Cancer-associated fibroblasts (CAFs) are central players in the microenvironment of solid tumors, affecting cancer progression and metastasis. CAFs have diverse phenotypes, origins and functions and consist of distinct subpopulations. Recent progress in single-cell RNA-sequencing technologies has enabled detailed characterization of the complexity and heterogeneity of CAF subpopulations in multiple tumor types. In this Review, we discuss the current understanding of CAF subsets and functions as elucidated by single-cell technologies, their functional plasticity, and their emergent shared and organ-specific features that could potentially be harnessed to design better therapeutic strategies for cancer. Lavie et al. review the recent advances in the field of cancer-associated fibroblasts, including their tissue-specific complexity and overall plasticity, as revealed by single-cell technologies. 

Cancer-associated fibroblasts in the single-cell era

The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth.

Abstract Mortality from breast cancer is almost exclusively a result of tumor metastasis and resistance to therapy and therefore understanding the underlying mechanisms is an urgent challenge. Chemotherapy, routinely used to treat breast cancer, induces extensive tissue damage, eliciting an inflammatory response that may hinder efficacy and promote metastatic relapse. Here we show that systemic treatment with doxorubicin, but not cisplatin, following resection of a triple-negative breast tumor induces the expression of complement factors in lung fibroblasts and modulates an immunosuppressive metastatic niche that supports lung metastasis. Complement signaling derived from cancer-associated fibroblasts (CAFs) mediates the recruitment of myeloid-derived suppressor cells (MDSCs) to the metastatic niche, thus promoting T cell dysfunction. Pharmacological targeting of complement signaling in combination with chemotherapy alleviates immune dysregulation and attenuates lung metastasis. Our findings suggest that combining cytotoxic treatment with blockade of complement signaling in triple-negative breast cancer patients may attenuate the adverse effects of chemotherapy, thus offering a promising approach for clinical use. 

https://www.nature.com/articles/s41467-022-33598-x.pdf

Chemotherapy-induced complement signaling modulates immunosuppression and metastatic relapse in breast cancer

Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.

/pdf/hallmarks-of-aging-based-dual-purpose-disease-and-age-1l7r3hk7.pdf

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

Lung cancer remains a major health problem despite the considerable research into prevention and treatment methods. Through a deeper understanding of tumors, patient‐specific ex vivo spheroid models with high specificity can be used to accurately investigate the cause, metastasis, and treatment strategies for lung cancer. Biofabricate lung tumors are presented, consisting of patient‐derived tumor spheroids, endothelial cells, and lung decellularized extracellular matrix, which maintain a radial oxygen gradient, as well as biophysicochemical behaviors of the native tumors for precision medicine. It is also demonstrated that the developed lung‐cancer spheroid model reproduces patient responses to chemotherapeutics and targeted therapy in a co‐clinical trial, with 85% accuracy, 86.7% sensitivity, and 80% specificity. RNA sequencing analysis validates that the gene expression in the spheroids replicates that in the patient's primary tumor. This model can be used as an ex vivo predictive model for personalized cancer therapy and to improve the quality of clinical care.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202206603

Sarah K. Deasy

Papers

A glitch in the matrix: organ-specific matrisomes in metastatic niches