Multiomics Analysis of Spatially Distinct Stromal Cells Reveals Tumor-Induced O-Glycosylation of the CDK4-pRB Axis in Fibroblasts at the Invasive Tumor Edge.

The invasive leading edge represents a potential gateway for tumor metastasis. The role of fibroblasts from the tumor edge in promoting cancer invasion and metastasis has not been comprehensively elucidated. We hypothesize that cross-talk between tumor and stromal cells within the tumor microenvironment results in activation of key biological pathways depending on their position in the tumor (edge vs. core). Here we highlight phenotypic differences between tumor-adjacent-fibroblasts (TAF) from the invasive edge and tumor core fibroblasts from the tumor core, established from human lung adenocarcinomas. A multiomics approach that includes genomics, proteomics, and O-glycoproteomics was used to characterize cross-talk between TAFs and cancer cells. These analyses showed that O-glycosylation, an essential posttranslational modification resulting from sugar metabolism, alters key biological pathways including the cyclin-dependent kinase 4 (CDK4) and phosphorylated retinoblastoma protein axis in the stroma and indirectly modulates proinvasive features of cancer cells. In summary, the O-glycoproteome represents a new consideration for important biological processes involved in tumor-stroma cross-talk and a potential avenue to improve the anticancer efficacy of CDK4 inhibitors. SIGNIFICANCE: A multiomics analysis of spatially distinct fibroblasts establishes the importance of the stromal O-glycoproteome in tumor-stroma interactions at the leading edge and provides potential strategies to improve cancer treatment. See related commentary by De Wever, p. 537.

A radiogenomic dataset of non-small cell lung cancer.

Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.