Tumor Antigens beyond the Human Exome.

With the advent of immunotherapeutics, a new era in the combat against cancer has begun. Particularly promising are neo-epitope-targeted therapies as the expression of neo-antigens is tumor-specific. In turn, this allows the selective targeting and killing of cancer cells whilst healthy cells remain largely unaffected. So far, many advances have been made in the development of treatment options which are tailored to the individual neo-epitope repertoire. The next big step is the achievement of efficacious "off-the-shelf" immunotherapies. For this, shared neo-epitopes propose an optimal target. Given the tremendous potential, a thorough understanding of the underlying mechanisms which lead to the formation of neo-antigens is of fundamental importance. Here, we review the various processes which result in the formation of neo-epitopes. Broadly, the origin of neo-epitopes can be categorized into three groups: canonical, noncanonical, and viral neo-epitopes. For the canonical neo-antigens that arise in direct consequence of somatic mutations, we summarize past and recent findings. Beyond that, our main focus is put on the discussion of noncanonical and viral neo-epitopes as we believe that targeting those provides an encouraging perspective to shape the future of cancer immunotherapeutics.

Exome-Based Amino Acid Optimization: A Dietary Strategy to Satisfy Human Nutritional Demands and Enhance Muscle Strength in Breast Tumor Mice Undergoing Chemotherapy.

Breast cancer patients undergoing chemotherapy often experience muscle wasting and weakness, which impact their quality of life. A potential solution lies in customizing amino acid compositions based on exome-derived formulations (ExAAs). The study hypothesized that tailoring dietary amino acids using ExAAs could enhance muscle health. Theoretical amino acid requirements were calculated from the genome's exome region, and a breast cancer mouse model undergoing paclitaxel treatment was established. The mice were supplemented with a cancer-specific nutritional formula (QJS), and the effects of QJS and amino acid-adjusted QJS (adjQJS) were compared. Both formulations improved the nutritional status without compromising tumor growth. Notably, adjQJS significantly enhanced muscle strength compared to QJS (1.51 ± 0.25 vs. 1.30 ± 0.08 fold change, p < 0.05). Transcriptome analysis revealed alterations in complement and coagulation cascades, with an observed upregulation of C3 gene expression in adjQJS. Immune regulation also changed, showing a decrease in B cells and an increase in monocytes in skeletal muscle with adjQJS. Importantly, adjQJS resulted in a notable increase in Alistipes abundance compared to QJS (10.19 ± 0.04% vs. 5.03 ± 1.75%). This study highlights the potential of ExAAs as valuable guide for optimizing amino acid composition in diets for breast cancer patients undergoing chemotherapy.

Genetic and therapeutic landscapes in cohort of pancreatic adenocarcinomas: next-generation sequencing and machine learning for full tumor exome analysis.

About 7% of all cancer deaths are caused by pancreatic cancer (PCa). PCa is known for its lowest survival rates among all oncological diseases and heterogenic molecular profile. Enormous amount of genetic changes, including somatic mutations, exceeds the limits of routine clinical genetic laboratory tests and further stagnates the development of personalized treatments. We aimed to build a mutational landscape of PCa in the Russian population based on full exome next-generation sequencing (NGS) of the limited group of patients. Applying a machine learning model on full exome individual data we received personalized recommendations for targeted treatment options for each clinical case and summarized them in the unique therapeutic landscape.

A Rare Inherited Bone Marrow Failure Syndrome Disclosed by Reanalysis of the Exome Data of a Patient Evaluated for Cytopenia and Dysmorphic Features.

BACKGROUND: Multisystemic findings of inherited bone marrow failure syndromes may cause difficulty in diagnosis. Exome sequencing (ES) helps to define the etiology of rare diseases and reanalysis offers a valuable new diagnostic approach. Herein, we present the clinical and molecular characteristics of a girl who was referred for cytopenia and frequent infections. CASE REPORT: A 5-year-old girl with cytopenia, dysmorphism, short stature, developmental delay, and myopia was referred for genetic counseling. Reanalysis of the ES data revealed a homozygous splice-site variant in the DNAJC21 (NM_001012339.3:c.983+1G>A), causing Shwachman-Diamond Syndrome (SDS). It was shown by the RNA sequencing that exon 7 was skipped, causing an 88-nucleotide deletion. CONCLUSIONS: Precise genetic diagnosis enables genetic counseling and improves patient management by avoiding inappropriate treatment and unnecessary testing. This report would contribute to the clinical and molecular understanding of this rare type of SDS caused by DNAJC21 variants and expand the phenotypic features of this condition.

ECOLE: Learning to call copy number variants on whole exome sequencing data.

Copy number variants (CNV) are shown to contribute to the etiology of several genetic disorders. Accurate detection of CNVs on whole exome sequencing (WES) data has been a long sought-after goal for use in clinics. This was not possible despite recent improvements in performance because algorithms mostly suffer from low precision and even lower recall on expert-curated gold standard call sets. Here, we present a deep learning-based somatic and germline CNV caller for WES data, named ECOLE. Based on a variant of the transformer architecture, the model learns to call CNVs per exon, using high-confidence calls made on matched WGS samples. We further train and fine-tune the model with a small set of expert calls via transfer learning. We show that ECOLE achieves high performance on human expert labelled data for the first time with 68.7% precision and 49.6% recall. This corresponds to precision and recall improvements of 18.7% and 30.8% over the next best-performing methods, respectively. We also show that the same fine-tuning strategy using tumor samples enables ECOLE to detect RT-qPCR-validated variations in bladder cancer samples without the need for a control sample. ECOLE is available at https://github.com/ciceklab/ECOLE .

SetQuence & SetOmic: Deep set transformers for whole genome and exome tumour analysis.

In oncology, Deep Learning has shown great potential to personalise tasks such as tumour type classification, based on per-patient omics data-sets. Being high dimensional, incorporation of such data in one model is a challenge, often leading to one-dimensional studies and, therefore, information loss. Instead, we first propose relying on non-fixed sets of whole genome or whole exome variant-associated sequences, which can be used for supervised learning of oncology-relevant tasks by our Set Transformer based Deep Neural Network, SetQuence. We optimise this architecture to improve its efficiency. This allows for exploration of not just coding but also non-coding variants, from large datasets. Second, we extend the model to incorporate these representations together with multiple other sources of omics data in a flexible way with SetOmic. Evaluation, using these representations, shows improved robustness and reduced information loss compared to previous approaches, while still being computationally tractable. By means of Explainable Artificial Intelligence methods, our models are able to recapitulate the biological contribution of highly attributed features in the tumours studied. This validation opens the door to novel directions in multi-faceted genome and exome wide biomarker discovery and personalised treatment among other presently clinically relevant tasks.

Diagnostic exome identifies a novel PRKG2 mutation in a proband with skeletal dysplasia.

This graphic abstract combines pedigree, dysmorphology features, radiographs, and the PRKG2 protein domain, specifically the CNB-A regulatory domain, which harbors a mutation resulting in premature protein termination.

An Exome Capture-Based RNA-Sequencing Assay for Genome-Wide Identification and Prioritization of Clinically Important Fusions in Pediatric Tumors.

This study reports the development of an exome capture-based RNA-sequencing assay to detect recurring and novel fusions in hematologic, solid, and central nervous system tumors. The assay used Twist Comprehensive Exome capture with either fresh or formalin-fixed samples and a bioinformatic platform that provides fusion detection, prioritization, and downstream curation. A minimum of 50 million uniquely mapped reads, a consensus read alignment/fusion calling approach using four callers (Arriba, FusionCatcher, STAR-Fusion, and Dragen), and custom software were used to integrate, annotate, and rank the candidate fusion calls. In an evaluation of 50 samples, the number of calls varied substantially by caller, from a mean of 24.8 with STAR-Fusion to 259.6 with FusionCatcher; only 1.1% of calls were made by all four callers. Therefore a filtering and ranking algorithm was developed based on multiple criteria, including number of supporting reads, calling consensus, genes involved, and cross-reference against databases of known cancer-associated or likely false-positive fusions. This approach was highly effective in pinpointing known clinically relevant fusions, ranking them first in 47 of 50 samples (94%). Detection of pathogenic gene fusions in three diagnostically challenging cases highlights the importance of a genome-wide and nontargeted method for fusion detection in pediatric cancer.

Ultra-rare genetic variation in relapsing polychondritis: a whole-exome sequencing study.

OBJECTIVE: Relapsing polychondritis (RP) is a systemic inflammatory disease of unknown aetiology. The objective of this study was to examine the contribution of rare genetic variations to RP. METHODS: We performed a case-control exome-wide rare variant association analysis that included 66 unrelated European American cases with RP and 2923 healthy controls (HC). Gene-level collapsing analysis was performed using Firth's logistics regression. Exploratory pathway analysis was performed using three different methods: Gene Set Enrichment Analysis, sequence kernel association test and higher criticism test. Plasma DCBLD2 levels were measured in patients with RP and HC using ELISA. RESULTS: In the collapsing analysis, RP was associated with a significantly higher burden of ultra-rare damaging variants in the DCBLD2 gene (7.6% vs 0.1%, unadjusted OR=79.8, p=2.93×10-7). Plasma DCBLD2 protein levels were significantly higher in RP than in HC (median 4.06 ng/µL vs 0.05 ng/µL, p<0.001). The pathway analysis revealed a statistically significant enrichment of genes in the tumour necrosis factor signalling pathway driven by rare damaging variants in RELB, RELA and REL using higher criticism test weighted by eigenvector centrality. CONCLUSIONS: This study identified specific rare variants in the DCBLD2 gene as a putative genetic risk factor for RP. These findings should be validated in additional patients with RP and supported by future functional experiments.

A new gene for autosomal dominant facial palsy/migraine identified in a family by whole exome sequencing.

BACKGROUND: Facial palsy manifests as unilateral or bilateral weakness and inability to move some of the facial muscles. The aetiology may be different including idiopathic, trauma, infections or brain tumours or it can be associated with chronic neurological diseases. For instance, in recurrent migraine, an increased risk of idiopathic facial palsy (often unilateral) has been observed. Migraine is a neurovascular disorder characterized by mild to severe intensity of headaches, often associated with neuro-ophthalmological symptoms. METHODS: A family is reported where five members were affected by facial palsy associated with other clinical features including migraine, diplopia, facial swelling, eye conjunctivitis following a vertical transmission. Whole exome sequencing was performed in three members (two affected and one healthy) in order to identify potential variants causative of their phenotype. RESULTS: A missense variant c.304G>A was found leading to the p.(Ala102Thr) substitution in the TRPM8 gene, previously related to migraine by genome wide association studies. This variant was classified as deleterious by several predictor tools, and the mutant residue was predicted to alter the protein structure in terms of flexibility and interactions with the surrounding residues. CONCLUSION: These findings suggest that TRPM8 could be a new causative gene further linking migraine and recurrent facial palsy.

Understanding the Impact of Population and Cancer Type on Tumor Mutation Burden Scores: A Comprehensive Whole-Exome Study in Cancer Patients From India.

PURPOSE: The purpose of this study was to understand the impact of population diversity and geographic variation on tumor mutation burden (TMB) scores across cancers and its implication on stratification of patients for immune checkpoint inhibitor (ICI) therapy. MATERIALS AND METHODS: This retrospective study used whole-exome sequencing (WES) to profile 1,233 Indian patients with cancer across 30 different cancer types and to estimate their TMB scores. A WES-based pipeline was adopted, along with an indigenously developed strategy for arriving at true somatic mutations. A robust unsupervised machine learning approach was used to understand the distribution of TMB scores across different populations and within the population. RESULTS: The results of the study showed a biphasic distribution of TMB scores in most cancers, with different threshold scores across cancer types. Patients with cancer in India had higher TMB scores compared with the Caucasian patients. We also observed that the TMB score value at 90th percentile (predicting high efficacy to ICI) was high in four different cancer types (sarcoma, ovary, head and neck, and breast) in the Indian cohort as compared with The Cancer Genome Atlas or public cohort. However, in lung and colorectal cancers, the TMB score distribution was similar between the two population cohorts. CONCLUSION: The findings of this study indicate that it is crucial to benchmark both cancer-specific and population-specific TMB distributions to establish a TMB threshold for each cancer in various populations. Additional prospective studies on much larger population across different cancers are warranted to validate this observation to become the standard of care.

Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients.

High-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a data interpretation issue. To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn's Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.

Multi-Trait Exome-Wide Association Study of Back Pain-Related Phenotypes.

Back pain (BP) is a major contributor to disability worldwide, with heritability estimated at 40-60%. However, less than half of the heritability is explained by common genetic variants identified by genome-wide association studies. More powerful methods and rare and ultra-rare variant analysis may offer additional insight. This study utilized exome sequencing data from the UK Biobank to perform a multi-trait gene-based association analysis of three BP-related phenotypes: chronic back pain, dorsalgia, and intervertebral disc disorder. We identified the SLC13A1 gene as a contributor to chronic back pain via loss-of-function (LoF) and missense variants. This gene has been previously detected in two studies. A multi-trait approach uncovered the novel FSCN3 gene and its impact on back pain through LoF variants. This gene deserves attention because it is only the second gene shown to have an effect on back pain due to LoF variants and represents a promising drug target for back pain therapy.

Integration of exome-seq and mRNA-seq using DawnRank, identified genes involved in innate immunity as drivers of breast cancer in the Indian cohort.

Genetic heterogeneity influences the prognosis and therapy of breast cancer. The cause of disease progression varies and can be addressed individually. To identify the mutations and their impact on disease progression at an individual level, we sequenced exome and transcriptome from matched normal-tumor samples. We utilised DawnRank to prioritise driver genes and identify specific mutations in Indian patients. Mutations in the C3 and HLA genes were identified as drivers of disease progression, indicating the involvement of the innate immune system. We performed immune profiling on 16 matched normal/tumor samples using CIBERSORTx. We identified CD8+ve T cells, M2 macrophages, and neutrophils to be enriched in luminal A and T cells CD4+naïve, natural killer (NK) cells activated, T follicular helper (Tfh) cells, dendritic cells activated, and neutrophils in triple-negative breast cancer (TNBC) subtypes. Weighted gene co-expression network analysis (WGCNA) revealed activation of T cell-mediated response in ER positive samples and Interleukin and Interferons in ER negative samples. WGCNA analysis also identified unique pathways for each individual, suggesting that rare mutations/expression signatures can be used to design personalised treatment.

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients.

Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we unveil cancer's evolutionary signatures tied to distinct disease outcomes, representing "favored trajectories" of acquisition of driver mutations detected in patients with similar prognosis. We present a framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) to extract such signatures from sequencing experiments generated by different technologies such as bulk and single-cell sequencing data. We apply ASCETIC to (i) single-cell data from 146 myeloid malignancy patients and bulk sequencing from 366 acute myeloid leukemia patients, (ii) multi-region sequencing from 100 early-stage lung cancer patients, (iii) exome/genome data from 10,000+ Pan-Cancer Atlas samples, and (iv) targeted sequencing from 25,000+ MSK-MET metastatic patients, revealing subtype-specific single-nucleotide variant signatures associated with distinct prognostic clusters. Validations on several datasets underscore the robustness and generalizability of the extracted signatures.

Advancing diagnosis and management of liver disease in adults through exome sequencing.

BACKGROUND: Whole-exome sequencing (WES) is an effective tool for diagnosis in patients who remain undiagnosed despite a comprehensive clinical work-up. While WES is being used increasingly in pediatrics and oncology, it remains underutilized in non-oncological adult medicine, including in patients with liver disease, in part based on the faulty premise that adults are unlikely to harbor rare genetic variants with large effect size. Here, we aim to assess the burden of rare genetic variants underlying liver disease in adults at two major tertiary referral academic medical centers. METHODS: WES analysis paired with comprehensive clinical evaluation was performed in fifty-two adult patients with liver disease of unknown etiology evaluated at two US tertiary academic health care centers. FINDINGS: Exome analysis uncovered a definitive or presumed diagnosis in 33% of patients (17/52) providing insight into their disease pathogenesis, with most of these patients (12/17) not having a known family history of liver disease. Our data shows that over two-thirds of undiagnosed liver disease patients attaining a genetic diagnosis were being evaluated for cholestasis or hepatic steatosis of unknown etiology. INTERPRETATION: This study reveals an underappreciated incidence and spectrum of genetic diseases presenting in adulthood and underscores the clinical value of incorporating exome sequencing in the evaluation and management of adults with liver disease of unknown etiology. FUNDING: S.V. is supported by the NIH/NIDDK (K08 DK113109 and R01 DK131033-01A1) and the Doris Duke Charitable Foundation Grant #2019081. This work was supported in part by NIH-funded Yale Liver Center, P30 DK34989.

Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk.

Linkage and candidate gene studies have identified several breast cancer susceptibility genes, but the overall contribution of coding variation to breast cancer is unclear. To evaluate the role of rare coding variants more comprehensively, we performed a meta-analysis across three large whole-exome sequencing datasets, containing 26,368 female cases and 217,673 female controls. Burden tests were performed for protein-truncating and rare missense variants in 15,616 and 18,601 genes, respectively. Associations between protein-truncating variants and breast cancer were identified for the following six genes at exome-wide significance (P < 2.5 × 10-6): the five known susceptibility genes ATM, BRCA1, BRCA2, CHEK2 and PALB2, together with MAP3K1. Associations were also observed for LZTR1, ATR and BARD1 with P < 1 × 10-4. Associations between predicted deleterious rare missense or protein-truncating variants and breast cancer were additionally identified for CDKN2A at exome-wide significance. The overall contribution of coding variants in genes beyond the previously known genes is estimated to be small.

Whole-Exome and Transcriptome Sequencing Expands the Genotype of Majewski Osteodysplastic Primordial Dwarfism Type II.

Microcephalic Osteodysplastic Primordial Dwarfism type II (MOPDII) represents the most common form of primordial dwarfism. MOPD clinical features include severe prenatal and postnatal growth retardation, postnatal severe microcephaly, hypotonia, and an increased risk for cerebrovascular disease and insulin resistance. Autosomal recessive biallelic loss-of-function genomic variants in the centrosomal pericentrin (PCNT) gene on chromosome 21q22 cause MOPDII. Over the past decade, exome sequencing (ES) and massive RNA sequencing have been effectively employed for both the discovery of novel disease genes and to expand the genotypes of well-known diseases. In this paper we report the results both the RNA sequencing and ES of three patients affected by MOPDII with the aim of exploring whether differentially expressed genes and previously uncharacterized gene variants, in addition to PCNT pathogenic variants, could be associated with the complex phenotype of this disease. We discovered a downregulation of key factors involved in growth, such as IGF1R, IGF2R, and RAF1, in all three investigated patients. Moreover, ES identified a shortlist of genes associated with deleterious, rare variants in MOPDII patients. Our results suggest that Next Generation Sequencing (NGS) technologies can be successfully applied for the molecular characterization of the complex genotypic background of MOPDII.

Analytic validation of NeXT Dx™, a comprehensive genomic profiling assay.

We describe the analytic validation of NeXT Dx, a comprehensive genomic profiling assay to aid therapy and clinical trial selection for patients diagnosed with solid tumor cancers. Proprietary methods were utilized to perform whole exome and whole transcriptome sequencing for detection of single nucleotide variants (SNVs), insertions/deletions (indels), copy number alterations (CNAs), and gene fusions, and determination of tumor mutation burden and microsatellite instability. Variant calling is enhanced by sequencing a patient-specific normal sample from, for example, a blood specimen. This provides highly accurate somatic variant calls as well as the incidental reporting of pathogenic and likely pathogenic germline alterations. Fusion detection via RNA sequencing provides more extensive and accurate fusion calling compared to DNA-based tests. NeXT Dx features the proprietary Accuracy and Content Enhanced technology, developed to optimize sequencing and provide more uniform coverage across the exome. The exome was validated at a median sequencing depth of >500x. While variants from 401 cancer-associated genes are currently reported from the assay, the exome/transcriptome assay is broadly validated to enable reporting of additional variants as they become clinically relevant. NeXT Dx demonstrated analytic sensitivities as follows: SNVs (99.4%), indels (98.2%), CNAs (98.0%), and fusions (95.8%). The overall analytic specificity was >99.0%.