Cellworks Biosimulation Combined with Tumor Microenvironment Modeling Enhances Predictions of IO Response in NSCLC Patients

Study finds utilizing genomic and transcriptomic data with biosimulation enhances personalized predictions of IO response in NSCLC

BARCELONA, Spain, September 16, 2024 – Cellworks Group Inc., a leader in Personalized Therapy Decision Support and Precision Drug Development, today announced results from a study using Cellworks computational biosimulation to predict how individual non-small cell lung cancer (NSCLC) patients will respond to immunotherapy (IO) treatment by combining biosimulation with personalized tumor microenvironment (P-TME) modeling. The research found that biosimulation is highly effective for predicting immunotherapy responses in NSCLC above and beyond PD-L1 and TMB.

Findings from the study were showcased in a poster presentation (168P) titled, Biosimulation Coupled with Personalized Tumor Microenvironment (P-TME) Modeling Predicts Response to Immunotherapy Treatment in NSCLC Patients, as part of the ESMO Congress 2024 held in Barcelona, Spain from September 13-17, 2024.

“Despite success of immunotherapy in treatment of NSCLC, existing biomarkers for predicting patient response have limitations,” said Himanshu Grover, first author of the study and Lead Scientist R&D at Cellworks. “This study sought to improve immunotherapy treatment predictions by comprehensive modeling of the intricate interactions between a tumor's genomic profile and its surrounding microenvironment. This approach not only deepens our understanding of how individual tumors respond to immunotherapy, but also has the potential to elevate the standards for personalized cancer treatment.”

"These findings demonstrate how integrating genomic and transcriptomic data in the Cellworks biosimulation models can improve predictions of immunotherapy responses for NSCLC patients,” said Dr. Michael Castro, Cellworks Chief Medical Officer. “With a better understanding of the tumor's genetic landscape and its microenvironment, we can offer more tailored and accurate predictions about treatment efficacy and identify actionable targets to disable immune evasion mechanisms. These findings underline the utility of comprehensive genomic analysis and computational biological modeling in diagnosing and overcoming immune evasion in patients with advanced cancer.”

Study Design

This research study utilized Cellworks computational biosimulation alongside whole exome sequencing (WES) data to simulate immunotherapy response and predict patient outcomes for NSCLC patients treated with Immunotherapy. To provide a more holistic picture of the TME, the study incorporated patient’s bulk transcriptomic data applying an algorithm developed to deconvolute the data into personalized TME cell fractions. Researchers used Cellworks mechanistic computational biology model (CBM) to integrate a patient's tumor genomic data, with bulk transcriptomic data, to predict both IO signaling pathway dysregulation and drug response.

Study Results

By deconvoluting bulk transcriptomic data from 59 NSCLC patients treated with immunotherapy, researchers identified 19 different TME cell types, generating personalized Tumor Cell Proportions (TCP). This analysis revealed distinct cellular composition patterns associated with immunotherapy response. For instance, the ratio of CXCL9+IFN+ M1-like macrophages to SPP1+CD163+ M2-like macrophages showed a strong positive correlation with progression-free survival. The study found that TCP was a strong predictor of Progression-Free Survival (PFS) in a Cox proportional hazards model (p-value< 0.0001). Combining immunotherapy efficacy score with TCP further enhanced the model's predictive power, delivering more accurate forecasts of treatment success compared to either marker alone.

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.

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 CLIA-certified computational laboratory in Franklin, Tennessee and a research and development facility in Bangalore, India. For more information, visit www.cellworks.life.

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