A new paper describes a systems biology approach to tackling the complexity of TIL populations with the aim of predicting anti-tumor activity. They profiled the reactivity of a ton of TIL populations, and correlated this inforamtion with a battery of surface markers. They fed the info into some sort of machine learning algorithim, which came up with some pretty clear-cut boolean style predictive rules that a mere organic being (aka tumor immunologist) could never have possibly concieved of with a million years of deductive reasoning and experimental testing.
Check it out:
Rule 1) If the CD8+CD28-CD152- subpopulation constitutes less than 43% of the entire TIL population AND the CD94+constitutes less than 0.4% of the entire TIL population, then the TIL population is tumor-reactive.
Also, by manipulating the relative proportions of different TIL subpopulations, they could affect reactivity as predicted by their adding machine.
Wicked. Who knew an abacus could do immunology?
See: Predicting and controlling the reactivity of immune cell populations against cancer