ABSTRACT
A significant proportion of colorectal cancer (CRC) patients develop peritoneal metastases (PM) in the course of their disease. PMs are associated with a poor quality of life, significant morbidity and dismal disease outcome. To improve care for this patient group, a better understanding of the molecular characteristics of CRC-PM is required. Here we present a comprehensive molecular characterization of a cohort of 52 patients. This reveals that CRC-PM represent a distinct CRC molecular subtype, CMS4, but can be further divided in three separate categories, each presenting with unique features. We uncover that the CMS4-associated structural protein Moesin plays a key role in peritoneal dissemination. Finally, we define specific evolutionary features of CRC-PM which indicate that polyclonal metastatic seeding underlies these lesions. Together our results suggest that CRC-PM should be perceived as a distinct disease entity.
Subject(s)
Colorectal Neoplasms , Neoplasms, Second Primary , Peritoneal Neoplasms , Colorectal Neoplasms/pathology , Humans , Peritoneal Neoplasms/genetics , Peritoneal Neoplasms/secondary , Peritoneum/metabolism , Quality of LifeABSTRACT
Effective treatments for pancreatic ductal adenocarcinoma (PDAC) are lacking, and targeted agents have demonstrated limited efficacy. It has been speculated that a rare population of cancer stem cells (CSCs) drives growth, therapy resistance, and rapid metastatic progression in PDAC. These CSCs demonstrate high clonogenicity in vitro and tumorigenic potential in vivo. However, their relevance in established PDAC tissue has not been determined. Here, we use marker-independent stochastic clonal labeling, combined with quantitative modeling of tumor expansion, to uncover PDAC tissue growth dynamics. We find that in contrast to the CSC model, all PDAC cells display clonogenic potential in situ. Furthermore, the proximity to activated cancer-associated fibroblasts determines tumor cell clonogenicity. This means that the microenvironment is dominant in defining the clonogenic activity of PDAC cells. Indeed, manipulating the stroma by Hedgehog pathway inhibition alters the tumor growth mode, revealing that tumor-stroma crosstalk shapes tumor growth dynamics and clonal architecture.
Subject(s)
Carcinoma, Pancreatic Ductal/pathology , Cell Lineage , Neoplastic Stem Cells/pathology , Pancreatic Neoplasms/pathology , Tumor Microenvironment , Anilides/pharmacology , Animals , Antineoplastic Agents/pharmacology , Cancer-Associated Fibroblasts/metabolism , Cancer-Associated Fibroblasts/pathology , Carcinoma, Pancreatic Ductal/drug therapy , Carcinoma, Pancreatic Ductal/genetics , Carcinoma, Pancreatic Ductal/metabolism , Cell Communication , Cell Line, Tumor , Cell Proliferation , Female , Hedgehog Proteins/antagonists & inhibitors , Hedgehog Proteins/metabolism , Humans , Male , Mice, Inbred NOD , Mice, Nude , Mice, SCID , Neoplastic Stem Cells/drug effects , Neoplastic Stem Cells/metabolism , Pancreatic Neoplasms/drug therapy , Pancreatic Neoplasms/genetics , Pancreatic Neoplasms/metabolism , Pyridines/pharmacology , Signal Transduction , Stromal Cells/metabolism , Stromal Cells/pathology , Time Factors , Tumor Burden , Xenograft Model Antitumor AssaysABSTRACT
Survival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing. Here, we introduce a scalable measure of chromosomal copy number heterogeneity (CNH) that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data and live cell imaging we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 37 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.