A precision medicine approach to metabolic therapy for breast cancer in mice

Abstract

Increasing evidence highlights approaches targeting metabolism as potential adjuvants to cancer therapy. Sodium-glucose transport protein 2 (SGLT2) inhibitors are the newest class of antihyperglycemic drugs. To our knowledge, SGLT2 inhibitors have not been applied in the neoadjuvant setting as a precision medicine approach for this devastating disease. Here, we treat lean breast tumor-bearing mice with the SGLT2 inhibitor dapagliflozin as monotherapy and in combination with paclitaxel chemotherapy. We show that dapagliflozin enhances the efficacy of paclitaxel, reducing tumor glucose uptake and prolonging survival. Further, the ability of dapagliflozin to enhance the efficacy of chemotherapy correlates with its effect to reduce circulating insulin in some but not all breast tumors. Our data suggest a genetic signature for breast tumors more likely to respond to dapagliflozin in combination with paclitaxel. In the current study, tumors driven by mutations upstream of canonical insulin signaling pathways responded to this combined treatment, whereas tumors driven by mutations downstream of canonical insulin signaling did not. These data demonstrate that dapagliflozin enhances the response to chemotherapy in mice with breast cancer and suggest that patients with driver mutations upstream of canonical insulin signaling may be most likely to benefit from this neoadjuvant approach.

Fig. 1. The SGLT2 inhibitor dapagliflozin outperforms diabetes drugs in other classes at lowering both fasting and postprandial plasma insulin concentrations.

Mice were fed a Western diet (60% calories from fat in chow, and 5% sucrose drinking water), for four weeks prior to initiation of drug treatment for two weeks. a Blood glucose. b Plasma insulin. In both panels, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by ANOVA with Tukey’s multiple comparisons test. In both panels, n = 5 per group.

Fig. 2. Dapagliflozin slows spontaneous tumor growth in lean and obese MMTV-PyMT mice, correlated with its effect to lower plasma insulin concentrations.

a Plasma insulin and (b) Tumor [¹⁴C] 2-deoxyglucose uptake in mice treated acutely with a single dose of dapagliflozin (2.5 mg/kg). c, d Blood glucose and plasma insulin in mice treated continuously for four weeks with dapagliflozin in drinking water. e Tumor [¹⁴C] 2-deoxyglucose uptake in mice treated chronically with dapagliflozin. In panels (a–e), mice were studied after a 6 h fast. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by ANOVA with Tukey’s multiple comparisons test. n = 5 per group. f Tumor growth. The mean ± S.E.M. of n = 5 per group is shown. Brackets denote statistically significant (P < 0.05) comparisons by ANOVA with Tukey’s multiple comparisons test.

Fig. 3. Dapagliflozin slows orthotopic 4T1 tumor growth in lean and obese mice, correlated with its effect to lower plasma insulin concentrations.

a Blood glucose and (b) Plasma insulin in 6 h fasted mice. c Tumor [¹⁴C] 2-deoxyglucose uptake. In panels (a–c), *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by ANOVA with Tukey’s multiple comparisons test. n = 5 per group. d Tumor growth. Data are the mean ± S.E.M. of n = 5 per group. The brackets denote statistically significant (P < 0.05) comparisons by ANOVA with Tukey’s multiple comparisons test.

Fig. 4. Dapagliflozin enhances the efficacy of chemotherapy in two murine models of breast cancer.

Tumor glucose uptake in (a) MMTV-PyMT and (b) 4T1 tumor-bearing mice. In panels (a, b), **P < 0.01, ****P < 0.0001. c Survival in MMTV-PyMT mice. The x-axis refers to weeks of life. d Survival in 4T1 tumor-bearing mice. The x axis refers to days after tumor cell injection. In panels (c, d), **P < 0.01, ****P < 0.0001 vs. vehicle, ⁺⁺P < 0.01, ⁺⁺⁺⁺P < 0.0001 vs. dapagliflozin, ##P < 0.01 vs. paclitaxel via the Mantel-Cox log-rank test, adjusted for multiple comparisons. The p values in the upper right corner of each survival curve refer to the overall curve comparison using the Mantel-Cox log-rank test. Glucose uptake was measured in n = 5 per group, and groups of 10 (c) and 5 (d) were studied to generate the survival curves.

Fig. 5. Dapagliflozin has no adverse effects to cause fatigue, anorexia, weight loss, or neuropathy in MMTV-PyMT or 4T1 tumor-bearing mice.

a, b Ad lib food intake, measured in metabolic cages. All metabolic cage studies in this figure were performed after 3 weeks of chemotherapy or vehicle treatment in MMTV-PyMT and 4T1 tumor-bearing mice, respectively. c, d Body weight, measured after 4 weeks of treatment. e, f Spontaneous activity. g, h Thermal latency during week 3 of chemotherapy or vehicle treatment. In all panels, *P < 0.05, **P < 0.01, ***P < 0.001 by ANOVA with Tukey’s multiple comparisons test. Groups of n = 5 were studied.

Fig. 6. Tumor drivers may predict the response to dapagliflozin as an adjunct to paclitaxel.

a–e Survival in EMT6, Ac711, M6, M158, and Eph 1424 tumor-bearing mice, respectively. Mice were examined daily by an investigator who was blinded as to group allocation. **P < 0.01 vs. vehicle, ++P < 0.01 vs. dapagliflozin, ##P < 0.01 vs. paclitaxel by the Mantel-Cox log-rank test, adjusted for multiple comparisons. The P values in the upper right corner of each survival curve refer to the overall curve comparison using the Mantel-Cox log-rank test. Groups of n = 5 were studied.

Fig. 7. Driver mutations tested in this study.

Green bubbles show models utilized herein.