Kocher, Jean-Pierre A.

Abstract Introduction: Single-cell RNA-sequencing (scRNA-seq) pairing with single-cell T-Cell-Receptor-sequencing (scTCR-seq) enables profiling of gene expression and TCR repertoires in individual T cells, elevating our understanding of T-cell-mediated immunity. Here, we present immunopipe, a comprehensive and flexible pipeline (https://github.com/pwwang/immunopipe) for analyzing the paired data. Besides the command-line tool, it offers a user-friendly web interface that allows users with varying programming expertise to configure, initiate, and monitor the pipeline. By combining extensive functionality with ease of use, immunopipe empowers users to unlock valuable insights from the data, which notably advances our understanding of immune response and fosters the development of innovative immunotherapies. Results: Immunopipe is a comprehensive analytical pipeline for scRNA-seq and scTCR-seq data, offering various modules leveraging existing tools and novel methods to ensure data quality and enable downstream analytics. It performs quality control for scRNA-seq data, clusters cells based on gene expression to facilitate the identification of cell populations and provides insights into T-cell heterogeneity and functional diversity through T-cell selection and clustering. Enrichment analysis identifies markers and pathways for T-cell characterization. Our automated T-cell annotation tool annotates cell types accurately and hierarchically. For scTCR-seq data, immunopipe analyzes TCR clones, including clonality, diversity, gene usage, and repertoire overlap. Clone residency analysis examines the presence of clones across samples. Integrative analyses combine scRNA-seq and scTCR-seq data, providing a comprehensive understanding of the cellular landscape. Immunopipe incorporates diverse cell group information to analyze T-cell populations, which enables granular perspectives of the data for comprehensive insights. Immunopipe offers a flexible platform, allowing users to customize the analyses as needed. It supports both core and optional analyses through configuration, where users have control over the adjustable parameters. The pipeline relies on cell grouping information that can be referenced from metadata or modified to generate new variables for analysis and data filtering. Immunopipe can be executed locally or on scheduler platforms, and a docker image is provided for compatibility and simplified installation. It also offers a web interface that facilitates configuration, execution monitoring, and access to results. Conclusion: Immunopipe is a flexible and user-friendly pipeline for analyzing scRNA-seq and scTCR-seq data. It empowers researchers to gain valuable insights that could enhance the understanding of immune responses and facilitate the development of innovative immunotherapies. Citation Format: Panwen Wang, Haidong Dong, Yue Yu, Shuwen Zhang, Zhifu Sun, Jean-Pierre A. Kocher, Junwen Wang, Lin Yi, Ying Li. Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline [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 4959.

Abstract Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor. Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNA-seq samples, and examined in an independent testing set of 521 TCGA colorectal cancer RNA-seq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNA-seq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNA-seq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNA-seq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNA-seq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Abstract Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor. Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNA-seq samples, and examined in an independent testing set of 521 TCGA colorectal cancer RNA-seq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ).From RNA-seq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNA-seq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNA-seq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNA-seq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Objective: Bringing together genomics, transcriptomics, proteomics, and other –omics technologies is an important step towards developing highly personalized medicine. However, instrumentation has advances far beyond expectations and now we are able to generate data faster than it can be interpreted. Materials and Methods: We have developed PANDA ( P athway AND A nnotation) Explorer, a visualization tool that integrates gene-level annotation in the context of biological pathways to help interpret complex data from disparate sources. PANDA is a web-based application that displays data in the context of well-studied pathways like KEGG, BioCarta, and PharmGKB. PANDA represents data/annotations as icons in the graph while maintaining the other data elements (i.e. other columns for the table of annotations). Custom pathways from underrepresented diseases can be imported when existing data sources are inadequate. PANDA also allows sharing annotations among collaborators. Results : In our first use case, we show how easy it is to view supplemental data from a manuscript in the context of a user’s own data. Another use-case is provided describing how PANDA was leveraged to design a treatment strategy from the somatic variants found in the tumor of a patient with metastatic sarcomatoid renal cell carcinoma. Conclusion : PANDA facilitates the interpretation of gene-centric annotations by visually integrating this information with context of biological pathways. The application can be downloaded or used directly from our website http://bioinformaticstools.mayo.edu/research/panda-viewer/.

We previously reported an extremely rare case of follicular dendritic cell sarcoma (FDCS) presented as a thyroid mass. Given the rarity of this disease, there are no personalized and molecularly targeted treatment options due to the lack of knowledge in the genomic makeup of the tumor. A 44-year-old white woman was diagnosed with an extranodal FDCS in thyroid. The patient underwent a total thyroidectomy, central compartment dissection, parathyroid re-implantation, and adjuvant radiation therapy. Tumor DNA sequencing of 236 genes by FoundationOne panel found truncating mutations in PTEN and missense mutations in RET and TP53. However, patient-matched germline DNA was not sequenced which is critical for identification of true somatic mutations. Furthermore, the FoundationOne panel doesn't measure genomic rearrangements which have been shown to be abundant in sarcomas and are associated with sarcoma tumorigenesis and progression. In the current study, we carried out comprehensive genomic sequencing of the tumor, adjacent normal tissues, and patient-matched blood, in an effort to understand the genomic makeup of this rare extranodal FDCS and to identify potential therapeutic targets. Eighty-one somatic point mutations were identified in tumor but not in adjacent normal tissues or blood. A clonal truncating mutation in the CLTCL1 gene, which stabilizes the mitotic spindle, was likely a driver mutation of tumorigenesis and could explain the extensive copy number aberrations (CNAs) and genomic rearrangements in the tumor including a chr15/chr17 local chromothripsis resulted in 6 expressed fusion genes. The fusion gene HDGFRP3→SHC4 led to a 200-fold increase in the expression of oncogene SHC4 which is a potential target of the commercial drug Dasatinib. Missense mutations in ATM and splice-site mutation in VEGFR1 were also detected in addition to the TP53 missense mutation reported by FoundationOne.

Abstract Motivation: Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. Results: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. Availability and Implementation: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS. Contact: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Motivation: RNA-seq has become the method of choice to quantify genes and exons, discover novel transcripts and detect fusion genes. However, reliable variant identification from RNA-seq data remains challenging because of the complexities of the transcriptome, the challenges of accurately mapping exon boundary spanning reads and the bias introduced during the sequencing library preparation. Method: We developed RVboost, a novel method specific for RNA variant prioritization. RVboost uses several attributes unique in the process of RNA library preparation, sequencing and RNA-seq data analyses. It uses a boosting method to train a model of ‘good quality’ variants using common variants from HapMap, and prioritizes and calls the RNA variants based on the trained model. We packaged RVboost in a comprehensive workflow, which integrates tools of variant calling, annotation and filtering. Results: RVboost consistently outperforms the variant quality score recalibration from the Genome Analysis Tool Kit and the RNA-seq variant-calling pipeline SNPiR in 12 RNA-seq samples using ground-truth variants from paired exome sequencing data. Several RNA-seq–specific attributes were identified as critical to differentiate true and false variants, including the distance of the variant positions to exon boundaries, and the percent of the reads supporting the variant in the first six base pairs. The latter identifies false variants introduced by the random hexamer priming during the library construction. Availability and implementation: The RVboost package is implemented to readily run in Mac or Linux environments. The software and user manual are available at http://bioinformaticstools.mayo.edu/research/rvboost/. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract MOTIVATION: Transcriptome sequencing (RNA-seq) is the most economical and information-rich next generation sequencing technology to study genomic medicine. Genomic features such as gene expression, transcript expression, fusion transcripts, long non-coding RNAs and expressed single nucleotide variants (eSNVs) can be obtained from RNA-seq data. There are several computational workflows to obtain gene counts, transcript counts and fusion gene candidates, but not a comprehensive system to identify expressed SNVs from RNA-sequencing data. METHODS: We have developed a novel computational system (The eSNV-Detect) with parallel alignment strategies to identify expressed SNVs accurately from tumor transcriptome sequencing data. Sequence-based features like reads supporting reference allele, alternate allele, ratio of alternate reads to total read depth, base qualities, mapping qualities, forward strand and reverse strand supporting reads etc. along with a variety of databases such as dbSNP, 1000 genome, 5400 exome, refGene, avsift, ljb_phylop from UCSC were used to call SNVs from RNA-sequencing data. For the expressed SNVs detected in tumors, we can also identify the amino acid change and classify the protein domains using our method. RESULTS: We have applied the eSNV-Detect method to call novel expressed SNVs in two tumors (one HER2 positive and one ER positive breast tumor). BWA and Bowtie were used to align the two breast tumors. Several filtering criteria's were applied to the tumors to remove any known (germ line) or low confidence SNVs. We randomly selected 83 and 31 eSNVs for the HER2 and ER positive tumors respectively and performed Sanger sequencing validation in tumor and matched normal tissues of the tumor. We were able to validate 81 out of 83 eSNVs in the HER2 tumor sample, and 29 out of 31 eSNVs in the ER positive tumor sample. Of the eSNVs that we validated 39/81 and 10/31 were localized to functional protein domains in HER2 and ER positive tumors respectively. We are also in the process of validating eSNVs detected from RNA-seq with the tumor and blood exome sequencing data we have for the same patients. Citation Format: Xiaojia Tang, Saurabh Baheti, Khader Shameer, Kevin J. Thompson, Jean-Pierre Kocher, Edith A. Perez, Aubrey E. Thompson, Krishna R. Kalari. The eSNV-Detect: a computational system to identify expressed single nucleotide variants from tumor transcriptomes. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3185. doi:10.1158/1538-7445.AM2013-3185

Abstract Motivation: The Biological Reference Repository (BioR) is a toolkit for annotating variants. BioR stores public and user-specific annotation sources in indexed JSON-encoded flat files (catalogs). The BioR toolkit provides the functionality to combine and retrieve annotation from these catalogs via the command-line interface. Several catalogs from commonly used annotation sources and instructions for creating user-specific catalogs are provided. Commands from the toolkit can be combined with other UNIX commands for advanced annotation processing. We also provide instructions for the development of custom annotation pipelines. Availability and implementation: The package is implemented in Java and makes use of external tools written in Java and Perl. The toolkit can be executed on Mac OS X 10.5 and above or any Linux distribution. The BioR application, quickstart, and user guide documents and many biological examples are available at http://bioinformaticstools.mayo.edu . Contact: Kocher.JeanPierre@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.