Automated mitotic spindle hotspot counts are highly associated with clinical outcomes in systemically untreated early-stage triple-negative breast cancer.

Operable triple-negative breast cancer (TNBC) has a higher risk of recurrence and death compared to other subtypes. Tumor size and nodal status are the primary clinical factors used to guide systemic treatment, while biomarkers of proliferation have not demonstrated value. Recent studies suggest that subsets of TNBC have a favorable prognosis, even without systemic therapy. We evaluated the association of fully automated mitotic spindle hotspot (AMSH) counts with recurrence-free (RFS) and overall survival (OS) in two separate cohorts of patients with early-stage TNBC who did not receive systemic therapy. AMSH counts were obtained from areas with the highest mitotic density in digitized whole slide images processed with a convolutional neural network trained to detect mitoses. In 140 patients from the Mayo Clinic TNBC cohort, AMSH counts were significantly associated with RFS and OS in a multivariable model controlling for nodal status, tumor size, and tumor-infiltrating lymphocytes (TILs) (p < 0.0001). For every 10-point increase in AMSH counts, there was a 16% increase in the risk of an RFS event (HR 1.16, 95% CI 1.08-1.25), and a 7% increase in the risk of death (HR 1.07, 95% CI 1.00-1.14). We corroborated these findings in a separate cohort of systemically untreated TNBC patients from Radboud UMC in the Netherlands. Our findings suggest that AMSH counts offer valuable prognostic information in patients with early-stage TNBC who did not receive systemic therapy, independent of tumor size, nodal status, and TILs. If further validated, AMSH counts could help inform future systemic therapy de-escalation strategies.

Breast Cancer Polygenic-Risk Score Influence on Risk-Reducing Endocrine Therapy Use: Genetic Risk Estimate (GENRE) Trial 1-Year and 2-Year Follow-Up.

Refinement of breast cancer risk estimates with a polygenic-risk score (PRS) may improve uptake of risk-reducing endocrine therapy (ET). A previous clinical trial assessed the influence of adding a PRS to traditional risk estimates on ET use. We stratified participants according to PRS-refined breast cancer risk and evaluated ET use and ET-related quality of life (QOL) at 1-year (previously reported) and 2-year follow-ups. Of 151 participants, 58 (38.4%) initiated ET, and 22 (14.6%) discontinued ET by 2 years; 42 (27.8%) and 36 (23.8%) participants were using ET at 1- and 2-year follow-ups, respectively. At the 2-year follow-up, 39% of participants with a lifetime breast cancer risk of 40.1% to 100.0%, 18% with a 20.1% to 40.0% risk, and 16% with a 0.0% to 20.0% risk were taking ET (overall P = 0.01). Moreover, 40% of participants whose breast cancer risk increased by 10% or greater with addition of the PRS to a traditional breast cancer-risk model were taking ET versus 0% whose risk decreased by 10% or greater (P = 0.004). QOL was similar for participants taking or not taking ET at 1- and 2-year follow-ups, although most who discontinued ET did so because of adverse effects. However, these QOL results may have been skewed by the long interval between QOL surveys and lack of baseline QOL data. PRS-informed breast cancer prevention counseling has a lasting, but waning, effect over time. Additional follow-up studies are needed to address the effect of PRS on ET adherence, ET-related QOL, supplemental breast cancer screening, and other risk-reducing behaviors. PREVENTION RELEVANCE: Risk-reducing medications for breast cancer are considerably underused. Informing women at risk with precise and individualized risk assessment tools may substantially affect the incidence of breast cancer. In our study, a risk assessment tool (IBIS-polygenic-risk score) yielded promising results, with 39% of women at highest risk starting preventive medication.

Integration of multiomics data shows down regulation of mismatch repair and tubulin pathways in triple-negative chemotherapy-resistant breast tumors.

BACKGROUND: Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Patients with TNBC are primarily treated with neoadjuvant chemotherapy (NAC). The response to NAC is prognostic, with reductions in overall survival and disease-free survival rates in those patients who do not achieve a pathological complete response (pCR). Based on this premise, we hypothesized that paired analysis of primary and residual TNBC tumors following NAC could identify unique biomarkers associated with post-NAC recurrence. METHODS AND RESULTS: We investigated 24 samples from 12 non-LAR TNBC patients with paired pre- and post-NAC data, including four patients with recurrence shortly after surgery (< 24 months) and eight who remained recurrence-free (> 48 months). These tumors were collected from a prospective NAC breast cancer study (BEAUTY) conducted at the Mayo Clinic. Differential expression analysis of pre-NAC biopsies showed minimal gene expression differences between early recurrent and nonrecurrent TNBC tumors; however, post-NAC samples demonstrated significant alterations in expression patterns in response to intervention. Topological-level differences associated with early recurrence were implicated in 251 gene sets, and an independent assessment of microarray gene expression data from the 9 paired non-LAR samples available in the NAC I-SPY1 trial confirmed 56 gene sets. Within these 56 gene sets, 113 genes were observed to be differentially expressed in the I-SPY1 and BEAUTY post-NAC studies. An independent (n = 392) breast cancer dataset with relapse-free survival (RFS) data was used to refine our gene list to a 17-gene signature. A threefold cross-validation analysis of the gene signature with the combined BEAUTY and I-SPY1 data yielded an average AUC of 0.88 for six machine-learning models. Due to the limited number of studies with pre- and post-NAC TNBC tumor data, further validation of the signature is needed. CONCLUSION: Analysis of multiomics data from post-NAC TNBC chemoresistant tumors showed down regulation of mismatch repair and tubulin pathways. Additionally, we identified a 17-gene signature in TNBC associated with post-NAC recurrence enriched with down-regulated immune genes.

Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment.

We identified resistance mechanisms to abiraterone acetate/prednisone (AA/P) in patients with metastatic castration-resistant prostate cancer (mCRPC) in the Prostate Cancer Medically Optimized Genome-Enhanced Therapy (PROMOTE) study.We analyzed whole-exome sequencing (WES) and RNA-sequencing data from 83 patients with metastatic biopsies before (V1) and after 12 weeks of AA/P treatment (V2). Resistance was determined by time to treatment change (TTTC).At V2, 18 and 11 of 58 patients had either short-term (median 3.6 months; range 1.4-4.5) or long-term (median 29 months; range 23.5-41.7) responses, respectively. Nonresponders had low expression of TGFBR3 and increased activation of the Wnt pathway, cell cycle, upregulation of AR variants, both pre- and posttreatment, with further deletion of AR inhibitor CDK11B posttreatment. Deletion of androgen processing genes, HSD17B11, CYP19A1 were observed in nonresponders posttreatment. Genes involved in cell cycle, DNA repair, Wnt-signaling, and Aurora kinase pathways were differentially expressed between the responder and non-responder at V2. Activation of Wnt signaling in nonresponder and deactivation of MYC or its target genes in responders was detected via SCN loss, somatic mutations, and transcriptomics. Upregulation of genes in the AURKA pathway are consistent with the activation of MYC regulated genes in nonresponders. Several genes in the AKT1 axis had increased mutation rate in nonresponders. We also found evidence of resistance via PDCD1 overexpression in responders. IMPLICATIONS: Finally, we identified candidates drugs to reverse AA/P resistance: topoisomerase inhibitors and drugs targeting the cell cycle via the MYC/AURKA/AURKB/TOP2A and/or PI3K_AKT_MTOR pathways.

Luminal androgen receptor breast cancer subtype and investigation of the microenvironment and neoadjuvant chemotherapy response.

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype with low overall survival rates and high molecular heterogeneity; therefore, few targeted therapies are available. The luminal androgen receptor (LAR) is the most consistently identified TNBC subtype, but the clinical utility has yet to be established. Here, we constructed a novel genomic classifier, LAR-Sig, that distinguishes the LAR subtype from other TNBC subtypes and provide evidence that it is a clinically distinct disease. A meta-analysis of seven TNBC datasets (n = 1086 samples) from neoadjuvant clinical trials demonstrated that LAR patients have significantly reduced response (pCR) rates than non-LAR TNBC patients (odds ratio = 2.11, 95% CI: 1.33, 2.89). Moreover, deconvolution of the tumor microenvironment confirmed an enrichment of luminal epithelium corresponding with a decrease in basal and myoepithelium in LAR TNBC tumors. Increased immunosuppression in LAR patients may lead to a decreased presence of cycling T-cells and plasma cells. While, an increased presence of myofibroblast-like cancer-associated cells may impede drug delivery and treatment. In summary, the lower levels of tumor infiltrating lymphocytes (TILs), reduced immune activity in the micro-environment, and lower pCR rates after NAC, suggest that new therapeutic strategies for the LAR TNBC subtype need to be developed.

Does a Multiple Myeloma Polygenic Risk Score Predict Overall Survival of Patients with Myeloma?

BACKGROUND: Genome-wide association studies (GWAS) of multiple myeloma in populations of European ancestry (EA) identified and confirmed 24 susceptibility loci. For other cancers (e.g., colorectum and melanoma), risk loci have also been associated with patient survival. METHODS: We explored the possible association of all the known risk variants and their polygenic risk score (PRS) with multiple myeloma overall survival (OS) in multiple populations of EA [the International Multiple Myeloma rESEarch (IMMEnSE) consortium, the International Lymphoma Epidemiology consortium, CoMMpass, and the German GWAS] for a total of 3,748 multiple myeloma cases. Cox proportional hazards regression was used to assess the association between each risk SNP with OS under the allelic and codominant models of inheritance. All analyses were adjusted for age, sex, country of origin (for IMMEnSE) or principal components (for the others) and disease stage (ISS). SNP associations were meta-analyzed. RESULTS: SNP associations were meta-analyzed. From the meta-analysis, two multiple myeloma risk SNPs were associated with OS (P < 0.05), specifically POT1-AS1-rs2170352 [HR = 1.37; 95% confidence interval (CI) = 1.09-1.73; P = 0.007] and TNFRSF13B-rs4273077 (HR = 1.19; 95% CI = 1.01-1.41; P = 0.04). The association between the combined 24 SNP MM-PRS and OS, however, was not significant. CONCLUSIONS: Overall, our results did not support an association between the majority of multiple myeloma risk SNPs and OS. IMPACT: This is the first study to investigate the association between multiple myeloma PRS and OS in multiple myeloma.

Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk.

Polygenic risk scores (PRSs) for a variety of diseases have recently been shown to have relative risks that depend on age, and genetic relative risks decrease with increasing age. A refined understanding of the age dependency of PRSs for a disease is important for personalized risk predictions and risk stratification. To further evaluate how the PRS relative risk for prostate cancer depends on age, we refined analyses for a validated PRS for prostate cancer by using 64,274 prostate cancer cases and 46,432 controls of diverse ancestry (82.8% European, 9.8% African American, 3.8% Latino, 2.8% Asian, and 0.8% Ghanaian). Our strategy applied a novel weighted proportional hazards model to case-control data to fully utilize age to refine how the relative risk decreased with age. We found significantly greater relative risks for younger men (age 30-55 years) compared with older men (70-88 years) for both relative risk per standard deviation of the PRS and dichotomized according to the upper 90th percentile of the PRS distribution. For the largest European ancestral group that could provide reliable resolution, the log-relative risk decreased approximately linearly from age 50 to age 75. Despite strong evidence of age-dependent genetic relative risk, our results suggest that absolute risk predictions differed little from predictions that assumed a constant relative risk over ages, from short-term to long-term predictions, simplifying implementation of risk discussions into clinical practice.

Prevalence of heavy chain MGUS by race and family history risk groups using a high-sensitivity screening method.

Mass-spectrometry (MS) assays detect lower levels of monoclonal proteins and result in earlier detection of monoclonal gammopathy of undetermined significance (MGUS). We examined heavy chain MGUS prevalence using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS among 3 risk groups, ages 50 or older: 327 African Americans (AA) and 1223 European Americans (EA) from a clinical biobank and 1093 unaffected first-degree relatives (FDR) of patients with hematologic disorders. Age- and sex-adjusted prevalence rates were directly standardized to 2010 United States population. Prevalence ratios were estimated for comparisons of AA and FDR to the EA group using the Poisson distribution. Results were also compared with population-based prevalence using conventional gel-based methods. Risk groups had similar sex and age distributions. MALDI-TOF MGUS prevalence was higher in the AA (16.5% [95% confidence interval (CI), 12.2%, 20.8%]) and FDR (18.3% [95% CI, 16.6%, 21.6%]) than in EA (10.8% [95% CI, 8.8%, 12.7%]), translating to prevalence ratios of 1.73 (95% CI, 1.31, 2.29) and 1.90 (95% CI, 1.55, 2.34), respectively. MALDI-TOF EA prevalence was over threefold higher than conventional estimates but showed similar age trends. Thus, the MALDI-TOF assay found greater numbers with MGUS but similar relative differences by race, family history, and age as prior studies.

Biomarkers for Predicting Abiraterone Treatment Outcome and Selecting Alternative Therapies in Castration-Resistant Prostate Cancer.

Approximately one-third of patients with metastatic castration-resistant prostate cancer (CRPC) exhibited primary abiraterone resistance. To identify alternative treatment for abiraterone nonresponders, we performed drug discovery analyses using the L1000 database using differentially expressed genes identified in tumor biopsies and patient-derived xenograft (PDX) tumors between abiraterone responders and nonresponders enrolled in PROMOTE trial. This approach identified 3 drugs, including topoisomerase II (TOP2) inhibitor mitoxantrone, CDK4/6 inhibitor palbociclib, and pan-CDK inhibitor PHA-793887. These drugs significantly suppressed the growth of abiraterone-resistant cell lines and PDX models. Moreover, we identified 11 genes targeted by all 3 drugs that were associated with worse outcomes in both the PROMOTE and Stand Up To Cancer cohorts. This 11-gene panel might also function as biomarkers to select the 3 alternative therapies for this subgroup of patients with CRPC, warranting further clinical investigation.

Estrogen receptor beta repurposes EZH2 to suppress oncogenic NFκB/p65 signaling in triple negative breast cancer.

Triple Negative Breast Cancer (TNBC) accounts for 15-20% of all breast cancer cases, yet is responsible for a disproportionately high percentage of breast cancer mortalities. Thus, there is an urgent need to identify novel biomarkers and therapeutic targets based on the molecular events driving TNBC pathobiology. Estrogen receptor beta (ERß) is known to elicit anti-cancer effects in TNBC, however its mechanisms of action remain elusive. Here, we report the expression profiles of ERß and its association with clinicopathological features and patient outcomes in the largest cohort of TNBC to date. In this cohort, ERß was expressed in approximately 18% of TNBCs, and expression of ERß was associated with favorable clinicopathological features, but correlated with different overall survival outcomes according to menopausal status. Mechanistically, ERß formed a co-repressor complex involving enhancer of zeste homologue 2/polycomb repressive complex 2 (EZH2/PRC2) that functioned to suppress oncogenic NFκB/RELA (p65) activity. Importantly, p65 was shown to be required for formation of this complex and for ERß-mediated suppression of TNBC. Our findings indicate that ERß+ tumors exhibit different characteristics compared to ERß- tumors and demonstrate that ERß functions as a molecular switch for EZH2, repurposing it for tumor suppressive activities and repression of oncogenic p65 signaling.

Nine-gene pharmacogenomics profile service: The Mayo Clinic experience.

PURPOSE: The Pharmacogenomics (PGx) Profile Service was a proof-of-concept project to implement PGx in patient care at Mayo Clinic. METHODS: Eighty-two healthy individuals aged 18 and older underwent genotyping of CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, SLCO1B1, HLA-B*58:01, and VKORC1. A PGx pharmacist was involved in ordering, meeting with patients, interpreting, reviewing, and documenting results. RESULTS: Ninety three percent were CYP1A2 rapid metabolizers, 92% CYP3A4 normal metabolizers, and 88% CYP3A5 poor metabolizers; phenotype frequencies for CYP2C19 and CYP2D6 varied. Seventy-three percent had normal functioning SLCO1B1 transporter, 4% carried the HLA-B*58:01 risk variant, and 35% carried VKORC1 and CYP2C9 variants that increased warfarin sensitivity. CONCLUSION: Pre-emptive PGx testing offered medication improvement opportunity in 56% of participants for commonly used medications. A collaborative approach involving a PGx pharmacist integrated within a clinical practice with regards to utility of PGx results allowed for implementation of the PGx Profile Service. KEY POINTS: The Mayo Clinic PGx (PGx) Profile Service was a proof-of-concept project to utilize PGx testing as another clinical tool to enhance medication selection and decrease serious adverse reactions or medication failures. Over one-half of participants in the pilot using the PGx Profile Service were predicted to benefit from pre-emptive PGx testing to guide pharmacotherapy. PGx pharmacists played a crucial role in the PGx Profile Service by educating participants, identifying medication-gene interactions, and providing evidence-based (CPIC and DPWG) PGx recommendations for past, current, and future medication us.

Penalized mediation models for multivariate data.

Statistical methods to integrate multiple layers of data, from exposures to intermediate traits to outcome variables, are needed to guide interpretation of complex data sets for which variables are likely contributing in a causal pathway from exposure to outcome. Statistical mediation analysis based on structural equation models provide a general modeling framework, yet they can be difficult to apply to high-dimensional data and they are not automated to select the best fitting model. To overcome these limitations, we developed novel algorithms and software to simultaneously evaluate multiple exposure variables, multiple intermediate traits, and multiple outcome variables. Our penalized mediation models are computationally efficient and simulations demonstrate that they produce reliable results for large data sets. Application of our methods to a study of vascular disease demonstrates their utility to identify novel direct effects of single-nucleotide polymorphisms (SNPs) on coronary heart disease and peripheral artery disease, while disentangling the effects of SNPs on the intermediate risk factors including lipids, cigarette smoking, systolic blood pressure, and type 2 diabetes.

Patient-Derived Xenograft Engraftment and Breast Cancer Outcomes in a Prospective Neoadjuvant Study (BEAUTY).

PURPOSE: Patient-derived xenografts (PDX) are a research tool for studying cancer biology and drug response phenotypes. While engraftment rates are higher for tumors with more aggressive characteristics, it is uncertain whether engraftment is prognostic for cancer recurrence. PATIENTS AND METHODS: In a prospective study of patients with breast cancer treated with neoadjuvant chemotherapy (NAC) with taxane ± trastuzumab followed by anthracycline-based chemotherapy, we report the association between breast cancer events and PDX engraftment using tumors derived from treatment naïve (pre-NAC biopsies from 113 patients) and treatment resistant (post-NAC at surgery from 34 patients). Gray test was used to assess whether the cumulative incidence of a breast cancer event differs with respect to either pre-NAC PDX engraftment or post-NAC PDX engraftment. RESULTS: With a median follow-up of 5.7 years, the cumulative incidence of breast cancer relapse did not differ significantly according to pre-NAC PDX engraftment (5-year rate: 13.6% vs. 13.4%; P = 0.89). However, the incidence of a breast event was greater for patients with post-NAC PDX engraftment (5-year rate: 50.0% vs. 19.6%), but this did not achieve significance (P = 0.11). CONCLUSIONS: In treatment-naïve breast cancer receiving standard NAC, PDX engraftment was not prognostic for breast cancer recurrence. Further study is needed to establish whether PDX engraftment in the treatment-resistant setting is prognostic for cancer recurrence.

Impact of variant-level batch effects on identification of genetic risk factors in large sequencing studies.

Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power and therefore often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed sequencing-related quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. In the Alzheimer's Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that a subset of top disease-risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordances come largely from quality differences and if the batch-specific variants have better quality.

Impact of Personalized Genetic Breast Cancer Risk Estimation With Polygenic Risk Scores on Preventive Endocrine Therapy Intention and Uptake.

Endocrine therapy is underutilized to reduce breast cancer incidence among women at increased risk. Polygenic risk scores (PRSs) assessing 77 breast cancer genetic susceptibility loci personalizes risk estimates. We examined effect of personalized PRS breast cancer risk prediction on intention to take and endocrine therapy uptake among women at increased risk. Eligible participants had a 10-year breast cancer risk ≥5% by Tyrer-Cuzick model [International Breast Cancer Intervention Study (IBIS)] or ≥3.0 % 5-year Gail Model risk with no breast cancer history or hereditary breast cancer syndrome. Breast cancer risk was estimated, endocrine therapy options were discussed, and endocrine therapy intent was assessed at baseline. After genotyping, PRS-updated breast cancer risk estimates, endocrine therapy options, and intent to take endocrine therapy were reassessed; endocrine therapy uptake was assessed during follow-up. From March 2016 to October 2017, 151 patients were enrolled [median (range) age, 56.1 (36.0-76.4 years)]. Median 10-year and lifetime IBIS risks were 7.9% and 25.3%. Inclusion of PRS increased lifetime IBIS breast cancer risk estimates for 81 patients (53.6%) and reduced risk for 70 (46.4%). Of participants with increased breast cancer risk by PRS, 39 (41.9%) had greater intent to take endocrine therapy; of those with decreased breast cancer risk by PRS, 28 (46.7%) had less intent to take endocrine therapy (P < 0.001). On multivariable regression, increased breast cancer risk by PRS was associated with greater intent to take endocrine therapy (P < 0.001). Endocrine therapy uptake was greater among participants with increased breast cancer risk by PRS (53.4%) than with decreased risk (20.9%; P < 0.001). PRS testing influenced intent to take and endocrine therapy uptake. Assessing PRS effect on endocrine therapy adherence is needed.Prevention Relevance: Counseling women at increased breast cancer risk using polygenic risk score (PRS) risk estimates can significantly impact preventive endocrine therapy uptake. Further development of PRS testing to personalize breast cancer risk assessments and endocrine therapy counselling may serve to potentially reduce the incidence of breast cancer in the future.

Coinherited genetics of multiple myeloma and its precursor, monoclonal gammopathy of undetermined significance.

So far, 23 germline susceptibility loci have been associated with multiple myeloma (MM) risk. It is unclear whether the genetic variation associated with MM susceptibility also predisposes to its precursor, monoclonal gammopathy of undetermined significance (MGUS). Leveraging 2434 MM cases, 754 MGUS cases, and 2 independent sets of controls (2567/879), we investigated potential shared genetic susceptibility of MM and MGUS by (1) performing MM and MGUS genome-wide association studies (GWAS); (2) validating the association of a polygenic risk score (PRS) based on 23 established MM loci (MM-PRS) with risk of MM, and for the first time with MGUS; and (3) examining genetic correlation of MM and MGUS. Heritability and genetic estimates yielded 17% (standard error [SE] ±0.04) and 15% (SE ±0.11) for MM and MGUS risk, respectively, and a 55% (SE ±0.30) genetic correlation. The MM-PRS was associated with risk of MM when assessed continuously (odds ratio [OR], 1.17 per SD; 95% confidence interval [CI], 1.13-1.21) or categorically (OR, 1.70; 95% CI, 1.38-2.09 for highest; OR, 0.71; 95% CI, 0.55-0.90 for lowest compared with middle quintile). The MM-PRS was similarly associated with MGUS (OR, 1.19 per SD; 95% CI, 1.14-1.26 as a continuous measure, OR, 1.77, 95%CI: 1.29-2.43 for highest and OR, 0.70, 95%CI: 0.50-0.98 for lowest compared with middle quintile). MM and MGUS associations did not differ by age, sex, or MM immunoglobulin isotype. We validated a 23-SNP MM-PRS in an independent series of MM cases and provide evidence for its association with MGUS. Our results suggest shared common genetic susceptibility to MM and MGUS.

Penalized models for analysis of multiple mediators.

Mediation analysis attempts to determine whether the relationship between an independent variable (e.g., exposure) and an outcome variable can be explained, at least partially, by an intermediate variable, called a mediator. Most methods for mediation analysis focus on one mediator at a time, although multiple mediators can be jointly analyzed by structural equation models (SEMs) that account for correlations among the mediators. We extend the use of SEMs for the analysis of multiple mediators by creating a sparse group lasso penalized model such that the penalty considers the natural groupings of parameters that determine mediation, as well as encourages sparseness of the model parameters. This provides a way to simultaneously evaluate many mediators and select those that have the most impact, a feature of modern penalized models. Simulations are used to illustrate the benefits and limitations of our approach, and application to a study of DNA methylation and reactive cortisol stress following childhood trauma discovered two novel methylation loci that mediate the association of childhood trauma scores with reactive cortisol stress levels. Our new methods are incorporated into R software called regmed.

Penalized variance components for association of multiple genes with traits.

Variance component models have gained popularity for genetic analyses, driven by their flexibility to simultaneously analyze multiple genetic variants in a gene by kernel statistics, and their ability to account for population stratification via genomic relationship matrices. For exploratory analyses with modest sample sizes and a potentially large number of variance components, it can be challenging to use standard maximum-likelihood or restricted maximum-likelihood methods to estimate variance components, because these iterative methods often fail to converge when likelihood surfaces are fairly flat, and standard-likelihood ratio statistical tests are not adequate. To overcome these limitations, we developed a penalized-likelihood model, whereby the penalty function follows the popular elastic-net approach, applying both L1 and L2 penalties to the variance components. By simulations, we demonstrate the potential gain in power by using both L1 and L2 penalties, and results from our simulations suggest that assigning 80% of the penalty parameter to the L1 penalty and 20% to the L2 penalty provides a reasonable balance between false-positive and false-negative results. Larger sample size improves the properties of our methods, at the cost of longer computation time. Application of our methods to a study of the influence of DNA methylation on levels of cortisol in reaction to stress testing shows how our method can be used to prioritize findings for further functional studies.

Deep sequencing across germline genome-wide association study signals relating to breast cancer events in women receiving aromatase inhibitors for adjuvant therapy of early breast cancer.

OBJECTIVE: To identify additional genetic variants beyond those observed in a previous genome-wide association study (GWAS) in women treated on the MA.27 clinical trial in which women were randomized to 5 years of adjuvant therapy with anastrozole or exemestane. PATIENTS AND METHODS: We performed a matched case-control study in 234 women who had a recurrence of breast cancer (cases) and 649 women who had not (controls). The analysis was restricted to White women with an estrogen receptor-positive breast cancer. Multiplex PCR-based targeted deep sequencing was performed of the MIR2052HG region on chromosome 8 between positions 75.4 and 75.7, a span of 300 kb, in an attempt to identify additional functional single nucleotide polymorphisms (SNPs). RESULTS: A total of 4677 unique variants were identified that had not been identified in the previous GWAS. Clinical Annotation of Variants analysis revealed 10 variants, including eight SNPs and two insertion-deletion mutations with moderate or high impact. However, none of the common and variant regions was significant after adjustment for the most significant SNP (rs13260300) identified in our previous GWAS. We performed haplotype analysis that revealed two regions in which the haplotypes lost significance when adjusted for this prior GWAS SNP and one region with two significant haplotypes (P = 0.046 and 0.031) after adjusting for the GWAS SNP. CONCLUSION: We were unable to identify common or rare variant regions that added value to the findings from our previous GWAS. We did find two haplotypes that were significant after adjusting for our top GWAS SNP but these were considered to be of marginal value.