Cellworks Biosimulation Predicts PARPi Benefit in Multiple Cancers

Cellworks Group Inc., a leader in personalized therapy decision support and precision drug development, has unveiled significant findings from a study leveraging the Cellworks Platform. This study aimed to predict homologous recombination deficiency (HRD) and assess the efficacy of PARP inhibitors (PARPi) in real-world patients with ovarian, pancreatic, prostate, and triple-negative breast cancers (TNBC). The research underscores the potential of the Cellworks mechanistic biosimulation model to surpass BRCA mutation status in predicting patient responses to PARPi therapies.

The study results were presented in a poster titled "Use of Biosimulation to Predict Homologous Recombination Deficiency and PARPi Benefit in Patients with Ovarian, Pancreatic, Prostate and Triple Negative Breast Cancers" during the ESMO Congress 2024 in Barcelona, Spain, from September 13-17, 2024.

Professor Daniel Palmer of the University of Liverpool, the principal investigator of the study, highlighted the limitations of current HRD tests, which primarily focus on BRCA status and may not identify all patients who could benefit from PARPi. By employing Cellworks' personalized therapy biosimulation, the study developed an HRD classifier that could predict PARPi benefits even in patients with wild-type BRCA. This advancement is crucial in identifying wild-type BRCA patients who might gain from PARPi therapy.

Dr. Michael Castro, Chief Medical Officer at Cellworks, emphasized that the study utilized the Cellworks mechanistic biology model, combined with patient-specific tumor genomic profiles, to pinpoint dysregulations in HR signaling pathways and predict varying responses to PARPi. This approach can identify HRD in patients who lack BRCA1 or BRCA2 mutations but could still benefit from PARPi therapy, paving the way for more personalized and effective treatments.

The study design involved Cellworks computational biosimulation on four real-world retrospective cohorts from The Cancer Genome Atlas (TCGA), encompassing patients with ovarian, pancreatic, prostate, and TNBC. The model's output, which represented key HR pathways, was employed to develop a classifier that distinguished HRD by comparing BRCA wild-type ovarian cancer patients to those with BRCA mutations. This classifier was then validated prospectively in independent sets of patients with ovarian, pancreatic, and prostate cancers. Efficacy scores based on biosimulated composite cell growth in response to the PARP inhibitor Olaparib were assessed in relation to the predicted HRD status in BRCA wild-type patients.

The study results demonstrated that the HRD classifier generated through Cellworks biosimulation was significantly correlated with BRCA status across all four validation sets. It showed strong predictiveness for BRCA status in ovarian, pancreatic, prostate, and TNBC cancers. Moreover, in all four cancer types, predicted PARPi efficacy was markedly higher in BRCA wild-type patients identified as HRD.

The Cellworks Platform performs computational biosimulation of protein-protein interactions, enabling the modeling of tumor behavior using comprehensive genomic data. This facilitates the evaluation of how personalized treatment strategies interact with a patient’s unique tumor network. Multi-omic data from individual patients or cohorts are used as input to the Cellworks Computational Biology Model (CBM) to generate specific disease models. The CBM is an extensive mechanistic network comprising over 6,000 human genes, 30,000 molecular species, and 600,000 molecular interactions. This model, along with associated drug models, is used to biosimulate the impact of specific compounds or drug combinations on the patient or cohort, producing therapy response predictions. The Cellworks CBM has been validated and applied to various clinical datasets, with results presented in over 125 publications and presentations globally.