This is my area of research, I likely know people on that MSK panel and I’m pretty familiar with the current research in “predictive analytics” in cancer. The state-of-the-art is pretty poor in this space; the cutting edge of this research is really about sequencing technology - uncovering the underlying mutations in your tumor. Once you have that basic data (e.g., “you have a BRAF mutation”), the rest is pretty simple - (“Take this BRAF inhibitor”).
We’re really not at the point where we’re able to take a complex integration of variables from your life history, like socioeconomic class, diet, etc., and produce any sort of better prediction about what your outcomes will be on various therapies. Mostly this is because we don’t have anything resembling a well-curated, comprehensive dataset covering these sorts of variables (imagine the nightmare in terms of coordination and medical privacy issues), but also because our understanding of cancer biology is still so piss-poor that the idea that we might be able to integrate additional information is a joke.
This story reminds me of the very-similar moment in the late 90s when Silicon Graphics (SGI) claimed that they were going to solve protein-folding using their advanced supercomputers. A few decades later, we’re still no closer to solving protein folding and SGI is defunct.