David Haussler Research Articles

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.

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

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

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.

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.

The Problem is Not Monsters: The FRANKENCON Panel on Science and Ethics.

In November of 2019, the University of California Santa Cruz hosted a 3-day interdisciplinary conference to commemorate the 200th anniversary of the publication of Frankenstein, by Mary Shelley. A panel of senior researchers convened to discuss the impact of the novel on modern discussions of scientific ethics. The panel featured Nandini Bhattacharya, George Blumenthal, Michael M. Chemers, David Haussler, and Jenny Reardon. In the process, the panelists acted as the Institutional Review Board for a proposal from Victor Frankenstein himself.

Abstract 6154: H3K27M gliomas are characterized by a stall in the epithelial-mesenchymal transition

Abstract Pediatric diffuse midline gliomas are lethal cancers, the majority of which harbor the H3 p.K27M mutations. Although it has potential implications on the treatment of diffuse midline glioma as a disease driver; the timing, cell type of origin, and effect of the H3 p.K27M mutation on early embryonic brain development is poorly understood. The purpose of our study is to elucidate the molecular mechanisms by which the histone H3 p.K27M mutation drives tumorigenesis of pediatric diffuse midline gliomas using the analysis of genomic datasets. Here, we performed differential RNA sequencing gene expression analysis of a cohort of H3K27M and H3 wild type (WT) pediatric diffuse midline gliomas, revealing that genes in the epithelial-mesenchymal transition (EMT) pathway were significantly differentially expressed between the mutant and WT tumors. Several EMTs are required for normal brain development. Overall, pre-EMT genes, including the master regulator of EMT SNAI1, were overexpressed in H3K27M tumors compared to the WT tumors, while post-EMT genes were underexpressed. We hypothesized that the H3 p.K27M mutation may lead to gliomagenesis by inducing a stall in the EMT in early brain development. To test this hypothesis, we examined published single-cell RNA sequencing data from pediatric diffuse midline gliomas alongside similar data from organoid models of neural development, collected from multiple developmental timepoints. This analysis revealed transcriptional similarities between H3K27M and pre-EMT neural stem cells. Currently, we are investigating the expression of EMT markers in H3K27M and WT pediatric glioma primary cell lines, using Western blotting, RT-PCR, and CRISPRi screening. In conclusion, we observed aberrant expression of genes involved in EMT in H3K27M pediatric gliomas. Our observations are consistent with a model in which the p.H3K27M mutation is associated with a pre-EMT cell phenotype, potentially due to an arrest in the EMT pathway or de-differentiation of mature astrocytes. Citation Format: ALLISON R. CHENEY, Lauren M. Sanders, Lucas Seninge, Holly C. Beale, Ellen Towle Kephart, Jacob Pfeil, Katrina Learned, A. Geoffrey Lyle, Isabel Bjork, David Haussler, Sofie R. Salama, Olena M. Vaske. H3K27M gliomas are characterized by a stall in the epithelial-mesenchymal transition [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6154.

Abstract 5464: Determining accuracy of RNA sequencing data for gene expression profiling of single samples

Abstract Gene expression analysis of single samples shows increasing promise for clinical applications. However, obtaining high quality RNA from a human tumor sample can be challenging because medical, surgical, and pathological requirements often lead to sparse or degraded RNA. The variability in RNA quality presents challenges for defining input sample requirements, which are required to calculate sensitivity, specificity and reference ranges as required for a Clinical Laboratory Improvement Amendments (CLIA)-approved test. Clinical analysis of a single RNA-Seq dataset for the purpose of gene expression profiling involves not only the patient's sample, but a comparison cohort. We use 12,236 total tumor samples and require at least 20 samples for within-disease comparisons. Many of these samples do not have associated metadata about the quality of the sample, and so we have prioritized quality measures that can be derived from the sequence data alone. In order to characterize variability present in RNA-Seq datasets, we analyzed paired-end Illumina RNA sequencing (RNA-Seq) data from 1088 tumor samples from 29 data providers. We categorized reads based on where and how well they map to the genome, as well as by their PCR duplicate status. We defined reference ranges for five types of reads found in sequencing data: unmapped (0-13%); multi-mapped (2-15%); mapped duplicate (2-66%); mapped non exonic (0-26%) and mapped, exonic, non-duplicate (MEND, 27-76%). Only 64% of the 1088 tumor samples had read type fractions within the reference ranges. Of the remainder, most exceeded the reference ranges of more than one type of read. We then measured the relationship of sensitivity and specificity to input MEND read depth. We subsampled 5 deeply sequenced samples. With each subsample, we identified exceptionally highly expressed genes and samples with similar gene expression profiles. With subsampling to 20 million MEND reads, we detected over-expressed genes (“up-outlier” genes) with a median sensitivity of 96.1% and specificity of 99.8%; sample similarity had 96.6% sensitivity and 100.0% specificity. We estimate that a sample sequenced to a depth of 70 million total reads will typically have sufficient data for the up-outlier and sample-similarity gene expression analysis assays described here. With this analysis, we have identified a conservative approach to measuring the quality of RNA-Seq read data, which can then be used to define the sensitivity and specificity of single-sample assays to support their ultimate clinical adoption. Citation Format: Holly C. Beale, Jacquelyn M. Roger, Matthew A. Cattle, Liam T. McKay, Katrina Learned, Geoff Lyle, Ellen T. Kephart, Rob Currie, Du Linh Lam, Lauren Sanders, Jacob Pfeil, John Vivian, Isabel Bjork, Sofie R. Salama, David Haussler, Olena M. Vaske. Determining accuracy of RNA sequencing data for gene expression profiling of single samples [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5464.

Abstract A44: Comparative gene expression analysis for identification and prioritization of therapeutic targets in a cohort of childhood cancers

Abstract Here we describe the utility of comparative RNA sequencing (RNA-seq) analysis in identifying cancer driver pathways and relevant therapeutics in childhood cancer, and we introduce a novel system for prioritizing gene targets based on analytical and published evidence. The purpose of our study was to evaluate the clinical utility of comparative N-of-1 gene expression analysis in identifying therapeutic options for pediatric patients with relapsed or recurrent cancers. The comparative analysis framework and large cancer background cohorts were developed at the Treehouse Childhood Cancer Initiative at UC Santa Cruz. We employed this analysis in a pilot study of 26 patients at the Lucile Packard Children’s Hospital. We analyzed RNA-seq data from 30 biopsied samples from 26 patient donors, including 16 sarcomas, 5 CNS tumors, 3 hematopoietic cancers, 1 liver, and 1 colon cancer. We compared gene expression from each child’s tumor biopsy to a background cohort of RNA-seq data from over 11,000 cancer patients, and also to a smaller cohort defined by molecular and histopathologic similarity to the child’s tumor biopsy. This comparison yielded outlier gene activations in the child’s biopsy compared to those in the background cohorts, reflecting cancer driver pathways in the child’s tumor that are actionable. We stratified each of the resulting therapeutic leads into 5 baskets: RTK activation, JAK/STAT signaling, PI3K/AKT/mTOR signaling, Cell Cycle activation, or Other. Because 27 of the 30 samples had more than one lead, we developed a novel scoring system to prioritize each sample’s therapeutic gene targets based on the analytical strength of the gene expression analysis result, and on published literature evidence for each gene as a biomarker indicative of drug response. We presented the results of our analysis in genomic consensus meetings at Stanford and surveyed the responses of clinicians and families on the utility of our analysis for treatment decisions. Here we report our experience with the method and highlight case studies in which this analysis informed treatment decisions. Citation Format: Lauren M. Sanders, A. Geoffrey Lyle, Holly C. Beale, Ellen Towle Kephart, Katrina Learned, Jennifer Peralez, Norman Lacayo, Arun Rangaswami, Sheri L. Spunt, Isabel Bjork, David Haussler, Sofie R. Salama, Olena M. Vaske. Comparative gene expression analysis for identification and prioritization of therapeutic targets in a cohort of childhood cancers [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A44.

Abstract B06: Candidate differentiation stall in epithelial mesenchymal transition in H3K27M diffuse midline glioma

Abstract The purpose of our study was to elucidate the molecular mechanisms by which the histone H3 K27M mutation drives tumorigenesis of pediatric gliomas. Though it has potential implications on the treatment of diffuse midline glioma as a disease driver, the timing, cell type of origin, and effect of the H3K27M mutation on early embryonic brain development are not fully characterized. Here, we performed differential expression analysis on a cohort of H3K27M and H3WT pediatric gliomas, revealing that genes in the epithelial-mesenchymal transition (EMT) pathway were significantly differentially expressed. SNAI1, the EMT master regulator, was significantly overexpressed in the H3K27M tumor cohort. Overall, pre-EMT genes were overexpressed in H3K27M tumors while post-EMT genes were underexpressed. We hypothesized that H3K27M may lead to gliomagenesis by stalling an EMT in early brain development and employed single-cell and bulk RNA sequencing data from cerebral organoids at multiple developmental timepoints to test this hypothesis. We observed that a long noncoding RNA (lncRNA) signature identified as transiently expressed in early brain development was preferentially expressed in H3K27M tumors. Cell type-specific lncRNA signatures had higher expression in H3K27M tumors for pre-EMT cell types, and higher expression in H3WT tumors for post-EMT cell types. Finally, t-SNE clustering of single-cell glioma RNA sequencing data with single-cell organoid data revealed transcriptional similarities between H3K27M and pre-EMT neural stem cells. In conclusion, we observed aberrant activity of the EMT in H3K27M gliomas. Our data suggest that the H3K27M mutation is associated with a pre-EMT cell phenotype, and that this mutation may cause EMT arrest or de-differentiation. Citation Format: Allison R. Cheney, Lauren M. Sanders, Lucas Seninge, Holly C. Beale, Ellen Towle Kephart, Jacob Pfeil, Katrina Learned, A. Geoffrey Lyle, Isabel Bjork, David Haussler, Sofie R. Salama, Olena M. Vaske. Candidate differentiation stall in epithelial mesenchymal transition in H3K27M diffuse midline glioma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B06.

Abstract IA02: The Treehouse Childhood Cancer Initiative

Abstract Each year over 4,000 children with cancer in the US lack adequate treatment options for their disease, underscoring the urgent need for new therapeutic targets and new therapies. Cancer genomic investigations focusing on the identification of recurrent DNA changes in tumors that could be targeted by therapies have been disappointing in the majority of pediatric cancers, as these diseases have lower rates of somatic mutation compared to adult cancers. To address this challenge, the UC Santa Cruz Genomics Institute launched the Treehouse Childhood Cancer Initiative, an innovative program that identifies novel therapeutic targets for difficult-to-treat pediatric cancers through leveraging the power of large, shared tumor RNA-sequencing datasets. Treehouse partners with pediatric cancer hospitals, including Lucille Packard Children’s Hospital at Stanford, UCSF Benioff Children’s Hospital, Children’s Hospital of Orange County, Nationwide Children’s Hospital, and British Columbia Children’s Hospital, to investigate whether the comparative RNA-seq approach is feasible and useful when applied to patients prospectively. A preliminary analysis of 126 patients from four hospitals showed that comparative RNA-seq analysis can identify druggable targets for over 74.3% of difficult-to-treat pediatric cancer patients, while state-of-the-art DNA analysis finds potential therapeutic options for only 45.9% of patients. Current work focuses on determining the clinical utility of the Treehouse approach. Citation Format: David Haussler. The Treehouse Childhood Cancer Initiative [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr IA02.

Abstract 911: UCSC Xena for cancer genomics visualization and interpretation

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. Our unique Visual Spreadsheet shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We offer dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, charts, statistical analyses, genomic signatures, comparative transcript views, and bookmarks. We link out to the UCSC Genome Browser, giving users additional genomic context for any gene or coordinate, as well as MuPIT/CRAVAT and TumorMap, to give users complementary views of the same data. Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, PCAWG, GDC, GTEx, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data. In addition to the commonly available SNPs, INDELs, copy number variation, and gene expression datasets, we support DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotypes, subtype classifications and biomarkers. A recompute of TCGA, TARGET and GTEx datasets through the same bioinformatics pipeline allows users to compare expression between tumor and normal tissues. All of our data is available for download via our python API or through AWS S3 buckets. A researcher can host their own data securely via private hubs running on a laptop or behind a firewall, with visual and analytical integration occurring only within the Xena Browser. The lightweight Xena Data Hubs are straightforward to install on Windows, Mac and Linux. Loading data is easy using either our application or command line interface. Our newest features include: * a new, more intuitive wizard to load your data into a local Hub * URL bookmarks to save interactive views for yourself or to share with collaborators * genomic signatures: dynamically build as a weighted sum over a set of genes * hierarchically cluster a list of genes, regulons or probes * upper vs lower quartile in a KM plot Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. UCSC Xena for cancer genomics visualization and interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 911.

Abstract 2466: Designing an intuitive visualization of BRCAness scores for clinicians

Abstract Objective: Genomic interrogation of tumors has changed the way we care for cancer patients. Monogenic, single mutation tests clearly and accurately define patient populations whose tumors will or will not likely respond to specific therapies. Next generation sequencing has enabled diagnostics derived from broader footprints, such as signatures of mutational processes. Currently, no universal guidelines exist on translating complex predictive signatures into the clinic, and their interpretability is a barrier to uptake by clinicians. Visually displaying genomic signatures may help clinicians interpret them. We designed a user-directed approach to developing understandable displays of an individual’s BRCAness signature through testing at clinical cancer conferences. Materials and Methods: We adopted a two-stage approach to optimizing user-testing methods. First, 44 attendees at the 2018 Gastrointestinal Cancer Symposium participated in a tablet-based test of a visualization of a patient’s BRCAness signature. We refined our testing methods and continued tabled-based testing of 51 attendees at the 2018 American Society of Clinical Oncology. Participants viewed slides explaining the signature and visualization. They were asked to interpret a patient’s BRCAness signature when provided information both as text and as a visualization. Participants received one of three similar versions of the visualization. Results: Participants interpreted patients’ BRCAness signatures faster when given a visual. When shown a visual indicating BRCAness scores of germline BRCA mutated tumors, participants interpreted the information fastest without sacrificing accuracy or confidence in their answer. Participants’ interpretation of an individual’s BRCAness signature was associated with their self-reported likelihood to recruit the individual to a clinical trial. Conclusion: Visualizing distributions of BRCAness signatures with germline BRCA mutations displayed helped participants respond faster without sacrificing accuracy or confidence in their answer. Testing attendees at cancer conferences presents a potential platform to include clinicians and researchers in the development of genomics visualization tools. Citation Format: Hannah R. Allegakoen, Mary Goldman, Bennett Caughey, Martin Consunji, Christopher C. Benz, David Haussler, Jingchun Zhu, Eric A. Collisson. Designing an intuitive visualization of BRCAness scores for clinicians [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2466.

Abstract B02: Distinct CD8+ T-cell recognition profiles driven by conformational plasticity of recurrent neuroblastoma neoepitopes

Abstract The identification of tumor neoantigens driving T-cell responses for immunotherapy poses a significant bottleneck in the development of approaches for precision cancer therapeutics. Here, we employ a bioinformatics method, Prediction Of T Cell Epitopes for Cancer Therapy (ProTECT), using sequencing data from neuroblastoma patients to identify a recurrent Anaplastic Lymphoma Kinase mutation (ALK R1275Q) that leads to two high-affinity neoepitopes when expressed in complex with common HLA alleles. Analysis of the x-ray structures of nonamer and decamer peptides bound to HLA-B*15:01 reveals that the neoepitopes adopt distinct conformations that confer measurable differences in stability in vitro, while the self-antigen is excluded from binding due to steric hindrance. MHC tetramer staining of peripheral blood mononuclear cells from HLA-matched donors shows that the two neoepitopes elicit distinct CD8+ T-cell recognition profiles. To expand the range of patients who could mount a T-cell response to our ALK neoepitopes, we additionally characterize HLA binding repertoire using Rosetta comparative modeling simulations. Subsequent determination of the x-ray structure of decamer-bound HLA-A*01:01 further validates the Rosetta predictions. This work provides a novel approach towards robust, high-throughput identification and detailed characterization of highly stable neoantigen/HLA targets with desired T-cell recognition features for cancer immunotherapy. Citation Format: Sofie R. Salama, Jugmohit S. Toor, Arjun A. Rao, Andrew C. McShan, Mark Yarmarkovich, Sarvind Tripathi, Santrupti Nerli, Ada A. Madejska, Son Nguyen, Karissa Yamaguchi, John M. Maris, David Haussler, Nikolaos G. Sgourakis. Distinct CD8+ T-cell recognition profiles driven by conformational plasticity of recurrent neuroblastoma neoepitopes [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr B02.

Abstract A22: Identification of gene expression differences between primary pediatric tumors and their PDX models

Abstract Molecularly targeted therapies inhibit specific cancer pathways and have fewer harmful side effects than cytotoxic chemotherapies; however, the development of targeted therapies for childhood cancers has lagged behind that of adult cancers. Although adult and childhood cancers have different molecular mechanisms, similar druggable cancer pathways may be activated. We propose using gene expression analysis to identify opportunities to reposition FDA-approved targeted therapies for childhood cancer patients. We developed an N-of-1 gene expression approach that leverages a compendium of ~11,000 cancer gene expression profiles. To evaluate the utility of this approach for identifying cancer driver pathways, we analyzed gene expression data for 9 patient-derived xenograft (PDX) tumors. PDXs were tested for sensitivity to selected targeted therapies. Our analysis correctly predicted drug sensitivity in 8 of the 9 PDXs based on overexpression of specific cancer pathways. We also compared gene expression between PDX and matched primary tumor samples (n=7). Using TumorMap analysis, we found that PDX tumors cluster with their matched primary tumor and other samples with a similar cancer diagnosis. We also ran differential expression analysis and found 260 overexpressed genes and 630 underexpressed genes in PDX tumors. Underexpressed genes were associated with immune functions, extracellular matrix proteins, and growth factor signaling. Overexpressed genes were involved in TNF-α; signaling. This analysis confirmed expected changes in gene expression while also identifying TNF-α; signaling as a potential biologic artifact of PDX tumors. These features may influence the results of PDX experiments and should be accounted for when using this model. Overall, the PDX model recapitulates gene expression features of pediatric cancer and is a powerful tool for evaluating novel precision medicine approaches. Citation Format: Jacob J. Pfeil, Alex G. Lee, Leanne C. Sayles, Ellen T. Kephart, Holly C. Beale, Lauren M. Sanders, Olena Morozova, Sofie R. Salama, Alejandro Sweet-Cordero, David Haussler. Identification of gene expression differences between primary pediatric tumors and their PDX models [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr A22.

Abstract 2274: Cancer genomics visualization and interpretation using UCSC Xena

Abstract The UCSC Xena platform (http://xena.ucsc.edu/) allows biologists and bioinformaticians to securely analyze and visualize functional genomics data. Our unique Visual Spreadsheet shows multiple data types side by side enabling discovery of correlations across and within genes and genomic regions. Dynamic Kaplan-Meier survival analysis assesses survival stratification in addition to scatter plots, bar graphs, and boxplots all shown with statistical tests. In addition to the commonly available SNPs, INDELs, CNV, and gene expression datasets, we support DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotypes, subtype classifications and biomarkers. Our new whole genome views allow users to easily visualize non-coding regions for both copy number variation and somatic mutations. All of our data is available for download via our python API or through AWS S3 buckets. Our expanding public Xena Data Hubs currently host 1500+ datasets from more than 35 cancer types, as well as Pan-Cancer datasets. In addition to serving seminal cancer genomic datasets to the scientific community, including the latest from the GDC, TCGA, TARGET, and ICGC, we also host 'normal tissue' datasets from GTEx. A recompute of TCGA, TARGET and GTEx datasets through the same bioinformatics pipeline allows users to compare expression between tumor and normal tissues. In addition to exploring these public datasets, the UCSC Xena Browser can easily display an investigator's genomic and clinical data on their own Xena Hub. By empowering users to install and load data into their own hub, our architecture ensures that the investigator's data remains private. The lightweight Xena Data Hubs are straightforward to install on Windows, Mac and Linux operating systems and loading data is easy using either our application or command line interface. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Cancer genomics visualization and interpretation using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2274.

Abstract 4890: A pan-cancer analysis framework for incorporating gene expression information into clinical interpretation of pediatric cancer genomic data

Abstract Genomic characterization used in pediatric cancer clinical trials is limited to the detection of somatic mutations and gene fusions in well-characterized cancer genes. However, these approaches do not reveal actionable therapeutic targets for the majority of pediatric cancer patients. Incorporation of gene expression information into clinical genomic analysis is hindered by the lack of appropriate computational methods, designed for single patients rather than patient cohorts. UC Santa Cruz Treehouse Childhood Cancer Initiative (treehousegenomics.soe.ucsc.edu) enables the incorporation of gene expression information into the genomic evaluation of pediatric cancer patients. We leverage large cancer RNA sequencing datasets, including The Cancer Genome Atlas, Therapeutically Applicable Research to Generate Effective Treatments, Medulloblastoma Advanced Genomics International Consortium, International Cancer Genome Consortium, and published research and clinical studies. Through our “pan-cancer analysis”, we compare each prospective tumor’s RNA sequencing and/or mutational profile to over 11,000 uniformly analyzed tumor profiles using our Tumor Map method. Tumor Map visualizes single tumors together with the reference compendium and identifies samples that are most similar to the given tumor based on the gene expression profiles. We also developed a gene expression outlier analysis to identify transcripts that are over expressed in the given tumor. These pan-cancer gene expression analyses are used in conjunction with mutation data to nominate molecular pathways that may be driving the disease in each child, providing useful information to the medical teams. We aim to evaluate this approach in partnership with pediatric cancer clinical genomic trials at Stanford University, UC San Francisco, Children’s Hospital of Orange County, University of Michigan, Children’s Mercy Hospital, and British Columbia Children’s Hospital. The analysis of the first 27 patients at Stanford, most with refractory solid tumors, provided evidence of the potential clinical utility of incorporating gene expression information into the genomic evaluation of pediatric cancer patients. In all cases, we identified candidate driver molecular pathways that could be targeted by existing FDA-approved therapies or therapies available through a clinical trial. The most frequently identified molecular targets were receptor tyrosine kinases and cyclin-dependent kinases. For 3 patients with no treatment options prior to our work, the analysis contributed to treatment decisions. This study provides a framework for incorporating gene expression information into the clinical interpretation of pediatric cancer genomic data. We underscore the importance of releasing the data to the community immediately following generation, so that they may benefit new patients. Citation Format: Olena Morozova, Yulia Newton, Avanthi Tayi Shah, Holly Beale, Du Linh Lam, John Vivian, Isabel Bjork, Theodore Goldstein, Josh Stuart, Sofie Salama, E. Alejandro Sweet-Cordero, David Haussler. A pan-cancer analysis framework for incorporating gene expression information into clinical interpretation of pediatric cancer genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4890. doi:10.1158/1538-7445.AM2017-4890

Abstract LB-338: A critical evaluation of genomic data sharing: Barriers to accessing pediatric cancer genomic datasets: a Treehouse Childhood Cancer Initiative experience

Abstract Genomic data sharing is increasingly recognized as critical to genomic research. The need is acute in pediatric cancer research due to the rarity of pediatric tumor types and paucity of pediatric cancer data, and in translational research to assess the impact of genomic research on human health. However, genomic data sharing is hindered by an absence of standards regarding timing, patient privacy, use agreement standards, and data characterization and quality. At UC Santa Cruz Treehouse Childhood Cancer Initiative (treehousegenomics.soe.ucsc.edu), we examine individual pediatric cancer tumor RNA sequencing profiles against a database of over 11,000 tumor RNA sequencing profiles from public genomic datasets such as The Cancer Genome Atlas, Therapeutically Applicable Research To Generate Effective Treatments, International Cancer Genome Consortium, and Medulloblastoma Advanced Genomics International, and pediatric cancer clinical trials with which we partner, such as those at Stanford University, UC San Francisco, Children’s Hospital of Orange County, and British Columbia Children’s Hospital. For over 18 months, we have worked systematically to enhance the Treehouse dataset by adding pediatric cancer data and presently underrepresented tumor types. The NIH and other leading funding agencies now regularly require grantees to make genomic data generated available to the research community, either post-publication or after an embargo period. We have combed websites and public repositories, searched PubMed, and contacted researchers directly. Finding data requires a mining of literature, often with limited information, and initiating the many different processes for requesting permission for these datasets, with different and often cumbersome data use obligations. The combination of cryptically named datasets, multiple data types and the practice of grouping datasets from multiple papers under a single study accession makes zeroing in on the correct dataset challenging. Downloading the genomic data is time-consuming, such that a dataset of under a 100 files can take up to a week to download under optimal conditions. Matching metadata is inconsistently available, often vague, sparse or error ridden. Only after months of identifying, permissioning for use, committing to use- and sharing-restricting terms, and downloading the genomic and metadata, is it possible to assess the quality, often discovering that data quality is low. We evaluate the barriers to data sharing based on the Treehouse experience and offer guidelines for timing, use agreement standards, and data characterization and quality, to enhance data sharing and outcomes for all pediatric cancer patients. Citation Format: Katrina Learned, Ann Durbin, Robert Currie, Holly Beale, Du Linh Lam, Theodore Goldstein, Sofie R. Salama, David Haussler, Olena Morozova, Isabel Bjork. A critical evaluation of genomic data sharing: Barriers to accessing pediatric cancer genomic datasets: a Treehouse Childhood Cancer Initiative experience [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-338. doi:10.1158/1538-7445.AM2017-LB-338

Abstract 4891: Identification of recurrent high-affinity MHC class I restricted neo-epitopes in neuroblastoma using ProTECT

Abstract Introduction: T-cells are trained to differentiate between cell-surface MHC-displayed peptide sequences from self- and non-self proteins and act on the latter. The numerous mutations often associated with cancers can occur in coding regions of the genome and modify the sequence of wild-type proteins, potentially creating targets for immunotherapies. We have developed an analysis pipeline ProTECT (Prediction of T-cell Epitopes for Cancer Therapy) to identify and rank neo-epitopes in terms of immunogenicity. Running ProTECT on a set of Neuroblastomas patients predicted hotspot mutations that bind well to high-frequency MHC alleles – combinations that would potentially benefit a large subset of NBL patients. Methods: ProTECT accepts paired tumor and normal DNA sequencing fastq files, and tumor RNA sequencing fastqs. Mutations are called using a panel of callers [1-4], and are annotated[5] to identify coding mutations. Prediction of self-MHC:mutated-peptides is carried out[6] and the final binding predictions are ranked using an in-house algorithm. Summary: Running ProTECT on 6 Neuroblastoma samples (NBL) from the TARGET (Therapeutically Applicable Research to Generate Effective Treatments) project revealed 2 well-known hotspot mutations in NBL (NRAS Q61K and ALK R1275Q) that bind to common MHC alleles (A*01:01 and B*15:01 respectively). We also found 2 closely related mutations in ALK F1174L and F1174I that are predicted to C*04:01 and C*07:02. We carried out in-vitro refolding and crystallization assays [7] for the five highest-ranking mutant NRAS and ALK R1275Q predictions. Properly conformed MHC trimers were verified by a monodisperse peak after anion exchange chromatography. SDS gel electrophoresis and Mass-spec confirmed bound peptide for 4/5 tested predictions and 3 of these were used to set up hanging-drop crystallization trials in various conditions. Positive hits were obtained for one (AQDIYRASY::HLA-B*15:01) and the structure was obtained at 1.7A. The structure suggested the binding of the 10-mer (AQDIYRASYY) to the MHC and this was shown to bind better than the 9-mer using Differential Scanning Fluorimetry[9]. We will run ProTECT on the remaining 100+ TARGET NBL trios, and on relevant cohorts within The Cancer Genome Atlas (TCGA). We aim to reveal clinically relevant hotspot-mutation:MHC pairs. Conclusion: We have described a pipeline for identification and ranking of therapeutically relevant neo-epitopes. We have predicted potential targets for NBL that have been validated in-vitro.

Abstract 2584: The UCSC Xena system for cancer genomics data visualization and interpretation

Abstract The UCSC Xena platform (http://xena.ucsc.edu/) allows biologists and bioinformaticians to securely analyze and visualize their private functional genomics data in the context of public genomic and clinical data sets. The Xena platform consists of a set of federated data hubs and the Xena browser, which integrates across hubs, providing one location to analyze and visualize all data. Our expanding public Xena Data Hubs currently hosts 1400+ data sets from more than 35 cancer types, as well as Pan-Cancer data sets. Our public data hubs serve seminal cancer genomics and functional genomics data set to the scientific community, including the latest TCGA, TARGET, ICGC, and GTEx data sets. We support most data types including somatic and germline SNPs, INDELs, large structural variants, CNV, gene-, transcript-, exon- protein-, miRNA-expression, DNA methylation, phenotypes, clinical data, subtype classifications and genomic biomarkers. Additionally, investigators’ own functional genomics data can be hosted on private hubs running on their laptop or behind the firewall. Data is integrated on the UCSC Xena Browser, allowing biologists to view and interpretation of their genomic data in the context of a large collection of cancer genomics data sets such as TCGA. The lightweight Xena data hubs are straightforward to install on Windows, Mac and Linux operating systems and loading data is easy using either our application or command line interface. This system of the browser and hubs helps researchers combine new or preliminary results from their laptops or internal servers, or even data from a new paper, securely with vetted data from the public sphere. Visualizations and analyses include dynamic Kaplan-Meier survival analysis to assess survival stratification by any information in addition to our visual spreadsheet, scatter plots and bar graphs. We seek feedback at our poster on new visualizations and functionalities. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. The UCSC Xena system for cancer genomics data visualization and interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2584. doi:10.1158/1538-7445.AM2017-2584

Getting Data Sharing Right to Help Fulfill the Promise of Cancer Genomics

Limited access to the profusion of sequence information derived from cancer patients worldwide stymies basic research and clinical decisions. Efforts are underway to streamline and safeguard data use.