Li Ding, Ph.D. - US grants

DESCRIPTION (provided by applicant): High-throughput sequencing (HTS) platforms are revolutionizing genomics and health research. The incredible throughput of new sequencing instruments has enabled sequencing of genomes, exomes, methylomes, and transcriptomes in both research and clinical settings. As the cost of DNA sequencing has plummeted, two important trends have become apparent. First, the cost of analysis, in terms of computing resources and personnel, will soon surpass the cost of data generation. This will increase the pressing demand for analytical algorithms that run faster, with fewer CPU/memory resources, while processing overgrowing data sets. Second, the advent of HTS technologies has put low-cost, high-throughput sequencing into the hands of small research labs and clinical investigators; groups that are not accustomed to dealing with this type and scale of data. These developments will undoubtedly yield an unprecedented number of new discoveries, clinical insights, and medical breakthroughs in the coming years, provided the outstanding issues of HTS data analysis (short read lengths, inherent errors, and sheer number of sequence reads) can be conclusively resolved. Until now, most HTS has taken place in large genome centers with teams of bioinformaticians and substantial computing infrastructures. There is an urgent need to make their analysis tools and next-generation pipelines available to the wider research community as easy to install and use packages. We have spent several years developing a computational framework and innovative tools for HTS data analysis, with a particular focus on the discovery and interpretation of genetic variants. Our goal in this proposal is to make these tools available to the wider community, both individually and as part of a complete informatics solution from alignment to detection to interpretation. The solution we describe is flexible and powerful enough to be adopted by experienced laboratories, while at the same time providing high quality, push-button analysis of sequence data for those with little bioinformatics expertise. The framework will run in the cloud or on a single CPU, enabling researchers, educators, and clinicians to speed the transition from sequencing technology adoption to biological knowledge and clinical application. PUBLIC HEALTH RELEVANCE: The promise of the personalized medicine will only be realized when each individual's genetic code can be read and analyzed in the clinical setting. Unfortunately, the associated technologies will generate massive amounts of data that are difficult to analyze and interpret. The software describe in this proposal will enable widespread and easy analysis and interpretation of genetic data, accelerating the overall understanding of genetic information and its application to human health.

DESCRIPTION (provided by applicant): Large-scale cancer genomics projects such as The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Pediatric Cancer Genome Project (PCGP) are producing a wealth of high throughput sequence data from a large number of cancer samples and their matched normals. These data hold great promise for understanding the genetic basis of cancer and also for the identification of germline susceptibility variants in cancer. Major advancements have been made to systematically catalog somatic variations in cancer genomes from these data sets. However, identifying and interpreting germline changes using data from these studies remains a significant challenge. The primary difficulty stems from 1) the lack of computational pipelines/tools to utilize tumor and normal sequencing data for simultaneous detection of somatic, germline, and LOH events at the nucleotide and chromosomal levels and 2) the lack of uniform bioinformatics analysis strategies for identifying and prioritizing deleterious candidate germline variants responsible for susceptibility. We will develop a computational pipeline for the identification and interpretation f germline alterations in cancer including single nucleotide variants, insertions and deletions (indels), copy number variations, and structural variants. This pipeline will be initially used to systematically analyze whole genome, exome, and RNA-sequencing data from over 5,000 cancer cases already generated by several major efforts and individual research groups and additional cases that will be made publicly available in the next several years. In silico predicte deleterious germline variants from these data will be used for statistical association analysis across groups stratified by age and cancer type to identify novel germline susceptibility variants, genes, and pathways involved in different cancer types. We will further investigate the potential interaction between germline susceptibility variants and somatic mutational landscape. Finally, both pipeline and results from this project will be made publically available, facilitating the analysis and interpretation by the research community of the ever- growing large-scale cancer sequencing data to better discover and understand germline susceptibility variants.

DESCRIPTION (provided by applicant): Although overt and latent viral infections are widespread in human populations, only few viruses have been linked to tumorigenesis. While this may be the result of coevolution between human and viruses, it appears more likely that the low number is a reflection of the difficulties in establishing causal relations between viral infection and cancer. For the few confirmed oncoviruses (HPV, HBV, HCV, EBV, etc.), some infected patients develop cancers with variable clinical course and presentation. We hypothesize that additional essential events in the host or the viruses are required for the cancers to initiate/progress and that many more cancers than we currently know have a viral connection. A wealth of cancer sequence data is being produced by multiple large- scale cancer projects, such as the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and Pediatric Cancer Genome Project (PCGP). In order to take advantage of these valuable data sets for testing the above hypothesis, we propose to develop a set of computational methods and analysis strategies to characterize the viromes, genomes, and transcriptomes in different cancers with known clinical features. In particular, we will first build a computational pipeline fr the identification and characterization of viruses in cancer. This effort will be augmented by developing statistical approaches to establish the association among virus characteristics, host genetic alterations, and clinical features (Aim 1). This pipeline will first be applied to detect ad characterize viruses in cancer types such as cervical cancer, head and neck cancer, and hepatocellular carcinoma, all of which are known to be associated with viral etiology. Beyond confirming the links between these cancers and their known oncoviruses, HPV and HBV/HCV respectively, we will aim at a thorough characterization of all genomic and transcriptomic changes in both host and virus. Combined with clinical features of the cancers, we expect to establish association between such changes and the status of the cancers (Aim 2). Taking a step further, we will also utilize this validated pipeline to systematically analyze sequence data from cancer types having some initial evidence of viral involvement from animal model and epidemiology studies, the aim being to perform more sensitive detections of cancer-causing viruses missed by traditional approaches and to establish the statistical association between viral infection and tumor formation using uniform and high quality data from a large number of tumor samples (Aim 3). The successful analysis of the viral and host genes, transcriptomes, and genomes of over 6,000 cancer cases from many cancer types already sequenced by several major efforts will produce, for the first time, a state-of-the-art knowledge base of the cancer virome. We anticipate that new pathogenic viruses and/or subtypes will be discovered and more cancers will be explained by viruses. This would lead to a paradigm shift for cancer prevention and treatment. Finally, both pipeline and results from this project will be made publically available, facilitating the analysis and interpretation by the research community to better discover and understand viruses in cancer.

PROJECT SUMMARY Large-scale cancer sequencing efforts provide a unique opportunity for the discovery of germline and somatic driver alterations influencing cancer susceptibility, initiation, progression, and clinical response. Detecting such alterations is fundamentally and technically challenging for several reasons including: 1) the combinatorially enormous number of ways that a genome can be altered, 2) the presence of various sized repeats, highly homologous gene families, and other contextual influences on alignment and detection accuracy, 3) systematic errors inherent in current sequencing technologies and tumor preservation techniques, and 4) intratumoral and intertumoral heterogeneity including clonality, purity, and lymphocyte infiltration. As a result, the full complement of driver events for the typical tumor still defies identification and, in many cases, no drivers can be found. Our recent work has also demonstrated that some types of indels/SVs such as complex indels, ITD/PTD (internal/partial tandem duplications), and homopolymer indels are often missed by existing approaches. Beyond detection challenges, functional interpretation of the impact of genomic alterations requires strategies that integrate WGS/exome, RNA-seq, and protein data to reveal translational, splicing, and protein structural effects. In addition, cooperative dynamics between germline and somatic alterations are usually missed, as these events have been analyzed independently. As cancer sequencing projects expand to include well-curated clinical phenotypes, methods necessary to understand the pathogenicity and druggability of driver alterations that underlie phenotypes such as drug resistance or exceptional responders are also urgently needed. To fully harness the power of large-scale cancer genomics and to facilitate advances in personalized medicine, our group proposes to focus on two core competencies, coding and non-coding mutations, outlined in the RFA. In collaboration with other GDACs, GDC, and AWGs, we will extend computational approaches that we have successfully established and applied for TCGA and ICGC projects to detect and functionally and clinically interpret germline and somatic drivers using sequencing data from GCC along with curated clinical data.

With the discovery of a number of new molecular targets and exponential increase in the number of anti-cancer agents, it has become imperative to optimize preclinical models in order to design rational clinical trials. Recognizing the unique advantage of the patient-derived xenografts (PDXs) models in drug testing and personalized medicine, physicians and researchers at Washington University School of Medicine (WUSM) and the Siteman Cancer Center have been actively generating and utilizing PDX models for cancer research and the evaluation of molecularly targeted agents. Building on our strength and existing infrastructure, we propose to establish the Washington University PDX Development and Trial Center (WU-PDTC), as part of PDXNet program, to promote preclinical testing in a collaborative nation-wide effort. The PDX core within WU-PDTC will develop and characterize at least 1000 new pathogen-free PDX models across major tumor types. The PDX models will be genomically and phenotypically characterized using the latest ?omics technologies and expertly analyzed using the most current data analysis pipelines that have been deployed for other large scale NCI programs. The Bioinformatics Core will integrate these analyzes with clinical annotation from the originating patient to include patient treatment history and tumor response (Aim 1). Our two research projects will conduct PDX clinical trials using single agent and combinational agents using drugs under NCI-IND (Aim 2). Project 1 will test pan- or beta isoform specific class I PI3K inhibitors in over 100 breast PDX models while Project 2 will study combinatorial approaches that overcome tumor intrinsic and extrinsic mechanisms to ERK inhibition in over 100 already available pancreatic PDX models. Proteogenomic and clinical response data will be collected in these models, as part of a broader effort of characterizing PDX models and conducting clinical correlation and treatment response analyses by the Bioinformatics Core (Aim 3). All relevant information, including proteogenomic features of PDX models and treatment/response history, will be tracked in a dedicated relational database that will be accessible to PDXNet and PDMR-FNLCR. The goal is to identify candidates for human clinical trials for the ET-CTN. In Aim 4, the WU-PDTC will leverage existing expertise and programs from the NCI-designated Comprehensive Siteman Cancer Center, Institute of Clinical and Translational Research (ITCS), McDonnell Genome Institute, Mallinkrodt Imaging Research Center, and Early Therapeutic Clinical Trials Network (ET-CTN) to support the goals of developing and utilizing PDX models to test and improve cancer treatment, in collaboration with other components of the PDXNet. Finally, WU-PDTC, through coordination by the Administration Core, will support pilot research projects utilizing the PDX resources and fostering collaboration across PDTCs, PNMR-FNLCR, and other NCI programs. The goal is to increase the spectrum of agents tested in PDX clinical trials and to improve the reliability, validation, utility, and standardization of PDX models through innovative Cross-PDXNet research. Our demonstrated capability in generating and utilizing PDX models, extensive existing infrastructure, and strong institutional commitment set a solid foundation for a successful PDTC that will help achieve goals set by NCI in advancing the use of PDX models in preclinical testing.

Project Summary Falling costs of generating genomic data and computational advances in discerning health-affecting variants therein are bringing personalized molecular medicine closer to reality. Progress has also been made on establishing guidelines (e.g., by the American College of Medical Genetics and Genomics) for the interpretation of sequence variants. However, the crucial step of systematically and accurately interpreting their clinical implications remains an unsolved problem. Specifically, clinical interpretation is technically challenging for several reasons, including: 1) the enormous number of variants in individual genomes, making it difficult to pinpoint causal variants, 2) limited functional/clinical data at the gene and variant levels, 3) discovery of novel clinical variants is a tedious low-throughput process using traditional laboratory and clinical approaches, and 4) conventional bioinformatics tools tend to have insufficient precision based on limitations imposed by linear sequence analysis alone. As a result, clinical genomics is still far too costly for routine clinical use. To meet the urgent need of high precision clinical variant interpretation, our proposal aims to 1) build upon existing clinical knowledge (ClinVar) from ClinGen efforts, 2) utilize rich human variation data in public databases (e.g., ExAC and dbSNP), and 3) leverage existing and upcoming sequencing data from large disease cohorts and small family studies; all to support developing/employing a cross-cutting computational/experimental strategy for clinical variant discovery at a massive scale across a broad spectrum of human diseases. We hypothesize that variants clustering in 3D spatial proximity to known pathogenic variants have high probabilities of affecting protein function. We hypothesize further that many pathogenic variants in databases such as ExAC remain undetected/hidden due to their recessive nature or their rarity that limits statistical power for detection in association analyses. To test these hypotheses and to establish a database for functionally important variants associated with human diseases, we propose to develop a software system called ClinPath3D to detect and characterize clinically relevant pathogenic variants. Essentially, it will utilize protein structures and variant pathogenicity potential to identify 3D spatial pathogenic variant clusters (PVCs) (Aim 1). We will then apply ClinPath3D to interpret rare variants of unknown significance (VUS) from the ExAC, dbSNP, and other variant databases using pathogenic variants obtained from ClinVar as nucleation points for clustering, all with a view toward discerning disease variants in the general population (Aim 2). Finally, we will use large sequencing data sets (CCDG, TopMed, UK100K) to statistically assess variant enrichment in specific disease cohorts and will further improve positive results by experimentally characterizing 50-100 high-priority variants in kinases and 50-100 in transcription factors (Aim 3). Results from these studies will contribute to clinical advancement in two key ways: (1) methodological improvement of identifying pathogenic/functional variants in patient genomes and (2) the building of a comprehensive database of clinically relevant variants across a broad spectrum of disease types.

Project Summary/Abstract: Administrative Core The Administrative Core will provide executive oversight and administrative support for the construction of the tumor atlases for TNBC, Pancreas and Glioblastoma by the Washington University Human Tumor Atlas Research Center (WU-HTARC). The Administrative Core will also provide the infrastructure for communications across the three disease groups that are the focus for the atlases and also for scientific collaborations within the Human Tumor Atlas Network and other companion NCI initiatives. To achieve these goals the admin core will: 1. Facilitate executive oversight of the HTARC and each of the Units. Facilitate executive oversight by providing timely reporting to the HTARC Leadership Committee, and by coordinating Leadership Committee meetings, and Internal Advisory Board meetings. 2. Provide administrative and fiscal oversight for all HTARC components. This will include but is not limited to the management of budgets and subcontracts as appropriate, and preparation of annual progress reports with input from the Unit Leaders. 3. Coordinate all HTARC meetings. Coordination of all internal HTARC and external HTAN meetings will be executed by the Center Administrator. 4. Facilitate HTARC-HTANet communications and collaborations. Facilitate HTARC-HTANet communications through development and maintenance of a HTARC website, monthly Unit meetings and Leadership Committee meetings, annual symposium, and monthly electronic newsletter. 5. Coordinate and manage the HTARC. Support developing collaborations to include pilot projects to be funded through the HTARC Pilot funds specifically for Trans-HTAN projects. 6. Provide general administrative support for HTARC investigators. Assist investigators with the preparation of scholarly presentations, publications, regulatory documentation, and all other paperwork generated by the HTARC and necessary to conduct HTAN collaborative work.

Project Summary/Abstract: Data Analysis Unit The over-arching goal of the Data Analysis Unit for the Washington University Human Tumor Atlas Research Center (WU-HTARC) is to provide bioinformatics tools and processing/analysis infrastructure for in-depth analyses of the data generated in the Characterization Unit. Most importantly, we will integrate data across both the methodological (omics/imaging/phenotypic analyses) and the dimensional (1D/2D/3D/time) spectrums into coherent and accessible tumor atlases for each of the three cancer types: GBM, PDAC, and BRCA/TNBC. At the basic level, each atlas will consist of first cataloging a variety of numerically-computed metrics for cell types, including fractions (1D), density, dispersion, and location measures for individual cell types and Euclidean measures of spatial interspersedness of different cell types, e.g. immune and tumor cells (2D and 3D), and how these metrics change with time. We will then correlate this information with both genomic analyses, such as mutation signatures, clonality, and significantly mutated genes/regions/pathways, proteomic and metabolomics analyses, and image-derived data. The core of the atlas will be a MySQL relational database that not only stores all collected data, but links them along these different dimensions. Users will interface with the atlas through a sophisticated viewer/query browser-based web portal that will support both traditional text-based queries, as well as spatial-based queries (shape, feature locations, etc.). The cohesion among the three atlases, in terms of the spectrum of data used and the approaches of their construction, will allow users to generate new types of hypotheses not now possible and to perform pan-cancer analyses to reveal commonalities and differences in the three representative solid tumors, and to potentially extrapolate these findings to other tumors.

Project Summary/Abstract: Overall Diverse areas of cancer research have progressed to the point that it is now feasible to meaningfully integrate research data and clinical information across the molecular, cellular, and tissue realms into a larger, more detailed picture of the onco-dynamics of cancer, including spatial-temporal details during cancer treatment and progression. Physicians and researchers at Washington University School of Medicine (WUSM) and the Siteman Cancer Center (WUSM-SCC) are longtime leaders in the allied sub-disciplines of cancer, including genomics, proteomics, imaging, functional characterization, pathology, clinical trials, and clinical care. WUSM- SCC is an NCI-designated Comprehensive Cancer Center, which sees ~9,000 new cancer patients annually. Building on our expertise, established infrastructure, large patient population, and extraordinary institutional commitment, we propose to develop the Washington University Human Tumor Atlas Research Center (WU- HTARC) within the NIH Human Tumor Analysis Network (HTAN). We will focus on generating organ-specific human tumor atlases for three high priority cancer types associated with exceptionally poor prognosis: the triple negative breast cancer (TNBC), glioblastoma (GBM), and pancreatic ductal adenocarcinoma (PDAC). Collectively, we will analyze ~1,600-2000 samples collected from spatially separated locations and at different time points along the clinical treatment course from 300-375 patients (selected from ~750 recruited patients) for the duration of the project. In addition to standard histopathological analyses, bulk DNA/RNA sequencing, proteomics, and clinical imaging, etc., we will conduct cutting-edge, comprehensive analyses, including single cell RNA-Seq (scRNA-Seq), multiplexed immunofluorescent protein localization (MxIF), mass cytometry/Cytometry by Time of Flight (CyTOF) cellular characterization, metabolomics analysis, innovative imaging, and 3-D modeling. We have established infrastructure covering the aforementioned areas, from specimen procurement (Biospecimen Unit), to multidisciplinary analyses modules (Characterization Unit), and to analysis pipelines (Data Analysis Unit). Data generated from this study will be valuable for revealing the clonal evolution of the tumor cells from longitudinally collected specimens and to reconstruct the tumor ecosystem involving non- cancer cells and acellular structures. Our atlases will have comprehensive data integration at the 3D level over time, providing unprecedented 4D models for the 3 selected cancer types. Our established infrastructure and continuous efforts in incorporating new technologies in omics, imaging, and informatics, will help ensure our atlases will be the state-of-the-art, taking full advantage of the latest progress in these fields and will continue to evolve beyond the pilot phase to facilitate cancer research and improve clinical care. The proposed atlases target a set of critically important clinical questions, including tumor resistance that has long been a challenge for GBM treatment and also an important clinical problem in BRCA/TNBC and PDAC, in which minority populations are disproportionately affected. Other emphases are BRCA response/resistance to chemotherapy, PDAC metastasis, and GBM local recurrence in conjunction with resistance to therapy. These atlases can cross reference each other for pan-cancer analyses. We will also seek to cooperate with any Pre- Cancer Atlas (PCA) centers studying these disease types to maximize the temporal continuity of research on these cancers. The similarities and differences among the three selected cancer types will provide synergy among the three atlases and will also allow us to accumulate valuable knowledge in atlas building for other cancer types. The data, specimens, and experience gained by our center will be shared with HTAN and the broader research community to foster the next important discoveries in personalized cancer medicine.

Overall Project Summary/Abstract Cellular senescence has been characterized as a state of irreversible cell-cycle arrest coupled with a secretory program that can profoundly impact the tissue microenvironment. Our current understanding of senescence is largely based on cell culture and model-based studies. Research on the relevant signaling pathways and mechanisms underlying cellular senescence across human tissues over time is lacking. Our ability to leverage recent advances in omics and molecular imaging technologies enables us to investigate the transcriptional changes and secretory features driving and/or associated with senescence at higher depths and resolution than ever before. Here, we propose to develop the Washington University Senescence Tissue Mapping Center (WU-SN-TMC) within the NIH Senescence Network (SenNet). Our WU-SN-TMC will develop cellular senescence atlases using 500 human samples from four essential tissue types: bone marrow, breast, colon, and liver. We will first optimize our omics and imaging technologies and platforms for capturing, detecting, characterizing, and visualizing senescent cells; develop computational tools and models for accurate identification of senescent cells and markers; construct breast, bone marrow, colon, and liver senescence atlases in spatial and temporal contexts; and assess the landscape and heterogeneity of senescence. With these initial atlases, we will further characterize, validate, and define cellular senescence phenotypes and biomarkers using perturbation methods and investigate the interactions between senescent cells and the senescence-associated microenvironment. Finally, we will work with other SenNet centers to build comprehensive, major organ/tissue senescence atlases by integrated and comparative studies of all SenNet data across tissue types, time, sex, age, and ancestry groups. As a member of the SenNet program, WU-SN- TMC will employ state-of-the-art omics and imaging technologies, including bulk proteogenomics, single cell sequencing, spatial transcriptomics, CODEX molecular imaging, 3D light sheet microscopy plus expansion technologies that are likely to mature over the funding period, such as single molecule sequencing, to generate high-resolution, multi-parameter biomarkers and maps of cellular senescence in the four tissue types selected. We have the established infrastructure and expertise to successfully conduct this work, including high quality biospecimen collection, omics and imaging data production, experimental confirmation and validation, and high throughput, standardized, and reproducible data analysis. In conclusion, we will work closely with other SenNet centers and the Consortium Organization and Data Coordination Center (CODCC), to generate comprehensive atlases across major human tissue types under various physiological conditions, including changes across the human lifespan.

PROJECT SUMMARY Vision. Participant engagement and sequencing research from the Washington University Participant Engagement and Cancer Genomic Sequencing Center (WU-PE-CGS) will fill critical gaps in knowledge, methodology, and characterization of understudied cancer populations, leading to optimal approaches to participant engagement, outreach, and communication in genomic characterization studies. Goal. The overall goal of the WU-PE-CGS is to build a rigorous, scientific evidence base for approaches direct engagement of cancer patients and post-treatment cancer survivors as participants in cancer research. Our focus is on rare and understudied cancer populations with significant disparities including cholangiocarcinoma, multiple myeloma, and colorectal cancer under age 50. Participant engagement strategies are most effective when they are adapted and implemented in real-world settings in partnership with community and patient advocacy stakeholders. Setting. Our Center will be housed in an exceptional environment that fosters transdisciplinary collaboration, catalyzes new ideas in patient engagement, and ensures support for patient engagement and genome sequencing that finds solutions for complex recruitment and engagement challenges in real-world settings with underrepresented patient populations. Significant matching contributions from Washington University will allow us to quickly and strategically invest in ideas. Aims. The specific aims of the Center are to: (1) Advance the field of participant engagement to study cancer disparities and rare cancers by conducting innovative and impactful direct stakeholder engagement with continuous evaluation and research; (2) Expand an exceptional, diverse team of investigators, patients, and advocacy stakeholders; (3) Address cancer disparities by understanding barriers to and improving the ability for disadvantaged and understudied populations to encounter, use, and benefit from genomic sequencing and analysis; (4) Organize and integrate Center units to facilitate transdisciplinary, team science within our Center and across the PE-CGS Network. Innovations and impact. The WU-PE-CGS builds on a long and outstanding record of leadership in both cancer disparities and genomic research across the cancer continuum. We will be particularly innovative and allow for a significant return on the scientific investment in several ways. First, our Center has distinctive features that include a combined focus on cancer disparities, the application of strategies to increase participant engagement in research, success in biospecimen acquisition, and exceptional genomic sequencing expertise. Second, we have assembled a diverse, world class team with strong linkages to multiple rare and understudied cancers. Third, we engage investigators from different disciplines and invest in the development of early career scholars. Fourth, we will strategically and creatively disseminate products in ways that will benefit researchers, practitioners, and community members. Fifth, we will partner with exceptional patient-centered and wide-reaching advocacy groups to engage patients, optimize recruitment, and seamlessly return results. Input from these groups, patients, and their families is a key strength that will leverage our track record of stakeholder-engaged research. And finally, we have developed a focused strategy for collective integration of our units. These synergies will allow our Center to become a national resource for optimal approaches to participant engagement, outreach, and communication in genomic characterization studies and other studies as technologies advance that will accelerate progress for both the scientific community, patients and their communities. In summary, we are uniquely situated to advance a network of participant engagement and sequencing researchers, integrate research with patients and their stakeholders, build intellectual capital, and significantly enhance the capacity for participant engagement and genomic characterization studies. This Center will ultimately benefit health systems, providers, and people with rare cancers and lead to a reduction in cancer disparities.

Short gut syndrome (SGS) results from the treatment of multiple conditions in adults and children. In children, the mortality associated with SGS is roughly 25%, making it one of the most lethal conditions in infancy and childhood. Morbidity among survivors is high with another 25% of children requiring a small bowel transplant. The current 5-year patient survival following a small bowel transplant is still roughly 58%. Intestinal failure associated liver disease (IFALD) represents a spectrum of liver injury including steatosis, cholestasis, fibrosis, and cirrhosis. IFALD is the leading indication for intestinal and/or multivisceral transplantation in children with SGS. The incidence of IFALD is roughly 50% in pediatric patients who receive parenteral nutrition (PN). The pathogenesis of IFALD is unique because SGS patients are enterally starved, have no insulin resistance, and are not obese. Using a PN-independent murine model of small bowel resection (SBR), we demonstrate perturbed gut barrier function and significant alterations in intestinal lipid signaling, severe hepatitis, cholestasis, necrosis, and regenerative nodules. In one mouse, we confirmed the development of HCC. Accordingly, our overarching hypothesis is that IFALD reflects a proinflammatory milieu within the remnant bowel along with profound alterations in lipid signaling within both intestine and liver to initiate hepatic injury, fibrosis, and ultimate progression to advanced liver injury. For this project, we have developed a multiple-PI proposal embracing world class expertise in intestinal adaptation responses to massive SBR (Warner/Rubin), intestinal and hepatic lipid signaling (Davidson) and genomics and metabolomics (Ding). In the first Specific Aim, we will focus on the intestinal contribution to liver injury and fibrosis. First, genetically altered mice will undergo SBR to determine the effect of impaired intestinal chylomicron assembly, disrupted intestinal expression of a major transcription factor involved with lipid sensing and signaling, and perturbed expression of an enterocyte cytoplasmic protein involved with absorption of long chain fatty acids on liver injury. We will then delineate the effects of varied dietary fat on liver injury, intestinal permeability, and portal venous cytokine production. Finally, we will determine the most important intestinal site of toll-like receptor 4 (TLR4) activity in the pathogenesis of altered gut permeability and resection-associated liver injury. The next Specific Aim will focus on the hepatic component of injury, steatosis, and fibrosis after SBR. We will delineate a temporal profile of lipidomic and lipogenic gene expression within the liver at multiple time points after SBR. We will determine whether alteration of the omega-6 to omega-3 ratio as well as disrupted expression of a major regulator of lipid synthesis contributes to advanced liver injury. Finally, we will elucidate a genomic and metabolomic profile in the liver of evolving liver injury in mice as well as in human patients with end stage IFALD. These findings may provide novel mechanistic insight into the etiology of IFALD.

Project Summary - Genome Characterization Unit (GCU) The GCU will provide comprehensive, end-to-end CLIA-compliant genomic testing and cutting-edge research- level analysis of patients with MM, CRC, and CHOL who are engaged by the PEU. This testing will be conducted on diagnostic tumor samples and matched normal tissue from 300 patients for each of the three cancer types during the WU PE-CGS research program. Additional follow-up samples from 150 of these patients will also be analyzed during disease progression. Sample processing by the GCU will include nucleic acid extraction and QC. Diagnostic samples will be analyzed using 250X tumor/normal exome sequencing (WES) with both somatic and germline analysis for gene-level single nucleotide variants (SNV and insertion/deletions (indels). Tumor-only WGS (60X) will be performed to detect tumor-associated structural mutations, and tumor RNA-seq will support, confirm, and extend findings from the DNA-based assays. Tumor tissue and/or cell-free DNA will also be analyzed with targeted deep sequencing (>10,000X) using unique molecular indexes (UMI) for sensitive detection and monitoring of tumor-associated mutations. All of these assays will be performed using CLIA-compliant procedures with integrated quality management practices. The GCU will also conduct research-level scRNA-Seq, proteomics, and cellular imaging studies on selected samples to enhance our understanding of these tumor types. Genomic assays will proceed according to a planned schedule for year-by-year combinations of diagnostic specimens and follow-up collections, with small numbers of candidates selected for research studies. Results from these assays will be returned to participants using a tiered reporting system that will depend on participant preference. Tier 1 results will highlight findings with established clinical relevance obtained from CLIA sequencing of individual participants, including pathogenic, tumor-associated somatic drivers and inherited mutations that are clinically actionable according to published guidelines and that will be reported using established categorization for somatic drivers and pathogenic germline variants, their clinical implications, and possible actions. Participants can also elect to receive Tier 2 results, which will be comprised of additional mutations from the same CLIA-compliant data that are identified with advanced methods and are predicted to be clinically relevant via functional annotation, as well as results from targeted sequencing of follow-up samples for monitoring tumor evolution over time. Research-level Tier 3 molecular studies may also be provided to participants as aggregate, deidentified reports that can be used to enhance and extend interpretations of their individualized CLIA results. These results will also be securely uploaded to the NCI Genomic Data Commons (GDC) for use by the cancer biology community. All GCU activities will be coordinated with the PEU and EOU to ensure clarity and consistency across the WU PE-CGS program.

Summary/Abstract Tremendous progress on cancer has been made at the molecular level over the past decade, largely due to the broad application of high throughput, large-scale bulk whole genome, exome and RNA sequencing. In particular, the discovery of numerous medium to high-penetrance drivers, characterization of pathogenic germline variants, and the revelation of many-to-many relationships of genes and pathways, have brought a fuller view of the combinatorial complexity of cancer. Indeed, newer technologies, like single-cell and spatial genomics methods, are now augmenting bulk sequence data to power deeper studies of cancer dynamics, such as heterogeneity, evolution, and interaction with the microenvironment. The current view is that such advanced data, augmented by improved bioinformatics analysis tools and larger, well-curated cohorts will enable medicine to push beyond statistical descriptions toward a genuine deterministic understanding of cancer. Toward this goal, our proposal seeks to extend and apply established bioinformatics systems to integrate the above technologies and leverage our broad range of capabilities and to support the NCI Genomic Characterization Network (NCI-GCN) and Center for Cancer Genomics (CCG) via three specific aims: (1) annotating and interpreting coding and non-coding somatic and germline alterations, (2) characterizing tumor cell populations, evolution, and the tumor microenvironment, and (3) unlocking biological and clinical insights at both the individual and cross-cancer (Pan-Cancer) levels to discern basic themes across the major human cancers. Our approach involves fluencies in four areas of core competence outlined in the program RFA: DNA mutations, long-read sequence analysis, scRNA-Seq analysis, and spatial genomics data analysis (with connection to digital imaging analysis).

Summary The GenitoUrinary Development Molecular Anatomy Project (GUDMAP) has been providing valuable references for the research community studying urogenital development and diseases. It is a recurring theme that rapidly advancing new technologies are instrumental in enhancing and expanding reference databases. Cross fertilization of atlas building efforts spanning various organ systems and disease types will undoubtedly boost the technology penetration across these projects. To build multi-dimensional atlases of developing urogenital organs that incorporate the latest multi-omics and spatial molecular mapping technologies, we have assembled a team with expertise both in urogenital development and multi-dimensional, multi-platform, molecular atlas building. We propose to utilize the infrastructure we developed at our institution for the NCI Human Tumor Atlas Network (HTAN) and other large scale projects as a springboard to help effectively and efficiently propel GUDMAP to the next level with transcriptome-wide coverage, single cell level resolution, and spatial mapping with unprecedented clarity. We will take advantage of our experience in the incorporation of single nucleus (sn) RNA-seq and snATAC-seq to establish a comprehensive epigenetic and transcriptomic landscape in targeted urogenital organs and structures (lower urinary tract (LUT), selected male reproductive organs, kidney vasculature, lymphatics, and nerves) at single cell resolution (Aim 1). We will then add the spatial dimension to this molecular landscape to build 2D and 3D molecular atlases by incorporating spatial transcriptomics (ST), CODEX, and light sheet microscopy (LSM) (Aim 2). In Aim 3, we will extend our study to disease models, focusing on murine models of congenital anomalies of the kidney and the urinary tract (CAKUT). With the proposed experiments (Fig. 1), we aim at building multidimensional molecular atlases for developing urogenital organs at unprecedented cellular resolution and gene coverage with the highest efficiency possible.