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Highlights • PDAC cells in the duodenal epithelium mimic intestinal cells and co-opt the basement membrane. • Intramucosal PDAC location is strongly coupled to the classical phenotype and to intestinal traits. • Intratumoral heterogeneity is linked to specific tissue compartments, which shape phenotype plasticity of PDAC cells., Introduction Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid tumors. Based on transcriptomic classifiers, basal-like and classical PDAC subtypes have been defined that differ in prognosis. Cells of both subtypes can coexist in individual tumors; however, the contribution of either clonal heterogeneity or microenvironmental cues to subtype heterogeneity is unclear. Here, we report the spatial tumor phenotype dynamics in a cohort of patients in whom PDAC infiltrated the duodenal wall, and identify the duodenal epithelium as a distinct PDAC microniche. Materials and methods We used serial multiplex quantitative immunohistochemistry (smq-IHC) for 24 proteins to phenotypically chart PDAC tumor cells in patients whose tumors infiltrated the duodenal epithelium. Additionally, we used a genetically engineered mouse model to study the PDAC cell phenotype in the small intestinal epithelium in a controlled genetic background. Result We show that pancreatic cancer cells revert to non-destructive growth upon integration into the duodenal epithelium, where they adopt traits of intestinal cell differentiation, associated with phenotypical stabilization of the classical subtype. The integrated tumor cells replace epithelial cells in an adenoma-like manner, as opposed to invasive growth in the submucosa. Finally, we show that this phenomenon is shared between species, by confirming duodenal integration and phenotypic switching in a genetic PDAC mouse model. Discussion Our results identify the duodenal epithelium as a distinct PDAC microniche and tightly link microenvironmental cue to cancer transcriptional subtypes. The phenomenon of “intestinal mimicry” provides a unique opportunity for the systematic investigation of microenvironmental influences on pancreatic cancer plasticity.

Cellular identity in complex multicellular organisms is determined in part by the physical organization of cells. However, large-scale investigation of the cellular interactome remains technically challenging. Here we develop cell interaction by multiplet sequencing (CIM-seq), an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between cell types present in a tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution into constituent cell types. CIM-seq estimates parameters such as number of cells and cell types in each multiplet directly from sequencing data, making it compatible with high-throughput droplet-based methods. When applied to gut epithelium or whole dissociated lung and spleen, CIM-seq correctly identifies known interactions, including those between different cell lineages and immune cells. In the colon, CIM-seq identifies a previously unrecognized goblet cell subtype expressing the wound-healing marker Plet1, which is directly adjacent to colonic stem cells. Our results demonstrate that CIM-seq is broadly applicable to unsupervised profiling of cell-type interactions in different tissue types.

Colorectal cancer liver metastases (CRLM) grow in two major patterns defined by the histomorphology of the invasion front, replacement or desmoplastic. The desmoplastic pattern, in which a stromal rim separates tumor cells and liver parenchyma is a strong positive prognostic factor, implying favorable biological features. However, the origin of the perimetastatic stroma is unknown and the underlying biological mechanisms are unclear. Here, we created spatial growth pattern maps of resected CRLM at cell-level resolution using digital pathology and quantified growth pattern heterogeneity at unprecedented resolution. We manually generated > 60’ 000 individual digital annotations on 543 metastases from 263 consecutive patients. We found that, in contrast to standard growth pattern assessments, high-resolution scoring revealed the prognostic outcome to be dependent on growth pattern proportions, such that survival improved with increasing fractions of desmoplastic encapsulation. The desmoplastic pattern was coupled to decreased tumor viability and to preoperative chemotherapy, hinting at a potentially causative connection of tumor viability and fibrotic encapsulation. Analyses of the cellular constituents of the rim revealed previously unrecognized liver parenchymal remnants. Spatial quantitation of liver remnants in the rim uncovered its gradual zonation from benign-like fibrosis at the liver side to tumor-associated stroma at the metastasis edge. Together, our data suggest that the perimetatstic “desmoplastic” capsule primarily results from a reparative hepatic process in conjunction with inefficient tumor cell colonization of liver plates, rather than from actively induced desmoplasia. We posit a model in which efficient replacement-type growth that precludes a mature hepatic injury reaction determines prognosis. Our results underscore tumor-cell replacement of hepatocytes as key for liver metastatic progression and suggest that the spatial heterogeneity of tumor invasion can be leveraged to understand fundamental mechanisms of metastatic growth.

Single cell sequencing methods facilitate the study of tissues at high resolution, revealing rare cell types with varying transcriptomes or genomes, but so far have been lacking the capacity to investigate cell-cell interactions. Here, we introduce CIM-seq, an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between every cell type in a given tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution of these into their constituent cell types using machine learning. CIM-seq is broadly applicable to studies that aim to simultaneously investigate the constituent cell types and the global interaction profile in a specific tissue.

A prototypic pediatric cancer that frequently shows activation of RAS signaling is embryonal rhabdomyosarcoma (ERMS). ERMS also show aberrant Hedgehog (HH)/GLI signaling activity and can be driven by germline mutations in this pathway. We show, that in ERMS cell lines derived from sporadic tumors i.e. from tumors not caused by an inherited genetic variant, HH/GLI signaling plays a subordinate role, because oncogenic mutations in HRAS, KRAS, or NRAS (collectively named oncRAS) inhibit the main HH target GLI1 via the MEK/ERK-axis, but simultaneously increase proliferation and tumorigenicity. oncRAS also modulate expression of stem cell markers in an isoform- and context-dependent manner. In Hh-driven murine ERMS that are caused by a Patched mutation, oncHRAS and mainly oncKRAS accelerate tumor development, whereas oncNRAS induces a more differentiated phenotype. These features occur when the oncRAS mutations are induced at the ERMS precursor stage, but not when induced in already established tumors. Moreover, in contrast to what is seen in human cell lines, oncRAS mutations do not alter Hh signaling activity and marginally affect expression of stem cell markers. Together, all three oncRAS mutations seem to be advantageous for ERMS cell lines despite inhibition of HH signaling and isoform-specific modulation of stem cell markers. In contrast, oncRAS mutations do not inhibit Hh-signaling in Hh-driven ERMS. In this model, oncRAS mutations seem to be advantageous for specific ERMS populations that occur within a specific time window during ERMS development. In addition, this window may be different for individual oncRAS isoforms, at least in the mouse.

Contains fulltext : 283492.pdf (Publisher’s version ) (Closed access) The first consensus guidelines for scoring the histopathological growth patterns (HGPs) of liver metastases were established in 2017. Since then, numerous studies have applied these guidelines, have further substantiated the potential clinical value of the HGPs in patients with liver metastases from various tumour types and are starting to shed light on the biology of the distinct HGPs. In the present guidelines, we give an overview of these studies, discuss novel strategies for predicting the HGPs of liver metastases, such as deep-learning algorithms for whole-slide histopathology images and medical imaging, and highlight liver metastasis animal models that exhibit features of the different HGPs. Based on a pooled analysis of large cohorts of patients with liver-metastatic colorectal cancer, we propose a new cut-off to categorise patients according to the HGPs. An up-to-date standard method for HGP assessment within liver metastases is also presented with the aim of incorporating HGPs into the decision-making processes surrounding the treatment of patients with liver-metastatic cancer. Finally, we propose hypotheses on the cellular and molecular mechanisms that drive the biology of the different HGPs, opening some exciting preclinical and clinical research perspectives.

Single cell sequencing methods facilitate the study of tissues at high resolution, revealing rare cell types with varying transcriptomes or genomes, but so far have been lacking the capacity to investigate cell-cell interactions. Here, we introduce CIM-seq, an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between every cell type in a given tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution of these into their constituent cell types using machine learning. CIM-seq is broadly applicable to studies that aim to simultaneously investigate the constituent cell types and the global interaction profile in a specific tissue.

Cellular identity in complex multicellular organisms is determined in part by the physical organization of cells. However, large-scale investigation of the cellular interactome remains technically challenging. Here we develop cell interaction by multiplet sequencing (CIM-seq), an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between cell types present in a tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution into constituent cell types. CIM-seq estimates parameters such as number of cells and cell types in each multiplet directly from sequencing data, making it compatible with high-throughput droplet-based methods. When applied to gut epithelium or whole dissociated lung and spleen, CIM-seq correctly identifies known interactions, including those between different cell lineages and immune cells. In the colon, CIM-seq identifies a previously unrecognized goblet cell subtype expressing the wound-healing marker Plet1, which is directly adjacent to colonic stem cells. Our results demonstrate that CIM-seq is broadly applicable to unsupervised profiling of cell-type interactions in different tissue types.

Cellular identity in complex multicellular organisms is strictly maintained over the course of life. This control is achieved in part by the organ structure itself, such that neighboring cells influence each other’s identity. However, large-scale investigation of the cellular interactome has been technically challenging. Here, we develop CIM-seq, an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between every cell type in a given tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution of these into their constituent cell types using machine learning. We use CIM-seq to define the cell interaction landscape of the mouse small intestinal and colonic epithelium, uncovering both known and novel interactions. Specifically, we find that the general architecture of the stem cell niche is radically different between the two tissues. In small intestine, the stem-Paneth cell interaction forms an exceptionally strong and exclusive niche, in which Paneth cells provide Wnt ligands1. In colonic epithelium, no similar compartment exists to support stem cells, and Wnt signaling is provided by a mesenchymal cell layer2,3. However, colonic stem cells are supported by an adjacent, previously unrecognized goblet cell subtype expressing the wound-healing marker Plet1, which is also highly upregulated during regeneration of colon epithelium. These results identify novel cellular interactions specific for the colonic stem cell niche and suggest an additional level of structural control in the colon. CIM-seq is broadly applicable to studies that aim to simultaneously investigate the constituent cell types and the global interaction profile in a specific tissue.

Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma with poor prognosis. RMS frequently show Hedgehog (HH) pathway activity, which is predominantly seen in the embryonal subtype (ERMS). They also show activation of Phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) signaling. Here we compared the therapeutic effectiveness and the impact on HH target gene expression of Smoothened (SMO) antagonists with those of the PI3K inhibitor pictilisib in ERMS with and without mutations in the HH receptor Patched1 (PTCH). Our data demonstrate that growth of ERMS showing canonical Hh signaling activity due to Ptch germline mutations is efficiently reduced by SMO antagonists. This goes along with strong downregulation of the Hh target Gli1. Likewise Ptch mutant tumors are highly responsive toward the PI3K inhibitor pictilisib, which involves modulation of AKT and caspase activity. Pictilisib also modulates Hh target gene expression, which, however, is rather not correlated with its antitumoral effects. In contrast, sporadic ERMS, which usually express HH target genes without having PTCH mutation, apparently lack canonical HH signaling activity. Thus, stimulation by Sonic HE (SHH) or SAG (Smoothened agonist) or inhibition by SMO antagonists do not modulate HH target gene expression. In addition, SMO antagonists do not provoke efficient anticancer effects and rather exert off-target effects. In contrast, pictilisib and other PI3K/AKT/mTOR inhibitors potently inhibit cellular growth. They also efficiently inhibit HH target gene expression. However, of whether this is correlated with their antitumoral effects it is not clear. Together, these data suggest that PI3K inhibitors are a good and reliable therapeutic option for all ERMS, whereas SMO inhibitors might only be beneficial for ERMS driven by PTCH mutations.