Mathematical model identifies effective drug combinations for non-small-cell lung cancer

Houston Methodist researchers have developed an advanced mathematical model that predicts how novel treatment combinations could significantly extend progression-free survival for patients with non-small-cell lung cancer (NSCLC), the most common type of lung cancer.

Through advanced mathematical modeling, a team led by Prashant Dogra, Ph.D., and Zhihui “Bill” Wang, Ph.D., from the Mathematics in Medicine Program at the Houston Methodist Research Institute, expanded on initial research done at MD Anderson Cancer Center on the molecule anti-miR-155 in mice. Dogra and Wang explored the clinical potential of anti-miR-155—a small RNA molecule—in simulated patients, identifying novel drug combinations that could significantly improve treatment efficacy and progression-free survival.

MicroRNA-155 (miR-155) is known to play a critical role in worsening treatment outcomes for NSCLC by contributing to drug resistance and immune suppression. In particular, elevated levels of miR-155 can help tumors evade immune detection and reduce the effectiveness of standard therapies, such as chemotherapy and immunotherapy. To counteract this, researchers have sought to use a synthetic therapeutic molecule, called anti-miR-155, to neutralize the negative effects of miR-155.

“By doing this, we boost the effectiveness of current standard-of-care treatments like cisplatin and immune checkpoint inhibitors, ultimately leading to improved survival rates, as shown in our model,” Wang said. “By neutralizing the overactivity of miR-155, we can restore the balance in the immune system and improve the efficacy of cancer treatments.”

Chemotherapy, immunotherapy and anti-miR-155 therapy can be seen as different yet complementary approaches to treating non-small-cell lung cancer, Wang added.

The researchers calibrated their computational model with preclinical data from the MD Anderson Cancer Center lab of George Calin, M.D., Ph.D., using information from mouse studies that provided real-world biological data on how anti-miR-155 behaves in the body, including how it affects tumor growth and drug resistance.

This allowed them to refine their mathematical model to ensure it accurately represented the relevant biological processes. They modified the model for application to humans by accounting for differences between species, such as body size and metabolism, to help simulate and predict how the treatment might work in humans.

Due to significant biological differences, there typically is uncertainty when transitioning from animal studies to clinical trials. Wang, Dogra and team’s mathematical model, however, helps address this by providing insight on how the treatment might work in diverse human patients through extensive computer simulations, predicting outcomes like progression-free survival and identifying the best drug combinations.

“By using a combination of in vivo data from animal studies and advanced mathematical modeling to predict how the therapy would perform in humans, this work bridges the critical gap between preclinical development and clinical translation of anti-miR-155, offering a clear path to testing this treatment in humans,” Dogra said. “This approach provides a strong foundation for designing more effective clinical trials and helps accelerate the process, making the transition from preclinical to clinical testing more efficient and targeted.”

Their next steps focus on further preclinical testing to confirm the safety and efficacy of the anti-miR-155 therapy in combination with standard-of-care drugs before progressing to human trials.

“Our approach to combining mathematical modeling with therapeutic development could revolutionize how we bring new cancer treatments to patients,” Wang said. “This goes beyond non-small-cell lung cancer. It could accelerate treatment development for many types of cancer.”

The method and findings of this study are described in a paper titled “Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer,” appearing last month in the journal Molecular Cancer.

Dogra and Wang are the corresponding authors on the study, and their collaborators were Vrushaly Shinglot, Javier Ruiz-Ramírez, Joseph Cave, Joseph D. Butner, Carmine Schiavone, Dan G. Duda, Ahmed O. Kaseb, Caroline Chung, Eugene J. Koay, Vittorio Cristini, Bulent Ozpolat and George A. Calin.