Abstract / Summary
Background Chemotherapy (CT) de-escalation strategies based on dual HER2 blockade with trastuzumab and pertuzumab (HP) have shown promising efficacy in HER2-positive (HER2+) early breast cancer (EBC), but biomarkers to guide patient selection are lacking. The Ataraxis Breast platform (ATX) uses artificial intelligence (AI) models integrating hematoxylin-eosin (H&E) whole-slide images and clinical variables to predict pathological complete response (pCR) and long-term outcomes. We evaluated ATX in the phase II PHERGain (NCT03161353) and PHERGain-2 (NCT04733118) trials. Methods The ATX NEO model was assessed for pCR prediction in PHERGain (n=306 evaluable patients) and PHERGain-2 (n=383 evaluable patients). The ATX RISK model was evaluated for prognosis in PHERGain (n=309 evaluable patients). Prespecified cutoffs classified patients into low-, medium-, and high-probability groups for pCR and low- or high-risk groups for recurrence. Associations with pCR were assessed using logistic regression. Prognostic performance was evaluated using Cox proportional hazards models, Kaplan-Meier estimates, and Harrell's C-index. Results Overall pCR rates were 40.2% in PHERGain and 60.3% in PHERGain-2. Continuous NEO scores were significantly associated with pCR in both PHERGain (odds ratio [OR], 1.28; 95% CI, 1.11-1.48; P<0.001) and PHERGain-2 (OR, 1.30; 95% CI, 1.13-1.52; P<0.001). Using prespecified cutoffs, pCR rates increased across low-, medium-, and high-probability groups (19.2%, 41.3%, and 48.3% in PHERGain; 29.5%, 61.6%, and 70.1% in PHERGain-2). In PHERGain, the RISK score was significantly associated with event-free survival (hazard ratio per 1-SD increase, 1.56; 95% CI, 1.17-2.08; P=0.002; C-index, 0.66). Five-year event-free survival was 93.9% in the low-risk group and 83.1% in the high-risk group. Conclusions NEO score was consistently associated with pCR across two independent HER2+ EBC trials, while RISK score provided prognostic information for long-term outcomes in PHERGain. Prespecified score categories identified clinically distinct response and risk groups. These findings support further evaluation of AI-based biomarkers to refine patient selection for CT de-escalation strategies in HER2+ EBC.