The Erlang distribution approximates the age distribution of incidence of childhood and young adulthood cancers

Belikov, Aleksey V.; Vyatkin, Alexey D.; Leonov, Sergey

doi:10.7717/peerj.11976

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Although both of these explanations are likely true to some degree, their contribution does not seem to be so large as to dramatically affect the results. The most probable explanation is the inherent stochasticity of carcinogenesis processes [26,27] combined with unequal strength of different driver events [28]. For example, patients having tumours with only one driver event were unlucky that this event happened (by chance) in one of the most crucial genes such as BRAF.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts

et al. 2022

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There is a growing need to develop novel therapeutics for targeted treatment of cancer. The prerequisite to success is the knowledge about which types of molecular alterations are predominantly driving tumorigenesis. To shed light onto this subject, we have utilized the largest database of human cancer mutations–TCGA PanCanAtlas, multiple established algorithms for cancer driver prediction (2020plus, CHASMplus, CompositeDriver, dNdScv, DriverNet, HotMAPS, OncodriveCLUSTL, OncodriveFML) and developed four novel computational pipelines: SNADRIF (Single Nucleotide Alteration DRIver Finder), GECNAV (Gene Expression-based Copy Number Alteration Validator), ANDRIF (ANeuploidy DRIver Finder) and PALDRIC (PAtient-Level DRIver Classifier). A unified workflow integrating all these pipelines, algorithms and datasets at cohort and patient levels was created. We have found that there are on average 12 driver events per tumour, of which 0.6 are single nucleotide alterations (SNAs) in oncogenes, 1.5 are amplifications of oncogenes, 1.2 are SNAs in tumour suppressors, 2.1 are deletions of tumour suppressors, 1.5 are driver chromosome losses, 1 is a driver chromosome gain, 2 are driver chromosome arm losses, and 1.5 are driver chromosome arm gains. The average number of driver events per tumour increases with age (from 7 to 15) and cancer stage (from 10 to 15) and varies strongly between cancer types (from 1 to 24). Patients with 1 and 7 driver events per tumour are the most frequent, and there are very few patients with more than 40 events. In tumours having only one driver event, this event is most often an SNA in an oncogene. However, with increasing number of driver events per tumour, the contribution of SNAs decreases, whereas the contribution of copy-number alterations and aneuploidy events increases.

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Section: Discussionmentioning

confidence: 99%

Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts

et al. 2022

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“…A puzzling question that remains in cancer genomics is why mutations in a given driver gene are typically confined to one or a few cancer types, resulting in each cancer type having its own unique set of driver genes ( Iranzo, Martincorena & Koonin, 2018 ). As mutations are supposed to happen randomly as a result of stochastic mutagenesis processes ( Belikov, 2017 ; Belikov, Vyatkin & Leonov, 2021 ), it is logical to suggest that mutations in different tissues can affect the same genes. However, the same mutation can be selected for in some tissues and selected against in others ( Levine, Jenkins & Copeland, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients

Belikov¹,

Vyatkin²,

Leonov³

2022

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Background Cancer driver genes are usually ranked by mutation frequency, which does not necessarily reflect their driver strength. We hypothesize that driver strength is higher for genes preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer. Methods We propose formulas for the Driver Strength Index (DSI) and the Normalized Driver Strength Index (NDSI), the latter independent of gene mutation frequency. We validate them using TCGA PanCanAtlas datasets, established driver prediction algorithms and custom computational pipelines integrating SNA, CNA and aneuploidy driver contributions at the patient-level resolution. Results DSI and especially NDSI provide substantially different gene rankings compared to the frequency approach. E.g., NDSI prioritized members of specific protein families, including G proteins GNAQ, GNA11 and GNAS, isocitrate dehydrogenases IDH1 and IDH2, and fibroblast growth factor receptors FGFR2 and FGFR3. KEGG analysis shows that top NDSI-ranked genes comprise EGFR/FGFR2/GNAQ/GNA11–NRAS/HRAS/KRAS–BRAF pathway, AKT1–MTOR pathway, and TCEB1–VHL–HIF1A pathway. Conclusion Our indices are able to select for driver gene attributes not selected by frequency sorting, potentially for driver strength. Genes and pathways prioritized are likely the strongest contributors to cancer initiation and progression and should become future therapeutic targets.

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“…Although both of these explanations are likely true to some degree, their contribution does not seem to be so large as to dramatically affect the results. The most probable explanation is the inherent stochasticity of carcinogenesis processes [21,22] combined with unequal strength of different driver events [23]. For example, patients having tumours with only one driver event were unlucky that this event happened (by chance) in one of the most crucial genes such as BRAF.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts

et al. 2021

Preprint

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BackgroundThere is a growing need to develop novel therapeutics for targeted treatment of cancer. The prerequisite to success is the knowledge about which types of molecular alterations are predominantly driving tumorigenesis – single nucleotide (SNA) or copy number (CNA), in oncogenes or in tumour suppressors, gains or losses of full chromosomes or chromosomal arms (aneuploidy). However, the number and proportion of various types of driver events per tumour is still not clear, neither for cancer in general, nor for individual cancer types, stages and patient demographics (age and gender).MethodsTo shed light onto this subject, we have utilized the largest database of human cancer mutations – TCGA PanCanAtlas, multiple established algorithms for cancer driver prediction (2020plus, CHASMplus, CompositeDriver, dNdScv, DriverNet, HotMAPS, IntOGen Plus, OncodriveCLUSTL, OncodriveFML) and developed four novel computational pipelines: SNADRIF (SNA DRIver Finder), GECNAV (Gene Expression-based CNA Validator), ANDRIF (ANeuploidy DRIver Finder) and PALDRIC (PAtient-Level DRIver Classifier). A unified workflow integrating all these pipelines, algorithms and datasets at cohort and patient levels was created.ResultsBy integrating results of various driver prediction algorithms, we have found that there are on average 20 driver events per tumour, of which 1.5 are hyperactivating SNAs in oncogenes, 10.5 are amplifications of oncogenes, 2 are homozygous inactivating SNAs or deletions of tumour suppressors, 1.5 are driver chromosome losses, 2 are driver chromosome gains, 1 is a driver chromosome arm loss, and 1.5 are driver chromosome arm gains. The average number of driver events per tumour varies strongly between cancer types, from 1.7 in thyroid carcinoma to 42.4 in ovarian carcinoma. In females, the number of driver events increases most dramatically until the age of menopause (50 y.o.), whereas in males until 70 y.o. Moreover, in females, the number of driver events increases abruptly from Stage I to Stage II, after which stays more or less constant, and this increase is due to CNAs and aneuploidy but not due to SNAs. In tumours having only one driver event, this event is a SNA in an oncogene. However, with increasing number of driver events per tumour, the contribution of SNAs and tumour suppressor events decreases, whereas the contribution of oncogene amplifications and aneuploidy events increases. Patients with two driver events per tumour are the most frequent, and there are very few patients with more than 50 events.ConclusionsAs half of all driver events in a patient’s tumour appear to be amplifications of oncogenes, we suggest that future therapeutics development efforts should be focused on targeting this alteration type. Therapies aimed at gains and losses of chromosomal arms and whole chromosomes also appear very promising. On the other hand, drugs aiming at point mutations and tumour suppressors are predicted to be less successful. Overall, our results provide valuable insights into the extent of driver alterations of different types in human tumours and suggest optimal targets for candidate therapeutics.

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The Erlang distribution approximates the age distribution of incidence of childhood and young adulthood cancers

Cited by 7 publications

References 43 publications

Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts

Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts

Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients

Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts

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