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

2021 ◽  
Author(s):  
Alexey D. Vyatkin ◽  
Danila V. Otnyukov ◽  
Sergey V. Leonov ◽  
Aleksey V. Belikov
Keyword(s):  
Human Cancer ◽  
Point Mutations ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Patient Level ◽  
Tumour Suppressors ◽  
Chromosome Arm ◽  
Cancer Mutations ◽  
Cancer Types ◽  
And Gender

AbstractBackgroundThere 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.

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

PLoS Genetics ◽  
2022 ◽  
Vol 18 (1) ◽  
pp. e1009996
Author(s):  
Alexey D. Vyatkin ◽  
Danila V. Otnyukov ◽  
Sergey V. Leonov ◽  
Aleksey V. Belikov
Keyword(s):  
Copy Number ◽  
Human Cancer ◽  
Single Nucleotide ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Patient Level ◽  
Tumour Suppressors ◽  
Chromosome Arm ◽  
Cancer Mutations ◽  
Cancer Types

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.

TCGA PanCanAtlas Data Analysis Suggests Multiple Possibilities for Personalized Cancer Therapy

2021 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Aleksey V. Belikov ◽  
Alexey D. Vyatkin ◽  
Danila V. Otnykov ◽  
Sergey V. Leonov
Keyword(s):  
Targeted Therapy ◽  
Tumor Suppressors ◽  
Human Cancer ◽  
Point Mutations ◽  
Driver Mutations ◽  
Primary Tumors ◽  
Cancer Driver ◽  
Significant Difference ◽  
Chromosome Arm ◽  
Personalized Cancer

Personalized cancer medicine holds promise for the future of cancer treatment. One of the keys to success is the knowledge of exact molecular alterations that drive tumorigenesis in a given patient, so that a suitable targeted therapy can be selected. However, the extent of such alterations, i.e., number of various kinds of driver mutations per patient, is still not known. We have utilized the largest database of human cancer mutations—TCGA PanCanAtlas, multiple popular algorithms for cancer driver prediction and several custom scripts to estimate the number of various kinds of driver mutations in primary tumors. We have found that there are on average 12 driver mutations per patient’s tumor, of which 0.6 are hyperactivating point mutations in oncogenes, 1.5 are amplifications of oncogenes, 0.1 have both in the same oncogene, 1.2 are inactivating point mutations in tumor suppressors, 2.1 are deletions in tumor suppressors, 0.3 have both in the same tumor suppressor, 1.5 are driver chromosome losses, 1 is driver chromosome gain, 2 are driver chromosome arm losses, and 1.5 are driver chromosome arm gains. The number of driver mutations per tumor gradually increased with age, from 6.7 for < 25 y.o. to 14.9 for > 85 y.o., and cancer stage, from 10.0 to 15.2. There was no significant difference between genders (12.0 in males vs. 11.9 in females). The number of driver mutations per tumor varied strongly between cancer types, from 1.2 in thyroid carcinoma to 23.8 in bladder carcinoma. Overall, our results provide valuable insights into the extent of driver alterations in tumors and suggest that multiple possibilities to choose a suitable targeted therapy exist in each patient.

scholarly journals An integrative analysis of the age-associated multi-omic landscape across cancers

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kasit Chatsirisupachai ◽  
Tom Lesluyes ◽  
Luminita Paraoan ◽  
Peter Van Loo ◽  
João Pedro de Magalhães
Keyword(s):  
The Cancer Genome Atlas ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Age Related ◽  
Incidence And Mortality ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Using Data ◽  
Transcriptomic Changes

AbstractAge is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterise genomic, transcriptomic and epigenetic alterations in relation to patients’ age across cancer types. We show that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations are identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences are found in gliomas and endometrial cancer. We identify age-related global transcriptomic changes and demonstrate that these genes are in part regulated by age-associated DNA methylation changes. This study provides a comprehensive, multi-omics view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.

scholarly journals Convergent mutations in tissue-specific regulatory regions reveal novel cancer drivers

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Jose A. Seoane ◽  
Richard Sallari ◽  
Jonathan K. Pritchard ◽  
Christina Curtis ◽  
...  
Keyword(s):  
Human Cancer ◽  
Cell Type ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Regulatory Regions ◽  
Tissue Specific ◽  
Cancer Driver ◽  
Regulatory Mutations ◽  
Cancer Types ◽  
Promoter Mutations

AbstractAlthough much effort has been devoted to identifying coding mutations across cancer types, regulatory mutations remain poorly characterized. Here, we describe a framework to identify non-coding drivers by aggregating mutations in cell-type specific regulatory regions for each gene. Application of this approach to 2,634 patients across 11 human cancer types identified 60 pan-cancer, 22 pan-breast and 192 cancer specific candidate driver genes that were enriched for expression changes. Analysis of high-throughput CRISPR knockout screens revealed large, cancer specific growth effects for these genes, on par with coding mutations and exceeding that for promoter mutations. Amongst the five candidate drivers selected for further analysis, four (IPO9, MED8, PLEKHA6, and OXNAD1) were associated with survival across multiple cancer types. These studies demonstrate the power of our cell-type aware, convergent regulatory framework to define novel tissue specific cancer driver genes, considerably expanding evidence of functional non-coding mutations in cancer.

scholarly journals An Integrative Analysis of the Age-Associated Genomic, Transcriptomic and Epigenetic Landscape across Cancers

2020 ◽  
Author(s):  
Kasit Chatsirisupachai ◽  
Tom Lesluyes ◽  
Luminita Paraoan ◽  
Peter Van Loo ◽  
João Pedro de Magalhães
Keyword(s):  
The Cancer Genome Atlas ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Age Related ◽  
Incidence And Mortality ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Using Data ◽  
Transcriptomic Changes

AbstractAge is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterised genomic, transcriptomic and epigenetic alterations in relation to patients’ age across cancer types. We showed that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations were identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences were found in gliomas and endometrial cancer. We identified age-related global transcriptomic changes and demonstrated that these genes are controlled by age-associated DNA methylation changes. This study provides a comprehensive view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.

scholarly journals Cellular Fitness Phenotypes of Cancer Target Genes from Oncobiology to Cancer Therapeutics

Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 433
Author(s):  
Bijesh George ◽  
P. Mukundan Pillai ◽  
Aswathy Mary Paul ◽  
Revikumar Amjesh ◽  
Kim Leitzel ◽  
...  
Keyword(s):  
Drug Targets ◽  
Target Genes ◽  
Human Cancer ◽  
Cancer Therapeutics ◽  
Survival Duration ◽  
Cancer Drug ◽  
Targeted Therapeutics ◽  
Cellular Targets ◽  
Human Cancer Cells ◽  
Cancer Types

To define the growing significance of cellular targets and/or effectors of cancer drugs, we examined the fitness dependency of cellular targets and effectors of cancer drug targets across human cancer cells from 19 cancer types. We observed that the deletion of 35 out of 47 cellular effectors and/or targets of oncology drugs did not result in the expected loss of cell fitness in appropriate cancer types for which drugs targeting or utilizing these molecules for their actions were approved. Additionally, our analysis recognized 43 cellular molecules as fitness genes in several cancer types in which these drugs were not approved, and thus, providing clues for repurposing certain approved oncology drugs in such cancer types. For example, we found a widespread upregulation and fitness dependency of several components of the mevalonate and purine biosynthesis pathways (currently targeted by bisphosphonates, statins, and pemetrexed in certain cancers) and an association between the overexpression of these molecules and reduction in the overall survival duration of patients with breast and other hard-to-treat cancers, for which such drugs are not approved. In brief, the present analysis raised cautions about off-target and undesirable effects of certain oncology drugs in a subset of cancers where the intended cellular effectors of drug might not be good fitness genes and that this study offers a potential rationale for repurposing certain approved oncology drugs for targeted therapeutics in additional cancer types.

scholarly journals Transporter associated with antigen processing 1 (TAP1) expression and prognostic analysis in breast, lung, liver, and ovarian cancer

2021 ◽  
Author(s):  
Anika Tabassum ◽  
Md. Nazmus Samdani ◽  
Tarak Chandra Dhali ◽  
Rahat Alam ◽  
Foysal Ahammad ◽  
...  
Keyword(s):  
Expression Pattern ◽  
Cancer Progression ◽  
Antigen Processing ◽  
Human Cancer ◽  
Analytical Data ◽  
Significant Negative Correlation ◽  
Cancer Tissue ◽  
Human Cancer Cell Lines ◽  
Prognostic Analysis ◽  
Cancer Types

Abstract Transporter associated with antigen processing 1 (TAP1) is a transporter protein that represent tumor antigen in the MHC I or HLA complex. Any defect in the TAP1 gene resulting in inadequate tumor tracking. TAP1 influences multidrug resistance (MDR) in human cancer cell lines and hinders the treatment during chemotherapeutic. The association of TAP1 in cancer progression remains mostly unknown and further study of the gene in relation with cancer need to conduct. Thus, the study has designed to analyze the association between the TAP1 with cancer by computationally. The expression pattern of the gene has determined by using ONCOMINE, GENT2, and GEPIA2 online platforms. The protein level of TAP1 was examined by the help of Human Protein Atlas. Samples with different clinical outcomes were investigated to evaluate the expression and promoter methylation in cancer vs. normal tissues by using UALCAN server. The copy number alteration, mutation frequency, and expression level of the gene in different cancer were analyzed by using cBioPortal server. The PrognoScan and KM plotter platforms were used to perform the survival analysis and represented graphically. Additionally, pathway and gene ontology (GO) features correlated to the TAP1 gene were analyzed and presented by bar charts. After arranging the data in a single panel like correlating expression to prognosis, mutational and alterations characteristic, and pathways analysis, we observed some interesting insights that emphasized the importance of the gene in cancer progression. The study found the relationship between the TAP1 expression pattern and prognosis in different cancer tissues and shows how TAP1 affects the clinical characteristics. The analytical data presented in the study is vital to learn about the effect of TAP1 in tumor tissue, where previously studies showing contradicting expression of TAP1 in cancer tissue. The analyzed data can also be utilized further to evade the threats against chemotherapy. Overall, the study provided a new aspect to consider the role of TAP1 gene in cancer progression and survival status. Key messages • This study demonstrated, for the first time, a correlation between the TAP1 gene and tumor progression. • An upregulation of TAP1 mRNA was demonstrated in various cancer types. • This study reported a significant negative correlation for TAP1 gene expression and the survival rate in different cancer types.

scholarly journals Inferring perturbation profiles of cancer samples

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.

Application Of LIF Technique In The Diagnosis Of Some Human Cancer Types

2009 ◽  
Author(s):  
A. El-Hussein ◽  
H. Ismail ◽  
A. K. Kasem ◽  
M. A. Harith ◽  
Mohamed Abdel Harith
Keyword(s):  
Human Cancer ◽  
Cancer Types

scholarly journals Regulatory mechanisms of phosphatase of regenerating liver (PRL)-3

2016 ◽  
Vol 44 (5) ◽  
pp. 1305-1312 ◽  
Author(s):  
Teresa Rubio ◽  
Maja Köhn
Keyword(s):  
Drug Development ◽  
Posttranslational Modifications ◽  
Human Cancer ◽  
Regulatory Mechanisms ◽  
Therapeutic Drug ◽  
Regenerating Liver ◽  
Cancer Types ◽  
Tumor Metastases ◽  
Phosphatase Of Regenerating Liver ◽  
Incomplete Picture

The phosphatase of regenerating liver (PRL)-3 is overexpressed in many human cancer types and tumor metastases when compared with healthy tissues. Different pathways and mechanisms have been suggested to modulate PRL-3 expression levels and activity, giving some valuable insights but still leaving an incomplete picture. Investigating these mechanisms could provide new targets for therapeutic drug development. Here, we present an updated overview and summarize recent findings concerning the different PRL-3 expression regulatory mechanisms and posttranslational modifications suggested to modulate the activity, localization, or stability of this phosphatase.

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