Immune checkpoint inhibitors (ICI) have the potential to induce durable therapeutic responses, yet response rates in breast cancer are modest and limited to particular subtypes. To expand the applicability of ICI, we examined the role of an essential autophagy gene, FIP200, which has been shown to be important for tumor progression in mammary tumors. Specific disruption of the autophagy function of FIP200 or complete ablation of FIP200 in genetic mouse models revealed that FIP200 autophagy function was required for progression of PyMT-driven mammary tumors. However, a noncanonical autophagy function of FIP200 was responsible for limiting T-cell recruitment and activation of the TBK1-IFN signaling axis. FIP200 also interacted with the TBK1 adaptor protein, AZI2, which was crucial for activation of TBK1 following FIP200 ablation. Accordingly, disrupting the noncanonical autophagy function of FIP200 in combination with ICI therapy led to superior, durable responses in immune-competent models of breast cancer. Collectively, these insights could guide future development of therapeutic agents against FIP200 for combinatorial ICI therapies in nonresponsive breast cancers.

Significance:

These findings show that deletion of FIP200 enhances immune checkpoint inhibitor efficacy in nonresponsive breast cancer.

Clinical application of immune checkpoint inhibitors (ICI) such as anti–PD-1 and anti–CTLA-4 therapy has expanded beyond tumors that have a high burden of nonsynonymous mutations (e.g., melanoma and lung cancer; refs. 1, 2). Remarkably, robust durable responses and prolonged overall survival can be achieved with these treatment modalities (3, 4). However, not all tumors respond, and in the case of breast cancer, current clinical trials are largely limited to triple-negative breast cancers (TNBC) and Her2+ breast cancers. Several biomarkers have been proposed to predict response to ICIs, and these include CD8+ T-cell density, PD-L1 expression, and the mutational load of tumors (5). TNBCs frequently display higher numbers of tumor-infiltrating lymphocytes (TIL) and are generally more mutagenic, hence the justification for its inclusion in ongoing clinical trials. Even then, objective response rates (ORR) that are reported for patients with advanced TNBC are less than 20% (6, 7), and in an unselected cohort of patients with breast cancer, the ORR has been reported to be much lower, at 4.8% (8). Accordingly, it remains a formidable challenge to expand the prominent benefits of immunotherapy to a large proportion of patients with breast cancer.

Macroautophagy (hereafter autophagy) is a process that enables cells to recycle cytoplasmic entities and organelles through the sequestration of cargo within double-membraned structures termed autophagosomes. This self-recycling mechanism allows for sustenance under nutrient-limiting conditions and the elimination of defective organelles or proteins (9). FIP200 (RB1CC1) is an essential autophagy gene that is part of the autophagy initiation complex, which includes ULK1, ATG13, and ATG101 (10). Autophagy has been shown to be important for the secretion of damage-associated molecular patterns in chemotherapy-treated colon carcinoma and melanoma cells (11, 12), which leads to efficient recruitment and activation of T cells to elicit antitumor immune responses. More recently, however, it has been shown in syngeneic transplant models of breast cancer and melanoma that inhibition of autophagy either through genetic depletion (Atg7 or Atg12) or pharmacologically (antimalarial treatment) in tumor cells does not impair antitumor immune responses (13). Nonetheless, a number of studies have shown that depletion of autophagy-related genes in the stromal or immune compartment can heighten antimicrobial or antitumor responses, implicating a role for autophagy in these processes (14–21). There are also reports of nonautophagic functions of autophagy-related genes in the regulation of inflammation (22–24), namely LC3-associated phagocytosis (16). Hence, the relative contributions of both the canonical-autophagy and nonautophagic functions of autophagy genes with regards to inflammation are of great interest. As such, there is a need to specifically dissect and distinguish the contribution of autophagy and nonautophagy functions of autophagy genes in the regulation of antitumor immunity, both in tumor and stromal cells.

We have shown previously that FIP200 ablation in MMTV-PyMT–driven mammary tumors can inhibit tumor growth and progression. In addition, FIP200 deletion also increased CD8+ TILs and the expression of IFN-responsive genes in these tumors (25). This suggests that FIP200 negatively regulates antitumor immune responses, but because FIP200 also has non–autophagy-related functions (26), it is possible that this negative regulation is due to the nonautophagy functions of FIP200 and may explain the different outcomes observed with antitumor immunity when other autophagy-related genes are depleted (11–13). Accordingly, we have recently developed a mouse model with a FIP200 knock-in allele that disrupts the association between FIP200 and ATG13, leading to specific impairment of autophagy (27). With this mouse model, we will be able to dissect the role of autophagy or nonautophagy functions of FIP200 in the regulation of tumor growth, progression, and antitumor immune responses.

In this study, we demonstrated that disruption of FIP200′s autophagy function is sufficient to inhibit the growth and metastatic potential of PyMT-driven mammary tumors, to a similar extent as depleting FIP200 completely. However, the nonautophagy function of FIP200 was responsible for increased T-cell recruitment and activation of the TBK1-IFN signaling axis. FIP200 was also found to interact with the TBK1 adaptor protein, AZI2, which was crucial for TBK1 activation upon FIP200 ablation. Because tumors with increased CD8+ T-cell density and IFN signaling have been associated with better responses to ICI therapy in various cancers (5, 28, 29), we further demonstrated the effects of ablating FIP200 in combination with anti–PD-1 and/or anti–CTLA-4 therapy to achieve prolonged durable responses in mice bearing PyMT tumors. Ultimately, we have unraveled a superior combinatorial therapeutic strategy for ICIs, and this could potentially expand the prominent benefits of ICIs to nonresponsive breast cancers.

Reagents and antibodies

ISG56-Luciferase plasmid was a gift from Dr. Saumendra Sarkar from the University of Pittsburgh, Pittsburgh, PA (30). pcDNA3.1-hTANK-FLAG construct was obtained from Dr. Ulrich Siebenlist (31), and pCMV3Tag8-HA-hSINTBAD-FLAG and pCMV3Tag8-HA-mNAP1-FLAG plasmids were a kind gift from Dr. Shitao Li (32). FIP200 plasmids (Myc-FIP200, Myc-FIP200-N859, Myc-FIP200-CC, and Myc-FIP200-CT) were generated as described previously (26), and Myc-FIP200-CT delta 20 amino acid variants were generated using Q5 site–directed mutagenesis kit (NEB). For gene silencing experiments, siRNAs used were AZI2 (D-014092-01-0002, D-014092-02-0002), TANK (D-005255-20-0002, D-005255-02-0002), and SINTBAD (D-020406-01-0002, D-020406-02-0002; Dharmacon). Antibodies used for immunoblotting include β-actin (Sigma; A5441), vinculin (Sigma; V4505), GAPDH (CST; 2118), FIP200 (CST; 12436), p62 (CST; 5114), LC3 (CST; 2775), phospho-TBK1 (CST; 5483), TBK1 (CST; 3504), IRF1 (CST; 8478), PARP (CST; 9532), Azi2 (Proteintech; 15042-1-AP), Tank (Proteintech; 27065-1-AP), and SINTBAD (CST; 8605). For IHC, antibodies used p62 (Enzo life Sciences; BML-PW9860), cleaved caspase-3 (CST; 9661), Ki67 (Spring Bioscience; m3062), and CD8 (eBioscience; 14-0808-80). For flow cytometry, antibodies used were CD45-Pacific blue (BioLegend; 103125), CD3-V500 (BioLegend; 100233), CD8-FITC (BioLegend; 100706), CD103-APC (BioLegend; 121413), and CD49b-APC (BioLegend; 108907).

Cell culture and treatment

Primary tumor cells and their derivatives were cultured in DMEM/F12 supplemented with 10% FBS, 10 ng/mL EGF, 20 mg/mL insulin, and 50 units/mL penicillin–streptomycin. The generation of FIP200f/f;PyMT;CreER cells and FIP200f/KI;PyMT;CreER cells has been described previously (27, 33), and deletion of Fip200 was induced by culturing with 100 nmol/L 4-hydroxytamoxifen (4-OHT). Transfection experiments were carried out using Lipofectamine 3000 Reagent (Invitrogen) for cell lines and Amaxa Basic Nucleofector Kit for electroporation for primary cells (Lonza). Production of lentivirus and transduction of cells were carried out as described previously (25). Each sgRNA sequence is as follows: sgRb1cc1 is 5′-caccg CTCCATTGACCACCAGAACC-3′. sgTbk1 is 5′-caccgCATAAGCTTCCTTCGCCCAG-3′. sgAzi2 is 5′-caccgATCTTCTACTAGCGTGTCCA-3′. MDA-MB-231 cells were obtained from the ATCC. Cell lines were maintained for less than 20 passages after collection or thawing. Mycoplasma testing was performed on a monthly basis.

Flow cytometry

Single-cell suspensions were prepared from primary tumors as described previously (34). Cells were blocked with TruStain fcX (anti-mouse CD16/32 antibody; BioLegend; 101319) before incubation with antibodies described above for 20 minutes at 4°C. Cells were then rinsed before sorting or analysis by FACSAria or FACSCanto instruments (BD Biosciences). Flow cytometry data were analyzed using FlowJo software.

Tumor mice and transplants

Ctrl-MT (Fip200f/f, MMTV-PyMT) and cKO-MT (Fip200f/f, MMTV-PyMT, MMTV-Cre) mice have been described previously (25). Brca1f/f Trp53f/f K14-Cre mice were a kind gift from Dr. Jos Jonkers (Netherlands Cancer Institute) and have been crossed with FIP200f/ki mice as described previously (35). To eliminate potential influence of mouse genetic background on the phenotypes, all strains were backcrossed to FVB/N genetic background (syngeneic to the MMTV-PyMT mice or Brca1f/f Trp53f/f K14-Cre mice used throughout the study) for at least 7 times (99.21%). Mice were housed and handled according to local, state, and federal regulations. All experimental procedures were carried out according to protocols approved by the Institutional Animal Care and Use Committee at University of Cincinnati (Cincinnati, OH). For transplantation experiments, 2 × 106 cells were prepared in PBS:Matrigel at a 1:1 ratio and were injected into the fourth mammary gland fat pad. Mice with transplanted tumors were randomized into respective treatment groups when the diameter of tumors reached to approximately 5 mm. Antibodies for treatment were obtained from BioXCell; anti-mouse PD-1 (BP0273), anti-mouse CTLA-4 (BE0131), rat IgG2a isotype control (BP0089), and Syrian hamster IgG control (BE0087). Amlexanox was obtained from MedChemExpress (HY-B0713) and administered at 25 mg/kg intraperitoneally daily for 7 days, upon the start of tamoxifen induction in vivo.

Histology and IHC

Formalin-fixed paraffin-embedded tumors were sectioned (5 μm) and stained for respective antigens as described previously (25). For antigen retrieval, slides were heated in citrate buffer in a pressure cooker. Quantification of positive cells was performed using Image J software.

Immunoblotting

Lysates were prepared from cells using modified RIPA buffer as described previously (25) with the addition of protease and phosphatase inhibitors according to manufacturer's instructions (Thermo Fisher Scientific). Protein concentrations were then quantified by bicinchoninic acid method, subjected to SDS–PAGE, and analyzed by immunoblotting as described previously (25). For phosphatase treatment, cell lysates without phosphatase inhibitors were incubated with FastAP at 37°C for 1 hour according to manufacturer's protocol (Thermo Fisher Scientific).

Quantitative PCR

Total RNA was isolated from cells using GeneGet RNA Purification Kit (Thermo Fisher Scientific) according to the manufacturer's instructions. Equal amounts of RNA were then reverse transcribed using iScript cDNA Synthesis Kit (Bio-Rad). qRT-PCR analysis was performed using iTaq Universal SYBR Green Supermix (Bio-rad) with primers used as follows: Actin-Forward; 5′-GGCTGTATTCCCCTCCATCG-3′, Actin-Reverse; 5′-CCAGTTGGTAACAATGCCATGT-3′, Ccl5-Forward; 5′-GCTGCTTTGCCTACCTCTCC-3′, Ccl5-Reverse; 5′-TCGAGTGACAAACACGACTGC-3′, Cxcl10-Forward; 5′-CCAAGTGCTGCCGTCATTTTC-3′, Cxcl10-Reverse; 5′-GGCTCGCAGGGATGATTTCAA-3′, Cxcl9-Forward; 5′-GGAGTTCGAGGAACCCTAGTG-3′, Cxcl9-Reverse; 5′-GGGATTTGTAGTGGATCGTGC.

Statistical analysis

Data were plotted as means, and error bars represent SD. One-way comparisons were tested using unpaired t test with Welch's correction for Figs. 2J, 3A–D, 3K–L, and 4H. For multiple comparisons, one-way ANOVA with Tukey post-hoc test was performed when datasets were all determined to be parametric by Shapiro–Wilk tests, in Figs. 1G, 2B, E, G, and 4C–F. For multiple comparisons when datasets were nonparametric as determined by Shapiro–Wilk tests, the Kruskal–Wallis test followed by Dunn's post-hoc test were used, in Figs. 1D, E, 2C, F, H, and 3H. For survival curves, log-rank tests were utilized. The threshold for statistical significance was P < 0.05. For analyses involving quantification of fields of view from histological samples, triplicate fields of views were averaged, and this was plotted to represent each single biological replicate.

Figure 1.

Specific targeting of FIP200′s autophagy function inhibits breast cancer development and metastasis. A, Tumor-free survival curves of Ctrl-MT (n = 64 mice), cKO-MT (n = 22 mice), and cKI-MT (n = 26 mice) cohorts. B, Tumor growth curves of Ctrl-MT (n = 30), cKO-MT (n = 12), and cKI-MT (n = 14) mice. C, Representative images for lung sections from Ctrl-MT, cKO-MT, and cKI-MT mice. Scale bar, 500 μm. D, Bar chart showing quantification for number of metastatic nodules per field of view (Ctrl-MT, n = 22 mice; cKO-MT, n = 8 mice; cKI-MT, n = 8) when compared at the same final timepoint. Kruskal–Wallis test followed by Dunn's post-hoc test was used. E, Bar chart showing quantification for number of metastatic nodules per field of view (Ctrl-MT, n = 11 mice; cKO-MT, n = 9 mice; cKI-MT, n = 9 mice) when lung sections from mice bearing similar tumor loads were compared. Kruskal–Wallis test followed by Dunn's post-hoc test was used. F, Immunoblots showing levels of FIP200, LC3B, and β-actin in Ctrl-MT, cKO-MT, and cKI-MT tumors (n = 4 for each sample). G, Bar charts showing quantification of relative LC3-II/LC3-I expression levels. Statistical significance was determined by ANOVA with Tukey post-hoc test. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001.

Figure 1.

Specific targeting of FIP200′s autophagy function inhibits breast cancer development and metastasis. A, Tumor-free survival curves of Ctrl-MT (n = 64 mice), cKO-MT (n = 22 mice), and cKI-MT (n = 26 mice) cohorts. B, Tumor growth curves of Ctrl-MT (n = 30), cKO-MT (n = 12), and cKI-MT (n = 14) mice. C, Representative images for lung sections from Ctrl-MT, cKO-MT, and cKI-MT mice. Scale bar, 500 μm. D, Bar chart showing quantification for number of metastatic nodules per field of view (Ctrl-MT, n = 22 mice; cKO-MT, n = 8 mice; cKI-MT, n = 8) when compared at the same final timepoint. Kruskal–Wallis test followed by Dunn's post-hoc test was used. E, Bar chart showing quantification for number of metastatic nodules per field of view (Ctrl-MT, n = 11 mice; cKO-MT, n = 9 mice; cKI-MT, n = 9 mice) when lung sections from mice bearing similar tumor loads were compared. Kruskal–Wallis test followed by Dunn's post-hoc test was used. F, Immunoblots showing levels of FIP200, LC3B, and β-actin in Ctrl-MT, cKO-MT, and cKI-MT tumors (n = 4 for each sample). G, Bar charts showing quantification of relative LC3-II/LC3-I expression levels. Statistical significance was determined by ANOVA with Tukey post-hoc test. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001.

Close modal
Figure 2.

Noncanonical autophagy function of FIP200 is responsible for suppressing CD8+ T-cell infiltration in breast cancer. A, Representative images of Ctrl-MT, cKO-MT, and cKI-MT tumors immunostained for cleaved caspase-3. Scale bar, 50 μm. B and C, Bar charts showing quantification of number of cleaved caspase-3–positive cells per field of view (Ctrl-MT, n = 9; cKO-MT, n = 4; cKI-MT, n = 4 mice; B) and number of Ki67-positive cells per field of view (Ctrl-MT; n = 8, cKO-MT; n = 5, cKI-MT; n = 5 mice; C). ANOVA with Tukey post-hoc test was used for B, whereas Kruskal–Wallis test followed by Dunn's post-hoc test was used for C. D, Representative images of Ctrl-MT, cKO-MT, and cKI-MT tumors immunostained for CD8. Scale bar, 100 μm. E, Bar chart showing quantification of CD8-positive cells per field of view (Ctrl-MT, n = 11; cKO-MT, n = 5; cKI-MT, n = 5 mice). ANOVA with Tukey post-hoc test was used. F–H, Flow cytometry analysis of CD8+ T cells (F), CD8+ CD103+ T cells (G), and CD49B+ NK cells (H) as a percentage of CD45+ cells in Ctrl-MT (n = 5), cKO-MT (n = 5), and cKI-MT (n = 5) tumors. Kruskal–Wallis test followed by Dunn's post-hoc test was used for F and H, whereas ANOVA with Tukey post-hoc test was used for G. I, Representative images of iKO (−Tam), iKO (+Tam), iKI (−Tam), and iKI (+Tam) tumors immunostained for CD8. Scale bar, 50 μm. J, Bar chart showing quantification of CD8-positive cells per field of view in iKO and iKI tumors (iKO-Tam, n = 6; iKO+Tam, n = 6; iKI-Tam, n = 5; iKI+Tam, n = 5 mice). Statistical significance was determined by unpaired t test with Welch's correction. *, P ≤ 0.05; **, P ≤ 0.01.

Figure 2.

Noncanonical autophagy function of FIP200 is responsible for suppressing CD8+ T-cell infiltration in breast cancer. A, Representative images of Ctrl-MT, cKO-MT, and cKI-MT tumors immunostained for cleaved caspase-3. Scale bar, 50 μm. B and C, Bar charts showing quantification of number of cleaved caspase-3–positive cells per field of view (Ctrl-MT, n = 9; cKO-MT, n = 4; cKI-MT, n = 4 mice; B) and number of Ki67-positive cells per field of view (Ctrl-MT; n = 8, cKO-MT; n = 5, cKI-MT; n = 5 mice; C). ANOVA with Tukey post-hoc test was used for B, whereas Kruskal–Wallis test followed by Dunn's post-hoc test was used for C. D, Representative images of Ctrl-MT, cKO-MT, and cKI-MT tumors immunostained for CD8. Scale bar, 100 μm. E, Bar chart showing quantification of CD8-positive cells per field of view (Ctrl-MT, n = 11; cKO-MT, n = 5; cKI-MT, n = 5 mice). ANOVA with Tukey post-hoc test was used. F–H, Flow cytometry analysis of CD8+ T cells (F), CD8+ CD103+ T cells (G), and CD49B+ NK cells (H) as a percentage of CD45+ cells in Ctrl-MT (n = 5), cKO-MT (n = 5), and cKI-MT (n = 5) tumors. Kruskal–Wallis test followed by Dunn's post-hoc test was used for F and H, whereas ANOVA with Tukey post-hoc test was used for G. I, Representative images of iKO (−Tam), iKO (+Tam), iKI (−Tam), and iKI (+Tam) tumors immunostained for CD8. Scale bar, 50 μm. J, Bar chart showing quantification of CD8-positive cells per field of view in iKO and iKI tumors (iKO-Tam, n = 6; iKO+Tam, n = 6; iKI-Tam, n = 5; iKI+Tam, n = 5 mice). Statistical significance was determined by unpaired t test with Welch's correction. *, P ≤ 0.05; **, P ≤ 0.01.

Close modal
Figure 3.

Loss of FIP200's nonautophagy function activates the TBK1-IRF-IFN signaling axis for proinflammatory chemokine expression. A–C, Bar charts showing the relative transcript levels of Ccl5 (A), Cxcl9 (B), and Cxcl10 (C) in iKO (−4OHT), iKO (+4OHT), iKI (-4OHT), and iKI (+4OHT) tumor cells, quantified via qRT-PCR (n = 4 for each sample). Statistical significance was determined by unpaired t test with Welch's correction. D, Bar charts showing the luciferase/Renilla luminescence ratio for iKO and iKI cells transfected with the ISG56-reporter plasmid (n = 3 for each sample). Statistical significance was determined by unpaired t test with Welch's correction. E, Immunoblots showing the levels of FIP200, p-TBK1, TBK1, and actin in iKO (-4OHT), iKO (+4OHT), iKI (-4OHT), and iKI (+4OHT) tumor cells. F, Immunoblots showing the levels of IRF1, PARP, and GAPDH in nuclear and cytoplasmic extracts of iKO and iKI cells. G, Representative images of Ctrl-BPK, cKO-BPK, and cKI-BPK tumors immunostained for CD8. Scale bar, 200 μm. H, Bar chart showing quantification of CD8-positive cells per field of view (Ctrl-BPK, n = 19; cKO-BPK, n = 10; cKI-BPK, n = 10 mice). Kruskal–Wallis test followed by Dunn's post-hoc test was used. I, Immunoblots showing the levels of FIP200, p-TBK1, TBK1, AZI2, and vinculin in BRCA1-Ctrl and BRCA1-FIP200KO tumor cells. J, Immunoblots showing the levels of FIP200, IRF1, PARP, and GAPDH in nuclear and cytoplasmic extracts of BRCA1-Ctrl and BRCA1-FIP200KO tumor cells. K, Bar charts showing the luciferase/Renilla luminescence ratio for BRCA1-Ctrl and BRCA1-FIP200KO cells transfected with the ISG56-reporter plasmid (n = 3 for each sample). Statistical significance was determined by unpaired t test with Welch's correction. L, Bar charts showing the relative transcript levels of Ccl5, Cxcl9, and Cxcl10 in BRCA1-Ctrl and BRCA1-FIP200KO tumor cells, quantified via qRT-PCR (n = 4 for each sample). Statistical significance was determined by unpaired t test with Welch's correction. *, P ≤ 0.05; **, P ≤ 0.01; ns, nonsignificant.

Figure 3.

Loss of FIP200's nonautophagy function activates the TBK1-IRF-IFN signaling axis for proinflammatory chemokine expression. A–C, Bar charts showing the relative transcript levels of Ccl5 (A), Cxcl9 (B), and Cxcl10 (C) in iKO (−4OHT), iKO (+4OHT), iKI (-4OHT), and iKI (+4OHT) tumor cells, quantified via qRT-PCR (n = 4 for each sample). Statistical significance was determined by unpaired t test with Welch's correction. D, Bar charts showing the luciferase/Renilla luminescence ratio for iKO and iKI cells transfected with the ISG56-reporter plasmid (n = 3 for each sample). Statistical significance was determined by unpaired t test with Welch's correction. E, Immunoblots showing the levels of FIP200, p-TBK1, TBK1, and actin in iKO (-4OHT), iKO (+4OHT), iKI (-4OHT), and iKI (+4OHT) tumor cells. F, Immunoblots showing the levels of IRF1, PARP, and GAPDH in nuclear and cytoplasmic extracts of iKO and iKI cells. G, Representative images of Ctrl-BPK, cKO-BPK, and cKI-BPK tumors immunostained for CD8. Scale bar, 200 μm. H, Bar chart showing quantification of CD8-positive cells per field of view (Ctrl-BPK, n = 19; cKO-BPK, n = 10; cKI-BPK, n = 10 mice). Kruskal–Wallis test followed by Dunn's post-hoc test was used. I, Immunoblots showing the levels of FIP200, p-TBK1, TBK1, AZI2, and vinculin in BRCA1-Ctrl and BRCA1-FIP200KO tumor cells. J, Immunoblots showing the levels of FIP200, IRF1, PARP, and GAPDH in nuclear and cytoplasmic extracts of BRCA1-Ctrl and BRCA1-FIP200KO tumor cells. K, Bar charts showing the luciferase/Renilla luminescence ratio for BRCA1-Ctrl and BRCA1-FIP200KO cells transfected with the ISG56-reporter plasmid (n = 3 for each sample). Statistical significance was determined by unpaired t test with Welch's correction. L, Bar charts showing the relative transcript levels of Ccl5, Cxcl9, and Cxcl10 in BRCA1-Ctrl and BRCA1-FIP200KO tumor cells, quantified via qRT-PCR (n = 4 for each sample). Statistical significance was determined by unpaired t test with Welch's correction. *, P ≤ 0.05; **, P ≤ 0.01; ns, nonsignificant.

Close modal

Specific targeting of FIP200's autophagy function inhibits breast cancer development and metastasis

To investigate the specific role of FIP200′s autophagy function in breast cancer in vivo, we first created mammary epithelial–specific FIP200 knock-in–mutant mice in the PyMT breast cancer model (FIP200f/KI;MMTV-Cre;MMTV-PyMT, designated as cKI-MT mice) by crossing our recently developed FIP200+/KI and FIP200f/+;MMTV-Cre;MMTV-PyMT mice (25, 27). Mammary epithelial cells in cKI-MT mice express only the FIP200-4A mutant from the KI allele (floxed allele deleted by MMTV-Cre), which specifically abrogates the autophagy function of FIP200 in these cells by disrupting its interaction with Atg13 (27). Mammary tumor development in these mice was monitored by physical palpation as described previously (25, 36). cKI-MT mice showed significantly suppressed tumor development compared with littermate controls (Ctrl-MT mice) and to a similar extent as that of cKO-MT mice described previously (Fig. 1A; ref. 25), suggesting that specific blockade of FIP200′s autophagy function is sufficient to inhibit mammary tumor formation in vivo. Blocking FIP200′s autophagy function specifically also inhibited mammary tumor growth (Fig. 1B), as well as metastasis to the lungs (Fig. 1C and D) in cKI-MT mice to a similar extent as cKO-MT mice, when mice were analyzed 3 weeks after tumor appearance. We also examined the number of metastatic nodules for the different mice with comparable primary tumor size (tumor burden of ∼1,500 mm3) and found significantly reduced number of nodules in both cKI-MT and cKO-MT mice (Fig. 1E), indicating that decreased metastasis upon ablating autophagy function of FIP200 was not simply due to the reduced tumor growth in these mice versus Ctrl-MT mice.

We next prepared lysates from multiple primary tumors in different mice and analyzed by Western blotting for FIP200 expression and autophagy in tumor cells. Figure 1F shows significantly reduced expression of FIP200 in the tumors from cKO-MT mice, as expected, and a slightly decreased level of FIP200 in tumors from cKI-MT mice (i.e., FIP200-4A–mutant protein encoded by the KI allele), possibly caused by the loss of the floxed allele of FIP200. Measurements of LC3-II/LC3-I ratios provided validation for autophagy blockage upon deletion of FIP200 in cKO-MT or loss of its autophagy function in cKI-MT tumors (Fig. 1F and G), as expected. Together, these results suggest that the decreased breast cancer development and metastasis upon FIP200 deletion is due to specific loss of its autophagy function rather than its nonautophagy functions.

Nonautophagy function of FIP200 is responsible for suppressing CD8+ T-cell infiltration in breast cancer

Our previous studies suggested that FIP200 deletion inhibited mammary tumorigenesis through both tumor cell–intrinsic mechanisms and increased infiltration of CD8+ effector T cells to enhance immune surveillance in cKO-MT mice (25). To evaluate whether either of these activities are mediated by autophagy and nonautophagy functions of FIP200, we examined tumor sections from cKI-MT mice in comparison with those from Ctrl-MT and cKO-MT mice. A trend of increased apoptosis was observed for both cKI-MT and cKO-MT tumors (Fig. 2A and B), but proliferation rates were comparable with Ctrl-MT as measured by Ki67 staining (Fig. 2C). In contrast to these similar tumor cell–intrinsic activities, we found that loss of the autophagy function of FIP200 in cKI-MT tumors was not sufficient to increase CD8+ TILs, but only total ablation of FIP200 in cKO-MT mice led to increased CD8+ TILs (Fig. 2D and E). Furthermore, analysis of immune cell populations by flow cytometry verified that cKO-MT, but not cKI-MT tumors, had increased levels of CD8+ T cells (Fig. 2F) and also revealed more specifically increased CD8+ CD103+ tissue resident memory T cells (Fig. 2G), relative to Ctrl-MT tumors. The increase in cytotoxic T cells was also accompanied by higher levels of CD49b+ natural killer (NK) cells in cKO-MT but not cKI-MT tumors (Fig. 2H). Overall, these results indicated that elevated antitumor immune response was a consequence of tumors that lack FIP200.

To further elucidate the mechanisms of increased CD8+ T-cell infiltration upon the loss of FIP200's nonautophagy functions, we prepared mammary tumor cells from FIP200f/KI;MMTV-PyMT mice (lacking MMTV-Cre) and infected them with MSCV.CreERT2, to allow for inducible deletion of FIP200, as described previously (33). The resulting FIP200f/KI;MMTV-PyMT:CreER (designated as iKI) and the previously described FIP200f/f;MMTV-PyMT:CreER (designated as iKO; ref. 33) tumor cells were transplanted into syngeneic FVB recipient mice, and the recipient mice were then treated with or without tamoxifen (TAM) to induce Cre expression and consequent deletion of the floxed FIP200 allele in these cells. FIP200 deletion in the established tumors (i.e., mice with iKO cell transplant + TAM) led to increased CD8+ T-cell infiltration (compared with the same mice - TAM), whereas blocking FIP200 autophagy function (i.e., mice with iKI cell transplant + TAM) did not show significant increase in CD8+ T-cell infiltration (Fig. 2I and J). These results are consistent with our observations in cKO-MT and cKI-MT mice, and together, they suggest that FIP200′s noncanonical autophagy function (lost in FIP200 deletion but not FIP200-4A mutation), but not its canonical autophagy function (lost in both), is required for suppressing CD8+ T-cell infiltration in breast cancer.

Loss of the nonautophagy function of FIP200 activates the TBK1-IRF-IFN signaling axis for proinflammatory chemokine expression

Our previous studies showed that ablation of FIP200 in cKO-MT tumors can lead to increased expression of IFN-responsive genes, including several chemokines such as Ccl5, Cxcl9, and Cxcl10 (25). Such changes are likely responsible for the increased CD8+ TILs in FIP200-null tumor cells in both cKO-MT mice (Fig. 2D and E; ref. 25) and recipient mice with iKO transplant following Tam (see Fig. 2I and J), as these chemokines have been associated with increased recruitment of TILs (37). To evaluate this notion further, we examined their expression in iKO and iKI tumor cells with or without 4OHT, the active metabolite of tamoxifen, for inducing deletion in these cells in vitro. Similar to tumor cells in cKO-MT mice, we found that FIP200 deletion increased expression of Ccl5, Cxcl9, and Cxcl10 in iKO cells (Fig. 3AC). In contrast, FIP200-4A mutation (i.e., iKI cells + 4OHT) led to a much smaller increase for Ccl5, and Cxcl10, and no difference for Cxcl9, despite similar levels of autophagy inhibition in both iKI and iKO cells after 4OHT treatment (Supplementary Fig. S1). Moreover, 4OHT treatment of iKO cells to delete FIP200, but not iKI cells for FIP200-4A mutation, increased IRF transcriptional activity relative to their respective controls (i.e., vehicle-treated cells) in ISG56 promoter-reporter assays (Fig. 3D). These results suggest that upregulation of these chemokines after the loss of noncanonical autophagy functions of FIP200 (but not loss of its canonical autophagy function) is responsible for the increased CD8+ TILs.

We next examined the levels of phosphorylated TBK1 in iKO and iKI cells with or without 4OHT treatment, as FIP200 has been implicated in the regulation of TBK1, a key node in IFN signaling (38). TBK1 phosphorylation increased specifically in iKO cells, but not iKI cells, following 4OHT treatment relative to the vehicle-treated, control cells (Fig. 3E). Moreover, we observed increased nuclear localization of interferon regulatory factor 1 (IRF1) in iKO cells, but not iKI cells, after 4OHT treatment (Fig. 3F). IHC analysis of PyMT tumors also revealed increased levels of nuclear IRF1 in cKO-MT but not cKI-MT tumors relative to Ctrl-MT tumors (Supplementary Fig. S2A and S2B). Importantly, the regulation of CD8+ TILs by FIP200 can be recapitulated in a mouse model of BRCA1-null basal-like breast cancer; Brca1F/F;Trp53F/F;K14-Cre driven tumors (Ctrl-BPK tumors; Fig. 3G). We observed that conditional deletion of FIP200 (cKO) increased CD8+ TIL infiltration in cKO-BPK mammary tumors, whereas mammary tumors expressing an autophagy-deficient mutant knock-in allele (cKI-BPK) did not exhibit any significant increase in CD8+ TILs (Fig. 3G and H). Ablation of FIP200 by CRISPR-CAS9–mediated targeting in BPK tumor cells also led to increased phosphorylation of TBK1 (Fig. 3I) and increased nuclear localization of IRF1 (Fig. 3J) in BRCA1-FIP200KO (transduced with sgFIP200) cells relative to BRCA1-Ctrl cells (transduced with sgEmpty). Concomitantly, increased ISG56-luciferase reporter activity (Fig. 3K) and increased expression of Ccl5 and Cxcl10 were also observed in these cells (Fig. 3L). This indicates that the negative regulation of TBK1-IRF signaling and tumor immune responses by FIP200 occurs in more than one breast cancer subtype.

To further determine whether the increased IRF activity and expression of proinflammatory cytokines upon ablation of FIP200 were a consequence of increased TBK1 activation, we genetically ablated TBK1 in 4OHT-treated iKO cells (i.e., FIP200 KO tumor cells) by CRISPR-CAS9 to generate FIP200/TBK1 2KO cells. We found that TBK1 KO in FIP200 KO tumor cells (Fig. 4A) reversed the increase in nuclear IRF1 levels by FIP200 ablation to a similar level as in vehicle-treated iKO cells (Fig. 4B). Accordingly, there was also less IRF transcription factor activity in FIP200/TBK1 2KO cells, as determined by ISG56-reporter assays (Fig. 4C). Further, the increased expression of Ccl5, Cxcl9, and Cxcl10 in FIP200 KO tumor cells relative to Ctrl tumor cells was also diminished in FIP200/TBK1 2KO cells (Fig. 4DF). We also attempted to determine the effects of TBK1 KO on CD8+ TIL infiltration, but iKO cells depleted of TBK1 did not form any tumors. It is possible that TBK1 is required for the survival of cells, especially in vivo (39, 40), and we observed increased cleaved caspase-3 levels upon TBK1 knockout (Supplementary Fig. S3A), but the effects on cell growth in vitro were not as prominent (Supplementary Fig. S3B). Alternatively, we have transplanted iKO (+Tam) tumors and treated them with the TBK1 inhibitor, amlexanox, and this led to a trend in decreased CD8 TILs in iKO (+Tam) tumors (Supplementary Fig. S3C and S3D). Together, these results suggest that FIP200 suppresses TBK1 phosphorylation and consequent upregulation of IFN signaling through its noncanonical autophagy functions, and loss of which triggers the TBK1-IRF-IFN signaling for CD8+ TIL recruitment in mammary tumors.

Figure 4.

TBK1 is required for increased IRF1 nuclear localization, ISG56-reporter activity, and expression of chemokines. A, Immunoblots showing the levels of FIP200, p-TBK1, TBK1, and vinculin in iKO (-4OHT), iKO (+4OHT) [FIP200 KO], and iKO (+4OHT +sgTBK1) [FIP200/TBK1 2KO] cells. B, Immunoblots showing the levels of FIP200, IRF1, PARP, and GAPDH in nuclear and cytoplasmic extracts of iKO (-4OHT), iKO (+4OHT) [FIP200 KO], and iKO (+4OHT +sgTBK1) [FIP200/TBK1 2KO] cells. C, Bar charts showing the luciferase/Renilla luminescence ratio for iKO (-4OHT), iKO (+4OHT) [FIP200 KO], and iKO (+4OHT +sgTBK1) [FIP200/TBK1 2KO] cells transfected with the ISG56-reporter plasmid (n = 3 for each sample). D–F, Bar charts showing the relative transcript levels of Ccl5 (D), Cxcl9 (E), and Cxcl10 (F) in iKO (-4OHT), iKO (+4OHT) [FIP200 KO], and iKO (+4OHT +sgTBK1) [FIP200/TBK1 2KO] tumor cells, quantified via qRT-PCR (n = 4 for each sample). Statistical significance was determined by ANOVA with Tukey post-hoc test for C–F. ****, P ≤ 0.0001.

Figure 4.

TBK1 is required for increased IRF1 nuclear localization, ISG56-reporter activity, and expression of chemokines. A, Immunoblots showing the levels of FIP200, p-TBK1, TBK1, and vinculin in iKO (-4OHT), iKO (+4OHT) [FIP200 KO], and iKO (+4OHT +sgTBK1) [FIP200/TBK1 2KO] cells. B, Immunoblots showing the levels of FIP200, IRF1, PARP, and GAPDH in nuclear and cytoplasmic extracts of iKO (-4OHT), iKO (+4OHT) [FIP200 KO], and iKO (+4OHT +sgTBK1) [FIP200/TBK1 2KO] cells. C, Bar charts showing the luciferase/Renilla luminescence ratio for iKO (-4OHT), iKO (+4OHT) [FIP200 KO], and iKO (+4OHT +sgTBK1) [FIP200/TBK1 2KO] cells transfected with the ISG56-reporter plasmid (n = 3 for each sample). D–F, Bar charts showing the relative transcript levels of Ccl5 (D), Cxcl9 (E), and Cxcl10 (F) in iKO (-4OHT), iKO (+4OHT) [FIP200 KO], and iKO (+4OHT +sgTBK1) [FIP200/TBK1 2KO] tumor cells, quantified via qRT-PCR (n = 4 for each sample). Statistical significance was determined by ANOVA with Tukey post-hoc test for C–F. ****, P ≤ 0.0001.

Close modal

FIP200 interacts with and regulates the phosphorylation of AZI2

We next sought to determine the potential molecular mechanisms for the increased TBK1 phosphorylation upon FIP200 deletion in tumor cells. Although FIP200 has been previously reported to coimmunoprecipitate with TBK1 (41), we were unable to confirm this interaction under the conditions we tested. Nonetheless, another previous study identified FIP200 as an interactor of SINTBAD/TBKBP1 (32), which is a known adaptor protein important in the regulation of TBK1 (42, 43). Hence, we set out to evaluate the potential interactions between FIP200 and several different TBK1 adaptor proteins, including SINTBAD, AZI2/NAP1, and TANK. Indeed, we could verify the interaction between HA/FLAG-SINTBAD and MYC-FIP200, but interestingly, we also found that FIP200 could interact with AZI2 (Fig. 5A). This is also true for the reciprocal IP with immunoprecipitation of FLAG tag (Supplementary Fig. S4A). Despite the ability of FIP200 to interact with AZI2 and SINTBAD, we were not able to co-IP FIP200 with TANK (Fig. 5A; Supplementary Fig. S4A), suggesting that there is specificity in the regulation of TBK1 adaptors by FIP200. To delineate the contribution of TBK1 adaptor proteins in the activation of TBK1 upon FIP200 ablation, we utilized siRNAs against TBK1 adaptor proteins to silent AZI2, TANK, and SINTBAD independently in MDA-MB-231 cells that were transduced with sgEmpty or sgFIP200 (Fig. 5B). Similar to iKO cells following 4OHT treatment (see Fig. 3E), FIP200 KO increased TBK1 phosphorylation in MDA-MB-231 (Fig. 5B) and MCF7 (Supplementary Fig. S4B) human breast cancer cells. Importantly, silencing of AZI2 was most effective in diminishing p-TBK1 (Fig. 5B), indicating that FIP200 regulates TBK1 activation largely in part through AZI2. The ablation of AZI2 in iKO (+4OHT) cells could also abolish TBK1 activation upon loss of FIP200 (Fig. 5C) and reduced IRF transcriptional activity, measured through ISG56-reporter assays (Supplementary Fig. S5A). However, the interaction between AZI2 and TBK1 was not increased in cells that were depleted of FIP200. This suggests that FIP200 does not compete with TBK1 for binding to AZI2, as a mechanism of inhibition (Supplementary Fig. S5B).

Figure 5.

FIP200 interacts with and regulates the phosphorylation of AZI2. A, Immunoblots of coimmunoprecipitation experiments with MYC antibody in HEK293 cells transfected with either HA/FLAG-AZI2, HA/FLAG-SINTBAD, or FLAG-TANK along with MYC-FIP200. B, Immunoblots showing the levels of FIP200, p-TBK1, TBK1, AZI2, TANK, SINTBAD, and vinculin in MDA-MB-231 Ctrl (sgEmpty) and MDA-MB-231 FIP200KO (sgFIP200) cells transfected with indicated siRNAs against AZI2, TANK, or SINTBAD. C, Immunoblots showing the levels of FIP200, AZI2, p-TBK1, TBK1, and actin in iKO sgEmpty and iKO sgAZI2 cells, ±4OHT treatment. D and E, Immunoblots of coimmunoprecipitation experiments with HA antibody in HEK293 cells transfected with MYC-FIP200 fragment (D) or deletion mutant constructs and HA/FLAG-AZI2 (E). F and G, Immunoblots showing levels of FIP200, AZI2, and vinculin in MDA-MB-231 (F) or iKO (G) cells ±phosphatase treatment.

Figure 5.

FIP200 interacts with and regulates the phosphorylation of AZI2. A, Immunoblots of coimmunoprecipitation experiments with MYC antibody in HEK293 cells transfected with either HA/FLAG-AZI2, HA/FLAG-SINTBAD, or FLAG-TANK along with MYC-FIP200. B, Immunoblots showing the levels of FIP200, p-TBK1, TBK1, AZI2, TANK, SINTBAD, and vinculin in MDA-MB-231 Ctrl (sgEmpty) and MDA-MB-231 FIP200KO (sgFIP200) cells transfected with indicated siRNAs against AZI2, TANK, or SINTBAD. C, Immunoblots showing the levels of FIP200, AZI2, p-TBK1, TBK1, and actin in iKO sgEmpty and iKO sgAZI2 cells, ±4OHT treatment. D and E, Immunoblots of coimmunoprecipitation experiments with HA antibody in HEK293 cells transfected with MYC-FIP200 fragment (D) or deletion mutant constructs and HA/FLAG-AZI2 (E). F and G, Immunoblots showing levels of FIP200, AZI2, and vinculin in MDA-MB-231 (F) or iKO (G) cells ±phosphatase treatment.

Close modal

To characterize the interaction between FIP200 andAZI2 with more precision, we analyzed a series of FIP200 segments for interactions with AZI2. We found that the FIP200 C-terminal region (CT: residues 1401–1591) as well as full-length FIP200, but not the N-terminal half (N859: residues 1–859) or the coiled coil domain (CC: residues 860–1373), bound to AZI2 (Fig. 5D). We then prepared another series of Myc-tagged FIP200 CT, each deleting 20 residues within 1401–1591 (i.e., 1401–1420, 1421–1440, etc.). These constructs were cotransfected with HA-AZI2 into HEK-293T cells to detect association by coimmunoprecipitation. We found that mutations lacking residues between 1401–1420, 1481–1540, and 1561–1580 showed significantly reduced binding to AZI2, relative to the control construct FIP200 CT (Fig. 5E), suggesting residues within these regions may mediate FIP200 interaction with AZI2. Interestingly, we also observed that deletion of FIP200 resulted in a band-shift for AZI2 (slower migrating band) in MDA-MB-231 cells (Fig. 5F), and this band-shift could be eliminated by phosphatase treatment. The same shift was also found in iKO and BPK cells upon FIP200 deletion (Figs. 3I and 5G), suggesting that the regulation of AZI2 phosphorylation by FIP200 is conserved between mouse and human breast cancer cells. Together, our results suggest the interesting possibility that noncanonical autophagy functions of FIP200 negatively regulate TBK1 through its interaction with AZI2.

Ablation of FIP200 enhanced the efficacy of ICI therapy in nonresponsive mammary tumors

As increased CD8+ TIL density and activation of IFN signaling have been reported to be associated with better ICI responses in various cancers (5, 28, 44, 45), hence we posit that specifically targeting the noncanonical autophagy function of FIP200 will improve ICI efficacy. In order to dissect more specifically the canonical autophagy and noncanonical autophagy roles of FIP200 in regulating enhanced responses to ICI therapy, we leveraged upon the iKI and iKO cells (Fig. 2J and K) that can be induced by tamoxifen in vivo. These cells were respectively transplanted into syngeneic FvB/N hosts, and when tumors reached about 5 mm in diameter (day 0), they were randomized into groups treated with three consecutive daily doses of tamoxifen to induce deletion of the floxed FIP200 allele or treated with corn oil as vehicle controls (Fig. 6A). Consequently, vehicle-treated mice (−Tam) and tamoxifen-treated mice (+Tam) were further randomized into cohorts that were treated either with isotype control antibody (IgG), anti–PD-1 antibody (PD-1), anti–CTLA-4 antibody (CTLA-4), or the combination of both anti–PD-1 and anti–CTLA-4 (ICI-Combo; Fig. 6A). By comparing the growth curves of iKI (−Tam) and iKI (+Tam) tumors, it is apparent that loss of the autophagy function of FIP200 retards tumor growth (Fig. 6B). However, the progression of iKI (+Tam) tumors was not affected by anti–PD-1 therapy relative to IgG-treated controls, and the same is true for iKI (−Tam) tumors (Fig. 6B and C). Due to the lack of treatment response upon anti–PD-1 therapy in iKI (−Tam) and iKI (+Tam) tumors, anti–CTLA-4 and ICI-Combo regiments were not tested further in these cohorts. These results suggest that the canonical autophagy function of FIP200 is not responsible for suppressing effective responses to anti–PD-1 therapy.

Figure 6.

Ablation of FIP200 enhanced the efficacy of ICI therapy in nonresponsive mammary tumors. A, Treatment regimen for transplanted iKO or iKI cells into syngeneic FVB/N mice. Mice cohorts were treated with vehicle or tamoxifen to induce deletion when tumors reached ∼50 mm3. A week later, IgG control, anti–PD-1, anti–CTLA-4, or ICI-Combo therapy was administered (8 mg/kg; i.p. every other day; four doses). B, Tumor growth curves for iKI (±Tam) tumors treated with IgG or anti–PD-1. C, Survival plots for iKI (±Tam) tumors treated with IgG or anti–PD-1. iKI-Tam IgG, n = 6; iKI-Tam PD-1, n = 5; iKI+Tam IgG, n = 6; iKI+Tam PD-1, n = 7. D, Tumor growth curves for iKO (−Tam) tumors treated with IgG, anti–PD-1, anti–CTLA-4, or ICI-Combo therapy (IgG, n = 8; PD-1, n = 4; CTLA-4, n = 4; ICI-Combo, n = 7). E, Tumor growth and relapse curves for iKO (+Tam) tumors treated with IgG, anti–PD-1, anti–CTLA-4, or ICI-Combo therapy (IgG, n = 14; PD-1, n = 7; CTLA-4, n = 6; ICI-Combo, n = 7). F and G, Survival plots for iKO (−Tam) tumors (F) and iKO (+Tam) tumors (G) treated with IgG, anti–PD-1, anti–CTLA-4, or ICI-Combo therapy.

Figure 6.

Ablation of FIP200 enhanced the efficacy of ICI therapy in nonresponsive mammary tumors. A, Treatment regimen for transplanted iKO or iKI cells into syngeneic FVB/N mice. Mice cohorts were treated with vehicle or tamoxifen to induce deletion when tumors reached ∼50 mm3. A week later, IgG control, anti–PD-1, anti–CTLA-4, or ICI-Combo therapy was administered (8 mg/kg; i.p. every other day; four doses). B, Tumor growth curves for iKI (±Tam) tumors treated with IgG or anti–PD-1. C, Survival plots for iKI (±Tam) tumors treated with IgG or anti–PD-1. iKI-Tam IgG, n = 6; iKI-Tam PD-1, n = 5; iKI+Tam IgG, n = 6; iKI+Tam PD-1, n = 7. D, Tumor growth curves for iKO (−Tam) tumors treated with IgG, anti–PD-1, anti–CTLA-4, or ICI-Combo therapy (IgG, n = 8; PD-1, n = 4; CTLA-4, n = 4; ICI-Combo, n = 7). E, Tumor growth and relapse curves for iKO (+Tam) tumors treated with IgG, anti–PD-1, anti–CTLA-4, or ICI-Combo therapy (IgG, n = 14; PD-1, n = 7; CTLA-4, n = 6; ICI-Combo, n = 7). F and G, Survival plots for iKO (−Tam) tumors (F) and iKO (+Tam) tumors (G) treated with IgG, anti–PD-1, anti–CTLA-4, or ICI-Combo therapy.

Close modal

Consistent with a lack of response to ICI therapy and similar to iKI (−Tam) tumors, PD-1, CTLA-4, or ICI-Combo treatment had negligible effects on tumor growth relative to IgG-treated tumors for iKO (−Tam) tumors (Fig. 6D, and F). In contrast, all IgG, single ICI, and combination ICI–treated iKO (+Tam) tumors regressed upon administration of tamoxifen (Fig. 6E), indicating that FIP200 contributes to tumor growth, as observed previously (33). However, iKO (+Tam) tumors that were treated with IgG relapsed and reached tumor endpoints after a median of about 76 days (Fig. 6E and G). On the other hand, iKO (+Tam) tumors treated with anti–PD-1 or anti–CTLA-4 had an extended median survival of 106 and 80 days, respectively (Fig. 6E and G). Intriguingly, although the combination of anti–PD-1 and anti–CTLA-4 therapy had no significant effect on the progression of iKO (−Tam) tumors (Fig. 6F), 2 of 7 (28.8%) of iKO (+Tam) tumors that were treated with the ICI combination regressed and remained progression-free for up to 250 days after tumor appearance (Fig. 6E and G). Together, these results demonstrate that blocking FIP200′s nonautophagy functions, but not the loss of its autophagy function, led to activation of TBK1-IRF-IFN signaling, CD8+ TIL infiltration, and enhanced the responses of ICI therapy in mammary tumors.

In this study, we have provided genetic proof of principle that targeting FIP200 can improve the efficacy of combination ICI therapy. Such a strategy is vital and imperative in breast cancers because the majority of breast cancers (especially luminal subtypes) do not respond to ICI therapy (6). This was evidently illustrated in our study, where anti–PD-1 in combination with anti–CTLA-4 had no significant effect on the growth of Ctrl-MT tumors, which are classified as luminal B tumors. Despite the ineffectiveness of combination ICI therapy on Ctrl-MT tumors, ablation of FIP200 was able to induce tumor regression, and remarkably, the administration of combination ICI therapy in tumors lacking FIP200 could prevent relapse and elicit prolonged durable responses. Notably, the median tumor-free survival for cKO-MT tumors treated with combination ICI therapy was improved significantly, and 28.8% of mice in this cohort remained tumor-free for up to 250 days. It is worth noting that this is a relatively long duration of follow-up and highlights the superior durable responses that can be achieved with this combination. These results illustrate the promising potential of targeting FIP200 as a means to sensitize nonresponsive luminal breast tumors to ICI therapy, and this may allow a larger cohort of patients with breast cancer to reap the benefits of immunotherapy.

As FIP200 is a large protein (∼200 kDa), it has multiple domains that can function independently to coordinate various cellular pathways (46). Among these, FIP200 has been identified as a key regulator of autophagy (10) that is required in the initiation complex, along with ULK1, ATG13, and ATG101. In order to specifically dissect its autophagy functions, we have recently developed mice with a knock-in allele of FIP200 that is unable to bind ATG13 and initiate autophagy (27). Through the use of this canonical autophagy-deficient allele of FIP200, we were able to dissect specifically for the first time in a mouse model of breast cancer that FIP200′s autophagy function promoted the initiation, growth, and metastasis of PyMT-driven mammary tumors. These observations are consistent with our previous studies demonstrating a protumorigenic role of FIP200 in breast cancer cells (25, 27, 34, 47), but more specifically provide justification for the inhibition of autophagy as a therapeutic strategy in breast cancer. Importantly, these observations have clinical implications, because autophagy inhibitors such as hydroxychloroquine are being tested in a number of clinical trials (48).

In addition to the role of FIP200 in canonical autophagy, our findings also demonstrated that FIP200 can regulate the activation of TBK1, a key player in the induction of IFN signaling (32). The negative regulation of TBK1 by FIP200 was not dependent on the ability of FIP200 to interact with ATG13, suggesting that this regulation does not involve the canonical autophagy function of FIP200. This is in line with a study that showed TBK1 activation was increased upon ablation of FIP200, and that the activation of TBK1 in these cells was important for ferritin turnover that was autophagy-independent (38). However, Goodwin and colleagues also observed TBK1 activation upon ULK1/2 or ATG13 ablation, whereas disrupting FIP200-ATG13 binding in our iKI (+4OHT) cells did not induce pronounced TBK1 activation. These findings may suggest that the negative regulation of TBK1 occurs through members of the ULK1 complex but does not require the interaction between FIP200 and ATG13 (i.e., distinct complex conformation despite the same complex members being involved). It is also worth noting that TBK1 itself is involved in autophagy and phosphorylates cargo receptors such as p62, NDP52, and TAX1BP1 to promote cargo binding (49). More recently, TBK1 has also been implicated in the initiation of autophagy, by phosphorylating Syntaxin 17 to promote the formation of the initiation complex (50). Although autophagy has been shown to negatively regulate TBK1 activation by degrading upstream activators such as MAVS (51) and STING (49), in our studies, the regulation of TBK1 by FIP200 occurs through a canonical autophagy-independent manner. It is possible that TBK1 regulation will depend on the sum of signaling inputs that are present within each cellular context, but TBK1 activation was observed upon FIP200 ablation in a wide range of breast cancer cells in this study (i.e., PyMT, BRCA-1 null, MDA-MB-231, and MCF-7 cells).

Activation of the TBK1-IFN signaling axis has emerged as an effective strategy to improve the efficacy of ICIs. Various modalities such as STING agonists (52) or poly(I:C) treatment (28), which converge on TBK1, have proven to be effective in making ICIs more efficacious. Moreover, the loss of IFN pathway genes has been described as a mechanism of resistance to anti–CTLA-4 therapy (45). Importantly, we observed a consistent association between improved ICI efficacy, increased CD8+ TILs, and TBK1 activity, where these parameters were increased upon ablation of FIP200 but not when the interaction between FIP200 and ATG13 was disrupted. The lack of responses to anti–PD-1 therapy and CD8+ T-cell infiltration in the cKI model is also consistent with a study that showed that autophagy inhibition either by genetic or pharmacological means did not affect antitumor T-cell recruitment and responses (13). Hence, our study highlights the need to target the noncanonical autophagy functions of FIP200 to improve ICI efficacy. Furthermore, we also demonstrated that the increased IRF activity and elevated expression of proinflammatory cytokines upon FIP200 ablation were dependent on TBK1 activation. As for the recruitment of CD8+ TILs, treatment with amlexanox, an inhibitor of TBK1 and IKKϵ, could diminish the number of CD8+ TILs (Supplementary Fig. S3C and S3D). This suggests that the increased TBK1 activation upon FIP200 depletion contributes to this process, but the lack of specificity of amlexanox is a limitation of this particular experiment. In addition, we have excluded the involvement of IFNAR1 signaling as a dominant upstream regulator of TBK1 and AZI2 (Supplementary Fig. S6). Mechanistically, we have discovered that FIP200 can interact with and regulate the phosphorylation of AZI2, a TBK1 adaptor protein. Our independent observations (Fig. 5AC) are consistent with a recently published study describing AZI2/SINTBAD binding regions within the C-terminus of FIP200 (53). Furthermore, genetic ablation of AZI2 in FIP200-null tumor cells indicated that AZI2 was a major contributor to TBK1 activation in these cells. To our knowledge, there have been no prior reports on the regulation of posttranslational modifications of AZI2. Hence, we have unraveled an additional layer of TBK1 regulation that involves FIP200. The specific phosphorylation site(s) of AZI2 that were negatively regulated by FIP200 and its significance for downstream signaling pathways are of great interest and could be exploited for pharmaceutical intervention in future studies.

Importantly, the regulation of proinflammatory chemokines that are important for CD8+ TIL infiltration was not limited to mouse mammary tumor cells. In fact, we also observed increased expression of CXCL10 in MDA-MB-231 cells upon FIP200 deletion (Supplementary Fig. S7). Accordingly, we then examined The Cancer Genome Atlas breast cancer cohort for correlations between FIP200 (RB1CC1) expression with TBK1, CCL5, CXCL9, and CXCL10 (Supplementary Fig. S8A). For the association between FIP200 and TBK1, there was a significant positive correlation (Supplementary Fig. S8B). However, because our results indicated a change in phospho-TBK1 levels rather than its expression upon FIP200 ablation, the significance of this correlation will need to be investigated in future studies. Coherently, there was an inverse correlation between FIP200 and CCL5 expression (Supplementary Fig. S8C). This would be in line with our observations that FIP200 limits the expression of chemokines. On the other hand, no significant associations were found between FIP200 levels with CXCL9 or CXCL10 (Supplementary Fig. S8D and S8E). It is possible that the lack of association between FIP200 and CXCL9/CXCL10 may be due to confounding factors such as stromal/immune cells expression of these genes in these whole tumor samples.

In summary, we have found that FIP200 plays multiple protumorigenic roles in mammary tumors. This includes a role in autophagy that is important for tumor initiation, growth, and metastasis. On the other hand, FIP200 also negatively regulates the AZI2/TBK1/IFN signaling axis (nonautophagy function) that impinges on the expression of proinflammatory cytokines, CD8+ T-cell infiltration, and ICI therapeutic efficacy. These observations provide rationale for targeting these separate functions of FIP200 in concert. Importantly, we have also demonstrated that FIP200 ablation in combination with ICIs can achieve prolonged durable responses in luminal breast tumors that are otherwise nonresponsive to ICI therapy.

S.K. Yeo reports grants from NIH during the conduct of the study. M.A. Haas reports grants from NCI during the conduct of the study. No potential conflicts of interest were disclosed by the other authors.

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data available from authors upon request.

T. Okamoto: Conceptualization, data curation, investigation, writing-original draft. S.K. Yeo: Conceptualization, data curation, formal analysis, investigation, writing-original draft, writing-review and editing. M. Hao: Resources, investigation. M.R. Copley: Investigation. M.A. Haas: Investigation. S. Chen: Conceptualization, resources, writing-original draft. J.-L. Guan: Conceptualization, supervision, funding acquisition, writing-review and editing.

We would like to thank Nyiva Muyanga for help with genotyping and the University of Cincinnati LAMS staff for their support with regards to mouse colony maintenance and husbandry. We appreciate the help from Glenn Doerman for graphics support and preparation of figures. We are grateful to members of the Guan lab for critical appraisal and suggestions in the preparation of this article. J.-L. Guan received NIH grants R01 CA211066, R01 HL073394, and R01 NS094144. S. Chen was supported by NSFC grants (81572877 and 81773132). S.K. Yeo received grants from NIH. M.A. Haas received grants from NCI.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Blankenstein
T
,
Coulie
PG
,
Gilboa
E
,
Jaffee
EM
. 
The determinants of tumour immunogenicity
.
Nat Rev Cancer
2012
;
12
:
307
13
.
2.
Bedognetti
D
,
Maccalli
C
,
Bader
SB
,
Marincola
FM
,
Seliger
B
. 
Checkpoint inhibitors and their application in breast cancer
.
Breast Care
2016
;
11
:
108
15
.
3.
Topalian
SL
,
Hodi
FS
,
Brahmer
JR
,
Gettinger
SN
,
Smith
DC
,
McDermott
DF
, et al
Safety, activity, and immune correlates of anti-PD-1 antibody in cancer
.
N Engl J Med
2012
;
366
:
2443
54
.
4.
Topalian
SL
,
Sznol
M
,
McDermott
DF
,
Kluger
HM
,
Carvajal
RD
,
Sharfman
WH
, et al
Survival, durable tumor remission, and long-term safety in patients with advanced melanoma receiving nivolumab
.
J Clin Oncol
2014
;
32
:
1020
30
.
5.
Topalian
SL
,
Taube
JM
,
Anders
RA
,
Pardoll
DM
. 
Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy
.
Nat Rev Cancer
2016
;
16
:
275
87
.
6.
Emens
LA
,
Kok
M
,
Ojalvo
LS
. 
Targeting the programmed cell death-1 pathway in breast and ovarian cancer
.
Curr Opin Obstet Gynecol
2016
;
28
:
142
7
.
7.
Nanda
R
,
Chow
LQ
,
Dees
EC
,
Berger
R
,
Gupta
S
,
Geva
R
, et al
Pembrolizumab in patients with advanced triple-negative breast cancer: phase Ib KEYNOTE-012 study
.
J Clin Oncol
2016
;
34
:
2460
7
.
8.
Dirix
L
,
Takacs
I
,
Nikolinakos
P
,
Jerusalem
G
,
Arkenau
H-T
,
Hamilton
E
, et al
Abstract S1–04: Avelumab (MSB0010718C), an anti-PD-L1 antibody, in patients with locally advanced or metastatic breast cancer: a phase Ib JAVELIN solid tumor trial
.
Cancer Res
2016
;
76
:
S1-04-S1
.
9.
Mizushima
N
,
Komatsu
M
. 
Autophagy: renovation of cells and tissues
.
Cell
2011
;
147
:
728
41
.
10.
Hara
T
,
Takamura
A
,
Kishi
C
,
Iemura
S
,
Natsume
T
,
Guan
JL
, et al
FIP200, a ULK-interacting protein, is required for autophagosome formation in mammalian cells
.
J Cell Biol
2008
;
181
:
497
510
.
11.
Michaud
M
,
Martins
I
,
Sukkurwala
AQ
,
Adjemian
S
,
Ma
Y
,
Pellegatti
P
, et al
Autophagy-dependent anticancer immune responses induced by chemotherapeutic agents in mice
.
Science
2011
;
334
:
1573
7
.
12.
Michaud
M
,
Xie
X
,
Bravo-San Pedro
JM
,
Zitvogel
L
,
White
E
,
Kroemer
G
. 
An autophagy-dependent anticancer immune response determines the efficacy of melanoma chemotherapy
.
Oncoimmunology
2014
;
3
:
e944047
.
13.
Starobinets
H
,
Ye
J
,
Broz
M
,
Barry
K
,
Goldsmith
J
,
Marsh
T
, et al
Antitumor adaptive immunity remains intact following inhibition of autophagy and antimalarial treatment
.
J Clin Invest
2016
;
126
:
4417
29
.
14.
DeVorkin
L
,
Pavey
N
,
Carleton
G
,
Comber
A
,
Ho
C
,
Lim
J
, et al
Autophagy regulation of metabolism is required for CD8
.
Cell Rep
2019
;
27
:
502
13
.
15.
Lim
J
,
Park
H
,
Heisler
J
,
Maculins
T
,
Roose-Girma
M
,
Xu
M
, et al
Autophagy regulates inflammatory programmed cell death via turnover of RHIM-domain proteins
.
Elife
2019
;
8
:
e44452
.
16.
Cunha
LD
,
Yang
M
,
Carter
R
,
Guy
C
,
Harris
L
,
Crawford
JC
, et al
LC3-associated phagocytosis in myeloid cells promotes tumor immune tolerance
.
Cell
2018
;
175
:
429
41
.
17.
Martin
PK
,
Marchiando
A
,
Xu
R
,
Rudensky
E
,
Yeung
F
,
Schuster
SL
, et al
Autophagy proteins suppress protective type I interferon signalling in response to the murine gut microbiota
.
Nat Microbiol
2018
;
3
:
1131
41
.
18.
Samie
M
,
Lim
J
,
Verschueren
E
,
Baughman
JM
,
Peng
I
,
Wong
A
, et al
Selective autophagy of the adaptor TRIF regulates innate inflammatory signaling
.
Nat Immunol
2018
;
19
:
246
54
.
19.
Yang
A
,
Herter-Sprie
G
,
Zhang
H
,
Lin
EY
,
Biancur
D
,
Wang
X
, et al
Autophagy sustains pancreatic cancer growth through both cell-autonomous and nonautonomous mechanisms
.
Cancer Discov
2018
;
8
:
276
87
.
20.
Poillet-Perez
L
,
Xie
X
,
Zhan
L
,
Yang
Y
,
Sharp
DW
,
Hu
ZS
, et al
Autophagy maintains tumour growth through circulating arginine
.
Nature
2018
;
563
:
569
73
.
21.
Karsli-Uzunbas
G
,
Guo
JY
,
Price
S
,
Teng
X
,
Laddha
SV
,
Khor
S
, et al
Autophagy is required for glucose homeostasis and lung tumor maintenance
.
Cancer Discov
2014
;
4
:
914
27
.
22.
Jung
H
,
Leal-Ekman
JS
,
Lu
Q
,
Stappenbeck
TS
. 
Atg14 protects the intestinal epithelium from TNF-triggered villus atrophy
.
Autophagy
2019
;
15
:
1990
2001
.
23.
Cui
J
,
Jin
S
,
Wang
RF
. 
The BECN1-USP19 axis plays a role in the crosstalk between autophagy and antiviral immune responses
.
Autophagy
2016
;
12
:
1210
1
.
24.
Wang
YT
,
Zaitsev
K
,
Lu
Q
,
Li
S
,
Schaiff
WT
,
Kim
KW
, et al
Select autophagy genes maintain quiescence of tissue-resident macrophages and increase susceptibility to Listeria monocytogenes
.
Nat Microbiol
2020
;
5
:
272
81
.
25.
Wei
H
,
Wei
S
,
Gan
B
,
Peng
X
,
Zou
W
,
Guan
JL
. 
Suppression of autophagy by FIP200 deletion inhibits mammary tumorigenesis
.
Genes Dev
2011
;
25
:
1510
27
.
26.
Gan
B
,
Peng
X
,
Nagy
T
,
Alcaraz
A
,
Gu
H
,
Guan
JL
. 
Role of FIP200 in cardiac and liver development and its regulation of TNFalpha and TSC-mTOR signaling pathways
.
J Cell Biol
2006
;
175
:
121
33
.
27.
Chen
S
,
Wang
C
,
Yeo
S
,
Liang
CC
,
Okamoto
T
,
Sun
S
, et al
Distinct roles of autophagy-dependent and -independent functions of FIP200 revealed by generation and analysis of a mutant knock-in mouse model
.
Genes Dev
2016
;
30
:
856
69
.
28.
Brockwell
NK
,
Owen
KL
,
Zanker
D
,
Spurling
A
,
Rautela
J
,
Duivenvoorden
HM
, et al
Neoadjuvant interferons: critical for effective PD-1-based immunotherapy in TNBC
.
Cancer Immunol Res
2017
;
5
:
871
84
.
29.
Minn
AJ
,
Wherry
EJ.
Combination cancer therapies with immune checkpoint blockade: convergence on interferon signaling
.
Cell
2016
;
165
:
272
5
.
30.
Zhu
J
,
Smith
K
,
Hsieh
PN
,
Mburu
YK
,
Chattopadhyay
S
,
Sen
GC
, et al
High-throughput screening for TLR3-IFN regulatory factor 3 signaling pathway modulators identifies several antipsychotic drugs as TLR inhibitors
.
J Immunol
2010
;
184
:
5768
76
.
31.
Chariot
A
,
Leonardi
A
,
Muller
J
,
Bonif
M
,
Brown
K
,
Siebenlist
U
. 
Association of the adaptor TANK with the I kappa B kinase (IKK) regulator NEMO connects IKK complexes with IKK epsilon and TBK1 kinases
.
J Biol Chem
2002
;
277
:
37029
36
.
32.
Li
S
,
Wang
L
,
Berman
M
,
Kong
YY
,
Dorf
ME
. 
Mapping a dynamic innate immunity protein interaction network regulating type I interferon production
.
Immunity
2011
;
35
:
426
40
.
33.
Wei
H
,
Wang
C
,
Groce
C
,
Guan
J
. 
p62/SQSTM1 synergizes with autophagy for tumor growth in vivo
.
Genes Dev
2014
;
28
:
1204
16
.
34.
Yeo
SK
,
Wen
J
,
Chen
S
,
Guan
JL
. 
Autophagy differentially regulates distinct breast cancer stem-like cells in murine models via EGFR/Stat3 and Tgfβ/Smad signaling
.
Cancer Res
2016
;
76
:
3397
410
.
35.
Yeo
SK
,
Paul
R
,
Haas
M
,
Wang
C
,
Guan
JL
. 
Improved efficacy of mitochondrial disrupting agents upon inhibition of autophagy in a mouse model of BRCA1-deficient breast cancer
.
Autophagy
2018
;
14
:
1214
25
.
36.
Luo
M
,
Fan
H
,
Nagy
T
,
Wei
H
,
Wang
C
,
Liu
S
, et al
Mammary epithelial-specific ablation of the focal adhesion kinase suppresses mammary tumorigenesis by affecting mammary cancer stem/progenitor cells
.
Cancer Res
2009
;
69
:
466
74
.
39.
Oelkrug
C
,
Ramage
JM
. 
Enhancement of T cell recruitment and infiltration into tumours
.
Clin Exp Immunol
2014
;
178
:
1
8
.
38.
Goodwin
JM
,
Dowdle
WE
,
DeJesus
R
,
Wang
Z
,
Bergman
P
,
Kobylarz
M
, et al
Autophagy-independent lysosomal targeting regulated by ULK1/2-FIP200 and ATG9
.
Cell Rep
2017
;
20
:
2341
56
.
39.
Xu
D
,
Jin
T
,
Zhu
H
,
Chen
H
,
Ofengeim
D
,
Zou
C
, et al
TBK1 Suppresses RIPK1-driven apoptosis and inflammation during development and in aging
.
Cell
2018
;
174
:
1477
91
.
40.
Lafont
E
,
Draber
P
,
Rieser
E
,
Reichert
M
,
Kupka
S
,
de Miguel
D
, et al
TBK1 and IKKϵ prevent TNF-induced cell death by RIPK1 phosphorylation
.
Nat Cell Biol
2018
;
20
:
1389
99
.
41.
Behrends
C
,
Sowa
ME
,
Gygi
SP
,
Harper
JW
. 
Network organization of the human autophagy system
.
Nature
2010
;
466
:
68
76
.
42.
Goncalves
A
,
Bürckstümmer
T
,
Dixit
E
,
Scheicher
R
,
Górna
MW
,
Karayel
E
, et al
Functional dissection of the TBK1 molecular network
.
PLoS One
2011
;
6
:
e23971
.
43.
Ryzhakov
G
,
Randow
F
. 
SINTBAD, a novel component of innate antiviral immunity, shares a TBK1-binding domain with NAP1 and TANK
.
EMBO J
2007
;
26
:
3180
90
.
44.
Ayers
M
,
Lunceford
J
,
Nebozhyn
M
,
Murphy
E
,
Loboda
A
,
Kaufman
DR
, et al
IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade
.
J Clin Invest
2017
;
127
:
2930
40
.
45.
Gao
J
,
Shi
LZ
,
Zhao
H
,
Chen
J
,
Xiong
L
,
He
Q
, et al
Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy
.
Cell
2016
;
167
:
397
404
.
46.
Gan
B
,
Guan
JL
. 
FIP200, a key signaling node to coordinately regulate various cellular processes
.
Cell Signal
2008
;
20
:
787
94
.
47.
Wei
H
,
Gan
B
,
Wu
X
,
Guan
JL
. 
Inactivation of FIP200 leads to inflammatory skin disorder, but not tumorigenesis, in conditional knock-out mouse models
.
J Biol Chem
2009
;
284
:
6004
13
.
48.
Chude
CI
,
Amaravadi
RK
. 
Targeting autophagy in cancer: update on clinical trials and novel inhibitors
.
Int J Mol Sci
2017
;
18
:
1279
.
49.
Prabakaran
T
,
Bodda
C
,
Krapp
C
,
Zhang
BC
,
Christensen
MH
,
Sun
C
, et al
Attenuation of cGAS-STING signaling is mediated by a p62/SQSTM1-dependent autophagy pathway activated by TBK1
.
EMBO J
2018
;
37
:
e97858
.
50.
Kumar
S
,
Gu
Y
,
Abudu
YP
,
Bruun
JA
,
Jain
A
,
Farzam
F
, et al
Phosphorylation of syntaxin 17 by TBK1 controls autophagy initiation
.
Dev Cell
2019
;
49
:
130
44
.
51.
Jin
S
,
Tian
S
,
Luo
M
,
Xie
W
,
Liu
T
,
Duan
T
, et al
Tetherin suppresses type I interferon signaling by targeting MAVS for NDP52-mediated selective autophagic degradation in human cells
.
Mol Cell
2017
;
68
:
308
22
.
52.
Ohkuri
T
,
Ghosh
A
,
Kosaka
A
,
Zhu
J
,
Ikeura
M
,
David
M
, et al
STING contributes to antiglioma immunity via triggering type I IFN signals in the tumor microenvironment
.
Cancer Immunol Res
2014
;
2
:
1199
208
.
53.
Ravenhill
BJ
,
Boyle
KB
,
von Muhlinen
N
,
Ellison
CJ
,
Masson
GR
,
Otten
EG
, et al
The cargo receptor NDP52 initiates selective autophagy by recruiting the ULK complex to cytosol-invading bacteria
.
Mol Cell
2019
;
74
:
320
9
.

Supplementary data