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Cancer-associated fibroblasts (CAF) may exert tumor-promoting and tumor-suppressive functions, but the mechanisms underlying these opposing effects remain elusive. Here, we sought to understand these potentially opposing functions by interrogating functional relationships among CAF subtypes, their mediators, desmoplasia, and tumor growth in a wide range of tumor types metastasizing to the liver, the most common organ site for metastasis. Depletion of hepatic stellate cells (HSC), which represented the main source of CAF in mice and patients in our study, or depletion of all CAF decreased tumor growth and mortality in desmoplastic colorectal and pancreatic metastasis but not in nondesmoplastic metastatic tumors. Single-cell RNA-Seq in conjunction with CellPhoneDB ligand-receptor analysis, as well as studies in immune cell-depleted and HSC-selective knockout mice, uncovered direct CAF-tumor interactions as a tumor-promoting mechanism, mediated by myofibroblastic CAF-secreted (myCAF-secreted) hyaluronan and inflammatory CAF-secreted (iCAF-secreted) HGF. These effects were opposed by myCAF-expressed type I collagen, which suppressed tumor growth by mechanically restraining tumor spread, overriding its own stiffness-induced mechanosignals. In summary, mechanical restriction by type I collagen opposes the overall tumor-promoting effects of CAF, thus providing a mechanistic explanation for their dual functions in cancer. Therapeutic targeting of tumor-promoting CAF mediators while preserving type I collagen may convert CAF from tumor promoting to tumor restricting.

Hepatocellular carcinoma (HCC), the fourth leading cause of cancer mortality worldwide, develops almost exclusively in patients with chronic liver disease and advanced fibrosis 1 , 2 . Here we interrogated functions of hepatic stellate cells (HSCs), the main source of liver fibroblasts 3 , during hepatocarcinogenesis. Genetic depletion, activation or inhibition of HSCs in mouse models of HCC revealed their overall tumour-promoting role. HSCs were enriched in the preneoplastic environment, where they closely interacted with hepatocytes and modulated hepatocarcinogenesis by regulating hepatocyte proliferation and death. Analyses of mouse and human HSC subpopulations by single-cell RNA sequencing together with genetic ablation of subpopulation-enriched mediators revealed dual functions of HSCs in hepatocarcinogenesis. Hepatocyte growth factor, enriched in quiescent and cytokine-producing HSCs, protected against hepatocyte death and HCC development. By contrast, type I collagen, enriched in activated myofibroblastic HSCs, promoted proliferation and tumour development through increased stiffness and TAZ activation in pretumoural hepatocytes and through activation of discoidin domain receptor 1 in established tumours. An increased HSC imbalance between cytokine-producing HSCs and myofibroblastic HSCs during liver disease progression was associated with increased HCC risk in patients. In summary, the dynamic shift in HSC subpopulations and their mediators during chronic liver disease is associated with a switch from HCC protection to HCC promotion. Subpopulations of cytokine-producing and myofibroblastic hepatic stellate cells, identified by single-cell RNA sequencing, protect against or promote the development of hepatocellular carcinoma via high expression of hepatocyte growth factor or type I collagen, respectively..

Cancer-associated fibroblasts (CAF) may exert tumor-promoting and tumor-suppressive functions, but the mechanisms underlying these opposing effects remain elusive. Here, we sought to understand these potentially opposing functions by interrogating functional relationships among CAF subtypes, their mediators, desmoplasia, and tumor growth in a wide range of tumor types metastasizing to the liver, the most common organ site for metastasis. Depletion of hepatic stellate cells (HSC), which represented the main source of CAF in mice and patients in our study, or depletion of all CAF decreased tumor growth and mortality in desmoplastic colorectal and pancreatic metastasis but not in nondesmoplastic metastatic tumors. Single-cell RNA-Seq in conjunction with CellPhoneDB ligand-receptor analysis, as well as studies in immune cell-depleted and HSC-selective knockout mice, uncovered direct CAF-tumor interactions as a tumor-promoting mechanism, mediated by myofibroblastic CAF-secreted (myCAF-secreted) hyaluronan and inflammatory CAF-secreted (iCAF-secreted) HGF. These effects were opposed by myCAF-expressed type I collagen, which suppressed tumor growth by mechanically restraining tumor spread, overriding its own stiffness-induced mechanosignals. In summary, mechanical restriction by type I collagen opposes the overall tumor-promoting effects of CAF, thus providing a mechanistic explanation for their dual functions in cancer. Therapeutic targeting of tumor-promoting CAF mediators while preserving type I collagen may convert CAF from tumor promoting to tumor restricting.

BackgroundAI models have been developed to better understand disease through histology or protein structure, however these tools cannot model the impact of therapeutics on disease in a cellular context. AI cell models could simulate cellular responses to therapeutics, enabling exploration of vast combinatorial target spaces too large for experiments. However, their use in drug discovery is limited by a lack of large-scale, relevant biological datasets.We introduce the gx1TM model, a foundation model of T cell biology and demonstrate how it can be used for target discovery at a scale beyond wet-lab experiments.MethodsThe gx1™ model, a transformer-based encoder-decoder model,1 was trained using masked expression modeling on a dataset of over 75M cells (30M+ T cells from ArsenalBio). To adapt the gx1TM model for target discovery in exhausted T cells, a genetic perturbation dataset of exhausted T cells was generated. We perturbed 551 genes using CRISPRi targeting in T cells from four donors.2 T cell exhaustion was induced in vitro via repeated antigen stimulation over 14 days.3 We measured T cell killing, growth, and performed single-cell RNA sequencing. These data were used to fine-tune a perturbation model. After training, the model’s performance was evaluated on a held-out donor and 50 held out perturbations. Finally, we conducted a virtual screen and validated novel targets with superior predicted function.ResultsThe perturbation model’s predictions were robust to technical covariates with high correlation between true and predicted values in held out donors and held out perturbations (figure 1a). Using the fine-tuned perturbation model we performed a virtual screen of 182,000 gene combinations and ranked by predicted cytotoxicity and proliferation. We selected 113 novel perturbations for validation. Experimental measures were correlated with predicted values (figure 1b). Similarly, predicted expression changes induced by perturbations were correlated with experimental data (log-fold change over controls Pearson’s r=0.67). This correlation was comparable to correlations between experimental replicates. Importantly, the model predicted gene perturbations that had superior function in comparison to perturbations in the training set were confirmed experimentally.ConclusionsThe gx1™ model, fine-tuned with perturbation data, accurately predicts T cell function and identifies novel genetic targets for enhancement. This AI-driven approach may significantly accelerate drug discovery by enabling virtual screens that are physically infeasible. More generally, this work suggests that cell foundation models may enable the discovery of new biology and the identification of new targets beyond the reach of physical experiments.ReferencesVaswani, Ashish, et al. ‘Attention is all you need.’ Advances in Neural Information Processing Systems. 2017;30.Ran F, Hsu P, Wright J, et al. Genome engineering using the CRISPR-Cas9 system. Nat Protoc. 2013;8:2281–2308. https://doi.org/10.1038/nprot.2013.143Wherry EJ, Kurachi M. Molecular and cellular insights into T cell exhaustion. Nature Reviews Immunology. 2015;15(8),486–499.Abstract 1125 Figure 1Performance of gx1 based perturbation model. [a] The left panel demonstrates experimental validation of model predictions of log fold change gene expression for each perturbation [b] experimental validation of functional predictions[Image Omitted. See PDF.]