Cancer-associated fibroblasts play a crucial role within the tumor microenvironment. However, a comprehensive characterization of CAF in colorectal cancer (CRC) is still missing. We combined scRNA-seq and spatial proteomics to decipher fibroblast heterogeneity in healthy human colon and CRC at high resolution. Analyzing nearly 23,000 fibroblasts, we identified 11 distinct clusters and verified them by spatial proteomics. Four clusters, consisting of myofibroblastic CAF (myCAF)-like, inflammatory CAF (iCAF)-like and proliferating fibroblasts as well as a novel cluster, which we named "T cell-inhibiting CAF" (TinCAF), were primarily found in CRC. This new cluster was characterized by the expression of immune-interacting receptors and ligands, including CD40 and NECTIN2. Co-culture of CAF and T cells resulted in a reduction of the effector T cell compartment, impaired proliferation, and increased exhaustion. By blocking its receptor interaction, we demonstrated that NECTIN2 was the key driver of T cell inhibition. Analysis of clinical datasets showed that NECTIN2 expression is a poor prognostic factor in CRC and other tumors. In conclusion, we identified a new class of immuno-suppressive CAF with features rendering them a potential target for future immunotherapies.

The WHO classification of pulmonary neuroendocrine tumors (PNETs) is also used to classify thymic NETs (TNETs) into typical and atypical carcinoid (TC and AC), large cell neuroendocrine carcinoma (LCNEC), and small cell carcinoma (SCC), but little is known about the usability of alternative classification systems. One hundred seven TNET (22 TC, 51 AC, 28 LCNEC, and 6 SCC) from 103 patients were classified according to the WHO, the European Neuroendocrine Tumor Society, and a grading-related PNET classification. Low coverage whole-genome sequencing and immunohistochemical studies were performed in 63 cases. A copy number instability (CNI) score was applied to compare tumors. Eleven LCNEC were further analyzed using targeted next-generation sequencing. Morphologic classifications were tested against molecular features. Whole-genome sequencing data fell into three clusters: CNIlow, CNIint, and CNIhigh. CNIlow and CNIint comprised not only TC and AC, but also six LCNECs. CNIhigh contained all SCC and nine LCNEC, but also three AC. No morphologic classification was able to predict the CNI cluster. Cases where primary tumors and metastases were available showed progression from low-grade to higher-grade histologies. Analysis of LCNEC revealed a subgroup of intermediate NET G3 tumors that differed from LCNEC by carcinoid morphology, expression of chromogranin, and negativity for enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2). TNETs fall into three molecular subgroups that are not reflected by the current WHO classification. Given the large overlap between TC and AC on the one hand, and AC and LCNEC on the other, we propose a morphomolecular grading system, Thy-NET G1-G3, instead of histologic classification for patient stratification and prognostication.