Incendia Therapeutics to Present with PathAI at 2024 ASCO Annual Meeting

CAMBRIDGE, Mass., Incendia Therapeutics, a precision oncology company, is making strides in developing innovative therapies aimed at reprogramming the tumor microenvironment (TME). The company recently announced its collaboration with PathAI, an AI-powered precision pathology company, to present significant data at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting, scheduled for May 31 – June 4, 2024, in Chicago, Illinois.

Laura Dillon, PhD, Vice President of Translational Medicine & Bioinformatics at Incendia Therapeutics, expressed her enthusiasm about presenting their collaborative research with PathAI at ASCO. The presentation will focus on digital pathology-based biomarkers and their potential to predict clinical outcomes. The data to be shared highlights the correlation between immune phenotypes predicted from hematoxylin and eosin (H&E)-stained whole slide images and survival rates following checkpoint inhibitor therapy in non-small cell lung cancer (NSCLC). Dillon emphasized that these findings could lead to improved patient outcomes in NSCLC by identifying PD-L1(-) patients who might benefit more from checkpoint inhibitors.

The presentation, titled "Correlation of immune phenotypes derived from H&E-stained whole slide images with prognosis and response to checkpoint inhibitors in NSCLC," will be showcased in a poster session. The session will take place on Monday, June 3, from 1:30 - 4:30 PM CDT (2:30 – 5:30 PM EDT). Presenting authors include Bahar Rahsepar, Senior AI Product Manager at PathAI, and Laura Dillon, Ph.D., from Incendia Therapeutics.

Key aspects to be covered in the presentation include the classification of tumors into inflamed, excluded, or desert categories based on the spatial patterns of tumor-infiltrating lymphocytes (TILs). This classification serves as a potential biomarker for patients likely to respond to checkpoint inhibitors (CPI). Traditional manual methods to assess these immune phenotypes (IPs) have been subjective and inconsistent, hindering their clinical application. However, the use of AI to predict IPs using patch-level TIL features has shown promising results. AI model-predicted IPs were found to be prognostic in The Cancer Genome Atlas (TCGA) NSCLC dataset and predictive of progression-free survival (PFS) in a CPI-treated clinical NSCLC cohort. Importantly, the association between IP and PFS was independent of PD-L1 status, suggesting that PD-L1(-) patients could be identified who might benefit more from CPI.

In the TCGA NSCLC cohort, patients with model-predicted inflamed immune phenotypes (iIP) and excluded IP (eIP) had significantly better overall survival (OS) compared to those with desert IP (dIP). Specifically, iIP (N=196) and eIP (N=607) patients showed a hazard ratio (HR) of 0.53 (p=0.003) and 0.59 (p=0.003), respectively, compared to dIP (N=80). Moreover, in the clinical cohort, the density of cancer tumor-infiltrating lymphocytes and the fraction of 'hot' epithelial patches were significantly associated with PFS (HR=0.64, q=0.04 and HR=0.69, q=0.04, respectively). Model-predicted eIP patients (N=46) had shorter PFS compared to iIP patients (N=39; HR=0.54, p=0.045). Among PD-L1(-) patients (N=43, tumor proportion score ≤1%), iIP patients exhibited longer PFS than eIP and dIP patients (HR=0.35, p=0.02), while no significant difference in PFS was observed for PD-L1(+) patients.

Incendia Therapeutics is committed to discovering and developing novel experimental therapeutics designed to reprogram the tumor microenvironment. Their platform relies on advanced research in spatial characterization of TME, multi-omics data integration, and comprehensive preclinical testing. The company's leading experimental molecule, PRTH-101, is currently in a Phase 1 clinical trial for treating advanced solid tumors.

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