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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

Reactive and nonreactive TILs exhibit distinct subpopulation signatures. Columns and rows correspond to TILs and subpopulations, respectively. Colors indicate the fraction of cells belonging to each subpopulation in each TIL. Unsupervised clustering was used on the rows and columns (see Materials and methods). The red and blue arrows represent nonreactive and reactive TILs, respectively. Two main clusters emerge characterized by CD4+ and CD8+ overabundant subpopulations. Interestingly, although the clustering procedure did not take into account TIL reactivity, the emerging clusters do separate nonreactive from reactive TILs (P<10−3).
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f3: Reactive and nonreactive TILs exhibit distinct subpopulation signatures. Columns and rows correspond to TILs and subpopulations, respectively. Colors indicate the fraction of cells belonging to each subpopulation in each TIL. Unsupervised clustering was used on the rows and columns (see Materials and methods). The red and blue arrows represent nonreactive and reactive TILs, respectively. Two main clusters emerge characterized by CD4+ and CD8+ overabundant subpopulations. Interestingly, although the clustering procedure did not take into account TIL reactivity, the emerging clusters do separate nonreactive from reactive TILs (P<10−3).

Mentions: The usage of differential gene expression signatures has become a well-established method for distinguishing between various cellular states and different pathological conditions (Golub et al, 1999). We extend this concept to heterogeneous cell populations, by using a similar notion of a ‘subpopulations signature.' Unsupervised hierarchical clustering was applied on the subpopulation signatures as shown in Figure 3, where each column corresponds to a TIL culture and the rows represent subpopulations. Two significant clusters emerge, each representing a profile of CD4 and CD8 enriched subsets. These two markers represent regulatory and cytotoxic T-cell subpopulations, respectively (Figure 1B). Interestingly, the two clusters also separate between nonreactive and reactive TILs (Fischer exact P<10−3). This suggests that TIL reactivity against melanoma is largely dictated by its subpopulation composition. We also observed that the nonreactive cluster is further divided into two subclusters, both of which are enriched with nonreactive TILs that have distinct profiles. The first is mostly CD4, whereas the other is a mixture of CD8 and CD4 subpopulation derivatives, suggesting CD4 dominance over CD8.


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)

Reactive and nonreactive TILs exhibit distinct subpopulation signatures. Columns and rows correspond to TILs and subpopulations, respectively. Colors indicate the fraction of cells belonging to each subpopulation in each TIL. Unsupervised clustering was used on the rows and columns (see Materials and methods). The red and blue arrows represent nonreactive and reactive TILs, respectively. Two main clusters emerge characterized by CD4+ and CD8+ overabundant subpopulations. Interestingly, although the clustering procedure did not take into account TIL reactivity, the emerging clusters do separate nonreactive from reactive TILs (P<10−3).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: Reactive and nonreactive TILs exhibit distinct subpopulation signatures. Columns and rows correspond to TILs and subpopulations, respectively. Colors indicate the fraction of cells belonging to each subpopulation in each TIL. Unsupervised clustering was used on the rows and columns (see Materials and methods). The red and blue arrows represent nonreactive and reactive TILs, respectively. Two main clusters emerge characterized by CD4+ and CD8+ overabundant subpopulations. Interestingly, although the clustering procedure did not take into account TIL reactivity, the emerging clusters do separate nonreactive from reactive TILs (P<10−3).
Mentions: The usage of differential gene expression signatures has become a well-established method for distinguishing between various cellular states and different pathological conditions (Golub et al, 1999). We extend this concept to heterogeneous cell populations, by using a similar notion of a ‘subpopulations signature.' Unsupervised hierarchical clustering was applied on the subpopulation signatures as shown in Figure 3, where each column corresponds to a TIL culture and the rows represent subpopulations. Two significant clusters emerge, each representing a profile of CD4 and CD8 enriched subsets. These two markers represent regulatory and cytotoxic T-cell subpopulations, respectively (Figure 1B). Interestingly, the two clusters also separate between nonreactive and reactive TILs (Fischer exact P<10−3). This suggests that TIL reactivity against melanoma is largely dictated by its subpopulation composition. We also observed that the nonreactive cluster is further divided into two subclusters, both of which are enriched with nonreactive TILs that have distinct profiles. The first is mostly CD4, whereas the other is a mixture of CD8 and CD4 subpopulation derivatives, suggesting CD4 dominance over CD8.

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