Cooney, Kathleen A.

PURPOSE Germline testing (GT) is a central feature of prostate cancer (PCA) treatment, management, and hereditary cancer assessment. Critical needs include optimized multigene testing strategies that incorporate evolving genetic data, consistency in GT indications and management, and alternate genetic evaluation models that address the rising demand for genetic services. METHODS A multidisciplinary consensus conference that included experts, stakeholders, and national organization leaders was convened in response to current practice challenges and to develop a genetic implementation framework. Evidence review informed questions using the modified Delphi model. The final framework included criteria with strong (> 75%) agreement (Recommend) or moderate (50% to 74%) agreement (Consider). RESULTS Large germline panels and somatic testing were recommended for metastatic PCA. Reflex testing—initial testing of priority genes followed by expanded testing—was suggested for multiple scenarios. Metastatic disease or family history suggestive of hereditary PCA was recommended for GT. Additional family history and pathologic criteria garnered moderate consensus. Priority genes to test for metastatic disease treatment included BRCA2, BRCA1, and mismatch repair genes, with broader testing, such as ATM, for clinical trial eligibility. BRCA2 was recommended for active surveillance discussions. Screening starting at age 40 years or 10 years before the youngest PCA diagnosis in a family was recommended for BRCA2 carriers, with consideration in HOXB13, BRCA1, ATM, and mismatch repair carriers. Collaborative (point-of-care) evaluation models between health care and genetic providers was endorsed to address the genetic counseling shortage. The genetic evaluation framework included optimal pretest informed consent, post-test discussion, cascade testing, and technology-based approaches. CONCLUSION This multidisciplinary, consensus-driven PCA genetic implementation framework provides novel guidance to clinicians and patients tailored to the precision era. Multiple research, education, and policy needs remain of importance.

ABSTRACTNext‐generation sequencing technologies have afforded unprecedented characterization of low‐frequency and rare genetic variation. Due to low power for single‐variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel‐machine regression and adaptive testing methods for aggregative rare‐variant association testing have been demonstrated to be powerful approaches for pathway‐level analysis, although these methods tend to be computationally intensive at high‐variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare‐variant analysis using component gene‐level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family‐wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case‐control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open‐source R code for public use to facilitate easy application of our methods to existing rare‐variant analysis results.

ABSTRACTRare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single‐marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden‐type approaches attempt to identify aggregation of RVs across case‐control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large‐scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway‐level RV analysis results from a prostate cancer (PC) risk case‐control sequencing study. Finally, we discuss potential extensions and future directions of this work.

Abstract African American Hereditary Prostate Cancer Study (AAHPC) was developed as a national collaboration to explore the role of genetics in the causation of hereditary prostate cancer (HPC) in African American (AA) men. AAHPC is in partnership with the International Consortium for Prostate Cancer Genetics (ICPCG), which conducts collaborative studies of HPC genetics in multiplex families. As part of an ICPCG sequencing study of 539 affected individuals from 366 HPC pedigrees, we performed whole exome sequencing in 21 ICPCG AA families, of which there were 14 AAHPC affected men from 11 pedigrees. The combined ICPCG AA cohort consisted of N=26 affected members. Post-variant calling quality control (QC) was implemented using Golden Helix SVS 8 software with filters set for removal of variants with Read Depth < 10, Quality Score < 20, Quality Score: Read Depth Ratio < 0.5, Call Rate < 0.75. Variants were additionally filtered by MAF based on the NHLBI ESP650051-V2 exomes variant frequencies for the AA population using a MAF threshold of 5%. Following QC, 176/299 SNVs and 20/39 INDELs remained for further analysis. In these analyses, we focused on 13 known cancer genes (MSR1, AR, BRCA1, BRCA2, BTNL2, EPHB2, CDH1, RNASEL, ELAC2, HOXB13, CHEK2, TP53 and NBN). Three sequenced families had > 1 affected members sequenced (2 or 3 per family) and the remaining 18 families had one member sequenced. Under the dominant model, our preliminary results show that no rare variants in the 13 candidate genes were found in 3/3 affecteds in two families. Rare SNVs in seven candidate genes (AR, CDH1, ELAC2, HOXB13, RNASEL, BRCA2 and EPHB2) were found in 2/3 affecteds for two families and 2/2 affecteds in one family. Several of the remaining 18 affected men (1 sequenced per pedigree) shared the same rare SNV in these candidate genes. For INDELs, rare variants in three candidate genes were found in pedigrees with ≥ 2 affecteds. Several of the remaining 18 affected men (one sequenced per pedigree) shared the same rare INDEL. Additional QC is underway to validate these variants and bioinformatic analyses are being used to predict effects of the variants in an effort to unravel the complex genetic heterogeneity of HPC in AA. Citation Format: Cheryl D. Cropp, Shannon K. McDonnell, Sumit Middha, Melissa DeRycke, Danielle M. Karyadi, Daniel Schaid, Stephen N. Thibodeau, William B. Isaacs, Elaine A. Ostrander, Janet Stanford, Kathleen A. Cooney, Joan E. Bailey-Wilson, John D. Carpten. Rare variant discovery in known cancer genes from whole-exome sequencingof African American hereditary prostate cancer families. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr B40.

Abstract Background: A rare nonconservative substitution (G84E) in the HOXB13 gene has been shown to be associated with risk of prostate cancer. DNA samples from male patients included in the Mayo Clinic Biobank (MCB) were genotyped to determine the frequency of the G84E mutation and its association with various cancers. Methods: Subjects were genotyped using a custom TaqMan (Applied Biosystems) assay for G84E (rs138213197). In addition to donating a blood specimen, all MCB participants completed a baseline questionnaire to collect information on medical history and family history of cancer. Results: Forty-nine of 9,012 male patients were carriers of G84E (0.5%). Thirty-one percent (n = 2,595) of participants had been diagnosed with cancer, including 51.1% of G84E carriers compared with just 30.6% of noncarriers (P = 0.004). G84E was most frequently observed among men with prostate cancer compared with men without cancer (P < 0.0001). However, the mutation was also more commonly observed in men with bladder cancer (P = 0.06) and leukemia (P = 0.01). G84E carriers were more likely to have a positive family history of prostate cancer in a first-degree relative compared to noncarriers (36.2% vs. 16.0%, P = 0.0003). Conclusions: Our study confirms the association between the HOXB13 G84E variant and prostate cancer and suggests a novel association between G84E and leukemia and a suggestive association with bladder cancer. Future investigation is warranted to confirm these associations in order to improve our understanding of the role of germline HOXB13 mutations in human cancer. Impact: The associations between HOXB13 and prostate, leukemia, and bladder suggest that this gene is important in carcinogenesis. Cancer Epidemiol Biomarkers Prev; 24(9); 1366–72. ©2015 AACR.

Abstract Background: Genome-wide association studies have identified multiple genetic variants associated with prostate cancer risk which explain a substantial proportion of familial relative risk. These variants can be used to stratify individuals by their risk of prostate cancer. Methods: We genotyped 25 prostate cancer susceptibility loci in 40,414 individuals and derived a polygenic risk score (PRS). We estimated empirical odds ratios (OR) for prostate cancer associated with different risk strata defined by PRS and derived age-specific absolute risks of developing prostate cancer by PRS stratum and family history. Results: The prostate cancer risk for men in the top 1% of the PRS distribution was 30.6 (95% CI, 16.4–57.3) fold compared with men in the bottom 1%, and 4.2 (95% CI, 3.2–5.5) fold compared with the median risk. The absolute risk of prostate cancer by age of 85 years was 65.8% for a man with family history in the top 1% of the PRS distribution, compared with 3.7% for a man in the bottom 1%. The PRS was only weakly correlated with serum PSA level (correlation = 0.09). Conclusions: Risk profiling can identify men at substantially increased or reduced risk of prostate cancer. The effect size, measured by OR per unit PRS, was higher in men at younger ages and in men with family history of prostate cancer. Incorporating additional newly identified loci into a PRS should improve the predictive value of risk profiles. Impact: We demonstrate that the risk profiling based on SNPs can identify men at substantially increased or reduced risk that could have useful implications for targeted prevention and screening programs. Cancer Epidemiol Biomarkers Prev; 24(7); 1121–9. ©2015 AACR.

Abstract Germ line mutations in several genes (BRCA1, BRCA2, and CHEK2) whose products are involved in the DNA damage–signaling pathway have been implicated in prostate cancer risk. To identify additional genes in this pathway that might confer susceptibility to this cancer, we analyzed a recently identified DNA damage–response gene, p53AIP1 (a gene encoding for p53-regulated apoptosis-inducing protein 1), for genetic variants in prostate cancer. Five novel germ line variants were identified. The two truncating variants (Ser32Stop and Arg21insG) were found in 3% (4 of 132) of unselected prostate tumor samples. Genotyping of the two variants in an additional 393 men with sporadic prostate cancer showed a frequency of 3.1% (12 of 393) in contrast to 0.6% (2 of 327) in 327 unaffected men (Fisher's exact test, P = 0.018), with an odds ratio (OR) of 5.1 [95% confidence interval (95% CI), 1.1-23.0]. In addition, two of six tumors carrying the truncating variants were associated with loss of heterozygosity of the wild-type alleles, suggesting that p53AIP1 may act as a tumor suppressor. We also showed that the truncated p53AIP1 was unable to induce apoptosis and suppress cell growth in HeLa and COS-7 cells. These results suggest that loss-of-function variants in p53AIP1 associated with the risk of sporadic prostate cancer and further support the concept that the genetic defects in the DNA damage–response genes play an important role in the development of prostate cancer. (Cancer Res 2006; 66(21): 10302-7)

Abstract The Nijmegen breakage syndrome 1 (NBS1) gene, which participates in DNA double strand break repair, has been postulated to be a susceptibility factor for a number of cancers, including prostate cancer. Numerous mutations have been identified in NBS1, including the founder mutation 657del5. In this study, a number of analyses were done to determine whether mutations in NBS1 are associated with an increased risk for prostate cancer. The frequency of the 657del5 mutation in both familial prostate cancer cases (1,819 affected men among 909 families) and sporadic prostate cancer cases (1,218 affected men) collected from five centers participating in the International Consortium for Prostate Cancer Genetics were compared with that found in 697 normal controls. Seven individuals were identified to carry the mutation among the 3,037 cases screened: four in the familial group (three from one family and one from another) and three in the sporadic cases. The carrier frequency was 0.22% (2 of 909) for the probands and 0.25% (3 of 1,218) for the sporadic cases of prostate cancer. The 657del5 mutation was not detected in either the 293 unaffected members of the prostate cancer families or in the 697 control samples tested. The entire NBS1 gene was also sequenced in 20 of the youngest affected individuals from the Finnish group of familial cases to identify the presence of possible mutations in this high-risk group. One rare (D95N) and one common (E185Q) missense alteration was identified. More detailed analyses of the E185Q polymorphism, along with a third rare variant (R215W), failed to show an association with prostate cancer. Because the 657del5 mutation was absent from the control population, we are unable to determine if this alteration predisposes to prostate cancer. However, our data does suggest that mutations within NBS1, and in particular, 657del5, do not significantly contribute to the overall prostate cancer burden within our patient samples. (Cancer Epidemiol Biomarkers Prev 2006;15(5):935–8)