Outlier Genes Research Articles

281. PROTEOMIC, TRANSCRIPTOMIC AND GENOMIC ANALYSIS OF ESOPHAGEAL ADENOCARCINOMA UNCOVERS CANDIDATE THERAPEUTIC TARGETS AND CANCER-SELECTIVE POST-TRANSCRIPTIONAL REGULATION

Abstract Background Addressing the poor prognosis associated with esophageal adenocarcinoma (EAC) has been hindered by the absence of biomarkers for early disease detection and therapeutic targets. Despite extensive research on the somatic mutations associated with EAC, the impact of genomic aberrations on protein expression and cancer phenotype remains unclear. Methods We conducted a quantitative proteomic analysis using tumour tissue and patient-matched adjacent normal esophageal and gastric tissues from 23 patients undergoing resection for EAC. We further examined the relationship between transcript and protein levels using patient-matched whole transcriptome RNA sequencing and proteomic data from 7 patients and integrated these findings with data from a cohort of 264 EAC patients with RNA sequencing and 454 patients with whole-genome sequencing, as well as external published datasets. Results We quantified protein expression from 5897 genes in EAC and normal tissues. Several potential biomarkers showing selective expression in EAC, such as the transmembrane protein GPA33, were identified. We validated the EAC-specific expression of GPA33 in an external cohort of 115 patients, suggesting its utility as a diagnostic and therapeutic target. Additionally, integrated analysis of protein and RNA expression in EAC and normal tissues revealed genes with discordant protein and RNA levels, indicating post-transcriptional regulation of protein expression. These outlier genes, including SLC25A30, TAOK2, and AGMAT, exhibited rare somatic mutations, suggesting post-transcriptional mechanisms driving this EAC-specific phenotype. AGMAT, as an example, was found to be overexpressed at the protein level in EAC compared to adjacent normal tissues, with a proposed EAC-selective, post-transcriptional mechanism regulating protein abundance. Conclusion Through quantitative proteomic analysis, we identified GPA33 as a candidate EAC biomarker. Integrated analysis of the proteome, transcriptome, and genome in EAC uncovered genes with tumor-selective post-transcriptional regulation of protein expression, offering potential exploitable vulnerabilities for therapeutic intervention.

Denoiseit: denoising gene expression data using rank based isolation trees

BackgroundSelecting informative genes or eliminating uninformative ones before any downstream gene expression analysis is a standard task with great impact on the results. A carefully curated gene set significantly enhances the likelihood of identifying meaningful biomarkers.MethodIn contrast to the conventional forward gene search methods that focus on selecting highly informative genes, we propose a backward search method, DenoiseIt, that aims to remove potential outlier genes yielding a robust gene set with reduced noise. The gene set constructed by DenoiseIt is expected to capture biologically significant genes while pruning irrelevant ones to the greatest extent possible. Therefore, it also enhances the quality of downstream comparative gene expression analysis. DenoiseIt utilizes non-negative matrix factorization in conjunction with isolation forests to identify outlier rank features and remove their associated genes.ResultsDenoiseIt was applied to both bulk and single-cell RNA-seq data collected from TCGA and a COVID-19 cohort to show that it proficiently identified and removed genes exhibiting expression anomalies confined to specific samples rather than a known group. DenoiseIt also showed to reduce the level of technical noise while preserving a higher proportion of biologically relevant genes compared to existing methods. The DenoiseIt Software is publicly available on GitHub at https://github.com/cobi-git/DenoiseIt

Genomics of natural populations: gene conversion events reveal selected genes within the inversions of Drosophila pseudoobscura.

When adaptive phenotypic variation or quantitative trait loci map within an inverted segment of a chromosome, researchers often despair because the suppression of crossing over will prevent the discovery of selective target genes that established the rearrangement. If an inversion polymorphism is old enough, then the accumulation of gene conversion tracts offers the promise that quantitative trait loci or selected loci within inversions can be mapped. The inversion polymorphism of Drosophila pseudoobscura is a model system to show that gene conversion analysis is a useful tool for mapping selected loci within inversions. D. pseudoobscura has over 30 different chromosomal arrangements on the third chromosome (Muller C) in natural populations and their frequencies vary with changes in environmental habitats. Statistical tests of five D. pseudoobscura gene arrangements identified outlier genes within inverted regions that had potentially heritable variation, either fixed amino acid differences or differential expression patterns. We use genome sequences of the inverted third chromosome (Muller C) to infer 98,443 gene conversion tracts for a total coverage of 142 Mb or 7.2× coverage of the 19.7 Mb chromosome. We estimated gene conversion tract coverage in the 2,668 genes on Muller C and tested whether gene conversion coverage was similar among arrangements for outlier vs non-outlier loci. Outlier genes had lower gene conversion tract coverage among arrangements than the non-outlier genes suggesting that selection removes exchanged DNA in the outlier genes. These data support the hypothesis that the third chromosome in D. pseudoobscura captured locally adapted combinations of alleles prior to inversion mutation events.

APPLYING HYPERGRAPHS TO STUDIES IN QUANTITATIVE BIOLOGY

The objective of this research is to demonstrate hypergraph versatility and applicability for modeling diverse biological systems. The inherent structure of hypergraphs allows for encoding of higher-order feature interactions, providing a flexible framework for efficient models that can enhance our understanding of physical phenomena and one that can be generalized across various datasets. By adopting innovative methods including centrality measure and populations of models rather than singular instances, biases and overfitting tendencies are mitigated, again presenting promise for application across a broad spectrum of biological systems. Furthermore, emphasis is placed on the significance of probabilistic distribution analysis in elucidating threshold selection and feature relevance while maintaining high levels of accuracy. Our results demonstrate the advantages of hypergraph models on two different datasets; with the first on gene expression and the identification of outlier genes and the second on classifying starch grains. There is significant scope in the application of the hypergraph to a wider class of biological systems, with the potential to improve understanding of the biological processes.

Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

BackgroundRare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging.MethodsTo address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes.ResultsWe found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities.ConclusionsAltogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.

Abstract PO2-07-01: Predicted Circadian-related Transcription Factors in Human Breast Cell Lines–A key to understanding breast cancer pathology?

Abstract Studies show that disrupted circadian clocks are associated with breast and other forms of cancer. However, the pathways and cell processes linking broken circadian clocks to breast cancer are still mostly unknown. In this study, we conducted a reanalysis of mRNA expression in breast cell lines, specifically MCF10a (a fibrocystic cell line) and MCF7 (a metastatic adenocarcinoma cell line). To achieve this, we utilized microarray data obtained from Gutierrez et al. in 2016, specifically the datasets GSE76370, GSE76368, and GSE76369. First, Metacycle, an R statistical package employing a cosine fitting algorithm, was used to identify circadian-oscillations in mRNA expression from both cell lines. MetaCycle (Wu et al., 2016) analysis produced an amplitude, phase, and period value for each circadian gene. Following the analysis, we conducted principal component analysis on the Metacycle output to identify any outlier genes. Subsequently, hierarchical clustering analysis was employed to group these outlier genes based on their oscillating expression levels, with the purpose of identifying clusters that exhibited similar period and phase patterns. We hypothesized that genes found in the same cluster would share common transcription factors that would drive and synchronize the expression of those genes. We tested this hypothesis and found that genes with the same circadian pattern did indeed share common transcription factor binding sites, using Ciiider 3.0 (Gearing et al, 2019), an internet-based tool for predicting and analyzing transcription factor binding sites. We found 96 genes grouped into 6 clusters for MCF10a, and 92 genes grouped into 6 for MCF7 cell lines. Arid3A binding sites were highly enriched across MCF7 and MCF10a. In MCF10a, where circadian oscillations were more pronounced, Sox5 binding sites were highly enriched for cluster 1, Meis1 was enriched for cluster 2, and Arid3A was enriched in clusters 3-6, in combination with other transcription factors. In MCF7, a different set of genes showed circadian-like oscillating expression patterns. However, these different genes showed enrichment of Arid3A across all clusters. Since Arid3A binding sites were found in genes involved in all of the MCF7 clusters, and found in four of the six clusters in MCF10a, further work is needed in understanding the additional regulatory mechanisms that shift the phase and period of the oscillations of these groups of genes. In addition, more work needs to be done to study the role of these transcription factors in cancer progression and basic cell processes. Most of these transcription factors are known to be related to breast or other forms of cancer. Though this list is not complete, some of the more enriched transcription factor binding sites include Arid3A, Meis1, Znf354c, in MCF10a and in MCF7 they include Arid3A, Znf354c, Nfatc2, and Ahr:Arnt. The most enriched transcription factor amongst these circadian-like genes is Arid3A. Arid3A is an H3K9me3 demethylase and may impact regulation of other genes through demethylation. Suppression of Arid3A is associated with leukemia. Meis1 is also implicated in leukemia and tumor growth, as well as sleep disorders. Arnt splice variants are also associated with breast cancer. Future research is needed to determine if these transcription factors are important for circadian-like control. Our in silico results suggest that genes found in the same clusters share their respective transcription factors that regulate the expression of these circadian-like genes. Citation Format: Yocanxóchitl Perfecto-Avalos, Janice Yoo, Cinthya Saravia Heyman, Princess Jemeala Deranamie, John Tran, Sean-Patrick Scott. Predicted Circadian-related Transcription Factors in Human Breast Cell Lines–A key to understanding breast cancer pathology? [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-01.

Massively parallel screen uncovers many rare 3' UTR variants regulating mRNA abundance of cancer driver genes.

Understanding the function of rare non-coding variants represents a significant challenge. Using MapUTR, a screening method, we studied the function of rare 3' UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare gnomAD variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated an interrogation of 11,929 somatic mutations, uncovering 3928 (33%) functional mutations in 155 cancer driver genes. Functional MapUTR variants were enriched in microRNA- or protein-binding sites and may underlie outlier gene expression in tumors. Further, we introduce untranslated tumor mutational burden (uTMB), a metric reflecting the amount of somatic functional MapUTR variants of a tumor and show its potential in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), demonstrating their cancer-driving potential. Our study elucidates the function of tens of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.

Multi-Omic Analysis of Esophageal Adenocarcinoma Uncovers Candidate Therapeutic Targets and Cancer-Selective Posttranscriptional Regulation

Efforts to address the poor prognosis associated with esophageal adenocarcinoma (EAC) have been hampered by a lack of biomarkers to identify early disease and therapeutic targets. Despite extensive efforts to understand the somatic mutations associated with EAC over the past decade, a gap remains in understanding how the atlas of genomic aberrations in this cancer impacts the proteome and which somatic variants are of importance for the disease phenotype. We performed a quantitative proteomic analysis of 23 EACs and matched adjacent normal esophageal and gastric tissues. We explored the correlation of transcript and protein abundance using tissue-matched RNA-seq and proteomic data from seven patients and further integrated these data with a cohort of EAC RNA-seq data (n = 264 patients), EAC whole-genome sequencing (n = 454 patients), and external published datasets. We quantified protein expression from 5879 genes in EAC and patient-matched normal tissues. Several biomarker candidates with EAC-selective expression were identified, including the transmembrane protein GPA33. We further verified the EAC-enriched expression of GPA33 in an external cohort of 115 patients and confirm this as an attractive diagnostic and therapeutic target. To further extend the insights gained from our proteomic data, an integrated analysis of protein and RNA expression in EAC and normal tissues revealed several genes with poorly correlated protein and RNA abundance, suggesting posttranscriptional regulation of protein expression. These outlier genes, including SLC25A30, TAOK2, and AGMAT, only rarely demonstrated somatic mutation, suggesting post-transcriptional drivers for this EAC-specific phenotype. AGMAT was demonstrated to be overexpressed at the protein level in EAC compared to adjacent normal tissues with an EAC-selective, post-transcriptional mechanism of regulation of protein abundance proposed. Integrated analysis of proteome, transcriptome, and genome in EAC has revealed several genes with tumor-selective, posttranscriptional regulation of protein expression, which may be an exploitable vulnerability.

Abstract 7322: Polygenic adaptation and co-regulatory dynamics in Chernobyl wolves: Unveiling immune and oncogenic stress interactions with implications for human cancer resilience

Abstract Wolves living in the Chernobyl Exclusion Zone (CEZ) for at least six generations have adapted to high radiation exposure and are a unique model for studying genetic selection under extreme conditions of oncogenic stress. The objective of this study was to build off previously described signatures of adaptation in CEZ wolves, identify networks of genes associated with immune adaptation in CEZ wolves, and determine prognostic significance of these genes in human cancer datasets to provide insight into immune adaptation and cancer immunity. RNA from blood samples of wolves in CEZ, Belarus (BLR), and Yellowstone (YLS) were sequenced previously. Here we analyzed these data by mapping wolf transcripts to human orthologs. Radiation exposure (in CEZ and BLR) was quantified by intramuscular Cesium-137. Differential gene expression (DESeq2), network co-expression analysis (lmQCM), and gene set variation analysis (GSVA) assessed transcriptomic changes between populations. Germline single nucleotide polymorphism (SNP) for all three populations were inferred from RNA-seq data. Genes differentially regulated between populations, including gene modules showing signatures of polygenic selection through increased rates of genetic divergence (p< 0.05) in CEZ wolf populations, were deemed as outlier genes. Those n=3,180 CEZ outlier genes were then investigated in human cancer data from The Cancer Genome Atlas (TCGA) to determine prognostic impact in 10 tumor types highly conserved between human and canines. GSVA confirmed activation of neutrophil and macrophage signatures in CEZ wolves and suppression of several adaptive immune-related signatures (p < 0.05 vs BLR and YLS). A signature including immune checkpoint targets was also significantly differentially expressed in CEZ wolves. In TCGA, Cox PH analysis identified 23 genes over-expressed in CEZ wolves with significant positive prognostic impact in ≥ 2 human tumor types. PTPN6, previously identified to have positive prognostic value in bladder cancer1, was also prognostic in three additional tumor types. In human bladder cancer, genes identified as differentially regulated in CEZ wolves were disproportionately associated with improved survival. As predicted, gene signature variations in CEZ wolves reveal a distinct immune profile, likely shaped by prolonged radiation exposure. These findings, along with evidence of polygenic selection, suggest adaptation to multigenerational radiation exposure (an oncogenic stress). Notably, the enrichment of genes with positive prognosis in human cancer overexpressed in CEZ wolves present a valuable model to explore genetic underpinnings of cancer immunity and advance our understanding of cancer resilience in humans. Reference: 1Shen C. et al. The Analysis of PTPN6 for Bladder Cancer: An Exploratory Study Based on TCGA. Dis Markers. 2020. Citation Format: Christina Y. Yu, Cara N. Love, Merzu Belete, Hareesh Chandrupatla, Lauren K. Brady, Sriram Sridhar, Shane C. Campbell-Staton. Polygenic adaptation and co-regulatory dynamics in Chernobyl wolves: Unveiling immune and oncogenic stress interactions with implications for human cancer resilience [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7322.

Abstract 1759: Integrative longitudinal genomic analysis of therapy-resistant and metastatic pediatric cancers

Abstract Pediatric cancer patients are commonly profiled with gene-panel sequencing tests that yield few actionable results, in part due to the complex genomic alterations that define these malignancies. We hypothesized that integration of whole-genome (WGS) and RNA sequencing (RNAseq), would lead to a more comprehensive understanding of these diseases. Our study is uniquely focused on metastatic and relapsed disease, whereas previous studies focused on primary cases. We also prioritized longitudinal profiling, including with deep sequencing, to capture tumor evolution across primary and metastatic sites and to quantify the utility of resampling. We assembled a cohort of 219 high-risk pediatric oncology patients, including solid tumors, CNS tumors, leukemias/lymphomas, patients with relapsed/refractory disease (75), metastatic disease at diagnosis (7), rare diagnoses (17), prior cancer history, and estimated overall survival <50%. We characterized 286 samples with WGS (tumor ~60X; germline ~30X) and/or RNAseq (polyA selected, ≥20 million reads), including 95 samples taken from 44 patients at different time points (diagnosis, relapse, etc.). Variants, structural rearrangements, mutational signatures, and copy-number alterations were identified using WGS. RNAseq was used to profile gene expression outliers, gene fusions, and expression of variants. Integrated results were used to prioritize potentially actionable variants. For 20 patients (44 samples), we performed targeted deep sequencing of the DNA (~500X). RNAseq identified potentially druggable outlier gene expression and fusions, including 102 novel fusions where the 3’ gene is overexpressed. WGS identified aneuploidy, loss of heterozygosity and whole genome duplication across histotypes. Mutational burden and mutational signatures analyses identified profound effects from platinum drugs on tumor evolution, but treatment effects were not universal. Multiple sampling per patient uncovered drastic spatial and temporal differences in the genomes and transcriptomes of these tumors. Custom deep sequencing confirmed these findings, captured evolution missed by WGS, and furthered our understanding of the complex impacts of treatment on clonal evolution. Histotypes differed by whether actionability was higher for WGS or RNAseq alone, but in 66% of samples modality integration increased actionability. Integration also identified a subset of tumors that may be amenable to immunotherapy, but which lack canonical markers of response. Longitudinal analysis highlighted both the opportunities and risks of targeted therapy, with targetable variants gained and lost between timepoints. Our study shows that integrated multi-modality sequencing can elucidate novel insights into the biology of pediatric cancers and identify potential therapeutic targets not detected using gene-panel testing alone. Citation Format: Henry J. Martell, Avanthi T. Shah, Alex G. Lee, Stanley G. Leung, Soo-Jin Cho, María Pons Ventura, Ana Golla, Amanda E. Marinoff, Elizabeth P. Young, Bogdan Tanasa, Inge Behroozfard, Heng-Yi Liu, Aviv Spillinger, Michelle L. Turski, Nicole Elzie-Tuttle, Carlos Espinosa-Mendez, Arun Rangaswami, Tabitha M. Cooney, Cassie Kline, Anurag Agrawal, Jennifer Michlisch, Elliot Stieglitz, Mignon Loh, Amit J. Sabnis, Kieuhoa T. Vo, Sheri Spunt, Norman Lacayo, Holly C. Beal, Florette K. Hazard, Sophie Salama, David Haussler, Olena M. Vaske, Marcus R. Breese, E. Alejandro Sweet-Cordero. Integrative longitudinal genomic analysis of therapy-resistant and metastatic pediatric cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1759.

Identification and analysis of mitochondria-related central genes in steroid-induced osteonecrosis of the femoral head, along with drug prediction.

Steroid-induced osteonecrosis of the femoral head (SONFH) is a refractory orthopedic hip joint disease that primarily affects middle-aged and young individuals. SONFH may be caused by ischemia and hypoxia of the femoral head, where mitochondria play a crucial role in oxidative reactions. Currently, there is limited literature on whether mitochondria are involved in the progression of SONFH. Here, we aim to identify and validate key potential mitochondrial-related genes in SONFH through bioinformatics analysis. This study aims to provide initial evidence that mitochondria play a role in the progression of SONFH and further elucidate the mechanisms of mitochondria in SONFH. The GSE123568 mRNA expression profile dataset includes 10 non-SONFH (non-steroid-induced osteonecrosis of the femoral head) samples and 30 SONFH samples. The GSE74089 mRNA expression profile dataset includes 4 healthy samples and 4 samples with ischemic necrosis of the femoral head. Both datasets were downloaded from the Gene Expression Omnibus (GEO) database. The mitochondrial-related genes are derived from MitoCarta3.0, which includes data for all 1136 human genes with high confidence in mitochondrial localization based on integrated proteomics, computational, and microscopy approaches. By intersecting the GSE123568 and GSE74089 datasets with a set of mitochondrial-related genes, we screened for mitochondrial-related genes involved in SONFH. Subsequently, we used the good Samples Genes method in R language to remove outlier genes and samples in the GSE123568 dataset. We further used WGCNA to construct a scale-free co-expression network and selected the hub gene set with the highest connectivity. We then intersected this gene set with the previously identified mitochondrial-related genes to select the genes with the highest correlation. A total of 7 mitochondrial-related genes were selected. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the selected mitochondrial-related genes using R software. Furthermore, we performed protein network analysis on the differentially expressed proteins encoded by the mitochondrial genes using STRING. We used the GSEA software to group the genes within the gene set in the GSE123568 dataset based on their coordinated changes and evaluate their impact on phenotype changes. Subsequently, we grouped the samples based on the 7 selected mitochondrial-related genes using R software and observed the differences in immune cell infiltration between the groups. Finally, we evaluated the prognostic significance of these features in the two datasets, consisting of a total of 48 samples, by integrating disease status and the 7 gene features using the cox method in the survival R package. We performed ROC analysis using the roc function in the pROC package and evaluated the AUC and confidence intervals using the ci function to obtain the final AUC results. Identification and analysis of 7 intersecting DEGs (differentially expressed genes) were obtained among peripheral blood, cartilage samples, hub genes, and mitochondrial-related genes. These 7 DEGs include FTH1, LACTB, PDK3, RAB5IF, SOD2, and SQOR, all of which are upregulated genes with no intersection in the downregulated gene set. Subsequently, GO and KEGG pathway enrichment analysis revealed that the upregulated DEGs are primarily involved in processes such as oxidative stress, release of cytochrome C from mitochondria, negative regulation of intrinsic apoptotic signaling pathway, cell apoptosis, mitochondrial metabolism, p53 signaling pathway, and NK cell-mediated cytotoxicity. GSEA also revealed enriched pathways associated with hub genes. Finally, the diagnostic value of these key genes for hormone-related ischemic necrosis of the femoral head (SONFH) was confirmed using ROC curves. BID, FTH1, LACTB, PDK3, RAB5IF, SOD2, and SQOR may serve as potential diagnostic mitochondrial-related biomarkers for SONFH. Additionally, they hold research value in investigating the involvement of mitochondria in the pathogenesis of ischemic necrosis of the femoral head.

Impact of Rare Structural Variant Events in Newly Diagnosed Multiple Myeloma.

Whole-genome sequencing (WGS) of patients with newly diagnosed multiple myeloma (NDMM) has shown recurrent structural variant (SV) involvement in distinct regions of the genome (i.e., hotspots) and causing recurrent copy-number alterations. Together with canonical immunoglobulin translocations, these SVs are recognized as "recurrent SVs." More than half of SVs were not involved in recurrent events. The significance of these "rare SVs" has not been previously examined. In this study, we utilize 752 WGS and 591 RNA sequencing data from patients with NDMM to determine the role of rare SVs in myeloma pathogenesis. Ninety-four percent of patients harbored at least one rare SV event. Rare SVs showed an SV class-specific enrichment within genes and superenhancers associated with outlier gene expression. Furthermore, known myeloma driver genes recurrently impacted by point mutations were dysregulated by rare SVs. Overall, we demonstrate the association of rare SVs with aberrant gene expression supporting a potential driver role in myeloma pathogenesis.

Evolutionary analyses of gene expression divergence in Panicum hallii: exploring constitutive and plastic responses using reciprocal transplants.

The evolution of gene expression is thought to be an important mechanism of local adaptation and ecological speciation. Gene expression divergence occurs through the evolution of cis- polymorphisms and through more widespread effects driven by trans-regulatory factors. Here, we explore expression and sequence divergence in a large sample of Panicum hallii accessions encompassing the species range using a reciprocal transplantation experiment. We observed widespread genotype and transplant site drivers of expression divergence, with a limited number of genes exhibiting genotype-by-site interactions. We used a modified Fst-Qst outlier approach (QPC analysis) to detect local adaptation. We identified 514 genes with constitutive expression divergence above and beyond the levels expected under neutral processes. However, no plastic expression responses met our multiple testing correction as QPC outliers. Constitutive QPC outlier genes were involved in a number of developmental processes and responses to abiotic environments. Leveraging earlier expression quantitative trait loci (eQTL) results (Lovell et al. 2018), we found a strong enrichment of expression divergence, including for QPC outliers, in genes previously identified with cis and cis-environment interactions but found no patterns related to trans-factors. Population genetic analyses detected elevated sequence divergence of promoters and coding sequence of constitutive expression outliers, but little evidence for positive selection on these proteins. Our results are consistent with a hypothesis of cis-regulatory divergence as a primary driver of expression divergence in P. hallii.

Selection on standing genetic variation mediates convergent evolution in extremophile fish.

Hydrogen sulfide is a toxic gas that disrupts numerous biological processes, including energy production in the mitochondria, yet fish in the Poecilia mexicana species complex have independently evolved sulfide tolerance several times. Despite clear evidence for convergence at the phenotypic level in these fishes, it is unclear if the repeated evolution of hydrogen sulfide tolerance is the result of similar genomic changes. To address this gap, we used a targeted capture approach to sequence genes associated with sulfide processes and toxicity from five sulfidic and five nonsulfidic populations in the species complex. By comparing sequence variation in candidate genes to a reference set, we identified similar population structure and differentiation, suggesting that patterns of variation in most genes associated with sulfide processes and toxicity are due to demographic history and not selection. But the presence of tree discordance for a subset of genes suggests that several loci are evolving divergently between ecotypes. We identified two differentiation outlier genes that are associated with sulfide detoxification in the mitochondria that have signatures of selection in all five sulfidic populations. Further investigation into these regions identified long, shared haplotypes among sulfidic populations. Together, these results reveal that selection on standing genetic variation in putatively adaptive genes may be driving phenotypic convergence in this species complex.

Origin matters: Using a local reference genome improves measures in population genomics.

Genome sequencing enables answering fundamental questions about the genetic basis of adaptation, population structure and epigenetic mechanisms. Yet, we usually need a suitable reference genome for mapping population-level resequencing data. In some model systems, multiple reference genomes are available, giving the challenging task of determining which reference genome best suits the data. Here, we compared the use of two different reference genomes for the three-spined stickleback (Gasterosteus aculeatus), one novel genome derived from a European gynogenetic individual and the published reference genome of a North American individual. Specifically, we investigated the impact of using a local reference versus one generated from a distinct lineage on several common population genomics analyses. Through mapping genome resequencing data of 60 sticklebacks from across Europe and North America, we demonstrate that genetic distance among samples and the reference genomes impacts downstream analyses. Using a local reference genome increased mapping efficiency and genotyping accuracy, effectively retaining more and better data. Despite comparable distributions of the metrics generated across the genome using SNP data (i.e. π, Tajima's D and FST ), window-based statistics using different references resulted in different outlier genes and enriched gene functions. A marker-based analysis of DNA methylation distributions had a comparably high overlap in outlier genes and functions, yet with distinct differences depending on the reference genome. Overall, our results highlight how using a local reference genome decreases reference bias to increase confidence in downstream analyses of the data. Such results have significant implications in all reference-genome-based population genomic analyses.

Genetic characterization of Macaca arctoides: A highlight of key genes and pathways.

When compared to the approximately 22 other macaque species, Macaca arctoides has many unique phenotypes. These traits fall into various phenotypic categories, including genitalia, coloration, mating, and olfactory traits. Here we used a previously identified whole genome set of 690 outlier genes to look for possible genetic explanations of these unique traits. Of these, 279 genes were annotated miRNAs, which are non-coding. Patterns within the remaining outliers in coding genes were investigated using GO (n = 370) and String (n = 383) analysis, which showed many interconnected immune-related genes. Further, we compared the outliers to candidate pathways associated with M. arcotides' unique phenotypes, revealing 10/690 outlier genes that overlapped these four pathways: hedgehog signaling, WNT signaling, olfactory, and melanogenesis. Of these, genes in all pathways except olfactory had higher FST values than the rest of the genes in the genome based on permutation tests. Overall, our results point to many genes each having a small impact on phenotype, working in tandem to cause large systemic changes. Additionally, these results may indicate pleiotropy. This seems to be especially true with the development and coloration of M. arctoides. Our results highlight that development, melanogenesis, immune function, and miRNAs may be heavily involved in M. arctoides' evolutionary history.

Abstract 6067: Characterizing the landscape of rare structural variant events in newly diagnosed multiple myeloma

Abstract Background: Structural variants (SV) are known to play a critical role in the pathogenesis of multiple cancer types. Using whole genome sequencing (WGS), we recently characterized the SV landscape of 752 newly diagnosed multiple myeloma (MM) patients, identifying 68 SV hotspots and 152 recurrent copy number aberrations (CNA; Rustad et al. Blood Cancer Discovery 2020). Despite comprehensive annotation, more than half of SVs were not linked to any known MM genomic driver. The biological impact of these SV events, here defined as rare SV, occurring in 93% (702/752) of patients, is unknown. Methods: To study the biological impact of rare SVs, we interrogated WGS (n=752) and RNAseq (n=591) in the CoMMpass trial. Recurrent SVs identified by involvement in canonical Ig translocations, recurrent CNAs, or SV hotspots were excluded. All SVs within an event must not involve a recurrent region to be defined rare. To determine SV class-specific gene relationships, breakpoint enrichment was compared against a permuted background model for each SV class and gene expression direction, up to 1 Mb. Genes were considered affected if expression was above a gene specific outlier Z-score of +/- 2. Lastly, we modeled breakpoint density to the nearest MM superenhancer up to 10 MB, and compared to permuted background rates. Results: Of the total 8,942 SVs, 4,959 (55%) were identified as rare. 201 (34%) patients had at least 1 enriched rare SV event associated with gene expression outliers. Amongst over-expressed gene outliers, rare templated insertions and duplications were enriched within the gene body and up to 1 MB away. Rare inversions were enriched in genes 100kb and 1MB away, and rare translocations were associated with outliers 1 MB away. Amongst under-expressed gene outliers, rare complex SVs were enriched within the gene body, while deletion SVs were enriched in the gene body and up to 1 MB away. Rare duplications, translocations and templated insertions were enriched up to 1 MB of superenhancers. Rare templated insertions were significantly enriched against the background model (p < 0.001, Fisher Exact). Overall, 82% (104/126) of gene outliers affected by rare templated insertions were associated with superenhancers, (95 over-expressed, e.g. IRF6 and 9 under-expressed), 54% (130/237) by rare translocations, (105 overexpressed, e.g. FAM46A and 25 under-expressed), and 55% (96/172) by rare duplication events, (93 overexpressed, e.g. MAPK13, and 7 under-expressed). In addition, among the 853 outlier genes affected by enriched rare SVs, at least 15 are involved with B-cell development, suggesting a potential driver role in myeloma pathogenesis. Conclusion: In summary, leveraging WGS and RNA-seq of clinical samples, we demonstrate that rare SVs are frequently associated with aberrant gene expression, expanding our understanding of their potential role in heterogenous clinical response in patients diagnosed with MM. Citation Format: Monika Chojnacka, Benjamin Diamond, Bachisio Ziccheddu, Even Rustad, Kylee Maclachlan, Marios Papadimitriou, Eileen Boyle, Patrick Blaney, Saad Usmani, Gareth Morgan, Ola Landgren, Francesco Maura. Characterizing the landscape of rare structural variant events in newly diagnosed multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6067.

Geography, environment, and colonization history interact with morph type to shape genomic variation in an Arctic fish.

Polymorphic species are useful models for investigating the evolutionary processes driving diversification. Such processes include colonization history as well as contemporary selection, gene flow, and genetic drift, which can vary between intraspecific morphs as a function of their distinct life histories. The interactive and relative influence of such evolutionary processes on morph differentiation critically informs morph-specific management decisions and our understanding of incipient speciation. We therefore investigated how geographic distance, environmental conditions, and colonization history interacted with morph migratory capacity in the highly polymorphic fish species, Arctic Charr (Salvelinus alpinus). Using an 87 k SNP chip we genetically characterized recently evolved anadromous, resident, and landlocked charr collected from 45 locations across a secondary contact zone of three charr glacial lineages in eastern Canada. A strong pattern of isolation by distance across all populations suggested geographic distance principally shaped genetic structure. Landlocked populations had lower genetic diversities and higher genetic differentiation than anadromous populations. However, effective population size was generally temporally stable in landlocked populations in comparison to anadromous populations. Genetic diversity positively correlated with latitude, potentially indicating southern anadromous populations' vulnerability to climate change and greater introgression between the Arctic and Atlantic glacial lineages in northern Labrador. Local adaptation was suggested by the observation of several environmental variables strongly associating with functionally relevant outlier genes including a region on chromosome AC21 potentially associated with anadromy. Our results demonstrate that gene flow, colonization history, and local adaptation uniquely interact to influence the genetic variation and evolutionary trajectory of populations.

Single Cell Multi-Omic Profiling of Acute Lymphoblastic Leukemia Identifies Altered Cellular Pathways and Highlights Heterogeneity in the Maturation Block of DUX4-Rearranged ALL

B-cell progenitor acute lymphoblastic leukemia (BCP-ALL) is the most common childhood malignancy. The established subtypes of BCP-ALL, such as BCR::ABL1-positive, ETV6::RUNX1-positive, and high hyperdiplod (HeH) ALL, are important for diagnosis, prognostication, and treatment stratification. However, we and others have described novel subtypes, of which DUX4-rearranged (DUX4-r) ALL is among the most common. The subtype-specific rearrangements of DUX4 result in aberrant expression of a truncated version of this gene, which normally encodes an embryonic transcription factor. ALL is typically associated with a good prognosis, and DUX4-r ALL represents a favorable subtype. However, the cytotoxic treatment is associated with significant short- and long-term side effects. By an increased understanding of the cellular and molecular characteristics of ALL, the development of more specific treatment alternatives with high efficacy and less toxicity should be facilitated. We have used a multimodal single cell approach to delineate the transcriptional, epigenetic and phenotypic characteristics of a cohort of childhood BCP-ALLs (n=23) with a focus on the novel DUX4-r ALL subtype (n=11). The cohort also included three established subtypes, with four samples each of BCR::ABL1, ETV6::RUNX1, and HeH. The leukemic blast cell populations of the DUX4-r cases (blast count at diagnosis: 70 - 96 %) displayed distinct gene expression profiles. To deduce where the blast cells were located along a normal differentiation axis, we projected the blast cells onto a reference force directed knn-graph of normal bone marrow (NBM) cells from five healthy donors (Fig. 1). The projection of cells allowed inferring the most likely stage of maturation for each cell, as well as visualizing the altered gene expression profile compared to their healthy counterparts using a "dissimilarity score". Several DUX4-r ALL (4/11) samples exhibited a lack of B-cell progenitors after a distinct point in the graph, suggesting a distinct maturation block. For the remaining samples, the blast cells were dispersed throughout the B-cell maturation axis, indicating a partial or a more heterogenous arrest among the leukemic blasts. We also classified the cell type of each cell in the ALL samples according to a NBM reference. With this method, most of the blast cells were characterized as early progenitor B-cells. However, other cell classifications also occurred. In fact, in five of eleven DUX4-r ALLs, more than 35% of the blast population were classified as a cell type distinct from B-cell progenitors. Interestingly, the classification of blast cells as granulocyte-macrophage progenitors (GMP) was particularly common among DUX4-r ALLs (2-30% of blast cells) compared to the other subtypes (0-12%). However, such cells displayed an altered gene expression profile compared to GMP cells in NBM, as manifested by a high dissimilarity score. Nevertheless, this could suggest a higher plasticity or a more myeloid-biased transcriptional state among the blast cells within the DUX4-r subtype compared to other subtypes. To further delineate altered pathways in DUX4-r blast cells, differential gene expression analyses were performed, both between DUX4-r blast cells and blast cells of other subtypes, as well as between DUX4-r blast cells and NBM progenitor B-cells. The DUX4-r blast cells were characterized by high expression of distinct outlier genes, including ANGPT2, MCAM, EGR1, PDGFRA, NR4A1, AFT3 and PTPRM. Notably, GSEA highlighted upregulation of gene sets involved in angiogenesis- and vasculature development. In conclusion, by single cell multi-omic profiling we demonstrate that BCP-ALL blast cells are associated with distinct patient-specific gene expression patterns that are strongly influenced by subtype. DUX4-r ALL blast cells are associated with a heterogenous maturation block, transcriptional features associated with angiogenesis- and vasculature development, and possibly also higher plasticity with a myeloid-biased transcriptional state. The results should, hopefully, form a basis towards improved future diagnostics and treatment stratification of BCP-ALL, and aid the discovery of targeted treatment alternatives. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Maximum SNP FST Outperforms Full-Window Statistics for Detecting Soft Sweeps in Local Adaptation.

Local adaptation can lead to elevated genetic differentiation at the targeted genetic variant and nearby sites. Selective sweeps come in different forms, and depending on the initial and final frequencies of a favored variant, very different patterns of genetic variation may be produced. If local selection favors an existing variant that had already recombined onto multiple genetic backgrounds, then the width of elevated genetic differentiation (high FST) may be too narrow to detect using a typical windowed genome scan, even if the targeted variant becomes highly differentiated. We, therefore, used a simulation approach to investigate the power of SNP-level FST (specifically, the maximum SNP FST value within a window, or FST_MaxSNP) to detect diverse scenarios of local adaptation, and compared it against whole-window FST and the Comparative Haplotype Identity statistic. We found that FST_MaxSNP had superior power to detect complete or mostly complete soft sweeps, but lesser power than full-window statistics to detect partial hard sweeps. Nonetheless, the power of FST_MaxSNP depended highly on sample size, and confident outliers depend on robust precautions and quality control. To investigate the relative enrichment of FST_MaxSNP outliers from real data, we applied the two FST statistics to a panel of Drosophila melanogaster populations. We found that FST_MaxSNP had a genome-wide enrichment of outliers compared with demographic expectations, and though it yielded a lesser enrichment than window FST, it detected mostly unique outlier genes and functional categories. Our results suggest that FST_MaxSNP is highly complementary to typical window-based approaches for detecting local adaptation, and merits inclusion in future genome scans and methodologies.