TY - JOUR
T1 - Predicting and controlling the reactivity of immune cell populations against cancer
AU - Oved, Kfir
AU - Eden, Eran
AU - Akerman, Martin
AU - Noy, Roy
AU - Wolchinsky, Ron
AU - Izhaki, Orit
AU - Schallmach, Ester
AU - Kubi, Adva
AU - Zabari, Naama
AU - Schachter, Jacob
AU - Alon, Uri
AU - Mandel-Gutfreund, Yael
AU - Besser, Michal J.
AU - Reiter, Yoram
PY - 2009/1/20
Y1 - 2009/1/20
N2 - 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.
AB - 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.
KW - Decision tree algorithms
KW - Heterogeneous cell population
KW - Subpopulation signature
KW - Systems immunology
KW - Tumor immunology
UR - http://www.scopus.com/inward/record.url?scp=66249138108&partnerID=8YFLogxK
U2 - 10.1038/msb.2009.15
DO - 10.1038/msb.2009.15
M3 - מאמר
C2 - 19401677
AN - SCOPUS:66249138108
VL - 5
JO - Molecular Systems Biology
JF - Molecular Systems Biology
SN - 1744-4292
M1 - 265
ER -