Novel breast cancer susceptibility loci under linkage peaks identified in African ancestry consortia.

BACKGROUND: Expansion of genome-wide association studies across population groups is needed to improve our understanding of shared and unique genetic contributions to breast cancer. We performed association and replication studies guided by a priori linkage findings from African ancestry (AA) relative pairs. METHODS: We performed fixed-effect inverse-variance weighted meta-analysis under three significant AA breast cancer linkage peaks (3q26-27, 12q22-23, and 16q21-22) in 9241 AA cases and 10 193 AA controls. We examined associations with overall breast cancer as well as estrogen receptor (ER)-positive and negative subtypes (193,132 SNPs). We replicated associations in the African-ancestry Breast Cancer Genetic Consortium (AABCG). RESULTS: In AA women, we identified two associations on chr12q for overall breast cancer (rs1420647, OR = 1.15, p = 2.50×10-6; rs12322371, OR = 1.14, p = 3.15×10-6), and one for ER-negative breast cancer (rs77006600, OR = 1.67, p = 3.51×10-6). On chr3, we identified two associations with ER-negative disease (rs184090918, OR = 3.70, p = 1.23×10-5; rs76959804, OR = 3.57, p = 1.77×10-5) and on chr16q we identified an association with ER-negative disease (rs34147411, OR = 1.62, p = 8.82×10-6). In the replication study, the chr3 associations were significant and effect sizes were larger (rs184090918, OR: 6.66, 95% CI: 1.43, 31.01; rs76959804, OR: 5.24, 95% CI: 1.70, 16.16). CONCLUSION: The two chr3 SNPs are upstream to open chromatin ENSR00000710716, a regulatory feature that is actively regulated in mammary tissues, providing evidence that variants in this chr3 region may have a regulatory role in our target organ. Our study provides support for breast cancer variant discovery using prioritization based on linkage evidence.

Genome- and transcriptome-wide association studies of 386,000 Asian and European-ancestry women provide new insights into breast cancer genetics.

By combining data from 160,500 individuals with breast cancer and 226,196 controls of Asian and European ancestry, we conducted genome- and transcriptome-wide association studies of breast cancer. We identified 222 genetic risk loci and 137 genes that were associated with breast cancer risk at a p < 5.0 × 10-8 and a Bonferroni-corrected p < 4.6 × 10-6, respectively. Of them, 32 loci and 15 genes showed a significantly different association between ER-positive and ER-negative breast cancer after Bonferroni correction. Significant ancestral differences in risk variant allele frequencies and their association strengths with breast cancer risk were identified. Of the significant associations identified in this study, 17 loci and 14 genes are located 1Mb away from any of the previously reported breast cancer risk variants. Pathways analyses including 221 putative risk genes identified multiple signaling pathways that may play a significant role in the development of breast cancer. Our study provides a comprehensive understanding of and new biological insights into the genetics of this common malignancy.

FAVOR: functional annotation of variants online resource and annotator for variation across the human genome.

Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizes and visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene- and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery.

A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which can carry as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG's applications to Alzheimer's disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.

A computational framework to unify orthogonal information in DNA methylation and copy number aberrations in cell-free DNA for early cancer detection.

Cell-free DNA (cfDNA) provides a convenient diagnosis avenue for noninvasive cancer detection. The current methods are focused on identifying circulating tumor DNA (ctDNA)s genomic aberrations, e.g. mutations, copy number aberrations (CNAs) or methylation changes. In this study, we report a new computational method that unifies two orthogonal pieces of information, namely methylation and CNAs, derived from whole-genome bisulfite sequencing (WGBS) data to quantify low tumor content in cfDNA. It implements a Bayes model to enrich ctDNA from WGBS data based on hypomethylation haplotypes, and subsequently, models CNAs for cancer detection. We generated WGBS data in a total of 262 samples, including high-depth (>20×, deduped high mapping quality reads) data in 76 samples with matched triplets (tumor, adjacent normal and cfDNA) and low-depth (~2.5×, deduped high mapping quality reads) data in 186 samples. We identified a total of 54 Mb regions of hypomethylation haplotypes for model building, a vast majority of which are not covered in the HumanMethylation450 arrays. We showed that our model is able to substantially enrich ctDNA reads (tens of folds), with clearly elevated CNAs that faithfully match the CNAs in the paired tumor samples. In the 19 hepatocellular carcinoma cfDNA samples, the estimated enrichment is as high as 16 fold, and in the simulation data, it can achieve over 30-fold enrichment for a ctDNA level of 0.5% with a sequencing depth of 600×. We also found that these hypomethylation regions are also shared among many cancer types, thus demonstrating the potential of our framework for pancancer early detection.

Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts.

Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (∼38× coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (∼10% and ∼1% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23 mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97 mg/L and major allele homozygotes with mean CRP of 4.11 mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.

MOTIVATION: Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk. RESULTS: With thousands of tissue- and cell-type-specific epigenomic features, we propose TVAR. This multi-label learning-based deep neural network predicts the functionality of non-coding variants in the genome based on eQTLs across 49 human tissues in the GTEx project. TVAR learns the relationships between high-dimensional epigenomics and eQTLs across tissues, taking the correlation among tissues into account to understand shared and tissue-specific eQTL effects. As a result, TVAR outputs tissue-specific annotations, with an average AUROC of 0.77 across these tissues. We evaluate TVAR's performance on four complex diseases (coronary artery disease, breast cancer, Type 2 diabetes and Schizophrenia), using TVAR's tissue-specific annotations, and observe its superior performance in predicting functional variants for both common and rare variants, compared with five existing state-of-the-art tools. We further evaluate TVAR's G-score, a scoring scheme across all tissues, on ClinVar, fine-mapped GWAS loci, Massive Parallel Reporter Assay (MPRA) validated variants and observe the consistently better performance of TVAR compared with other competing tools. AVAILABILITY AND IMPLEMENTATION: The TVAR source code and its scores on the ClinVar catalog, fine mapped GWAS Loci, high confidence eQTLs from GTEx dataset, and MPRA validated functional variants are available at https://github.com/haiyang1986/TVAR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization.

BACKGROUND: Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders with a strong genetic basis. Large scale sequencing studies have identified over one hundred ASD risk genes. Nevertheless, the vast majority of ASD risk genes remain to be discovered, as it is estimated that more than 1000 genes are likely to be involved in ASD risk. Prioritization of risk genes is an effective strategy to increase the power of identifying novel risk genes in genetics studies of ASD. As ASD risk genes are likely to exhibit distinct properties from multiple angles, we reason that integrating multiple levels of genomic data is a powerful approach to pinpoint genuine ASD risk genes. RESULTS: We present BNScore, a Bayesian model selection framework to probabilistically prioritize ASD risk genes through explicitly integrating evidence from sequencing-identified ASD genes, biological annotations, and gene functional network. We demonstrate the validity of our approach and its improved performance over existing methods by examining the resulting top candidate ASD risk genes against sets of high-confidence benchmark genes and large-scale ASD genome-wide association studies. We assess the tissue-, cell type- and development stage-specific expression properties of top prioritized genes, and find strong expression specificity in brain tissues, striatal medium spiny neurons, and fetal developmental stages. CONCLUSIONS: In summary, we show that by integrating sequencing findings, functional annotation profiles, and gene-gene functional network, our proposed BNScore provides competitive performance compared to current state-of-the-art methods in prioritizing ASD genes. Our method offers a general and flexible strategy to risk gene prioritization that can potentially be applied to other complex traits as well.

Incorporating European GWAS findings improve polygenic risk prediction accuracy of breast cancer among East Asians.

Previous genome-wide association studies (GWASs) have been largely focused on European (EUR) populations. However, polygenic risk scores (PRSs) derived from EUR have been shown to perform worse in non-EURs compared with EURs. In this study, we aim to improve PRS prediction in East Asians (EASs). We introduce a rescaled meta-analysis framework to combine both EUR (N = 122,175) and EAS (N = 30,801) GWAS summary statistics. To improve PRS prediction in EASs, we use a scaling factor to up-weight the EAS data, such that the resulting effect size estimates are more relevant to EASs. We then derive PRSs for EAS from the rescaled meta-analysis results of EAS and EUR data. Evaluated in an independent EAS validation data set, this approach increases the prediction liability-adjusted Nagelkerke's pseudo R2 by 40%, 41%, and 5%, respectively, compared with PRSs derived from an EAS GWAS only, EUR GWAS only, and conventional fixed-effects meta-analysis of EAS and EUR data. The PRS derived from the rescaled meta-analysis approach achieved an area under the receiver operating characteristic curve (AUC) of 0.6059, higher than AUC = 0.5782, 0.5809, 0.6008 for EAS, EUR, and conventional meta-analysis of EAS and EUR. We further compare PRSs constructed by single-nucleotide polymorphisms that have different linkage disequilibrium (LD) scores and minor allele frequencies (MAFs) between EUR and EAS, and observe that lower LD scores or MAF in EAS correspond to poorer PRS performance (AUC = 0.5677, 0.5530, respectively) than higher LD scores or MAF (AUC = 0.589, 0.5993, respectively). We finally build a PRS stratified by LD score differences in EUR and EAS using rescaled meta-analysis, and obtain an AUC of 0.6096, with improvement over other strategies investigated.

Evaluating breast cancer predisposition genes in women of African ancestry.

PURPOSE: Studies conducted primarily among European ancestry women reported 12 breast cancer predisposition genes. However, etiologic roles of these genes in breast cancer among African ancestry women have been less well-investigated. METHODS: We conducted a case-control study in African American women, which included 1117 breast cancer cases and 2169 cancer-free controls, and a pooled analysis, which included 7096 cases and 8040 controls of African descent. Odds ratios of associations with breast cancer risk were estimated. RESULTS: Using sequence data, we identified 61 pathogenic variants in 12 breast cancer predisposition genes, including 11 pathogenic variants not yet reported in previous studies. Pooled analysis showed statistically significant associations of breast cancer risk with pathogenic variants in BRCA1, BRCA2, PALB2, ATM, CHEK2, TP53, NF1, RAD51C, and RAD51D (all P < .05). The associations with BRCA1, PALB2, and RAD51D were stronger for estrogen receptor (ER)-negative than for ER-positive breast cancer (P heterogeneity < .05), whereas the association with CHEK2 was stronger for ER-positive than for ER-negative breast cancer. CONCLUSION: Our study confirmed previously identified associations of breast cancer risk with BRCA1, BRCA2, PALB2, ATM, TP53, NF1, and CHEK2 and provided new evidence to extend the associations of breast cancer risk with RAD51C and RAD51D, which was identified previously in European ancestry populations, to African ancestry women.

Whole genome sequence association with E-selectin levels reveals loss-of-function variant in African Americans.

E-selectin mediates the rolling of circulating leukocytes during inflammatory processes. Previous genome-wide association studies in European and Asian individuals have identified the ABO locus associated with E-selectin levels. Using Trans-Omics for Precision Medicine whole genome sequencing data in 2249 African Americans (AAs) from the Jackson Heart Study, we examined genome-wide associations with soluble E-selectin levels. In addition to replicating known signals at ABO, we identified a novel association of a common loss-of-function, missense variant in Fucosyltransferase 6 (FUT6; rs17855739,p.Glu274Lys, P = 9.02 × 10-24) with higher soluble E-selectin levels. This variant is considerably more common in populations of African ancestry compared to non-African ancestry populations. We replicated the association of FUT6 p.Glu274Lys with higher soluble E-selectin in an independent population of 748 AAs from the Women's Health Initiative and identified an additional pleiotropic association with vitamin B12 levels. Despite the broad role of both selectins and fucosyltransferases in various inflammatory, immune and cancer-related processes, we were unable to identify any additional disease associations of the FUT6 p.Glu274Lys variant in an electronic medical record-based phenome-wide association scan of over 9000 AAs.

Discovery of structural deletions in breast cancer predisposition genes using whole genome sequencing data from > 2000 women of African-ancestry.

Single germline nucleotide pathogenic variants have been identified in 12 breast cancer predisposition genes, but structural deletions in these genes remain poorly characterized. We conducted in-depth whole genome sequencing (WGS) in genomic DNA samples obtained from 1340 invasive breast cancer cases and 675 controls of African ancestry. We identified 25 deletions in the intragenic regions of ten established breast cancer predisposition genes based on a consensus call from six state-of-the-art SV callers. Overall, no significant case-control difference was found in the frequency of these deletions. However, 1.0% of cases and 0.3% of controls carried any of the eight putative protein-truncating rare deletions located in BRCA1, BRCA2, CDH1, TP53, NF1, RAD51D, RAD51C and CHEK2, resulting in an odds ratio (OR) of 3.29 (95% CI 0.74-30.16). We also identified a low-frequency deletion in NF1 associated with breast cancer risk (OR 1.93, 95% CI 1.14-3.42). In addition, we detected 56 deletions, including six putative protein-truncating deletions, in suspected breast predisposition genes. This is the first large study to systematically search for structural deletions in breast cancer predisposition genes. Many of the deletions, particularly those resulting in protein truncations, are likely to be pathogenic. Results from this study, if confirmed in future large-scale studies, could have significant implications for genetic testing for this common cancer.

A Comprehensive cis-eQTL Analysis Revealed Target Genes in Breast Cancer Susceptibility Loci Identified in Genome-wide Association Studies.

Genome-wide association studies (GWASs) have identified more than 150 common genetic loci for breast cancer risk. However, the target genes and underlying mechanisms remain largely unknown. We conducted a cis-expression quantitative trait loci (cis-eQTL) analysis using normal or tumor breast transcriptome data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project. We identified a total of 101 genes for 51 lead variants after combing the results of a meta-analysis of METABRIC and TCGA, and the results from GTEx at a Benjamini-Hochberg (BH)-adjusted p < 0.05. Using luciferase reporter assays in both estrogen-receptor positive (ER+) and negative (ER-) cell lines, we showed that alternative alleles of potential functional single-nucleotide polymorphisms (SNPs), rs11552449 (DCLRE1B), rs7257932 (SSBP4), rs3747479 (MRPS30), rs2236007 (PAX9), and rs73134739 (ATG10), could significantly change promoter activities of their target genes compared to reference alleles. Furthermore, we performed in vitro assays in breast cancer cell lines, and our results indicated that DCLRE1B, MRPS30, and ATG10 played a vital role in breast tumorigenesis via certain disruption of cell behaviors. Our findings revealed potential target genes for associations of genetic susceptibility risk loci and provided underlying mechanisms for a better understanding of the pathogenesis of breast cancer.

Integration of circulating tumor cell and neutrophil-lymphocyte ratio to identify high-risk metastatic castration-resistant prostate cancer patients.

BACKGROUND: The neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and circulating tumor cells (CTCs) have been associated with survival in castration-resistant prostate cancer (CRPC). However, no study has examined the prognostic value of NLR and PLR in the context of CTCs. METHODS: Baseline CTCs from mCRPC patients were enumerated using the CellSearch System. Baseline NLR and PLR values were calculated using the data from routine complete blood counts. The associations of CTC, NLR, and PLR values, individually and jointly, with progression-free survival (PFS) and overall survival (OS), were evaluated using Kaplan-Meier analysis, as well as univariate and multivariate Cox models. RESULTS: CTCs were detected in 37 (58.7%) of 63 mCRPC patients, and among them, 16 (25.4%) had ≥5 CTCs. The presence of CTCs was significantly associated with a 4.02-fold increased risk for progression and a 3.72-fold increased risk of death during a median follow-up of 17.6 months. OS was shorter among patients with high levels of NLR or PLR than those with low levels (log-rank P = 0.023 and 0.077). Neither NLR nor PLR was individually associated with PFS. Among the 37 patients with detectable CTCs, those with a high NLR had significantly shorter OS (log-rank P = 0.024); however, among the 26 patients without CTCs, the OS difference between high- and low-NLR groups was not statistically significant. Compared to the patients with CTCs and low NLR, those with CTCs and high levels of NLR had a 3.79-fold risk of death (P = 0.036). This association remained significant after adjusting for covariates (P = 0.031). Combination analyses of CTC and PLR did not yield significant results. CONCLUSION: Among patients with detectable CTCs, the use of NLR could further classify patients into different risk groups, suggesting a complementary role for NLR in CTC-based prognostic stratification in mCRPC.

DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data.

Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.

Large-Scale Genome-Wide Association Study of East Asians Identifies Loci Associated With Risk for Colorectal Cancer.

BACKGROUND & AIMS: Genome-wide association studies (GWASs) have associated approximately 50 loci with risk of colorectal cancer (CRC)-nearly one third of these loci were initially associated with CRC in studies conducted in East Asian populations. We conducted a GWAS of East Asians to identify CRC risk loci and evaluate the generalizability of findings from GWASs of European populations to Asian populations. METHODS: We analyzed genetic data from 22,775 patients with CRC (cases) and 47,731 individuals without cancer (controls) from 14 studies in the Asia Colorectal Cancer Consortium. First, we performed a meta-analysis of 7 GWASs (10,625 cases and 34,595 controls) and identified 46,554 promising risk variants for replication by adding them to the Multi-Ethnic Global Array (MEGA) for genotype analysis in 6445 cases and 7175 controls. These data were analyzed, along with data from an additional 5705 cases and 5961 controls genotyped using the OncoArray. We also obtained data from 57,976 cases and 67,242 controls of European descent. Variants at identified risk loci were functionally annotated and evaluated in correlation with gene expression levels. RESULTS: A meta-analyses of all samples from people of Asian descent identified 13 loci and 1 new variant at a known locus (10q24.2) associated with risk of CRC at the genome-wide significance level of P < 5 × 10-8. We did not perform experiments to replicate these associations in additional individuals of Asian ancestry. However, the lead risk variant in 6 of these loci was also significantly associated with risk of CRC in European descendants. A strong association (44%-75% increase in risk per allele) was found for 2 low-frequency variants: rs201395236 at 1q44 (minor allele frequency, 1.34%) and rs77969132 at 12p11.21 (minor allele frequency, 1.53%). For 8 of the 13 associated loci, the variants with the highest levels of significant association were located inside or near the protein-coding genes L1TD1, EFCAB2, PPP1R21, SLCO2A1, HLA-G, NOTCH4, DENND5B, and GNAS. For other intergenic loci, we provided evidence for the possible involvement of the genes ALDH7A1, PRICKLE1, KLF5, WWOX, and GLP2R. We replicated findings for 41 of 52 previously reported risk loci. CONCLUSIONS: We showed that most of the risk loci previously associated with CRC risk in individuals of European descent were also associated with CRC risk in East Asians. Furthermore, we identified 13 loci significantly associated with risk for CRC in Asians. Many of these loci contained genes that regulate the immune response, Wnt signaling to ß-catenin, prostaglandin E2 catabolism, and cell pluripotency and proliferation. Further analyses of these genes and their variants is warranted, particularly for the 8 loci for which the lead CRC risk variants were not replicated in persons of European descent.

Prognostic value of HER2 status on circulating tumor cells in advanced-stage breast cancer patients with HER2-negative tumors.

PURPOSE: Discordance between HER2 expression in tumor tissue (tHER2) and HER2 status on circulating tumor cells (cHER2) has been reported. It remains largely underexplored whether patients with tHER2-/cHER2+ can benefit from anti-HER2 targeted therapies. METHODS: cHER2 status was determined in 105 advanced-stage patients with tHER2- breast tumors. Association between cHER2 status and progression-free survival (PFS) was analyzed by univariate and multivariate Cox models and survival differences were compared by Kaplan-Meier method. RESULTS: Compared to the patients with low-risk cHER2 (cHER2+ < 2), those with high-risk cHER2 (cHER2+ ≥ 2) had shorter survival time and an increased risk for disease progression (hazard ratio [HR] 2.16, 95% confidence interval [CI] 1.20-3.88, P = 0.010). Among the patients with high-risk cHER2, those who received anti-HER2 targeted therapies had improved PFS compared with those who did not (HR 0.30, 95% CI 0.10-0.92, P = 0.035). In comparison, anti-HER2 targeted therapy did not affect PFS among those with low-risk cHER2 (HR 0.70, 95% CI 0.36-1.38, P = 0.306). Similar results were obtained after adjusting covariates. A longitudinal analysis of 67 patients with cHER2 detected during follow-ups found that those whose cHER2 status changed from high-risk at baseline to low-risk at first follow-up exhibited a significantly improved survival compared to those whose cHER2 remained high-risk (median PFS: 11.7 weeks vs. 2.0 weeks, log-rank P = 0.001). CONCLUSION: In advanced-stage breast cancer patients with tHER2- tumors, cHER2 status has the potential to guide the use of anti-HER2 targeted therapy in patients with high-risk cHER2.

De novo pattern discovery enables robust assessment of functional consequences of non-coding variants.

MOTIVATION: Given the complexity of genome regions, prioritize the functional effects of non-coding variants remains a challenge. Although several frameworks have been proposed for the evaluation of the functionality of non-coding variants, most of them used 'black boxes' methods that simplify the task as the pathogenicity/benign classification problem, which ignores the distinct regulatory mechanisms of variants and leads to less desirable performance. In this study, we developed DVAR, an unsupervised framework that leverage various biochemical and evolutionary evidence to distinguish the gene regulatory categories of variants and assess their comprehensive functional impact simultaneously. RESULTS: DVAR performed de novo pattern discovery in high-dimensional data and identified five regulatory clusters of non-coding variants. Leveraging the new insights into the multiple functional patterns, it measures both the between-class and the within-class functional implication of the variants to achieve accurate prioritization. Compared to other two-class learning methods, it showed improved performance in identification of clinically significant variants, fine-mapped GWAS variants, eQTLs and expression-modulating variants. Moreover, it has superior performance on disease causal variants verified by genome-editing (like CRISPR-Cas9), which could provide a pre-selection strategy for genome-editing technologies across the whole genome. Finally, evaluated in BioVU and UK Biobank, two large-scale DNA biobanks linked to complete electronic health records, DVAR demonstrated its effectiveness in prioritizing non-coding variants associated with medical phenotypes. AVAILABILITY AND IMPLEMENTATION: The C++ and Python source codes, the pre-computed DVAR-cluster labels and DVAR-scores across the whole genome are available at https://www.vumc.org/cgg/dvar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis.

PURPOSE: The increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease. METHODS: We estimated genetically predicted expression (GReX) of cystic fibrosis transmembrane conductance regulator (CFTR) and tested for association with clinical diagnoses in the Vanderbilt University biobank (N = 9142 persons of European descent with 71 cases of CF). The top associated EHR phenotypes were assessed in combination as a phenotype risk score (PheRS) for discriminating CF case status in an additional 2.8 million patients from Vanderbilt University Medical Center (VUMC) and 125,305 adult patients including 25,314 CF cases from MarketScan, an independent external cohort. RESULTS: GReX of CFTR was associated with EHR phenotypes consistent with CF. PheRS constructed using the EHR phenotypes and weights discovered by the genetic associations improved discriminative power for CF over the initially proposed PheRS in both VUMC and MarketScan. CONCLUSION: Our study demonstrates the power of EHRs for clinical description of CF and the benefits of using a genetics-informed weighing scheme in construction of a phenotype risk score. This research may find broad applications for phenomic studies of Mendelian disease genes.

Multi-regional sequencing reveals intratumor heterogeneity and positive selection of somatic mtDNA mutations in hepatocellular carcinoma and colorectal cancer.

Recent studies have revealed significant intratumor heterogeneity (ITH) of nuclear genome mutations and highlighted its function in tumor progression and treatment resistance. However, the ITH of somatic mitochondrial DNA (mtDNA) mutations detected in cancers remains unknown. In this study, we performed multiregional mtDNA sequencing of tumor and paratumor tissue samples from 12 hepatocellular carcinoma (HCC) and 13 colorectal cancer (CRC) patients. A substantial level of mtDNA mutations was found in paired non-HCC inflammatory tissues, suggesting that these tissues might not be mtDNA-genetically "normal." Moreover, our data indicated that the ITH of somatic mtDNA mutations was a common feature in HCC and CRC patients. In addition, we found that shared mutations which were observed in at least 2 samples in each patient exhibited a significantly higher heteroplasmic level than mutations that were private to a specific tumor region from both HCC (p = 0.039) and CRC patients (p = 0.001). The heteroplasmic level of shared mutations was positively correlated with intratumoral recurrence of mtDNA mutations. We also found that shared mutations in tumor tissues with a higher degree of pathogenicity risk exhibited a higher heteroplasmic level and intratumoral recurrence in both HCC and CRC patients. These findings suggest that some mtDNA mutations may undergo positive selection during the clonal expansion. Taken together, our analyses identified various levels of ITH of somatic mtDNA mutations in HCC and CRC patients and provided evidence supporting the positive selection working on some somatic mtDNA mutations in tumor tissues.