scholarly journals ConsensusDriver improves upon individual algorithms for predicting driver alterations in different cancer types and individual patients – a toolbox for precision oncology

2017 ◽  
Author(s):  
Denis Bertrand ◽  
Sibyl Drissler ◽  
Burton Chia ◽  
Jia Yu Koh ◽  
Li Chenhao ◽  
...  
Keyword(s):  
Cancer Genomics ◽  
Clinical Phenotype ◽  
Genetic Alterations ◽  
Driver Gene ◽  
Prediction Methods ◽  
Precision Oncology ◽  
Cancer Driver ◽  
Recurrent Mutations ◽  
Cancer Types ◽  
Patient Subgroups

AbstractBackgroundIn recent years, several large-scale cancer genomics studies have helped generate detailed molecular profiling datasets for many cancer types and thousands of patients. These datasets provide a unique resource for studying cancer driver prediction methods and their utility for precision oncology, both to predict driver genetic alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.MethodsWe performed the most comprehensive assessment to date of 18 driver gene prediction methods, on more than 3,400 tumour samples, from 15 cancer types, to determine their suitability in guiding precision medicine efforts. These methods have diverse approaches, which can be classified into five categories:functionalimpact on proteins in general (FI) or specific tocancer (FIC),cohort-basedanalysis for recurrent mutations (CBA),mutations withexpressioncorrelation (MEC) and methods that use geneinteractionnetwork-basedanalysis (INA).ResultsThe performance of driver prediction methods varies considerably, with concordance with a gold-standard varying from 9% to 68%. FI methods show relatively poor performance (concordance <22%) while CBA methods provide conservative results, but require large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provide the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of drivers, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity). This tool can be applied to predict driver alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.ConclusionExisting cancer driver prediction methods are based on very different assumptions and each of them can only detect a particular subset of driver events. Consensus-based methods, like ConsensusDriver, are thus a promising approach to harness the strengths of different driver prediction paradigms.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals Bayesian inference of cancer driver genes using signatures of positive selection

2017 ◽  
Author(s):  
Luis Zapata ◽  
Hana Susak ◽  
Oliver Drechsel ◽  
Marc R. Friedländer ◽  
Xavier Estivill ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Large Fraction ◽  
Driver Gene ◽  
Tumor Type ◽  
Sequencing Data ◽  
Cancer Etiology ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Types ◽  
Cancer Cell Fraction

AbstractTumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian inference model for driver prediction. In an in-depth benchmark, we demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.

scholarly journals The cancer-mutation network and the number and specificity of driver mutations

2017 ◽  
Author(s):  
Jaime Iranzo ◽  
Iñigo Martincorena ◽  
Eugene V. Koonin
Keyword(s):  
Linear Regression ◽  
Cancer Genomics ◽  
Driver Mutations ◽  
Bipartite Network ◽  
Cancer Driver ◽  
Cancer Mutation ◽  
Cancer Types ◽  
Extensive Information ◽  
The Mean ◽  
Network Component

AbstractCancer genomics has produced extensive information on cancer-associated genes but the number and specificity of cancer driver mutations remains a matter of debate. We constructed a bipartite network in which 7665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer-associated genes. We show that 27% of the tumors can be assigned to statistically supported modules, most of which encompass 1-2 cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene-specificity of driver mutations. Linear regression of the mutational loads in cancer-associated genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers is ~2, with a range of 1 to 5. Cancers that are associated to modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.

scholarly journals Whole exome precision oncology targeting synthetic lethal vulnerabilities across the tumor transcriptome

2020 ◽  
Author(s):  
Joo Sang Lee ◽  
Nishanth Ulhas Nair ◽  
Lesley Chapman ◽  
Sanju Sinha ◽  
Kun Wang ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Patient Treatment ◽  
Precision Oncology ◽  
Driver Genes ◽  
Patient Response ◽  
Synthetic Lethal ◽  
Cancer Driver ◽  
Whole Exome ◽  
Transcriptomics Data ◽  
Cancer Types

AbstractPrecision oncology has made significant advances in the last few years, mainly by targeting actionable mutations in cancer driver genes. However, the proportion of patients whose tumors can be targeted therapeutically remains limited. Recent studies have begun to explore the benefit of analyzing tumor transcriptomics data to guide patient treatment, raising the need for new approaches for systematically accomplishing that. Here we show that computationally derived genetic interactions can successfully predict patient response. Assembling a broad repertoire of 32 datasets spanning more than 1,500 patients and including both tumor transcriptomics and response data, we predicted the response in 17 out of 21 targeted and 8 out of 11 checkpoint therapy datasets across 8 different cancer types with considerable accuracy, without ever training on these datasets. Analyzing the recently published multi-arm WINTHER trial, we show that the fraction of patients benefitting from transcriptomic-based treatments could potentially be markedly increased from 15% to about 85% by targeting synthetic lethal vulnerabilities in their tumors. In summary, this is the first computational approach to obtain considerable predictive performance across many different targeted and immunotherapy datasets, providing a promising new way for guiding cancer treatment based on the tumor transcriptomics of cancer patients.

scholarly journals Pan-cancer detection of driver genes at the single-patient resolution

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Joel Nulsen ◽  
Hrvoje Misetic ◽  
Christopher Yau ◽  
Francesca D. Ciccarelli
Keyword(s):  
False Positive Rate ◽  
Genetic Alterations ◽  
Therapeutic Interventions ◽  
Precision Oncology ◽  
Cancer Type ◽  
Rare Cancer ◽  
Driver Genes ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

Abstract Background Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. Results We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. Conclusions sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Vol 17 (6) ◽  
pp. e1009085
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals Evaluating the Evaluation of Cancer Driver Genes

2016 ◽  
Author(s):  
Collin J. Tokheim ◽  
Nickolas Papadopoulis ◽  
Kenneth W. Kinzler ◽  
Bert Vogelstein ◽  
Rachel Karchin
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Gold Standard ◽  
Gene Prediction ◽  
Mutation Rates ◽  
Driver Gene ◽  
Prediction Methods ◽  
Driver Genes ◽  
Cancer Driver ◽  
Human Cancers

AbstractSequencing has identified millions of somatic mutations in human cancers, but distinguishing cancer driver genes remains a major challenge. Numerous methods have been developed to identify driver genes, but evaluation of the performance of these methods is hindered by the lack of a gold standard, i.e., bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied when a gold standard is not available. We used this framework to compare the performance of eight driver gene prediction methods. One of these methods, newly described here, incorporated a machine learning-based ratiometric approach. We show that the driver genes predicted by each of these eight methods vary widely. Moreover, the p-values reported by several of the methods were inconsistent with the uniform values expected, thus calling into question the assumptions that were used to generate them. Finally, we evaluated the potential effects of unexplained variability in mutation rates on false positive driver gene predictions. Our analysis points to the strengths and weaknesses of each of the currently available methods and offers guidance for improving them in the future.SignificanceModern large-scale sequencing of human cancers seeks to comprehensively discover mutated genes that confer a selective advantage to cancer cells. Key to this effort has been development of computational algorithms to find genes that drive cancer, based on their patterns of mutation in large patient cohorts. However, since there is no generally accepted gold standard of driver genes, it has been difficult to quantitatively compare these methods. We present a new machine learning method for driver gene prediction and a rigorous protocol to evaluate and compare prediction methods. Our results suggest that most current methods do not adequately account for heterogeneity in the number of mutations expected by chance and consequently have many false positive calls. The problem is most acute for cancers with high mutation rates and comprehensive discovery of drivers in these cancers may be more difficult than currently anticipated.

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 Novel ratio-metric features enable the identification of new driver genes across cancer types

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Malvika Sudhakar ◽  
Raghunathan Rengaswamy ◽  
Karthik Raman
Keyword(s):  
Cancer Genomics ◽  
Low Frequency ◽  
Tumour Suppressor Genes ◽  
Functional Categories ◽  
Driver Genes ◽  
Nonsense Mutations ◽  
Cancer Driver ◽  
Selective Growth Advantage ◽  
Cancer Types ◽  
Pan Cancer

AbstractAn emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. While several driver genes have been discovered, many remain undiscovered, especially those mutated at a low frequency across samples. This study defines new features and builds a pan-cancer model, cTaG, to identify new driver genes. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. The model classifies genes into the functional categories of driver genes, tumour suppressor genes (TSGs) and oncogenes (OGs), having distinct mutation type profiles. We overcome overfitting and show that certain mutation types, such as nonsense mutations, are more important for classification. Further, cTaG was employed to identify tissue-specific driver genes. Some known cancer driver genes predicted by cTaG as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential new driver genes for further experimental screening. cTaG is available at https://github.com/RamanLab/cTaG.

scholarly journals Novel ratio-metric features enable the identification of new driver genes across cancer types

2020 ◽  
Author(s):  
Malvika Sudhakar ◽  
Raghunathan Rengaswamy ◽  
Karthik Raman
Keyword(s):  
High Probability ◽  
Cancer Genomics ◽  
Low Frequency ◽  
Mutation Rates ◽  
Tumour Suppressor Genes ◽  
Driver Genes ◽  
Nonsense Mutations ◽  
Cancer Driver ◽  
Selective Growth Advantage ◽  
Cancer Types

ABSTRACTAn emergent area of cancer genomics has been the identification of driver genes. Driver genes confer a selective growth advantage to the cell and push it towards tumorigenesis. Functionally, driver genes can be divided into two categories, tumour suppressor genes (TSGs) and oncogenes (OGs), which have distinct mutation type profiles. While several driver genes have been discovered, many remain undiscovered, especially those that are mutated at a low frequency across samples. The current methods are not sufficient to predict all driver genes because the underlying characteristics of these genes are not yet well understood. Thus, to predict novel genes, we need to define new features and models that are not biased and identify genes that might otherwise be overshadowed by mutation profiles of recurrent driver genes. In this study, we define new features and build a model to identify novel driver genes. We overcome overfitting and show that certain mutation types such as nonsense mutations are more important for classification. Some known cancer driver genes, which are predicted by the model as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential novel driver genes for further experimental screening.

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