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/.

Currently, more than 33 million peoples have been infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and more than a million people died from coronavirus disease 2019 (COVID-19), a disease caused by the virus. There have been multiple reports of autoimmune and inflammatory diseases following SARS-CoV-2 infections. There are several suggested mechanisms involved in the development of autoimmune diseases, including cross-reactivity (molecular mimicry). A typical workflow for discovering cross-reactive epitopes (mimotopes) starts with a sequence similarity search between protein sequences of human and a pathogen. However, sequence similarity information alone is not enough to predict cross-reactivity between proteins since proteins can share highly similar conformational epitopes whose amino acid residues are situated far apart in the linear protein sequences. Therefore, we used a hidden Markov model-based tool to identify distant viral homologs of human proteins. Also, we utilized experimentally determined and modeled protein structures of SARS-CoV-2 and human proteins to find homologous protein structures between them. Next, we predicted binding affinity (IC50) of potentially cross-reactive T-cell epitopes to 34 MHC allelic variants that have been associated with autoimmune diseases using multiple prediction algorithms. Overall, from 8,138 SARS-CoV-2 genomes, we identified 3,238 potentially cross-reactive B-cell epitopes covering six human proteins and 1,224 potentially cross-reactive T-cell epitopes covering 285 human proteins. To visualize the predicted cross-reactive T-cell and B-cell epitopes, we developed a web-based application “Molecular Mimicry Map (3M) of SARS-CoV-2” (available at https://ahs2202.github.io/3M/). The web application enables researchers to explore potential cross-reactive SARS-CoV-2 epitopes alongside custom peptide vaccines, allowing researchers to identify potentially suboptimal peptide vaccine candidates or less ideal part of a whole virus vaccine to design a safer vaccine for people with genetic and environmental predispositions to autoimmune diseases. Together, the computational resources and the interactive web application provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune disease following COVID-19.

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.

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As the third enzyme of the pentose phosphate pathway (PPP), abnormally elevated levels of 6-phosphogluconate dehydrogenase (6PGD) have been documented in various human cancers. We demonstrate that reduced cereblon (CRBN) protein expression is the underlying mechanism of elevated 6PGD expression in metastatic prostate cancer cells. We establish 6PGD as a new endogenous substrate for CRBN by demonstrating that it interacts directly with CRBN and is ubiquitinated by CRL4CRBN. In addition, CRBN negatively regulates prostate cancer cell progression and metastasis, as abnormally high 6PGD, in the absence of sufficient CRBN, enhances the metastatic potential of prostate cancer in vitro and in vivo. Our findings show convincingly that carbohydrate metabolism regulated by 6PGD is linked to prostate cancer metastasis via CRBN. Based on these data, we propose that the 6PGD-CRBN axis may be a suitable target for further research into new therapeutics for mitigating prostate cancer metastasis. Citation Format: Koushik Guchhait, Hyeon Seung Yoon, Seungheon Shin, Hyun-Su An, Hye Seung Nam, Francisco D. Yanqui-Rivera, Samara M. Oña, Miguel Á. Mendez, Seokjae Park, Eun-Kyoung Kim, Jong Yeon Hwang, Jee-Young Han, Doo Yong Chung, Daeho Park, Su-Geun Yang, Chul-Seung Park, Steve K. Cho. Cereblon inhibits prostate cancer progression and metastasis by negatively regulating 6PGD [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB157.