scholarly journals Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations

2014 ◽  
Author(s):  
Özgün Babur ◽  
Mithat Gönen ◽  
Bülent Arman Aksoy ◽  
Nikolaus Schultz ◽  
Giovanni Ciriello ◽  
...  
Keyword(s):  
Cancer Genomics ◽  
Single Gene ◽  
Mutual Exclusion ◽  
Selective Advantage ◽  
Driver Mutations ◽  
Mutual Exclusivity ◽  
Genomic Alterations ◽  
Single Function ◽  
Current Sample ◽  
Systematic Identification

Recent cancer genome studies have identified numerous genomic alterations in cancer genomes. It is hypothesized that only a fraction of these genomic alterations drive the progression of cancer -- often called driver mutations. Current sample sizes for cancer studies, often in the hundreds, are sufficient to detect pivotal drivers solely based on their high frequency of alterations. In cases where the alterations for a single function are distributed among multiple genes of a common pathway, however, single gene alteration frequencies might not be statistically significant. In such cases, we expect to observe that most samples are altered in only one of those alternative genes because additional alterations would not convey an additional selective advantage to the tumor. This leads to a mutual exclusion pattern of alterations, that can be exploited to identify these groups. We developed a novel method for the identification of sets of mutually exclusive gene alterations in a signaling network. We scan the groups of genes with a common downstream effect, using a mutual exclusivity criterion that makes sure that each gene in the group significantly contributes to the mutual exclusivity pattern. We have tested the method on all available TCGA cancer genomics datasets, and detected multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.

scholarly journals Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Özgün Babur ◽  
Mithat Gönen ◽  
Bülent Arman Aksoy ◽  
Nikolaus Schultz ◽  
Giovanni Ciriello ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Mutual Exclusivity ◽  
Genomic Alterations ◽  
Systematic Identification

scholarly journals Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact

2021 ◽  
Author(s):  
Bengi Ruken Yavuz ◽  
Chung-Jung Tsai ◽  
Ruth Nussinov ◽  
Nurcan Tuncbag
Keyword(s):  
Cell Lines ◽  
Drug Response ◽  
Cancer Genomics ◽  
Single Gene ◽  
Clinical Information ◽  
Driver Mutations ◽  
Patient Specific ◽  
Cancer Data ◽  
Activated State ◽  
Pan Cancer

Transforming patient-specific molecular data into clinical decisions is fundamental to personalized medicine. Despite massive advancements in cancer genomics, to date driver mutations whose frequencies are low, and their observable transformation potential is minor have escaped identification. Yet, when paired with other mutations in cis, such 'latent driver' mutations can drive cancer. Here, we discover potential 'latent driver' double mutations. We applied a statistical approach to identify significantly co-occurring mutations in the pan-cancer data of mutation profiles of ~80,000 tumor sequences from the TCGA and AACR GENIE databases. The components of same gene doublets were assessed as potential latent drivers. We merged the analysis of the significant double mutations with drug response data of cell lines and patient derived xenografts (PDXs). This allowed us to link the potential impact of double mutations to clinical information and discover signatures for some cancer types. Our comprehensive statistical analysis identified 228 same gene double mutations of which 113 mutations are cataloged as latent drivers. Oncogenic activation of a protein can be through either single or multiple independent mechanisms of action. Combinations of a driver mutation with either a driver, a weak driver, or a strong latent driver have the potential of a single gene leading to a fully activated state and high drug response rate. Tumor suppressors require higher mutational load to coincide with double mutations compared to oncogenes which implies their relative robustness to losing their functions. Evaluation of the response of cell lines and patient-derived xenograft data to drug treatment indicate that in certain genes double

scholarly journals Pervasive conditional selection of driver mutations and modular epistasis networks in cancer

2022 ◽  
Author(s):  
Jaime Iranzo ◽  
George Gruenhagen ◽  
Jorge Calle-Espinosa ◽  
Eugene V. Koonin
Keyword(s):  
Cancer Genomics ◽  
Mutual Exclusion ◽  
Driver Mutations ◽  
Tumor Evolution ◽  
Cancer Genes ◽  
Cancer Subtypes ◽  
Cancer Driver ◽  
Conditional Selection ◽  
Tcga Dataset ◽  
Complex Scenario

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistatic interactions and their quantitative effect on tumor evolution remains a challenge. We developed a method to quantify COnditional SELection on the Excess of Nonsynonymous Substitutions (Coselens) in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identified 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection accounts for 25-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario, where gene-specific across-pathway interactions shape differentiated cancer subtypes.

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 Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression

2015 ◽  
Author(s):  
Giulio Caravagna ◽  
Alex Graudenzi ◽  
DANIELE RAMAZZOTTI ◽  
Rebeca Sanz-Pamplona ◽  
Luca De Sano ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Current Knowledge ◽  
Population Level ◽  
Selective Advantage ◽  
Explanatory Models ◽  
Driver Mutations ◽  
Cross Sectional ◽  
Progression Model ◽  
Colorectal Cancer Progression ◽  
Ngs Data

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent works on "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications as it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression, as well as to suggest novel experimentally verifiable hypotheses.

scholarly journals Discovering the drivers of clonal hematopoiesis

2020 ◽  
Author(s):  
Oriol Pich ◽  
Iker Reyes-Salazar ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
Positive Selection ◽  
Molecular Mechanisms ◽  
Somatic Mutations ◽  
Cancer Genomics ◽  
Variant Calling ◽  
Selective Advantage ◽  
Cancer Genes ◽  
Driver Genes ◽  
Hematopoietic Stem ◽  
Clonal Hematopoiesis

AbstractMutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) in certain conditions drive clonal hematopoiesis (CH). While some CH drivers have been identified experimentally or through epidemiological studies, the compendium of all genes able to drive CH upon mutations in HSCs is far from complete. We propose that identifying signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, similarly as done to identify cancer genes. Using a reverse somatic variant calling approach, we repurposed whole-genome and whole-exome blood/tumor paired samples of more than 12,000 donors from two large cancer genomics cohorts to identify blood somatic mutations. The application of IntOGen, a robust driver discovery pipeline, to blood somatic mutations across both cohorts, and more than 24,000 targeted sequenced samples yielded a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers all known CH genes, and discovers novel candidates. Generating this compendium is an essential step to understand the molecular mechanisms of CH and to accurately detect individuals with CH to ascertain their risk to develop related diseases.

Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

2020 ◽  
Vol 18 (03) ◽  
pp. 2050016 ◽  
Author(s):  
Jorge Francisco Cutigi ◽  
Adriane Feijo Evangelista ◽  
Adenilso Simao
Keyword(s):  
Computational Methods ◽  
Complex Disease ◽  
Cancer Genomics ◽  
Genetic Alterations ◽  
Driver Mutations ◽  
Easy Access ◽  
Computational Perspective ◽  
High Level ◽  
Passenger Mutations ◽  
Cancer Bioinformatics

Cancer is a complex disease caused by the accumulation of genetic alterations during the individual’s life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.

scholarly journals Evolution of the chloroplast genome

2003 ◽  
Vol 358 (1429) ◽  
pp. 99-107 ◽  
Author(s):  
Christopher J. Howe ◽  
Adrian C. Barbrook ◽  
V. Lila Koumandou ◽  
R. Ellen R. Nisbet ◽  
Hamish A. Symington ◽  
...  
Keyword(s):  
Borrelia Burgdorferi ◽  
Chloroplast Genome ◽  
Genome Organization ◽  
Plastid Genome ◽  
Single Gene ◽  
Selective Advantage ◽  
Nucleotide Composition ◽  
Large Circle ◽  
Nuclear Genomes ◽  
Adenine Thymine

We discuss the suggestion that differences in the nucleotide composition between plastid and nuclear genomes may provide a selective advantage in the transposition of genes from plastid to nucleus. We show that in the adenine, thymine (AT)–rich genome of Borrelia burgdorferi several genes have an AT–content lower than the average for the genome as a whole. However, genes whose plant homologues have moved from plastid to nucleus are no less AT–rich than genes whose plant homologues have remained in the plastid, indicating that both classes of gene are able to support a high AT–content. We describe the anomalous organization of dinoflagellate plastid genes. These are located on small circles of 2–3 kbp, in contrast to the usual plastid genome organization of a single large circle of 100–200 kbp. Most circles contain a single gene. Some circles contain two genes and some contain none. Dinoflagellate plastids have retained far fewer genes than other plastids. We discuss a similarity between the dinoflagellate minicircles and the bacterial integron system.

Frequency of actionable genomic alterations in early-stage lung adenocarcinoma (LA) detected by next-generation sequencing (NGS).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e17541-e17541
Author(s):  
Kai Wang ◽  
Philip Stephens ◽  
Roman Yelensky ◽  
Jeffrey S. Ross ◽  
Vincent A. Miller ◽  
...  
Keyword(s):  
Early Stage ◽  
Mtor Inhibitors ◽  
Driver Mutations ◽  
Tumor Type ◽  
Genomic Alterations ◽  
Genomic Libraries ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Ffpe Tissues ◽  
Risk Patients

e17541 Background: Early stage LA (≤T1N0) has a variable natural history. Although many patients are cured (~2/3) with surgery, many tumors recur and better methods are needed to distinguish these subsets and tailor therapy accordingly. We undertook a pilot study of NGS in such tumors to characterize the frequency and diversity of actionable genomic alterations in this setting. Methods: Formalin-fixed paraffin embedded (FFPE) tissues from 21 archival samples collected from 2007 to 2011 were obtained commercially with accompanying clinical and pathologic data. Clinical characteristics: female (17)/male (4); T1N0 (18), AIS (2), MIA (1). Pathologic LA subtype: acinar (n=8), mucinous (n=4), lepidic (n=4), and mixed (n=2) in addition to AIS (n=2) and MIA (n=1). Targeted NGS performed in a CLIA laboratory (Foundation Medicine) gave evaluable results in all cases. Genomic libraries were captured for 3230 exons in 182 cancer-related genes plus 37 introns from 14 genes often rearranged in cancer and sequenced to average median depth of 640X with 99% of bases covered >100X. Results: Forty-one driver mutations were identified in 17 genes among the 21 patients (avg= 1.9) including 28 base substitutions, 5 indels, 4 amplifications, 3 homozygous deletions and 1 rearrangement. Notably, 90% of cases (19/21) harbored at least one alteration that can be classified as actionable-linked to an approved therapy in LA or another tumor type or a clinical trial. Examples include alterations in EGFR (n=6), NF1 (n=2, PI3K/mTOR inhibitors), CDNK2A (n=1, CDK inhibitors), EML4-ALK (fusion, n=1, ALK inhibitor), ERBB2 (n=1, TKI inhibitors), STK11 (n=1, mTOR inhibitors), and MDM2 (n=1, nutlins). Conclusions: NGSidentified drivergenomic alterations in T1N0 LA, MIA and AIS. On average, two known driver mutations were identified per tumor, many of which are associated with existing or emerging targeted therapies. This provides rationale for future investigation of larger series of T1N0 LA, AIS and MIA, using NGS to evaluate the prognostic value of these changes and could presage adjuvant trials in higher risk patients.

Development and validation of a real-world clinicogenomic database.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 2514-2514 ◽  
Author(s):  
Gaurav Singal ◽  
Peter Grant Miller ◽  
Vineeta Agarwala ◽  
Jie He ◽  
Anala Gossai ◽  
...  
Keyword(s):  
Real World ◽  
Cancer Genomics ◽  
False Negative ◽  
Genomic Data ◽  
Female Gender ◽  
Driver Mutations ◽  
New Paradigm ◽  
Process Data ◽  
Genomic Databases ◽  
Negative Results

2514 Background: Genomic findings have diagnostic, prognostic, and predictive utility in clinical oncology. Population studies have been limited by reliance on trials, registries, or institutional chart review, which are costly and represent narrow populations. Integrating electronic health record (EHR) and genomic data collected as part of routine clinical practice may overcome these hurdles. Methods: Patients in the Flatiron Health Database with non-small cell lung cancer (NSCLC) who underwent comprehensive genomic profiling (CGP) by Foundation Medicine were included. EHR processing included structured data harmonization and abstraction of variables from unstructured documents. EHR and CGP data were de-identified and linked in a HIPAA-compliant process. Data included clinical characteristics, alterations across > 300 genes, tumor mutation burden (TMB), therapies and associated real-world responses, progression, and overall survival (OS). Results: The cohort (n = 1619) had expected clinical (mean age 66; 75% with smoking hx; 80% non-squamous) and genomic (18% EGFR; 4% ALK; 1% ROS1) properties of NSCLC. Presence of a driver mutation (EGFR, ALK, ROS1, MET, BRAF, RET, or ERBB2; n = 576) was associated with younger age, female gender, non-smoking, improved OS (35 vs 19 mo, LR p < 0.0001), and prolonged survival when treated with NCCN-recommended therapy (42 vs 28 mo, LR p = 0.001). CGP identified false negative results in up to 30% of single-biomarker tests for EGFR, ALK, and ROS1. CGP accuracy was supported by clinical outcomes. For example, 5 patients with prior negative ALK-fusion testing began ALK-directed therapy after positive CGP results. All 5 exhibited at least a partial response as recorded in the EHR by treating clinicians. Immunotherapy was used in 22% of patients (n = 353). TMB predicted response to nivolumab, including in PD-L1 negative populations. We recapitulated known associations with smoking, histology, and driver mutations. Conclusions: We present and validate a new paradigm for rapidly generating large, research-grade, longitudinal clinico-genomic databases by linking genomic data with EHR clinical annotation. This method offers a powerful tool for understanding cancer genomics and advancing precision medicine.

Sign in / Sign up

Export Citation Format

Share Document