Cancer heterogeneity: origins and implications for genetic association studies (original) (raw)
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Acta Oncologica, 2009
Sequencing of the human genome has recently been completed and mapping of the complete genomic variation is ongoing. During the last decade there has been a huge expansion of studies of genetic variants, both with respect to association studies of disease risk and for studies of genetic factors of prognosis and treatments response, i.e., pharmacogenomics. The use of genetics to predict a patient's risk of disease or treatment response is one step toward an improved personalised prevention and screening modality for the prevention of cancer and treatment selection. The technology and statistical methods for completing whole genome tagging of variants and genome wide association studies has developed rapidly over the last decade. After identifying the genetic loci with the strongest, statistical associations with disease risk, future studies will need to further characterise the genotype-phenotype relationship to provide a biological basis for prevention and treatment decisions according to genetic profile. This review discusses some of the general issues and problems of study design; we also discuss challenges in conducting valid association studies in rare cancers such as paediatric brain tumours, where there is support for genetic susceptibility but difficulties in assembling large sample sizes. The clinical interpretation and implementation of genetic association studies with respect to disease risk and treatment is not yet well defined and remains an important area of future research.
A Compendium of Genome-Wide Associations for Cancer: Critical Synopsis and Reappraisal
Journal of The National Cancer Institute, 2010
Since 2007, genome-wide association (GWA) studies have identified numerous well-supported, novel genetic risk loci for common cancers; however, there are concerns that this technology is reaching its limits. We provide an overview of GWAidentified genetic associations with solid tumors. We simulated the distribution of population risk alleles for colorectal, prostate, testicular, and thyroid cancers based on genetic variants identified in GWA studies. We also evaluated whether statistical power to detect typical genetic effects could be improved with studies performing GWA analyses of all available samples rather than multistage designs. Fifty-six eligible articles yielded 92 eligible associations between cancer phenotypes and genetic variants with a median per-allele odds ratio (OR) of 1.22 (interquartile range = 1.15-1.36). Half of the associations pertained to prostate, colorectal, or breast cancer. Individuals at the upper quartile of simulated risk had only 2.1-to 4.2-fold higher relative risk than those in the lower quartile. Comprehensive evaluation of currently available samples with GWA platforms would yield few additional variants with per-allele OR = 1.4, but many more variants with OR = 1.2 could be detected; statistical power to detect weak associations (OR = 1.07) would still be negligible. The GWA approach is effective in identifying common genetic variants with moderate effect; however, identifying loci with very small effects and rare variants will require major new efforts. At present, the utility of GWA-identified risk loci in risk stratification for cancer is limited. J Natl Cancer Inst 2010;102:846-858 jnci.oxfordjournals.org JNCI | Review 847
Computational Biology, 2009
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Molecular Cancer, 2008
It is increasingly clear that complex networks of relationships between genes and/or proteins govern neoplastic processes. Our understanding of these networks is expanded by the use of functional genomic and proteomic approaches in addition to computational modeling. Concurrently, whole-genome association scans and mutational screens of cancer genomes identify novel cancer genes. Together, these analyses have vastly increased our knowledge of cancer, in terms of both "part lists" and their functional associations. However, genetic interactions have hitherto only been studied in depth in model organisms and remain largely unknown for human systems. Here, we discuss the importance and potential benefits of identifying genetic interactions at the human genome level for creating a better understanding of cancer susceptibility and progression and developing novel effective anticancer therapies. We examine gene expression profiles in the presence and absence of co-amplification of the 8q24 and 20q13 chromosomal regions in breast tumors to illustrate the molecular consequences and complexity of genetic interactions and their role in tumorigenesis. Finally, we highlight current strategies for targeting tumor dependencies and outline potential matrix screening designs for uncovering molecular vulnerabilities in cancer cells.
Carcinogenesis, 2013
Dozens of common genetic variants associated with cancer risk have been identified through genome-wide association studies (GWASs). However, these variants only explain a modest fraction of the heritability of disease. The missing heritability has been attributed to several factors, among them the existence of genetic interactions (G × G). Systematic screens for G × G in model organisms have revealed their fundamental influence in complex phenotypes. In this scenario, G × G overlap significantly with other types of gene and/or protein relationships. Here, by integrating predicted G × G from GWAS data and complex-and context-defined gene coexpression profiles, we provide evidence for G × G associated with cancer risk. G × G predicted from a breast cancer GWAS dataset identified significant overlaps [relative enrichments (REs) of 8-36%, empirical P values < 0.05 to 10-4 ] with complex (non-linear) gene coexpression in breast tumors. The use of gene or protein data not specific for breast cancer did not reveal overlaps. According to the predicted G × G, experimental assays demonstrated functional interplay between lipoma-preferred partner and transforming growth factor-β signaling in the MCF10A non-tumorigenic mammary epithelial cell model. Next, integration of pancreatic tumor gene expression profiles with pancreatic cancer G × G predicted from a GWAS corroborated the observations made for breast cancer risk (REs of 25-59%). The method presented here can potentially support the identification of genetic interactions associated with cancer risk, providing novel mechanistic hypotheses for carcinogenesis.
The Journal of Pathology, 2010
There are now about 100 genes known to cause Mendelian inherited cancer syndromes, but these only explain a minor part of the familial clustering of the common cancers. The increased familial relative risk of cancer in the general population must largely involve genes of low-or moderate-penetrance. Until recently, attempts to identify cancer predisposition genes with low penetrance had proved similarly unrewarding. However, in the past 2 years, developments in this area have been rapid. In particular, the 'common disease-common variant' model of predisposition has come to the fore. In this model, alleles of high frequency (typically >10%) and low penetrance (typically < two-fold increased lifetime risk) contribute substantially to susceptibility to the common human diseases, including cancers. Many common risk alleles for cancer have been found by genome-wide association studies (GWASs) in the form of tagging SNPs, although identification of the disease-causing variants generally remains a difficult problem. The 'common disease-common variant' model has recently been criticized by proponents of a 'common disease-rare variant' model. In fact, the conflict between the models is false and a more continuous approach, bounded only by technical limitations and sample sizes, appears to be more appropriate. In this review, we summarize the general findings from cancer GWASs and their problems, and discuss the issues of finding rarer variants and other forms of cancer-predisposing variation, such as copy number polymorphisms.
2010
There are now about 100 genes known to cause Mendelian inherited cancer syndromes, but these only explain a minor part of the familial clustering of the common cancers. The increased familial relative risk of cancer in the general population must largely involve genes of low-or moderate-penetrance. Until recently, attempts to identify cancer predisposition genes with low penetrance had proved similarly unrewarding. However, in the past 2 years, developments in this area have been rapid. In particular, the 'common disease-common variant' model of predisposition has come to the fore. In this model, alleles of high frequency (typically >10%) and low penetrance (typically < two-fold increased lifetime risk) contribute substantially to susceptibility to the common human diseases, including cancers. Many common risk alleles for cancer have been found by genome-wide association studies (GWASs) in the form of tagging SNPs, although identification of the disease-causing variants generally remains a difficult problem. The 'common disease-common variant' model has recently been criticized by proponents of a 'common disease-rare variant' model. In fact, the conflict between the models is false and a more continuous approach, bounded only by technical limitations and sample sizes, appears to be more appropriate. In this review, we summarize the general findings from cancer GWASs and their problems, and discuss the issues of finding rarer variants and other forms of cancer-predisposing variation, such as copy number polymorphisms.
General lessons from large-scale studies to identify human cancer predisposition genes
The Journal of Pathology, 2009
There are now about 100 genes known to cause Mendelian inherited cancer syndromes, but these only explain a minor part of the familial clustering of the common cancers. The increased familial relative risk of cancer in the general population must largely involve genes of low-or moderate-penetrance. Until recently, attempts to identify cancer predisposition genes with low penetrance had proved similarly unrewarding. However, in the past 2 years, developments in this area have been rapid. In particular, the 'common disease-common variant' model of predisposition has come to the fore. In this model, alleles of high frequency (typically >10%) and low penetrance (typically < two-fold increased lifetime risk) contribute substantially to susceptibility to the common human diseases, including cancers. Many common risk alleles for cancer have been found by genome-wide association studies (GWASs) in the form of tagging SNPs, although identification of the disease-causing variants generally remains a difficult problem. The 'common disease-common variant' model has recently been criticized by proponents of a 'common disease-rare variant' model. In fact, the conflict between the models is false and a more continuous approach, bounded only by technical limitations and sample sizes, appears to be more appropriate. In this review, we summarize the general findings from cancer GWASs and their problems, and discuss the issues of finding rarer variants and other forms of cancer-predisposing variation, such as copy number polymorphisms.
2015
Background: Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites. Methods: Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers. Results: GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl 2, on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of mu...