Pathology of Tumors Associated With Pathogenic Germline Variants in 9 Breast Cancer Susceptibility Genes

IMPORTANCE

Rare germline genetic variants in several genes are associated with increased breast cancer (BC) risk, but their precise contributions to different disease subtypes are unclear. This information is relevant to guidelines for gene panel testing and risk prediction.

OBJECTIVE

To characterize tumors associated with BC susceptibility genes in large-scale population- or hospital-based studies.

DESIGN, SETTING, AND PARTICIPANTS

The multicenter, international case-control analysis of the BRIDGES study included 42 680 patients and 46 387 control participants, comprising women aged 18 to 79 years who were sampled independently of family history from 38 studies. Studies were conducted between 1991 and 2016. Sequencing and analysis took place between 2016 and 2021.

EXPOSURES

Protein-truncating variants and likely pathogenic missense variants in ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, RAD51D, and TP53.

MAIN OUTCOMES AND MEASURES

The intrinsic-like BC subtypes as defined by estrogen receptor, progesterone receptor, and ERBB2 (formerly known as HER2) status, and tumor grade; morphology; size; stage; lymph node involvement; subtype-specific odds ratios (ORs) for carrying protein-truncating variants and pathogenic missense variants in the 9 BC susceptibility genes.

RESULTS

The mean (SD) ages at interview (control participants) and diagnosis (cases) were 55.1 (11.9) and 55.8 (10.6) years, respectively; all participants were of European or East Asian ethnicity. There was substantial heterogeneity in the distribution of intrinsic subtypes by gene. RAD51C, RAD51D, and BARD1 variants were associated mainly with triple-negative disease (OR, 6.19 [95% CI, 3.17-12.12]; OR, 6.19 [95% CI, 2.99-12.79]; and OR, 10.05 [95% CI, 5.27-19.19], respectively). CHEK2 variants were associated with all subtypes (with ORs ranging from 2.21-3.17) except for triple-negative disease. For ATM variants, the association was strongest for the hormone receptor (HR)+ERBB2- high-grade subtype (OR, 4.99; 95% CI, 3.68-6.76). BRCA1 was associated with increased risk of all subtypes, but the ORs varied widely, being highest for triple-negative disease (OR, 55.32; 95% CI, 40.51-75.55). BRCA2 and PALB2 variants were also associated with triple-negative disease. TP53 variants were most strongly associated with HR+ERBB2+ and HR-ERBB2+ subtypes. Tumors occurring in pathogenic variant carriers were of higher grade. For most genes and subtypes, a decline in ORs was observed with increasing age. Together, the 9 genes were associated with 27.3% of all triple-negative tumors in women 40 years or younger.

CONCLUSIONS AND RELEVANCE

The results of this case-control study suggest that variants in the 9 BC risk genes differ substantially in their associated pathology but are generally associated with triple-negative and/or high-grade disease. Knowing the age and tumor subtype distributions associated with individual BC genes can potentially aid guidelines for gene panel testing, risk prediction, and variant classification and guide targeted screening strategies.

Rare protein-coding variants implicate genes involved in risk of suicide death

Identification of genetic factors leading to increased risk of suicide death is critical to combat rising suicide rates, however, only a fraction of the genetic variation influencing risk has been accounted for. To address this limitation, we conducted the first comprehensive analysis of rare genetic variation in suicide death leveraging the largest suicide death biobank, the Utah Suicide Genetic Risk Study (USGRS). We conducted a single-variant association analysis of rare (minor allele frequency <1%) putatively functional single-nucleotide polymorphisms (SNPs) present on the Illumina PsychArray genotyping array in 2,672 USGRS suicide deaths of non-Finnish European (NFE) ancestry and 51,583 NFE controls from the Genome Aggregation Database. Secondary analyses used an independent control sample of 21,324 NFE controls from the Psychiatric Genomics Consortium. Five novel, high-impact, rare SNPs were identified with significant associations with suicide death (SNAPC1, rs75418419; TNKS1BP1, rs143883793; ADGRF5, rs149197213; PER1, rs145053802; and ESS2, rs62223875). 119 suicide decedents carried these high-impact SNPs. Both PER1 and SNAPC1 have other supporting gene-level evidence of suicide risk, and psychiatric associations exist for PER1 (bipolar disorder, schizophrenia), and for TNKS1BP1 and ESS2 (schizophrenia). Three of the genes (PER1, TNKS1BP1, and ADGRF5), together with additional genes implicated by genome-wide association studies on suicidal behavior, showed significant enrichment in immune system, homeostatic and signal transduction processes. No specific diagnostic phenotypes were associated with the subset of suicide deaths with the identified rare variants. These findings suggest an important role for rare variants in suicide risk and implicate genes and gene pathways for targeted replication.

Browser-based Data Annotation, Active Learning, and Real-Time Distribution of Artificial Intelligence Models: From Tumor Tissue Microarrays to COVID-19 Radiology

BACKGROUND

Artificial intelligence (AI) is fast becoming the tool of choice for scalable and reliable analysis of medical images. However, constraints in sharing medical data outside the institutional or geographical space, as well as difficulties in getting AI models and modeling platforms to work across different environments, have led to a "reproducibility crisis" in digital medicine.

METHODS

This study details the implementation of a web platform that can be used to mitigate these challenges by orchestrating a digital pathology AI pipeline, from raw data to model inference, entirely on the local machine. We discuss how this federated platform provides governed access to data by consuming the Application Program Interfaces exposed by cloud storage services, allows the addition of user-defined annotations, facilitates active learning for training models iteratively, and provides model inference computed directly in the web browser at practically zero cost. The latter is of particular relevance to clinical workflows because the code, including the AI model, travels to the user's data, which stays private to the governance domain where it was acquired.

RESULTS

We demonstrate that the web browser can be a means of democratizing AI and advancing data socialization in medical imaging backed by consumer-facing cloud infrastructure such as Box.com. As a case study, we test the accompanying platform end-to-end on a large dataset of digital breast cancer tissue microarray core images. We also showcase how it can be applied in contexts separate from digital pathology by applying it to a radiology dataset containing COVID-19 computed tomography images.

CONCLUSIONS

The platform described in this report resolves the challenges to the findable, accessible, interoperable, reusable stewardship of data and AI models by integrating with cloud storage to maintain user-centric governance over the data. It also enables distributed, federated computation for AI inference over those data and proves the viability of client-side AI in medical imaging.

AVAILABILITY

The open-source application is publicly available at , with a short video demonstration at .

Abstract 263: Novel transcriptomic framework captures prognostic and predictive markers in CLL

Chronic lymphocytic leukemia (CLL) is a hematological malignancy where malignant B-cells arise in the bone marrow and circulate in the blood. The disease course in CLL is heterogeneous. Some patients require immediate, aggressive treatment, while others do not require treatment for many years. There are a variety of prognostic and predictive markers for CLL, and research for new markers is ongoing. Our goal is to define multiple, informative, transcriptome variables for use in epidemiology and clinical studies, providing an agnostic framework to represent the many sources of heterogeneity that exist across CLL patients. Data cleaning and normalization were designed with parametric modeling in mind. After pre-processing, principal component analysis (PCA) was used to define orthogonal, quantitative components, referred to as spectra, to parameterize the transcriptome space. Each patient receives a set of quantitative values; one for each of the variables. Each of these variables is a multi-gene expression biomarker. Bulk RNA-sequencing was performed on treatment naïve CD19+/CD5+ sorted B-cells on the HiSeq4000 or NovaSeq platforms. Transcript-based read counts were generated from FASTQ files using Salmon. High-quality genes were selected, read counts internally normalized, and corrected for batch effects using ComBat. Pre-processing resulted in a final set of 8,895 quality-controlled, autosomal, protein-coding genes. PCA resulted in 13 spectra representing 55.7% of the total variance across all 202 CLL transcriptomes. To assess how well our novel CLL spectra framework captured known molecular marks for prognosis, we investigated associations between spectra with IGHV mutational status (determined using MiXCR) and CD49d expression. In multivariable analysis, the model including all spectra significantly predicted IGHV mutational status (p&lt;2.0x10-20). A highly significant model was also found for quantitative CD49d expression (which was not a gene in the framework, p&lt;7.4x10-32). Using matched germline DNA and tumor DNA sequencing we identified somatic CNVs and mutations using GATK and Strelka. We then assessed how well our novel framework captured these DNA characteristics. Significant spectra-based models were found that predicted common CLL CNVs (11q23 del, 13q14 del, 17p13 del and trisomy 12) as well as a complex karyotype (&gt;2 large CNVs) phenotype. Presence of ATM, NOTCH1, and TP53 protein-altering mutations was also independently captured in the framework. An agnostic framework of quantitative spectra (transcriptome variables) was able to identify known expression-based and genomic tumor features. This indicates that spectra provide a flexible intrinsic framework to represent tumor characteristics. Spectra are independent and designed to be used as predictor variables, alongside other covariates, in outcome modeling and have the potential to improve both epidemiology and clinical studies.

Citation Format: Julie Ellen Feusier, Rosalie G. Waller, Michael J. Madsen, Brian Avery, Nicola J. Camp. Novel transcriptomic framework captures prognostic and predictive markers in CLL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 263.

Genetic determinants of multiple myeloma risk within the Wnt/beta-catenin signaling pathway

BACKGROUND

Aberrant Wnt/beta-catenin pathway activation is implicated in Multiple Myeloma (MM) development, but little is known if genetic variants within this pathway contribute to MM susceptibility.

METHODS

We performed a discovery candidate pathway analysis in 269 non-Hispanic white MM cases and 272 controls focusing on 171 variants selected from 26 core genes within the Wnt/beta-catenin pathway. Significant candidate variants (P < 0.05) were selected for validation in internal and external non-Hispanic white populations totaling 818 cases and 1209 controls. We also examined significant variants in non-Hispanic black and Hispanic case/control study populations to identify potential differences by race/ethnicity. Possible biological functions of candidate variants were predicted in silico.

RESULTS

Seven variants were significantly associated with MM risk in non-Hispanic whites in the discovery population, of which LRP6:rs7966410 (OR: 0.57; 95 % CI: 0.38-0.88; P = 9.90 × 10^-3) and LRP6:rs7956971 (OR: 0.64; 95 % CI: 0.44-0.95; P = 0.027) remained significant in the internal and external populations. CSNK1D:rs9901910 replicated among all three racial/ethnic groups, with 2-6 fold increased risk of MM (OR: 2.40; 95 % CI: 1.67-3.45; P = 2.43 × 10^-6 - non-Hispanic white; OR: 6.42; 95 % CI: 2.47-16.7; P = 3.14 × 10^-4 - non-Hispanic black; OR: 4.31; 95 % CI: 1.83-10.1; P = 8.10 × 10^-4 - Hispanic). BTRC:rs7916830 was associated with a significant 37 % and 24 % reduced risk of MM in the non-Hispanic white (95 % CI: 0.49-0.82; P = 5.60 × 10^-4) and non-Hispanic Black (95 % CI: 0.60-0.97; P = 0.028) population, respectively. In silico tools predicted that these loci altered function through via gene regulation.

CONCLUSION

We identified several variants within the Wnt/beta-catenin pathway associated with MM susceptibility. Findings of this study highlight the potential genetic role of Wnt/beta-catenin signaling in MM etiology among a diverse patient population.

Genome-wide homozygosity and risk of four non-Hodgkin lymphoma subtypes

AIM

Recessive genetic variation is thought to play a role in non-Hodgkin lymphoma (NHL) etiology. Runs of homozygosity (ROH), defined based on long, continuous segments of homozygous SNPs, can be used to estimate both measured and unmeasured recessive genetic variation. We sought to examine genome-wide homozygosity and NHL risk.

METHODS

We used data from eight genome-wide association studies of four common NHL subtypes: 3061 chronic lymphocytic leukemia (CLL), 3814 diffuse large B-cell lymphoma (DLBCL), 2784 follicular lymphoma (FL), and 808 marginal zone lymphoma (MZL) cases, as well as 9374 controls. We examined the effect of homozygous variation on risk by: (1) estimating the fraction of the autosome containing runs of homozygosity (FROH); (2) calculating an inbreeding coefficient derived from the correlation among uniting gametes (F3); and (3) examining specific autosomal regions containing ROH. For each, we calculated beta coefficients and standard errors using logistic regression and combined estimates across studies using random-effects meta-analysis.

RESULTS

We discovered positive associations between FROH and CLL (β = 21.1, SE = 4.41, P = 1.6 × 10-6) and FL (β = 11.4, SE = 5.82, P = 0.02) but not DLBCL (P = 1.0) or MZL (P = 0.91). For F3, we observed an association with CLL (β = 27.5, SE = 6.51, P = 2.4 × 10-5). We did not find evidence of associations with specific ROH, suggesting that the associations observed with FROH and F3 for CLL and FL risk were not driven by a single region of homozygosity.

CONCLUSION

Our findings support the role of recessive genetic variation in the etiology of CLL and FL; additional research is needed to identify the specific loci associated with NHL risk.

Shared genomic segment analysis in a large high-risk chronic lymphocytic leukemia pedigree implicates CXCR4 in inherited risk

AIM

Chronic lymphocytic leukemia (CLL) has been shown to cluster in families. First-degree relatives of individuals with CLL have an ~8 fold increased risk of developing the malignancy. Strong heritability suggests pedigree studies will have good power to localize pathogenic genes. However, CLL is relatively rare and heterogeneous, complicating ascertainment and analyses. Our goal was to identify CLL risk loci using unique resources available in Utah and methods to address intra-familial heterogeneity.

METHODS

We identified a six-generation high-risk CLL pedigree using the Utah Population Database. This pedigree contains 24 CLL cases connected by a common ancestor. We ascertained and genotyped eight CLL cases using a high-density SNP array, and then performed shared genomic segment (SGS) analysis - a method designed for extended high-risk pedigrees that accounts for heterogeneity.

RESULTS

We identified a genome-wide significant region (P = 1.9 × 10-7, LOD-equivalent 5.6) at 2q22.1. The 0.9 Mb region was inherited through 26 meioses and shared by seven of the eight genotyped cases. It sits within a ~6.25 Mb locus identified in a previous linkage study of 206 small CLL families. Our narrow region intersects two genes, including CXCR4 which is highly expressed in CLL cells and implicated in maintenance and progression.

CONCLUSION

SGS analysis of an extended high-risk CLL pedigree identified the most significant evidence to-date for a 0.9 Mb CLL disease locus at 2q22.1, harboring CXCR4. This discovery contributes to a growing literature implicating CXCR4 in inherited risk to CLL. Investigation of the segregating haplotype in the pedigree will be valuable for elucidating risk variant(s).

Sequencing at lymphoid neoplasm susceptibility loci maps six myeloma risk genes

Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10-6), EOMES (P = 6.0 × 10-6) and BTNL2 (P = 2.1 × 10-3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.

Duo Shared Genomic Segment analysis identifies a genome-wide significant risk locus at 18q21.33 in myeloma pedigrees

AIM

High-risk pedigrees (HRPs) are a powerful design to map highly penetrant risk genes. We previously described Shared Genomic Segment (SGS) analysis, a mapping method for single large extended pedigrees that also addresses genetic heterogeneity inherent in complex diseases. SGS identifies shared segregating chromosomal regions that may inherit in only a subset of cases. However, single large pedigrees that are individually powerful (at least 15 meioses between studied cases) are scarce. Here, we expand the SGS strategy to incorporate evidence from two extended HRPs by identifying the same segregating risk locus in both pedigrees and allowing for some relaxation in the size of each HRP.

METHODS

Duo-SGS is a procedure to combine single-pedigree SGS evidence. It implements statistically rigorous duo-pedigree thresholding to determine genome-wide significance levels that account for optimization across pedigree pairs. Single-pedigree SGS identifies optimal segments shared by case subsets at each locus across the genome, with nominal significance assessed empirically. Duo-SGS combines the statistical evidence for SGS segments at the same genomic location in two pedigrees using Fisher's method. One pedigree is paired with all others and the best duo-SGS evidence at each locus across the genome is established. Genome-wide significance thresholds are determined through distribution-fitting and the Theory of Large Deviations. We applied the duoSGS strategy to eleven extended, myeloma HRPs.

RESULTS

We identified one genome-wide significant region at 18q21.33 (0.85 Mb, P = 7.3 × 10-9) which contains one gene, CDH20. Thirteen regions were genome-wide suggestive: 1q42.2, 2p16.1, 3p25.2, 5q21.3, 5q31.1, 6q16.1, 6q26, 7q11.23, 12q24.31, 13q13.3, 18p11.22, 18q22.3 and 19p13.12.

CONCLUSION

Our results provide novel risk loci with segregating evidence from multiple HRPs and offer compelling targets and specific segment carriers to focus a future search for functional variants involved in inherited risk formyeloma.

Natural history of monoclonal B-cell lymphocytosis among relatives in CLL families

Chronic lymphocytic lymphoma (CLL) has one of the highest familial risks among cancers. Monoclonal B-cell lymphocytosis (MBL), the precursor to CLL, has a higher prevalence (13%-18%) in families with 2 or more members with CLL compared with the general population (5%-12%). Although, the rate of progression to CLL for high-count MBLs (clonal B-cell count ≥500/µL) is ∼1% to 5%/y, no low-count MBLs have been reported to progress to date. We report the incidence and natural history of MBL in relatives from CLL families. In 310 CLL families, we screened 1045 relatives for MBL using highly sensitive flow cytometry and prospectively followed 449 of them. MBL incidence was directly age- and sex-adjusted to the 2010 US population. CLL cumulative incidence was estimated using Kaplan-Meier survival curves. At baseline, the prevalence of MBL was 22% (235/1045 relatives). After a median follow-up of 8.1 years among 449 relatives, 12 individuals progressed to CLL with a 5-year cumulative incidence of 1.8%. When considering just the 139 relatives with low-count MBL, the 5-year cumulative incidence increased to 5.7%. Finally, 264 had no MBL at baseline, of whom 60 individuals subsequently developed MBL (2 high-count and 58 low-count MBLs) with an age- and sex-adjusted incidence of 3.5% after a median of 6 years of follow-up. In a screening cohort of relatives from CLL families, we reported progression from normal-count to low-count MBL to high-count MBL to CLL, demonstrating that low-count MBL precedes progression to CLL. We estimated a 1.1% annual rate of progression from low-count MBL, which is in excess of that in the general population.

Breast Cancer Risk Factors and Survival by Tumor Subtype: Pooled Analyses from the Breast Cancer Association Consortium

BACKGROUND

It is not known whether modifiable lifestyle factors that predict survival after invasive breast cancer differ by subtype.

METHODS

We analyzed data for 121,435 women diagnosed with breast cancer from 67 studies in the Breast Cancer Association Consortium with 16,890 deaths (8,554 breast cancer specific) over 10 years. Cox regression was used to estimate associations between risk factors and 10-year all-cause mortality and breast cancer-specific mortality overall, by estrogen receptor (ER) status, and by intrinsic-like subtype.

RESULTS

There was no evidence of heterogeneous associations between risk factors and mortality by subtype (P adj > 0.30). The strongest associations were between all-cause mortality and BMI ≥30 versus 18.5-25 kg/m2 [HR (95% confidence interval (CI), 1.19 (1.06-1.34)]; current versus never smoking [1.37 (1.27-1.47)], high versus low physical activity [0.43 (0.21-0.86)], age ≥30 years versus <20 years at first pregnancy [0.79 (0.72-0.86)]; >0-<5 years versus ≥10 years since last full-term birth [1.31 (1.11-1.55)]; ever versus never use of oral contraceptives [0.91 (0.87-0.96)]; ever versus never use of menopausal hormone therapy, including current estrogen-progestin therapy [0.61 (0.54-0.69)]. Similar associations with breast cancer mortality were weaker; for example, 1.11 (1.02-1.21) for current versus never smoking.

CONCLUSIONS

We confirm associations between modifiable lifestyle factors and 10-year all-cause mortality. There was no strong evidence that associations differed by ER status or intrinsic-like subtype.

IMPACT

Given the large dataset and lack of evidence that associations between modifiable risk factors and 10-year mortality differed by subtype, these associations could be cautiously used in prognostication models to inform patient-centered care.

Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10^-7 either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04-1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.

Common genetic polymorphisms contribute to the association between chronic lymphocytic leukaemia and non-melanoma skin cancer

BACKGROUND

Epidemiological studies have demonstrated a positive association between chronic lymphocytic leukaemia (CLL) and non-melanoma skin cancer (NMSC). We hypothesized that shared genetic risk factors between CLL and NMSC could contribute to the association observed between these diseases.

METHODS

We examined the association between (i) established NMSC susceptibility loci and CLL risk in a meta-analysis including 3100 CLL cases and 7667 controls and (ii) established CLL loci and NMSC risk in a study of 4242 basal cell carcinoma (BCC) cases, 825 squamous cell carcinoma (SCC) cases and 12802 controls. Polygenic risk scores (PRS) for CLL, BCC and SCC were constructed using established loci. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Higher CLL-PRS was associated with increased BCC risk (OR4th-quartile-vs-1st-quartile = 1.13, 95% CI: 1.02-1.24, Ptrend = 0.009), even after removing the shared 6p25.3 locus. No association was observed with BCC-PRS and CLL risk (Ptrend = 0.68). These findings support a contributory role for CLL in BCC risk, but not for BCC in CLL risk. Increased CLL risk was observed with higher SCC-PRS (OR4th-quartile-vs-1st-quartile = 1.22, 95% CI: 1.08-1.38, Ptrend = 1.36 × 10-5), which was driven by shared genetic susceptibility at the 6p25.3 locus.

CONCLUSION

These findings highlight the role of pleiotropy regarding the pathogenesis of CLL and NMSC and shows that a single pleiotropic locus, 6p25.3, drives the observed association between genetic susceptibility to SCC and increased CLL risk. The study also provides evidence that genetic susceptibility for CLL increases BCC risk.

Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women

BACKGROUND

Genetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking.

METHODS

We used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity.

RESULTS

Protein-truncating variants in 5 genes (ATM, BRCA1, BRCA2, CHEK2, and PALB2) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (BARD1, RAD51C, RAD51D, and TP53) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in ATM and CHEK2, odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in BARD1, BRCA1, BRCA2, PALB2, RAD51C, and RAD51D, odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in ATM, CHEK2, and TP53 were associated with a risk of breast cancer overall with a P value of less than 0.001. For BRCA1, BRCA2, and TP53, missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants.

CONCLUSIONS

The results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.).

Abstract PO-01: Transcriptional dimensions provide a framework for describing tumor heterogeneity in CLL

Our goal is to define multiple informative transcriptome variables that capture sources of heterogeneity in chronic lymphocytic leukemia (CLL) cells, for flexible modeling in epidemiology and clinical studies. CD19+/CD5+ B-cells were sorted from whole blood of 227 CLL patients and RNA was sequenced on the HiSeq4000 or NovaSeq platforms as part of the ORIEN Avatar Initiative. Transcript-based read counts were generated from FASTQ files using Salmon. High-quality genes were selected and read counts internally normalized and corrected for batch effects using ComBat. Preprocessing resulted in a final set of 8,716 quality-controlled, autosomal, protein-coding genes. PCA was performed and, using a scree test, we selected 14 dimensions that represented 55.9% of the total variance across the CLL patients' transcriptomes. Fourteen quantitative, orthogonal CLL dimension variables were calculated for all 227 patients. By design, these CLL dimensions capture transcriptome variance and provide novel multi-gene expression biomarkers. We assessed whether these CLL transcriptome dimensions captured known clinically relevant molecular differences. First, we investigated associations with IGVH mutational status (determined using MiXCR). CLL dimension variables 1, 5, 6, and 8 predicted IGHV mutational status (p=4.6x10-16). Next, we investigated associations with ZAP70 and CD38 biomarkers, calculated by their expression in the RNA sequencing data using a separate pipeline and correcting for batch effects by ComBat (neither gene was in the 8,716 genes retained for PCA). CLL dimension variables 3, 5, 6, 7, and 8 significantly predicted Zap70 expression (p=1.6x10-38). CLL dimension variables 2, 3, 5, and 6 significantly predicted CD38 expression (p=3.1x10-31). Transcriptome dimension variables provide a flexible intrinsic framework to describe heterogeneity across CLL patients. We have shown that our transcriptome dimensions capture IGHV mutational status and ZAP70 and CD38 expression, all biomarkers for prognosis. Future work will include exploring the ability of the 14 dimensions to capture other known important molecular markers for CLL, including somatic deletion of 17p deletion, somatic mutational patterns, microsatellite instability, and previously described expression-based subgroups. Transcriptome dimensions are designed for utility as predictor variables, alongside other covariates, in parametric modeling, and have the potential to improve both epidemiology and clinical studies.

Citation Format: Julie E. Feusier, Rosalie G. Waller, Michael J. Madsen, Brian Avery, Nicola J. Camp. Transcriptional dimensions provide a framework for describing tumor heterogeneity in CLL [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-01.

Abstract A40: Characterization of quantitative gene-expression dimensions in myeloma tumors

Background: Clinical management and research of multiple myeloma (MM), a cancer of plasma cells, is limited by tumor heterogeneity. A standard approach to deconstruct tumor heterogeneity is to use hierarchical clustering techniques to determine mutually exclusive categorical subtypes. However, categorical subtypes may fail to capture potential important variations that cross tumor subtypes. An alternate approach is to determine orthogonal, quantitative tumor dimensions where each dimension is an independent tumor characteristic. We hypothesize that using a quantitative framework for tumor heterogeneity in MM will uncover biologically relevant components to tumors and may reflect specific molecular liabilities and therapeutic vulnerabilities. Further, these quantitative characteristics may be more homogeneous genetically and useful for germline gene mapping.

Objective: Identify orthogonal, quantitative dimensions in MM tumors using gene expression.

Data: RNA sequencing on treatment-naïve, CD138 sorted tumor cells from 768 individuals. Publicly available from the MM Research Foundation’s Clinical Outcomes on MM Genetic Profiles Assessment (CoMMpass) Interim Analysis 12a. SALMON transcripts per million adjusted expression estimates on 16,870 protein coding genes.

Analyses: Multistage singular value decomposition (SVD) to 1) select representative genes, and 2) characterize the orthogonal, quantitative tumor dimensions. Stage 1: Genes that contribute most to the initial SVD will be selected as representative genes. Stage 2: SVD on selected genes to identify quantitative gene expression tumor dimensions. Each dimension is a linear combination of the representative genes. Future work will associate the quantitative dimensions with demographic, clinical, and genetic (germline and somatic) characteristics, in addition to response to treatment using penalized linear regression modeling.

Conclusions: We present a new approach for the characterization of MM tumors using a more sophisticated quantitative framework that will facilitate more flexibility for subsequent statistical modeling. Improved measures for tumors have the potential to provide increased power for identification of association between tumor characteristics and genetic (germline or somatic) characteristics with ultimate potential for genetic counseling, insights into mechanism, risk stratification, response to treatment, and new candidates for precision therapeutics.

Citation Format: Rosalie G. Waller, Michael J. Madsen, John Gardner, Douglas Sborov, Nicola J. Camp. Characterization of quantitative gene-expression dimensions in myeloma tumors [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A40.

21-gene recurrence score testing utilization among older women from different races: A population-based study

OBJECTIVES

The influence of older age at diagnosis in combination with race/ethnicity on utilization and results of the 21-gene recurrence score (RS) assay for breast cancer (BC) patients is not fully understood. Our objectives were to evaluate the utilization of RS among older women with BC, the likelihood of a high-risk RS, and factors associated with breast cancer-specific mortality (BCSM) among older patients across different races.

MATERIALS AND METHODS

We utilized the Surveillance, Epidemiology, and Results (SEER) database with linked RS results to evaluate women with estrogen receptor-positive BC diagnosed 2004-2015. Multivariable logistic regression was used to describe the differences in utilization of RS testing and the association of high-risk RS according to patient characteristics. The Cox proportional hazards model was used to analyze factors associated with BCSM.

RESULTS

We found that 20.4% (109,244/536,555) of all women ≥18 and 14.3% (33,584/235,171) of women ≥65 underwent RS testing. Non-whites had lower odds of RS testing at younger ages whereas among women ≥65 there was no significant difference. After taking into account stage and grade, being ≥65 reduced the odds of high-risk RS in all races except American Indian/Alaskan Native. Age ≥ 65 was independently associated with increased hazard BCSM. Among women ≥65 with high-risk RS, chemotherapy was associated with lower hazard of BCSM in all races.

CONCLUSIONS

Older women are less likely to be tested for RS, but also less likely to have high-risk RS. Older women with high-risk RS, when given chemotherapy have reduced BCSM across all races.

Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.

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.

Harnessing Population Pedigree Data and Machine Learning Methods to Identify Patterns of Familial Bladder Cancer Risk

BACKGROUND

Relatives of patients with bladder cancer have been shown to be at increased risk for kidney, lung, thyroid, and cervical cancer after correcting for smoking-related behaviors that may concentrate in some families. We demonstrate a novel approach to simultaneously assess risks for multiple cancers to identify distinct multicancer configurations (multiple different cancer types that cluster in relatives) surrounding patients with familial bladder cancer.

METHODS

This study takes advantage of a unique population-level data resource, the Utah Population Database (UPDB), containing vast genealogy and statewide cancer data. Familial risk is measured using standardized incidence risk (SIR) ratios that account for sex, age, birth cohort, and person-years of the pedigree members.

RESULTS

We identify 1,023 families with a significantly higher bladder cancer rate than population controls (familial bladder cancer). Familial SIRs are then calculated across 25 cancer types, and a weighted Gower distance with K-medoids clustering is used to identify familial multicancer configurations (FMC). We found five FMCs, each exhibiting a different pattern of cancer aggregation. Of the 25 cancer types studied, kidney and prostate cancers were most commonly enriched in the familial bladder cancer clusters. Laryngeal, lung, stomach, acute lymphocytic leukemia, Hodgkin disease, soft-tissue carcinoma, esophageal, breast, lung, uterine, thyroid, and melanoma cancers were the other cancer types with increased incidence in familial bladder cancer families.

CONCLUSIONS

This study identified five familial bladder cancer FMCs showing unique risk patterns for cancers of other organs, suggesting phenotypic heterogeneity familial bladder cancer.

IMPACT

FMC configurations could permit better definitions of cancer phenotypes (subtypes or multicancer) for gene discovery and environmental risk factor studies.

Lipid Trait Variants and the Risk of Non-Hodgkin Lymphoma Subtypes: A Mendelian Randomization Study

BACKGROUND

Lipid traits have been inconsistently linked to risk of non-Hodgkin lymphoma (NHL). We examined the association of genetically predicted lipid traits with risk of diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and marginal zone lymphoma (MZL) using Mendelian randomization (MR) analysis.

METHODS

Genome-wide association study data from the InterLymph Consortium were available for 2,661 DLBCLs, 2,179 CLLs, 2,142 FLs, 824 MZLs, and 6,221 controls. SNPs associated (P < 5 × 10-8) with high-density lipoprotein (HDL, n = 164), low-density lipoprotein (LDL, n = 137), total cholesterol (TC, n = 161), and triglycerides (TG, n = 123) were used as instrumental variables (IV), explaining 14.6%, 27.7%, 16.8%, and 12.8% of phenotypic variation, respectively. Associations between each lipid trait and NHL subtype were calculated using the MR inverse variance-weighted method, estimating odds ratios (OR) per standard deviation and 95% confidence intervals (CI).

RESULTS

HDL was positively associated with DLBCL (OR = 1.14; 95% CI, 1.00-1.30) and MZL (OR = 1.09; 95% CI, 1.01-1.18), while TG was inversely associated with MZL risk (OR = 0.90; 95% CI, 0.83-0.99), all at nominal significance (P < 0.05). A positive trend was observed for HDL with FL risk (OR = 1.08; 95% CI, 0.99-1.19; P = 0.087). No associations were noteworthy after adjusting for multiple testing.

CONCLUSIONS

We did not find evidence of a clear or strong association of these lipid traits with the most common NHL subtypes. While these IVs have been previously linked to other cancers, our findings do not support any causal associations with these NHL subtypes.

IMPACT

Our results suggest that prior reported inverse associations of lipid traits are not likely to be causal and could represent reverse causality or confounding.

Family Study Designs Informed by Tumor Heterogeneity and Multi-Cancer Pleiotropies: The Power of the Utah Population Database

BACKGROUND

Previously, family-based designs and high-risk pedigrees have illustrated value for the discovery of high- and intermediate-risk germline breast cancer susceptibility genes. However, genetic heterogeneity is a major obstacle hindering progress. New strategies and analytic approaches will be necessary to make further advances. One opportunity with the potential to address heterogeneity via improved characterization of disease is the growing availability of multisource databases. Specific to advances involving family-based designs are resources that include family structure, such as the Utah Population Database (UPDB). To illustrate the broad utility and potential power of multisource databases, we describe two different novel family-based approaches to reduce heterogeneity in the UPDB.

METHODS

Our first approach focuses on using pedigree-informed breast tumor phenotypes in gene mapping. Our second approach focuses on the identification of families with similar pleiotropies. We use a novel network-inspired clustering technique to explore multi-cancer signatures for high-risk breast cancer families.

RESULTS

Our first approach identifies a genome-wide significant breast cancer locus at 2q13 [P = 1.6 × 10-8, logarithm of the odds (LOD) equivalent 6.64]. In the region, IL1A and IL1B are of particular interest, key cytokine genes involved in inflammation. Our second approach identifies five multi-cancer risk patterns. These clusters include expected coaggregations (such as breast cancer with prostate cancer, ovarian cancer, and melanoma), and also identify novel patterns, including coaggregation with uterine, thyroid, and bladder cancers.

CONCLUSIONS

Our results suggest pedigree-informed tumor phenotypes can map genes for breast cancer, and that various different cancer pleiotropies exist for high-risk breast cancer pedigrees.

IMPACT

Both methods illustrate the potential for decreasing etiologic heterogeneity that large, population-based multisource databases can provide.See all articles in this CEBP Focus section, "Modernizing Population Science."

Genetically Determined Height and Risk of Non-hodgkin Lymphoma

Although the evidence is not consistent, epidemiologic studies have suggested that taller adult height may be associated with an increased risk of some non-Hodgkin lymphoma (NHL) subtypes. Height is largely determined by genetic factors, but how these genetic factors may contribute to NHL risk is unknown. We investigated the relationship between genetic determinants of height and NHL risk using data from eight genome-wide association studies (GWAS) comprising 10,629 NHL cases, including 3,857 diffuse large B-cell lymphoma (DLBCL), 2,847 follicular lymphoma (FL), 3,100 chronic lymphocytic leukemia (CLL), and 825 marginal zone lymphoma (MZL) cases, and 9,505 controls of European ancestry. We evaluated genetically predicted height by constructing polygenic risk scores using 833 height-associated SNPs. We used logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) for association between genetically determined height and the risk of four NHL subtypes in each GWAS and then used fixed-effect meta-analysis to combine subtype results across studies. We found suggestive evidence between taller genetically determined height and increased CLL risk (OR = 1.08, 95% CI = 1.00-1.17, p = 0.049), which was slightly stronger among women (OR = 1.15, 95% CI: 1.01-1.31, p = 0.036). No significant associations were observed with DLBCL, FL, or MZL. Our findings suggest that there may be some shared genetic factors between CLL and height, but other endogenous or environmental factors may underlie reported epidemiologic height associations with other subtypes.

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.