On September 16, 2024 Cellworks Group Inc., a leader in Personalized Therapy Decision Support and Precision Drug Development, reported results from a study using the Cellworks Platform to predict homologous recombination deficiency (HRD) and the effectiveness of PARP inhibitors (PARPi) in real-world cohorts of patients with ovarian, pancreatic, prostate, and triple-negative breast cancers (TNBC) (Press release, Cellworks, SEP 16, 2024, View Source [SID1234646682]). This research highlights the potential of the Cellworks mechanistic biosimulation model to go beyond BRCA mutation status and provide more comprehensive predictions of patient response to PARPi therapies.

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Results from the study were showcased in a poster presentation titled, Use of Biosimulation to Predict Homologous Recombination Deficiency and PARPi Benefit in Patients with Ovarian, Pancreatic, Prostate and Triple Negative Breast Cancers, as part of the ESMO (Free ESMO Whitepaper) Congress 2024 held in Barcelona, Spain from September 13-17, 2024.

"PARP inhibitors have become a standard treatment for cancers characterized by homologous recombination deficiency," said Professor Daniel Palmer, Molecular and Clinical Cancer Medicine, University of Liverpool, and Principal Investigator of the study. "However, current HRD tests do not capture all patients who might benefit from PARPi, often focusing primarily on BRCA status. By using Cellworks personalized therapy biosimulation in this study, we produced an HRD classifier that was predictive of PARPi benefit in patients with wild-type BRCA. This is an important step towards using biosimulation to identify wild-type BRCA patients who may benefit from PARPi therapy."

"Biosimulation incorporates multiple levels of genomic, transcriptomic, and protein regulation, capturing the molecular interactions of key homologous recombination components," said Dr. Michael Castro, Cellworks Chief Medical Officer. "This study utilized Cellworks mechanistic biology model along with a patient’s tumor-based genomic profile to identify dysregulations in HR signaling pathways and predict differential responses to PARPi. Through this approach, we can capture patients with HRD but lacking BRCA1 or BRCA2 mutations who can benefit from PARPi therapy, leading to more personalized and effective treatment strategies."

Study Design

Cellworks computational biosimulation was performed on four real-world retrospective cohorts from The Cancer Genome Atlas (TCGA), including ovarian, pancreatic, prostate, and triple-negative breast cancer patients. The model output, representing key HR pathways, was used to develop a classifier that distinguishes HRD by comparing BRCA wild-type (WT) ovarian cancer patients (n=32) to BRCA-mutated patients (n=187). This locked classifier was then prospectively validated in independent sets of ovarian, pancreatic, and prostate cancer patients (n=336, 428, 189 respectively). Efficacy scores, based on biosimulated composite cell growth in response to the PARP inhibitor Olaparib, were evaluated in relation to the predicted HRD status in BRCA wild-type patients.

Study Results

The HRD classifier produced through Cellworks biosimulation was significantly associated with BRCA status across all four validation sets, demonstrating strong predictiveness for BRCA status in ovarian (AUC = 0.863, p < 0.001), pancreatic (AUC = 0.759, p = 0.002), prostate (AUC = 0.717, p < 0.001), and TNBC (AUC = 0.88, p < 0.001) cancers. Additionally, in all four cancer types, predicted PARPi efficacy was significantly higher in BRCA wild-type patients identified as HRD (ovarian p = 0.026, prostate p < 0.001, pancreatic p < 0.001, TNBC p < 0.001).

The Cellworks Platform

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