Tracking Cancer Genetic Evolution using OncoTrack

Talukder, Asoke K.; Agarwal, Mahima; Buetow, Kenneth H.; Denèfle, Patrice

doi:10.1038/srep29647

Cited by 6 publications

(4 citation statements)

References 89 publications

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“…This gene has been associated to diverse type of cancer including esophageal adenocarcinoma, pancreas, Melanoma, Lung, and prostate [26]. It was frequently mutated in lung cancer cell line genomes [27] and pancreatic tumors [28], but it was not conclusively classified as a driver gene.…”

Section: Discussionmentioning

confidence: 99%

Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes

2019

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BackgroundIdentifying cancer driver genes (CDG) is a crucial step in cancer genomic toward the advancement of precision medicine. However, driver gene discovery is a very challenging task because we are not only dealing with huge amount of data; but we are also faced with the complexity of the disease including the heterogeneity of background somatic mutation rate in each cancer patient. It is generally accepted that CDG harbor variants conferring growth advantage in the malignant cell and they are positively selected, which are critical to cancer development; whereas, non-driver genes harbor random mutations with no functional consequence on cancer. Based on this fact, function prediction based approaches for identifying CDG have been proposed to interrogate the distribution of functional predictions among mutations in cancer genomes (eLS 1–16, 2016). Assuming most of the observed mutations are passenger mutations and given the quantitative predictions for the functional impact of the mutations, genes enriched of functional or deleterious mutations are more likely to be drivers. The promises of these methods have been continually refined and can therefore be applied to increase accuracy in detecting new candidate CDGs. However, current function prediction based approaches only focus on coding mutations and lack a systematic way to pick the best mutation deleteriousness prediction algorithms for usage.ResultsIn this study, we propose a new function prediction based approach to discover CDGs through a gene-based permutation approach. Our method not only covers both coding and non-coding regions of the genes; but it also accounts for the heterogeneous mutational context in cohort of cancer patients. The permutation model was implemented independently using seven popular deleteriousness prediction scores covering splicing regions (SPIDEX), coding regions (MetaLR, and VEST3) and pan-genome (CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding). We applied this new approach to somatic single nucleotide variants (SNVs) from whole-genome sequences of 119 breast and 24 lung cancer patients and compared the seven deleteriousness prediction scores for their performance in this study.ConclusionThe new function prediction based approach not only predicted known cancer genes listed in the Cancer Gene Census (CGC), but also new candidate CDGs that are worth further investigation. The results showed the advantage of utilizing pan-genome deleteriousness prediction scores in function prediction based methods. Although VEST3 score, a deleteriousness prediction score for missense mutations, has the best performance in breast cancer, it was topped by CADD and Fathmm-MKL coding, two pan-genome deleteriousness prediction scores, in lung cancer.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0452-9) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes

2019

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“…However, to develop an understanding of system-wide patterns between all the predictors, recurrence, and OS, a network approach is more suitable. Relevance, or correlation networks [11–14] can be created using a similarity measure. Therefore, we create a mutual information (MI) network and subsequently a Euclidean distance based complete-linkage agglomerative hierarchical clustering of the most closely associated variables.…”

Section: Methodsmentioning

confidence: 99%

Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients

et al. 2019

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Currently in patients with bladder cancer, various clinical evaluations (imaging, operative findings at transurethral resection and radical cystectomy, pathology) are collectively used to determine disease status and prognosis, and recommend neoadjuvant, definitive and adjuvant treatments. We analyze the predictive power of these measurements in forecasting two key long-term outcomes following radical cystectomy, i.e., cancer recurrence and survival. Information theory and machine learning algorithms are employed to create predictive models using a large prospective, continuously collected, temporally resolved, primary bladder cancer dataset comprised of 3503 patients (1971-2016). Patient recurrence and survival one, three, and five years after cystectomy can be predicted with greater than 70% sensitivity and specificity. Such predictions may inform patient monitoring schedules and post-cystectomy treatments. The machine learning models provide a benchmark for predicting oncologic outcomes in patients undergoing radical cystectomy and highlight opportunities for improving care using optimal preoperative and operative data collection.

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“…In another transcriptome study, PLPPR1, PLPPR3 and PLPPR5 are among the top 25 most differentially expressed membrane proteins in pediatric cancer types ( Orentas et al, 2012 ). PLPPR2 has been associated with several cancers including colorectal, pancreatic and breast cancer cell lines and tissues ( Sagiv et al, 2008 ; Li et al, 2015 ; Talukder et al, 2016 ; Boonsongserm et al, 2019 ). For example, PLPPR2 expression is deregulated in colorectal cancer patient samples and cells ( Boonsongserm et al, 2019 ), while in breast cancer samples, PLPPR2 shows high frequency of an aa substitution at T278 (PLPPR2 T278S), potentially worsening breast cancer outcome ( Li et al, 2015 ).…”

Section: Expression and Localization Of Phospholipid Phosphatase- Rel...mentioning

confidence: 99%

Plasma membrane phospholipid phosphatase-related proteins as pleiotropic regulators of neuron growth and excitability

Fuchs

Bareesel²,

Kroon³

et al. 2022

Front. Mol. Neurosci.

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Tracking Cancer Genetic Evolution using OncoTrack

Cited by 6 publications

References 89 publications

Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes

Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes

Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients

Plasma membrane phospholipid phosphatase-related proteins as pleiotropic regulators of neuron growth and excitability

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