifocal and recurrent nature of bladder cancer has been explained by field cancerization of the bladder urothelium. To shed light on field cancerization in the bladder, we investigated the mutational landscape of normal appearing urothelium and paired bladder tumors from four patients. Sequencing of 509 cancer driver genes revealed the presence of 2-16 mutations exclusively localized in normal tissue (average target read depth 634x). Furthermore, 6-13 mutations were shared between tumor and normal samples and 8-75 mutations were exclusively detected in tumor samples. More mutations were observed in normal samples from patients with multifocal disease compared to patients with unifocal disease. Mutations in normal samples had low allele frequencies compared to tumor mutations (p<2.2*10−16). Furthermore, significant differences in the type of nucleotide changes between tumor, normal and shared mutations (p=2.7*10−8) were observed, and mutations in APOBEC context were observed primarily among tumor mutations (p=0.026). No differences in functional impact between normal, shared and tumor mutations were observed (p=0.23). Overall, these findings support the theory of multiple fields in the bladder, and document non-tumor specific driver mutations to be present in normal appearing bladder tissue.

scholarly journals Mutational Analysis of Field Cancerization in Bladder Cancer

2020 ◽  
Vol 6 (3) ◽  
pp. 253-264 ◽  
Author(s):  
Trine Strandgaard ◽  
Iver Nordentoft ◽  
Philippe Lamy ◽  
Emil Christensen ◽  
Mathilde Borg Houlberg Thomsen ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Normal Tissue ◽  
Mutational Analysis ◽  
Read Depth ◽  
Field Cancerization ◽  
Driver Mutations ◽  
Driver Genes ◽  
Bladder Tissue ◽  
Cancer Driver ◽  
Multifocal Disease

BACKGROUND: Morphologically normal tissue, adjacent to tumors, contains multiple molecular changes, the so-called field cancerization. The multifocal and recurrent nature of bladder cancer has been hypothesized to originate from this. However, further studies are required to explore the mutational composition of normal tissue adjacent to tumors. OBJECTIVE: To analyze field cancerization in bladder cancer patients using a non-tumor guided approach. METHODS: We investigated the mutational landscape of normal appearing urothelium and paired bladder tumors from four patients by applying deep-targeted sequencing. RESULTS: Sequencing of 509 cancer driver genes revealed the presence of 2– 13 mutations exclusively localized in normal tissue (average target read depth 634×). Furthermore, 6– 13 mutations were shared between tumor and normal samples and 8– 75 mutations were exclusively detected in tumor samples. More mutations were observed in normal samples from patients with multifocal disease compared to patients with unifocal disease. Mutations in normal samples had lower variant allele fractions (VAF) compared to tumor mutations (p < 2.2*10–16). Furthermore, significant differences in the type of nucleotide changes between tumor, normal and shared mutations (p = 2.2*10–5) were observed, and mutations in APOBEC context were observed primarily among tumor mutations (p = 0.02). No differences in functional impact between normal, shared and tumor mutations were observed (p = 0.61). CONCLUSION: Overall, these findings support the presence of more than one field in the bladder, and document non-tumor specific driver mutations to be present in normal appearing bladder tissue.

scholarly journals Mutational analysis of driver genes with tumor suppressive and oncogenic roles in gastric cancer

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3585 ◽  
Author(s):  
Tianfang Wang ◽  
Yining Liu ◽  
Min Zhao
Keyword(s):  
Gastric Cancer ◽  
Cancer Progression ◽  
Complex Disease ◽  
Mutational Analysis ◽  
Driver Mutations ◽  
Driver Genes ◽  
Protein Coding ◽  
Mirna Genes ◽  
Genetic Mechanisms ◽  
New Treatment

Gastric cancer (GC) is a complex disease with heterogeneous genetic mechanisms. Genomic mutational profiling of gastric cancer not only expands our knowledge about cancer progression at a fundamental genetic level, but also could provide guidance on new treatment decisions, currently based on tumor histology. The fact that precise medicine-based treatment is successful in a subset of tumors indicates the need for better identification of clinically related molecular tumor phenotypes, especially with regard to those driver mutations on tumor suppressor genes (TSGs) and oncogenes (ONGs). We surveyed 313 TSGs and 160 ONGs associated with 48 protein coding and 19 miRNA genes with both TSG and ONG roles. Using public cancer mutational profiles, we confirmed the dual roles of CDKN1A and CDKN1B. In addition to the widely recognized alterations, we identified another 82 frequently mutated genes in public gastric cancer cohort. In summary, these driver mutation profiles of individual GC will form the basis of personalized treatment of gastric cancer, leading to substantial therapeutic improvements.

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 Diversity spectrum analysis identifies mutation-specific effects of cancer driver genes

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Xiaobao Dong ◽  
Dandan Huang ◽  
Xianfu Yi ◽  
Shijie Zhang ◽  
Zhao Wang ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Spectrum Analysis ◽  
Driver Mutations ◽  
Driver Gene ◽  
Cancer Type ◽  
Driver Genes ◽  
Drug Responses ◽  
Cancer Driver ◽  
Specific Effects ◽  
Cancer Driver Genes

AbstractMutation-specific effects of cancer driver genes influence drug responses and the success of clinical trials. We reasoned that these effects could unbalance the distribution of each mutation across different cancer types, as a result, the cancer preference can be used to distinguish the effects of the causal mutation. Here, we developed a network-based framework to systematically measure cancer diversity for each driver mutation. We found that half of the driver genes harbor cancer type-specific and pancancer mutations simultaneously, suggesting that the pervasive functional heterogeneity of the mutations from even the same driver gene. We further demonstrated that the specificity of the mutations could influence patient drug responses. Moreover, we observed that diversity was generally increased in advanced tumors. Finally, we scanned potentially novel cancer driver genes based on the diversity spectrum. Diversity spectrum analysis provides a new approach to define driver mutations and optimize off-label clinical trials.

scholarly journals WITER: A powerful method for the estimation of cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

2018 ◽  
Author(s):  
Lin Jiang ◽  
Jingjing Zheng ◽  
Johnny Sheung Him Kwan ◽  
Sheng Dai ◽  
Cong Li ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Alternative Methods ◽  
Driver Mutations ◽  
Driver Genes ◽  
Cancer Driver ◽  
Binomial Regression ◽  
Background Mutation Rate ◽  
Technical Advances ◽  
Cancer Driver Genes

AbstractGenomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modeling distribution of background mutations, existing statistical methods are often underpowered to discriminate driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), to detect cancer-driver genes showing an excess of somatic mutations. By solving the problem of inaccurately modeling background mutations, this approach works even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in all tested cancers. Applying this approach, we estimated 178 driver genes in 26 different cancers types. In silico validation confirmed 90.5% of predicted genes as likely known drivers and 7 genes unique for individual cancers as likely new drivers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects, rescuing more genes missed by alternative tools.

scholarly journals A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

2018 ◽  
Author(s):  
Paul Ashford ◽  
Camilla S.M. Pang ◽  
Aurelio A. Moya-García ◽  
Tolulope Adeyelu ◽  
Christine A. Orengo
Keyword(s):  
Zinc Finger Protein ◽  
Point Mutations ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Driver Genes ◽  
Recurrent Point ◽  
Cancer Driver ◽  
Functional Sites ◽  
Cancer Driver Genes ◽  
Family Based

Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) – structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated.Our approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.

scholarly journals A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Paul Ashford ◽  
Camilla S. M. Pang ◽  
Aurelio A. Moya-García ◽  
Tolulope Adeyelu ◽  
Christine A. Orengo
Keyword(s):  
Driver Mutations ◽  
Driver Genes ◽  
Cancer Driver ◽  
Functional Family ◽  
Cancer Driver Genes ◽  
Domain Functional ◽  
Family Based

scholarly journals WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts

2019 ◽  
Vol 47 (16) ◽  
pp. e96-e96 ◽  
Author(s):  
Lin Jiang ◽  
Jingjing Zheng ◽  
Johnny S H Kwan ◽  
Sheng Dai ◽  
Cong Li ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Alternative Methods ◽  
Small Samples ◽  
Driver Mutations ◽  
Driver Genes ◽  
Cancer Driver ◽  
Binomial Regression ◽  
Technical Advances ◽  
Cancer Driver Genes

Abstract Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modelling distribution of background mutation counts, existing statistical methods are often underpowered to discriminate cancer-driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER, http://grass.cgs.hku.hk/limx/witer or KGGSeq,http://grass.cgs.hku.hk/limx/kggseq/), to detect cancer-driver genes showing an excess of somatic mutations. By fitting the distribution of background mutation counts properly, this approach works well even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in most tested cancers. Applying this approach, we estimated 229 driver genes in 26 different types of cancers. In silico validation confirmed 78% of predicted genes as likely known drivers and many other genes as very likely new drivers for corresponding cancers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects and rescue of more genes missed by alternative tools in moderate or small samples.

scholarly journals CancerGeneNet: linking driver genes to cancer hallmarks

2019 ◽  
Vol 48 (D1) ◽  
pp. D416-D421 ◽  
Author(s):  
Marta Iannuccelli ◽  
Elisa Micarelli ◽  
Prisca Lo Surdo ◽  
Alessandro Palma ◽  
Livia Perfetto ◽  
...  
Keyword(s):  
Large Fraction ◽  
Driver Mutations ◽  
Gene Products ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Hallmarks ◽  
Disease Phenotypes ◽  
Regulatory Circuits ◽  
Cancer Drivers ◽  
Cancer Phenotypes

Abstract CancerGeneNet (https://signor.uniroma2.it/CancerGeneNet/) is a resource that links genes that are frequently mutated in cancers to cancer phenotypes. The resource takes advantage of a curation effort aimed at embedding a large fraction of the gene products that are found altered in cancer cells into a network of causal protein relationships. Graph algorithms, in turn, allow to infer likely paths of causal interactions linking cancer associated genes to cancer phenotypes thus offering a rational framework for the design of strategies to revert disease phenotypes. CancerGeneNet bridges two interaction layers by connecting proteins whose activities are affected by cancer drivers to proteins that impact on the ‘hallmarks of cancer’. In addition, CancerGeneNet annotates curated pathways that are relevant to rationalize the pathological consequences of cancer driver mutations in selected common cancers and ‘MiniPathways’ illustrating regulatory circuits that are frequently altered in different cancers.

scholarly journals Mutational likeliness and entropy help to identify driver mutations and their functional role in cancer

2018 ◽  
Author(s):  
Giorgio Mattiuz ◽  
Salvatore Di Giorgio ◽  
Lorenzo Tofani ◽  
Antonio Frandi ◽  
Francesco Donati ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Somatic Mutations ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Loss Of Function ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Genomes ◽  
Passenger Mutations ◽  
Mutational Processes

AbstractAlterations in cancer genomes originate from mutational processes taking place throughout oncogenesis and cancer progression. We show that likeliness and entropy are two properties of somatic mutations crucial in cancer evolution, as cancer-driver mutations stand out, with respect to both of these properties, as being distinct from the bulk of passenger mutations. Our analysis can identify novel cancer driver genes and differentiate between gain and loss of function mutations.

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