Jian Zhang

BACKGROUND: RNA sequencing has become a ubiquitous technology used throughout life sciences as an effective method of measuring RNA abundance quantitatively in tissues and cells. The increase in use of RNA-seq technology has led to the continuous development of new tools for every step of analysis from alignment to downstream pathway analysis. However, effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts. RESULTS: Using the workflow management system Snakemake we have developed a user friendly, fast, efficient, and comprehensive pipeline for RNA-seq analysis. VIPER (Visualization Pipeline for RNA-seq analysis) is an analysis workflow that combines some of the most popular tools to take RNA-seq analysis from raw sequencing data, through alignment and quality control, into downstream differential expression and pathway analysis. VIPER has been created in a modular fashion to allow for the rapid incorporation of new tools to expand the capabilities. This capacity has already been exploited to include very recently developed tools that explore immune infiltrate and T-cell CDR (Complementarity-Determining Regions) reconstruction abilities. The pipeline has been conveniently packaged such that minimal computational skills are required to download and install the dozens of software packages that VIPER uses. CONCLUSIONS: VIPER is a comprehensive solution that performs most standard RNA-seq analyses quickly and effectively with a built-in capacity for customization and expansion.

BACKGROUND: Metastases to bone are a frequent complication of human prostate cancer and result in the development of osteoblastic lesions that include an underlying osteoclastic component. Previous studies in rodent models of breast and prostate cancer have established that receptor activator of NF-kappaB ligand (RANKL) inhibition decreases bone lesion development and tumor growth in bone. RANK is essential for osteoclast differentiation, activation, and survival via its expression on osteoclasts and their precursors. RANK expression has also been observed in some tumor cell types such as breast and colon, suggesting that RANKL may play a direct role on tumor cells. METHODS: Male CB17 severe combined immunodeficient (SCID) mice were injected with PC3 cells intratibially and treated with either PBS or human osteprotegerin (OPG)-Fc, a RANKL antagonist. The formation of osteolytic lesions was analyzed by X-ray, and local and systemic levels of RANKL and OPG were analyzed. RANK mRNA and protein expression were assessed on multiple prostate cancer cell lines, and events downstream of RANK activation were studied in PC3 cells in vitro. RESULTS: OPG-Fc treatment of PC3 tumor-bearing mice decreased lesion formation and tumor burden. Systemic and local levels of RANKL expression were increased in PC3 tumor bearing mice. PC3 cells responded to RANKL by activating multiple signaling pathways which resulted in significant changes in expression of genes involved in osteolysis and migration. RANK activation via RANKL resulted in increased invasion of PC3 cells through a collagen matrix. CONCLUSION: These data demonstrate that host stromal RANKL is induced systemically and locally as a result of PC3 prostate tumor growth within the skeleton. RANK is expressed on prostate cancer cells and promotes invasion in a RANKL-dependent manner.

Abstract Purpose: Cancer antigen–specific T cells are key components in antitumor immune response, yet their identification in the tumor microenvironment remains challenging, as most cancer antigens are unknown. Recent advance in immunology suggests that similar T-cell receptor (TCR) sequences can be clustered to infer shared antigen specificity. This study aims to identify antigen-specific TCRs from the tumor genomics sequencing data. Experimental Design: We used the TRUST (Tcr Repertoire Utilities for Solid Tissue) algorithm to assemble the TCR hypervariable CDR3 regions from 9,700 bulk tumor RNA-sequencing (RNA-seq) samples, and developed a computational method, iSMART, to group similar TCRs into antigen-specific clusters. Integrative analysis on the TCR clusters with multi-omics datasets was performed to profile cancer-associated T cells and to uncover novel cancer antigens. Results: Clustered TCRs are associated with signatures of T-cell activation after antigen encounter. We further elucidated the phenotypes of clustered T cells using single-cell RNA-seq data, which revealed a novel subset of tissue-resident memory T-cell population with elevated metabolic status. An exciting application of the TCR clusters is to identify novel cancer antigens, exemplified by our identification of a candidate cancer/testis gene, HSFX1, through integrated analysis of HLA alleles and genomics data. The target was further validated using vaccination of humanized HLA-A*02:01 mice and ELISpot assay. Finally, we showed that clustered tumor-infiltrating TCRs can differentiate patients with early-stage cancer from healthy donors, using blood TCR repertoire sequencing data, suggesting potential applications in noninvasive cancer detection. Conclusions: Our analysis on the antigen-specific TCR clusters provides a unique resource for alternative antigen discovery and biomarker identification for cancer immunotherapies.