Identifying subgroups and treatment heterogeneity in non-small cell lung cancer with survival outcomes.

Non-small cell lung cancer is the most common type of lung cancer, making up about 85% of all lung cancer cases. It affects hundreds of thousands of people each year worldwide. For some patients, immune treatments—also known as immunotherapy—can help the body’s own immune system fight cancer. However, not all patients respond the same way to these treatments.

One of the main goals in cancer research is to figure out which patients are most likely to benefit from immunotherapy. To do this, scientists study "biomarkers," which are signs in the body—such as proteins or genetic patterns—that may help predict how well a person will respond to treatment. Two promising biomarkers for non-small cell lung cancer are PD-L1 (programmed death-ligand 1), a protein that inhibits immune responses by binding to a protein called PD-1 on T-cells, preventing them from attacking other cells, including cancer cells, and bTMB (blood tumor mutational burden), a measure of the total number of genetic mutations found in circulating tumor DNA (ctDNA) in the blood. These have shown some potential in helping predict survival outcomes after immunotherapy.

In earlier studies, researchers often used a statistical measure called a “hazard ratio(HR)” to evaluate whether treatments worked well overall or for certain subgroups of patients. A hazard ratio is a relative measure of event occurrence between two groups over time; under the Cox's–Lehmann framework, it assumes proportional hazards, meaning one group consistently experiences events faster or slower than the other throughout follow-up. But this approach can sometimes give misleading results when patient responses are very different across groups. As a result, doctors may not get a clear picture of who truly benefits from the treatment.

To improve this, we will use individual patient-level data—detailed information from each patient in past clinical trials. With this data, we will create and test a new way to measure how well treatments work, using improved statistical methods. Our goal is to better understand how different groups of patients respond to immunotherapy and to find clearer ways to identify who is most likely to benefit from it.

This research could help doctors make more personalized treatment decisions for people with non-small cell lung cancer, ensuring that each patient receives the care that is most likely to help them.