Alvaro N.A. Monteiro

Principles for the post-GWAS functional characterization of cancer risk loci

Genome wide association studies (GWAS) have identified more than 200 mostly new common low-penetrance susceptibility loci for cancers. The predicted risk associated with each locus is generally modest (with a per-allele odds ratio typically less than 2) and so, presumably, are the functional effects of individual genetic variants conferring disease susceptibility. Perhaps the greatest challenge in the ‘post-GWAS’ era is to understand the functional consequences of these loci. Biological insights can then be translated to clinical benefits, including reliable biomarkers and effective strategies for screening and disease prevention. The purpose of this article is to propose principles for the initial functional characterization of cancer risk loci, with a focus on non-coding variants, and to define ‘post-GWAS’ functional characterization. By December 2010, there were 1,212 published GWAS studies1 reporting significant (P < 5 × 10−8) associations for 210 traits (Table 1), and the Catalog of Published GWAS states that by March 2011, 812 publications reported 3,977 SNP associations1. This is likely a small fraction of the common susceptibility loci of low penetrance that will eventually be identified. Despite these successes in identifying risk loci, the causal variant and/or the molecular basis of risk etiology has been determined for only a small fraction of these associations2–4. Plausible candidate genes can be based on proximity to risk loci, but few have so far been defined in a more systematic manner (Supplementary Table 1). Table 1 The genomic context in which a variant is found can be used as preliminary functional analysis Increased investment in post-GWAS functional characterization of risk loci5 has now been advocated across diseases and for cardiovascular disease and diabetes6. For cancer biology, the complex interplay between genetics and the environment in many cancers poses a particularly exciting challenge for post-GWAS research. Here we suggest a systematic strategy for understanding how cancer-associated variants exert their effects. We mostly refer to SNPs throughout the paper, but we recognize that other types of common genetic (for example, copy number variants) or epigenetic variation may influence risk. Our understanding of the way in which a risk variant initiates disease pathogenesis progresses from statistical association between genetic variation and trait or disease variation to functionality and causality. The functional consequences of variants in protein-coding regions causing most monogenic disorders are more readily interpreted because we know the genetic code. For non-Mendelian or multifactorial traits, most of the common DNA variants have so far mapped to non-protein–coding regions2, where our understanding of functional consequences and causality is more rudimentary. Our hypothesis is that the trait-associated alleles exert their effects by influencing transcriptional output (such as transcript levels and splicing) through multiple mechanisms. We emphasize appropriate assays and models to test the functional effects of both SNPs and genes mapping to cancer predisposition loci. Although much of what is written is applicable to alleles discovered for any trait, the section on modeling gene effects will emphasize measuring cancer-related phenotypes. At some loci, multiple, independently associated risk alleles rather than single risk alleles may be functionally responsible for the occurrence of disease. Genotyping susceptibility loci (and their correlated variants) in multiple populations with different linkage disequilibrium (LD) structures may prove effective in substantially reducing the number of potentially causative variants (that is, the same causal variant may segregate in multiple populations), as shown for the FGFR2 locus in breast cancer7, but for most loci there will remain a set of potentially causative variants that cannot be separated at the statistical level from case-control genotype data. A susceptibility locus should be re-sequenced to ascertain all genetic variation, identifying candidate functional or causal variants and identifying candidate causal genes. Ideally, the identification of a causal SNP would be the next step to reveal the molecular mechanisms of risk modification. Practically, however, it is unclear what the criteria for causality should be, particularly in non-protein–coding regions. Thus, although we propose a framework set of analyses (Box 1), we acknowledge that the techniques and methods will continue to evolve with the field. Box 1 Strategies to progress from tag SNP to mechanism Target resequencing efforts using linkage disequilibrium (LD) structure. Use other populations to refine LD regions (for example African ancestry with shorter LD and more heterogeneity). Determine expression levels of nearby genes as a function of genotype at each locus (eQTL). Characterize gene regulatory regions by multiple empirical techniques bearing in mind that these are tissue and context specific. Combine regulatory regions with risk loci using coordinates from multiple reference genomes to capture all variation within the shorter regulatory regions that correlates with the tag SNP at each locus. Multiple experimental manipulations in model systems are needed to progressively implicate transcription units (genes) in mechanisms relevant to the associated loci: Knockouts of regulatory regions in animal (difficult and may be limited by functional redundancy, but new targeting methods in rat are promising) models followed by genome-wide expression analysis. Use chromatin association methods (3C, CHIA-PET) of regulatory regions to determine the identity of target genes (compare with eQTL data). Targeted gene perturbations in somatic cell models. Explore fully genome-wide eQTL and miRNA quantitative variation correlation in relevant tissues and cells. Explore epigenetic mechanisms in the context of genome-wide genetic polymorphism. Employ cell models and tissue reconstructions to evaluate mechanisms using gene perturbations and polymorphic variants. The human cancer cell xenograft has re-emerged as a minimal in vivo validation of these models. Above all, resist the temptation to equate any partial functional evidence as sufficient. Published claims of functional relevance should be fully evaluated using the steps detailed above.

Integrated Evaluation of DNA Sequence Variants of Unknown Clinical Significance: Application to BRCA1 and BRCA2

Many sequence variants in predisposition genes are of uncertain clinical significance, and classification of these variants into high- or low-risk categories is an important problem in clinical genetics. Classification of such variants can be performed by direct epidemiological observations, including cosegregation with disease in families and degree of family history of the disease, or by indirect measures, including amino acid conservation, severity of amino acid change, and evidence from functional assays. In this study, we have developed an approach to the synthesis of such evidence in a multifactorial likelihood-ratio model. We applied this model to the analysis of three unclassified variants in BRCA1 and three in BRCA2. The evidence strongly suggests that two variants (C1787S in BRCA1 and D2723H in BRCA2) are deleterious, three (R841W in BRCA1 and Y42C and P655R in BRCA2) are neutral, and one (R1699Q in BRCA1) remains of uncertain significance. These results provide a demonstration of the utility of the model.

ENIGMA-Evidence-based network for the interpretation of germline mutant alleles: An international initiative to evaluate risk and clinical significance associated with sequence variation in BRCA1 and BRCA2 genes

As genetic testing for predisposition to human diseases has become an increasingly common practice in medicine, the need for clear interpretation of the test results is apparent. However, for many disease genes, including the breast cancer susceptibility genes BRCA1 and BRCA2, a significant fraction of tests results in the detection of a genetic variant for which disease association is not known. The finding of an “unclassified” variant (UV)/variant of uncertain significance (VUS) complicates genetic test reporting and counseling. As these variants are individually rare, a large collaboration of researchers and clinicians will facilitate studies to assess their association with cancer predisposition. It was with this in mind that the ENIGMA consortium (www.enigmaconsortium.org) was initiated in 2009. The membership is both international and interdisciplinary, and currently includes more than 100 research scientists and clinicians from 19 countries. Within ENIGMA, there are presently six working groups focused on the following topics: analysis, clinical, database, functional, tumor histopathology, and mRNA splicing. ENIGMA provides a mechanism to pool resources, exchange methods and data, and coordinately develop and apply algorithms for classification of variants in BRCA1 and BRCA2. It is envisaged that the research and clinical application of models developed by ENIGMA will be relevant to the interpretation of sequence variants in other disease genes. Hum Mutat 33:2–7, 2012.

A review of a multifactorial probability-based model for classification of BRCA1 and BRCA2 variants of uncertain significance (VUS).

Clinical mutation screening of the BRCA1 and BRCA2 genes for the presence of germline inactivating mutations is used to identify individuals at elevated risk of breast and ovarian cancer. Variants identified during screening are usually classified as pathogenic (increased risk of cancer) or not pathogenic (no increased risk of cancer). However, a significant proportion of genetic tests yields variants of uncertain significance (VUS) that have undefined risk of cancer. Individuals carrying these VUS cannot benefit from individualized cancer risk assessment. Recently, a quantitative “posterior probability model” for assessing the clinical relevance of VUS in BRCA1 or BRCA2, which integrates multiple forms of genetic evidence has been developed. Here, we provide a detailed review of this model. We describe the components of the model and explain how these can be combined to calculate a posterior probability of pathogenicity for each VUS. We explain how the model can be applied to public data and provide tables that list the VUS that have been classified as not pathogenic or pathogenic using this method. While we use BRCA1 and BRCA2 VUS as examples, the method can be used as a framework for classification of the pathogenicity of VUS in other cancer genes. Hum Mutat 33:8–21, 2012.

Determination of cancer risk associated with germ line BRCA1 missense variants by functional analysis

Germ line inactivating mutations in BRCA1 confer susceptibility for breast and ovarian cancer. However, the relevance of the many missense changes in the gene for which the effect on protein function is unknown remains unclear. Determination of which variants are causally associated with cancer is important for assessment of individual risk. We used a functional assay that measures the transactivation activity of BRCA1 in combination with analysis of protein modeling based on the structure of BRCA1 BRCT domains. In addition, the information generated was interpreted in light of genetic data. We determined the predicted cancer association of 22 BRCA1 variants and verified that the common polymorphism S1613G has no effect on BRCA1 function, even when combined with other rare variants. We estimated the specificity and sensitivity of the assay, and by meta-analysis of 47 variants, we show that variants with <45% of wild-type activity can be classified as deleterious whereas variants with >50% can be classified as neutral. In conclusion, we did functional and structure-based analyses on a large series of BRCA1 missense variants and defined a tentative threshold activity for the classification missense variants. By interpreting the validated functional data in light of additional clinical and structural evidence, we conclude that it is possible to classify all missense variants in the BRCA1 COOH-terminal region. These results bring functional assays for BRCA1 closer to clinical applicability.

Structure-based assessment of missense mutations in human BRCA1: implications for breast and ovarian cancer predisposition.

The BRCA1 gene from individuals at risk of breast and ovarian cancers can be screened for the presence of mutations. However, the cancer association of most alleles carrying missense mutations is unknown, thus creating significant problems for genetic counseling. To increase our ability to identify cancer-associated mutations in BRCA1, we set out to use the principles of protein three-dimensional structure as well as the correlation between the cancer-associated mutations and those that abolish transcriptional activation. Thirty-one of 37 missense mutations of known impact on the transcriptional activation function of BRCA1 are readily rationalized in structural terms. Loss-of-function mutations involve nonconservative changes in the core of the BRCA1 C-terminus (BRCT) fold or are localized in a groove that presumably forms a binding site involved in the transcriptional activation by BRCA1; mutations that do not abolish transcriptional activation are either conservative changes in the core or are on the surface outside of the putative binding site. Next, structure-based rules for predicting functional consequences of a given missense mutation were applied to 57 germ-line BRCA1 variants of unknown cancer association. Such a structure-based approach may be helpful in an integrated effort to identify mutations that predispose individuals to cancer.

p53 Acetylation Is Crucial for Its Transcription-independent Proapoptotic Functions

Acetylation of p53 at carboxyl-terminal lysine residues enhances its transcriptional activity associated with cell cycle arrest and apoptosis. Here we demonstrate that p53 acetylation at Lys-320/Lys-373/Lys-382 is also required for its transcription-independent functions in BAX activation, reactive oxygen species production, and apoptosis in response to the histone deacetylase inhibitors (HDACi) suberoylanilide hydroxamic acid and LAQ824. Knock-out of p53 markedly reduced HDACi-induced apoptosis. Unexpectedly, expression of transactivation-deficient p53 variants sensitized p53-null cells to HDACi-mediated BAX-dependent apoptosis, whereas knockdown of endogenous mutant p53 in cancer cells reduced HDACi-mediated cytotoxicity. Evaluation of the mechanisms controlling this response led to the discovery of a novel interaction between p53 and Ku70. The association between these two proteins was acetylation-independent, but acetylation of p53 could prevent and disrupt the Ku70-BAX complex and enhance apoptosis. These results suggest a new mechanism of acetylated p53 transcription-independent regulation of apoptosis.

Assessment of functional effects of unclassified genetic variants

Inherited predisposition to disease is often linked to reduced activity of a disease associated gene product. Thus, quantitation of the influence of inherited variants on gene function can potentially be used to predict the disease relevance of these variants. While many disease genes have been extensively characterized at the functional level, few assays based on functional properties of the encoded proteins have been established for the purpose of predicting the contribution of rare inherited variants to disease. Much of the difficulty in establishing predictive functional assays stems from the technical complexity of the assays. However, perhaps the most challenging aspect of functional assay development for clinical testing purposes is the absolute requirement for validation of the sensitivity and specificity of the assays and the determination of positive predictive values (PPVs) and negative predictive values (NPVs) of the assays relative to a “gold standard” measure of disease predisposition. In this commentary, we provide examples of some of the functional assays under development for several cancer predisposition genes (BRCA1, BRCA2, CDKN2A, and mismatch repair [MMR] genes MLH1, MSH2, MSH6, and PMS2) and present a detailed review of the issues associated with functional assay development. We conclude that validation is paramount for all assays that will be used for clinical interpretation of inherited variants of any gene, but note that in certain circumstances information derived from incompletely validated assays may be valuable for classification of variants for clinical purposes when used to supplement data derived from other sources. Hum Mutat 29(11), 1314–1326, 2008.

Comprehensive analysis of missense variations in the BRCT domain of BRCA1 by structural and functional assays.

Genetic screening of the breast and ovarian cancer susceptibility gene BRCA1 has uncovered a large number of variants of uncertain clinical significance. Here, we use biochemical and cell-based transcriptional assays to assess the structural and functional defects associated with a large set of 117 distinct BRCA1 missense variants within the essential BRCT domain of the BRCA1 protein that have been documented in individuals with a family history of breast or ovarian cancer. In the first method, we used limited proteolysis to assess the protein folding stability of each of the mutants compared with the wild-type. In the second method, we used a phosphopeptide pull-down assay to assess the ability of each of the variants to specifically interact with a peptide containing a pSer-X-X-Phe motif, a known functional target of the BRCA1 BRCT domain. Finally, we used transcriptional assays to assess the ability of each BRCT variant to act as a transcriptional activation domain in human cells. Through a correlation of the assay results with available family history and clinical data, we define limits to predict the disease risk associated with each variant. Forty-two of the variants show little effect on function and are likely to represent variants with little or no clinical significance; 50 display a clear functional effect and are likely to represent pathogenic variants; and the remaining 25 variants display intermediate activities. The excellent agreement between the structure/function effects of these mutations and available clinical data supports the notion that functional and structure information can be useful in the development of models to assess cancer risk.

A guide for functional analysis of BRCA1 variants of uncertain significance

Germline mutations in the tumor suppressor gene BRCA1 confer an estimated lifetime risk of 56–80% for breast cancer and 15–60% for ovarian cancer. Since the mid 1990s when BRCA1 was identified, genetic testing has revealed over 1,500 unique germline variants. However, for a significant number of these variants, the effect on protein function is unknown making it difficult to infer the consequences on risks of breast and ovarian cancers. Thus, many individuals undergoing genetic testing for BRCA1 mutations receive test results reporting a variant of uncertain clinical significance (VUS), leading to issues in risk assessment, counseling, and preventive care. Here, we describe functional assays for BRCA1 to directly or indirectly assess the impact of a variant on protein conformation or function and how these results can be used to complement genetic data to classify a VUS as to its clinical significance. Importantly, these methods may provide a framework for genome‐wide pathogenicity assignment. Hum Mutat 33:1526–1537, 2012.