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Hammel, P.

Publications and source records attributed to Hammel, P..

2 recordsLinked to original sources

PTPRZ1-targeting RNA CAR-T cells exert antigen-specific and bystander antitumor activity in glioblastoma

The great success of chimeric antigen receptor (CAR)-T cell therapy in B-cell malignancies has prompted its translation to solid tumors. In the case of glioblastoma (GBM), clinical trials have shown modest efficacy, but anti-GBM CAR-T cells are being intensely developed. In this study, we selected PTPRZ1 as an attractive new target for GBM treatment. We isolated six anti-human PTPRZ1 scFv from a human phage display library and produced 2nd generation CAR-T cells in an RNA format. Patient-derived GBM PTPRZ1-knock-in cell lines were used to select the CAR construct (471_28z), which showed high cytotoxicity while consistently displaying high CAR expression. CAR-T cells incorporating 471_28z were able to release IFN-{gamma}, IL-2, TNF-, Granzyme B, IL-17A, IL-6, and soluble FasL, and displayed low tonic signaling. Additionally, they maintained an effector memory phenotype after in vitro killing. Importantly, 471_28z CAR-T cells displayed strong bystander killing against PTPRZ1-negative cell lines after pre-activation by PTPRZ1-positive tumor cells, but did not kill antigen-negative non-tumor cells. In an orthotopic xenograft tumor model using NSG mice, a single dose of anti-PTPRZ1 CAR-T cells significantly delayed tumor growth. Taken together, these results validate the use of PTPRZ1 as a new GBM target and prompt the use of anti-PTPRZ1 CAR-T cells for clinical translation.

immunology↗

PACpAInt: a deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides

Pancreatic ductal adenocarcinoma (PAC) is a highly heterogeneous and plastic tumor with different transcriptomic molecular subtypes that hold great prognostic and theranostic values. We developed PACpAInt, a multistep approach using deep learning models to determine tumor cell type and their molecular phenotype on routine histological preparation at a resolution enabling to decipher complete intratumor heterogeneity on a massive scale never achieved before. PACpAInt effectively identified molecular subtypes at the slide level in three validation cohorts and had an independent prognostic value. It identified an interslide heterogeneity within a case in 39% of tumors that impacted survival. Diving at the cell level, PACpAInt identified "pure" classical and basal-like main subtypes as well as an intermediary phenotype and hybrid tumors that co-carried both classical and basal-like phenotypes. These novel artificial intelligence-based subtypes, together with the proportion of basal-like cells within a tumor had a strong prognostic impact.

cancer biology↗