PATTERNS OF SEX-BIASED GENE EXPRESSION AND GENE PATHWAY EVOLUTION IN DROSOPHILA

Sexual dimorphism, or the phenotypic differences that exist between males and females of the same species, is widespread throughout nature. Sexually dimorphic traits are primarily generated by differences in gene expression between the sexes, commonly known as sex-biased gene expression. In this dissertation, I explore the evolutionary patterns and consequences of sex-biased gene expression across Drosophila species. The most obvious sexually dimorphic characteristics exist in adult stages and consequently patterns of sex-bias in early Drosophila development have not been well-studied. In chapter 1, I examine patterns of sex-biased gene expression during ontogeny in two closely related Drosophila species belonging to the D. pseudoobscura group (D. pseudoobscura and D. persimilis). This study provides insight into global patterns of sex-bias gene expression throughout development between species. The visual pathway in Drosophila shows abundant evidence for sex-biased and species-specific differential gene expression. In chapter 2, across 12 different Drosophila species, I examine rates of protein sequence evolution of genes in this pathway to determine if observed differences in gene expression correlate with rates of evolutionary change at the level of protein sequence. As a whole the visual pathway in Drosophila exhibits strong conservation at the level of protein sequence over 65 million years of evolutionary time suggesting that observed differences in levels of transcription are the result of differences in the underlying regulatory mechanisms. The comparative molecular evolutionary analysis of the visual pathway revealed a novel isoform-specific lineage-specific duplication event of the key signal transduction activator gene G-alpha-q. In D. melanogaster, G-alpha-q is present as a single-copy and alternatively spliced in a tissue- and isoform-specific manner. The same gene is duplicated in an isoform-specific manner in the species belonging to the subgenus Drosophila such that each duplicate appears to retain the exon complement of only one of the splice-variants. In chapter 3, using experimental and computational approaches, I examine the evolution of the gene structure and expression of these novel isoform-specific duplicates. This analysis revealed a mechanism by which duplicate genes can evolve novel functions and expression patterns (including sex-biased expression patterns) while retaining their ancestral functions

Multi-omics inference of differential breast cancer-related transcriptional regulatory network gene hubs between young Black and White patients.

OBJECTIVE: Breast cancers (BrCA) are a leading cause of illness and mortality worldwide. Black women have a higher incidence rate relative to white women prior to age 40 years, and a lower incidence rate after 50 years. The objective of this study is to identify -omics differences between the two breast cancer cohorts to better understand the disparities observed in patient outcomes. MATERIALS AND METHODS: Using Standard SQL, we queried ISB-CGC hosted Google BigQuery tables storing TCGA BrCA gene expression, methylation, and somatic mutation data and analyzed the combined multi-omics results using a variety of methods. RESULTS: Among Stage II patients 50 years or younger, genes PIK3CA and CDH1 are more frequently mutated in White (W50) than in Black or African American patients (BAA50), while HUWE1, HYDIN, and FBXW7 mutations are more frequent in BAA50. Over-representation analysis (ORA) and Gene Set Enrichment Analysis (GSEA) results indicate that, among others, the Reactome Signaling by ROBO Receptors gene set is enriched in BAA50. Using the Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER) algorithm, putative top 20 master regulators identified include NUPR1, NFKBIL1, ZBTB17, TEAD1, EP300, TRAF6, CACTIN, and MID2. CACTIN and MID2 are of prognostic value. We identified driver genes, such as OTUB1, with suppressed expression whose DNA methylation status were inversely correlated with gene expression. Networks capturing microRNA and gene expression correlations identified notable microRNA hubs, such as miR-93 and miR-92a-2, expressed at higher levels in BAA50 than in W50. DISCUSSION/CONCLUSION: The results point to several driver genes as being involved in the observed differences between the cohorts. The findings here form the basis for further mechanistic exploration

Abstract C002: Interleukin-1 receptor accessory protein (IL1RAP) overexpression is associated with worse prognosis in PDAC and is targetable by nadunolimab

Abstract Introduction: The pancreatic tumor microenvironment (TME) is a major driver of tumor progression, chemoresistance and immune suppression, and the IL-1 axis has been implicated in tumor-promoting signaling networks. The IL1RAP-IL1R1 receptor complex is required for both IL-1α and IL-1β signaling and is expressed on tumor and stromal/immune cells. Nadunolimab is a fully humanized, ADCC-enhanced IgG1 antibody that targets IL1RAP and disrupts both IL-1α/IL-1β signaling. In the phase 1/2a trial CANFOUR (NCT03267316) in which 73 PDAC patients with previously untreated, locally advanced or metastatic PDAC received nadunolimab with gemcitabine/nab-paclitaxel (GN), encouraging preliminary data show median iPFS of 7.2 mo, median OS of 12.9 mo and 1-year survival of 58%. The present analyses investigated the correlation between IL1RAP expression and disease severity as well as therapeutic efficacy of nadunolimab/GN. Methods: Evaluable screening biopsies from 46 CANFOUR PDAC patients were stained for IL1RAP by immunohistochemistry. mRNA data was obtained from the GTEx, TCGA and Pancreatic Cancer Action Network’s Know Your Tumor (KYT) databases and correlated to patient data (TCGA, KYT) and mutational data (KYT). Results: IL1RAP expression was detected on tumor cells, fibroblasts, and infiltrating immune cells in tumor biopsies. Notably, all tumor cells expressed IL1RAP and stratification of evaluable samples into high or low tumor cell expression of IL1RAP revealed significantly prolonged median OS on nadunolimab/GN in IL1RAP high compared to IL1RAP low patients (14.2 vs 10.6 mo, p=0.017). This was also reflected in longer iPFS (8.0 vs 5.8 mo), 1-year survival (69 vs 40%) and iORR (52 vs 32%). Although varying in intensity, IL1RAP expression on stromal cells did not correlate significantly with treatment efficacy. In interrogating bulk RNA seq data from the GTEx, TCGA and KYT databases, IL1RAP was overexpressed in pancreatic cancer compared to normal tissue, with higher expression in advanced stage disease. Moreover, as KRAS mutations promote tumor inflammation and KRASG12D has been implicated in activating the IL-1 axis, we observed that KRASG12D tumors had a higher expression of IL1RAP and IL-1α, but not IL-1β, compared to KRAS-wildtype tumors. In both early and late-stage PDAC, high levels of IL1RAP strongly correlated with poor survival (both p<0.0001). Patients responding to GN tended to have lower IL1RAP levels than patients with progressive or stable disease, in stark contrast to the results with nadunolimab/GN. To understand the effects of nadunolimab/GN in the TME, spatial transcriptomics in screening and on-treatment biopsies are being analyzed with Nanostring GeoMx. Conclusion: IL1RAP is upregulated in pancreatic cancer, its expression correlates with oncogenic KRAS driver mutations and is strongly associated with poor survival. The IL1RAP-targeting antibody nadunolimab has shown promising target-dependent efficacy in combination with 1st line chemotherapy, indicating that IL1RAP is a both highly relevant and targetable protein in PDAC. Citation Format: Eric Van Cutsem, Kawther Abdilleh, Jashodeep Datta, Manuel Hidalgo, Camilla Rydberg Millrud, Petter Skoog, Annika Sanfridson, Dominique Tersago, David Liberg. Interleukin-1 receptor accessory protein (IL1RAP) overexpression is associated with worse prognosis in PDAC and is targetable by nadunolimab [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C002

A cloud-based resource for genome coordinate-based exploration and large-scale analysis of chromosome aberrations and gene fusions in cancer.

Cytogenetic analysis provides important information on the genetic mechanisms of cancer. The Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (Mitelman DB) is the largest catalog of acquired chromosome aberrations, presently comprising \u3e70 000 cases across multiple cancer types. Although this resource has enabled the identification of chromosome abnormalities leading to specific cancers and cancer mechanisms, a large-scale, systematic analysis of these aberrations and their downstream implications has been difficult due to the lack of a standard, automated mapping from aberrations to genomic coordinates. We previously introduced CytoConverter as a tool that automates such conversions. CytoConverter has now been updated with improved interpretation of karyotypes and has been integrated with the Mitelman DB, providing a comprehensive mapping of the 70 000+ cases to genomic coordinates, as well as visualization of the frequencies of chromosomal gains and losses. Importantly, all CytoConverter-generated genomic coordinates are publicly available in Google BigQuery, a cloud-based data warehouse, facilitating data exploration and integration with other datasets hosted by the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC) Resource. We demonstrate the use of BigQuery for integrative analysis of Mitelman DB with other cancer datasets, including a comparison of the frequency of imbalances identified in Mitelman DB cases with those found in The Cancer Genome Atlas (TCGA) copy number datasets. This solution provides opportunities to leverage the power of cloud computing for low-cost, scalable, and integrated analysis of chromosome aberrations and gene fusions in cancer

PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trial.

ObjectivesIn this paper, we discuss leveraging cloud-based platforms to collect, visualize, analyze, and share data in the context of a clinical trial. Our cloud-based infrastructure, Patient Repository of Biomolecular Entities (PRoBE), has given us the opportunity for uniform data structure, more efficient analysis of valuable data, and increased collaboration between researchers.Materials and methodsWe utilize a multi-cloud platform to manage and analyze data generated from the clinical Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2 (I-SPY 2 TRIAL). A collaboration with the Institute for Systems Biology Cancer Gateway in the Cloud has additionally given us access to public genomic databases. Applications to I-SPY 2 data have been built using R Shiny, while leveraging Google's BigQuery tables and SQL commands for data mining.ResultsWe highlight the implementation of PRoBE in several unique case studies including prediction of biomarkers associated with clinical response, access to the Pan-Cancer Atlas, and integrating pathology images within the cloud. Our data integration pipelines, documentation, and all codebase will be placed in a Github repository.Discussion and conclusionWe are hoping to develop risk stratification diagnostics by integrating additional molecular, magnetic resonance imaging, and pathology markers into PRoBE to better predict drug response. A robust cloud infrastructure and tool set can help integrate these large datasets to make valuable predictions of response to multiple agents. For that reason, we are continuously improving PRoBE to advance the way data is stored, accessed, and analyzed in the I-SPY 2 clinical trial

PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trial.

Objectives: In this paper, we discuss leveraging cloud-based platforms to collect, visualize, analyze, and share data in the context of a clinical trial. Our cloud-based infrastructure, Patient Repository of Biomolecular Entities (PRoBE), has given us the opportunity for uniform data structure, more efficient analysis of valuable data, and increased collaboration between researchers. Materials and Methods: We utilize a multi-cloud platform to manage and analyze data generated from the clinical Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2 (I-SPY 2 TRIAL). A collaboration with the Institute for Systems Biology Cancer Gateway in the Cloud has additionally given us access to public genomic databases. Applications to I-SPY 2 data have been built using R Shiny, while leveraging Google\u27s BigQuery tables and SQL commands for data mining. Results: We highlight the implementation of PRoBE in several unique case studies including prediction of biomarkers associated with clinical response, access to the Pan-Cancer Atlas, and integrating pathology images within the cloud. Our data integration pipelines, documentation, and all codebase will be placed in a Github repository. Discussion and conclusion: We are hoping to develop risk stratification diagnostics by integrating additional molecular, magnetic resonance imaging, and pathology markers into PRoBE to better predict drug response. A robust cloud infrastructure and tool set can help integrate these large datasets to make valuable predictions of response to multiple agents. For that reason, we are continuously improving PRoBE to advance the way data is stored, accessed, and analyzed in the I-SPY 2 clinical trial