On November 12, 2024 Cellworks Group Inc., a leader in Personalized Therapy Decision Support and Precision Drug Development, reported results from a study demonstrating the efficacy of the Cellworks Therapy Response Index (TRI) combined with personalized tumor microenvironment (TME) modeling in predicting overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving immunotherapy (IO) (Press release, Cellworks, NOV 12, 2024, View Source [SID1234648228]). This comprehensive approach enables more precise identification of patient-specific dysregulation in immunotherapy signaling pathways, resulting in better predictions of treatment responses.
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Results from the study were showcased in a poster presentation titled, A Therapy Response Index Coupled with Personalized Tumor Microenvironment Modeling Predicts Overall Survival for Immunotherapy Treatment in NSCLC Patients, as part of the SITC (Free SITC Whitepaper) 2024 Annual Meeting held in Houston, Texas from November 6-10, 2024.
"In this study, we tackle one of the biggest challenges in cancer therapy today – accurately predicting how patients will respond to immunotherapy," said Dr. James Wingrove, Vice President of Clinical Solutions at Cellworks and presenting author for the study. "Although immunotherapy has demonstrated significant success in treating NSCLC, existing tools for predicting individual patient outcomes are inadequate. This research marks a crucial step forward in understanding the intricate relationship between tumor genomics and the tumor microenvironment, paving the way for more personalized and effective treatment strategies."
"This study highlights Cellworks’ commitment to advancing personalized oncology by providing tools that go beyond conventional prediction methods," said Dr. Michael Castro, Chief Medical Officer at Cellworks. "By integrating the Cellworks TRI score with detailed tumor microenvironment modeling, we’re able to offer a level of insight into immunotherapy outcomes that has the potential to transform how NSCLC patients are treated. This approach doesn’t just predict survival—it enables a deeper understanding of the unique cellular interactions within each patient’s tumor, opening the door to more effective, personalized treatment strategies."
Study Design
The study involved the development and validation of an algorithm to deconvolute cell proportion and cell-specific gene expression from bulk transcriptome data. Utilizing a reference matrix generated from single-cell NSCLC data (n=771,891 cells), the algorithm was applied to RNA-seq data from 63 NSCLC patients who had received immunotherapy. A personalized Computational Biosimulation Model (CBM) of tumor genomic networks was generated for each patient using whole exome sequencing data, and a Therapy Response Index (TRI) score was calculated.
Study Results
The tumor microenvironment composition significantly predicted overall survival in NSCLC patients, as shown in a Cox proportional hazards model (LR ꭓ² = 20.12, p-value = 7.29e-6, HR = 0.157). Combining the TRI score with tumor microenvironment cell proportions (TCP) further improved the model’s ability to predict OS (LR ꭓ² = 24.21, p-value = 8.63e-07, HR = 0.11). The study revealed specific immune markers correlated with improved outcomes, including the ratio of CXCL9+ M1-like macrophages to SPP1+ CD163+ M2-like macrophages, which positively correlated with longer survival. Patients with high interferon+ macrophages and higher activated CD8+ T cells also demonstrated better outcomes, while elevated neutrophil presence was associated with reduced survival.
The Cellworks Platform, CBM and TRI
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, 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.