Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology.

Background & Aims: Patients with hepatocellular carcinoma (HCC) displaying overexpression of immune gene signatures are likely to be more sensitive to immunotherapy, however, the use of such signatures in clinical settings remains challenging. We thus aimed, using artificial intelligence (AI) on whole-slide digital histological images, to develop models able to predict the activation of 6 immune gene signatures.

Methods: AI models were trained and validated in 2 different series of patients with HCC treated by surgical resection.

Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing?

Background: Fibroblast growth factor receptor (FGFR) inhibitor treatment has become the first clinically approved targeted therapy in bladder cancer. However, it requires previous molecular testing of each patient, which is costly and not ubiquitously available.

Objective: To determine whether an artificial intelligence system is able to predict mutations of the FGFR3 gene directly from routine histology slides of bladder cancer.

Serum levels of soluble B and T lymphocyte attenuator predict overall survival in patients undergoing immune checkpoint inhibitor therapy for solid malignancies.

Therapy with immune checkpoint inhibitors (ICIs) can lead to durable tumor control in patients with various advanced stage malignancies. However, this is not the case for all patients, leading to an ongoing search for biomarkers predicting response and outcome to ICI. The B and T lymphocyte attenuator (BTLA) is an immune checkpoint expressed on immune cells that was shown to modulate therapeutic responses.