Pulsatile MEK Inhibition Improves Anti-tumor Immunity and T Cell Function in Murine Kras Mutant Lung Cancer

Abstract

KRAS is one of the driver oncogenes in non-small-cell lung cancer (NSCLC) but remains refractory to current modalities of targeted pathway inhibition, which include inhibiting downstream kinase MEK to circumvent KRAS activation. Here, we show that pulsatile, rather than continuous, treatment with MEK inhibitors (MEKis) maintains T cell activation and enables their proliferation. Two MEKis, selumetinib and trametinib, induce T cell activation with increased CTLA-4 expression and, to a lesser extent, PD-1 expression on T cells in vivo after cyclical pulsatile MEKi treatment. In addition, the pulsatile dosing schedule alone shows superior anti-tumor effects and delays the emergence of drug resistance. Furthermore, pulsatile MEKi treatment combined with CTLA-4 blockade prolongs survival in mice bearing tumors with mutant Kras. Our results set the foundation and show the importance of a combinatorial therapeutic strategy using pulsatile targeted therapy together with immunotherapy to optimally enhance tumor delay and promote long-term anti-tumor immunity.

Figure 1. MEK Inhibition Affects Murine Kras Mutant Tumor Growth and Murine T Cell Signaling

(A) pERK expression in various Kras mutant lung cancer cell lines after trametinib treatment by western blot. (B) Viability of lung tumor cell lines after selumetinib treatment. Samples were biological replicates. (C) Survival of HKP1 lung-cancer-bearing mice after 3 weeks selumetinib treatment. (D) pERK expression in CD4+ and CD8+ T cells from HKP1 tumor-bearing lungs after selumetinib or trametinib treatment by flow cytometry. Samples were biological replicates. *p < 0.05; **p < 0.01; ***p < 0.001; and ****p < 0.0001, Welch’s t test. NS, not significant. Error bars represent SD. The experiments were performed 2–3 times, and representative results are shown here.

Figure 2. Short Schedule of MEKi Treatment Alters T Cell Activation Status Ex Vivo

(A) Schema of ex vivo short versus long treatment experiment. (B) CTLA-4, PD1, Ki-67, and 4–1BB expression in CD8+ T cells and CD4+Foxp3 cells by flow cytometry after selumetinib (left) or trametinib (right) treatment for 96 hr. *p < 0.05; **p < 0.01, Welch’s test. Error bars represent SD. Samples were biological replicates. The experiment was performed twice, and representative results are shown here.

Figure 3. Short Treatment of MEKis Alters T Cell Priming Ex Vivo

(A) Schema of short treatment on Pmel-1 CD8+ T cells with human gp100 peptide pulse. (B) Flow cytometry plots of CD44 and CD62L markers on CD8+ T cells after 5 days of priming. (C) Frequency of CD44 CD62L subsets from CD8+ T cells by flow cytometry analysis. Average percentage of each subset is presented. (D) Frequency of CD44+ CD62L cell population by flow cytometry. (E) IFNγ production from supernatant at day 5 by cytokine profiling. *p < 0.05; **p < 0.01; ***p < 0.001, Welch’s test. Error bars represent SD. Samples were biological replicates. The experiment was performed twice, and representative results are shown here.

Figure 4. Pulsatile Treatment of Selumetinib Induces CTLA-4 and PD-1 Expression In Vivo

HKP1 transplantable lung-tumor-bearing mice were treated with selumetinib (25 mg/kg, BID) as presented in (A). After 2 weeks of treatment, lungs were collected and analyzed by flow cytometry. (A) Schema of selumetinib treatment in HKP1 lung-tumor-bearing mice in vivo. (B) Frequency of CD3+ T cell subsets in lung tumors by flow cytometry. (C) Ki-67 of diverse cell populations in lung tumors by flow cytometry. (D) Scatterplots of PD-1 and CTLA-4 marker (left) and co-inhibitory marker expression from CD3+ T cell subsets of lung tumors by flow cytometry (right). Gating controls are samples without either PD-1 or CTLA-4 antibodies. *p < 0.05; **p < 0.01; ***p < 0.001, Mann-Whitney test. Samples were biological replicates. The experiment was performed 3 times, and representative results are shown here.

Figure 5. Pulsatile Schedule of MEKi Treatment Delays Tumor Growth In Vivo

(A) Schema of selumetinib treatment in KRAS^G12C mutant genetically engineered mouse model (GEMM) of lung cancer. Treatment schedule for continuous treatment (upper panel, 25 mg/kg, BID) and pulsatile treatment (lower panel, 25 mg/kg, BID). (B) Waterfall plot showing tumor volume change at indicated time points after the continuous treatment of selumetinib. (C) Waterfall plot showing tumor volume change at indicated time points after the treatment of pulsatile dosing of either vehicle (left panel) or selumetinib (right panel). (D) Representative images of immunohistochemistry (IHC) staining of pERK (left panels) and multiplicative quick scores for quantification of pERK½ staining with vehicle control, pulsatile selumetinib, or continuous selumetinib for tumor tissue samples at the end of the treatment (right panel). Scale bars, 100 mm. *p < 0.05; ****p < 0.0001. (E) Progression-free survival of KRAS^G12C mice treated with vehicle control, pulsatile selumetinib, or continuous selumetinib. **p < 0.01; ***p < 0.001. Samples were biological replicates. This treatment study was performed three times, and results from all mice have been combined as presented.

Figure 6. Co-inhibitory Signaling Was Altered Differentially by Continuous versus Pulsatile Treatment of MEKis

(A) Flow cytometry analysis of KRAS^G12C mutant GEMM lung-tumor-infiltrating T cell subpopulations: CD4+, CD8+, and Tregs (CD4+Foxp3+) after continuous (left) or pulsatile (right) treatment with selumetinib as presented in Figure 5A. Lung tumors were collected at the end of treatment. *p < 0.05. NS, not significant. (B) Representative flow cytometry analysis of PD-1 levels in both CD4+ and CD8+ tumor-infiltrating T cells after continuous treatment of selumetinib. (C) Quantification of inhibitory immune checkpoint molecule expression on CD4+ (upper) and CD8+ (lower) T cells after 3 weeks of continuous selumetinib treatment. *p < 0.05; **p < 0.01. (D) Quantification of inhibitory molecules within CD4+ (left) and CD8+ (right) T lymphocyte subpopulations after 3 cycles of pulsatile selumetinib treatment. **p < 0.01. Samples were biological replicates. All mice were recruited at the same time for the treatment, and results from all mice are shown here.

Figure 7. Pulsatile Treatment of Selumetinib with High Dosage Impacts Immune Microenvironment Differently and Enhances Survival in Combination with Anti-CTLA-4 Treatment

(A) Schema of dosing and sample collection after either high-dose (Hi; 600 mg/kg/day; left panel) or low-dose (Lo; 50 mg/kg/day; right panel) selumetinib treatment. Kras^G12DTrp53^fl/fl murine transplantable tumors were treated with different dosages of selumetinib. Mouse lung tumors were collected at indicated time points. Samples were biological replicates. (B) Flow cytometry analysis of different tumor-infiltrating T cell subpopulations within total infiltrating CD45+ leukocytes at indicated time points (left). PD-L1 expression levels on tumor cells (EpCAM+), myeloid cells (CD11b+), and T cells (CD4+ and CD8+) (middle); and Ki-67 expression (right). (C) Quantification of inhibitory immune checkpoint molecules expressed on CD4+ (left) and CD8+ (right) T cells. (D) Schema of selumetinib and anti-CTLA-4 treatment on LLC transplantable tumor model. (E) Survival curve from the selumetinib and anti-CTLA-4 treatment combination in immune-competent mice (C57BL/6J). (F) Survival curve from the selumetinib and anti-CTLA-4 treatment combination in immune-deficient mice (Rag1 / ). The color code is as same as in (F). (G) Survival of the pulsatile selumetinib and anti-CTLA-4 treatment group. Survival analysis was done by Log-rank (Mantel-Cox) test. * < 0.05; ** < 0.01. Samples were biological replicates. The experiment was performed 2–3 times, and representative results are shown here.