PSMA-targeting TGFß-insensitive armored CAR T cells in metastatic castration-resistant prostate cancer: a phase 1 trial.

Chimeric antigen receptor (CAR) T cells have demonstrated promising efficacy, particularly in hematologic malignancies. One challenge regarding CAR T cells in solid tumors is the immunosuppressive tumor microenvironment (TME), characterized by high levels of multiple inhibitory factors, including transforming growth factor (TGF)-ß. We report results from an in-human phase 1 trial of castration-resistant, prostate cancer-directed CAR T cells armored with a dominant-negative TGF-ß receptor (NCT03089203). Primary endpoints were safety and feasibility, while secondary objectives included assessment of CAR T cell distribution, bioactivity and disease response. All prespecified endpoints were met. Eighteen patients enrolled, and 13 subjects received therapy across four dose levels. Five of the 13 patients developed grade ≥2 cytokine release syndrome (CRS), including one patient who experienced a marked clonal CAR T cell expansion, >98% reduction in prostate-specific antigen (PSA) and death following grade 4 CRS with concurrent sepsis. Acute increases in inflammatory cytokines correlated with manageable high-grade CRS events. Three additional patients achieved a PSA reduction of ≥30%, with CAR T cell failure accompanied by upregulation of multiple TME-localized inhibitory molecules following adoptive cell transfer. CAR T cell kinetics revealed expansion in blood and tumor trafficking. Thus, clinical application of TGF-ß-resistant CAR T cells is feasible and generally safe. Future studies should use superior multipronged approaches against the TME to improve outcomes.

A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosis and Staging of Pancreatic Ductal Adenocarcinoma.

PURPOSE: To determine whether a multianalyte liquid biopsy can improve the detection and staging of pancreatic ductal adenocarcinoma (PDAC). EXPERIMENTAL DESIGN: We analyzed plasma from 204 subjects (71 healthy, 44 non-PDAC pancreatic disease, and 89 PDAC) for the following biomarkers: tumor-associated extracellular vesicle miRNA and mRNA isolated on a nanomagnetic platform that we developed and measured by next-generation sequencing or qPCR, circulating cell-free DNA (ccfDNA) concentration measured by qPCR, ccfDNA KRAS G12D/V/R mutations detected by droplet digital PCR, and CA19-9 measured by electrochemiluminescence immunoassay. We applied machine learning to training sets and subsequently evaluated model performance in independent, user-blinded test sets. RESULTS: To identify patients with PDAC versus those without, we generated a classification model using a training set of 47 subjects (20 PDAC and 27 noncancer). When applied to a blinded test set (N = 136), the model achieved an AUC of 0.95 and accuracy of 92%, superior to the best individual biomarker, CA19-9 (89%). We next used a cohort of 20 patients with PDAC to train our model for disease staging and applied it to a blinded test set of 25 patients clinically staged by imaging as metastasis-free, including 9 subsequently determined to have had occult metastasis. Our workflow achieved significantly higher accuracy for disease staging (84%) than imaging alone (accuracy = 64%; P < 0.05). CONCLUSIONS: Algorithmically combining blood-based biomarkers may improve PDAC diagnostic accuracy and preoperative identification of nonmetastatic patients best suited for surgery, although larger validation studies are necessary.