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Krebs, N.

Publications and source records attributed to Krebs, N..

2 recordsLinked to original sources

Spatial tumor immune heterogeneity facilitates subtype co-existence and therapy response via AP1 dichotomy in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) displays a high degree of spatial subtype heterogeneity. This intratumoral co-existence of classical and basal-like programs is evident in multi-scale transcriptomic and spatial analyses of resected, advanced-stage and chemotherapy-treated specimens and reciprocally linked to a diverse stromal immune microenvironment as well as worse clinical outcome. However, the underlying mechanisms of intratumoral subtype heterogeneity remain largely unclear. Here, by combining preclinical models, multi-center clinical, bulk and compartment-specific transcriptomic, proteomic, and bioimaging data from human specimens, we identified an interplay between neoplastic intrinsic AP1 transcription factor dichotomy and extrinsic CD68+ macrophages as a driver of intratumoral subtype co-existence along with an immunosuppressive tumor microenvironment with T cell exclusion. Our ATAC-, ChIP-, and RNA-seq analyses revealed that JUNB/AP1- and HDAC-mediated epigenetic programs repress pro-inflammatory immune signatures in tumor cells, antagonizing cJUN/AP1 signaling to favor a therapy-responsive classical neoplastic identity. Through the tumor microenvironment, this dichotomous regulation was further amplified via regional macrophage populations. Moreover, CD68+/TNF-+ cells associated with a reactive phenotype and reduced CD8+ T cell infiltration in human PDAC tumors. Consequently, combined anti-TNF- immunotherapy and chemotherapy resulted in reduced macrophage counts and promoted CD3+/CD8+ T cell infiltration in basal-like PDAC, leading to improved survival in preclinical murine models. We conclude that tumor cell intrinsic epigenetic programs, together with extrinsic microenvironmental cues, facilitate intratumoral subtype heterogeneity and disease progression.

cancer biology↗

Hourglass, a tool to mine bioimaging data, uncovers sex-disparities in the IL-6-associated T cell response in pancreatic tumors

Recent advances in digital pathology have led to an explosion in high-content multidimensional imaging approaches. Yet, our ability to gainfully process, visualize, integrate and mine the resulting mass of bioimaging data remains a challenge. We have developed Hourglass, an open access user-friendly software that streamlines complex biology-driven post-processing and visualization of multiparametric data. Directed at datasets derived from tissue microarrays or imaging methods that analyze multiple regions of interest per patient specimen, Hourglass systematically organizes observations across spatial and global levels as well as within patient subgroups. Application of Hourglass to our large and complex pancreatic cancer bioimaging dataset (540,617 datapoints derived from 26 bioimaging analyses applied to 596 specimens from 165 patients) consolidated a breadth of known IL-6 functions in a well-annotated human pancreatic cancer cohort and uncovered new unprecedented insights into a sex-linked Interleukin-6 (IL-6) association with immune phenotypes. Specifically, regional effects of IL-6 on the intratumoral T cell response were restricted to male patients only. In conclusion, Hourglass facilitates multi-layered knowledge extraction from complex multiparametric bioimaging datasets and provides tailored analytical means to productively harness heterogeneity at the sample and patient level.

cancer biology↗