Ann W. Silk, MD, of Rutgers Cancer Institute of New Jersey, New Brunswick, presented the day’s highlights and commented that the study by Hellmann et al shows the utility, and, in fact, the necessity, of developing a new means of estimating outcomes for patients treated with immunotherapies.
“Immunotherapy is so impactful that we have to change the assumptions in the mathematical models we use to analyze the data,” Dr. Silk said. “Mathematically, the conventional parametric statistical model assumes that long-term survival equals zero. The long-term survival we are now seeing in [immunotherapy] studies necessitates alternate modeling.”
In the current analysis, long-term survival plateaus at around 21% to 25% in the KEYNOTE trials of programmed cell death ligand 1 (PD-L1)–positive advanced non–small cell lung cancer. Because the anti–programmed cell death protein 1 (PD-1) agents have activity in so many tumors, “modeling of long-term survival is increasingly important,” and Dr. Hellmann’s model is an “efficient tool” for that purpose, she said. Its application to clinical trial design may help bring drugs to the clinic faster, she added.
For clinicians, the findings from this analysis show “that the plateau on the survival curves that we have seen so many times is now well accepted,” she said. “We can counsel our patients that if they reach a certain point—about 3 years posttreatment—they are likely to survive for years to come.” ■
Disclosure: Dr. Silk reported no potential conflicts of interest.
Statistical modeling of long-term survival from the KEYNOTE trials of the programmed cell death protein 1 (PD-1)–inhibitor pembrolizumab (Keytruda) estimates that one-quarter of appropriately selected patients with advanced non–small cell lung cancer (NSCLC) may attain long-term survival.1Error...