Insights into Mechanisms of Pheochromocytomas and Paragangliomas Driven by Known or New Genetic Drivers

Pheochromocytomas and paragangliomas are rare tumors of neural crest origin. Their remarkable genetic diversity and high heritability have enabled discoveries of bona fide cancer driver genes with an impact on diagnosis and clinical management and have consistently shed light on new paradigms in cancer. In this review, we explore unique mechanisms of pheochromocytoma and paraganglioma initiation and management by drawing from recent examples involving rare mutations of hypoxia-related genes VHL, EPAS1 and SDHB, and of a poorly known susceptibility gene, TMEM127. These models expand our ability to predict variant pathogenicity, inform new functional domains, recognize environmental-gene connections, and highlight persistent therapeutic challenges for tumors with aggressive behavior.

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Evaluating the evaluation of cancer driver genes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1616440113 ◽

2016 ◽

Vol 113 (50) ◽

pp. 14330-14335 ◽

Cited By ~ 160

Author(s):

Collin J. Tokheim ◽

Nickolas Papadopoulos ◽

Kenneth W. Kinzler ◽

Bert Vogelstein ◽

Rachel Karchin

Keyword(s):

Somatic Mutations ◽

Gene Prediction ◽

Gene Mutations ◽

Evaluation Framework ◽

Mutation Rates ◽

Driver Gene ◽

Driver Genes ◽

Cancer Driver ◽

Bona Fide ◽

Cancer Driver Genes

Sequencing 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, that is, bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied to driver gene prediction methods. We used this framework to compare the performance of eight such methods. One of these methods, described here, incorporated a machine-learning–based ratiometric approach. We show that the driver genes predicted by each of the 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.

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Faculty Opinions recommendation of Evaluating the evaluation of cancer driver genes.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727060594.793535346 ◽

2017 ◽

Author(s):

Ron Shamir

Keyword(s):

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes

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

Scientific Reports ◽

10.1038/s41598-021-92014-4 ◽

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.

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Ranking cancer drivers via betweenness-based outlier detection and random walks

BMC Bioinformatics ◽

10.1186/s12859-021-03989-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Cesim Erten ◽

Aissa Houdjedj ◽

Hilal Kazan

Keyword(s):

Cancer Genomics ◽

Interaction Network ◽

Molecular Data ◽

Alternative Methods ◽

Patient Specific ◽

Cancer Genes ◽

Driver Genes ◽

Cancer Driver ◽

Protein Protein Interaction ◽

Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

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driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

BMC Bioinformatics ◽

10.1186/s12859-021-04203-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ege Ülgen ◽

O. Uğur Sezerman

Keyword(s):

Biological Knowledge ◽

Driver Gene ◽

Driver Genes ◽

Cancer Driver ◽

Prior Biological Knowledge ◽

Wilcoxon Rank Sum Test ◽

Cancer Genomes ◽

Novel Method ◽

Cancer Driver Genes ◽

Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

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Morphological Characterization, Variability and Diversity among Vegetable Soybean (Glycine max L.) Genotypes

Plants ◽

10.3390/plants10040671 ◽

2021 ◽

Vol 10 (4) ◽

pp. 671

Author(s):

Nagaraju Shilpashree ◽

Sarojinikunjamma Nirmala Devi ◽

Dalasanuru Chandregowda Manjunathagowda ◽

Anjanappa Muddappa ◽

Shaimaa A. M. Abdelmohsen ◽

...

Keyword(s):

Genetic Diversity ◽

Genetic Improvement ◽

Genotypic Variation ◽

Morphological Characterization ◽

High Yield ◽

Vegetable Soybean ◽

Breeding Lines ◽

Commercial Cultivation ◽

Soybean Genotypes ◽

High Heritability

Vegetable soybean production is dependent on the development of vegetable type varieties that would be achieved by the use of germplasm to evolve new agronomically superior yielding vegetable type with beneficial biochemical traits. This can be accomplished by a better understanding of genetics, which is why the research was conducted to reveal the quantitative genetics of vegetable soybean genotypes. Genetic variability of main morphological traits in vegetable soybean genotypes and their divergence was estimated, as a result of the magnitude of genotypic variation (GV), and phenotypic variation (PV) of traits varied among the genotypes. All traits showed high heritability (h2) associated with high genetic advance percentage mean (GAM). Therefore, these variable traits are potential for genetic improvement of vegetable type soybean. Genetic diversity is the prime need for breeding, and the magnitude of genetic diversity values were maximized among specific genotypes. Eight clusters were found for all genotypes; cluster VIII and cluster I were considered to have the most diversity. Cluster VIII consisted of two genotypes (GM-6 and GM-27), based on the mean outcomes of the high yield attributing traits. Hence, these two (GM-6, GM-27) genotypes can be advanced for commercial cultivation; furthermore, other genotypes can be used as source of breeding lines for genetic improvement of vegetable soybean.

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Current cancer driver variant predictors learn to recognize driver genes instead of functional variants

BMC Biology ◽

10.1186/s12915-020-00930-0 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Daniele Raimondi ◽

Antoine Passemiers ◽

Piero Fariselli ◽

Yves Moreau

Keyword(s):

Data Sets ◽

Precision Oncology ◽

Complex Task ◽

Excellent Performance ◽

Driver Genes ◽

Significant Drop ◽

Data Set ◽

Gene Effects ◽

Cancer Driver ◽

Functional Variants

Abstract Background Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenesis and precision oncology. Various bioinformatics methods have attempted to solve this complex task. Results In this study, we investigate the assumptions on which these methods are based, showing that the different definitions of driver and passenger variants influence the difficulty of the prediction task. More importantly, we prove that the data sets have a construction bias which prevents the machine learning (ML) methods to actually learn variant-level functional effects, despite their excellent performance. This effect results from the fact that in these data sets, the driver variants map to a few driver genes, while the passenger variants spread across thousands of genes, and thus just learning to recognize driver genes provides almost perfect predictions. Conclusions To mitigate this issue, we propose a novel data set that minimizes this bias by ensuring that all genes covered by the data contain both driver and passenger variants. As a result, we show that the tested predictors experience a significant drop in performance, which should not be considered as poorer modeling, but rather as correcting unwarranted optimism. Finally, we propose a weighting procedure to completely eliminate the gene effects on such predictions, thus precisely evaluating the ability of predictors to model the functional effects of single variants, and we show that indeed this task is still open.

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Inferring perturbation profiles of cancer samples

Bioinformatics ◽

10.1093/bioinformatics/btab113 ◽

2021 ◽

Author(s):

Martin Pirkl ◽

Niko Beerenwinkel

Keyword(s):

Indirect Evidence ◽

R Package ◽

The Cancer Genome Atlas ◽

Supplementary Information ◽

Patient Specific ◽

Driver Genes ◽

Cancer Driver ◽

Molecular Alterations ◽

Incomplete Coverage ◽

Gene Perturbations

Abstract Motivation Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. Results We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas. Availability and implementation The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi. Supplementary information Supplementary data are available at Bioinformatics online.

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