Cancer Genomic Datasets Research Articles

POU4F3 Is a Sensitive and Specific Marker of Merkel Cell Carcinoma

Although of therapeutic importance, a single sensitive and specific immunostain to distinguish Merkel cell carcinoma (MCC) from mimics is not currently available. In addition, single tumor cells are difficult to detect in sentinel lymph node biopsy. Leveraging publicly available data sets of 9264 solid tumors and >600,000 single-cell transcriptomes, we identified POU4F3 to be a specific marker of MCC. Analyses of Pan-Cancer RNA bulk sequencing data of 24 tumor types from Tumor Cancer Genomic Atlas data sets as well as non-Tumor Cancer Genomic Atlas small cell lung carcinoma and MCC data sets confirmed POU4F3 specificity for MCC. Single-cell RNA-sequencing analyses also confirmed the lack of POU4F3 expression in lung small cell carcinoma as well as a variety of normal tissues. Nuclear POU4F3 immunohistochemical expression was noted in 98.7% of 153 MCCs and in only 1.7% of mimics (3 of 180 cases, including 95 small cell carcinomas, of which 55 were from lungs and the remainder from other sites). Three POU4F3-positive non-MCC cases were from lungs (2 cases) and vagina (1 case). All 153 tested MCC cases were negative for ASCL1, a key transcriptional regulator highly expressed in small cell lung carcinoma. NeuroD1 was seen in a subset of MCC cases (20.9%, 32/153). POU4F3 immunostain was performed on 29 sentinel lymph nodes, and strong POU4F3 nuclear expression facilitated the ease of metastasis detection, even single tumor cells. Our study built on prior works shows that POU4F3 is a sensitive and specific clinical marker of MCC.

Optimized twin spatio-temporal convolutional neural network with squeezenet transfer learning model for cancer miRNA biomarker classification

Cancer MicroRNA (miRNA) biomarkers are small RNA molecules identified in cancer cells that indicate the presence or progression of cancer. miRNA is detected through circulating (liquid biopsy) and tissue-based approaches. Handling the data involves sequencing, followed by bioinformatic analysis to interpret the miRNA profiles. The valuable knowledge is extracted from the cancer genomic datasets using data mining and machine learning techniques, although the accuracy of certain classes has been unsatisfied. To address this issue, an optimized twin spatio-temporal convolutional neural network with SqueezeNet transfer learning model for cancer miRNA biomarker classification (TSTCNN-SNet-AGT-CBC) is proposed. The input imageries are gathered from Cancer Genome Atlas dataset. The noise is reduced and image quality is improved in preprocessing by applying Reliable asynchronous sampled-data filtering (RASDF). Then, features are extracted and the cancer miRNA is effectively classified as a diagnosis, therapy, and prognostic normal region using twin spatio-temporal convolutional neural network with SqueezeNet. The batch normalization layer of the twin Spatio-Temporal Convolutional Neural Network (TSTCNN) is eliminated and added with Squeeze Net Layer (SNet). Then, the Artificial Gorilla Troops Optimization (AGTO) is used for tuning the TSTCNN-SNet optimum weight parameter. The performance of proposed technique is examined under performance metrics like precision, sensitivity, F-scores and accuracy. The proposed TSTCNN-SNet-AGT-CBC method attains 24.52 %, 20.97 % and 16.36 % higher accuracy and 23.68 %, 18.77 %, and 41.36 % greater precision when compared with existing techniques.

Correlation of syndecan gene amplification with metastatic potential and clinical outcomes in carcinomas.

Cell surface receptors play crucial roles in cellular responses to extracellular ligands, helping to modulate the functions of a cell based on information coming from outside the cell. Syndecan refers to a family of cell adhesion receptors that regulate both extracellular and cytosolic events. Alteration of syndecan expression disrupts regulatory mechanisms in a cell type-specific fashion, often leading to serious diseases, notably cancer. Given the multifaceted functions and distinct tissue distributions of syndecan, it will be important to unravel the gene-level intricacies of syndecan expression and thereby further understand its involvement in various carcinogenic processes. Although accumulating evidence indicates that the protein expression patterns of syndecan family members are significantly altered in cancer cells, the underlying gene-level mechanisms remain largely unknown. This review endeavors to explore syndecan gene expression levels across different cancer types by scrutinizing extensive cancer genome datasets using tools such as cBioPortal. Our analysis unveils that somatic mutations in SDC genes are rare occurrences, whereas copy number alterations are frequently observed across diverse cancers, particularly in SDC2 and SDC4. Notably, amplifications of SDC2 and SDC4 correlate with heightened metastatic potential and dismal prognosis. This underscores the recurrent nature of SDC2 and SDC4 amplifications during carcinogenesis and sheds light on their role in promoting cancer activity through augmented protein expression. The identification of these amplifications not only enriches our understanding of carcinogenic mechanisms but also hints at the potential therapeutic avenue of targeting SDC2 and SDC4 to curb cancer cell proliferation and metastasis.

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression.

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

Accelerated somatic mutation calling for whole-genome and whole-exome sequencing data from heterogenous tumor samples.

Accurate detection of somatic mutations in DNA sequencing data is a fundamental prerequisite for cancer research. Previous analytical challenges were overcome by consensus mutation calling from four to five popular callers. This, however, increases the already nontrivial computing time from individual callers. Here, we launch MuSE 2, powered by multistep parallelization and efficient memory allocation, to resolve the computing time bottleneck. MuSE 2 speeds up 50 times more than MuSE 1 and eight to 80 times more than other popular callers. Our benchmark study suggests combining MuSE 2 and the recently accelerated Strelka2 achieves high efficiency and accuracy in analyzing large cancer genomic data sets.

High expression of NXPH4 correlates with poor prognosis, metabolic reprogramming, and immune infiltration in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is a prevalent gastrointestinal malignant disease with high mortality rate, and identification of novel prognostic biomarkers and therapeutic targets is urgently needed. Although neurexophilin 4 (NXPH4) has been investigated in several tumors, its role in COAD remains unclear. The aim of this study was to explore the prognostic value and potential functions of NXPH4 in COAD. The expression of NXPH4 in COAD were analyzed using The Cancer Genome Atlas (TCGA) and datasets from the Gene Expression Omnibus (GEO) database. The prognostic value of NXPH4 was determined using Kaplan-Meier analysis and Cox regression analysis. To investigate the possible mechanism underlying the role of NXPH4 in COAD, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were employed. The correlation between NXPH4 expression and immune cell infiltration levels was examined thorough single-sample gene set enrichment analysis (ssGSEA). Furthermore, the competing endogenous RNA (ceRNA) regulatory network that may be involved in NXPH4 in COAD was predicted and constructed through a variety of databases. NXPH4 expression was significantly higher in COAD tissue compared with normal colon tissues. Meanwhile, high expression of NXPH4 was associated with poor prognosis in COAD patients. GO-KEGG and GSEA analyses indicated that NXPH4 was associated with glycolysis and hypoxia pathway, and may promote COAD progression and metastasis by modulating metabolic reprogramming. ssGSEA analysis demonstrated that NXPH4 expression also associated with immune infiltration. Furthermore, we identified various microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) as upstream regulators of NXPH4 in COAD. The present study revealed that high expression of NXPH4 is associated with tumor progression, metabolic reprogramming, and immune infiltration. These findings suggest that NXPH4 could serve as a reliable prognostic biomarker and a promising therapeutic target in COAD.

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients.

Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined. We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD. Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion. Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

Abstract 7406: Visualization and analysis of cancer genomics data using UCSC Xena

Abstract UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux. Questions Xena can help you answer:* Is overexpression of this gene associated with lower/higher survival?* What genes are differentially expressed between these two groups of samples?* What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, GSEA, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. New features include: * Genome-wide differential gene expression analysis * GSEA analysis * Select samples directly from the screen for filtering and creating subgroups * Loading of Microsoft Excel files Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8 Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [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 7406.

Coactosin-Like Protein 1 (COTL1) Could Be an Immunological and Prognostic Biomarker: From Pan-Cancer Analysis to Low-Grade Glioma Validation.

Cancer represents a widespread global health challenge impacting millions of individuals worldwide. Identifying new targets for cancer treatment is a crucial step in developing more effective therapies. Among these potential targets, Coactosin-like protein 1 (COTL1), a cytoskeleton-associated protein with critical roles in cell migration, adhesion, and signaling, has shown involvement in tumor progression. GSCA, TIMER, SangerBox database were used to explore the COTL1 expression across different tumor types. We employed the TCGA Pan-Atlas Cancer Genomics Dataset, which is available through the cBioportal platform, to explore genetic alterations in COTL1. We conduct a comprehensive analysis of COTL1, encompassing gene expression, clinical prognosis, RNA modification, immunotherapy, and cancer stemness through SangerBox database. Clinical samples were validated using immunohistochemistry. Our analysis revealed that COTL1 is highly expressed in most cancers and correlates with decreased survival in Glioma, Glioblastoma multiforme, and pan-kidney cohorts. Furthermore, COTL1 was found to be associated with DNA and RNA stemness in 20 and 22 different tumor types, respectively. Additionally, COTL1 showed positive correlations with immunological checkpoints and immune infiltration cells. It was also linked to tumor mutation burden (TMB), microsatellite instability (MSI), neoantigen (NEO), and programmed death ligand 1 (PD-L1), all of which are potential targets for immunotherapies. Moreover, a favorable relationship was demonstrated between genomic-instability markers such as heterozygosity (LOH), homologous recombination deficiency (HRD), and mutant allele tumor heterogeneity (MATH) with COTL1. Furthermore, our findings confirmed a positive correlation between COTL1 expression, CD8, and PD-L1 in LGG, as well as an association of high COTL1 expression with decreased patient survival in LGG. Based on these compelling findings, COTL1 may hold significant clinical implications for the development of novel cancer therapies and serve as a potential target for tumors associated with immunotherapy in the future.

Abstract B032: Combined A2AR and PARP inhibition in homologous recombination deficient (HRD) castrate-resistant prostate cancer (CRPC)

Abstract Background: In response to cellular death and hypoxia, ATP is passively released into the extracellular space. ATP undergoes rapid stepwise conversion to the potent, immune-suppressive adenosine by ectoenzymes CD39 and CD73. Engagement of adenosine with the A2A and A2B receptors (A2AR, A2BR) promote the suppressive effects of adenosine. Early-phase clinical trials in men with metastatic castration-resistant prostate cancer (CRPC) suggest that A2AR antagonism may mitigate adenosine-driven immunosuppression in the tumor microenvironment (TME). The effect of A2AR antagonism on tumor cells themselves is unknown. Investigating combination treatments to promote both immune and tumor-intrinsic anti-tumor activity, we hypothesize that inhibition of adenosine signaling through the A2AR combined with PARP inhibition is an effective combination therapy for men with homologous repair deficient (HRD) CRPC. Methods: Here, we interrogate publicly available prostate cancer genomic datasets in the cBioPortal to evaluate the incidence of mutational and copy number alterations in the ENTPD1 (CD39), NT5E (CD73), and ADORA2A (A2AR) genes. Using an A2AR antagonist, we examine the effects of blocking A2AR signaling on tumor cell viability and expression of CD39, CD73, A2AR, and PARP expression in two androgen-insensitive human prostate cancer cell lines, DU145 and PC3. In DU145 and PC3 xenograft models, we tested A2AR antagonist, PARP inhibitor, or a combination of A2AR antagonist and PARP inhibitor, compared to vehicle alone on tumor growth. Results: Assessment of mutational and copy number alterations of ENTPD1 (CD39), NT5E (CD73), and ADORA2A (A2AR) in localized and metastatic prostate cancer reveals that the ADORA2A gene is amplified 10 times more frequently (2.4% vs. 0.2%, p<0.01) in the metastatic setting. Stratifying the metastatic cohort by anti-androgen treatments, we found that in patients with prior abiraterone and/or enzalutamide exposure, ADORA2A was more commonly amplified (4.4% vs. 0.7%, p<0.005), whereas ENTPD1 or NT5E were not. In both DU145 and PC3 cell lines, A2AR antagonism drives a dose dependent increase in cell death and expressions of A2AR, CD39, and CD73, especially in the DU145 cell line characterized by multiple missense mutations in key genes in the homologous recombination pathway. In both the DU145 and PC3 xenograft models, treatment with an A2AR antagonist or PARP inhibitor monotherapy did not significantly influence tumor volumes. However, when combined, A2AR antagonism plus PARP inhibition resulted in a 59% reduction in tumor volume (196.3 mm3 vs. 479.6 mm3, p<0.001) in the DU145 model, compared to vehicle-treated control. In the PC3 model, with fewer alterations to the HR pathway, monotherapy also had no influence on tumor volume. When combined, A2AR antagonism plus PARP inhibition caused a 43% reduction in volume (304.1 mm3 vs. 538.2.0 mm3, p<0.05). Conclusion: These data suggest that combined A2AR and PARP inhibition may be an effective therapeutic combination for HRD metastatic castration-resistant prostate cancer. Citation Format: Patrick J. McCann, Wendy Mao, Matthew Chaimowitz, Erika Houthys, Reece Marillier, Shruti Bansal, Julia An, Jason A. Piersaint, Namita Sen, Charles G. Drake, Catherine S. Spina. Combined A2AR and PARP inhibition in homologous recombination deficient (HRD) castrate-resistant prostate cancer (CRPC) [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B032.

Abstract 2066: Visualization and analysis of cancer genomics data using UCSC Xena

Abstract UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux. Questions Xena can help you answer: * Is overexpression of this gene associated with lower/higher survival? * What genes are differentially expressed between these two groups of samples? * What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: * Genome-wide differential gene expression analysis * Select samples directly from the screen for filtering and creating subgroups * Violin plots on any numerical data * Loading of Microsoft Excel files Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8 Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2066.

Convex Multi-View Clustering Via Robust Low Rank Approximation With Application to Multi-Omic Data.

Recent advances in high throughput technologies have made large amounts of biomedical omics data accessible to the scientific community. Single omic data clustering has proved its impact in the biomedical and biological research fields. Multi-omic data clustering and multi-omic data integration techniques have shown improved clustering performance and biological insight. Cancer subtype clustering is an important task in the medical field to be able to identify a suitable treatment procedure and prognosis for cancer patients. State of the art multi-view clustering methods are based on non-convex objectives which only guarantee non-global solutions that are high in computational complexity. Only a few convex multi-view methods are present. However, their models do not take into account the intrinsic manifold structure of the data. In this paper, we introduce a convex graph regularized multi-view clustering method that is robust to outliers. We compare our algorithm to state of the art convex and non-convex multi-view and single view clustering methods, and show its superiority in clustering cancer subtypes on publicly available cancer genomic datasets from the TCGA repository. We also show our method's better ability to potentially discover cancer subtypes compared to other state of the art multi-view methods.

The Predictive and Prognostic Role of RAS-RAF-MEK-ERK Pathway Alterations in Breast Cancer: Revision of the Literature and Comparison with the Analysis of Cancer Genomic Datasets.

Although gene alterations of the RAS/RAF/MEK/ERK pathway are uncommon in breast cancer, this pathway is frequently activated in breast tumors, implying its role in tumor progression. We describe, after a revision of the literature, the frequency and types of gene alterations affecting this pathway in breast cancer by analyzing some public datasets from cBioPortal. Moreover, we consider their prognostic and predictive impact on treatment response, along with the role of transcriptomic predictors of RAS pathway activation. Our analysis shows that the driver alterations in RAS/RAF/MEK/ERK pathway-related genes are detected in 11% of primary breast cancers. The most frequently mutated genes are NF1 and KRAS, while copy number alterations mainly affect KRAS and BRAF, especially in basal-like tumors. The subgroup of patients carrying these alterations shows a worse prognosis; alterations in NF1 and RAF1 are associated with significantly reduced breast-cancer-specific survival in multivariate analysis. The literature review shows that the pathway is implicated, either by genetic or epigenetic alterations or by signaling network adaptations, in the mechanisms of sensitivity and resistance to a wide range of drugs used in the treatment of breast cancer. A thorough understanding of these alterations is critical for developing combination therapies that can delay or overcome drug resistance.

Pervasive conditional selection of driver mutations and modular epistasis networks in cancer.

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.

Abstract 1155: cBioPortal for cancer genomics

Abstract cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale cancer genomics data sets. cBioPortal provides a user-friendly interface that integrates genomic and clinical data, and provides a suite of visualizations and analyses, including OncoPrints, mutation “lollipop” plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization. cBioPortal also integrates external tools including CIViC, Cancer Digital Slide Archive, Next-Generation Clustered Heat Map, IGV and Bioconductor to facilitate interpretation. The public site (https://www.cbioportal.org) is accessed by ~35,000 unique visitors each month and hosts data from >325 studies spanning individual labs and large consortia. In addition, >67 instances of cBioPortal are installed at academic institutions and pharmaceutical/biotechnology companies worldwide. In 2021 we added data from 32 studies, totaling >24,000 samples, to the public site. All data is also available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub/. We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of >135,000 clinically sequenced samples from 19 institutions (https://genie.cbioportal.org). In addition, the GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations, including response, outcome, and treatment histories. The first BPC release contains data from >1,800 non-small cell lung cancer samples and will be released in early 2022. The growing GENIE cohort and the BPC clinical data have driven a number of recent developments, including performance improvements (the load time for the GENIE cohort was reduced from minutes to seconds). To leverage the BPC clinical data, we enabled sample selection based on treatment status, extended support for outcome analysis, and enhanced the patient timeline representation to incorporate response data. Additional development work has focused on improvements to variant interpretation, enhancements to the Mutations tab, and support for novel molecular assays via the ‘generic assay’ data type. Documentation on these new features and many others is available at https://www.cbioportal.org/news. cBioPortal is fully open source (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Bilkent University and The Hyve. We welcome open source contributions from others in the cancer research community. Citation Format: Jianjiong Gao, Tali Mazor, Ino de Bruijn, Adam Abeshouse, Diana Baiceanu, Ziya Erkoc, Elena Garcia Lara, Benjamin Gross, David M. Higgins, Prasanna K. Jagannathan, Priti Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Sander Rodenburg, Baby A. Satravada, Robert Sheridan, Lucas Sikina, Jessica Singh, S. Onur Sumer, Yichao Sun, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Kees van Bochove, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. cBioPortal for cancer genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1155.

Abstract 635: Concordance between dogs and humans: The use of AI in evaluating clinical cancer genomic datasets

Abstract Spontaneous tumors in dogs are highly similar to human cancers histologically, genetically, molecularly and clinically. In this presentation we explore similarities found across species when associating cancer biomarkers and outcomes. Our findings validate concordance across the cancer journeys. This is important to deepen the field of comparative oncology and affirming spontaneous cancer in dogs as an ideal ‘model’ for precision oncology, building upon recent work in tumor biology concordance. Our machine learning analysis prognosis and predictive effect of genomic alterations in 1303 client-owned tumor-bearing dogs identified TP53 and PTPRD as prognostic markers for poor survival in dogs with an overall survival hazard ratio (OS HR) of 1.732, 1.914, respectively (P<0.001 and P=0.023). These results are aligned with non-small cell lung cancer, metastatic breast cancer and pancreatic cancer in people correlated with poor survival when carrying TP53 mutation, and PTPRD also correlated with poor survival in gliomas. Besides that, we also identified ALB1 and PIK3CA prognostic markers for poor survival in canine tumors with an overall survival hazard ratio (OS HR) of 1.707, 1.689, respectively (P=0.01, and P=0.002). This data reaffirms the similarity between canine and human cancers not just in the genomic profiles but also the tumor biology and treatment strategy for both species. Among tumor types, lymphoma and hemangiosarcoma showed the worst survival times (N=32, OS HR=1.929, P=0.002, and N=202, OS HR=1.743, P<0.001). Interestingly, we also identified that dogs with RET mutations respond better to toceranib, a veterinary oral multikinase inhibitor against RET, VEGFR2, PDGFR, FLT3 and c-KIT; this is in concordance with sunitinib a tyrosine kinase inhibitor that target RET used in humans thyroid carcinomas and other tumors carrying RET mutations. To the best of our knowledge, this is the largest canine tumor clinical genomic dataset analysis. Applying real-world evidence and data tools in the FidoCure® dataset, we successfully identified associations between gene mutations and survival associated with response to target therapy treatment. These associations can benefit dogs by enabling better therapeutic recommendations and also benefit human cancer research since canine models recapitulate human disease with intact immune systems and similar tumor genomic profiles, confirmed by the FidoCure® database, accelerating clinical studies of novel treatments that currently have significant barriers to study at scale. Citation Format: Kevin Wu, Lucas Rodrigues, Gerald Post, Garrett Harvey, Aubrey Miller, Lindsay Lambert, Christina Lopes, Benjamin Lewis, James Zou. Concordance between dogs and humans: The use of AI in evaluating clinical cancer genomic datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 635.

Abstract LB503: Using JBrowse 2 plugins to visualize cancer genomic data

Abstract Genome browsers are essential tools for integrating, exploring, and visualizing data from large cancer genomics datasets. JBrowse 2 is a JavaScript genome browser with novel features for visualizing structural variants, syntenic alignments, and multiple types of genomic data. Although JBrowse 2 is available as a web-based or desktop application, the capabilities and features are not fixed and can be expanded through a comprehensive plugin system. To help address users' needs, we launched the new JBrowse 2 Plugin Store (https://jbrowse.org/jb2/plugin_store/). The overarching goals of the Plugin Store are to showcase the crucial features plugins add to JBrowse 2, to enable researchers to search for plugins they need, and for developers to highlight their plugins. The plugin system spans all aspects of the JBrowse 2 application and enables new track types (e.g. Manhattan plots, Hi-C data, ideograms), data adapters (e.g. API endpoint adapters for NCI Genomic Data Commons and the International Cancer Genome Consortium), and views (e.g. dot plots and multiple sequence alignments). Here, we present the capabilities of JBrowse 2 plugins and describe usage scenarios for cancer biologists and bioinformaticians, and software developers. Citation Format: Robin Andrew Haw, Colin Diesh, Caroline Bridge, Rob Buels, Garrett Stevens, Peter Xie, Teresa Martinez, Elliot Hershberg, Junjun Zhang, Shihab Dider, Scott Cain, Lincoln Stein, Ian Holmes. Using JBrowse 2 plugins to visualize cancer genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB503.

Abstract 5039: Visualization and analysis of cancer genomics data using UCSC Xena

Abstract UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. The toll supports Mac, Windows, and Linux. Questions Xena can help you answer: 1) Is overexpression of this gene associated with lower/higher survival? 2) What genes are differentially expressed between these two groups of samples? 3) What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression, and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: - Genome-wide differential gene expression analysis - New interface for filtering samples and creating subgroups - New interface to create charts and graphs - Violin plots on any numerical data - Loading of Microsoft Excel files - A new Publication Page showcasing publications and authors that use UCSC Xena Our beta prototype for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5039.

EHF is a novel regulator of cellular redox metabolism and predicts patient prognosis in HNSCC.

Head and Neck Squamous Cell Carcinoma (HNSCC) is a heterogeneous disease with relatively high morbidity and mortality rates. The lack of effective therapies, high recurrence rates and drug resistance driven in part, by tumor heterogeneity, contribute to the poor prognosis for patients diagnosed with this cancer. This problem is further exacerbated by the fact that key regulatory factors contributing to the disease diversity remains largely elusive. Here, we have identified EHF as an important member of the ETS family of transcription factors that is highly expressed in normal oral tissues, but lost during HNSCC progression. Interestingly, HNSCC tumors and cell lines exhibited a dichotomy of high and low EHF expression, and patients whose tumors retained EHF expression showed significantly better prognosis, suggesting a potential tumor suppressive role for EHF. To address this, we have performed gain and loss of function studies and leveraged bulk and single-cell cancer genomic datasets to identify global EHF targets by RNA-sequencing (RNA-seq) and Chromatin Immunoprecipitation and next generation sequencing (ChIP-seq) experiments of HNSCC cell lines. These mechanistic studies have revealed that EHF, acts as a regulator of a broad spectrum of metabolic processes, specifically targeting regulators of redox homeostasis such as NRF2 and SOX2. Our immunostaining results confirm the mutually exclusive expression patterns of EHF and SOX2 in HNSCC tumors and suggest a possible role for these two factors in establishing discrete metabolic states within the tumor microenvironment. Taken together, EHF may serve as a novel prognostic marker for classifying HNSCC patients for actionable and targeted therapeutic intervention.

Bayesian networks elucidate complex genomic landscapes in cancer

Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they can be constructed from observed data and can provide a guiding, graphical tool in exploring such relations. Here we propose BNs for elucidating the relations between driver events in large cancer genomic datasets. We present a methodology that is specifically tailored to biologists and clinicians as they are the main producers of such datasets. We achieve this by using an optimal BN learning algorithm based on well established likelihood functions and by utilising just two tuning parameters, both of which are easy to set and have intuitive readings. To enhance value to clinicians, we introduce (a) the use of heatmaps for families in each network, and (b) visualising pairwise co-occurrence statistics on the network. For binary data, an optional step of fitting logic gates can be employed. We show how our methodology enhances pairwise testing and how biologists and clinicians can use BNs for discussing the main relations among driver events in large genomic cohorts. We demonstrate the utility of our methodology by applying it to 5 cancer datasets revealing complex genomic landscapes. Our networks identify central patterns in all datasets including a central 4-way mutual exclusivity between HDR, t(4,14), t(11,14) and t(14,16) in myeloma, and a 3-way mutual exclusivity of three major players: CALR, JAK2 and MPL, in myeloproliferative neoplasms. These analyses demonstrate that our methodology can play a central role in the study of large genomic cancer datasets.