Background: The objective of this comprehensive pan-cancer study is to evaluate the potential of deep learning (DL) for molecular profiling of multi-omic biomarkers directly from hematoxylin and eosin (H&E)-stained whole slide images.
Methods: A total of 12,093 DL models predicting 4031 multi-omic biomarkers across 32 cancer types were trained and validated. The study included a broad range of genetic, transcriptomic, and proteomic biomarkers, as well as established prognostic markers, molecular subtypes, and clinical outcomes.
Urological malignancies, represented mainly by prostate, bladder, and renal cancers, are some of the leading causes of cancer-related mortalities worldwide. Despite various efforts over decades to develop early detection tests and effective therapeutic paradigms, the response rate to the existing treatments remains low for both primary and late stage/recurrent phases of these cancers. The evolving landscape of molecular diagnostics, aiming to make the diagnosis and treatment more patient-driven, underpins precision oncology and particularly intends to rationally profile individual tumors and highlight the mechanistic insight and complexity of tumor microenvironment in order to develop biomarkers of toxicity risks and response prediction in a clinically oriented dynamical setting.
View Article and Find Full Text PDFPurpose: The tumor suppressor protein p53 is known to control cell cycle arrest and apoptosis. Lupeol is a phytochemical that has been found to induce apoptosis in different cancer types through the extrinsic pathway. As yet, however, its role in the induction of cell cycle arrest and apoptosis through the intrinsic pathway in head and neck cancer has not been investigated.
View Article and Find Full Text PDFEpidermal growth factor receptor (EGFR) pathway is overexpressed in head and neck cancer (HNC). Lupeol, a natural triterpene (phytosterol found in fruits, vegetables, etc.), has been reported to be effective against multiple cancer indications.
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