A novel conditional active biologic anti-EpCAM x anti-CD3 bispecific antibody with synergistic tumor selectivity for cancer immunotherapy.

Epithelial cell adhesion molecule (EpCAM) is a transmembrane glycoprotein that plays several roles in cancer biology. EpCAM is an attractive therapeutic target because of its expression in most solid tumors. However, targeting EpCAM has been challenging because it is also highly expressed in normal epithelial tissues. Initial attempts to develop EpCAM-specific T-cell engagers were unsuccessful due to severe cytokine release effects, as well as serious on-target, off-tumor drug-related toxicities. We developed novel, conditionally active biological (CAB) bispecific antibodies that bind to both EpCAM and CD3 in an acidic tumor microenvironment. In healthy tissues, binding to EpCAM and CD3 is greatly reduced by a novel, dual CAB selection, where each binding domain is independently blocked by the presence of physiological chemicals known as Protein-associated Chemical Switches (PaCS). The CAB anti-EpCAM T-cell engagers displayed the anticipated bispecific binding properties and mediated the potent lysis of EpCAM-positive cancer cell lines through the recruitment of T cells in the tumor microenvironment. Xenograft studies showed that the efficacy of CAB bispecific antibodies is similar to that of a non-CAB anti-EpCAM bispecific antibody, but they have markedly reduced toxicity in non-human primates, indicating an unprecedentedly widened therapeutic index of over 100-fold. These preclinical results indicate that the dual CAB bispecific antibody is potentially both a powerful and safe therapeutic platform and a promising T cell-engaging treatment for patients with EpCAM-expressing tumors.

Development of a novel conditionally active EpCAM-specific T-cell engager with enhanced safety and tolerability for treatment of solid tumors.

An Interactive App with Multi-parametric MRI - Whole-Mount Histology Correlation for Enhanced Prostate MRI Training of Radiology Residents.

RATIONALE AND OBJECTIVES: To validate the educational value of a newly created learning application in enhancing prostate MRI training of radiologists for detecting prostate cancer using an observer study. MATERIALS AND METHODS: An interactive learning app, LearnRadiology, was developed using a web-based framework to display multi-parametric prostate MRI images with whole-mount histology for 20 cases curated for unique pathology and teaching points. Twenty new prostate MRI cases, different from the ones used in the web app, were uploaded on 3D Slicer. Three radiologists (R1: radiologist; R2, R3: residents) blinded to pathology results were asked to mark areas suspected of cancer and provide a confidence score (1-5, with 5 being high confidence level). Then after a minimum memory washout period of 1 month, the same radiologists used the learning app and then repeated the same observer study. The diagnostic performance for detecting cancers before and after accessing the learning app was measured by correlating MRI with whole-mount pathology by an independent reviewer. RESULTS: The 20 subjects included in the observer study had 39 cancer lesions (13 Gleason 3 + 3, 17 Gleason 3 + 4, 7 Gleason 4 + 3, and 2 Gleason 4 + 5 lesions). The sensitivity (R1: 54% â†’ 64%, P = 0.08; R2: 44% â†’ 59%, P = 0.03; R3: 62% â†’ 72%, P = 0.04) and positive predictive value (R1: 68% â†’ 76%, P = 0.23; R2: 52% â†’ 79%, P = 0.01; R3: 48% â†’ 65%, P = 0.04) for all 3 radiologists improved after using the teaching app. The confidence score for true positive cancer lesion also improved significantly (R1: 4.0 ± 1.0 â†’ 4.3 ± 0.8; R2: 3.1 ± 0.8 â†’ 4.0 ± 1.1; R3: 2.8 ± 1.2 â†’ 4.1 ± 1.1; P < 0.05). CONCLUSION: The web-based and interactive LearnRadiology app learning resource can support medical student and postgraduate education by improving diagnostic performance of trainees for detecting prostate cancer.