New Study Introduces a Personalized Therapy Selection Approach in NSCLC Poised to Transform Treatment Decisions
Cellworks Group Inc., a leader in Personalized Therapy Decision Support and Best-in-Class PTRS, today announced results from the landmark myCare-040 clinical study, which demonstrates the predictive capability of the Cellworks Platform in guiding chemotherapy treatment decisions for patients with metastatic non-small cell lung cancer (NSCLC) receiving immune checkpoint inhibitors (ICIs). The findings advance a personalized approach to NSCLC treatment selection that maximizes benefit and minimizes unnecessary toxicity.
Results from the myCare-040 study were showcased in a poster presentation titled, Computational Modeling of Comprehensive Genomic Profiling to Predict Chemotherapy Benefit in Advanced NSCLC, as part of the IASLC 2025 World Conference on Lung Cancer (WCLC25) hosted by the International Association for the Study of Lung Cancer taking place September 6-9, 2025 at the Fira de Barcelona Convention Center Gran Via in Barcelona, Spain.
"Although combination chemo-immunotherapy remains a standard first-line option for advanced NSCLC, the incremental benefit of adding chemotherapy varies substantially among patients," said Charu Aggarwal, MD, MPH, FASCO, Leslye M. Heisler Professor of Lung Cancer Excellence in the Perelman School of Medicine at the University of Pennsylvania, and Co-Principal Investigator of the study. "The findings from this study reveal a novel personalized computational biosimulation approach capable of identifying patients most likely to benefit from combination chemo-immunotherapy rather than immunotherapy alone, thereby reducing exposure to treatment-related toxicities in those unlikely to respond. This innovation constitutes an important advancement in personalized therapy selection for NSCLC."
"The use of broad-based molecular testing has helped identify patients with actionable biomarkers who may benefit from targeted therapies," said Tejas Patil, MD, Assistant Professor of Medicine-Medical Oncology, University of Colorado School of Medicine, and Co-Principal Investigator of the study. "In particular, PD-L1 testing has gained relevance as an important biomarker in determining the role of immune checkpoint inhibitors, especially for those patients whose PD-L1 expression levels are 50% or greater. However, we also know that this is an imperfect biomarker and does not capture the full spectrum of which patients may benefit from chemotherapy with immune checkpoint inhibitors. In this study, we utilized Cellworks computational modeling to determine which patients receiving first line cancer treatment would benefit from the addition of chemotherapy with immune checkpoint inhibitors. This represents a clinically meaningful step forward in personalized medicine for NSCLC."
"While PD-L1 expression is widely used to guide treatment decisions in NSCLC, it is not a reliable predictor of chemo-immunotherapy benefit," said James Wingrove, PhD, Chief Development Officer at Cellworks. "Chemo-immunotherapy response depends on complex interactions between tumor biology and the immune system-far more than any single biomarker, including PD-L1, can capture. In this study, we leveraged NGS and biosimulated tumor behavior to deliver a personalized prediction of each patient's likelihood of responding to chemo-immunotherapy versus immunotherapy alone. This approach offers a level of personalization that PD-L1 alone simply cannot provide."
To address the variability in chemotherapy benefit among patients with advanced NSCLC, Cellworks developed a mechanistic computational biology model (CBM) that uses tumor-specific genomic data obtained through NGS. The model maps signaling pathway dysregulation to predict how a patient will respond to immune checkpoint inhibitor (ICI) therapy alone or in combination with chemotherapy (ICI+C).
Key Findings
- TRI Predicts Overall Survival: TRI was significantly associated with overall survival (OS) after adjusting for clinical and genomic risk factors, including PD-L1 expression (LR p 0.019).
- ?TRI Identifies High-Benefit Patients: 22% of patients in the validation cohort (High Benefit ?TRI score 16) gained a median OS increase of 8.3 months with chemo-immunotherapy and achieved a >15% absolute improvement in OS at 24 months.
- No Benefit for Low ?TRI Patients: 78% of patients in the validation cohort (No Benefit ?TRI score 16) showed no survival advantage from the addition of chemotherapy to ICI.
- PD-L1 Independence: ?TRI predictions were independent of PD-L1 status, underscoring its value as a complementary biomarker for treatment decisions.
- Guiding Treatment Decisions: ?TRI distinguished patients likely to benefit from chemo-immunotherapy versus ICI alone.
Study Design
In this study, researchers applied Cellworks mechanistic Computational Biology Model (CBM) to develop a novel classifier (?TRI) using clinical and comprehensive genomic data from two real-world retrospective cohorts of patients with non-squamous, metastatic NSCLC (n=1,549). To validate the classifier, an independent cohort of 328 patients diagnosed with advanced NSCLC between April 2017 and November 2022 (mean age 66.4 years, 43% female, 75% receiving front-line chemo-immunotherapy) was analyzed. The computational model, classifier, and clinical threshold (?TRI score 16) were locked prior to validation, and performance was then assessed in this independent patient cohort.
The Cellworks Platform
The Cellworks Platform performs computational biosimulation of protein-protein interactions, enabling in silico modeling of tumor behavior using genomic data derived from next-generation sequencing (NGS). 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 score for a specific therapy. The Cellworks CBM has been tested and applied against various clinical datasets with results provided in more than 125 presentations and publications with global collaborators.
About Cellworks Group
Cellworks Group, Inc. is dedicated to improving patient outcomes by harnessing the power of computational science to deliver Personalized Therapy Decision Support and Best-in-Class PTRS solutions. The Cellworks Platform predicts patient-specific therapy response for oncology and other serious diseases using a breakthrough Computational Biology Model (CBM) and biosimulation technology. Cellworks is backed by Artiman Ventures, Bering Capital, Sequoia Capital, UnitedHealth Group and Agilent Ventures. Headquartered in South San Francisco, the company also operates a CLIA-certified computational lab in Franklin, Tennessee. Learn more at www.cellworks.life.
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