Faculty Opinions recommendation of Mutational landscape and significance across 12 major cancer types.

2017 ◽  
Author(s):  
Ian D Hickson
Keyword(s):  
Mutational Landscape ◽  
Cancer Types

scholarly journals Tumor mutational landscape is a record of the pre-malignant state

2019 ◽  
Author(s):  
Kirsten Kübler ◽  
Rosa Karlić ◽  
Nicholas J. Haradhvala ◽  
Kyungsik Ha ◽  
Jaegil Kim ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Dna Sequences ◽  
Chromatin Structure ◽  
Somatic Mutations ◽  
Major Influence ◽  
Cell Of Origin ◽  
Regional Patterns ◽  
Cancer Subtypes ◽  
Mutational Landscape ◽  
Cancer Types

ABSTRACTChromatin structure has a major influence on the cell-specific density of somatic mutations along the cancer genome. Here, we present a pan-cancer study in which we searched for the putative cancer cell-of-origin of 2,550 whole genomes, representing 32 cancer types by matching their mutational landscape to the regional patterns of chromatin modifications ascertained in 104 normal tissue types. We found that, in almost all cancer types, the cell-of-origin can be predicted solely from their DNA sequences. Our analysis validated the hypothesis that high-grade serous ovarian cancer originates in the fallopian tube and identified distinct origins of breast cancer subtypes. We also demonstrated that the technique is equally capable of identifying the cell-of-origin for a series of 2,044 metastatic samples from 22 of the tumor types available as primaries. Moreover, cancer drivers, whether inherited or acquired, reside in active chromatin regions in the respective cell-of-origin. Taken together, our findings highlight that many somatic mutations accumulate while the chromatin structure of the cell-of-origin is maintained and that this historical record, captured in the DNA, can be used to identify the often elusive cancer cell-of-origin.

scholarly journals Mutational landscape and significance across 12 major cancer types

Nature ◽  
2013 ◽  
Vol 502 (7471) ◽  
pp. 333-339 ◽  
Author(s):  
Cyriac Kandoth ◽  
Michael D. McLellan ◽  
Fabio Vandin ◽  
Kai Ye ◽  
Beifang Niu ◽  
...  
Keyword(s):  
Mutational Landscape ◽  
Cancer Types

scholarly journals Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Cell Reports ◽  
2018 ◽  
Vol 23 (1) ◽  
pp. 282-296.e4 ◽  
Author(s):  
Michael Seiler ◽  
Shouyong Peng ◽  
Anant A. Agrawal ◽  
James Palacino ◽  
Teng Teng ◽  
...  
Keyword(s):  
Splicing Factor ◽  
Mutational Landscape ◽  
Functional Consequences ◽  
Cancer Types

Cancer in agricultural populations

2020 ◽  
pp. 837-837
Author(s):  
K. . Togawa
Keyword(s):  
Risk Factors ◽  
General Population ◽  
Cohort Studies ◽  
Health Effects ◽  
Cancer Incidence ◽  
Lower Prevalence ◽  
International Consortium ◽  
Adverse Health Effects ◽  
Specific Cancer ◽  
Cancer Types

Agricultural workers can be exposed to a wide variety of agents (e.g. pesticides), some of which may have adverse health effects, such as cancer. To study the health effects of agricultural exposures, an international consortium of agricultural cohort studies, AGRICOH, was established. The present analysis compared cancer incidence between the AGRICOH cohorts and the general population and found lower overall cancer incidence in the AGRICOH cohorts, with some variation across cohorts for specific cancer types. The observed lower cancer incidence may be due to healthy worker bias or lower prevalence of risk factors in the agricultural populations. Further analysis is underway.

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

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