Large-scale Genomic Analysis Research Articles

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. We present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflow simplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflow for three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. AnVILWorkflow lowers the barrier to utilizing AnVIL’s resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

Age and Sex Affects the Frequency and Mutation Type of FGFR2 Alterations in Cholangiocarcinoma

PURPOSE FGFR2 alterations are infrequent but currently represent the most common targetable mutation in cholangiocarcinoma. We performed a large-scale genomic analysis to assess associations between FGFR2 and other driver gene alterations in cholangiocarcinoma with patient demographics. METHODS Clinical and molecular data for patients with intrahepatic cholangiocarcinoma were abstracted from the Foundation Medicine (FMI) and Genomics, Evidence, Neoplasia, Information, Exchange (GENIE) genomic databases. The Chi-squared test was used to assess the association between sex and alterations in driver genes. FGFR2 fusion partners were analyzed using descriptive statistics. RESULTS Across both FMI (N = 7,859) and GENIE (N = 1,395) cohorts, FGFR2 rearrangements were more frequent in female than male patients with cholangiocarcinoma (odds ratio, 1.69 and 2.45). By contrast, FGFR2 missense mutations, TP53 mutations, and KRAS mutations were not associated with sex. Stratifying patients by age groups showed that FGFR2 rearrangements were specifically enriched in younger ages (<45 years). Although the most common FGFR2 fusion partners were shared across both sexes, a subset of fusion partners was recurrent and uniquely associated with female or male patients. CONCLUSION FGFR2 rearrangements but not other driver alterations in cholangiocarcinoma are concentrated in young female patients, where the prevalence reached nearly 20% across two large patient cohorts. These results suggest that the molecular basis of FGFR2 rearrangements may be distinct between sexes. These results indicate the importance of genetic testing in cholangiocarcinoma, particularly for younger patients, and suggest that patient recruitment strategies in trials for FGFR2-targeting therapies should seek to reduce barriers to enhance the enrollment of young female patients.

Cyanobacteria-cyanophage interactions between freshwater and marine ecosystems based on large-scale cyanophage genomic analysis

The disparities in harmful algal blooms dynamics are largely attributed to variations in cyanobacteria populations within aquatic ecosystems. However, cyanobacteria-cyanophage interactions and their role in shaping cyanobacterial populations has been previously underappreciated. To address this knowledge gap, we isolated and sequenced 42 cyanophages from diverse water sources in China, with the majority (n = 35) originating from freshwater sources. We designated these sequences as the “Novel Cyanophage Genome sequence Collection” (NCGC). NCGC displayed notable genetic variations, with 95 % (40/42) of the sequences representing previously unidentified taxonomic ranks. By integrating NCGC with public data of cyanophages and cyanobacteria, we found evidence for more frequent historical cyanobacteria-cyanophage interactions in freshwater ecosystems. This was evidenced by a higher prevalence of prophage integrase-related genes in freshwater cyanophages (37.97 %) than marine cyanophages (7.42 %). In addition, freshwater cyanophages could infect a broader range of cyanobacteria orders (n = 4) than marine ones (n = 0). Correspondingly, freshwater cyanobacteria harbored more defense systems per million base pairs in their genomes, indicating more frequent phage infections. Evolutionary and cyanophage epidemiological studies suggest that interactions between cyanobacteria and cyanophages in freshwater and marine ecosystems are interconnected, and that brackish water can act as a transitional zone for freshwater and marine cyanophages. In conclusion, our research significantly expands the genetic information database of cyanophage, offering a wider selection of cyanophages to control harmful cyanobacterial blooms. Additionally, we represent a pioneering large-scale and comprehensive analysis of cyanobacteria and cyanophage sequencing data, and it provides theoretical guidance for the application of cyanophages in different environments.

Interpreting and visualizing pathway analyses using embedding representations with PAVER.

Omics studies use large-scale high-throughput data to explain changes underlying different traits or conditions. However, omics analysis often results in long lists of pathways that are difficult to interpret. Therefore, it is of interest to describe a tool named PAVER (Pathway Analysis Visualization with Embedding Representations) for large scale genomic analysis. PAVER curates similar pathways into groups, identifies the pathway most representative of each group, and provides publication-ready intuitive visualizations. PAVER clusters pathways defined by their vector embedding representations and then identifies the term most cosine similar to its respective cluster's average embedding. PAVER can integrate multiple pathway analyses, highlight relevant biological insights, and work with any pathway database.

HOFE: an interactive forensic entomological database.

The significance of entomological evidence in inferring the time, location and cause of death has been demonstrated both theoretically and practically. With the advancement of sequencing technologies, reports have emerged on necrophagous insects' nuclear genomes, transcriptomes, proteomes and mitochondrial genomes. However, within the field of forensic entomology, there is currently no available database that can integrate, store and share the resources of necrophagous insects. The absence of a database poses an inconvenience to the application of entomological evidence in judicial practice and hampers the development of the forensic entomology discipline. Given this, we have developed the Home Of Forensic Entomology database, encompassing 10 core functional modules: Home, Browse, Mitochondria, Proteome, JBrowse, Search, BLAST, Tools, Case base and Maps. Notably, the 'Tools' module enables multiple sequence alignment analysis (Muscle), homologous protein prediction (Genewise), primer design (Primer), large-scale genomic analysis (Lastz), Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, as well as expression profiling (PCA Analysis, Hcluster and Correlation Heatmap). In addition, the present database also works as an interactive platform for researchers by sharing forensic entomological case reports and uploading data and material. This database provides potential visitors with a comprehensive function for multi-omics data analysis, offers substantial references to researchers and criminal scene investigators and facilitates the utilization of entomological evidence in court. Database URL: http://ihofe.com/.

Integrative genomic reconstruction of carbohydrate utilization networks in bifidobacteria: global trends, local variability, and dietary adaptation.

Bifidobacteria are among the earliest colonizers of the human gut, conferring numerous health benefits. While multiple Bifidobacterium strains are used as probiotics, accumulating evidence suggests that the individual responses to probiotic supplementation may vary, likely due to a variety of factors, including strain type(s), gut community composition, dietary habits of the consumer, and other health/lifestyle conditions. Given the saccharolytic nature of bifidobacteria, the carbohydrate composition of the diet is one of the primary factors dictating the colonization efficiency of Bifidobacterium strains. Therefore, a comprehensive understanding of bifidobacterial glycan metabolism at the strain level is necessary to rationally design probiotic or synbiotic formulations that combine bacterial strains with glycans that match their nutrient preferences. In this study, we systematically reconstructed 66 pathways involved in the utilization of mono-, di-, oligo-, and polysaccharides by analyzing the representation of 565 curated metabolic functional roles (catabolic enzymes, transporters, transcriptional regulators) in 2973 non-redundant cultured Bifidobacterium isolates and metagenome-assembled genomes (MAGs). Our analysis uncovered substantial heterogeneity in the predicted glycan utilization capabilities at the species and strain level and revealed the presence of a yet undescribed phenotypically distinct subspecies-level clade within the Bifidobacterium longum species. We also identified Bangladeshi isolates harboring unique gene clusters tentatively implicated in the breakdown of xyloglucan and human milk oligosaccharides. Predicted carbohydrate utilization phenotypes were experimentally characterized and validated. Our large-scale genomic analysis considerably expands the knowledge of carbohydrate metabolism in bifidobacteria and provides a foundation for rationally designing single- or multi-strain probiotic formulations of a given bifidobacterial species as well as synbiotic combinations of bifidobacterial strains matched with their preferred carbohydrate substrates.

Large-scale genomic analysis of the domestic dog informs biological discovery.

Recent advances in genomics, coupled with a unique population structure and remarkable levels of variation, have propelled the domestic dog to new levels as a system for understanding fundamental principles in mammalian biology. Central to this advance are more than 350 recognized breeds, each a closed population that has undergone selection for unique features. Genetic variation in the domestic dog is particularly well characterized compared with other domestic mammals, with almost 3000 high-coverage genomes publicly available. Importantly, as the number of sequenced genomes increases, new avenues for analysis are becoming available. Herein, we discuss recent discoveries in canine genomics regarding behavior, morphology, and disease susceptibility. We explore the limitations of current data sets for variant interpretation, tradeoffs between sequencing strategies, and the burgeoning role of long-read genomes for capturing structural variants. In addition, we consider how large-scale collections of whole-genome sequence data drive rare variant discovery and assess the geographic distribution of canine diversity, which identifies Asia as a major source of missing variation. Finally, we review recent comparative genomic analyses that will facilitate annotation of the noncoding genome in dogs.

CMAPLE: efficient phylogenetic inference in the pandemic era.

We have recently introduced MAPLE (MAximum Parsimonious Likelihood Estimation), a new pandemic-scale phylogenetic inference method exclusively designed for genomic epidemiology. In response to the need for enhancing MAPLE's performance and scalability, here we present two key components: (1) CMAPLE software, a highly optimized C++ reimplementation of MAPLE with many new features and advancements; and (2) CMAPLE library, a suite of Application Programming Interfaces to facilitate the integration of the CMAPLE algorithm into existing phylogenetic inference packages. Notably, we have successfully integrated CMAPLE into the widely used IQ-TREE 2 software, enabling its rapid adoption in the scientific community. These advancements serve as a vital step towards better preparedness for future pandemics, offering researchers powerful tools for large-scale pathogen genomic analysis.

Comparative genomics of the closely related fungal genera Cryptococcus and Kwoniella reveals karyotype dynamics and suggests evolutionary mechanisms of pathogenesis.

In exploring the evolutionary trajectories of both pathogenesis and karyotype dynamics in fungi, we conducted a large-scale comparative genomic analysis spanning the Cryptococcus genus, encompassing both global human fungal pathogens and nonpathogenic species, and related species from the sister genus Kwoniella. Chromosome-level genome assemblies were generated for multiple species, covering virtually all known diversity within these genera. Although Cryptococcus and Kwoniella have comparable genome sizes (about 19.2 and 22.9 Mb) and similar gene content, hinting at preadaptive pathogenic potential, our analysis found evidence of gene gain (via horizontal gene transfer) and gene loss in pathogenic Cryptococcus species, which might represent evolutionary signatures of pathogenic development. Genome analysis also revealed a significant variation in chromosome number and structure between the 2 genera. By combining synteny analysis and experimental centromere validation, we found that most Cryptococcus species have 14 chromosomes, whereas most Kwoniella species have fewer (11, 8, 5, or even as few as 3). Reduced chromosome number in Kwoniella is associated with formation of giant chromosomes (up to 18 Mb) through repeated chromosome fusion events, each marked by a pericentric inversion and centromere loss. While similar chromosome inversion-fusion patterns were observed in all Kwoniella species with fewer than 14 chromosomes, no such pattern was detected in Cryptococcus. Instead, Cryptococcus species with less than 14 chromosomes showed reductions primarily through rearrangements associated with the loss of repeat-rich centromeres. Additionally, Cryptococcus genomes exhibited frequent interchromosomal translocations, including intercentromeric recombination facilitated by transposons shared between centromeres. Overall, our findings advance our understanding of genetic changes possibly associated with pathogenicity in Cryptococcus and provide a foundation to elucidate mechanisms of centromere loss and chromosome fusion driving distinct karyotypes in closely related fungal species, including prominent global human pathogens.

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines.

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. Here we present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflowsimplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflowfor three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. While some limitations exist around workflow customization, AnVILWorkflowlowers the barrier to taking advantage of AnVIL's resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

Large-scale transcriptomic and genomic analyses reveal a novel functional gene SERPINB6 for chicken carcass traits

BackgroundCarcass traits are crucial indicators of meat production efficiency. However, the molecular regulatory mechanisms associated with these traits remain unclear.ResultsIn this study, we conducted comprehensive transcriptomic and genomic analyses on 399 Tiannong partridge chickens to identify key genes and variants associated with carcass traits and to elucidate the underlying regulatory mechanisms. Based on association analyses with the elastic net (EN) model, we identified 12 candidate genes (AMY1A, AP3B2, CEBPG, EEF2, EIF4EBP1, FGFR1, FOXD3, GOLM1, LOC107052698, PABPC1, SERPINB6 and TBC1D16) for 4 carcass-related traits, namely live weight, dressed weight, eviscerated weight, and breast muscle weight. SERPINB6 was identified as the only overlapping gene by 3 analyses, EN model analysis, weighted gene co-expression network analysis and differential expression analysis. Cell-level experiments confirmed that SERPINB6 promotes the proliferation of chicken DF1 cells and primary myoblasts. Further expression genome-wide association study and association analysis indicated that rs317934171 is the critical site that enhances SERPINB6 expression. Furthermore, a dual-luciferase reporter assay proved that gga-miR-1615 targets the 3′UTR of SERPINB6.ConclusionsCollectively, our findings reveal that SERPINB6 serves as a novel gene for chicken carcass traits by promoting fibroblast and myoblast proliferation. Additionally, the downstream variant rs317934171 regulates SERPINB6 expression. These results identify a new target gene and molecular marker for the molecular mechanisms of chicken carcass traits.

Large-scale bacterial genomic and metagenomic analysis reveals Pseudomonas aeruginosa as potential ancestral source of tigecycline resistance gene cluster tmexCD-toprJ

BackgroundThe global dissemination of the multidrug resistance efflux pump gene cluster tmexCD-toprJ has greatly weakened the effects of multiple antibiotics, including tigecycline. However, the potential origin and transmission mechanisms of the gene cluster remain unclear. MethodsHere, we concluded a comprehensive bioinformatics analysis on integrated 73,498 bacterial genomes, including Pseudomonas spp., Klebsiella spp., Aeromonas spp., Proteus spp., and Citrobacter spp., along with 1,152 long-read metagenomic datasets to trace the origin and propagation of tmexCD-toprJ. ResultsOur results demonstrated that tmexCD-toprJ was predominantly found in Pseudomonas aeruginosa sourced from human hosts in Asian countries and North American countries. Phylogenetic and genomic feature analyses showed that tmexCD-toprJ was likely evolved from mexCD-oprJ of some special clones of P. aeruginosa. Furthermore, metagenomic analysis confirmed that P. aeruginosa is the only potential ancestral bacterium for tmexCD-toprJ. A putative mobile genetic structure harboring tmexCD-toprJ, int-int-hp-hp-tnfxB-tmexCD-toprJ, was the predominant genetic context of tmexCD-toprJ across various bacterial genera, suggesting that the two integrase genes play a pivotal role in the horizontal transmission of tmexCD-toprJ. ConclusionsBased on these findings, it is almost certain that the tmexCD-toprJ gene cluster was derived from P. aeruginosa and further spread to other bacteria.

Radiogenomics in breast cancer: a review

INTRODUCTION: Early detection of breast cancer is essential in the diagnosis and treatment of this disease. The results of core biopsy, immunohistochemical methods or removed surgical specimens contributed to the identification of molecular markers. Currently, the attention of researchers is attracted by the anatomical and functional features of tumor tissue obtained using imaging methods. The correlation of specific phenotypes based on images (radiomics) with large-scale genomic analysis (genomics) is a new field of research called “radiogenomics” or, more precisely, “image genomics”. This new field examines the relationship between diagnostic methods and gene data, as well as with other clinically relevant information.OBJECTIVE: To analyze current trends in the development of radiogenomics in the study of breast cancer based on the literature data.MATERIALS AND METHODS: The medical literature was searched using information and analytical databases Cochrane, Medline, and Elibrary using the text search queries “radiogenomics of breast cancer”, “mammography and radiogenomics”, “magnetic resonance imaging and radiogenomics”, “ultrasonic radiogenomics”.RESULTS: We found strong evidence that there is a moderate relationship between imaging characteristics and genomic characteristics of breast cancer. However, the results obtained have a number of limiting factors that distort the overall picture.CONCLUSION: Precision medicine can be optimized based on the genotypic and phenotypic characteristics of the tumor. However, the development of this direction requires new research and the development of databases using a multicenter approach.

Insights into the Inherited Basis of Valvular Heart Disease.

PURPOSEOF REVIEW: Increases in the availability of genetic data and advances in the tools and methods for their analyses have enabled well-powered genetic association studies that have significantly enhanced our understanding of the genetic factors underlying both rare and common valve diseases. Valvular heart diseases, such as congenital valve malformations and degenerative valve lesions, increase the risk of heart failure, arrhythmias, and sudden death. In this review, we provide an updated overview of our current understanding of the genetic mechanisms underlying valvular heart diseases. With a focus on discoveries from the past 5years, we describe recent insights into genetic risk and underlying biological pathways. RECENT FINDINGS: Recently acquired knowledge around valvular heart disease genetics has provided important insights into novel mechanisms related to disease pathogenesis. Newly identified risk loci associated valvular heart disease mainly regulate the composition of the extracellular matrix, accelerate the endothelial-to-mesenchymal transition, contribute to cilia formation processes, and play roles in lipid metabolism. Large-scale genomic analyses have identified numerous risk loci, genes, and biological pathways associated with degenerative valve disease and congenital valve malformations. Shared risk genes suggest common mechanistic pathways for various valve pathologies. More recent studies have combined cardiac magnetic resonance imaging and machine learning to offer a novel approach for exploring genotype-phenotype relationships regarding valve disease. Progress in the field holds promise for targeted prevention, particularly through the application of polygenic risk scores, and innovative therapies based on the biological mechanisms for predominant forms of valvular heart diseases.

The new paradigm of psychiatry precision medicine and its emerging clinical framework

IntroductionPrecision medicine is a promising approach to improving the prevention, prediction and treatment of disease, based on individual characteristics and biomarkers/genetic variants shared by specific subgroups of patients.ObjectivesThis study aims to address the new paradigm of precision medicine in psychiatry and to discuss, through the literature, its emerging clinical framework.MethodsWe conducted an exhaustive review of the scientific literature using PubMed database and Google Scholar, with “Precision Medicine in Psychiatry” as keywords.ResultsOur review revealed that while psychiatrists have long practiced a personalized therapeutic approach with, for example, treatment choices guided by individual criteria, the methods for achieving this objectively have until now been largely lacking. This dilemma has begun to be resolved with the implementation of data analysis methods such as machine learning and large-scale genomic analysis studies. The goals of precision psychiatry involved the delineation of genetic risk factors using GWAS, the redefinition of the functional domains involved in mental disorders and pharmacological repositioning. The highly polygenic nature of mental disorders and the failure of GWAS to confirm the role of candidate genes have suggested that a systems genetic approach that considers function at the network level would provide a better approach to the problem of linking heterogeneous genetic risk factors and brain mechanisms. In addition, the growing evidence that certain disorders such as psychotic disorders are syndromes rather than diseases in their own right suggests that many conditions currently recognized as such may have similar underlying patterns of cognitive dysfunction and neurobiological abnormalities that will need to be reclassified.ConclusionsThe application of precision medicine in psychiatry is still in its infancy. Numerous research programs creating large multimodal databases with multiple data on brain imaging, genetics, etc. will soon support the clinical deployment of precision medicine in psychiatry.Disclosure of InterestNone Declared

3Dmapper: a command line tool for BioBank-scale mapping of variants to protein structures.

The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis. To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable. https://github.com/vicruiser/3Dmapper.

Molecular Pathology of Thyroid Tumors: Essential Points to Comprehend Regarding the Latest WHO Classification.

In 2022, the new WHO Classification of Endocrine and Neuroendocrine Tumors, Fifth Edition (beta version) (WHO 5th), was published. Large-scale genomic analyses such as The Cancer Genome Atlas (TCGA) have revealed the importance of understanding the molecular genetics of thyroid tumors. Consequently, the WHO 5th was fundamentally revised, resulting in a systematic classification based on the cell of origin of tumors and their clinical risk. This paper outlines the following critical points of the WHO 5th. 1. Genetic mutations in follicular cell-derived neoplasms (FDNs) highlight the role of mutations in the MAP kinase pathway, including RET, RAS, and BRAF, as drivers of carcinogenesis. Differentiated thyroid cancers such as follicular thyroid carcinoma (FTC) and papillary thyroid carcinoma (PTC) have specific genetic alterations that correlate with morphological classifications: RAS-like tumors (RLTs) and BRAF p.V600E-like tumors (BLTs), respectively. 2. The framework for benign lesions has been revised. The WHO 5th introduces a new category: "developmental abnormalities". Benign FDNs comprise "thyroid follicular nodular disease", follicular thyroid adenoma (FTA), FTA with papillary architecture, and oncocytic adenoma (OA). "Hürthle cell adenoma/carcinoma" is renamed oncocytic adenoma/carcinoma of the thyroid (OA/OCA), which can be distinguished from FTA/FTC by its unique genetic background. 3. Low-risk tumors include NIFTP, TT-UMP, and HTT, and they have an extremely low malignant potential or an uncertain malignant potential. 4. PTC histological variants are reclassified as "subtypes" in the WHO 5th. 5. The concept of high-grade carcinomas is introduced, encompassing poorly differentiated thyroid carcinoma (PDTC), differentiated high-grade thyroid carcinoma (DHGTC), and high-grade medullary thyroid carcinoma (MTC). 6. Squamous cell carcinoma is included in anaplastic thyroid carcinoma (ATC) in the WHO 5th due to their shared genetic and prognostic features. 7. Other miscellaneous tumors are categorized as salivary-gland-type carcinomas of the thyroid, thyroid tumors of uncertain histogenesis, thymic tumors within the thyroid, and embryonal thyroid neoplasms. The WHO 5th thus emphasizes the importance of classifying tumors based on both genetic abnormalities and histomorphology. This approach aids in achieving accurate pathological diagnosis and facilitates the early selection of appropriate treatment options, including molecular targeted therapies.

Abstract 1852: NCI’s Clinical Proteomic Tumor Analysis Consortium: A proteogenomic cancer analysis program

Abstract The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (NCI’s CPTAC) is a national effort to accelerate the understanding of cancer biology through the integration of large-scale proteome and genome analysis, or proteogenomics. The program which is composed of a Tumor Characterization Program and a Translational Research Program (proteomics.cancer.gov) aims to leverage the investments made in cancer research by understanding the molecular changes by proteogenomically characterizing cancers and accelerating the basic discovery toward clinical impact. All data and analytical tools are made broadly available to the research community through public databases to maximize utility and public benefit.CPTAC teams have characterized a plethora of treatment-naïve tumor types, including colorectal, ovarian, breast, clear cell renal cell carcinoma, uterine corpus endometrial carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, brain (including adult, pediatric, and adolescent and young adult), head and neck squamous cell carcinoma, and pancreatic ductal adenocarcinoma (proteomics.cancer.gov/resources/milestones-and-publications). Ongoing projects include acute myeloid leukemia (AML), cutaneous melanoma, sarcoma, oligodendroglioma, gastric, prostate, liver, and thyroid cancers. In the PTRC Program, CPTAC is partnering with NCI-sponsored clinical trials to support clinically relevant research projects that would elucidate biological mechanisms of therapeutic response, resistance, and/or toxicity in cancers including AML, multiple myeloma, melanoma, and lung cancers. All mass spectrometry-based proteomic, genomic, and imaging data (histopathology and radiology) are made publicly available at the CPTAC Proteomic Data Commons (pdc.cancer.gov), Genomic Data Commons (gdc.cancer.gov), and the Cancer Imaging Archive (cancerimagingarchive.net)/Imaging Data Commons (imaging.datacommons.cancer.gov) respectively. CPTAC is also supporting development of new proteogenomic data analysis tools (proteomics.cancer.gov/resources/computational-tools). In addition, the CPTAC Assay Portal (assays.cancer.gov) is a public resource populated with mass spectrometry-based targeted proteomic assays developed by the consortium for quantitatively measuring proteins of interest, including those discovered through comprehensive tumor characterization. Lastly, well-characterized monoclonal antibodies targeting cancer-specific proteins and peptides are also made available at CPTAC’s Antibody Portal (antibodies.cancer.gov). Citation Format: Mehdi Mesri, Eunkyung An, Xu Zhang, Jasmin Bavarva, Ana I. Robles, Tara Hiltke, Henry Rodriguez. NCI’s Clinical Proteomic Tumor Analysis Consortium: A proteogenomic cancer analysis program [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 1852.

Abstract 6248: Multilayered molecular interactions underlying glioblastoma pathogenesis through multi-omics analysis

Abstract Glioblastoma, IDH-wild type (GBM) is the most common malignant and treatment-refractory brain tumor with a dismal prognosis. Despite the identification of various driver genes in previous large-scale genomic analyses, there has been limited progress in the development of novel treatments. Acquiring an extensive understanding of the molecular mechanisms underlying GBM pathogenesis is crucial to improve prognostic outcomes. We analyzed 289 whole-genome sequencing data (WGS) including 159 unpublished deep WGS (≥ ×120 coverage) along with RNA-seq, DNA methylation array, and assay for transposase-accessible chromatin with sequencing (ATAC-seq) to uncover the molecular mechanisms regulating development and progression of GBM. Our deep WGS enables us to delineate a fine view of clonal architecture, where mutational signature varies between clonal and subclonal mutations, suggesting that different mutational processes contribute to GBM pathogenesis depending on its developmental stage. The differentiation status of tumor cells detected by transcriptional deconvolution analysis is associated with multi-layer profiles including genomic alterations, expression subtypes, DNA methylation, and open chromatin status. Tumors predominantly comprised of differentiated cells display genetic and epigenetic profiles that align with the classical subtype, whereas tumors predominantly composed of stem-like cells exhibit profiles consistent with the proneural subtype. ATAC-seq unveils genome-wide chromatin accessibility features associated with gene expression subtypes. Motif enrichment analysis of differentially accessible sites among the subtypes identified specific transcription factors. The proneural subtype is enriched for the SOX10 binding motif, which is associated with regulating cell states, and exhibits higher expression of SOX10 compared to other subtypes. Conversely, the mesenchymal and classical subtypes are enriched for the CREB1 binding motif, which promotes cell proliferation and angiogenesis through TGFβ2 upregulation, wherein TGFβ2 expression is significantly elevated. These findings support a model in which the difference in chromatin structure also regulates the progression of GBM.Our analysis reveals the multi-layer molecular interactions underlying the progression of GBM enhancing our understanding of the pathogenesis. Citation Format: Takuma Nakashima, Yusuke Funakoshi, Ryo Yamamoto, Yuriko Sugihara, Shohei Nambu, Yoshiki Arakawa, Shota Tanaka, Joji Ishida, Ryuta Saito, Ryosuke Hanaya, Koji Yoshimoto, Yoshitaka Narita, Hiromichi Suzuki. Multilayered molecular interactions underlying glioblastoma pathogenesis through multi-omics analysis [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 6248.

Abstract 774: Genetic architecture, evolutionary genomics, and genomic risk of prostate cancer in sub-Saharan Africa

Abstract Men of African descent have the highest prostate cancer (CaP) incidence and mortality rates, yet the genetic basis of CaP in African men has been understudied. We performed the first large-scale genomic analysis of CaP across sub-Saharan Africa (SSA). We evaluated the evolutionary history of CaP-associated loci, generated a novel genomic risk score, inferred ancestry-specific genetic architectures, and fine-mapped disease associations using in 3,963 CaP cases and 3,509 controls recruited in Ghana, Nigeria, Senegal, South Africa, and Uganda through the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network. Fifteen independent associations at 8q24.21, 6q22.1, and 11q13.3 reached genome-wide significance, including four novel associations. Multiple lead SNPs were private alleles in SSA, a pattern arising from recent mutations and the out-of-Africa bottleneck. These SSA-specific alleles contribute to haplotypes with odds ratios for CaP above 2.4. The genetic architecture of CaP differs across SSA, with effect size differences contributing more to this heterogeneity than allele frequency differences. Population genetic analyses reveal that SSA CaP associations are largely governed by neutral evolution, as opposed to natural selection. This finding is consistent with the late onset of this disease. Focusing on 2,502 age-matched cases and 2,222 non-cancer controls, we generated a novel genomic predictor of CaP. We then assessed the extent to which this polygenic risk score was correlated with age of onset, PSA levels, genetic ancestry, and family history of study participants. The performance of this novel polygenic risk score was tested on other populations of African descent, as well as populations with non-African ancestry. Our findings emphasize the utility of conducting genetic studies in diverse populations. Citation Format: Timothy R. Rebbeck, Rohini Janivara, Wenlong C. Chen, Ujani Hazra, MADCaP Network, Joseph Lachance. Genetic architecture, evolutionary genomics, and genomic risk of prostate cancer in sub-Saharan Africa [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 774.