Classifying cancer-associated fibroblasts-The good, the bad, and the target.

Cancer-associated fibroblasts (CAFs) are heterogeneous and ubiquitous stromal cells within the tumor microenvironment (TME). Numerous CAF types have been described, typically using single-cell technologies such as single-cell RNA sequencing. There is no general classification system for CAFs, hampering their study and therapeutic targeting. We propose a simple CAF classification system based on single-cell phenotypes and spatial locations of CAFs in multiple cancer types, assess how our scheme fits within current knowledge, and invite the CAF research community to further refine it.

Cancer-associated fibroblast phenotypes are associated with patient outcome in non-small cell lung cancer.

Despite advances in treatment, lung cancer survival rates remain low. A better understanding of the cellular heterogeneity and interplay of cancer-associated fibroblasts (CAFs) within the tumor microenvironment will support the development of personalized therapies. We report a spatially resolved single-cell imaging mass cytometry (IMC) analysis of CAFs in a non-small cell lung cancer cohort of 1,070 patients. We identify four prognostic patient groups based on 11 CAF phenotypes with distinct spatial distributions and show that CAFs are independent prognostic factors for patient survival. The presence of tumor-like CAFs is strongly correlated with poor prognosis. In contrast, inflammatory CAFs and interferon-response CAFs are associated with inflamed tumor microenvironments and higher patient survival. High density of matrix CAFs is correlated with low immune infiltration and is negatively correlated with patient survival. In summary, our data identify phenotypic and spatial features of CAFs that are associated with patient outcome in NSCLC.

Acquired resistance to anti-PD1 therapy in patients with NSCLC associates with immunosuppressive T cell phenotype.

Immune checkpoint inhibitor treatment has the potential to prolong survival in non-small cell lung cancer (NSCLC), however, some of the patients develop resistance following initial response. Here, we analyze the immune phenotype of matching tumor samples from a cohort of NSCLC patients showing good initial response to immune checkpoint inhibitors, followed by acquired resistance at later time points. By using imaging mass cytometry and whole exome and RNA sequencing, we detect two patterns of resistance¨: One group of patients is characterized by reduced numbers of tumor-infiltrating CD8+ T cells and reduced expression of PD-L1 after development of resistance, whereas the other group shows high CD8+ T cell infiltration and high expression of PD-L1 in addition to markedly elevated expression of other immune-inhibitory molecules. In two cases, we detect downregulation of type I and II IFN pathways following progression to resistance, which could lead to an impaired anti-tumor immune response. This study thus captures the development of immune checkpoint inhibitor resistance as it progresses and deepens our mechanistic understanding of immunotherapy response in NSCLC.

Cancer-associated fibroblast classification in single-cell and spatial proteomics data.

Cancer-associated fibroblasts (CAFs) are a diverse cell population within the tumour microenvironment, where they have critical effects on tumour evolution and patient prognosis. To define CAF phenotypes, we analyse a single-cell RNA sequencing (scRNA-seq) dataset of over 16,000 stromal cells from tumours of 14 breast cancer patients, based on which we define and functionally annotate nine CAF phenotypes and one class of pericytes. We validate this classification system in four additional cancer types and use highly multiplexed imaging mass cytometry on matched breast cancer samples to confirm our defined CAF phenotypes at the protein level and to analyse their spatial distribution within tumours. This general CAF classification scheme will allow comparison of CAF phenotypes across studies, facilitate analysis of their functional roles, and potentially guide development of new treatment strategies in the future.

Missing-in-Metastasis/Metastasis Suppressor 1 Regulates B Cell Receptor Signaling, B Cell Metabolic Potential, and T Cell-Independent Immune Responses.

Efficient generation of antibodies by B cells is one of the prerequisites of protective immunity. B cell activation by cognate antigens via B cell receptors (BCRs), or pathogen-associated molecules through pattern-recognition receptors, such as Toll-like receptors (TLRs), leads to transcriptional and metabolic changes that ultimately transform B cells into antibody-producing plasma cells or memory cells. BCR signaling and a number of steps downstream of it rely on coordinated action of cellular membranes and the actin cytoskeleton, tightly controlled by concerted action of multiple regulatory proteins, some of them exclusive to B cells. Here, we dissect the role of Missing-In-Metastasis (MIM), or Metastasis suppressor 1 (MTSS1), a cancer-associated membrane and actin cytoskeleton regulating protein, in B cell-mediated immunity by taking advantage of MIM knockout mouse strain. We show undisturbed B cell development and largely normal composition of B cell compartments in the periphery. Interestingly, we found that MIM-/- B cells are defected in BCR signaling in response to surface-bound antigens but, on the other hand, show increased metabolic activity after stimulation with LPS or CpG. In vivo, MIM knockout animals exhibit impaired IgM antibody responses to immunization with T cell-independent antigen. This study provides the first comprehensive characterization of MIM in B cells, demonstrates its regulatory role for B cell-mediated immunity, as well as proposes new functions for MIM in tuning receptor signaling and cellular metabolism, processes, which may also contribute to the poorly understood functions of MIM in cancer.