Abstract SY34-01: Genome-wide association studies in cancer: A step in the right direction
Authors: Chanock Stephen J. 
Published: August 2012

Abstract
For many years, investigators have pursued the heritable contribution of cancer, first in families with multiple affected individuals and more recently in unrelated population based studies. Family studies have identified highly penetrant mutations in genes for a range of cancers, such as breast, colon, ovarian and malignant melanoma as well as in families with a spectrum of less common cancers as observed in TP53 mutations in Li-Fraumeni Syndrome. Since familial cancers represent a small fraction of cancer risk in the general population, investigators have pursued alternative approaches in population-based studies, first beginning with candidate genes, based on ‘favorite genes’ but only a handful of conclusive associations have been confirmed. Based on a rapidly developed annotation of human genetic variation, in the last five years, it has been possible to use microarray chip technology to scan large sets of cases and controls in search of common variants conclusively associated with susceptibility to one or more types of cancer, using an approach known as a genome-wide association study (GWAS).
GWAS have emerged as a powerful tool for the discovery of regions that harbor common and perhaps uncommon variants that confer risk to different types of cancer using an ‘agnostic’ approach, based on statistical evidence. GWAS begin with the scanning of sets of cases and controls with commercial microarray chips with single nucleotide polymorphisms (SNPs) followed by efforts to replicate the promising signals in additional data sets. At the beginning of 2012, already 215 regions have been conclusively associated with the etiology of two-dozen cancers. The majority of regions are associated with one type of cancer and surprisingly, none of these regions have been conclusively associated with clinical outcomes for the cancer. At least eight multi-cancer susceptibility regions have been uncovered, providing new insights into perhaps common mechanisms underlying carcinogenesis, in some instances suggesting novel shared mechanisms. In the near future, many more regions are expected to be reported because of large scale efforts, including meta-analyses and scans with next generation microarray chips that survey a larger proportion of the spectrum of genetic variation.
With rare exception, the estimated odds ratios for SNPs discovered in cancer GWAS are well below 1.5, suggesting that many different regions contribute to risk for most cancers. For example, in prostate cancer, at least four-dozen distinct susceptibility loci, harboring common variants that have been identified by GWAS so far yet surprisingly, no single locus distinguishes between aggressive and non-aggressive disease. This observation suggests that there could be regions that distinctly contribute to cancer risk and cancer outcomes (e.g., aggressive or metastatic disease). For lung cancer, a disease strongly driven by exposure to tobacco products, so far, only a handful of regions have been conclusively established and notably, the strongest signal on chromosome 15 could be related to smoking.
A large number of common alleles contribute to the heritability of a specific cancer. Since each region confers a small contribution to the risk for cancer, it is daunting to consider any single SNP as a clinical test. Since the complex genomic architecture of each cancer differs, additional genotyping and sequence analysis will be required to comprehensively catalog susceptibility alleles followed by the formidable task of understanding the interactions between genetic regions as well as the environment. So far, the discovery of regions has been based on an approach that looks at the additive model for each region. As larger studies are collected, it will be possible to investigate epistasis and gene-environment interactions.
While the age of GWAS has uncovered many different regions across the genome, the daunting challenge resides in pursuing each region to identify and characterize the genetic variants that are directly associated with risk or protection from the cancer of interest. For each new region, investigators face the arduous task of interrogating each region beginning with fine-mapping necessary to prioritize the set subset of variants worthy of the pursuit of the biological basis for the direct association of one or more variants. Distinct from the initial GWAS scan, the investigation of each region necessitates inspection of the specific linkage disequilibrium patterns for common and uncommon variants (and rare variants for the exceptions when ‘synthetic associations’ can explain the common allele's signal). Furthermore, it is possible to examine signals in populations with different genetic histories in order to use the differences in linkage disequilibrium patterns to narrow the regions of interest for follow-up studies. Re-sequence analysis together with in silico mapping using the HapMap and 1000 Genome Project data sets enable investigators to generate a comprehensive set of common and uncommon variants within a region of interest. Bioinformatic analyses of the genetic context can be invaluable in the prioritization of variants for specific study in laboratory assays or model organisms, enhanced by a close reading of the available literature for the trait and region implicated in cancer susceptibility.
Nearly all GWAS signals have mapped to non-coding regions, strongly suggesting the impact of genetic variants on regulatory mechanisms in neighboring ‘plausible’ genes (e.g., FGFR2 and breast cancer, MSMB/NCOA4 and prostate cancer) but also genes at a distance (e.g., eQTL type analyses). The functional basis of the direct association can illuminate basic pathways, perturbed in cancer susceptibility, which over time could provide new insights into strategies to intervene or diagnose cancer at an earlier stage. In this regard, one of the remarkable aspects of GWAS is the synthesis of disciplines in that the discovery of biological insights results from epidemiologists working with geneticists.
Since we remain early in the discovery of common genetic variants associated with risk for distinct cancers, it is not surprising that the application of common SNPs to disease prediction is premature. Common genetic variants represent only a proportion of genetic variants that contribute to disease risk; uncommon, rare and copy number variants will undoubtedly contribute to risk in both familial and unrelated settings, and their relative contributions are expected to vary by cancer site. Once a more comprehensive set of variants has been assembled, risk prediction models should be expected to improve in precision but each will be limited by the overall heredity of the cancer. At the same time, it will be necessary to assess different strategies to address distinct clinical and public health questions. It is likely that reclassification of individuals based on genetic risk profiles or alternatively, the identification of subsets of individuals at high risk could be used to determine the optimal timing of a diagnostic or therapeutic intervention. It will be critical to assess the applicability of genetic tests in specific clinical settings, such as when to perform screening tests with calculable risks (e.g., biopsies or chemoprevention) before incorporating SNPs into clinical practice. To incorporate the fruits of current genomic observations, new studies will need to be designed to validate the utility of known genetic variants in assessing risk for cancer as well as its outcomes.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr SY34-01. doi:1538-7445.AM2012-SY34-01