Germline homozygosity and allelic imbalance of HLA-I are common in esophagogastric adenocarcinoma and impair the repertoire of immunogenic peptides.

BACKGROUND: The individual HLA-I genotype is associated with cancer, autoimmune diseases and infections. This study elucidates the role of germline homozygosity or allelic imbalance of HLA-I loci in esophago-gastric adenocarcinoma (EGA) and determines the resulting repertoires of potentially immunogenic peptides. METHODS: HLA genotypes and sequences of either (1) 10 relevant tumor-associated antigens (TAAs) or (2) patient-specific mutation-associated neoantigens (MANAs) were used to predict good-affinity binders using an in silico approach for MHC-binding (www.iedb.org). Imbalanced or lost expression of HLA-I-A/B/C alleles was analyzed by transcriptome sequencing. FluoroSpot assays and TCR sequencing were used to determine peptide-specific T-cell responses. RESULTS: We show that germline homozygosity of HLA-I genes is significantly enriched in EGA patients (n=80) compared with an HLA-matched reference cohort (n=7605). Whereas the overall mutational burden is similar, the repertoire of potentially immunogenic peptides derived from TAAs and MANAs was lower in homozygous patients. Promiscuity of peptides binding to different HLA-I molecules was low for most TAAs and MANAs and in silico modeling of the homozygous to a heterozygous HLA genotype revealed normalized peptide repertoires. Transcriptome sequencing showed imbalanced expression of HLA-I alleles in 75% of heterozygous patients. Out of these, 33% showed complete loss of heterozygosity, whereas 66% had altered expression of only one or two HLA-I molecules. In a FluoroSpot assay, we determined that peptide-specific T-cell responses against NY-ESO-1 are derived from multiple peptides, which often exclusively bind only one HLA-I allele. CONCLUSION: The high frequency of germline homozygosity in EGA patients suggests reduced cancer immunosurveillance leading to an increased cancer risk. Therapeutic targeting of allelic imbalance of HLA-I molecules should be considered in EGA.

Prediction of homologous recombination deficiency from Oncomine Comprehensive Assay Plus correlating with SOPHiA DDM HRD Solution.

OBJECTIVE: Poly(ADP-ribose) polymerase (PARP) inhibitors are used for targeted therapy for ovarian cancer with homologous recombination deficiency (HRD). In this study, we aimed to develop a homologous recombination deficiency prediction model to predict the genomic integrity (GI) index of the SOPHiA DDM HRD Solution from the Oncomine Comprehensive Assay (OCA) Plus. We also tried to a find cut-off value of the genomic instability metric (GIM) of the OCA Plus that correlates with the GI index of the SOPHiA DDM HRD Solution. METHODS: We included 87 cases with high-grade ovarian serous carcinoma from five tertiary referral hospitals in Republic of Korea. We developed an HRD prediction model to predict the GI index of the SOPHiA DDM HRD Solution. As predictor variables in the model, we used the HRD score, which included percent loss of heterozygosity (%LOH), percent telomeric allelic imbalance (%TAI), percent large-scale state transitions (%LST), and the genomic instability metric (GIM). To build the model, we employed a penalized logistic regression technique. RESULTS: The final model equation is -21.77 + 0.200 × GIM + 0.102 × %LOH + 0.037 × %TAI + 0.261 × %LST. To improve the performance of the prediction model, we added a borderline result category to the GI results. The accuracy of our HRD status prediction model was 0.958 for the test set. The accuracy of HRD status using GIM with a cut-off value of 16 was 0.911. CONCLUSION: The Oncomine Comprehensive Assay Plus provides a reliable biomarker for homologous recombination deficiency.

Leveraging Off-Target Reads in Panel Sequencing for Homologous Recombination Repair Deficiency Screening in Tumor.

Targeted tumor only sequencing has become a standard practice in cancer diagnostics. This study aims to develop an approach for robust copy number variant calling in tumor samples using only off-target region (OTR) reads. We also established a clinical use case for homologous recombination deficiency (HRD) score estimation (HRDest) using the sum of telomeric-allelic imbalance and large-scale state transition scores without the need for loss of heterozygosity information. A strong correlation was found between HRD score and the sum of telomeric-allelic imbalance + large-scale state transition in The Cancer Genome Atlas cohort (ρ = 0.99, P < 2.2 × 10-16) and in a clinical in-house cohort of 34 tumors (ρ = 0.9, P = 5.1 × 10-13) comparing whole-exome sequencing and targeted sequencing data. HRDest scores from 1086 clinical cases were compared with The Cancer Genome Atlas data set. There were no significant differences in HRD score distribution within the analyzed tumor types. As a control, commercially available HRD standards were also sequenced, and the HRDest scores obtained from the OTR reads were well within the HRD reference range provided by the manufacturer. In conclusion, OTR reads of tumor-only panel sequencing can be used to determine genome-wide copy number variant profiles and to approximate HRD scores.

KRAS allelic imbalance drives tumour initiation yet suppresses metastasis in colorectal cancer in vivo.

Oncogenic KRAS mutations are well-described functionally and are known to drive tumorigenesis. Recent reports describe a significant prevalence of KRAS allelic imbalances or gene dosage changes in human cancers, including loss of the wild-type allele in KRAS mutant cancers. However, the role of wild-type KRAS in tumorigenesis and therapeutic response remains elusive. We report an in vivo murine model of colorectal cancer featuring deletion of wild-type Kras in the context of oncogenic Kras. Deletion of wild-type Kras exacerbates oncogenic KRAS signalling through MAPK and thus drives tumour initiation. Absence of wild-type Kras potentiates the oncogenic effect of KRASG12D, while incidentally inducing sensitivity to inhibition of MEK1/2. Importantly, loss of the wild-type allele in aggressive models of KRASG12D-driven CRC significantly alters tumour progression, and suppresses metastasis through modulation of the immune microenvironment. This study highlights the critical role for wild-type Kras upon tumour initiation, progression and therapeutic response in Kras mutant CRC.

A pan-tissue survey of mosaic chromosomal alterations in 948 individuals.

Genetic mutations accumulate in an organism's body throughout its lifetime. While somatic single-nucleotide variants have been well characterized in the human body, the patterns and consequences of large chromosomal alterations in normal tissues remain largely unknown. Here, we present a pan-tissue survey of mosaic chromosomal alterations (mCAs) in 948 healthy individuals from the Genotype-Tissue Expression project, augmenting RNA-based allelic imbalance estimation with haplotype phasing. We found that approximately a quarter of the individuals carry a clonally-expanded mCA in at least one tissue, with incidence strongly correlated with age. The prevalence and genome-wide patterns of mCAs vary considerably across tissue types, suggesting tissue-specific mutagenic exposure and selection pressures. The mCA landscapes in normal adrenal and pituitary glands resemble those in tumors arising from these tissues, whereas the same is not true for the esophagus and skin. Together, our findings show a widespread age-dependent emergence of mCAs across normal human tissues with intricate connections to tumorigenesis.

SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty.

Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.

Genetic analysis and allele-specific expression of SMAD7 3'UTR variants in human colorectal cancer reveal a novel somatic variant exhibiting allelic imbalance.

BACKGROUND: Considering the impact of SMAD7 deregulation in colorectal cancer (CRC) progression and the significance of single nucleotide variant (SNV)-mediated disruptions of microRNA (miRNA)-dependent regulation for cancer susceptibility, our study aimed to analyze genetic variation in the SMAD7 3' untranslated region ( 3'UTR) in CRC, measure differences in allelic mRNA expression, and evaluate its interference with miRNA-mediated post-transcriptional regulation. PATIENTS AND METHODS: This study included 80 patients with different CRC stages and six human colon cancer cell lines of various histological origins. SMAD7 3'UTR was analyzed by direct sequencing, followed by the relative quantification of differential allelic expression of detected variants by allele-specific qRT-PCR. In silico tools were employed for predictions of regulatory consequences of detected variants. RESULTS: A total of four different SNVs in one cell line and nine patients were found, among which were a novel somatic point variant and three already known germline variants (rs16950113, rs1050799536, and rs1043778717). All evaluated SNVs exhibited variable extents of allelic imbalance in expression. In silico analysis predicted significant effects of SNVs on miRNA binding efficiency, with each SNV disrupting existing and creating new target sites for one or more miRNAs. CONCLUSION: Imbalance observed in the expression of SNV alleles altering miRNA binding suggests that all investigated SNVs are potential contributing factors impacting SMAD7 expression regulation in CRC that further studies should investigate.

Diagnostic and therapeutic potential of RNASET2 in Crohn's disease: Disease-risk polymorphism modulates allelic-imbalance in expression and circulating protein levels and recombinant-RNASET2 attenuates pro-inflammatory cytokine secretion.

Ribonuclease T2 gene (RNASET2) variants are associated in genome wide association studies (GWAS) with risk for several autoimmune diseases, including Crohn's disease (CD). In T cells, a functional and biological relationship exists between TNFSF15-mediated enhancement of IFN-γ production, mucosal inflammation and RNASET2. Disease risk variants are associated with decreased mRNA expression and clinical characteristics of severe CD; however, functional classifications of variants and underlying molecular mechanisms contributing to pathogenesis remain largely unknown. In this study we demonstrate that allelic imbalance of RNASET2 disease risk variant rs2149092 is associated with transcriptional and post-transcriptional mechanisms regulating transcription factor binding, promoter-transactivation and allele-specific expression. RNASET2 mRNA expression decreases in response to multiple modes of T cell activation and recovers following elimination of activator. In CD patients with severe disease necessitating surgical intervention, preoperative circulating RNASET2 protein levels were decreased compared to non-IBD subjects and rebounded post-operatively following removal of the inflamed region, with levels associated with allelic carriage. Furthermore, overexpression or treatment with recombinant RNASET2 significantly reduced IFN-γ secretion. These findings reveal that RNASET2 cis- and trans-acting variation contributed regulatory complexity and determined expression and provide a basis for linking genetic variation with CD pathobiology. These data may ultimately identify RNASET2 as an effective therapeutic target in a subset of CD patients with severe disease.

Precise identification of cancer cells from allelic imbalances in single cell transcriptomes.

A fundamental step of tumour single cell mRNA analysis is separating cancer and non-cancer cells. We show that the common approach to separation, using shifts in average expression, can lead to erroneous biological conclusions. By contrast, allelic imbalances representing copy number changes directly detect the cancer genotype and accurately separate cancer from non-cancer cells. Our findings provide a definitive approach to identifying cancer cells from single cell mRNA sequencing data.

Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms.

While many germline cancer risk variants have been identified through genome-wide association studies (GWAS), the mechanisms by which these variants operate remain largely unknown. Here we used 406 cancer ATAC-Seq samples across 23 cancer types to identify 7,262 germline allele-specific accessibility QTLs (as-aQTLs). Cancer as-aQTLs had stronger enrichment for cancer risk heritability (up to 145 fold) than any other functional annotation across seven cancer GWAS. Most cancer as-aQTLs directly altered transcription factor (TF) motifs and exhibited differential TF binding and gene expression in functional screens. To connect as-aQTLs to putative risk mechanisms, we introduced the regulome-wide associations study (RWAS). RWAS identified genetically associated accessible peaks at >70% of known breast and prostate loci and discovered new risk loci in all examined cancer types. Integrating as-aQTL discovery, motif analysis and RWAS identified candidate causal regulatory elements and their probable upstream regulators. Our work establishes cancer as-aQTLs and RWAS analysis as powerful tools to study the genetic architecture of cancer risk.

Association between germline variants and somatic mutations in colorectal cancer.

Colorectal cancer (CRC) is a heterogeneous disease with evidence of distinct tumor types that develop through different somatically altered pathways. To better understand the impact of the host genome on somatically mutated genes and pathways, we assessed associations of germline variations with somatic events via two complementary approaches. We first analyzed the association between individual germline genetic variants and the presence of non-silent somatic mutations in genes in 1375 CRC cases with genome-wide SNPs data and a tumor sequencing panel targeting 205 genes. In the second analysis, we tested if germline variants located within previously identified regions of somatic allelic imbalance were associated with overall CRC risk using summary statistics from a recent large scale GWAS (n≃125 k CRC cases and controls). The first analysis revealed that a variant (rs78963230) located within a CNA region associated with TLR3 was also associated with a non-silent mutation within gene FBXW7. In the secondary analysis, the variant rs2302274 located in CDX1/PDGFRB frequently gained/lost in colorectal tumors was associated with overall CRC risk (OR = 0.96, p = 7.50e-7). In summary, we demonstrate that an integrative analysis of somatic and germline variation can lead to new insights about CRC.

Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets.

MOTIVATION: Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected. RESULTS: We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes. AVAILABILITY AND IMPLEMENTATION: The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Allelic imbalance of HLA-B expression in human lung cells infected with coronavirus and other respiratory viruses.

The human leucocyte antigen (HLA) loci have been widely characterized to be associated with viral infectious diseases using either HLA allele frequency-based association or in silico predicted studies. However, there is less experimental evidence to link the HLA alleles with COVID-19 and other respiratory infectious diseases, particularly in the lung cells. To examine the role of HLA alleles in response to coronavirus and other respiratory viral infections in disease-relevant cells, we designed a two-stage study by integrating publicly accessible RNA-seq data sets, and performed allelic expression (AE) analysis on heterozygous HLA genotypes. We discovered an increased AE pattern accompanied with overexpression of HLA-B gene in SARS-CoV-2-infected human lung epithelial cells. Analysis of independent data sets verified the respiratory virus-induced AE of HLA-B gene in lung cells and tissues. The results were further experimentally validated in cultured lung cells infected with SARS-CoV-2. We further uncovered that the antiviral cytokine IFNß contribute to AE of the HLA-B gene in lung cells. Our analyses provide a new insight into allelic influence on the HLA expression in association with SARS-CoV-2 and other common viral infectious diseases.

STAT3-mediated allelic imbalance of novel genetic variant Rs1047643 and B-cell-specific super-enhancer in association with systemic lupus erythematosus.

Mapping of allelic imbalance (AI) at heterozygous loci has the potential to establish links between genetic risk for disease and biological function. Leveraging multi-omics data for AI analysis and functional annotation, we discovered a novel functional risk variant rs1047643 at 8p23 in association with systemic lupus erythematosus (SLE). This variant displays dynamic AI of chromatin accessibility and allelic expression on FDFT1 gene in B cells with SLE. We further found a B-cell restricted super-enhancer (SE) that physically contacts with this SNP-residing locus, an interaction that also appears specifically in B cells. Quantitative analysis of chromatin accessibility and DNA methylation profiles further demonstrated that the SE exhibits aberrant activity in B cell development with SLE. Functional studies identified that STAT3, a master factor associated with autoimmune diseases, directly regulates both the AI of risk variant and the activity of SE in cultured B cells. Our study reveals that STAT3-mediated SE activity and cis-regulatory effects of SNP rs1047643 at 8p23 locus are associated with B cell deregulation in SLE.

Interpreting coronary artery disease GWAS results: A functional genomics approach assessing biological significance.

Genome-wide association studies (GWAS) have implicated 58 loci in coronary artery disease (CAD). However, the biological basis for these associations, the relevant genes, and causative variants often remain uncertain. Since the vast majority of GWAS loci reside outside coding regions, most exert regulatory functions. Here we explore the complexity of each of these loci, using tissue specific RNA sequencing data from GTEx to identify genes that exhibit altered expression patterns in the context of GWAS-significant loci, expanding the list of candidate genes from the 75 currently annotated by GWAS to 245, with almost half of these transcripts being non-coding. Tissue specific allelic expression imbalance data, also from GTEx, allows us to uncover GWAS variants that mark functional variation in a locus, e.g., rs7528419 residing in the SORT1 locus, in liver specifically, and rs72689147 in the GUYC1A1 locus, across a variety of tissues. We consider the GWAS variant rs1412444 in the LIPA locus in more detail as an example, probing tissue and transcript specific effects of genetic variation in the region. By evaluating linkage disequilibrium (LD) between tissue specific eQTLs, we reveal evidence for multiple functional variants within loci. We identify 3 variants (rs1412444, rs1051338, rs2250781) that when considered together, each improve the ability to account for LIPA gene expression, suggesting multiple interacting factors. These results refine the assignment of 58 GWAS loci to likely causative variants in a handful of cases and for the remainder help to re-prioritize associated genes and RNA isoforms, suggesting that ncRNAs maybe a relevant transcript in almost half of CAD GWAS results. Our findings support a multi-factorial system where a single variant can influence multiple genes and each genes is regulated by multiple variants.

mRNA analysis revealed a novel pathogenic EIF2S3 variant causing MEHMO syndrome.

EIF2S3 pathogenic variants have been shown to cause MEHMO syndrome - a rare X-linked intellectual disability syndrome. In most cases, DNA diagnostics of MEHMO syndrome is performed using exome sequencing. We describe two cousins with profound intellectual disability, severe microcephaly, microgenitalism, hypoglycemia, epileptic seizures, and hypertrichosis, whose clinical symptoms allowed us to suspect MEHMO syndrome. To confirm this diagnosis, we designed an mRNA analysis for the EIF2S3 gene. It is a cost-effective method to detect coding sequence variants in multi-exonic genes, as well as splicing defects and allelic imbalance. Our mRNA sequence analysis revealed a novel EIF2S3 variant c.820C>G in both cousins. We also found the same variant in female family members in the heterozygous state. To investigate the pathogenicity of the c.820C>G variant, we performed expression analysis, which showed that the DDIT3 transcript level was significantly increased in the patient relative to the controls. We, thus, demonstrate that mRNA analysis is an efficient tool for performing genetic testing in patients with distinct phenotypic features.

Impact of homologous recombination status and responses with veliparib combined with first-line chemotherapy in ovarian cancer in the Phase 3 VELIA/GOG-3005 study.

OBJECTIVE: In the Phase 3 VELIA trial (NCT02470585), PARP inhibitor (PARPi) veliparib was combined with first-line chemotherapy and continued as maintenance for patients with ovarian carcinoma enrolled regardless of chemotherapy response or biomarker status. Here, we report exploratory analyses of the impact of homologous recombination deficient (HRD) or proficient (HRP) status on progression-free survival (PFS) and objective response rates during chemotherapy. METHODS: Women with Stage III-IV ovarian carcinoma were randomized to veliparib-throughout, veliparib-combination-only, or placebo. Stratification factors included timing of surgery and germline BRCA mutation status. HRD status was dichotomized at genomic instability score 33. During combination therapy, CA-125 levels were measured at baseline and each cycle; radiographic responses were assessed every 9 weeks. RESULTS: Of 1140 patients randomized, 742 had BRCA wild type (BRCAwt) tumors (HRP, n = 373; HRD/BRCAwt, n = 329). PFS hazard ratios between veliparib-throughout versus control were similar in both BRCAwt populations (HRD/BRCAwt: 22.9 vs 19.8 months; hazard ratio 0.76; 95% confidence interval [CI] 0.53-1.09; HRP: 15.0 vs 11.5 months; hazard ratio 0.765; 95% CI 0.56-1.04). By Cycle 3, the proportion with ≥90% CA-125 reduction from baseline was higher in those receiving veliparib (pooled arms) versus control (34% vs 23%; P = 0.0004); particularly in BRCAwt and HRP subgroups. Complete response rates among patients with measurable disease after surgery were 24% with veliparib (pooled arms) and 18% with control. CONCLUSIONS: These results potentially broaden opportunities for PARPi utilization among patients who would not qualify for frontline PARPi maintenance based on other trials.

Polymorphisms in Genes Involved in Osteoblast Differentiation and Function Are Associated with Anthropometric Phenotypes in Spanish Women.

Much of the genetic variance associated with osteoporosis is still unknown. Bone mineral density (BMD) is the main predictor of osteoporosis risk, although other anthropometric phenotypes have recently gained importance. The aim of this study was to analyze the association of SNPs in genes involved in osteoblast differentiation and function with BMD, body mass index (BMI), and waist (WC) and hip (HC) circumferences. Four genes that affect osteoblast differentiation and/or function were selected from among the differentially expressed genes in fragility hip fracture (FOXC1, CTNNB1, MEF2C, and EBF2), and an association study of four single-nucleotide polymorphisms (SNPs) was conducted in a cohort of 1001 women. Possible allelic imbalance was also studied for SNP rs87939 of the CTNNB1 gene. We found significant associations of SNP rs87939 of the CTNNB1 gene with LS-sBMD, and of SNP rs1366594 of the MEF2C gene with BMI, after adjustment for confounding variables. The SNP of the MEF2C gene also showed a significant trend to association with FN-sBMD (p = 0.009). A possible allelic imbalance was ruled out as no differences for each allele were detected in CTNNB1 expression in primary osteoblasts obtained from homozygous women. In conclusion, we demonstrated that two SNPs in the MEF2C and CTNNB1 genes, both implicated in osteoblast differentiation and/or function, are associated with BMI and LS-sBMD, respectively.

Power calculator for detecting allelic imbalance using hierarchical Bayesian model.

OBJECTIVE: Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI exist, but methods are needed to estimate type I error and power for detecting AI and difference of AI between conditions. As the costs of the technology plummet, what is more important: reads or replicates? RESULTS: We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10, 20, and 30%, respectively, deviation from allelic balance in a condition with power > 80%. A minimum of 960 and 240 allele specific reads divided equally among 8 replicates is needed to detect a 20 or 30% difference in AI between conditions with comparable power. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions.