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Although the role of RNA binding proteins (RBPs) in extracellular RNA (exRNA) biology is well established, their exRNA cargo and distribution across biofluids are largely unknown. To address this gap, we extend the exRNA Atlas resource by mapping exRNAs carried by extracellular RBPs (exRBPs). This map was developed through an integrative analysis of ENCODE enhanced crosslinking and immunoprecipitation (eCLIP) data (150 RBPs) and human exRNA profiles (6,930 samples). Computational analysis and experimental validation identified exRBPs in plasma, serum, saliva, urine, cerebrospinal fluid, and cell-culture-conditioned medium. exRBPs carry exRNA transcripts from small non-coding RNA biotypes, including microRNA (miRNA), piRNA, tRNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), Y RNA, and lncRNA, as well as protein-coding mRNA fragments. Computational deconvolution of exRBP RNA cargo reveals associations of exRBPs with extracellular vesicles, lipoproteins, and ribonucleoproteins across human biofluids. Overall, we mapped the distribution of exRBPs across human biofluids, presenting a resource for the community.

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays., iScience, 25 (8), ISSN:2589-0042

To enable computational analysis of regulatory networks within the cancer cell in its natural tumor microenvironment, we develop a two-stage histoepigenetic analysis method. The first stage involves iterative computational deconvolution to estimate sample-specific cancer-cell intrinsic expression of a gene of interest. The second stage places the gene within a network module. We validate the method in simulation experiments, show improved performance relative to differential expression analysis from bulk samples, and apply it to illuminate the role of the mesothelin (MSLN) network in pancreatic ductal adenocarcinoma (PDAC). The network analysis and subsequent experimental validation in a panel of PDAC cell lines suggests AKT activation by MSLN through two known activators, retinoic acid receptor gamma (RARG) and tyrosine kinase non receptor 2 (TNK2). Taken together, these results demonstrate the potential of histoepigenetic analysis to reveal cancer-cell specific molecular interactions directly from patient tumor profiles.

Most cancers harbor a diverse collection of cell types including a typically heterogeneous cancer cell fraction. To reconstruct cell-intrinsic and heterotypic interactions driving tumor progression, we combine the XDec deconvolution method with cell-type-specific gene expression correlation analysis into the XDec-CHI method. XDec-CHI identifies intra- and inter-cellular pathways using correlation and places them in the context of specific tumor subtypes, as defined by the state of constituent cancer cells. We make the method web-accessible for analysis of publicly accessible pancreatic ductal adenocarcinoma, breast, head and neck, glioblastoma, and glioma tumors. We apply the method to TCGA and ICGC datasets to identify immune-suppressive interactions within PDAC tumors that are relevant for immunotherapies targeting PD-L1. Subtype-specific interactions derived from correlative analyses validated in co-culture experiments suggest PDAC subtypes have distinct therapeutic weaknesses, with Basal-like and

In response to signals from the embryonic testis, the germ cell intrinsic factor NANOS2 coordinates a transcriptional program necessary for the differentiation of pluripotent-like primordial germ cells toward a unipotent spermatogonial stem cell fate. Emerging evidence indicates that genetic risk factors contribute to testicular germ cell tumor initiation by disrupting sex-specific differentiation. Here, using the 129.MOLF-Chr19 mouse model of testicular teratomas and a NANOS2 reporter allele, we report that the developmental phenotypes required for tumorigenesis, including failure to enter mitotic arrest, retention of pluripotency and delayed sex-specific differentiation, were exclusive to a subpopulation of germ cells failing to express NANOS2. Single-cell RNA sequencing revealed that embryonic day 15.5 NANOS2-deficient germ cells and embryonal carcinoma cells developed a transcriptional profile enriched for MYC signaling, NODAL signaling and primed pluripotency. Moreover, lineage-tracing experiments demonstrated that embryonal carcinoma cells arose exclusively from germ cells failing to express NANOS2. Our results indicate that NANOS2 is the nexus through which several genetic risk factors influence tumor susceptibility. We propose that, in the absence of sex specification, signals native to the developing testis drive germ cell transformation.

With recent advances in distinguishing Pancreatic Ductal Adenocarcinoma (PDAC) subtypes based on transcriptional profiles, it is becoming clear that different subtypes have distinct therapeutic responses– e.g. Basal-like tumors tend to show less response to chemotherapy than Classical tumors. Here, we asked if the Basal-like, Classical A, and Classical B PDAC subtypes show distinct immuno-suppressive molecular profiles of relevance for targeted immunotherapy of PDAC. Toward this goal, we leveraged two large, public sequencing cohorts – TCGA and ICGC. To accommodate the large variation in cell type composition among the tumors across these cohorts, we applied the novel expression deconvolution (XDec) method to computationally deconvolute the bulk RNA-seq profiles. The method identified cell-type composition of each tumor and identified distinct Basal-like, Classical A, and Classical B subtypes, thus allowing classification of each tumor based on the state of its constituent cancer cell fraction. We further extended the method to also infer cell-type specific gene expression for each tumor sample and to identify correlation of cell-type specific gene expression levels across cohorts. Using the extended method, we determined that Basal-like cancer cells are CD274 high, Classical B cancer cell CD274 expression correlated strongly with MSLN and RELA (a recently proposed mechanism controlling PD-L1), and Classical A cancer cells had low CD274 expression. Because heterotypic interactions may also contribute toward an immuno-suppressive microenvironment, we next correlated expression levels between cancer and stromal cell types. Cancer cell MSLN expression correlated strongly with stromal CD274 expression which also correlated with stromal IFNGR1 and IFNγ. Using co-culture experiments we validated the causative relationship between cancer cell MSLN and stromal INFGR1 and CD274. Taken together, our results suggest that PDAC subtypes have distinct therapeutic vulnerabilities, with Basal-like and MSLN-high Classical B patients more likely to respond to PD-L1 targeting therapies. Moreover, we provided a methodological advance by extending the XDec method and by making it web-accessible, thus allowing the community to derive hypotheses testable in cell line models by performing correlations of cell-type specific gene expression levels deconvoluted from bulk RNA-seq profiles of PDAC patient tumors. Citation Format: Emily L. LaPlante, Dongliang Liu, Aleksandar Milosavljevic, Qizhi Yao. Basal-like, Classical A, and Classical B subtypes of pancreatic cancer show distinct immuno-suppressive molecular profiles [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-004.

Background: High mesothelin (MSLN) expression has been associated with poor prognosis in patients with pancreatic ductal adenocarcinoma (PDAC); however, the effect of MSLN exert on PDAC tumor microenvironment (TME) is largely unknown. Methods: MSLN over-expressed (MSLN-OE) and knocked-down (MSLN-KD) PDAC cells were established to investigate the MSLN-PD-L1 relationship in PDAC. Using an MSLN overexpressed Panc02 (Panc02-MSLN) cell line, we established an orthotopic PDAC mouse model. All mice were euthanized on day 27 after cell inoculation and the tumor-infiltrating leukocytes (TILs) were isolated for mass cytometry (CyTOF) study. The gene expression correlation between MSLN and a panel of immune cell markers was assessed by using a dataset that includes149 PDAC patients in the TCGA-PAAD cohort. Results: MSLN upregulated PD-L1 expression at both mRNA and protein levels in PDAC. MSLN was able to enhance PD-L1 transcription by recruitment of NF-kB P65 to the PD-L1 promoter. The Orthotopic implanted Panc02-MSLN group showed significantly increased tumor size, tumor weight, upregulated PD-L1, and reduced survival when compared with the vector control group. CyTOF analysis of the total CD45+ hematopoietic cells infiltrated to the TME revealed 13 distinct cell subsets. In MSLN-high tumors, there is a significant expansion of neutrophils, eosinophils, PD-1+CD8+, and CD8+NK cells. In addition, pan T cells, regulatory T cells (Treg), CD8+, and PD-1+CD4+ cells were increased. A considerable decrease of tumor-associated macrophages (TAM), dendritic cells, B cells, NK cells, and a trend towards decreased Ly6C+CD8+ cells was also found. Within the TAM subset, a decreased iNOS+M1 to Relm-a+M2 ratio was detected. Furthermore, although there were increased CD8+NK cells, we found more immunosuppressive markers such as PD-1, CTLA4, LAG3, KLRG1, and TIM3, and a decreased activation marker CD69 and transcription factor T-bet. Intriguingly, gene expression correlation analysis with 149 PDAC patients cohort in TCGA dataset showed that MSLN expression negatively correlates with the mRNA levels of the B220 marker of B cells, the F4/80 marker of macrophages, as well as the CD4, CD8, CD69, granzyme B, IL-2 and IFN-γ markers of T-cell activation. Conclusions: We showed here that MSLN could significantly modulate the TME, possibly through the upregulation of PD-L1 expression. Compared with MSLN-low PDAC tumors, MSLN-high tumors recruit diverse immunosuppressive rather than immunoreactive leukocytes into tumor tissues to constitute an immunologically cold TME. Since targeting MSLN or PD-L1 as a monotherapy exhibited only modest objective response rates in the clinic, our findings may provide an alternative strategy by combining the depletion of MSLN with immune checkpoint inhibitors, which could result in improved treatment efficacy in PDAC. Citation Format: Dongliang Liu, Ethan Poteet, Zhengdong Liang, Emily Laplante, Lisa Brubaker, Sadhna Dhingra, Aleksandar Milosavljevic, Changyi Chen, Qizhi Cathy Yao. Effects of mesothelin exert on tumor microenvironment in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-115.