Javaheri, T. | bioRxiv Science

bioRxiv2024

YAP1 is a key regulator of EWS::FLI1-dependent malignant transformation upon IGF-1 mediated reprogramming of bone mesenchymal stem cells

AbstractEwing sarcoma (EwS) is an aggressive cancer of adolescents in need of effective treatments. Insulin like growth factor (IGF) 1 was previously reported an autocrine growth factor for EwS, but only 10% of patients responded to IGF-1 receptor blockade. Although presumed to originate from mesenchymal progenitors during bone development, targeting of the EwS driver oncogene EWS::FLI1 to the mesenchymal lineage in a conditional mouse model did not result in tumor formation but led to skeletal malformations and perinatal death. We report that transient exposure to IGF-1 concentrations mimicking serum levels during puberty reprogrammed limb-derived mesenchymal cells of EWS::FLI1-mutant mice to stable transformation and tumorigenicity. We identified a modular mechanism of IGF-1-driven tumor promotion in the early steps of EwS pathogenesis, in which Yap1 plays a central role. Pharmacologic Yap1/Tead inhibition reversed the transformed phenotype of EWS::FLI1 expressing cells. Our data provide a rationale for combined IGF-1R and YAP/TEAD inhibition in the treatment of EwS patients. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=185 SRC="FIGDIR/small/603565v1_ufig1.gif" ALT="Figure 1"> View larger version (87K): org.highwire.dtl.DTLVardef@a6a298org.highwire.dtl.DTLVardef@1fcab8aorg.highwire.dtl.DTLVardef@1621e27org.highwire.dtl.DTLVardef@a9994e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

XKidneyOnco: An Explainable Framework to Classify Renal Oncocytoma and Chromophobe Renal Cell Carcinoma with a Small Sample Size

Renal oncocytoma and chromophobe renal cell carcinoma are two kidney cancer types that present a diagnostic challenge to pathologists and other clinicians due to their microscopic similarities. While RO is a benign renal neoplasm, ChRCC is considered malignant. Therefore, the differentiation between the two is crucial. In this study, we introduce an explainable framework to accurately differentiate ChRCC from RO, histologically. Our approach examined H&E-stained images of 656 ChRCC and 720 RO, and achieved a diagnostic accuracy of 88.2%, the sensitivity of 87%, and 100% specificity for explainable AI, which either outperforms or operate on par with convolutional neural network (CNN) models. Besides, we enrolled 44 pathology experts (including pathologists and pathology trainees) to differentiate the two tumors. The average accuracy of pathologists was 73%, which is 15.2% lower than our framework. These results indicate that the combination of human expert along with explainable AI achieve higher accuracy in differentiating the two tumors, while it reduces the workload of experts and offers the desired explainability for the medical experts.

pathology↗