Large Genomic Sequences Research Articles

Copy number variation introduced by a massive mobile element facilitates global thermal adaptation in a fungal wheat pathogen

Copy number variation (CNV) can drive rapid evolution in changing environments. In microbial pathogens, such adaptation is a key factor underpinning epidemics and colonization of new niches. However, the genomic determinants of such adaptation remain poorly understood. Here, we systematically investigate CNVs in a large genome sequencing dataset spanning a worldwide collection of 1104 genomes from the major wheat pathogen Zymoseptoria tritici. We found overall strong purifying selection acting on most CNVs. Genomic defense mechanisms likely accelerated gene loss over episodes of continental colonization. Local adaptation along climatic gradients was likely facilitated by CNVs affecting secondary metabolite production and gene loss in general. One of the strongest loci for climatic adaptation is a highly conserved gene of the NAD-dependent Sirtuin family. The Sirtuin CNV locus localizes to an ~68-kb Starship mobile element unique to the species carrying genes highly expressed during plant infection. The element has likely lost the ability to transpose, demonstrating how the ongoing domestication of cargo-carrying selfish elements can contribute to selectable variation within populations. Our work highlights how standing variation in gene copy numbers at the global scale can be a major factor driving climatic and metabolic adaptation in microbial species.

The genomic landscape of Ménière's disease: a path to endolymphatic hydrops

BackgroundMénière's disease (MD) is a disorder of the inner ear that causes episodic bouts of severe dizziness, roaring tinnitus, and fluctuating hearing loss. To date, no targeted therapy exists. As such, we have undertaken a large whole genome sequencing study on carefully phenotyped unilateral MD patients with the goal of gene/pathway discovery and a move towards targeted intervention. This study was a retrospective review of patients with a history of Ménière's disease. Genomic DNA, acquired from saliva samples, was purified and subjected to whole genome sequencing.ResultsStringent variant calling, performed on 511 samples passing quality checks, followed by gene-based filtering by recurrence and proximity in molecular interaction networks, led to 481 high priority MD genes. These high priority genes, including MPHOSPH8, MYO18A, TRIOBP, OTOGL, TNC, and MYO6, were previously implicated in hearing loss, balance, and cochlear function, and were significantly enriched in common variant studies of hearing loss. Validation in an independent MD cohort confirmed 82 recurrent genes. Pathway analysis pointed to cell–cell adhesion, extracellular matrix, and cellular energy maintenance as key mediators of MD. Furthermore, the MD-prioritized genes were highly expressed in human inner ear hair cells and dark/vestibular cells, and were differentially expressed in a mouse model of hearing loss.ConclusionBy enabling the development of model systems that may lead to targeted therapies and MD screening panels, the genes and variants identified in this study will inform diagnosis and treatment of MD.

FinaleToolkit: Accelerating Cell-Free DNA Fragmentation Analysis with a High-Speed Computational Toolkit.

Cell-free DNA (cfDNA) fragmentation pattern represents a promising non-invasive biomarker for disease diagnosis and prognosis. Numerous fragmentation features, such as end motif and window protection score (WPS), have been characterized in cfDNA genomic sequencing. However, the analytical tools developed in these studies are often not released to the liquid biopsy community or are inefficient for genome-wide analysis in large datasets. To address this gap, we have developed FinaleToolkit, a fast and memory efficient Python package designed to generate comprehensive fragmentation features from large cfDNA genomic sequencing data. For instance, FinaleToolkit can generate genome-wide WPS features from a ~100X cfDNA whole-genome sequencing (WGS) dataset in 1.2 hours using 16 CPU cores, offering up to a ~50-fold increase in processing speed compared to original implementations in the same dataset. We have benchmarked FinaleToolkit against original studies or implementations where possible, confirming its efficacy. Furthermore, FinaleToolkit enabled the genome-wide analysis of fragmentation patterns over arbitrary genomic intervals, significantly boosting the performance for cancer early detection. FinaleToolkit is open source and thoroughly documented with both command line interface and Python application programming interface (API) to facilitate its widespread adoption and use within the research community: https://github.com/epifluidlab/FinaleToolkit.

NGS panels' capture of biomarkers associated with targeted therapies in clinical trials.

e13508 Background: ASCO and NCCN guidelines strongly encourage participation in clinical trials though participation rates remain low. Although up to 60% of trials require biomarker information as a prerequisite to participation, guidelines are silent on biomarker testing to assess eligibility. To help inform policy development, we analyzed commercially available NGS panels to assess the capture rate of the specific gene variants required for targeted therapies in clinical trials. Methods: Publicly available gene variant information on 12 high volume NGS tissue and plasma commercial assays representative of panel size was accessed. The JAX-CKB database was queried to identify genomic prerequisites in active phase I-III clinical trials; mRNA and protein expression biomarkers were excluded. We used unpaired t-testing to compare the ability of large versus small panels to detect variants required for targeted treatments. (Somatic gene variant annotations and related content have been powered by The Jackson Laboratory Clinical Knowledgebase (JAX-CKBTM)). Results: Overall, 185 genes were linked to eligibility for targeted therapies in clinical trials (Table). Large panel assays (>80 genes) had a higher average capture rate compared to smaller panels, (84% vs. 46%, p value < 0.000002). Similarly, capture rates in select cancers were higher in larger panels: lung (85% vs 59%), breast (92% vs 54%), prostate (86% vs 34%), pancreatic (84% vs 54%), colorectal (88% vs 52%). All results were statistically significant (p < 0.05). Smaller panels were more likely to exclude large genes (e.g. BRCA), genome-wide signatures (e.g. TMB), fusions, and copy number variants. Conclusions: NGS-based panels with more than 80 genes had higher capture rates for biomarkers required for clinical trials of targeted therapies. Clinicians searching for all relevant treatment options for patients should include large genomic sequencing panels to maximize identification of potential clinical trial eligibility. Professional societies and policy makers interested in enhancing clinical trial enrollment rates should consider the panel size-related variability in capture rates of genetic variants when developing recommendations. [Table: see text]

Participation in genetic screening: testing different outreach methods across a diverse hospital system based patient population.

Introduction: Genomics has the potential to transform medicine by identifying genetic risk factors that predispose people to certain illnesses. Use of genetic screening is rapidly expanding and shifting towards screening all patients regardless of known risk factors, but research is limited on the success of broad population-level outreach for genetic testing and the effectiveness of different outreach methods across diverse populations. In this study, we tested the effectiveness of Digital Only (emailing and texting) and Brochure Plus Digital (mailed brochure, emailing, and texting) outreach to encourage a diverse patient population to participate in a large hospital system's whole genome sequencing program. Methods: Disproportionate stratified sampling was used to create a study population more demographically diverse than the eligible population and response rates were analyzed overall and by demographics to understand the effectiveness of different outreach strategies. Results: 7.5% of all eligible patients enrolled in the program. While approximately 70% of patients invited to complete genetic testing identified in their EHR as being Hispanic, Black or African America, Asian, or another non-White race, these patients generally enrolled at lower rates than the overall population. Other underrepresented groups had higher enrollment rates including people with Medicaid coverage (8.7%) and those residing in rural areas (10.6%). We found no significant difference in enrollment rates between our Digital-Only and our Brochure Plus Digital outreach approaches in the overall population, but enrollment rates were significantly higher for Asian patients and patients who resided in rural areas in the Brochure Plus Digital group. Across both outreach approaches, links provided in emails were most commonly used for enrollment. Discussion: Our study reveals expected enrollment rates for proactive outreach by a hospital system for genetic testing in a diverse population. As more hospital systems are adopting population-scale genetic testing, these findings can inform future outreach efforts to recruit patients for genetic testing including those patients traditionally underrepresented in genomics.

Next-Generation Sequencing and Triple-Negative Breast Cancer: Insights and Applications.

The poor survival of triple-negative breast cancer (TNBC) is due to its aggressive behavior, large heterogeneity, and high risk of recurrence. A comprehensive molecular investigation of this type of breast cancer using high-throughput next-generation sequencing (NGS) methods may help to elucidate its potential progression and discover biomarkers related to patient survival. In this review, the NGS applications in TNBC research are described. Many NGS studies point to TP53 mutations, immunocheckpoint response genes, and aberrations in the PIK3CA and DNA repair pathways as recurrent pathogenic alterations in TNBC. Beyond their diagnostic and predictive/prognostic value, these findings suggest potential personalized treatments in PD -L1-positive TNBC or in TNBC with a homologous recombination deficit. Moreover, the comprehensive sequencing of large genomes with NGS has enabled the identification of novel markers with clinical value in TNBC, such as AURKA, MYC, and JARID2 mutations. In addition, NGS investigations to explore ethnicity-specific alterations have pointed to EZH2 overexpression, BRCA1 alterations, and a BRCA2-delaAAGA mutation as possible molecular signatures of African and African American TNBC. Finally, the development of long-read sequencing methods and their combination with optimized short-read techniques promise to improve the efficiency of NGS approaches for future massive clinical use.

Variants near X‐Chromosome Genes NLGN4X and PTCHD1 are Significantly Associated with Alzheimer’s Disease Risk

AbstractBackgroundGenome‐wide association studies (GWAS) for AD have identified numerous associated loci but have been focused only on the autosomes. We sought to identify novel AD‐associated genes on X‐Chromosome (X‐Chr).MethodWe evaluated the association of AD with X‐Chr variants in GWAS datasets assembled by the Alzheimer Disease Genetics Consortium including 26,322 non‐Hispanic White (NHW) individuals using logistic regression models with covariates for age, sex and principal components of ancestry. The number of independent SNPs in X‐Chr was calculated by linkage disequilibrium pruning yielding a study‐wide significance threshold of P<7.58×10−7. Genes near SNPs surpassing this threshold were further evaluated by expression quantitative trait locus (eQTL) in the BRAINEAC database and differential gene expression analyses using RNA‐seq data obtained from autopsied brains contained (GEO: GSE44772 and GSE33000). We also tested association of AD with missense or splicing variants with minor allele count (MAC)≥10 in the same genes using GENESIS and whole exome sequencing (WES) data obtained by the Alzheimer Disease Sequencing Project (ADSP) for 11,172 NHWs.ResultWe identified novel associations with variants near NLGN4X (rs34056759; minor allele frequency [MAF] = 0.15, OR = 1.15, P = 5.14×10−7) and PTCHD1 (rs5970663; MAF = 0.30, OR = 1.13, P = 8.26×10−7). The rs5970663 risk allele C was significantly associated with increased expression of PTCHD1 in the medulla region, and PTCHD1 expression was significantly higher in AD cases than in controls in the prefrontal cortex (P<2.1×10−8) and visual cortex (P<2.3×10−4). Rs34056759 was not associated with NLGN4X expression, but NLGN4X expression was significantly lower in AD cases compared to controls in the prefrontal cortex (P<8.7×10−7). A rare PTCHD1 variant (rs201933353; MAF = 0.001) was nominally associated with AD among 6,318 women (OR = 3.72, P = 0.06).ConclusionWe identified novel associations of AD with X‐Chr variants near NLGN4X and PTCHD1, loci that were previously associated with autism spectrum disorder. We plan to replicate, fine‐map, and annotate the candidate loci identified in this study using large whole genome sequencing ADSP datasets.

Genetic landscape of human platelet antigen variants in the Indian population analysed from 1029 whole genomes

AbstractGenetic variants in human platelet antigens (HPAs) considered allo‐ or auto antigens are associated with various disorders, including neonatal alloimmune thrombocytopenia, platelet transfusion refractoriness and post‐transfusion purpura. Although global differences in genotype frequencies were observed, the distributions of HPA variants in the Indian population are largely unknown. This study aims to explore the landscape of HPA variants in India to provide a basis for risk assessment and management of related complications. Population‐specific frequencies of genetic variants associated with the 35 classes of HPAs (HPA‐1 to HPA‐35) were estimated by systematically analysing genomic variations of 1029 healthy Indian individuals as well as from global population genome datasets. Allele frequencies of the most clinically relevant HPA systems in the Indian population were found as follows, HPA‐1a – 0.884, HPA‐1b – 0.117, HPA‐2a – 0.941, HPA‐2b – 0.059, HPA‐3a – 0.653, HPA‐3b – 0.347, HPA‐4a – 0.999, HPA‐4b – 0.0010, HPA‐5a – 0.923, HPA‐5b – 0.077, HPA‐6a – 0.998, HPA‐6b – 0.002, HPA‐15a – 0.582 and HPA‐15b – 0.418. This study provides the first comprehensive analysis of HPA allele and genotype frequencies using large scale representative whole genome sequencing data of the Indian population.

Next-Generation Sequencing Markup Language (NGSML): A Medium for the Representation and Exchange of NGS Data.

With the increasing demand for low-cost high-throughput sequencing of large genomes, next-generation sequencing (NGS) technology has developed rapidly. NGS can not only be used in basic scientific research but also in clinical diagnostics and healthcare. Numerous software systems and tools have been developed to analyze NGS data, and various data formats have been produced to accommodate different sequencing equipment providers or analytical software. However, the data interoperability between these tools brings great challenges to researchers. A generic format that could be shared by most of the software and tools in the NGS field would make data interoperability and sharing easier. In this paper, we defined a general XML-based NGS markup language (NGSML) format for the representation and exchange of NGS data. We also developed a user-friendly GUI tool, NGSMLEditor, for presenting, creating, editing, and converting NGSML files. By using NGSML, various types of NGS data can be saved in one unified format. Compared with the unstructured plain text file, a structured data format based on XML technology solves the incompatibility of various NGS data formats. The NGSML specifications are freely available from http://www.sysbio.org.cn/NGSML. NGSMLEditor is open source under GNU GPL and can be downloaded from the website.

The dynamics of genome size and GC contents evolution in genus Nicotiana.

Hybridization and Polyploidization are most common of the phenomenon observed in plants, especially in the genus Nicotiana leading to the duplication of genome. Although genomic changes associated with these events has been studied at various levels but the genome size and GC content variation is less understood because of absence of sufficient genomic data. In this study the flow cytometry technique was used to uncover the genome size and GC contents of 46 Nicotiana species and we compared the genomic changes associated with the hybridization events along evolutionary time scale. The genome size among Nicotiana species varied between 3.28 pg and 11.88 pg whereas GC contents varied between 37.22% and 51.25%. The tetraploid species in genus Nicotiana including section Polydiclae, Repandae, Nicotiana, Rustica and Sauveolentes revealed both up and downsizing in their genome sizes when compared to the sum of genomes of their ancestral species. The genome sizes of three homoploid hybrids were found near their ancestral species. Loss of large genome sequence was observed in the evolutionary more aged species (>10 Myr) as compared to the recently evolved one's (<0.2 Myr). The GC contents were found homogenous with a mean difference of 2.46% among the Nicotiana species. It is concluded that genome size change appeared in either direction whereas the GC contents were found more homogenous in genus Nicotiana.

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

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 summarizesand 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.

Abstract 13496: Sequencing in Over 50,000 Cases Identifies Coding and Structural Variation Underlying Atrial Fibrillation Risk

Introduction: Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with substantial morbidity and mortality. AF is known to have a heritable component, with &gt;100 associated common variant loci. Rare variant studies have yielded limited robust associations for AF. We aimed to utilize large genome and exome sequencing data to discover rare genetic variants conferring large effects on AF risk. Methods: We meta-analyzed genome and exome sequencing data from 36 studies, including TOPMed, CCDG, UK Biobank and FOURIER. We performed exome-wide gene burden testing of rare (MAF&lt;0.1%) loss-of-function and deleterious missense variants, and single variant testing of low-frequency and rare (MAF&lt;1%) coding variants. Within genome sequenced samples, we performed gene burden testing of rare structural variants. Novel signals were replicated in MyCode. Finally, we functionally validated a novel gene by siRNA knockdown in pluripotent-induced atrial cardiomyocytes. Results: We included 52,416 AF cases and 277,762 controls, of which 49.6% were female, 83.4% were of European ancestry, and the mean baseline age was 56 years. In analysis of rare coding variation, we identified 4 novel genes associated with AF, including MYBPC3 (OR 3.5, P =2.1x10 -15 ), LMNA (OR 5.7, P =8.8x10 -11 ), PKP2 (OR 1.9, P =5.2x10 -8 ) and KDM5B (OR 2.3, P =3.0x10 -6 ). These signals were robust to removal of heart failure and cardiomyopathy cases and were replicated in independent datasets. Single variant analysis identified 2 novel signals in FAM189A2 (OR 3.9, P =7.86x10 -8 ) and ZFC3H1 (OR 5.9, P =9.7x10 -8 ). Rare deletions in CTNNA3 (OR 4.5, P =7.0x10 -9 ) were associated with increased AF risk and were supported by independent coding variant analyses, while duplications of GATA4 (OR 0.24, P =2.1x10 -5 ) were associated with reduced AF risk. In functional studies, knockdown of KDM5B resulted in shortening of the atrial action potential duration. Conclusions: Our analyses show the contribution of rare coding and structural variants to AF risk, highlight the shared genetic pathways underlying cardiomyopathy and AF, and implicate the histone demethylase gene KDM5B in AF susceptibility. In sum, we expanded our understanding of the rare variant architecture of this common arrhythmia.

The location of the t(4;14) translocation breakpoint within the NSD2 gene identifies a subset of patients with high-risk NDMM

AbstractAlthough translocation events between chromosome 4 (NSD2 gene) and chromosome 14 (immunoglobulin heavy chain [IgH] locus) (t(4;14)) is considered high risk in newly diagnosed multiple myeloma (NDMM), only ∼30% to 40% of t(4;14) patients are clinically high risk. We generated and compared a large whole genome sequencing (WGS) and transcriptome (RNA sequencing) from 258 t(4;14) (n = 153 discovery, n = 105 replication) and 183 non-t(4;14) NDMM patients with associated clinical data. A landmark survival analysis indicated only ∼25% of t(4;14) patients had an overall survival (OS) <24 months, and a comparative analysis of the patient subgroups identified biomarkers associated with this poor outcome, including translocation breakpoints located in the NSD2 gene and expression of IgH-NSD2 fusion transcripts. Three breakpoints were identified and are designated as: “no-disruption” (upstream of NSD2), “early-disruption” (in the 5' UTR), and “late-disruption” (within the NSD2 gene). Our results show a significant difference in OS based on the location of DNA breakpoints (median OS 28.6 “late-disruption” vs 59.2 “early disruption” vs 75.1 months “no disruption”). These findings have been replicated in an independent replication dataset. Also, univariate and multivariate analysis suggest high-risk markers such as del17p, 1p independently contribute to poor outcome in t(4;14) MM patients.

Enrichment of the VNTR PER3 variant in DSWPD patients: a large whole genome sequencing analysis

Enrichment of the VNTR PER3 variant in DSWPD patients: a large whole genome sequencing analysis

DeepRF: A deep learning method for predicting metabolic pathways in organisms based on annotated genomes

The rapid increase of metabolomics has led to an increasing focus on metabolic pathway modeling and reconstruction. In particular, reconstructing an organism's metabolic network based on its genome sequence is a key challenge in systems biology. The method used to address this problem predicts the presence or absence of metabolic pathways from known pathways in a reference database. However, this method is based on manual metabolic pathway construction and cannot be used for large genome sequencing data. To address such problems, we apply a supervised machine learning approach consisting of deep neural networks to learn feature representations of metabolic pathways and feed these representations into random forests to predict metabolic pathways. The supervised learning model, DeepRF, predicts all known and unknown metabolic pathways in an organism. Evaluation of DeepRF on over 318,016 instances shows that the model can predict metabolic pathways with high-performance metrics accuracy (>97%), recall (>95%), and precision (>99%). Comparing DeepRF with other methods in the literature shows that DeepRF produces more reliable results than other methods.

Abstract 1938: Large-scale identification of mutation activity and drug sensitivity in BRAF via a novel multi-label multi-task CNN model

Abstract Precision medicine has allowed for many drugs to be developed for frequently occurring well studied oncogenic mutations such as NTRK fusions or EGFR exon 19 mutations. However, large scale genomic sequencing of patient samples shows that tumors harbor many mutations classified as Variants of Uncertain Significance (VUS). Such mutations could have a significant role in tumor progression and can thus serve as potential drug targets. Therefore, understanding and characterizing the functional significance of VUSs and their response to targeted agents is essential. Here we present a novel machine learning (ML) model consisting of a multi-label, multi-task deep convolutional neural network followed by a decision tree-based regression model. Focusing on alterations in BRAF, we used data from a cell-based assay that measures the activity of signaling pathway activation. This is done using fluorescent imaging of cells expressing a mutated protein together with a fluorescently labeled signaling pathway reporter, providing the input to our model. We trained the model on 3 types of cell images: cells transfected with WT BRAF, BRAF V600E, and BRAF V600E treated with a high dose of Vemurafenib. We use two datasets to evaluate our performance: a set of 17 known active BRAF fusions, as well as a set of 16 known active non-V600E mutations. We also compare our performance to previously published single-task ML model that aims to detect both activity and response. The two methodologies are compared via two criteria: ability to detect activity of the mutations in the dataset, as well as ability to predict response to Vemurafenib or to FORE8394, a drug previously unseen by the model. We show that while both single-task and multi-task models identify all 17 known active fusions as oncogenic, the multi-task does slightly better on the non-V600E mutations, correctly identifying 15/16 of the active mutations vs 10/16 for the single-task model. Comparing drug response, the multi-task model has higher sensitivity in detecting active mutations as responsive to Vemurafenib or FORE8394, including all V600 mutations which are known responders to Vemurafenib and for whom the single-task model does not capture response. Following the training and validation, given a dataset of &amp;gt;300 previously unseen BRAF mutations, the multi-task model is then able to predict both the mutation’s oncogenicity level as well as its expected response to the given drug. Interestingly, the multi-task model also suggests a different drug response profile for vemurafenib compared to FORE8394. We conclude that our novel multi-task model provides an accurate and efficient method for uncovering the actionable and treatable mutational landscape of a drug for patients with mutations in BRAF. It can thus be viewed as a step forward in developing sensitive methodologies for determining patients that are more susceptible to benefit from a potential drug. Citation Format: Ilona Kifer, Arie Aizenman, Natalie Fillipov-Levy, Zohar Barbash, Michael Vidne, Gabi Tarcic. Large-scale identification of mutation activity and drug sensitivity in BRAF via a novel multi-label multi-task CNN model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1938.

Repeat large panel genomic sequencing identifies actionable alterations and characterizes the genomic landscape in patients with metastatic solid tumors.

3076 Background: The implementation of genomic profiling with next generation sequencing has revolutionized the field of precision oncology. Comprehensive genomic testing of tumors to identify actionable genomic alterations is now commonly performed in the care of patients with advanced/metastatic disease. Although the genomic profile of tumors has been shown to evolve with progression and intervening treatments, the role of repeat genomic testing is not well established. We sought to determine the evolution of actionable genomic alterations in patients undergoing repeat genomic testing on the same comprehensive genomic panel. Methods: We retrospectively examined the molecular profiles and medical records of 262 patients with metastatic solid tumors treated in MD Anderson who underwent genomic testing on the same panel (Oncomine, Thermo Fisher) for the detection of somatic mutations in the coding sequence of 143 cancer-related genes, on at least 2 separate occasions. Genomic alterations were reviewed by a central Precision Oncology Decision Support (PODS) team in order to provide annotations at the alteration level on the functional significance. Results: 262 patients underwent repeat genomic testing using the same genomic panel on samples collected at different time points from July 2010 to Dec 2021 across tumor types. Changes in alterations (gain or loss) were identified on repeat testing in most patients (66%) We then specifically assessed changes in alterations that were categorized as actionable if annotated by the PODS team at the time of reporting. A gain or loss of an actionable alteration was detected in 38% (100/262) patients. New actionable alterations were frequently identified (73%; 73/100), while 41% had loss of an actionable alteration (41/100). 14% had both loss and gain of actionable alteration on repeat testing; 58% had new actionable alteration identified alone; 27% had loss of actionable alteration only. Actionable alterations identified on repeat testing included alterations in PI3K/AKT (27%), EGFR (15%), and MAPK (16%). On repeat testing, changes in ³2 actionable alterations were frequently identified in the same test (43%). Conclusions: Repeat large panel genomic testing identifies both gain and loss of actionable alterations in patients with advanced metastatic cancers. Actionable aberrations frequently co-exist with alterations in a variety of other genes, which highlights the complexities of treating patients with metastatic cancer on progression of disease and suggests that tailored combination strategies may be necessary in these patients.

Large‐scale profiling of physiologically relevant naturally occurring rare GPCR variants using the bioSensAll® technology

G protein‐coupled receptors (GPCRs) represent the largest class of membrane‐bound proteins which regulate diverse physiological processes by engaging multiple signaling pathways. GPCR signaling can be differentially modulated by specific ligands to selectively promote the engagement of different subsets of signaling pathways, an outcome known as biased signaling. Although dysregulation of GPCR activity has been linked to pathological consequences, the involvement of their signaling pathways toward specific clinical repercussions remain unresolved. Thus, linking the role of GPCR signaling pathways to distinct physiological processes is not only important for understanding GPCR biology, but also essential for successfully targeting these receptors as therapeutic avenues. Here, to exploit the potential of naturally occurring GPCR variants emerging from large scale exome and genome sequencing efforts, we assessed the signaling profiles of rare variants across different GPCRs found in human populations with documented clinical phenotypes. Signaling profiles were assessed through an automated platform using our bioSensAll® technology, which measures the activity of various signaling pathways using a panel of 16 selective bioluminescence resonance energy transfer (BRET)‐based biosensors that monitor the activation of heterotrimeric G proteins (Gαs, Gαi1, Gαi2, GαoA, GαoB, Gαz, Gαq, Gα11, Gα14, Gα15/16, Gα12, Gα13) or recruitment of β‐arrestins (β‐arrestin 1 and β‐arrestin 2) to the plasma membrane upon agonist stimulation. To achieve the large‐scale screen in a high throughput system, bioSensAll® assays were miniaturized and adapted to a 384‐well format. HEK293 cells were co‐transfected with receptors and biosensors, and a scheduling software was used to coordinate the interaction between various instruments via a robotic microplate mover. Analysis was performed through an automated tool to generate and compile signaling profiles for each variant normalized to wild‐type receptor, revealing gain‐ or loss‐of‐function properties induced by variants across different signaling pathways, thus also allowing us to identify genetic alterations that impart biased signaling profiles. By pairing the multiparametric signaling profiles of variants found in human populations generated through our automated bioSensAll® platform with information on their associated clinical phenotypes, we can uncover associations between altered receptor pharmacology and clinical outcomes to accelerate the discovery of disease‐relevant targets and improve translation from bench to clinic.

Development and identification of a sex-specific molecular marker in Dai-qu stock large yellow croaker (Larimichthys crocea)

Dai-qu stock large yellow croaker (Larimichthys crocea) is an important marine economic fish and geographical population in the East China Sea. Traditionally, large yellow croakers can be divided into three geographical populations as follows: Dai-qu, Min-yuedong and Nao-zhou stocks. In the present study, a sex-specific molecular marker in Dai-qu stock was developed from loci between dmrt1 and cfap157. Based on large yellow croaker genome sequence and a well characterized sex-determining region on chromosome 22, 147 pairs of primers were designed for screening the sex-specific markers by PCR amplification. At last, 1 pair of primers were successfully screened and identified. The results showed that the primers only amplified 500 bp bands in females, whereas, 500 bp bands and 390 bp specific bands were amplified in males. Cloning and sequencing analysis of PCR products showed that females and males share the almost same 500 bp sequence, and the males have a specific deletion of 110 bp than females sequence. 276 individuals of Dai-qu stock large yellow croaker were used to verification this sex-specific marker, and compared with gonadal histological results, the accuracy of the specific primer reached 100%, indicating that the primer can effectively and accurately identify the genetic sex of Dai-qu stock large yellow croaker. In this study, a sex-specific marker was developed and identified, which further verified that the Dai-qu stock large yellow croaker also belonged to XX♀-XY♂ type, providing a useful tool for clarifying the molecular mechanism of sex determination and promoting sex-controlled breeding.