Cellular heterogeneity has been classically described within cellular populations, both in the settings of cell culture and in vivo. Heterogeneity, as an absolute property of a cellular population and collection of molecular readouts, can be difficult to interpret. However, relative differences in heterogeneity, such as may be due to epigenetics, genetics, or environmental conditions, may be more interpretable, in particular when tested for correlation with functional differences.
In the context of differences due to pharmacological perturbations, heterogeneity may be observed before or after treatment. In earlier work (Slack et al, 2008
), the ability to distinguish mechanistic classes of perturbations based on heterogeneous cancer cell responses was studied. In contrast, here we investigated whether patterns of basal signaling heterogeneity contained information predictive of subsequent population response to perturbation. We used drug sensitivity classification to provide an objective test of whether our decomposition of heterogeneity contained biologically relevant information. (It was not the goal of this study to develop or optimize predictors for drug sensitivities that outperform other methods.) We modeled the (quasi-equilibrium) distributions of cell signaling phenotypes present within populations from snapshots of large numbers of cells (Chang et al, 2008
; Huang et al, 2009
), and found that measures of these distributions served as informative, predictive readouts of population-level responses to perturbation. Our approach allowed us to decompose heterogeneous cellular distributions into a small number of more phenotypically homogenous states (), compare and group populations based on their patterns of heterogeneity (), identify a consistent relationship between heterogeneity and function across multiple sets of general signaling markers () and, finally, test whether a common model of basal signaling heterogeneity could be used to predict drug sensitivities across different cancer populations (). In general, characterization of the ensemble of subpopulation mixture may be required to distinguish functional differences among populations. However, in certain cases, (de-) enrichment for specific subpopulations may be sufficient to account for overall functional differences. For example, in MS1, enrichment for subpopulation pairs (S1, S4) or (S2, S3) separated paclitaxel-sensitive from -nonsensitive clones (Supplementary Figure 6
). Future studies are required to investigate the deeper molecular states of specific subpopulations (Loo et al, 2009a
) and their relationship to drug response. We note that in this study, cellular phenotypes were captured on the basis of the spatial colocalization patterns of signaling activity readouts from fixed cells. The physical sorting and subsequent investigation of our identified subpopulations remain challenging.
Important questions remain, such as the origins and evolution of the phenotypic diversification, why our decomposition of heterogeneity predicts drug responsiveness in our defined culture conditions, and why classification is possible on the basis of a limited number of biomarkers that were not chosen based on a prior knowledge of the biology of drug responsiveness, but rather on a general survey of pathways implicated in cancer. The observed heterogeneity among the H460 clones could be due to several factors, including differences in epigenetic states and genetic diversity that may have been present within the parent population or evolved within the clones during their short time of establishment. Regardless, we found that a simple description of the observed heterogeneity contained functional information. One possibility for our success using a limited number of biomarkers may be that our subpopulations reveal ‘deeper' underlying states that broadly reflect signaling in multiple pathways, and thus may be distinguishable by a small number of ‘general' signaling markers. Another possibility is that our approach has connected the characteristic behaviors of regulatory networks in two operating regimes: namely, networks operating within each cancer clone shape the stochastic distributions of cell signaling states in unchallenged conditions (Huang et al, 2009
) as well as determine an overall population response to an acute challenge (i.e. drug treatment). It is also interesting to speculate whether patterns of heterogeneity observed in primary cancer samples can be interpreted to reveal clinically important information. Importantly, the answer to this question is independent of whether profiles of clinical and cell line samples directly share common signatures. Nevertheless, the potential to study the physiological states of cell populations at a resolution greater than population averages, yet more summarized than individual cells, is highly compelling and our approach may help to interpret heterogeneity observed in healthy and diseased tissues.