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. 2009 Apr 28;5:265. doi: 10.1038/msb.2009.15

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Copyright © 2009, EMBO and Nature Publishing Group

This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited. Creation of derivative works is permitted but the resulting work may be distributed only under the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission.

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Simple rules based on subpopulation frequencies can predict TIL reactivity. (A) A decision tree algorithm was used to generate a simple set of four rules for classifying TIL functionality (see Materials and methods). Each rule is a path from the tree root (top) to one of the leaves (bottom). (B) IFN-γ levels of reactive TILs can be described as a function of two subpopulation fractions with positive and negative weights. Each dot is a reactive TIL. The y-axis is the empirical IFN-γ measurements and the x-axis is the theoretical IFN-γ levels calculated using the following model:

Overall, IFN-γ levels of reactive TILs can be described to a large extent as a balance between two opposing subpopulations with positive and negative effects.

Inline graphic — Simple rules based on subpopulation frequencies can predict TIL reactivity. (A) A decision tree algorithm was used to generate a simple set of four rules for classifying TIL functionality (see Materials and methods). Each rule is a path from the tree root (top) to one of the leaves (bottom). (B) IFN-γ levels of reactive TILs can be described as a function of two subpopulation fractions with positive and negative weights. Each dot is a reactive TIL. The y-axis is the empirical IFN-γ measurements and the x-axis is the theoretical IFN-γ levels calculated using the following model:

Overall, IFN-γ levels of reactive TILs can be described to a large extent as a balance between two opposing subpopulations with positive and negative effects.