For someone with high risk melanoma, the hardest part often comes after surgery. The tumor is gone but you are left knowing there is a real chance it comes back. You are monitored, you wait, and historically there has not been much that is built specifically for you in that window. That is what makes the news this morning so important $MRNA and $MRK announced that their personalized mRNA vaccine intismeran combined with Keytruda succeeded in Phase 3 for melanoma. The trial was stopped at its first interim analysis because the benefit was clear, slowing both recurrence and spread. Full efficacy data has not been released yet, but if this holds it would be the first randomized Phase 3 validation of a personalized neoantigen mRNA cancer vaccine. It shows mRNA being used in a very different way from how we first understood it. To make something like this work you need to solve two problems, and that is where $PSNL and $TEM become relevant. First you need to read the individual with extreme precision. You have to sequence the tumor, figure out which neoantigens are unique to that patient, build a vaccine around that, and then track whether any residual disease remains. Merck has used Personalis for that testing layer. PSNL and its NeXT platform is designed for that kind of sensitive, tumor informed analysis. It provides the composite biomarker and minimal residual disease tracking that a personalized approach depends on. Without that measurement, you cannot build or monitor a vaccine for one person. Second you need to make sense of that measurement at scale, which is where Tempus comes in. $TEM has been building its role with Merck over time, and earlier this year they expanded into a multiyear collaboration focused on AI driven precision medicine across Merck's oncology portfolio. What Tempus brings is different from Personalis. It aggregates deidentified clinical and molecular data and uses its Lens platform to find patterns, identify biomarkers, and help guide how a therapy like this should be developed and for whom it is most likely to work. It is the system that helps take a highly personal assay and place it in a much larger learning model. So what the patient experiences as a single personalized therapy is actually the product of an integrated system. PSNL helps define what is unique about that person's cancer. TEM helps provide the data foundation to understand that uniqueness and accelerate development around it. Merck brings both into a clinical program. This is a turning point in precision medicine in my opinion . It points to a model where after surgery we are moving from watching and waiting to actively building something for the individual to help prevent recurrence, and it highlights why both the diagnostic and the data platforms behind it are central to that shift.