Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood

Abstract

Cytokines mediate cell-to-cell communication across the immune system and therefore are critical to immunosurveillance in cancer and other diseases. Several cytokines show dysregulated abundance or signaling responses in breast cancer, associated with the disease and differences in survival and progression. Cytokines operate in a coordinated manner to affect immune surveillance and regulate one another, necessitating a systems approach for a complete picture of this dysregulation. Here, we profiled cytokine signaling responses of peripheral immune cells from breast cancer patients as compared to healthy controls in a multidimensional manner across ligands, cell populations, and responsive pathways. We find alterations in cytokine responsiveness across pathways and cell types that are best defined by integrated signatures across dimensions. Alterations in the abundance of a cytokine's cognate receptor do not explain differences in responsiveness. Rather, alterations in baseline signaling and receptor abundance suggesting immune cell reprogramming are associated with altered responses. These integrated features suggest a global reprogramming of immune cell communication in breast cancer.

Keywords: Cytokines; autoimmunity; breast cancer; immunology; tensor factorization.

Fig. 1.. Systematically profiling PBMC cytokine signaling across several dimensions.

(a) Schematic of the experimental approach. Human PBMCs were harvested from healthy and BC cohorts and subsequently treated with a panel of 7 cytokine/growth factor combinations. Response was quantified using 27-channel flow cytometry, through which the response of 23 different cell types was quantified. (b) Schematic of structure of the dataset and how it was divided for visualization in c–f. (c–f) Heatmap of phosphorylated STAT3 (c), STAT5 (d), STAT6 (e), and STAT1 (f) measurements for each treatment (X axis), and patient/cell type pair (Y axis). The signal was normalized to the maximum observed signal across patients. A small number of missing values were imputed for select patients in response to TGFβ and the IFNγ/IL-6 combination.

Fig. 2.. Canonical polyadic decomposition (CPD) of cytokine response identifies several response patterns strongly associated with BC.

(a) Schematic of the CPD. The signaling data is organized into a four-dimensional tensor with axes for each patient, treatment, cell type, and signaling marker. This tensor is reduced into the sum of the outer products of vectors (components) associated with each dimension. (b) Percent variance reconstructed (R2X) versus the number of components used. (c) The remaining error on reconstruction, normalized to the total dataset variance, versus the size of the dataset after decomposition using CPD or PCA. (d) The accuracy of a logistic regression classifier upon 10-fold cross-validation, using the CPD patient factors with varying numbers of components. (e) The weights associated with each component for a logistic regression classifier fit to patient factors using a 12-component decomposition (healthy = 0, BC = 1). (f) A heatmap of the patient factor matrix, with the patients hierarchically clustered. Patient status is indicated by the coloring along the bottom. (g–i) Component values for each treatment (g), cell type (h), and signaling marker (i). (j) Classification accuracy for logistic regression classifiers using all pairs of components.

Fig. 3.. Immune dysregulation is more prominently reflected in patterns of signaling response changes.

(a) STAT3 phosphorylation response to IL-10 across cell types, grouped by patient status. Cell types with statistically significant differences between BC and healthy status are colored red. (b) Baseline pSTAT3 in untreated cells across cell types, grouped by patient status. (c) Baseline pSTAT3 versus IL-10-induced pSTAT3 in CD8-positive cells, across patients. (d) pSTAT3 response to IL-10 across patients in B cells versus CD8 TCM. (e) Baseline untreated versus IL-2-induced STAT5 phosphorylation in CD8-positive cells. (f) IL-2-induced pSTAT5 in CD8-positive cells and Tregs. (g) Components 4 and 5 from the CPD factorization in Figure 2. (h) The difference in average Smad1/2 phosphorylation between the BC and healthy cohorts versus the same quantity for STAT4 phosphorylation, plotted for each cell type. (i–j) Baseline pSmad2/3 versus pSTAT4 across patients in CD8-positive cells (i) and Tregs (j).

Fig. 4.. CPD reveals patterns of receptor abundance variation.

(a) Schematic of CPD. Receptor data is organized into a three-dimensional tensor with axes of patient, cell type, and receptor. This tensor is reduced into sum of the outer products of rank 1 tensors (components), allowing for easy visualization. (b) Percent variance reconstructed (R2X) versus the number of components used. (c) The remaining error on reconstruction, normalized to the total dataset variance, versus the size of the dataset after decomposition using CPD or PCA. (d) The accuracy of a logistic regression classifier upon 10-fold cross-validation, using the CPD patient factors with varying numbers of components. (e) The weights associated with each component for a logistic regression classifier fit to patient factors using a 5-component decomposition (healthy = 0, BC = 1). (f) A heatmap of the patient factor matrix, with the patients hierarchically clustered. Patient status is indicated by the coloring along the bottom. (g–h) Component values for each receptor (g) and cell type (h).

Fig. 5.. Dissecting the patterns of receptor variation suggest concerted molecular programs.

(a) Mean relative abundance of PD-1 across CD8-positive cell types, grouped by patient disease status. (b) Mean relative abundance of PD-L1 across CD8-positive and B cells, grouped by patient disease status. (c) Mean relative abundance of IL6Rα across CD8-positive and B cells, grouped by patient disease status. (d) Mean relative abundance of IL2Rɑ in Tregs, grouped by patient disease status. (e–h) Correlation across patients between CD8⁺ TEM PD-L1 and CD20 B PD-L1 (e), CD8⁺ IL6Rα and CD20 B IL6Rα (f), Treg IL2Rɑ and CD8 TEM PD-L1 (g), and Treg IL2Rɑ and CD20 B IL6Rα (h). (i) ROC curve for the separation provided by several of the receptor amounts measurements, alongside component 2. *, **, and *** represent p-values less than 0.05, 0.005, and 0.0005, respectively.

Fig. 6.. Receptor abundance defines cell type- but not BC-specific responses.

(a) Patient-average IFN-induced STAT1 phosphorylation versus IFNɑR1 abundance across cell types. Induced responses are reported as z-scored delta MFI. (b) Patient-average IL-2-induced pSTAT5 versus IL2Rα abundance across cell types. (c) IL10R abundance across cell types, grouped by patient status. (d) IL-10-induced pSTAT3 versus IL10R abundance in classical monocytes, across patients. Cell types which feature statistically significant responses between BC and healthy status are listed in red. (e) IL2Rɑ abundance across cell types, grouped by patient status. (f) IL2Rβ abundance across cell types, grouped by patient status. (g–j) IL-2-induced pSTAT5 versus IL2Rɑ (g/h) or IL2Rβ (i/j) in CD8⁺ cells (g/i) or Tregs (h/j).

Fig. 7.. Patterns of coordinated receptor-signaling dysregulation suggest mechanisms of response reprogramming both globally and in a cell type-specific manner.

(a–b) Partial correlations among measurements with statistically significant differences in CD8⁺ (a) or CD4⁺ (b) T cells. Correlations are calculated across patients, specifically within the BC cohort. (c) Correlations across all patients among the receptor and signaling component patterns.