Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment.

Cancer-associated fibroblasts (CAFs) are the predominant components of the tumor microenvironment (TME) and influence cancer hallmarks, but without systematic investigation on their ubiquitous characteristics across different cancer types. Here, we perform pan-cancer analysis on 226 samples across 10 solid cancer types to profile the TME at single-cell resolution, illustrating the commonalities/plasticity of heterogenous CAFs. Activation trajectory of the major CAF types is divided into three states, exhibiting distinct interactions with other cell components, and relating to prognosis of immunotherapy. Moreover, minor CAF components represent the alternative origin from other TME components (e.g., endothelia and macrophages). Particularly, the ubiquitous presentation of endothelial-to-mesenchymal transition CAF, which may interact with proximal SPP1+ tumor-associated macrophages, is implicated in endothelial-to-mesenchymal transition and survival stratifications. Our study comprehensively profiles the shared characteristics and dynamics of CAFs, and highlight their heterogeneity and plasticity across different cancer types. Browser of integrated pan-cancer single-cell information is available at https://gist-fgl.github.io/sc-caf-atlas/ .

Pheophorbide A and SN38 conjugated hyaluronan nanoparticles for photodynamic- and cascadic chemotherapy of cancer stem-like ovarian cancer.

In this study, we designed photo-triggered reactive oxygen species (ROS)-generating pheophorbide A and ROS-cleavable thioketal-SN38 conjugated hyaluronan-cholesterol nanoparticles (PheoA-SN38-HC NPs). And we observed the combined therapeutic effects of PheoA-SN38-HC NPs against HEY-T30 human ovarian cancer (OC) model. Clinical Proteomic Tumor Analysis Consortium (CPTAC) data showed that the expression of cancer stem cell (CSC) markers (CD44, ALDH1A1, and CD117) is highly associated with poor clinical outcomes in OC patients. We proved that HEY-T30 cells overexpress CSC markers and much more invasive than other cancer cells. Flow cytometry (FACS) and microscopic analysis revealed the active targeting property of PheoA-SN38-HC NPs to CD44+ HEY-T30 cells. Moreover, the combination therapeutic effect of PheoA-SN38-HC NPs was clearly demonstrated against in vitro HEY-T30 cells and an in vivo xenograft mouse model. In particular, the paracrine cytotoxic effect of SN38 probably compensates the locoregional therapeutic limitation of photodynamic therapy.

CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins.

The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.