Marek Kimmel, professor of statistics at Rice University (left) and Olga Gorlova, associate professor of medicine at the Baylor College of Medicine (right)
Rice University and Baylor College of Medicine researchers have been awarded a two-year, $800,000 grant from the Department of Defense (DoD) to develop a new approach that leverages advanced computational models to revolutionize early diagnosis, prognosis, and therapeutic interventions for lung cancer.
The project is co-led by Marek Kimmel, a professor of statistics at Rice University, and Olga Yurievna Gorlova, an associate professor of medicine at Baylor College of Medicine. Together, their work will allow clinicians to track the incredibly complex, shifting mutational landscape of individual lung tumors, giving them the predictive power to anticipate how a patient's disease will progress, respond to treatment, or potentially recur.
The Challenge: Moving Beyond "One-Size-Fits-All" Treatments
Globally, lung cancer ranks among the deadliest and most aggressive malignancies, carrying a sobering five-year survival rate of just 25%. This high mortality rate is primarily driven by two factors:
- Late-Stage Detection: Patients are frequently diagnosed only after the disease has advanced, leaving few effective therapeutic options.
- Intratumor Heterogeneity: Lung cancers are not molecularly uniform; they are driven by a chaotic mix of multiple genetic mutations, unique to each patient and tumor.
Because each tumor features a distinct cellular fingerprint, traditional "one-size-fits-all" treatments often fail, leaving behind drug-resistant cells that cause the cancer to regrow.
“For years, the field has been studying what molecular features make some tumors grow faster than others,” said Gorlova. “We decided to go a step further to examine mutations and growth dynamics in each tumor cell to find the most aggressive and treatment-resistant group of cells within an individual.”
The Solution: Building Personalized Molecular Maps
To overcome these hurdles, Kimmel and Gorlova’s combinatorial approach pairs advanced genetic sequencing with novel computational modeling. This technique maps the exact mutations within individual cells of early-stage lung tumors, providing an unprecedented cell-by-cell breakdown picture of each tumor’s inner molecular makeup. Mapping these unique genetic landscapes allows oncologists to replace guesswork with a precise and effective diagnostic and treatment approach tailored to each patient.
“Early detection of aggressively growing cancer cells will help clinicians assess each patient’s risk for malignancy and recurrence, as well as help them in designing an effective treatment plan for each patient,” said Kimmel. “We are hopeful that the foundational data we generate in this study will support future clinical trials to test the clinical adoption of this method to reduce mortality and to improve survival outcomes for lung cancer patients.”
“This work was supported by the Department of Defense through the Peer Reviewed Cancer Research Program under Award No. CA250098. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the Department of Defense.”