Cellworks Personalized Therapy Biosimulation Study Produces Superior OS and DFS Predictions for Gastroesophageal Cancer Patients

Peer-Reviewed Research Indicates Whole Genome Biosimulation Holds Promise for Personalized Therapy Selection in Clinical Settings

Cellworks Group Inc., a leader in Personalized Therapy Decision Support and Precision Drug Development, today announced the results from a real-world study of gastroesophageal cancer which found that Therapy Response Index (TRI) scores generated using the Cellworks Biosimulation Platform predicted overall survival (OS) above and beyond standard clinical factors, including patient age, sex, and tumor-node-metastasis (TNM) staging. The study also showed a significant association between a patient’s TRI score and disease-free survival (DFS) and tumor regression grade (TRG).

The peer-reviewed paper, ‘Integration of genomic aberrations to predict clinical outcomes for patients with gastroesophageal adenocarcinoma receiving neoadjuvant chemotherapy,’ is available at ESMO Gastrointestinal Oncology.

Despite progress in gastroesophageal cancer therapy, only a small proportion of patients attain long-term survival. Previous precision-based strategies have been constrained by focusing on single biomarker–drug associations. This oversimplified approach to the disease has resulted in a wide spectrum of patient outcomes, with long-term survival rates spanning from 25% to 75% using multi-modality therapy — a combination of chemotherapy, radiation, surgical resection, and adjuvant nivolumab, which has become the standard of care.

“The focus for guiding treatment decisions for gastroesophageal patients has centered on specific biomarkers such as HER2, MSI, PD-L1 and CLD18.2,” said Dr. Elizabeth Smyth, Oxford University Hospitals NHS Foundation Trust; and Co-Principal Investigator of the study. “However, further refinement of treatment selection using computational biomarkers might be possible. This study highlights the unique value that Cellworks computational biosimulation can bring to the treatment decision process by utilizing a patient's comprehensive genomic profile and biosimulating all possible therapy combinations. This approach could offer a promising avenue for recommending a treatment strategy at an individual level, thereby improving patient outcomes."

"The size and complexity of comprehensive genomic panels pose a formidable challenge, making it difficult for oncologists to translate the intricate biology into actionable clinical decisions,” said Dr. Rebecca Fitzgerald, MD, Professor of Cancer Prevention at the University of Cambridge; Director of the Early Cancer Institute; and Co-Principal Investigator of the study. “This study demonstrates that utilizing whole genome sequencing of a patient’s gastroesophageal cancer along with Cellworks personalized biosimulation approach has the potential to highlight when alternative treatment strategies such as fluoropyrimidines, taxanes, anthracyclines or platinum compounds may yield superior outcomes for a specific patient."

"The transition from small panel NGS to whole exome and whole genome sequencing brings new insights about each cancer’s complex proteogenomic network,” said Dr. Michael Castro M.D., Oncologist at Beverly Hills Cancer Center; and Chief Medical Officer at Cellworks, Group Inc. “These insights reveal the basis for drug sensitivity and resistance that allows oncologists to select the optimal combination therapy for individual patients and creates the possibility of targeting the sources of therapeutic resistance. The view that genomic information is ‘unactionable’ is transformed by artificial intelligence (AI)-level molecular diagnosis provided by Cellworks’ biosimulation. Most genomic information that can be measured is highly relevant to tumor biology and treatment response but is beyond the grasp of busy clinicians. With Cellworks biosimulation, the oncogene-only approach to precision medicine is already obsolete.”

Clinical Study

Methods

The performance of Cellworks in silico biosimulation and Therapy Response Index (TRI) scores were studied in patients with gastroesophageal adenocarcinoma (OGA) with operable cancers from the UK Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) International Cancer Genome Consortium study. A total of 270 patients with OGA were selected who had 50x whole genome sequencing carried out on tissue derived from either biopsy or resection within 12 months of treatment. Patients were treated with chemotherapy drugs or regimes according to UK clinical guidelines.

Biosimulation was carried out utilizing the Cellworks computational biology model (CBM), which uses both mechanistic and statistical approaches to integrate a patient’s genomic aberrations, revealing signaling pathway dysregulation and variable drug response. Using patient-specific predictions derived from the CBM, a TRI score was used to predict therapeutic overall survival (OS), disease-free survival (DFS) and tumor regression grade (TRG).

Results

The results of the study revealed that the association of TRI value with overall survival (OS) for gastroesophageal adenocarcinoma patients was significant (P = 0.0012) above and beyond standard clinical factors including patient age, sex, tumor-node-metastasis (TNM) stage and neoadjuvant therapy. The association between TRI values and disease-free survival (DFS) was also significant (P = 0.0288). In addition, the TRI values optimized for tumor regression grade (TRG) displayed a significant association (P = 0.0011).

Conclusions

This study concluded that TRI scores for gastroesophageal adenocarcinoma patients predict OS and DFS beyond clinical factors. These results highlight the clinical value of employing Cellworks biosimulation for personalized therapy selection and warrant additional clinical evaluation.

Cellworks Platform and Therapy Response Index (TRI)

The Cellworks Platform biosimulates the impact of specific drug compounds on an individual patient or class of patients using their genomic profile. 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, which are statistically modeled to produce a qualitative Therapy Response Index (TRI) score, scaled from 0 (unfavorable outcome) to 100 (favorable outcome) for a specific therapy. The Cellworks CBM has been tested and applied against various clinical datasets with results provided in over 125 presentations and publications with global collaborators.

About Cellworks Group

Cellworks Group, Inc. is a leader in Personalized Therapy Decision Support and Precision Drug Development. The Cellworks Platform predicts therapy response for individual patients and patient cohorts using a breakthrough Computational Biology Model (CBM) and biosimulation technology. Backed by Artiman Ventures, Bering Capital, Sequoia Capital, UnitedHealth Group and Agilent Ventures, Cellworks has the world’s strongest trans-disciplinary team of molecular biologists, cellular pathway modelers and software engineers working toward a common goal – attacking serious diseases to improve the lives of patients. The company is based in South San Francisco, California with a research and development facility in Bangalore, India. For more information, visit cellworks.life.

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