Ricerc@Sapienza

OBJECTIVE: Interventional oncology (IO) is an emergent field in interventional radiology that can be considered the fourth pillar of oncology. Interventional oncology has the unique capability to treat malignancy in a loco-regional fashion enabling curative (percutaneous ablation), disease stabilization (intra-arterial chemo/radioembolization), and palliative treatment (such as biliary drainage or nephrostomy). The whole arsenal of IO acts by inducing necrosis and apoptosis, with interactions with the tumour's microenvironment potentially crucial for oncological outcomes.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis.

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and