The UCSC Genome Browser database: 2017 update

Since its 2001 debut, the University of California, Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu/) team has provided continuous support to the international genomics and biomedical communities through a web-based, open source platform designed for the fast, scalable display of sequence alignments and annotations landscaped against a vast collection of quality reference genome assemblies. The browser's publicly accessible databases are the backbone of a rich, integrated bioinformatics tool suite that includes a graphical interface for data queries and downloads, alignment programs, command-line utilities and more. This year's highlights include newly designed home and gateway pages; a new 'multi-region' track display configuration for exon-only, gene-only and custom regions visualization; new genome browsers for three species (brown kiwi, crab-eating macaque and Malayan flying lemur); eight updated genome assemblies; extended support for new data types such as CRAM, RNA-seq expression data and long-range chromatin interaction pairs; and the unveiling of a new supported mirror site in Japan.

Comparative Tumor RNA Sequencing Analysis for Difficult-to-Treat Pediatric and Young Adult Patients With Cancer

IMPORTANCE

Pediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes.

OBJECTIVE

To evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children's Hospital (n = 31), Lucile Packard Children's Hospital at Stanford University (n = 80), CHOC Children's Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017.

EXPOSURES

Participants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant's tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient's tumor. Results of the UCSC analysis were presented to clinical partners.

MAIN OUTCOMES AND MEASURES

Feasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information.

RESULTS

Among 144 samples from children and young adults (median age at diagnosis, 9 years; range, 0-26 years; 72 of 118 [61.0%] male [26 patients sex unknown]) with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq-derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%).

CONCLUSIONS AND RELEVANCE

This study's findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets.

Intra-ureteric capsaicin in loin pain haematuria syndrome: efficacy and complications

OBJECTIVE

To evaluate the safety and efficacy of intra-ureteric capsaicin for loin pain haematuria syndrome (LPHS).

PATIENTS AND METHODS

In an open prospective pilot study, four middle-aged patients (three women and one man) with LPHS resistant to therapies such as splanchnic nerve block, psychological treatment or renal autotransplantation (one) were assessed. An intra-ureteric infusion of capsaicin (30 mg/100 mL of 30% alcohol in saline) for 30 min with bladder irrigation was administered under general anaesthesia, with a subsequent intravenous patient-controlled narcotic analgesic pump for pain control. Double-concentration capsaicin was used for second infusions, if necessary when the response to the earlier infusion was inadequate or incomplete.

RESULTS

The first patient had experienced reduced pain levels for the first 3 months only, with no benefit from the subsequent treatments with higher doses of capsaicin (60 mg). The second patient with recurrent pain in an autotransplanted kidney had no benefit from either a 30 or 60 mg capsaicin infusion a month apart, but developed a fibrotic stricture at the transplant pelvi-ureteric junction, requiring pyelocystoplasty. The third patient with concurrent depression had no benefit from a 30-mg infusion of capsaicin. The fourth patient experienced no pain relief from a 30 mg infusion of capsaicin but developed proteinuria secondary to mesangial proliferative glomerulonephritis, ureteric inflammation needing stenting within 7 days of treatment and subsequently nephrectomy for a nonfunctioning kidney at 3 months.

CONCLUSION

Intra-ureteric capsaicin was neither effective nor safe in LPHS; the contribution of the alcohol diluent cannot be excluded.

The case for using mapped exonic non-duplicate reads when reporting RNA-sequencing depth: examples from pediatric cancer datasets

BACKGROUND

The reproducibility of gene expression measured by RNA sequencing (RNA-Seq) is dependent on the sequencing depth. While unmapped or non-exonic reads do not contribute to gene expression quantification, duplicate reads contribute to the quantification but are not informative for reproducibility. We show that mapped, exonic, non-duplicate (MEND) reads are a useful measure of reproducibility of RNA-Seq datasets used for gene expression analysis.

FINDINGS

In bulk RNA-Seq datasets from 2,179 tumors in 48 cohorts, the fraction of reads that contribute to the reproducibility of gene expression analysis varies greatly. Unmapped reads constitute 1-77% of all reads (median [IQR], 3% [3-6%]); duplicate reads constitute 3-100% of mapped reads (median [IQR], 27% [13-43%]); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (median [IQR], 25% [16-37%]). MEND reads constitute 0-79% of total reads (median [IQR], 50% [30-61%]).

CONCLUSIONS

Because not all reads in an RNA-Seq dataset are informative for reproducibility of gene expression measurements and the fraction of reads that are informative varies, we propose reporting a dataset's sequencing depth in MEND reads, which definitively inform the reproducibility of gene expression, rather than total, mapped, or exonic reads. We provide a Docker image containing (i) the existing required tools (RSeQC, sambamba, and samblaster) and (ii) a custom script to calculate MEND reads from RNA-Seq data files. We recommend that all RNA-Seq gene expression experiments, sensitivity studies, and depth recommendations use MEND units for sequencing depth.

Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

PURPOSE

Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample.

METHODS

We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression.

RESULTS

We demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns.

CONCLUSION

This exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.

Abstract 2466: Identifying confidently measured genes in single pediatric cancer patient samples using RNA sequencing

In the UC Santa Cruz Treehouse Childhood Cancer Initiative (treehousegenomics.soe.ucsc.edu), we are exploring the utility of using RNA-Seq analysis of tumor samples from children to identify potential novel therapeutic options for each individual. Within a single RNA-Seq data set, the gene expression measurements are not equally accurate. The identification of activated, druggable pathways requires accurate gene-level expression measurements.

We receive samples from a variety of clinical and research settings, and the quantity and complexity of the available input material and the depth of sequencing differ. These factors inspired us to develop a tool that will allow us to identify accurate measurements in most RNA-Seq samples we receive.

First, we characterized the relationship between depth of sequencing and the accuracy of the gene expression measurement. We analyzed subsets of reads in samples with more than 50 million Uniquely Mapped, Exonic, Non-duplicate (UMEND) reads. UMEND reads typically constitute over 80% of the reads in a high quality experiment with sufficient starting material. We compared gene expression across the subsets of reads to calculate how many UMEND reads are required to produce consistent measurements. We found that, on average, genes expressed at 1-5 TPM in our data require 30 million reads to be accurately measured. For this calculation, we define accuracy as the condition in which 75% of genes are measured to within 25% of the true value.

Secondly, we use these known relationships to identify genes that have been accurately measured in our tumor RNA-Seq samples. For a sample with 15 million UMEND reads, we find that genes expressed above 5 TPM can be accurately measured and are retained. In the first twelve samples analyzed, samples with more than 10 million UMEND reads retained at least 46% of the genes expressed above zero. We exclude as references those samples with fewer than 10 million UMEND reads due to the marked gene loss after thresholding for this group.

Using accurately measured genes allows us to more confidently assess similarity to other samples, identify enriched pathways, and confirm the expression of drug targets and related molecules under consideration. For example, we reconsidered the CDK4 inhibitor Palbociclib in one patient because the expression of RB1, downstream effector required for Palbociclib-mediated tumor cell death, was under our accuracy threshold. Accuracy thresholds can also be used in experiment planning.

Accuracy thresholding allows us to better assess the value of an RNA-Seq data set and, if necessary, identify the subset of genes whose expression can be confidently considered in a clinical setting. Our experience points to the importance of careful quality control in this process.

Citation Format: Holly Beale, Du Linh Lam, John Vivian, Yulia Newton, Avanthi Tayi Shah, Isabel Bjork, Ted Goldstein, Angela N. Brooks, Josh Stuart, Sofie Salama, E. Alejandro Sweet-Cordero, David Haussler1, Olena Morozova. Identifying confidently measured genes in single pediatric cancer patient samples using RNA sequencing [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 2466. doi:10.1158/1538-7445.AM2017-2466

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

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 615: Spatially resolved expression of T cell receptors elucidates spatial relationships between T cells, immune infiltration, and cancer-associated pathways

Spatial distribution of T cells is key in understanding the escape of tumors from immune surveillance via the adaptive immune response, including interactions between immune cells and the surrounding tumor microenvironment. T cells are critical to the adaptive immune response to pathogens and cancers, mediating an antigen-specific response through both specificity and diversity of T cell receptor (TCR) clonotypes. Many methods exist to determine specific clonotypes and overall TCR diversity present from bulk tissues or sorted cell populations; however, nearly all fail to capture spatial orientation and arrangement of T cells engaging with their microenvironment, and most require large amounts of starting material from precious samples. Here, we present a TCR expression profiling panel for the GeoMx® Digital Spatial Profiler that can be combined with the GeoMx Cancer Transcriptome Atlas (CTA) or Human Whole Transcriptome Atlas (WTA) on archival formalin-fixed paraffin embedded (FFPE) tissue specimens. This represents the first commercial spatial expression profiling assay for the simultaneous quantification of TCR constant, variable, and joining segments in situ.

We show reliable sensitivity and specificity (>90%) with respect to orthogonal sequencing and robust detection of TCR chains with evidence of clonal expansion and CD8 infiltration across tumor regions in colorectal cancer tissue. These events also corresponded to increased signatures of exhaustion from the T cells and suggest that the T cells resident in or near the tumor are tumor-specific and poised for activation via checkpoint blockade. Signaling pathways and tumor-specific signatures were also evaluated to look for mechanisms through which tumor cells respond to T cell infiltration. We further validated the performance of the TCR probe pool in cell pellet arrays with orthogonal TCR sequencing, tonsil and colorectal cancer tissues.

Together, the combination of our TCR add-on panel with the CTA or WTA illuminates T cell phenotypes, signaling pathways, population dynamics, and transcriptomic changes, yielding an unparalleled view of the T cell response in any context.

FOR RESEARCH USE ONLY. Not for use in diagnostic procedures.

Citation Format: Katrina van Raay, Michelle Kriner, Jason Reeves, Erin Piazza, Hargita Kaplan, John Vivian, Francis Fernandez, Margaret Hoang, Joseph Beechem. Spatially resolved expression of T cell receptors elucidates spatial relationships between T cells, immune infiltration, and cancer-associated pathways [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 615.

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

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 P2-01-11: Single-cell sequencing of the blood T cell repertoire before and after trastuzumab treatment in early stage HER2+ breast cancer

Background: Human epidermal growth factor receptor 2 (HER2) positivity in breast cancer portends an aggressive tumor behavior and poor prognosis and is associated with a positive response to trastuzumab treatment. Prior immunohistochemistry and RNA sequencing of breast tumor tissues suggest that trastuzumab may recruit and activate anti-tumor T cells. Tumor infiltrating lymphocytes have been associated with improved response in patients with HER2+ early breast cancer treated with neoadjuvant trastuzumab plus chemotherapy. However, these cells have not previously been characterized at the single cell level in tumor tissue or in the periphery. Assessing the T cell component in the peripheral blood via single-cell sequencing enables high sensitivity detection of rare cells, may identify T cell antigen receptors (TCR) involved in the anti-tumor response, and could lead to a non-invasive means of monitoring trastuzumab-mediated immune activity. Here we perform single cell sequencing of the blood T cell repertoire in breast cancer patients pre- and post-trastuzumab treatment. Methods: To characterize T cell response in trastuzumab plus chemotherapy treated patients, we profiled peripheral CD3+ T cells using 10x Genomics VDJ single-cell sequencing in paired samples from five patients with HER2+ breast cancer. Patients had stage IIA (n=2), stage IIIC (n=2) or stage IV (n=1) breast cancer and were treated with preoperative docetaxel, carboplatin, trastuzumab, pertuzumab (TCHP). Two patients had a pathological complete response (pCR), and three patients had partial clinical response with residual disease at surgery. Peripheral blood mononuclear cells (PMBCs) were collected at a C1D1 pre-treatment and day 1 of cycles 3, 4, or 5. Results: Eleven T cell subpopulations, including naïve and memory CD4+ and CD8+ T cells, activated CD4+ and CD8+ T effector cells, activated CD4+ T regulatory cells, were characterized in the five patients’ peripheral blood based on their transcriptional profiles. T cell subpopulation distribution and clonal expansion profiles were similar in pre- and post- treatment samples in all five donors. Large T cell clonal expansions were detected in the peripheral blood of the two patients who had a pCR, but were not detected in the three patients who had residual disease at surgery. The patients who had a pCR exhibited large expansions in activated CD8+ terminal effector memory/effector memory (TEM/EM) cells followed by expansions in activated CD4+ TEM/EM cells. A minor increasing trend in activated CD4+ Treg cells was observed across all patients upon treatment with TCHP. Conclusions: Single-cell sequencing enables high-resolution characterization of immune cell subsets and represents a promising approach to assess the immune response to trastuzumab and other cancer therapeutics. In this study, single-cell sequencing of peripheral CD3+ T cells identified clonal expansions in activated CD8+ and CD4+ T cells in HER2+ breast cancer patients who had a pCR with preoperative TCHP treatment. These data are consistent with the model that T cells play a key role in trastuzumab-mediated tumor control, and warrant further investigation in a larger sample population.

Citation Format: John Vivian, Kimberly Walter, Elliott Drabek, Nicole Haaser, Maren K. Levin, Esther San Roman Rodriguez, Kendra Peck, Ngan Nguyen, Carl Millward, Jonathan Benjamin, William H. Robinson, Joyce A. O'Shaughnessy. Single-cell sequencing of the blood T cell repertoire before and after trastuzumab treatment in early stage HER2+ breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-01-11.

Mobilization of innate and adaptive antitumor immune responses by the RNP-targeting antibody ATRC-101

Immunotherapy approaches focusing on T cells have provided breakthroughs in treating solid tumors. However, there remains an opportunity to drive anticancer immune responses via other cell types, particularly myeloid cells. ATRC-101 was identified via a target-agnostic process evaluating antibodies produced by the plasmablast population of B cells in a patient with non-small cell lung cancer experiencing an antitumor immune response during treatment with checkpoint inhibitor therapy. Here, we describe the target, antitumor activity in preclinical models, and data supporting a mechanism of action of ATRC-101. Immunohistochemistry studies demonstrated tumor-selective binding of ATRC-101 to multiple nonautologous tumor tissues. In biochemical analyses, ATRC-101 appears to target an extracellular, tumor-specific ribonucleoprotein (RNP) complex. In syngeneic murine models, ATRC-101 demonstrated robust antitumor activity and evidence of immune memory following rechallenge of cured mice with fresh tumor cells. ATRC-101 increased the relative abundance of conventional dendritic cell (cDC) type 1 cells in the blood within 24 h of dosing, increased CD8+ T cells and natural killer cells in blood and tumor over time, decreased cDC type 2 cells in the blood, and decreased monocytic myeloid-derived suppressor cells in the tumor. Cellular stress, including that induced by chemotherapy, increased the amount of ATRC-101 target in tumor cells, and ATRC-101 combined with doxorubicin enhanced efficacy compared with either agent alone. Taken together, these data demonstrate that ATRC-101 drives tumor destruction in preclinical models by targeting a tumor-specific RNP complex leading to activation of innate and adaptive immune responses.