Accurate modeling of HER2 positive breast cancer disease progression with a biophysical modeling software


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 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=1.5×10-10, 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.

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