Literature DB >> 22858414

Cancer heterogeneity: origins and implications for genetic association studies.

Davnah Urbach1, Mathieu Lupien, Margaret R Karagas, Jason H Moore.   

Abstract

Genetic association studies have become standard approaches to characterize the genetic and epigenetic variability associated with cancer development, including predispositions and mutations. However, the bewildering genetic and phenotypic heterogeneity inherent in cancer both magnifies the conceptual and methodological problems associated with these approaches and renders difficult the translation of available genetic information into a knowledge that is both biologically sound and clinically relevant. Here, we elaborate on the underlying causes of this complexity, illustrate why it represents a challenge for genetic association studies, and briefly discuss how it can be reconciled with the ultimate goals of identifying targetable disease pathways and successfully treating individual patients.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 22858414      PMCID: PMC3477266          DOI: 10.1016/j.tig.2012.07.001

Source DB:  PubMed          Journal:  Trends Genet        ISSN: 0168-9525            Impact factor:   11.639


  80 in total

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Review 3.  Evolution of the cancer genome.

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Journal:  Trends Genet       Date:  2012-02-16       Impact factor: 11.639

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5.  Genome-wide methylation analysis identifies genes specific to breast cancer hormone receptor status and risk of recurrence.

Authors:  Mary Jo Fackler; Christopher B Umbricht; Danielle Williams; Pedram Argani; Leigh-Ann Cruz; Vanessa F Merino; Wei Wen Teo; Zhe Zhang; Peng Huang; Kala Visvananthan; Jeffrey Marks; Stephen Ethier; Joe W Gray; Antonio C Wolff; Leslie M Cope; Saraswati Sukumar
Journal:  Cancer Res       Date:  2011-08-08       Impact factor: 12.701

Review 6.  What does physics have to do with cancer?

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Journal:  Nat Rev Cancer       Date:  2011-08-18       Impact factor: 60.716

Review 7.  Cancer as an evolutionary and ecological process.

Authors:  Lauren M F Merlo; John W Pepper; Brian J Reid; Carlo C Maley
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Review 8.  Architecture of inherited susceptibility to common cancer.

Authors:  Olivia Fletcher; Richard S Houlston
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Review 9.  The cancer genome.

Authors:  Michael R Stratton; Peter J Campbell; P Andrew Futreal
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10.  Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.

Authors:  Jiarui Ding; Ali Bashashati; Andrew Roth; Arusha Oloumi; Kane Tse; Thomas Zeng; Gholamreza Haffari; Martin Hirst; Marco A Marra; Anne Condon; Samuel Aparicio; Sohrab P Shah
Journal:  Bioinformatics       Date:  2011-11-13       Impact factor: 6.937

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  8 in total

1.  A multiclass likelihood ratio approach for genetic risk prediction allowing for phenotypic heterogeneity.

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2.  Regularized machine learning in the genetic prediction of complex traits.

Authors:  Sebastian Okser; Tapio Pahikkala; Antti Airola; Tapio Salakoski; Samuli Ripatti; Tero Aittokallio
Journal:  PLoS Genet       Date:  2014-11-13       Impact factor: 5.917

3.  Exomes of Ductal Luminal Breast Cancer Patients from Southwest Colombia: Gene Mutational Profile and Related Expression Alterations.

Authors:  Carolina Cortes-Urrea; Fernando Bueno-Gutiérrez; Melissa Solarte; Miguel Guevara-Burbano; Fabian Tobar-Tosse; Patricia E Vélez-Varela; Juan Carlos Bonilla; Guillermo Barreto; Jaime Velasco-Medina; Pedro A Moreno; Javier De Las Rivas
Journal:  Biomolecules       Date:  2020-04-30

4.  Discovery of VEGFR2 inhibitors by integrating naïve Bayesian classification, molecular docking and drug screening approaches.

Authors:  Xiaocong Pang; Wenwen Lian; Lvjie Xu; Jinhua Wang; Hao Jia; Baoyue Zhang; Ai-Lin Liu; Guan-Hua Du
Journal:  RSC Adv       Date:  2018-01-30       Impact factor: 4.036

5.  Genetic variants and their interactions in disease risk prediction - machine learning and network perspectives.

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6.  A discovery study of daunorubicin induced cardiotoxicity in a sample of acute myeloid leukemia patients prioritizes P450 oxidoreductase polymorphisms as a potential risk factor.

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Journal:  Front Genet       Date:  2013-11-11       Impact factor: 4.599

7.  SPARCoC: a new framework for molecular pattern discovery and cancer gene identification.

Authors:  Shiqian Ma; Daniel Johnson; Cody Ashby; Donghai Xiong; Carole L Cramer; Jason H Moore; Shuzhong Zhang; Xiuzhen Huang
Journal:  PLoS One       Date:  2015-03-13       Impact factor: 3.240

8.  Relationship Between Interleukin-10 Gene C-819T Polymorphism and Gastric Cancer Risk: Insights From a Meta-Analysis.

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  8 in total

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