GIT1 protects against breast cancer growth through negative regulation of Notch

doi:10.1038/s41467-022-28631-y

. 2022 Mar 22;13(1):1537.

doi: 10.1038/s41467-022-28631-y.

GIT1 protects against breast cancer growth through negative regulation of Notch

Songbai Zhang¹, Ayako Miyakawa^{1

2}, Malin Wickström³, Cecilia Dyberg³, Lauri Louhivuori¹, Manuel Varas-Godoy^{1

4}, Kati Kemppainen^{5

6}, Shigeaki Kanatani¹, Dagmara Kaczynska¹, Ivar Dehnisch Ellström¹, Lotta Elfman³, Pauliina Kronqvist⁷, Heli Repo⁷, Katsuhiko Mikoshiba^{8

9

10}, Cecilia Sahlgren^{5

6

11}, John Inge Johnsen³, Per Uhlén¹²

Affiliations

¹ Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
² Department of Molecular Medicine and Surgery, Karolinska University Hospital, Stockholm, Sweden.
³ Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
⁴ Centro de Biología Celular y Biomedicina (CEBICEM), Facultad de Medicina y Ciencia, Universidad San Sebastián, Santiago, Chile.
⁵ Turku Bioscience, Åbo Akademi University and University of Turku, Turku, Finland.
⁶ Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland.
⁷ Department of Pathology, University of Turku, Turku, Finland.
⁸ Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
⁹ RIKEN Center for Life Science Technologies (CLST), Chuo-ku, Kobe, Japan.
¹⁰ Department of Biomolecular Science, Faculty of Science, Toho University, Chiba, Japan.
¹¹ Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands.
¹² Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. per.uhlen@ki.se.

PMID: 35318302
PMCID: PMC8940956
DOI: 10.1038/s41467-022-28631-y

GIT1 protects against breast cancer growth through negative regulation of Notch

Songbai Zhang et al. Nat Commun. 2022.

. 2022 Mar 22;13(1):1537.

doi: 10.1038/s41467-022-28631-y.

Authors

Affiliations

¹ Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
² Department of Molecular Medicine and Surgery, Karolinska University Hospital, Stockholm, Sweden.
³ Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
⁴ Centro de Biología Celular y Biomedicina (CEBICEM), Facultad de Medicina y Ciencia, Universidad San Sebastián, Santiago, Chile.
⁵ Turku Bioscience, Åbo Akademi University and University of Turku, Turku, Finland.
⁶ Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland.
⁷ Department of Pathology, University of Turku, Turku, Finland.
⁸ Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
⁹ RIKEN Center for Life Science Technologies (CLST), Chuo-ku, Kobe, Japan.
¹⁰ Department of Biomolecular Science, Faculty of Science, Toho University, Chiba, Japan.
¹¹ Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands.
¹² Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. per.uhlen@ki.se.

PMID: 35318302
PMCID: PMC8940956
DOI: 10.1038/s41467-022-28631-y

Abstract

Hyperactive Notch signalling is frequently observed in breast cancer and correlates with poor prognosis. However, relatively few mutations in the core Notch signalling pathway have been identified in breast cancer, suggesting that as yet unknown mechanisms increase Notch activity. Here we show that increased expression levels of GIT1 correlate with high relapse-free survival in oestrogen receptor-negative (ER(-)) breast cancer patients and that GIT1 mediates negative regulation of Notch. GIT1 knockdown in ER(-) breast tumour cells increased signalling downstream of Notch and activity of aldehyde dehydrogenase, a predictor of poor clinical outcome. GIT1 interacts with the Notch intracellular domain (ICD) and influences signalling by inhibiting the cytoplasm-to-nucleus transport of the Notch ICD. In xenograft experiments, overexpression of GIT1 in ER(-) cells prevented or reduced Notch-driven tumour formation. These results identify GIT1 as a modulator of Notch signalling and a guardian against breast cancer growth.

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Fig. 1. Downregulated GIT1 expression in ER(−) breast tumours is associated with poor relapse-free survival in patients.**
a, b Immunostaining of GIT1 and ERα in one ER(+) and one ER(−) patient breast tumour section (a) and the quantitative analysis of GIT1 immunofluorescence relative to DAPI (b; ER(+) (n = 45) versus ER(−) (n = 30), P = 0.0006, t test). Scale bar, 10 μm. c Mass spectrometry data from CPTAC of the GIT1 protein levels in ER(+) and ER(−) patients (ER(+) (n = 51) versus ER(−) (n = 11), P = 0.024, t test). d, e, Western blots of GIT1 in various human breast cancer cells (d) and the quantitative analysis (e; n = 7 independent biological replicates, 184A1 versus BT474, P = 0.0004; MCF7, P = 0.0003; MDA-MB-134-VI, P = 0.015; MDA-MB-361, P < 0.0001; HCC1954, P = 0.017; MDA-MB-157, P = 0.023; MDA-MB-231, P = 0.0067; t tests). ER, oestrogen receptor. PR, progesterone receptor. HER2, human epidermal growth factor receptor 2. f Kaplan–Meier plot of relapse-free survival of the breast cancer patients from the TCGA database stratified by GIT1 expression (n = 292, P = 0.0084, log-rank test). g–i Kaplan–Meier plots of relapse-free survival of all breast cancer (BC) patients (g; n = 3,310, P < 0.0001, log-rank test), ER(+) patients (h; n = 2527, P = 0.032, log-rank test), and ER(−) patients (i; n = 779, P = 0.0007, log-rank test) from KM-plotter stratified by GIT1 expression. Hazard ratios (HRs) and their 95% confidence intervals (95%CI) are indicated. j, Forest plot of HRs for survival analysis of all BC, ER(+), and ER(−) patients stratified by GIT1 expression (ER(+) (n = 2527) versus ER(−) (n = 779), P = 0.047, one-sided unpaired t test). Error bars show 95%CI. All data are shown as the mean ± s.e.m. For the box plots, the centre line shows the median, the plus sign shows the mean, the upper and lower boundaries of the box show the upper and lower quartiles, and the whiskers show the minimum and maximum values. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by two-sided unpaired t tests. Source data are provided as a Source Data file.

**Fig. 2. GIT1 regulates Notch signalling, ALDH1 activity, and colony formation.**
a Immunostaining of GIT1 and Notch1 ICD (N1ICD) in one ER(+) and one ER(−) breast cancer sample. Nuclei were detected using DAPI. Images from representative micrographs; the experiment was repeated n = 6 times for ER(+) samples and n = 4 times for ER(−) samples with similar results. Scale bar, 10 μm. b–d Luciferase reporter assays of 12xCSL-Luc (b; n = 5, Ctrl-siRNA versus DAPT, P < 0.0001; Ctrl-siRNA versus GIT1-siRNA1, P = 0.0038; one-way ANOVA, F_2,12 = 64.17, P < 0.0001 (shaded area); mRFP versus GIT1-mRFP, P < 0.0001; t tests) and western blots of Hey1 (c) and the quantitative analysis (d; n = 6, Ctrl-shRNA versus GIT1-shRNA2, P = 0.030; mRFP versus GIT1-mRFP; P = 0.0002; t tests) in MDA-MB-231 cells treated as indicated. e, f Volcano plot of differentially expressed genes for high and low GIT1 e; n = 2509 patients) and a correlation analysis between GIT1 and ALDH1A1 mRNA expression (f; n = 2509 patients, Spearman ρ = −0.45, P < 0.0001) in breast cancer samples from the METABRIC database. Breast cancer stemness genes are indicated. g, h Flow cytometric analysis of Aldefluor-assayed MDA-MB-231 cells treated as indicated (g) and the quantitative analysis (h; n = 5, vehicle versus DAPT, P = 0.0014, t test; Ctrl-shRNA versus GIT1-shRNA2, P = 0.031; GIT1-shRNA2 versus GIT1-shRNA2 + DAPT, P = 0.040; Ctrl-shRNA + DAPT versus GIT1-shRNA2 + DAPT, P = 0.44; one-way ANOVA, F_3,16 = 7.216, P = 0.0028 (shaded area); mRFP versus GIT1-mRFP, P = 0.025, t test). SSC, side scatter. i Clonogenic assay of MDA-MB-231 cells treated as indicated (n = 6, vehicle versus DAPT, P < 0.0001, t test; Ctrl-shRNA versus GIT1-shRNA2, P < 0.0001; GIT1-shRNA2 versus GIT1-shRNA2 + DAPT, P < 0.0001; Ctrl-shRNA + DAPT (n = 3) versus GIT1-shRNA2 + DAPT (n = 3), P = 1.00; one-way ANOVA, F_3,14 = 57.84, P < 0.0001 (shaded area); mRFP (n = 5) versus GIT1-mRFP (n = 6), P = 0.010, t test). j, k Quantitative analyses of Cyclin A (j; n = 3, vehicle versus DAPT, P = 0.036, t test; Ctrl-shRNA versus GIT1-shRNA2, P = 0.0032; GIT1-shRNA2 versus GIT1-shRNA2 + DAPT, P < 0.0001; Ctrl-shRNA + DAPT versus GIT1-shRNA2 + DAPT, P = 0.84; one-way ANOVA, F_3,8 = 32.06, P < 0.0001 (shaded area); mRFP versus GIT1-mRFP, P = 0.028, t test) and Cyclin B1 (k; n = 3, vehicle versus DAPT, P = 0.024, t test; Ctrl-shRNA versus GIT1-shRNA2, P = 0.049; GIT1-shRNA2 versus GIT1-shRNA2 + DAPT, P = 0.0022; Ctrl-shRNA + DAPT versus GIT1-shRNA2 + DAPT, P = 0.94; one-way ANOVA, F_3,8 = 15.92, P = 0.0010 (shaded area); mRFP versus GIT1-mRFP, P = 0.043, t test) from western blots of MDA-MB-231 cells treated as indicated. All data are shown as the mean ± s.e.m. n denotes the number of biologically independent replicates, unless stated otherwise. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, not significant by two-sided unpaired t tests or one-way ANOVA with Tukey’s post hoc comparison. Source data are provided as a Source Data file.

**Fig. 3. GIT1 directly binds to the Notch intracellular domain.**
a, b Co-IP of GIT1 with Notch1 from lysates of the indicated mouse organs (a) and human breast cell lines (b). c Co-IP of the Notch1-2 ICDs with GIT1 from lysates of the MDA-MB-231 cells treated as indicated. d, Schematic diagrams of recombinant fusion protein fragments. e, f GIT1 pulled down by GST Notch fragments from MDA-MB-231 cell lysates. g Pulldown of purified GIT1-His by purified GST-N1ICD/N. CB, Coomassie Blue. Images from representative blots; the experiment was repeated n = 3 times with similar results. *, indicates recombinant GST-fused peptides. Source data are provided as a Source Data file.

**Fig. 4. GIT1 regulates the subcellular distribution of the Notch ICD.**
a–c Quantitative analyses of Notch1 (a; n = 4, vehicle versus DAPT, P = 0.032; Ctrl-shRNA versus GIT1-shRNA2, P = 0.26; mRFP versus GIT1-mRFP, P = 0.40; t tests) and the Notch1 ICD (N1ICD) (b; n = 4, vehicle versus DAPT, P < 0.0001; Ctrl-shRNA versus GIT1-shRNA2, P = 0.67; mRFP versus GIT1-mRFP, P = 0.46; t tests) western blots (c) from lysates of MDA-MB-231 cells treated as indicated. d–f Quantitative analyses of cytosolic (d; n = 7, vehicle versus DAPT, P = 0.013; Ctrl-shRNA versus GIT1-shRNA2, P = 0.0045; mRFP versus GIT1-mRFP, P = 0.035; t tests) and nuclear (e; n = 7, vehicle versus DAPT, P = 0.0001; Ctrl-shRNA versus GIT1-shRNA2, P = 0.0042; mRFP versus GIT1-mRFP, P = 0.0025; t tests) subcellular fractionation assays with western blots (f) from MDA-MB-231 cells treated as indicated. g, h Confocal images (g) and quantitative analysis of EGFP intensities in the cytoplasm and nucleus (h; Notch1ΔE−EGFP (n = 150 cells) versus: Notch1ΔE−EGFP + DAPT (n = 79 cells), P < 0.0001; Notch1ΔE-mutNLS1+2−EGFP (n = 180 cells), P < 0.0001; Notch1ΔE-mutNLS1+2−EGFP + DAPT (n = 63 cells), P < 0.0001; one-way ANOVA, F_3,468 = 42.82, P < 0.0001) of MDA-MB-231 cells treated as indicated. Nuclei were detected using DAPI. i, j Luciferase reporter assays of Notch1 ICD (UAS-Luc) in MDA-MB-231 cells with knockdown or overexpression of GIT1 (i; n = 4, vehicle versus DAPT, P = 0.0019; Ctrl-siRNA versus GIT1-siRNA1, P = 0.020; mRFP versus GIT1-mRFP, P = 0.027; t tests) or mutated NLS1 and NLS2 (j; n = 7, Notch1ΔE-GVP versus: Notch1ΔE-GVP + DAPT, P < 0.0001; Notch1ΔE-mutNLS1+2-GVP, P < 0.0001; Notch1ΔE-mutNLS1+2-GVP + DAPT, P < 0.0001; one-way ANOVA, F_3,24 = 1828, P < 0.0001). Scale bar, 10 μm. All data are shown as the mean ± s.e.m. n denotes the number of biologically independent replicates, unless stated otherwise. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, not significant by two-sided unpaired t tests or one-way ANOVA with Tukey’s post hoc comparison. Source data are provided as a Source Data file.

**Fig. 5. GIT1 suppresses the in vivo growth of TNBC cells.**
a–c, k Kaplan–Meier plots showing the percentage of tumour-free mice out of ten mice transplanted with MDA-MB-231 (a, k) or HCC1395 cells (b, c) stably expressing the indicated plasmids. All injection sites were assessed independently, and a tumour was defined as >100 mm³ (MDA-MB-231) and >500 mm³ (HCC1395). Comparisons of Kaplan–Meier curves, MDA-MB-231 implants: Ctrl-shRNA versus GIT1-mRFP, P < 0.0001; Ctrl-shRNA versus GIT1-shRNA2, P = 0.12 (a); HCC1395 implants: LV-mRFP versus LV-GIT1-mRFP, P = 0.012 (b); LV-Ctrl-shRNA versus LV-GIT1-shRNA3, P = 0.025 (c); MDA-MB-231 implants: Ctrl-shRNA versus DNMM1, P = 0.0038; Ctrl-shRNA versus GIT1-shRNA2 + DNMM1, P = 0.030; DNMM1 versus GIT1-shRNA2 + DNMM1, P = 0.58 (k); log-rank tests. d Scatter plot of tumour volume over time in the mice transplanted with HCC1395 cells expressing LV-GIT1-shRNA3 and LV-GIT1-mRFP. Time is days from the tumour volume >100 mm³. Solid line, exponential regression. Shaded area, 95% confidence bands. Comparison of growth rate constants k (Methods) for LV-GIT1-shRNA3 (n = 290 tumour measurements, k = 0.057) and LV-GIT1-mRFP (n = 440 tumour measurements, k = 0.023), P < 0.0001; F-test. e, f Tumour doubling time (e) and volume change (f) for LV-GIT1-shRNA3 versus LV-Ctrl-shRNA and LV-GIT1-mRFP versus LV-mRFP. Solid line, exponential regression. Shaded area, 95% confidence interval. g, j Flow cytometric analysis of ALDH1A1+ cells (g; n = 4, Ctrl-shRNA versus GIT1-shRNA2, P = 0.0090, t test; Ctrl-shRNA versus GIT1-shRNA2, P = 0.019; Ctrl-shRNA versus DNMM1, P = 0.018; GIT1-shRNA2 + DNMM1 versus DNMM1, P = 1.00; one-way ANOVA, F_2,9 = 7.877, P = 0.011 (shaded area)) and Hey1+ cells (j, n = 4, Ctrl-shRNA versus GIT1-shRNA2, P = 0.012, t test; Ctrl-shRNA versus GIT1-shRNA2, P = 0.0063; Ctrl-shRNA versus DNMM1, P = 0.0059; GIT1-shRNA2 + DNMM1 versus DNMM1, P = 1.00; one-way ANOVA, F_2,9 = 11.61, P = 0.0032 (shaded area)) in MDA-MB-231 xenograft tumours. h, i Confocal images of the Notch1 ICD (N1ICD)-immunolabelled MDA-MB-231 xenograft tumours (h) and quantitative analysis (i; n = 8 tumours, P = 0.0004, t test). Nuclei were detected using DAPI. Scale bar, 5 μm. All data are shown as the mean ± s.e.m. n denotes the number of biologically independent replicates, unless stated otherwise. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, not significant by two-sided unpaired t test, F-test, or one-way ANOVA with Tukey’s post hoc comparison. Source data are provided as a Source Data file.

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References

1. Ferlay J, et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer. 2010;127:2893–2917. - PubMed
1. Andersson ER, Lendahl U. Therapeutic modulation of Notch signalling—are we there yet? Nat. Rev. Drug Discov. 2014;13:357–378. - PubMed
1. Lafkas D, et al. Therapeutic antibodies reveal Notch control of transdifferentiation in the adult lung. Nature. 2015;528:127–131. - PubMed
1. Wu Y, et al. Therapeutic antibody targeting of individual Notch receptors. Nature. 2010;464:1052–1057. - PubMed
1. Dickson BC, et al. High-level JAG1 mRNA and protein predict poor outcome in breast cancer. Mod. Pathol. 2007;20:685–693. - PubMed

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[1] Ferlay J, et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer. 2010;127:2893–2917. - PubMed

[2] Ferlay J, et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer. 2010;127:2893–2917. - PubMed

[3] Andersson ER, Lendahl U. Therapeutic modulation of Notch signalling—are we there yet? Nat. Rev. Drug Discov. 2014;13:357–378. - PubMed

[4] Andersson ER, Lendahl U. Therapeutic modulation of Notch signalling—are we there yet? Nat. Rev. Drug Discov. 2014;13:357–378. - PubMed

[5] Lafkas D, et al. Therapeutic antibodies reveal Notch control of transdifferentiation in the adult lung. Nature. 2015;528:127–131. - PubMed

[6] Lafkas D, et al. Therapeutic antibodies reveal Notch control of transdifferentiation in the adult lung. Nature. 2015;528:127–131. - PubMed

[7] Wu Y, et al. Therapeutic antibody targeting of individual Notch receptors. Nature. 2010;464:1052–1057. - PubMed

[8] Wu Y, et al. Therapeutic antibody targeting of individual Notch receptors. Nature. 2010;464:1052–1057. - PubMed

[9] Dickson BC, et al. High-level JAG1 mRNA and protein predict poor outcome in breast cancer. Mod. Pathol. 2007;20:685–693. - PubMed

[10] Dickson BC, et al. High-level JAG1 mRNA and protein predict poor outcome in breast cancer. Mod. Pathol. 2007;20:685–693. - PubMed

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GIT1 protects against breast cancer growth through negative regulation of Notch

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GIT1 protects against breast cancer growth through negative regulation of Notch

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