Evaluating immunotherapeutic outcomes in triple-negative breast cancer with a cholesterol radiotracer in mice

Abstract

Evaluating the response to immune checkpoint inhibitors (ICIs) remains an unmet challenge in triple-negative breast cancer (TNBC). The requirement for cholesterol in the activation and function of T cells led us to hypothesize that quantifying cellular accumulation of this molecule could distinguish successful from ineffective checkpoint immunotherapy. To analyze accumulation of cholesterol by T cells in the immune microenvironment of breast cancer, we leveraged the PET radiotracer, eFNP-59. eFNP-59 is an analog of cholesterol that our group validated as an imaging biomarker for cholesterol uptake in preclinical models and initial human studies. In immunocompetent mouse models of TNBC, we found that elevated uptake of exogenous labeled cholesterol analogs functions as a marker for T cell activation. When comparing ICI-responsive and -nonresponsive tumors directly, uptake of fluorescent cholesterol and eFNP-59 increased in T cells from ICI-responsive tumors. We discovered that accumulation of cholesterol by T cells increased in ICI-responding tumors that received anti-PD-1 checkpoint immunotherapy. In patients with TNBC, tumors containing cycling T cells had features of cholesterol uptake and trafficking within those populations. These results suggest that uptake of exogenous cholesterol analogs by tumor-infiltrating T cells allows detection of T cell activation and has potential to assess the success of ICI therapy.

Keywords: Breast cancer; Diagnostic imaging; Immunology; Immunotherapy; Oncology.

Figure 1. E0771 tumors respond to anti–PD-1 immunotherapy, while AT-3 tumors do not.

Three days after orthotopically injecting E0771 or AT-3 breast cancer cells plus mouse mammary fibroblasts into syngeneic C57BL/6J mice, we randomly assigned animals to treatment with anti–PD-1 antibody or PBS vehicle every 3 days for 4 doses total. Graphs show mean values ± SEM (symbols) and calculated logistic regression (smooth line) for E0771 (A) or AT-3 (D) tumors (n = 6 control; n = 8 anti–PD-1) treated with anti–PD-1 antibody or PBS. (B and E) Growth of E0771 and AT3 tumor growth, respectively, for individual mice over time. Three tumors from the anti–PD-1 group failed to grow tumors and are overlapped on the x axis of panel B. We analyzed differences in tumor growth data by logistic regression. Survival curves demonstrate that anti–PD-1 treatment significantly prolonged survival for mice with E0771 tumors (C) but not with AT-3 (F), as analyzed by the Mantel-Cox test.

Figure 2. T cells from ICI-responsive versus -nonresponsive tumors have greater uptake of fluorescent cholesterol.

T cells were isolated from ICI-responsive EO771 or ICI-nonresponsive AT-3 tumors and left unstimulated (us) or restimulated on anti-CD3e–coated (stim) dishes for 18 hours in the presence of 3-NBD–labeled cholesterol. We determined uptake of labeled cholesterol in activated, CD69⁺ CD8⁺ (A) or CD4⁺ (B) T cells by flow cytometry. CD8⁺ (C) and CD4⁺ (D) T cells from EO771 tumors show significantly greater percentages of CD69⁺ cells with uptake of 3-NBD cholesterol than from ICI-nonresponsive AT-3 tumors. Stimulated CD8⁺ (E) and CD4⁺ (F) T cells from EO771 tumors also exhibited significantly higher mean fluorescence intensity (MFI) for cholesterol uptake. Data are combined from 2 experiments. *P < 0.05, **P < 0.01 by 2-tailed Student’s t test (C and D) or nonparametric Mann-Whitney test (E and F).

Figure 3. T cells in ICI-responsive EO771 tumors show greater uptake of fluorescent cholesterol in vivo.

We injected C57BL/6J mice intraperitoneally with cholesterol labeled with BODIPY and euthanized animals 24 hours later to collect and dissociate tumors for flow cytometry (n = 5 each for EO771 and AT-3). Plots for (A) CD8⁺ and (B) CD4⁺ T cells show accumulation of BODIPY-cholesterol in cells from individual tumors from EO771 and AT-3 tumors relative to vehicle only or isotype antibody control. (C) CD8⁺ and (D) CD4⁺ T cells in EO771 tumors showed significantly higher fold change accumulation of fluorescent cholesterol relative to FMO control. (E) CD8⁺, but not (F) CD4⁺, T cells in EO771 tumors also expressed higher levels of PD-1. **P < 0.01,***P < 0.001, ****P < 0.0001 for differences between means using nonparametric Mann-Whitney tests (C and D; n = 5 mice per group), while differences between T cell population percentages were assessed using 2-tailed Student’s t test (E and F).

Figure 4. eFNP-59 uptake in T cells correlates directly with activation, differing from exhaustion.

(A) Mice were inoculated with E0771 and treated with and without anti–PD-1 immunotherapy. T cells were extracted, activated, and activation status compared to cholesterol uptake as in the diagram. (B) Cholesterol uptake was first compared between activation and immunotherapy groups with different amounts of eFNP-59. Using the data from the 1000-nCi treatment, (C) T cell activation marker CD69 was compared to normalized activity in a scatter plot and (D) multiparametric plot also assessing activation signal with pseudocolor scale, estimation of triple-positive T cells with bubble scale, and information on immunotherapy status (samples above or below dashed line). (E) Normalized activity (cholesterol uptake) was directly compared to triple-positive T cells and then again as (F) a multiparametric plot with pseudocolor plot for double-positive T cells, bubble size for anti-CD3 stimulus, and immunotherapy status (above or below dashed line). ****P < 0.0001 by least squares fit.

Figure 5. T cells in ICI-responsive tumors had greater uptake of eFNP-59.

When tumors reached approximately 70 mm², we treated mice with anti–PD-1 antibody or control for 4 days. Mice were then injected with 100 μCi of eFNP-59 followed by T cell isolation protocols. (A) Graph shows uptake of eFNP-59 per microgram of spleen tissue measured by scintigraphy. Symbols show individual mice with annotations for mean values and standard deviations. (B) We isolated CD4⁺ and CD8⁺ tumor-infiltrating lymphocytes (TILs) by positive selection with immunomagnetic beads and determined accumulation of eFNP-59 normalized to total cell protein with a BCA assay. Representative data from 2 experimental replicates, with statistical comparisons by 1-way ANOVA with Dunn’s multiple-comparison test. *P < 0.05; **P < 0.01.

Figure 6. Cycling T cell populations in patients with TNBC upregulate genes related to cholesterol metabolism.

(A) Reanalysis of single-cell RNA sequencing data (29) with Cellenics software displays Louvain clusters of annotated T cell states, including custom cell sets derived from these clusters: T memory/resting, highly activated, T effector/transitional state, and T effector/dysfunctional. (B) Plot shows proportions of the absolute counts across for various T cell subsets across TNBC patients (n = 8429 cells from 9 tumors). (C) Clustered averaged gene expression data reveal upregulation of relevant genes involved in uptake and intracellular trafficking of cholesterol in cycling T cells. (D–I) Violin plots of normalized expression of specific genes: (D) ANXA2 (cholesterol distribution to the plasma membrane), (E) LAMTOR1 (endosomal transport), (F) STARD3NL (endosomal transport), (G) COMMD1 (LDLR recycling), (H) LDLR (cholesterol uptake), and (I) SREBF2 (positive regulation of cholesterol uptake and synthesis). The ⱡ symbol indicates P < 10^–20 between the cycling cluster and other T cell clusters, as determined by Welch’s t test.