The old blog still looks pretty ok after two years of inactivity! I ought to have remembered how the startup game goes … raise my head after a few crunch cycles and I’ve got a lot less hair and every article on CNN is about some angry old guy tweeting stuff. Huh.
Anyways, here at Adaptive we’ve been busy creating more awesome. My blog hiatus has been all about scaling up the clinical business around our new clonoSEQ assay. Beyond getting a completely new chemistry validated and into production, we’ve automated reporting and report delivery, created whole new products to support clinical trials, implemented new robots and sample tracking systems, and much much more. Progress every day.
But today is really special, which is what brought me back here to share.
We’re creating a Universal Diagnostic.
We’ve just announced a significant collaboration between Adaptive and Microsoft. Our shared goal is to create a “universal” diagnostic test — a simple blood draw that tells a story about every condition and every disease impacting your health. And to be clear, this is not speculative — we have a plan and every reason to believe that we will nail it. I am super, super psyched.
As we’ve discussed previously, Adaptive’s core technology allows us to describe the millions of T-Cell (and B-Cell) receptors that make up a person’s immune repertoire. Each of these receptors is a specific match for a unique “antigen” (a protein from an attacker such as cancer or Ebola). T-Cells circulate in blood and lymph; when they happen to bump into their soulmate antigen, they go into action — making copies of themselves and killing bad guys Rambo-style.
Cool thing is, this happens even when you have very tiny trace amounts of antigen in your system — so if we could match receptors to the antigens they are fighting, we could recognize and diagnose the corresponding condition earlier and more accurately than most current methods.
Even better, this is a general-purpose mechanism — the receptors are unique, but the actions of binding and responding are the same for every antigen. Since our test can read all the receptors at once, we’d be able to see everything that you are responding to with just one test (and as a bonus, things you have memory for).
Sounds pretty simple: Get the list of receptors, see which ones are activated, match them up to antigens, here’s your universal health picture, thank you very much and have a nice day. Woot!
Wait it’s still hard.
Two problems. First, nobody has been able to do this matching of receptor to antigen at any kind of scale. We’ve been able to quantify the receptor sequences here at Adaptive for years, but it’s completely non-obvious what any given receptor will bind to.
Perhaps not surprisingly, we’ve been working hard on this problem, and at this point can do it really well. Our MIRA assay follows the classic Adaptive chemistry + computation playbook, using combinatorics to multiplex a bunch of binding experiments onto one plate — resulting in thousands of binding pairs every time we do a run. So good news on that front.
The second problem, though, is that there are simply way, way too many possible TCRs for us to ever exhaustively map the entire space using MIRA. We’re talking like 10^16 possible receptors, and millions of potential antigens. And to make matters worse, people evolve different receptors to do the same thing — my TCRs for defending against chicken pox likely look completely different from yours, even though they respond to the same antigens.
Here’s where Microsoft and machine learning enter the game. By using our MIRA-derived data as a training set, we believe that we can train computer models to predict the binding properties of receptors we’ve never seen before. There’s actually a bunch of early evidence that this will work — we’ve done some work with CMV, and a couple of great small-scale examples showed up in Nature earlier this year (here and here).
Machine Learning is everywhere these days — and for good reason. Anywhere there are rules and patterns hiding in data, new ML techniques are proving exceptionally good at figuring them out. T-Cell binding is a physical phenomenon — gene sequences translate to protein sequences translate to physical structures translate to binding — so it’s a perfect application of these new tools.
Microsoft Research has been a pioneer in ML and artificial intelligence for years — it is incredibly exciting to be collaborating with them in a way that is so completely synergistic. There is not a doubt in my mind that over the next few years, Adaptive MIRA + Microsoft ML will deliver a Universal Diagnostic to the world.
Do that, and we really have fundamentally changed medicine together.
Time to play.
(NewCo) All We Have Yet To Understand