Highlights d We investigate the relationship of patient age and ICB therapy biomarkers d Favorable ICB biomarkers are generally more prevalent in elderly patients d The CAMA web application provides a multi-omics atlas of aging in cancer

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Single-cell RNA-seq (scRNA-seq) technologies can uncover changes in the molecular states that underlie cellular phenotypes. However, to understand dynamic cellular processes, computational tools are needed to extract temporal information from the snapshots of cellular gene expression that scRNA-seq provides. To address this challenge, we have developed a neural ordinary differential equation based method, RNAForecaster, for predicting gene expression states in single cells for multiple future time steps. We demonstrate that RNAForecaster can accurately predict future expression states in simulated scRNA-seq data. We then show that using metabolic labeling scRNA-seq data from constitutively dividing cells, RNAForecaster accurately recapitulates many of the expected changes in gene expression during progression through the cell cycle over a three day period. Finally, we extend RNAForecaster to use unspliced and spliced counts from scRNA-seq to predict the impact of knock down experiments in pancreatic development. Thus, RNAForecaster enables estimation of future expression states in biological systems.

Background The majority of pancreatic ductal adenocarcinomas (PDAC) are diagnosed at the metastatic stage, and standard therapies have limited activity with a dismal 5-year survival rate of only 8%. The liver and lung are the most common sites of PDAC metastasis, and each have been differentially associated with prognoses and responses to systemic therapies. A deeper understanding of the molecular and cellular landscape within the tumor microenvironment (TME) metastasis at these different sites is critical to informing future therapeutic strategies against metastatic PDAC. Results By leveraging combined mass cytometry, immunohistochemistry, and RNA sequencing, we identify key regulatory pathways that distinguish the liver and lung TMEs in a preclinical mouse model of metastatic PDAC. We demonstrate that the lung TME generally exhibits higher levels of immune infiltration, immune activation, and pro-immune signaling pathways, whereas multiple immune-suppressive pathways are emphasized in the liver TME. We then perform further validation of these preclinical findings in paired human lung and liver metastatic samples using immunohistochemistry from PDAC rapid autopsy specimens. Finally, in silico validation with transfer learning between our mouse model and TCGA datasets further demonstrates that many of the site-associated features are detectable even in the context of different primary tumors. Conclusions Determining the distinctive immune-suppressive features in multiple liver and lung TME datasets provides further insight into the tissue specificity of molecular and cellular pathways, suggesting a potential mechanism underlying the discordant clinical responses that are often observed in metastatic diseases.

Recently, extensions for relatlonal database management systems (DBMS) have been proposed to support also herarch& structures (complex objects) These extensions have been mamly unplemented on top of an exlstmg DBMS Such an approach leads to many dlsadvantages not only from the conceptual pomt of view but also from performance aspects Thus paper reports on a 3-year effort to design and prototype a DBMS to support a generahzed relatlonal data model, called extended NFZ (Non Fist Normal Form) data model which treats flat relations, hyts, and hlerarctical structures m a umform way The log& data model, a language for thts model, and altematlves for storage structures to unplement generabzed relations are presented and discussed

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Spatial transcriptomics (ST) is a powerful approach for cancers molecular and cellular characterization. Pancreatic intraepithelial neoplasia (PanIN) is a pancreatic ductal adenocarcinoma (PDAC) premalignancy diagnosed from formalin-fixed and paraffin-embedded (FFPE) specimens limiting single-cell based investigations. We developed a new FFPE ST analysis protocol for PanINs complemented with novel transfer learning approaches. The first transfer learning approach, to assign cell types to ST spots and integrate the transcriptional signatures, shows that PanINs are surrounded by PDAC cancer associated fibroblasts (CAFs) subtypes, including the rare antigen-presenting CAFs. Furthermore, most PanINs are of the classical PDAC subtype while one sample expresses cancer stem cell markers. A second transfer learning approach, to integrate ST PanIN data with PDAC scRNA-seq data, identifies a shift between inflammatory and proliferative signaling as PanINs progress to PDAC. Our data support a model of inflammatory signaling and PanIN-CAF interactions promoting premalignancy progression and PDAC immunosuppressive characteristics.

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