Abstract P1-04-08: Independent Validation of a Novel, Non-invasive Approach to Predict Pathologic Complete Response (pCR) in a Blinded, Prospectively-Run Single Center Trial

Abstract Background: As pathologic complete response (pCR) is correlated with higher rates of event free survival, accurately forecasting pCR advances our collective endeavors in precision oncology to discern and translate individual patient-specific data into risk stratification. We developed the TumorScope engine, a software platform that utilizes pretreatment diagnostic data to build a computational tumor model that simulates in vivo tumor characteristics and interactions, incorporating morphology, metabolism, vascularity, and nutrient and drug delivery. This non-invasive approach enables accurate forecasting of a patient’s response to physician-chosen neoadjuvant chemotherapy-based treatment (NAC). Here we validate the prognostic capacity of this technology at a single site cancer center. Methods: A blinded, prospective trial using retrospective data was conducted at UNC. The study cohort included patients aged 18 years or older diagnosed with any subtype of breast cancer who were treated with a NAC regimen and had a pre-treatment T1-weighted dynamic contrast enhanced (DCE) MRI available. Pre-treatment diagnostic and planned treatment data (demographics, drug regimen, receptor status (ER/PR/HER2), DCE MRI, and pathology) were input into the TumorScope engine to simulate predicted final tumor volumes (Vt) for each tumor and predict pCR or residual disease (RD); pCR predictions were compared to post-surgery pathologic assessments defined as ypT0/is/N0. Predicted pCR was set at pre-defined threshold of predicted Vt less than 0.01 cm^3, or at least a 99.9% Vt reduction. Results: One hundred and fifty subjects with 157 tumors were enrolled in the study. After excluding missing data (absent DCE-MRI), a total of 143 cases in 136 patients were included. The majority of patients self-identified as Caucasian (63%) or African American (23%). TumorScope had a pCR overall prediction accuracy of 92.3% (95% CI: 86.7 - 96.1%) with a sensitivity of 90.9 % (95% CI: 75.7 - 98.1 %) and specificity of 92.7% (95% CI: 86.2 – 96.8%). Based on our subgroup analysis, predictive accuracy remained reliable for HR+/HER2- (n=65; 95.4%), HR+/HER2+ (n=20; 85.0%), HR-/HER2+ (n=21; 85.7%) and TNBC (n=37; 94.6%) subtypes. Predictive performance remained stable across ethnic subtypes and tumor grade (see Table 1). Conclusion: The TumorScope noninvasive method that incorporates imaging, pathologic, demographic and planned treatment data appears to accurately predict an individual patient’s probability of pCR across clinical subtypes. Table 1. TumorScope prediction performance. Citation Format: Cherie Kuzmiak, Terry S. Hartman, Thad Benefield, Joseph Peterson, Anuja K. Antony, Tushar Pandey, John A. Cole Jr, Benjaminc C. Calhoun, Lisa Carey. Independent Validation of a Novel, Non-invasive Approach to Predict Pathologic Complete Response (pCR) in a Blinded, Prospectively-Run Single Center Trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-04-08.