DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes

Melloni, Giorgio; Ogier, Alessandro; Pretis, Stefano de; Mazzarella, Luca; Pelizzola, Mattia; Pelicci, Pier Giuseppe; Riva, Laura

doi:10.1186/gm563

Cited by 26 publications

(22 citation statements)

References 57 publications

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“…A remarkable finding was that PBF consistently induced promigratory and proinvasive phenotypes across a panel of different cell types. Our results therefore imply that PBF may represent a novel marker of the ability of cells to escape tumors and invade, which provides further insight for the recent identification of PBF as a central driver gene in human cancers across multiple tumor types including thyroid (27). …”

Section: Discussionmentioning

confidence: 59%

Pro-invasive Effect of Proto-oncogene PBF Is Modulated by an Interaction with Cortactin

Watkins

Imruetaicharoenchoke

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et al. 2016

The Journal of Clinical Endocrinology & Metabolism

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Context:Metastatic disease is responsible for the majority of endocrine cancer deaths. New therapeutic targets are urgently needed to improve patient survival rates.Objective:The proto-oncogene PTTG1-binding factor (PBF/PTTG1IP) is overexpressed in multiple endocrine cancers and circumstantially associated with tumor aggressiveness. This study aimed to understand the role of PBF in tumor cell invasion and identify possible routes to inhibit its action.Design, Setting, Patients, and Interventions:Thyroid, breast, and colorectal cells were transfected with PBF and cultured for in vitro analysis. PBF and cortactin (CTTN) expression was determined in differentiated thyroid cancer and The Cancer Genome Atlas RNA-seq data.Primary Outcome Measure:Pro-invasive effects of PBF were evaluated by 2D Boyden chamber, 3D organotypic, and proximity ligation assays.Results:Our study identified that PBF and CTTN physically interact and co-localize, and that this occurs at the cell periphery, particularly at the leading edge of migrating cancer cells. Critically, PBF induces potent cellular invasion and migration in thyroid and breast cancer cells, which is entirely abrogated in the absence of CTTN. Importantly, we found that CTTN is over-expressed in differentiated thyroid cancer, particularly in patients with regional lymph node metastasis, which significantly correlates with elevated PBF expression. Mutation of PBF (Y174A) or pharmacological intervention modulates the PBF: CTTN interaction and attenuates the invasive properties of cancer cells.Conclusion:Our results demonstrate a unique role for PBF in regulating CTTN function to promote endocrine cell invasion and migration, as well as identify a new targetable interaction to block tumor cell movement.

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Section: Discussionmentioning

confidence: 59%

Pro-invasive Effect of Proto-oncogene PBF Is Modulated by an Interaction with Cortactin

Watkins

Imruetaicharoenchoke

Read

et al. 2016

The Journal of Clinical Endocrinology & Metabolism

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“…However, only a limited number of genes have been fully vetted as cancer drivers. In previous work, driver prediction has been benchmarked by significant overlap with the Cancer Gene Census (CGC) 11 , which is a manually curated list of likely but not necessarily validated drivers 8,9,12 , by agreement with a consensus gene list of drivers predicted by multiple methods 13 , and by number of "suspicious" predicted driver genes with no clear relevance to cancer 5 . To our knowledge, a systematic framework for the evaluation of somatic mutations that can be generally applied has not been previously developed.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Evaluation of Cancer Driver Genes

Tokheim

Papadopoulis

Kinzler

et al. 2016

Preprint

148

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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, 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. Tokheim et al., page 3Significance Modern 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.

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“…Identification of driver genes has been an important focus area of cancer research because these genes are potential targets for therapy and biomarkers. Different approaches have been used for identification using mutational information [17, 18, 30], gene expression levels [31], protein structural information [32], network analysis [33, 34] or using multiple data sources [31]. Advances in sequencing technologies have made mutational information easily available, and different tools have been developed to identify driver genes.…”

Section: Discussionmentioning

confidence: 99%

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

Sudhakar

Rengaswamy

Raman

2020

Preprint

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An 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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DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes

Cited by 26 publications

References 57 publications

Pro-invasive Effect of Proto-oncogene PBF Is Modulated by an Interaction with Cortactin

Pro-invasive Effect of Proto-oncogene PBF Is Modulated by an Interaction with Cortactin

Evaluating the Evaluation of Cancer Driver Genes

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

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