Predicting and controlling the reactivity of immune cell populations against cancer

Kfir Oved^1,a, Eran Eden^2,a, Martin Akerman¹, Roy Noy¹, Ron Wolchinsky¹, Orit Izhaki³, Ester Schallmach³, Adva Kubi³, Naama Zabari³, Jacob Schachter³, Uri Alon^2,4, Yael Mandel-Gutfreund¹, Michal J Besser³ & Yoram Reiter¹

^aThese authors contributed equally to this work

Article highlights

Synopsis

A major challenge in molecular and cellular biology is to understand the collective function of cell populations made of different cell types that have complex networks of direct and indirect cell–cell interactions. Can simple rules be found to understand and even control the function of such complex and heterogeneous populations?

To address this question, we used as a model system a cell population mixture, called tumor-infiltrating lymphocytes (TILs). TILs are an immune population composed of different lymphocytic subpopulations that are derived from a tumor mass and have specificity and potential reactivity against the tumor (1). Presently, TILs are used in a clinical protocol called adoptive cell transfer for the treatment of metastatic melanoma (2). Despite their clinical importance, little is known about the underlying composition and cellular interactions that govern the degree of TIL reactivity against melanoma and consequentially on how to control their reactivity.

We began this study by examining the composition and reactivity of 91 TILs that were extracted from surgically removed metastatic tumors of 27 different melanoma patients. We characterized the cellular composition of each of these TILs by measuring the frequencies of over 100 different subpopulations that reside in them by using multicolor flow cytometry. Each of the subpopulations showed a unique set of receptors or membrane markers. The collective output of each TIL was defined in terms of its IFN- secretion, which is a major criterion for determining T-cell activity, after incubation with autologous melanoma. This procedure was used to classify 39 TIL cultures as reactive and 52 as nonreactive.

First, we asked whether any individual subpopulation can effectively predict the collective reactivity of the entire population. In particular, subpopulations containing the CD4⁺ and CD8⁺ receptors were examined due to their well-established role in T-cell responses. We found that none of the individual subpopulations examined in this study, including CD4⁺ and CD8⁺, could accurately predict the collective response of the heterogeneous TIL population to autologous cancer cells.

Next, we turned to examine the similarity between different TILs by comparing their sets of subpopulation frequencies, which we term 'subpopulation signatures.' Unsupervised clustering of the subpopulation signatures revealed that despite the large number of possible subpopulation combinations TILs tend to fall into one of two main clusters (Figure 3). Interestingly, the first cluster was highly enriched with nonreactive TILs, whereas the other was enriched with reactive ones (P<10³).

Figure 3

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).

Full figure and legend (593K)Figures & Tables index

These results suggest that although individual subpopulations are not accurate predictors, by using a combination of several subpopulations frequencies one may be able to predict the collective TIL reactivity. To test this, we applied decision tree algorithms on the subpopulation signatures and were able to extract a simple set of subpopulation-based rules that accurately predicted TIL reactivity (89% total accuracy). Furthermore, by using a linear model that is based on balance between two CD8⁺ subsets with antagonistic effects, we could predict not only whether a TIL is reactive but its actual level of reactivity in terms of INF- secretion.

These results raised the conjecture of whether one could control reactivity of TILs by rationally manipulating their subpopulation fractions using the subpopulation-based rules. To examine this, we rationally manipulated the subpopulation frequencies of a fresh cohort of 12 nonreactive TILs by selective enrichment and depletion of specific subpopulations. Remarkably, this manipulation restored the anti-tumor reactivity of 9 of the 12 nonreactive TILs (Figure 5). Additionally, by depleting and enriching the complementary set subpopulations, we were able to turn a fresh cohort of five reactive TILs into nonreactive ones.

Figure 5

Rational subpopulation manipulation can change TIL anti-tumor reactivity and is accompanied by a shift in subpopulation signature. (A) IFN- levels of 12 TILs before (red bars) and after (blue bars) rational subpopulation depletion and enrichment are compared. Nine of the original nonreactive TILs show significant increase in IFN- levels with up to 106-fold increase observed for TIL #2. Incubation of TILs in the control experiments with culture media or unrelated melanoma (white bars) indicates that the increase in IFN- secretion does not occur spontaneously and is tumor (HLA) specific. (B) The shift in reactivity can be explained in terms of a shift in subpopulation signature. The subpopulation fractions of 10 TILs before and after subpopulation manipulation are shown. The rows and columns correspond to different subpopulations and TILs, respectively. Two ways unsupervised clustering was performed on the rows and columns. The 10 nonreactive TILs prior to the manipulation are designated by a red color and the letter 'P.' Ten TILs after manipulation are designated by the letter 'A' with blue and yellow corresponding to reactive and nonreactive, respectively. Eight of the nonreactive TILs before manipulation became reactive, seven of which also showed a shift from a nonreactive subpopulation signature to a reactive one as indicated by the blue arrows going from right to left. The two TILs that remained nonreactive after manipulation exhibited either a minor change or a negative change in subpopulation signature as indicated by the red arrows. (C) The transformation of a nonreactive TIL to a reactive one can be described as a path between two points in the subpopulation space. In order to visualize the TILs positions in the multidimensional subpopulation space, we applied PCA, which is a method for dimensionality reduction (see Materials and methods). This enabled us a simple 2D visualization of the different TILs. The x and y-axes are the principal components capturing 49 and 24% of the total variance in the data. The x-axis captures a shift from CD8⁺ and CD28^- enriched subpopulation to CD4⁺ and CD28⁺ subpopulations, whereas the y-axis reflects a combination of additional subpopulations (see Supplementary Figure S4 for subpopulations coefficients defining each of the principal components). The figure shows a subspace region that is overpopulated with reactive TILs. The change in reactivity can be visualized as a path from a nonreactive TIL to a TIL that resides in the reactive subspace (e.g. see dotted arrow).

Full figure and legend (738K)Figures & Tables index

These results show the effectiveness of using a TIL's subpopulation signature to predict and even control its reactivity against tumors. The scheme presented in this work may be extended to predict and control the outputs of other types of heterogeneous cell populations in fields such as stem cells, tumor immunology, and tissue engineering.

MORE ARTICLES LIKE THIS

These links to content published by NPG are automatically generated.