Cancer-associated fibroblasts (CAFs) are indispensable architects of the tumor microenvironment. They perform the essential functions of extracellular matrix deposition, stromal remodeling, tumor vasculature modulation, modification of tumor metabolism, and participation in crosstalk between cancer and immune cells. In this review, we discuss our current understanding of the principal differences between normal fibroblasts and CAFs, the origin of CAFs, their functions, and ultimately, highlight the intimate connection of CAFs to virtually all of the hallmarks of cancer. We address the remarkable degree of functional diversity and phenotypic plasticity displayed by CAFs and strive to stratify CAF biology among different tumor types into practical functional groups. Finally, we summarize the status of recent and ongoing trials of CAF-directed therapies and contend that the paucity of trials resulting in Food and Drug Administration (FDA) approvals thus far is a consequence of the failure to identify targets exclusive of pro-tumorigenic CAF phenotypes that are mechanistically linked to specific CAF functions. We believe that the development of a unified CAF nomenclature, the standardization of functional assays to assess the loss-of-function of CAF properties, and the establishment of rigorous definitions of CAF subpopulations and their mechanistic functions in cancer progression will be crucial to fully realize the promise of CAF-targeted therapies.

Background: Neoadjuvant therapy (NAT) emerged as the standard of care for patients with pancreatic ductal adenocarcinoma (PDAC) who undergo surgery; however, surgery is morbid, and tools to predict resection margin status (RMS) and prognosis in the preoperative setting are needed. Radiomic models, specifically delta radiomic features (DRFs), may provide insight into treatment dynamics to improve preoperative predictions. Methods: We retrospectively collected clinical, pathological, and surgical data (patients with resectable, borderline, locally advanced, and metastatic disease), and pre/post-NAT contrast-enhanced computed tomography (CT) scans from PDAC patients at the University of Texas Southwestern Medical Center (UTSW; discovery) and Humanitas Hospital (validation cohort). Gross tumor volume was contoured from CT scans, and 257 radiomics features were extracted. DRFs were calculated by direct subtraction of pre/post-NAT radiomic features. Cox proportional models and binary prediction models, including/excluding clinical variables, were constructed to predict overall survival (OS), disease-free survival (DFS), and RMS. Results: The discovery and validation cohorts comprised 58 and 31 patients, respectively. Both cohorts had similar clinical characteristics, apart from differences in NAT (FOLFIRINOX vs. gemcitabine/nab-paclitaxel; p < 0.05) and type of surgery resections (pancreatoduodenectomy, distal or total pancreatectomy; p < 0.05). The model that combined clinical variables (pre-NAT carbohydrate antigen (CA) 19-9, the change in CA19-9 after NAT (∆CA19-9), and resectability status) and DRFs outperformed the clinical feature-based models and other radiomics feature-based models in predicting OS (UTSW: 0.73; Humanitas: 0.66), DFS (UTSW: 0.75; Humanitas: 0.64), and RMS (UTSW 0.73; Humanitas: 0.69). Conclusions: Our externally validated, predictive/prognostic delta-radiomics models, which incorporate clinical variables, show promise in predicting the risk of predicting RMS in NAT-treated PDAC patients and their OS or DFS.

End-of-life events related to carcinoma lethality are poorly characterized. Herein we conducted an observational, prospective, case–control study enrolling 21 patients with solid tumors and 10 patients without known malignancy, complemented by a retrospective validation cohort of 1,250 patients with cancer. In our prospective cohort, we observed spikes in circulating tumor cell (CTC) counts, particularly clusters, immediately before death (P < 0.0001), as well as pathological evidence of macrovascular infiltration and large-vessel occlusion obtained through rapid autopsy. In the validation cohort, radiological evidence of macrovascular infiltration emerged as the strongest predictor of poor survival—independent of metastasis—in treatment-homogeneous patients with colorectal, lung, ovarian, hepatocellular or pancreatic cancer (hazard ratios = 4.0–22.4). Collectively, these findings suggest that macrovascular infiltration and spikes in CTC clusters with consequent vascular failure could be pivotal end-of-life events associated with cancer lethality, providing a rationale for future trials aimed at curbing infiltration into large vessels.

Single-cell RNA-sequencing is an invaluable research tool that allows for the investigation of gene expression in heterogeneous cancer cell populations in ways that bulk RNA-seq cannot. However, normal (i.e., non tumor) cells in cancer samples have the potential to confound the downstream analysis of single-cell RNA-seq data. Existing methods for identifying cancer and normal cells include copy number variation inference, marker-gene expression analysis, and expression-based clustering. This work aims to extend the existing approaches for identifying cancer cells in single-cell RNA-seq samples by incorporating variant calling and the identification of putative driver alterations. We found that putative driver alterations can be detected in single-cell RNA-seq data obtained with full-length transcript technologies and noticed that a subset of cells in tumor samples are enriched for putative driver alterations as compared to normal cells. Furthermore, we show that the number of putative driver alterations and inferred copy number variation are not correlated in all samples. Taken together, our findings suggest that augmenting existing cancer-cell filtering methods with variant calling and analysis can increase the number of tumor cells that can be confidently included in downstream analyses of single-cell full-length transcript RNA-seq datasets.

, show promise in predicting the risk of PRMS in NAT-treated PDAC patients and their overall or diseasefree survival (

Breast cancer progression to visceral organs such as the lung and liver is associated with poor prognosis. Yet, these organs have diverse cellular microenvironments that may differentially influence cancer cell behavior. Here, we show that breast cancer metastases differ in their capacity for secondary seeding: the liver fosters metastasis-promoting properties and boosts secondary spread, whereas the lung restricts these processes, favoring local growth. Using patient data and rapid autopsy tissues, mouse models with barcode-mediated tracing, niche-labeling technology, and single-cell analysis of tumor cells and their direct microenvironment, we dissect the cellular and molecular microenvironmental factors underlying this differential behavior. Mechanistically, we identify BMP2-producing endothelial cells as critical players within the liver metastatic niche, enhancing metastasis-to-metastasis dissemination. Targeting the BMP2 signaling axis suppresses secondary spread of liver-derived breast cancer lesions. Altogether, we reveal site-specific behavior of lung and liver metastases in breast cancer, highlighting microenvironmental mechanisms and therapeutic opportunities for intervention.