Breast cancer (BC) progression during NAT is associated with development of distant metastases, positive LN status, and decreased OS/RFS. These can occur in the context of clinical trials and therapy de-escalation, where the focus is on delivering effective NAT to patients while reducing drug toxicity. The risks added by disease progression underscore the need for early identification of NAT progressors. To this end, we replicated the NeoSphere study in silico using TumorScope (TS), a biophysical modeling software, focusing on predicting disease progression during NAT. The NeoSphere trial studied the efficacy of docetaxel (T), pertuzumab (P), and trastuzumab (H) in combination with one another over 254 operable BC patients distributed across four study arms. We selected past BC patients with accompanying standard of care clinical data that matched NeoSphere sample composition based on patient and tumor characteristics. A total, 144 patients were included across four study arms (TH, THP, HP, and TP). Parameters from the NeoSphere study were mirrored where possible. Simulation generated volume trajectories of individual tumor’s response to therapy. Disease progressors were identified based on tumor volume at the final simulation timepoint compared to the first simulation timepoint. We then compared group means and proportions between progressors and responders using Welch’s two-sample t-test, and Fisher’s exact test, respectively. We replicated the NeoSphere trial using TS. pCR rates across study arms closely mirrored those of the actual trial. In the HP arm of our trial, we identified 12 (12/144) progressors. No difference was found when comparing it to that observed in the NeoSphere trial (p=1.00, OR=1.12). As expected, percent change in tumor volume from initial to final timepoints for the progressor group was significantly higher than the responder group (n=121, t=19.2, p=1.5x10-10, mean progressor=38.7, mean responder=-75.9). The progressor group was enriched with higher grade tumors (t=2.85, p=0.01), as well as HR-negative tumors (p=0.002, OR=7.54) compared to the responder group, and had lower HER2 receptor FISH ratios (t=-3.4, p=0.002). There were no differences observed between groups age, cancer subtype, or AJCC tumor stage (p>0.05). After trial replication, we identified clinical features that separated progressors from responders, which are being assessed for development of individualized predictive biomarkers of disease progression. While work is ongoing in the field to identify biomarkers of BC progression, it is evident that single markers are not sufficient. Comprehensive, multi-modal biomarkers of disease progression must be developed and applied to patient sub-populations to garner effective predictions. Using biophysical simulations, we are able to investigate the impact of drug delivery/sensitivity, metabolism, and spatial heterogeneity on BC progression.

Citation Format: John Pfeiffer, Tim Foley, Eduardo Braun, Anu Antony, Lance Munn, Joseph R. Peterson, John A. Cole, The SimBioSys Team. Accurate modeling of HER2 positive breast cancer disease progression with a biophysical modeling software [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1917.