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Predicting and controlling the reactivity of immune cell populations against cancer.

Oved K, Eden E, Akerman M, Noy R, Wolchinsky R, Izhaki O, Schallmach E, Kubi A, Zabari N, Schachter J, Alon U, Mandel-Gutfreund Y, Besser MJ, Reiter Y - Mol. Syst. Biol. (2009)

Bottom Line: Here, we asked whether one can predict and even control this reactivity.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.Altogether, this work describes a general framework for predicting and controlling the output of a cell mixture.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Technion Israel Institute of Technology, Haifa, Israel.

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 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.

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Related in: MedlinePlus

Individual subpopulations are partially predictive of TIL reactivity. For each subpopulation, blue and red dots indicate 39 reactive and 52 nonreactive TILs. The y-axis is the percentage of cells that belong to a specific subpopulation. The black horizontal bars indicate the optimal cutoff for classifying reactive and nonreactive TILs. The MCC classification accuracy of each subpopulation is shown at the bottom.
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f2: Individual subpopulations are partially predictive of TIL reactivity. For each subpopulation, blue and red dots indicate 39 reactive and 52 nonreactive TILs. The y-axis is the percentage of cells that belong to a specific subpopulation. The black horizontal bars indicate the optimal cutoff for classifying reactive and nonreactive TILs. The MCC classification accuracy of each subpopulation is shown at the bottom.

Mentions: To study whether any individual subpopulation can be used to differentiate between reactive and nonreactive TILs, we performed TIL reactivity prediction based on individual subpopulation frequencies (see Materials and methods). The prediction accuracies of different subpopulations ranged between 0 and 0.49 in terms of Matthews correlation coefficients (MCCs) with sensitivity of up to 80% and a specificity of 68% (Figure 2; Materials and methods). These limited classification accuracies suggest that the frequency of any individual subpopulations is a limited predictor of the cell mixture collective output.


Predicting and controlling the reactivity of immune cell populations against cancer.

Oved K, Eden E, Akerman M, Noy R, Wolchinsky R, Izhaki O, Schallmach E, Kubi A, Zabari N, Schachter J, Alon U, Mandel-Gutfreund Y, Besser MJ, Reiter Y - Mol. Syst. Biol. (2009)

Individual subpopulations are partially predictive of TIL reactivity. For each subpopulation, blue and red dots indicate 39 reactive and 52 nonreactive TILs. The y-axis is the percentage of cells that belong to a specific subpopulation. The black horizontal bars indicate the optimal cutoff for classifying reactive and nonreactive TILs. The MCC classification accuracy of each subpopulation is shown at the bottom.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2683719&req=5

f2: Individual subpopulations are partially predictive of TIL reactivity. For each subpopulation, blue and red dots indicate 39 reactive and 52 nonreactive TILs. The y-axis is the percentage of cells that belong to a specific subpopulation. The black horizontal bars indicate the optimal cutoff for classifying reactive and nonreactive TILs. The MCC classification accuracy of each subpopulation is shown at the bottom.
Mentions: To study whether any individual subpopulation can be used to differentiate between reactive and nonreactive TILs, we performed TIL reactivity prediction based on individual subpopulation frequencies (see Materials and methods). The prediction accuracies of different subpopulations ranged between 0 and 0.49 in terms of Matthews correlation coefficients (MCCs) with sensitivity of up to 80% and a specificity of 68% (Figure 2; Materials and methods). These limited classification accuracies suggest that the frequency of any individual subpopulations is a limited predictor of the cell mixture collective output.

Bottom Line: Here, we asked whether one can predict and even control this reactivity.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.Altogether, this work describes a general framework for predicting and controlling the output of a cell mixture.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Technion Israel Institute of Technology, Haifa, Israel.

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 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.

Show MeSH
Related in: MedlinePlus