Molecular profiling of a real-world breast cancer cohort with genetically inferred ancestries reveals actionable tumor biology differences between European ancestry and African ancestry patient populations.

BACKGROUND: Endocrine-resistant HR+/HER2- breast cancer (BC) and triple-negative BC (TNBC) are of interest for molecularly informed treatment due to their aggressive natures and limited treatment profiles. Patients of African Ancestry (AA) experience higher rates of TNBC and mortality than European Ancestry (EA) patients, despite lower overall BC incidence. Here, we compare the molecular landscapes of AA and EA patients with HR+/HER2- BC and TNBC in a real-world cohort to promote equity in precision oncology by illuminating the heterogeneity of potentially druggable genomic and transcriptomic pathways. METHODS: De-identified records from patients with TNBC or HR+/HER2- BC in the Tempus Database were randomly selected (N = 5000), with most having stage IV disease. Mutations, gene expression, and transcriptional signatures were evaluated from next-generation sequencing data. Genetic ancestry was estimated from DNA-seq. Differences in mutational prevalence, gene expression, and transcriptional signatures between AA and EA were compared. EA patients were used as the reference population for log fold-changes (logFC) in expression. RESULTS: After applying inclusion criteria, 3433 samples were evaluated (n = 623 AA and n = 2810 EA). Observed patterns of dysregulated pathways demonstrated significant heterogeneity among the two groups. Notably, PIK3CA mutations were significantly lower in AA HR+/HER2- tumors (AA = 34% vs. EA = 42%, P < 0.05) and the overall cohort (AA = 28% vs. EA = 37%, P = 2.08e-05). Conversely, KMT2C mutation was significantly more frequent in AA than EA TNBC (23% vs. 12%, P < 0.05) and HR+/HER2- (24% vs. 15%, P = 3e-03) tumors. Across all subtypes and stages, over 8000 genes were differentially expressed between the two ancestral groups including RPL10 (logFC = 2.26, P = 1.70e-162), HSPA1A (logFC = - 2.73, P = 2.43e-49), ATRX (logFC = - 1.93, P = 5.89e-83), and NUTM2F (logFC = 2.28, P = 3.22e-196). Ten differentially expressed gene sets were identified among stage IV HR+/HER2- tumors, of which four were considered relevant to BC treatment and were significantly enriched in EA: ERBB2_UP.V1_UP (P = 3.95e-06), LTE2_UP.V1_UP (P = 2.90e-05), HALLMARK_FATTY_ACID_METABOLISM (P = 0.0073), and HALLMARK_ANDROGEN_RESPONSE (P = 0.0074). CONCLUSIONS: We observed significant differences in mutational spectra, gene expression, and relevant transcriptional signatures between patients with genetically determined African and European ancestries, particularly within the HR+/HER2- BC and TNBC subtypes. These findings could guide future development of treatment strategies by providing opportunities for biomarker-informed research and, ultimately, clinical decisions for precision oncology care in diverse populations.

Multiomics in primary and metastatic breast tumors from the AURORA US network finds microenvironment and epigenetic drivers of metastasis.

The AURORA US Metastasis Project was established with the goal to identify molecular features associated with metastasis. We assayed 55 females with metastatic breast cancer (51 primary cancers and 102 metastases) by RNA sequencing, tumor/germline DNA exome and low-pass whole-genome sequencing and global DNA methylation microarrays. Expression subtype changes were observed in ~30% of samples and were coincident with DNA clonality shifts, especially involving HER2. Downregulation of estrogen receptor (ER)-mediated cell-cell adhesion genes through DNA methylation mechanisms was observed in metastases. Microenvironment differences varied according to tumor subtype; the ER+/luminal subtype had lower fibroblast and endothelial content, while triple-negative breast cancer/basal metastases showed a decrease in B and T cells. In 17% of metastases, DNA hypermethylation and/or focal deletions were identified near HLA-A and were associated with reduced expression and lower immune cell infiltrates, especially in brain and liver metastases. These findings could have implications for treating individuals with metastatic breast cancer with immune- and HER2-targeting therapies.

Novel genetic variants associated with mortality after unrelated donor allogeneic hematopoietic cell transplantation.

BACKGROUND: Identification of non-human leukocyte antigen (HLA) genetic risk factors could improve survival after allogeneic blood or marrow transplant (BMT) through matching at additional loci or individualizing risk prediction. We hypothesized that non-HLA loci contributed significantly to 1-year overall survival (OS), disease related mortality (DRM) or transplant related mortality (TRM) after unrelated donor (URD)BMT. METHODS: We performed a genome-wide association study (GWAS) in 2,887 acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) and acute lymphoblastic leukemia (ALL) patients and their ≥8/8 HLA-matched URDs comprising two independent cohorts treated from 2000-2011. FINDINGS: Using meta-analyses of both cohorts, genome-wide significant associations (p < 5 × 10-8) were identified in: recipient genomes with OS at MBNL1 (rs9990017, HR = 1.4, 95% CI 1.24-1.56, p = 3.3 × 10-8) and donor-recipient genotype mismatch with OS at LINC02774 (rs10927108, HR = 1.34, 95% CI 1.21-1.48, p = 2.0 × 10-8); donor genomes with DRM at PCNX4 (rs79076914, HR = 1.7, 95% CI 1.41-2.05, p = 3.15 × 10-8), LINC01194 (rs79498125, HR = 1.86, 95% CI 1.49-2.31, p = 2.84 × 10-8), ARID5B (rs2167710, HR = 1.5, 95% CI 1.31-1.73, p = 6.9 × 10-9) and CT49 (rs32250, HR = 1.44, 95% CI1.26-1.64, p = 2.6 × 10-8); recipient genomes at PILRB with TRM (rs141591562, HR = 2.33, 95% CI 1.74-3.12, p = 1.26 × 10-8) and donor-recipient genotype mismatch between EPGN and MTHF2DL with TRM (rs75868097, HR = 2.66, 95% CI 1.92-3.58, p = 4.6 × 10-9). Results publicly available at https://fuma.ctglab.nl/browse. INTERPRETATION: These data provide the first evidence that non-HLA common genetic variation at novel loci with biochemical function significantly impacts 1-year URD-BMT survival. Our findings have implications for donor selection, could guide treatment strategies and provide individualized risk prediction after future validation and functional studies. FUNDING: This project was funded by grants from the National Institutes of Health, USA.

Genome-Wide Association Analyses Identify Variants in IRF4 Associated With Acute Myeloid Leukemia and Myelodysplastic Syndrome Susceptibility.

The role of common genetic variation in susceptibility to acute myeloid leukemia (AML), and myelodysplastic syndrome (MDS), a group of rare clonal hematologic disorders characterized by dysplastic hematopoiesis and high mortality, remains unclear. We performed AML and MDS genome-wide association studies (GWAS) in the DISCOVeRY-BMT cohorts (2,309 cases and 2,814 controls). Association analysis based on subsets (ASSET) was used to conduct a summary statistics SNP-based analysis of MDS and AML subtypes. For each AML and MDS case and control we used PrediXcan to estimate the component of gene expression determined by their genetic profile and correlate this imputed gene expression level with risk of developing disease in a transcriptome-wide association study (TWAS). ASSET identified an increased risk for de novo AML and MDS (OR = 1.38, 95% CI, 1.26-1.51, Pmeta = 2.8 × 10-12) in patients carrying the T allele at s12203592 in Interferon Regulatory Factor 4 (IRF4), a transcription factor which regulates myeloid and lymphoid hematopoietic differentiation. Our TWAS analyses showed increased IRF4 gene expression is associated with increased risk of de novo AML and MDS (OR = 3.90, 95% CI, 2.36-6.44, Pmeta = 1.0 × 10-7). The identification of IRF4 by both GWAS and TWAS contributes valuable insight on the role of genetic variation in AML and MDS susceptibility.

Prognostic impact of pre-transplant chromosomal aberrations in peripheral blood of patients undergoing unrelated donor hematopoietic cell transplant for acute myeloid leukemia.

To improve risk stratification and treatment decisions for patients with acute myeloid leukemia (AML) undergoing hematopoietic cell transplantation (HCT). We used SNP-array data from the DISCOVeRY-BMT study to detect chromosomal aberrations in pre-HCT peripheral blood (collected 2-4 weeks before the administration of conditioning regimen) from 1974 AML patients who received HCT between 2000 and 2011. All aberrations detected in ≥ 10 patients were tested for their association with overall survival (OS), separately by remission status, using the Kaplan-Meier estimator. Cox regression models were used for multivariable analyses. Follow-up was through January 2019. We identified 701 unique chromosomal aberrations in 285 patients (7% of 1438 in complete remission (CR) and 36% of 536 not in CR). Copy-neutral loss-of-heterozygosity (CNLOH) in chr17p in CR patients (3-year OS = 20% vs. 50%, with and without chr17p CNLOH, p = 0.0002), and chr13q in patients not in CR (3-year OS = 4% vs. 26%, with and without chr13q CNLOH, p < 0.0001) are risk factors for poor survival. Models adjusted for clinical factors showed approximately three-fold excess risk of post-HCT mortality with chr17p CNLOH in CR patients (hazard ratio, HR = 3.39, 95% confidence interval CI 1.74-6.60, p = 0.0003), or chr13q CNLOH in patients not in CR (HR = 2.68, 95% CI 1.75-4.09, p < 0.0001). The observed mortality was mostly driven by post-HCT relapse (HR = 2.47, 95% CI 1.01-6.02, p = 0.047 for chr17p CNLOH in CR patients, and HR = 2.58, 95% CI 1.63-4.08, p < 0.0001 for chr13q CNLOH in patients not in CR. Pre-transplant CNLOH in chr13q or chr17p predicts risk of poor outcomes after unrelated donor HCT in AML patients. A large prospective study is warranted to validate the results and evaluate novel strategies to improve survival in those patients.

High dimensional model representation of log likelihood ratio: binary classification with SNP data.

BACKGROUND: Developing binary classification rules based on SNP observations has been a major challenge for many modern bioinformatics applications, e.g., predicting risk of future disease events in complex conditions such as cancer. Small-sample, high-dimensional nature of SNP data, weak effect of each SNP on the outcome, and highly non-linear SNP interactions are several key factors complicating the analysis. Additionally, SNPs take a finite number of values which may be best understood as ordinal or categorical variables, but are treated as continuous ones by many algorithms. METHODS: We use the theory of high dimensional model representation (HDMR) to build appropriate low dimensional glass-box models, allowing us to account for the effects of feature interactions. We compute the second order HDMR expansion of the log-likelihood ratio to account for the effects of single SNPs and their pairwise interactions. We propose a regression based approach, called linear approximation for block second order HDMR expansion of categorical observations (LABS-HDMR-CO), to approximate the HDMR coefficients. We show how HDMR can be used to detect pairwise SNP interactions, and propose the fixed pattern test (FPT) to identify statistically significant pairwise interactions. RESULTS: We apply LABS-HDMR-CO and FPT to synthetically generated HAPGEN2 data as well as to two GWAS cancer datasets. In these examples LABS-HDMR-CO enjoys superior accuracy compared with several algorithms used for SNP classification, while also taking pairwise interactions into account. FPT declares very few significant interactions in the small sample GWAS datasets when bounding false discovery rate (FDR) by 5%, due to the large number of tests performed. On the other hand, LABS-HDMR-CO utilizes a large number of SNP pairs to improve its prediction accuracy. In the larger HAPGEN2 dataset FTP declares a larger portion of SNP pairs used by LABS-HDMR-CO as significant. CONCLUSION: LABS-HDMR-CO and FPT are interesting methods to design prediction rules and detect pairwise feature interactions for SNP data. Reliably detecting pairwise SNP interactions and taking advantage of potential interactions to improve prediction accuracy are two different objectives addressed by these methods. While the large number of potential SNP interactions may result in low power of detection, potentially interacting SNP pairs, of which many might be false alarms, can still be used to improve prediction accuracy.

Multiple functional variants in the IL1RL1 region are pretransplant markers for risk of GVHD and infection deaths.

Graft-versus-host disease (GVHD) and infections are the 2 main causes of death without relapse after allogeneic hematopoietic cell transplantation (HCT). Elevated soluble serum simulation-2 (sST2), the product of IL1RL1 in plasma/serum post-HCT, is a validated GVHD biomarker. Hundreds of SNPs at 2q12.1 have been shown to be strongly associated with sST2 concentrations in healthy populations. We therefore hypothesized that the donor genetic variants in IL1RL1 correlate with sST2 protein levels associated with patient survival outcomes after HCT. We used DISCOVeRY-BMT (Determining the Influence of Susceptibility Conveying Variants Related to 1-Year Mortality after Blood and Marrow Transplantation), a genomic study of >3000 donor-recipient pairs, to inform our hypothesis. We first measured pre-HCT plasma/serum sST2 levels in a subset of DISCOVeRY-BMT donors (n = 757) and tested the association of donor sST2 levels with donor single nucleotide polymorphisms (SNPs) in the 2q12.1 region. Donor SNPs associated with sST2 levels were then tested for association with recipient death caused by acute GVHD (aGVHD)-, infection-, and transplant-related mortality in cohorts 1 and 2. Meta-analyses of cohorts 1 and 2 were performed using fixed-effects inverse variance weighting, and P values were corrected for multiple comparisons. Donor risk alleles in rs22441131 (P meta = .00026) and rs2310241 (P meta = .00033) increased the cumulative incidence of aGVHD death up to fourfold and were associated with high sST2 levels. Donor risk alleles at rs4851601 (P meta = 9.7 × 10-7), rs13019803 (P meta = 8.9 × 10-6), and rs13015714 (P meta = 5.3 × 10-4) increased cumulative incidence of infection death to almost sevenfold and were associated with low sST2 levels. These functional variants are biomarkers of infection or aGVHD death and could facilitate donor selection, prophylaxis, and a conditioning regimen to reduce post-HCT mortality.

gwasurvivr: an R package for genome-wide survival analysis.

SUMMARY: To address the limited software options for performing survival analyses with millions of SNPs, we developed gwasurvivr, an R/Bioconductor package with a simple interface for conducting genome-wide survival analyses using VCF (outputted from Michigan or Sanger imputation servers), IMPUTE2 or PLINK files. To decrease the number of iterations needed for convergence when optimizing the parameter estimates in the Cox model, we modified the R package survival; covariates in the model are first fit without the SNP, and those parameter estimates are used as initial points. We benchmarked gwasurvivr with other software capable of conducting genome-wide survival analysis (genipe, SurvivalGWAS_SV and GWASTools). gwasurvivr is significantly faster and shows better scalability as sample size, number of SNPs and number of covariates increases. AVAILABILITY AND IMPLEMENTATION: gwasurvivr, including source code, documentation and vignette are available at: http://bioconductor.org/packages/gwasurvivr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Replication and validation of genetic polymorphisms associated with survival after allogeneic blood or marrow transplant.

Multiple candidate gene-association studies of non-HLA single-nucleotide polymorphisms (SNPs) and outcomes after blood or marrow transplant (BMT) have been conducted. We identified 70 publications reporting 45 SNPs in 36 genes significantly associated with disease-related mortality, progression-free survival, transplant-related mortality, and/or overall survival after BMT. Replication and validation of these SNP associations were performed using DISCOVeRY-BMT (Determining the Influence of Susceptibility COnveying Variants Related to one-Year mortality after BMT), a well-powered genome-wide association study consisting of 2 cohorts, totaling 2888 BMT recipients with acute myeloid leukemia, acute lymphoblastic leukemia, or myelodysplastic syndrome, and their HLA-matched unrelated donors, reported to the Center for International Blood and Marrow Transplant Research. Gene-based tests were used to assess the aggregate effect of SNPs on outcome. None of the previously reported significant SNPs replicated at P < .05 in DISCOVeRY-BMT. Validation analyses showed association with one previously reported donor SNP at P < .05 and survival; more associations would be anticipated by chance alone. No gene-based tests were significant at P < .05. Functional annotation with publicly available data shows these candidate SNPs most likely do not have biochemical function; only 13% of candidate SNPs correlate with gene expression or are predicted to impact transcription factor binding. Of these, half do not impact the candidate gene of interest; the other half correlate with expression of multiple genes. These findings emphasize the peril of pursing candidate approaches and the importance of adequately powered tests of unbiased genome-wide associations with BMT clinical outcomes given the ultimate goal of improving patient outcomes.

Genetic association with B-cell acute lymphoblastic leukemia in allogeneic transplant patients differs by age and sex.

The incidence and mortality rates of B-cell acute lymphoblastic leukemia (B-ALL) differ by age and sex. To determine if inherited genetic susceptibility contributes to these differences we performed 2 genome-wide association studies (GWAS) by age, sex, and subtype and subsequent meta-analyses. The GWAS included 446 B-ALL cases, and 3027 healthy unrelated blood and marrow transplant (BMT) donors as controls from the Determining the Influence of Susceptibility Conveying Variants Related to One-Year Mortality after BMT (DISCOVeRY-BMT) study. We identified 1 novel variant, rs189434316, significantly associated with odds of normal cytogenetic B-ALL (odds ratio from meta-analysis [ORmeta] = 3.7; 95% confidence interval [CI], 2.5, 6.2; P value from meta-analysis [Pmeta] = 6.0 × 10-9). The previously reported pediatric B-ALL GWAS variant, rs11980379 (IKZF1), replicated in B-ALL pediatric patients (ORmeta = 2.3; 95% CI, 1.5, 3.7; Pmeta = 1.0 × 10-9), with evidence of heterogeneity (P = .02) between males and females. Sex differences in single-nucleotide polymorphism effect were seen in those >15 years (OR = 1.7; 95% CI, 1.4, 2.2, PMales = 6.38 × 10-6/OR = 1.1; 95% CI, 0.8, 1.5; PFemales = .6) but not ≤15 years (OR = 2.3; 95% CI, 1.4, 3.8; PMales = .0007/OR = 1.9; 95% CI, 1.2, 3.2; PFemales = .007). The latter association replicated in independent pediatric B-ALL cohorts. A previously identified adolescent and young-adult onset ALL-associated variant in GATA3 is associated with B-ALL risk in those >40 years. Our findings provide more evidence of the influence of genetics on B-ALL age of onset and we have shown the first evidence that IKZF1 associations with B-ALL may be sex and age specific.