Dr Bazhenova on Improving Biomarker Development in Lung Cancer

Lyudmila A. Bazhenova, MD, clinical professor, medicine, The University of California, San Diego (UCSD); medical oncologist, Moores Cancer Center, UCSD Health, discusses steps that the lung cancer field can take to improve biomarker research and increase patient responses to immunotherapy regimens.

The intricate and adaptable nature of the immune system poses significant challenges to immunotherapy development in lung cancer, Bazhenova begins. When administered as monotherapy, immunotherapy is unlikely to yield long-term efficacy, necessitating the development of combination therapies, she says. The expanding arsenal of immunotherapy agents—including PD-1/PD-L1 inhibitors, drugs targeting TIGIT and LAG-3, cell therapies, vaccines, and bispecific antibodies—presents a nearly limitless array of potential combinations, Bazhenova notes. The process of determining the optimal treatment combinations for individual patients is complex, Bazhenova says. For instance, opening numerous clinical trials without sufficient patient enrollment to each dilutes their effectiveness, leading to suboptimal outcomes, she explains.

A major hurdle in drug development is the dynamic nature of the biomarkers that are used to predict response to immunotherapy, according to Bazhenova. Current biomarkers are not static, but evolve as the tumor progresses, highlighting a critical gap in early drug development strategies, she reports. To address this gap, increased investment in translational research is needed, Bazhenova emphasizes. These studies should focus on outcomes beyond response rates and progression-free survival, such as predicting responses to immunotherapy, she says.

The PIONeeR trial (NCT03493581) exemplifies this research approach, incorporating extensive correlative analyses of biomarkers to better understand patient responses, Bazhenova notes. Future advancements in this area may involve leveraging machine learning and artificial intelligence to identify the most effective biomarkers, she explains. Learning from negative trials is also essential, she adds. Even when a study does not meet its primary end point, valuable insights can be gained from the research by identifying subsets of patients who may benefit from the treatment, Bazhenova concludes.