scholarly journals Tracking Subclonal Mutation Frequencies Throughout Lymphomagenesis Identifies Cancer Drivers in Mouse Models of Lymphoma

2017 ◽  
Author(s):  
Philip Webster ◽  
Joanna C. Dawes ◽  
Hamlata Dewchand ◽  
Katalin Takacs ◽  
Barbara Iadarola ◽  
...  
Keyword(s):  
Statistical Power ◽  
Tumor Development ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Local Variance ◽  
Non Hodgkin Lymphoma ◽  
Cancer Mutations ◽  
Selective Forces ◽  
Bona Fide ◽  
Subclonal Mutation

ABSTRACTDetermining whether recurrent but rare cancer mutations are bona fide driver mutations remains a bottleneck in cancer research. Here we present the most comprehensive analysis of retrovirus driven lymphomagenesis produced to date, sequencing 700,000 mutations from >500 malignancies collected at time points throughout tumor development. This enabled identification of positively selected events, and the first demonstration of negative selection of mutations that may be deleterious to tumor development indicating novel avenues for therapy. Customized sequencing and bioinformatics methodologies were developed to quantify subclonal mutations in both premalignant and malignant tissue, greatly expanding the statistical power for identifying driver mutations and yielding a high-resolution, genome wide map of the selective forces surrounding cancer gene loci. Screening two BCL2 transgenic models confirms known drivers of human B-cell non-Hodgkin lymphoma, and implicates novel candidates including modifiers of immunosurveillance such as co-stimulatory molecules and MHC loci. Correlating mutations with genotypic and phenotypic features also gives robust identification of known cancer genes independently of local variance in mutation density. An online resource http://mulv.lms.mrc.ac.uk allows customized queries of the entire dataset.

scholarly journals In silico saturation mutagenesis of cancer genes

2020 ◽  
Author(s):  
Ferran Muiños ◽  
Francisco Martinez-Jimenez ◽  
Oriol Pich ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
In Silico ◽  
Tumor Development ◽  
Saturation Mutagenesis ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Mutation Probability ◽  
Tumor Types ◽  
Tumor Somatic Mutations

SummaryExtensive bioinformatics analysis of datasets of tumor somatic mutations data have revealed the presence of some 500-600 cancer driver genes. The identification of all potential driver mutations affecting cancer genes is essential to implement precision cancer medicine and to understand the interplay of mutation probability and selection in tumor development. Here, we present an in silico saturation mutagenesis approach to identify all driver mutations in 568 cancer genes across 66 tumor types. For most cancer genes the mutation probability across tissues --underpinned by active mutational processes-- influences which driver variants have been observed, although this differs significantly between tumor suppressor and oncogenes. The role of selection is apparent in some of the latter, the observed and unobserved driver mutations of which are equally likely to occur. The number of potential driver mutations in a cancer gene roughly determines how many mutations are available for detection across newly sequenced tumors.

scholarly journals The whole-genome panorama of cancer drivers

2017 ◽  
Author(s):  
Radhakrishnan Sabarinathan ◽  
Oriol Pich ◽  
Iñigo Martincorena ◽  
Carlota Rubio-Perez ◽  
Malene Juul ◽  
...  
Keyword(s):  
Cancer Genomics ◽  
Tumor Development ◽  
Point Mutations ◽  
Driver Mutations ◽  
Whole Genome ◽  
Cancer Genes ◽  
Accurate Identification ◽  
Personalized Cancer Medicine ◽  
Cancer Medicine ◽  
Personalized Cancer

SUMMARYThe advance of personalized cancer medicine requires the accurate identification of the mutations driving each patient’s tumor. However, to date, we have only been able to obtain partial insights into the contribution of genomic events to tumor development. Here, we design a comprehensive approach to identify the driver mutations in each patient’s tumor and obtain a whole-genome panorama of driver events across more than 2,500 tumors from 37 types of cancer. This panorama includes coding and non-coding point mutations, copy number alterations and other genomic rearrangements of somatic origin, and potentially predisposing germline variants. We demonstrate that genomic events are at the root of virtually all tumors, with each carrying on average 4.6 driver events. Most individual tumors harbor a unique combination of drivers, and we uncover the most frequent co-occurring driver events. Half of all cancer genes are affected by several types of driver mutations. In summary, the panorama described here provides answers to fundamental questions in cancer genomics and bridges the gap between cancer genomics and personalized cancer medicine.

scholarly journals Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases

2019 ◽  
Author(s):  
Zheng Hu ◽  
Zan Li ◽  
Zhicheng Ma ◽  
Christina Curtis
Keyword(s):  
Clonal Evolution ◽  
Tumor Development ◽  
Distant Metastases ◽  
Driver Mutations ◽  
Sequencing Data ◽  
Primary Tumors ◽  
Lung Cancer Patients ◽  
Systemic Spread ◽  
Cancer Spread ◽  
Early Tumor

AbstractMetastasis is the primary cause of cancer-related deaths, but the natural history, clonal evolution and impact of treatment are poorly understood. We analyzed exome sequencing data from 457 paired primary tumor and metastatic samples from 136 breast, colorectal and lung cancer patients, including untreated (n=99) and treated (n=100) metastatic tumors. Treated metastases often harbored private ‘driver’ mutations whereas untreated metastases did not, suggesting that treatment promotes clonal evolution. Polyclonal seeding was common in untreated lymph node metastases (n=17/29, 59%) and distant metastases (n=20/70, 29%), but less frequent in treated distant metastases (n=9/94, 10%). The low number of metastasis-private clonal mutations is consistent with early metastatic seeding, which we estimated commonly occurred 2-4 years prior to diagnosis across these cancers. Further, these data suggest that the natural course of metastasis is selectively relaxed relative to early tumor development and that metastasis-private mutations are not drivers of cancer spread but instead associated with drug resistance.

scholarly journals Secondary Somatic Mutations in G-Protein-Related Pathways and Mutation Signatures in Uveal Melanoma

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1688 ◽  
Author(s):  
Francesca Piaggio ◽  
Veronica Tozzo ◽  
Cinzia Bernardi ◽  
Michela Croce ◽  
Roberto Puzone ◽  
...  
Keyword(s):  
G Protein ◽  
Uveal Melanoma ◽  
Tumor Development ◽  
Splicing Factor ◽  
Driver Mutations ◽  
Driver Gene ◽  
Protein Subunit ◽  
Mutational Signatures ◽  
Kegg Pathways ◽  
Tyrosine Kinase 2

Background: Uveal melanoma (UM), a rare cancer of the eye, is characterized by initiating mutations in the genes G-protein subunit alpha Q (GNAQ), G-protein subunit alpha 11 (GNA11), cysteinyl leukotriene receptor 2 (CYSLTR2), and phospholipase C beta 4 (PLCB4) and by metastasis-promoting mutations in the genes splicing factor 3B1 (SF3B1), serine and arginine rich splicing factor 2 (SRSF2), and BRCA1-associated protein 1 (BAP1). Here, we tested the hypothesis that additional mutations, though occurring in only a few cases (“secondary drivers”), might influence tumor development. Methods: We analyzed all the 4125 mutations detected in exome sequencing datasets, comprising a total of 139 Ums, and tested the enrichment of secondary drivers in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that also contained the initiating mutations. We searched for additional mutations in the putative secondary driver gene protein tyrosine kinase 2 beta (PTK2B) and we developed new mutational signatures that explain the mutational pattern observed in UM. Results: Secondary drivers were significantly enriched in KEGG pathways that also contained GNAQ and GNA11, such as the calcium-signaling pathway. Many of the secondary drivers were known cancer driver genes and were strongly associated with metastasis and survival. We identified additional mutations in PTK2B. Sparse dictionary learning allowed for the identification of mutational signatures specific for UM. Conclusions: A considerable part of rare mutations that occur in addition to known driver mutations are likely to affect tumor development and progression.

scholarly journals Distinct genomic profiles of gestational choriocarcinoma, a unique cancer of pregnant tissues

2020 ◽  
Vol 52 (12) ◽  
pp. 2046-2054
Author(s):  
Seung-Hyun Jung ◽  
Youn Jin Choi ◽  
Min Sung Kim ◽  
Hyeon-Chun Park ◽  
Mi-Ryung Han ◽  
...  
Keyword(s):  
Poor Prognosis ◽  
Neutral Loss ◽  
Hydatidiform Mole ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Single Nucleotide ◽  
Gestational Choriocarcinoma ◽  
Whole Exome ◽  
Late Event ◽  
Invasive Mole

AbstractLittle is known about genomic alterations of gestational choriocarcinoma (GC), unique cancer that originates in pregnant tissues, and the progression mechanisms from the nonmalignant complete hydatidiform mole (CHM) to GC. Whole-exome sequencing (20 GCs) and/or single-nucleotide polymorphism microarray (29 GCs) were performed. We analyzed copy-neutral loss-of-heterozygosity (CN-LOH) in 29 GCs that exhibited androgenetic CN-LOHs (20 monospermic, 8 dispermic) and no CN-LOH (one with NLRP7 mutation). Most GCs (25/29) harboring recurrent copy number alterations (CNAs) and gains on 1q21.1-q44 were significantly associated with poor prognosis. We detected five driver mutations in the GCs, most of which were chromatin remodeling gene (ARID1A, SMARCD1, and EP300) mutations but not in common cancer genes such as TP53 and KRAS. One patient’s serial CHM/invasive mole/GC showed consistent CN-LOHs, but only the GC harbored CNAs, indicating that CN-LOH is an early pivotal event in HM-IM-GC development, and CNAs may be a late event that promotes CHM progression to GC. Our data indicate that GCs have unique profiles of CN-LOHs, mutations and CNAs that together differentiate GCs from non-GCs. Practically, CN-LOH and CNA profiles are useful for the molecular diagnosis of GC and the selection of GC patients with poor prognosis for more intensive treatments, respectively.

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 Advances in Proteomic Technologies and Its Contribution to the Field of Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-25 ◽  
Author(s):  
Mehdi Mesri
Keyword(s):  
Basic Science ◽  
Tumor Development ◽  
Treatment Strategies ◽  
Science Research ◽  
Biological Processes ◽  
Cancer Genes ◽  
Cancer Genetic ◽  
Care Plans ◽  
The Past ◽  
To Come

Systematic studies of the cancer genome have generated a wealth of knowledge in recent years. These studies have uncovered a number of new cancer genes not previously known to be causal targets in cancer. Genetic markers can be used to determine predisposition to tumor development, but molecularly targeted treatment strategies are not widely available for most cancers. Precision care plans still must be developed by understanding and implementing basic science research into clinical treatment. Proteomics is continuing to make major strides in the discovery of fundamental biological processes as well as more recent transition into an assay platform capable of measuring hundreds of proteins in any biological system. As such, proteomics can translate basic science discoveries into the clinical practice of precision medicine. The proteomic field has progressed at a fast rate over the past five years in technology, breadth and depth of applications in all areas of the bioscience. Some of the previously experimental technical approaches are considered the gold standard today, and the community is now trying to come to terms with the volume and complexity of the data generated. Here I describe contribution of proteomics in general and biological mass spectrometry in particular to cancer research, as well as related major technical and conceptual developments in the field.

PADGE: analysis of heterogeneous patterns of differential gene expression

2007 ◽  
Vol 32 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Li Li ◽  
Amitabha Chaudhuri ◽  
John Chant ◽  
Zhijun Tang
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Statistical Power ◽  
Profile Analysis ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Cancer Genes ◽  
Data Set ◽  
Differential Gene

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644–648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269–2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/ .

scholarly journals Pathogenetic Analysis of Sinonasal Teratocarcinosarcomas Reveal Actionable β-catenin Overexpression and a β-catenin Mutation

2017 ◽  
Vol 78 (04) ◽  
pp. 346-352 ◽  
Author(s):  
Megan Yanik ◽  
Megan Scott ◽  
Carol Bradford ◽  
Jonathan McHugh ◽  
Scott McLean ◽  
...  
Keyword(s):  
Treatment Options ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Molecular Alterations ◽  
Online Databases ◽  
Activating Mutation ◽  
Cancer Mutations ◽  
Cancer Genome Atlas ◽  
Sinonasal Teratocarcinosarcoma ◽  
Index Tumor

Objective Sinonasal teratocarcinosarcomas are rare, aggressive tumors of the skull base. Treatment options are limited and outcomes are poor. Little is known in regard to the genetic factors regulating these tumors. Characterization of actionable molecular alterations in these tumors could provide potentially successful therapeutic options. Methods We performed targeted exome sequencing on an index sinonasal teratocarcinosarcoma specimen to identify potential driver mutations. We performed immunohistochemical stains for β-catenin on paraffin-embedded tissue on the index tumor and a subsequent teratocarcinosarcoma. Online databases of cancer mutations (Catalogue of Somatic Mutations in Cancer and The Cancer Genome Atlas) were accessed. Results We identified an activating p.S45F mutation in β-catenin in our index sinonasal teratocarcinosarcoma. This mutation results in constitutive signaling in the Wnt/β-catenin pathway. We confirmed β-catenin overexpression and nuclear localization via immunohistochemistry in the index tumor and a second patient. The p.S45F activating mutation was found in a variety of solid tumors, and accounts for 3.3 to 10.4% of all known β-catenin mutations. Conclusion We identified a potential driver mutation in β-catenin in a sinonasal teratocarcinosarcoma, resulting in β-catenin overexpression. These findings suggest a role for the Wnt/β-catenin pathway in sinonasal teratocarcinosarcoma tumorigenesis and a role for anti-β-catenin targeted therapy.

scholarly journals Protein Domain Hotspots Reveal Functional Mutations across Genes in Cancer

2015 ◽  
Author(s):  
Martin L Miller ◽  
Ed Reznik ◽  
Nicholas P Gauthier ◽  
Bülent Arman Aksoy ◽  
Anil Korkut ◽  
...  
Keyword(s):  
Statistical Power ◽  
Cancer Genomics ◽  
Protein Domains ◽  
Rapid Expansion ◽  
Protein Domain ◽  
Cancer Genes ◽  
Sequence Alignments ◽  
Hotspot Analysis ◽  
Functional Interpretation ◽  
The Impact

In cancer genomics, frequent recurrence of mutations in independent tumor samples is a strong indication of functional impact. However, rare functional mutations can escape detection by recurrence analysis for lack of statistical power. We address this problem by extending the notion of recurrence of mutations from single genes to gene families that share homologous protein domains. In addition to lowering the threshold of detection, this sharpens the functional interpretation of the impact of mutations, as protein domains more succinctly embody function than entire genes. Mapping mutations in 22 different tumor types to equivalent positions in multiple sequence alignments of protein domains, we confirm well-known functional mutation hotspots and make two types of discoveries: 1) identification and functional interpretation of uncharacterized rare variants in one gene that are equivalent to well-characterized mutations in canonical cancer genes, such as uncharacterizedERBB4(S303F) mutations that are analogous to canonicalERRB2(S310F) mutations in the furin-like domain, and 2) detection of previously unknown mutation hotspots with novel functional implications. With the rapid expansion of cancer genomics projects, protein domain hotspot analysis is likely to provide many more leads linking mutations in proteins to the cancer phenotype.

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