JCO Article Insights: Nivolumab + Relatlimab v Nivolumab + Ipilimumab in Melanoma

In this JCO Article Insights episode, Rohit Singh provides a summary on "First-Line Nivolumab Plus Relatlimab Versus Nivolumab Plus Ipilimumab in Advanced Melanoma: An Indirect Treatment Comparison Using RELATIVITY-047 and CheckMate 067 Trial Data", by Long et al, published in the November issue of the Journal of Clinical Oncology. The article provides insights into the use of the two dual immune checkpoint inhibitor regimens in patients with untreated advanced melanoma.

TRANSCRIPT

Rohit Singh: Hello and welcome to JCO Article Insights. I'm your host Rohit Singh, Assistant Professor at the University of Vermont Cancer Center and today we'll be discussing the article “First-Line Nivolumab Plus Relatlimab Versus Nivolumab Plus Ipilimumab in Advanced Melanoma: An Indirect Treatment Comparison Using RELATIVITY-047 and CheckMate 067 Trials,” authored by Dr. Georgina Long from the Melanoma Institute of Australia and her colleagues. 

So as we know, nivolumab plus relatlimab and nivo plus ipi, I'm going to refer to as ipi-nivo moving forward, are dual immune checkpoint inhibitors regimens that are approved for treating patients with advanced melanoma based on the phase 2 and 3 RELATIVITY-047 and phase 3 CheckMate 067 trials respectively. Nivo plus relatlimab is the only dual PD-1 and LAG-3 inhibitor regimen approved for treating patients with advanced melanoma and relatlimab is the first in class human IgG4 LAG-3 blocking antibody. Ipi plus nivo is a dual PD-1 and CTLA-4 inhibitor regimen. 

So this paper basically is an indirect treatment comparison using a patient level database from these trials and this pretty much was conducted because of the absence of head to head trials looking at different regimens in advanced melanoma in first line setting. In this trial, the authors tried to compare these two trials. However, it's always hard to compare two different trials and we usually don't do cross trial comparisons. The problem is that the groups might be different to begin with. For example, one group might have younger patients, healthier patients, while the other might have older or sicker. These differences can make it hard to tell if the treatment caused improvement or if the groups were different to begin with. In this trial, researchers use inverse probability of treatment weighting to adjust the baseline differences between the two patient groups or between these two trials. Inverse probability of treatment weighting is a method used in research to help make a fair comparison between two groups when studying how a treatment intervention works. Basically, IPTW helps level the playing field between the two groups or like two trials for this paper. So, it calculates the likelihood of receiving a treatment. For each person, for each patient, researchers estimate the chance they would have gotten the treatment based on their characteristics like age, health, condition, their baseline staging, and based on that they create weights. People who are less likely to get the treatment but did are given more weight, and those who are very likely to get the treatment are given less weight. The same is done for the group that didn't get the treatment, and then they rebalance the groups. By applying these weights the group becomes more similar in their characteristics as if everyone had an equal chance of getting the treatment. This way, IPTW helps researchers focus on the effect of treatment itself and other differences between the groups. It's like adjusting the scales to make sure you are comparing apples to apples. 

The key outcomes the authors are looking at in this one was progression free survivals, overall su