单细胞成像揭示胰腺导管腺癌中具有基因组关联的协同肿瘤-免疫-基质谱

Pancreatic ductal adenocarcinoma (PDAC) has consistent genomic drivers and established classical and basal transcriptional subtypes, but despite this, there exist many intermediate state tumors, a multiplicity of copy number aberrations and low frequency mutations, and a complex, spatially heterogeneous tumor microenvironment. These elements are not random; they are evidence of coordinated biological programs and evolving signaling networks that underlie tumor progression. Merging data from these varied molecular and spatial layers is essential to clinically define and therapeutically target a) tumors that fall into the ‘grey zone' of pancreatic cancer subtyping and b) specific collusive epithelial-stromal niches.To identify the in situ interdependencies between distinct tumor cell states, fibroblast phenotypes, deposited extracellular matrix, immune infiltrate, and vasculature, we performed imaging mass cytometry on three serial sections of a PDAC tissue microarray (221 resected tumors, ~4 cores each), generating >800 multiplexed images (40-43 channels) each focused on deeply profiling a different cell lineage. We captured 76 immune and stromal cell types and states, as well as six cancer cell types that recapitulated the classical and basal PDAC signatures, plus four discrete “intermediate” states with distinct associations to RNA subtype (n = 92), tumor ploidy (n = 192), and patient outcome. We clustered our immune and stromal cell populations to define 8 recurrent microenvironments, and found the microenvironment dominated by CD105+ CAFs was significantly spatially associated to classical tumor cells with strong epithelial differentiation transcription factor expression (pairwise Fisher's exact test, odds ratio = 3.7), ECM-rich microenvironments were proximal to basal tumor cells (odds ratio = 4.3), and pMLC2+ CAFs were enriched near the poor-prognosis, low-ploidy S100A4+ tumor phenotype (odds ratio = 4.2). We additionally denote a specific fibroblast-centric microenvironment associated with neoadjuvant treated tumors (n = 26). Using matched 30X whole-genome sequencing (n = 192), we found specific mutations and copy number changes that were associated changes in tumor and microenvironment composition, and performed Lasso-based machine learning to determine the most important cross-omic features for overall survival prediction. Together, our findings define a phenotypic and molecular framework of PDAC from genome to tumor-microenvironment, provide insight into the connection between tumor phenotype and stromal niches, and offer a refined basis for patient stratification.