Predicting and controlling the reactivity of immune cell populations against cancer

Kfir Oved, Eran Eden, Martin Akerman, Roy Noy, Ron Wolchinsky, Orit Izhaki, Ester Schallmach, Adva Kubi, Naama Zabari, Jacob Schachter, Uri Alon, Yael Mandel-Gutfreund, Michal J. Besser, Yoram Reiter

Research output: Contribution to journalArticlepeer-review

Abstract

Heterogeneous cell populations form an interconnected network that determine their collective output. One example of such a heterogeneous immune population is tumor-infiltrating lymphocytes (TILs), whose output can be measured in terms of its reactivity against tumors. While the degree of reactivity varies considerably between different TILs, ranging from null to a potent response, the underlying network that governs the reactivity is poorly understood. Here, we asked whether one can predict and even control this reactivity. To address this we measured the subpopulation compositions of 91 TILs surgically removed from 27 metastatic melanoma patients. Despite the large number of subpopulations compositions, we were able to computationally extract a simple set of subpopulation-based rules that accurately predict the degree of reactivity. This raised the conjecture of whether one could control reactivity of TILs by manipulating their subpopulation composition. Remarkably, by rationally enriching and depleting selected subsets of subpopulations, we were able to restore anti-tumor reactivity to nonreactive TILs. Altogether, this work describes a general framework for predicting and controlling the output of a cell mixture.

Original languageEnglish
Article number265
JournalMolecular Systems Biology
Volume5
DOIs
StatePublished - 20 Jan 2009
Externally publishedYes

Keywords

  • Decision tree algorithms
  • Heterogeneous cell population
  • Subpopulation signature
  • Systems immunology
  • Tumor immunology

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