Lung cancer is the leading cause of cancer-related deaths. While the recent use of immune checkpoint inhibitors significantly improves patient outcomes, responsiveness remains restricted to a small proportion of patients. Conventional dendritic cells (DCs) play a major role in anticancer immunity. In mice, two subpopulations of DCs are found in the lung: DC2s (CD11b+Sirpα+) and DC1s (CD103+XCR1+), the latest specializing in the promotion of anticancer immune responses. However, the impact of lung cancer on DC populations and the consequent influence on the anticancer immune response remain poorly understood. To address this, DC populations were studied in murine models of Lewis Lung Carcinoma (LLC) and melanoma-induced lung metastasis (B16F10). We report that direct exposure to live or dead cancer cells impacts the capacity of DCs to differentiate into CD103+ DC1s, leading to profound alterations in CD103+ DC1 proportions in the lung. In addition, we observed the accumulation of CD103loCD11b+ DCs, which express DC2 markers IRF4 and Sirpα, high levels of T-cell inhibitory molecules PD-L1/2 and the regulatory molecule CD200. Finally, DC1s were injected in combination with an immune checkpoint inhibitor (anti-PD-1) in the B16F10 model of resistance to the anti-PD-1 immune checkpoint therapy; the co-injection restored sensitivity to immunotherapy. Thus, we demonstrate that lung tumor development leads to the accumulation of CD103loCD11b+ DCs with a regulatory potential combined with a reduced proportion of highly-specialized antitumor CD103+ DC1s, which could promote cancer growth. Additionally, promoting an anticancer DC signature could be an interesting therapeutic avenue to increase the efficacy of existing immune checkpoint inhibitors.
Lung cancer is the leading cause of cancer-related deaths. While the recent use of immune checkpoint inhibitors significantly improves patient outcomes, responsiveness remains restricted to a small proportion of patients. Conventional dendritic cells (DCs) play a major role in anticancer immunity. In mice, two subpopulations of DCs are found in the lung: DC2s (CD11b+Sirpα+) and DC1s (CD103+XCR1+), the latest specializing in the promotion of anticancer immune responses. However, the impact of lung cancer on DC populations and the consequent influence on the anticancer immune response remain poorly understood. To address this, DC populations were studied in murine models of Lewis Lung Carcinoma (LLC) and melanoma-induced lung metastasis (B16F10). We report that direct exposure to live or dead cancer cells impacts the capacity of DCs to differentiate into CD103+ DC1s, leading to profound alterations in CD103+ DC1 proportions in the lung. In addition, we observed the accumulation of CD103loCD11b+ DCs, which express DC2 markers IRF4 and Sirpα, high levels of T-cell inhibitory molecules PD-L1/2 and the regulatory molecule CD200. Finally, DC1s were injected in combination with an immune checkpoint inhibitor (anti-PD-1) in the B16F10 model of resistance to the anti-PD-1 immune checkpoint therapy; the co-injection restored sensitivity to immunotherapy. Thus, we demonstrate that lung tumor development leads to the accumulation of CD103loCD11b+ DCs with a regulatory potential combined with a reduced proportion of highly-specialized antitumor CD103+ DC1s, which could promote cancer growth. Additionally, promoting an anticancer DC signature could be an interesting therapeutic avenue to increase the efficacy of existing immune checkpoint inhibitors.
Currently, lung cancer remains the most lethal cancer in industrialized countries. Despite significant advances in conventional therapies, the five-year survival rate remains lower than 20% in most countries [1]. To support tumor development, cancer induces an immunoregulatory environment that reduces the anticancer function of immune cells [2]. Consequently, immunotherapies recently emerged as a new strategy to restore the natural antitumor immune response, and significantly improve survival. Immune checkpoint inhibitors (ICIs) that target the PD-1/PD-L1 axis are the most commonly used immunotherapy in patients with non-small cell lung carcinoma (NSCLC) and are now approved as first-line treatment in several countries [3, 4]. In normal conditions, the interaction between PD-L1 expressed on antigen-presenting cells and PD-1 present on T cells limits the T cell response to prevent auto-immunity. However, in cancer, PD-L1 is overexpressed by cancer cells and immune cells which are present within the tumor environment, leading to the inhibition of the cytotoxic T cells, which are crucial for the anticancer immune response [5]. While in some cases PD-1/PD-L1 inhibitors successfully restore the function of cytotoxic T cells and significantly improve patient survival, their effectiveness is limited to only a small proportion of patients [3, 6]. There is therefore an urgent need to better understand anticancer immune responses.CD8 T cells or cytotoxic T cells are major effectors of ICIs and play an important role in the natural anticancer immune response. Indeed, CD8 T cell activation is initiated via antigen presentation by conventional dendritic cells (DCs) [7]. In the lung, DCs are a heterogeneous population, which in the past was divided according to surface marker expression, which can be highly variable based on the inflammatory context [8, 9] and differs between humans and mice [10]. Recently, DCs were thoroughly re-characterized based on cellular developmental pathways. This allowed the emergence of a new consensus in DC classification that better translates from mice to humans [10], where lung DCs comprise a mixture of CD103+XCR1+ (mice)/CD141+XCR1+ (human) DC1s that depend on both BATF3 and IRF8 transcription factors for their development, and CD11b+Sirpα+ (mice and human) DC2s, which express the IRF4 transcription factor [10, 11]. DC1s are a major component of anticancer immune responses. Indeed, the absence of DC1 populations in Batf3 or Irf8 mice favours the growth of primary tumors or metastasis progression [12-14]. Furthermore, DC1s specialize in IL-12 production, trafficking of tumor antigens to draining lymph nodes and cross-presentation of tumor antigens to CD8 T cells [12-16]. Finally, DC1s also play an important role in immune checkpoints immunotherapies, as Batf3 mice do not respond to this type of treatment [17]. The contribution of DC2s in anticancer immune response is not well established, but some propose they are necessary to induce antitumor CD4 T cell immunity [18].Despite this wealth of knowledge on anticancer immune responses, lung cancer immunotherapy remains weakly effective [3, 6]. This may stem in part from the current lack of knowledge on the impact of lung cancer on local DC populations, which are crucial in anticancer immunity. Previous studies by our group suggested that various inflammatory contexts profoundly impact the local DC signature, as well as disease progression [8, 9, 19]. Specifically, we demonstrated that the proportions of CD103+ DC1s are drastically reduced under inflammatory conditions. We, therefore, set out to verify whether the development of lung cancer alters local DC populations, and whether enriching local DCs with high levels of anticancer CD103+ DC1s could favourably impact the anticancer lung response. Using mouse models of lung cancer and melanoma-induced lung metastasis, we demonstrate that the cancer microenvironment decreases the proportions of anticancer CD103+ DC1s. In return, we observed an unpredicted increase in a CD103loCD11b+ DC population, which strongly expresses PD-L1/2 and CD200 regulatory molecules. Finally, we show that enriching the local DC population with CD103+ DC1s supports a more efficient response to anti-PD-1s. These results suggest that lung tumor progression alters the local DC population signature to favour tumor growth and underline new mechanisms explaining the inability of the local DC1s to naturally regulate tumor growth, and possible resistance to anti-PD-1 therapies.
Materials and methods
Mice
C57Bl/6J mice were purchased from Jackson Laboratories and bred in a pathogen-free animal unit (Centre de recherche de l’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Québec, QC, Canada). 8–12 weeks male and female mice were used for in vivo cancer models and 6–12 weeks male and female were used for in vitro protocols. Protocols were approved by local ethics committees and followed Canadian animal care guidelines.
In vivo tumor models
For the induction of lung tumor models, mice were injected intravenously (i.v.) with 2.5 x 105 B16F10 melanoma cells (ATCC, catalog no. CRL-6475) or 106 Lewis lung carcinoma (LLC) cells (ATCC, catalog no. CRL-1642), previously grown in DMEM media (Wisent) supplemented with 10% FBS (Wisent). 18 days following cancer cells injection, mice were euthanized and lungs were collected. An arbitrary cancer score indicative of the number and size of tumors was fixed from 0 (no visible tumor) to 5 (highest score). In the B16F10 lung metastasis model, 200 μg/mice anti-mouse PD-1 antibody (BioXcell, catalog no. BP0033-2) or 200 μg/mice Armenian hamster IgG isotype control (BioXcell, catalog no. BP0091) were administered via intraperitoneal injection on day 4, 8, 11 and 14 in combination with an i.v. injection of 3 x 105 XCR1+ FLT3L-BMDCs on day 0, 4, 8, 11 and 14 (Fig 7C) [20-22].
Fig 7
Injection of XCR1+ DC1 improves sensitivity to anti-PD-1 treatment.
(A) FLT3L-BMDCs were stimulated with GM-CSF (Day 0). Either live B16F10/ LLC cells or a B16F10/ LLC antigenic preparation was added on day 0 or day 2 and ΔIL-12 p40 MFI was measured by flow cytometry on day 3. (B) Two days following GM-CSF stimulation FLT3L-BMDCs were stimulated for 24h with CFSE-treated B16F10 cells and CFSE MFI was measured by flow cytometry in CD11c+MHC II+ DC ± stimulation with CFSE-B16F10. (C) Schematic representation of the timeline treatment with the anti-PD-1 and XCR1+ DC1 injections (D-E-F) Analysis of mice lungs (D) 9 days or (E-F) 18 days after B16F10 injections. (D) The total number of lung CD103+XCR1+ DC1 24h after the last DC injection. (E) Lung index (lung weight/mice weight), cancer score (indicative of the number and size of tumors) and lung total cell number. (F) Lung ratios of CD8 T cell and NK cell numbers relative to the number and size of tumors (cancer score). CD8 T cells were identified as CD45+, CD19-, CD90.2+, CD4-, CD8+ and NK cells were identified as CD19-, CD3e-, B220-, CD49b+, NK1.1+ by flow cytometry analysis. (A) Data are presented as individual dots with means. (B to F) Data are expressed as mean ± SEM. (A-B-E-F, except E panel 3) n = 5–11 pooled from two independent experiments. (D, E panel 3) n = 5 representative of two independent experiments.* = p < 0.05, compared as indicated in the graph; ϕ = p < 0.05 compared to naïve mice. P-values were determined using (B) paired t-test, (D) unpaired t-test and (E-F) one-way ANOVA followed by Tukey’s multiple comparisons test.
Production of FLT3L bone marrow-derived DCs (FLT3L-BMDCs)
Bone marrow cells were isolated by flushing marrow from tibias and femurs using a 27-gauge needle and PBS. Cells were cultured at 1.5 x 106 cells/ml for 7 days in RPMI 1640 media (Wisent) supplemented with 10% FBS (Wisent), 50 μM β-mercaptoethanol, antibiotic/anti-mycotic (Wisent) and 100 ng/ml FMS-like tyrosine kinase 3 ligand (FLT3L) (Peprotech, catalog no. 250-31L). On day 7, 10 ng/ml of Granulocyte-macrophage colony-stimulating factor (GM-CSF) (Peprotech, catalog no. 315–03) was added to the culture. For the stimulation of FLT3L-BMDCs, 104 live or an antigenic preparation (obtained by two cycles of freeze and thaw) of B16F10 or LLC cells per million of DCs were added. In some stimulations, DCs were segregated from live cancer cells with a 0.4 μm cell culture insert (Falcon). For transfer experiments, FLT3L-BMDCs were stimulated on day 7 with GM-CSF and on day 9 with live B16F10 cells and harvested on day 10. Before the injection, XCR1+ FLT3L-BMDCs were isolated using XCR1-APC (Biolegend) and EasySep™ Mouse APC Positive Selection Kit II (Stemcell).
FLT3L-BMDCs phagocytosis assay
B16F10 cells were stained for 20 minutes using the CellTrace™ CFSE Cell Proliferation Kit (Invitrogen) according to the manufacturer’s instructions, and washed. 104 CFSE-B16F10 cells per million of DCs were then used to stimulate FLT3L-BMDCs for 24h. DC CFSE expression was measured by flow cytometry.
Flow cytometry
For flow cytometry analysis, the lung tissue was digested with 200 U/mL Collagenase IV (Sigma-Aldrich) for 45 min at 37°C and pressed through a 70 μm cell strainer. Red blood cells were lysed with ammonium chloride solution. Antibodies used were CD103-PE, CD103-biotin, CD103-APC-Cy7, CD11c-BV711, CD11c-BV785, I-A/I-E (MHC II)-Pacific Blue, CD172a (Sirpα)-APC-Cy7, CD19-biotin, CD90.2-biotin, CCR2-biotin, Ly-6C-APC-Cy7, IRF4-PE, CD86-APC-Cy7, CD197 (CCR7)-APC, TGF-β1-APC, TNF-APC-Cy7, H-2Kb/H-2Db (MHC I)-APC, CD200-PE, PD-L1-APC, PD-L2-PE, CD366 (TIM-3)-APC, IL-12/IL-23 p40-APC, XCR1-APC, XCR1-BV650 (Biolegend), NK1.1-biotin (Ablab), CD11b-Pe-Cy7, CD103-PE, Sirpα-BV711, Zbtb46-PE (BD Bioscience), CD11b-AF700 (eBioscience), IRF8-APC (Miltenyi Biotec) and CD80-biotin (BD PHARMINGEN). For cytokine intracellular staining, lung-isolated cells or FLT3L-BMDCs were stimulated for 4h with 50 ng/ml Phorbol 12-myristate 13-acetate (PMA) (Sigma-Aldrich), 500 ng/ml Ionomycin (Sigma-Aldrich) and 10 μg/ml brefeldin A (Sigma-Aldrich) at 37⁰C. Intracellular staining was performed using the True-Nuclear™ Transcription Factor Buffer Set (Biolegend) according to the manufacturer’s instructions. Cells were analyzed using a BD LSR Fortessa cytometer (BD Biosciences) and FlowJo software V10 (BD). Doublets were discarded from the analysis by sequentially selecting the linear population from FSC-A/FSC-H and SSC-A/SSC-H dot plots. When indicated, autofluorescent cells were removed from the analysis using the FITC channel. At least, 2 x 105 lung-isolated cells and 3 x 104 FLT3L-BMDCs were processed. Mean fluorescence intensity (MFI) data were analyzed as Δ MFI, which corresponds to the MFI of the antigen-positive population minus the MFI of the fluorescence minus one (FMO) control of this population.
Statistics
Data are presented as mean ± SEM. Data were tested for normality and homogeneity of variance using GraphPad Prism software, and the required statistical analysis was performed according to the normality of data, as suggested by the software. Accordingly, statistical analysis for multiple comparisons was performed using an ANOVA table followed by Tukey’s multiple comparison tests. Non-multiple comparisons were analyzed using paired or unpaired t-tests. Statistical significance was determined at p < 0.05.
Results
Cancer development decreases the proportions of lung CD103+XCR1+ DC1s
To evaluate the impact of the lung cancer environment on DC populations, two different cancer models were used. The first (LLC) is an orthotopic model of lung cancer that develops as a squamous cell carcinoma. The second one uses B16F10 melanoma cells and is a pulmonary metastatic model. The development of lung tumors resulted in an increased lung index (lung weight/mice weight) and total lung cell numbers in both cancer models (Fig 1A) compared to naïve mice. Total DCs were characterized (as previously published by our group [8, 9]) as auto-fluorescent-, CD90.2-, CD19-, NK1.1-, MHC II+ and CD11c+ (see Fig 1B for DC gating). The presence of LLC and B16F10-induced tumors strongly impacted the relative proportions of these lung DC populations. The percentage of lung CD103+XCR1+ DC1s gradually decreased and reciprocally, the percentage of CD11b+Sirpα+ DC2s increased slightly in both models (Fig 1C–1E). These results indicate that tumor development impacts the balance between CD103+XCR1+ DC1s and CD11b+Sirpα+ DC2s at the expense of the antitumor DC1 population.
Fig 1
Lung tumor development decreases the proportions of CD103+XCR1+ DC1s.
Analysis of lung tumors and DC populations following i.v. injection of B16F10 or LLC cancer cells. (A) Lung index (lung weight/mice weight) and total lung cell number. (B) Gating strategy for the identification of total DCs. DCs were gated on auto‐fluorescence-, NK1.1-, CD90.2-, CD19-, MHC IIHi and CD11c+. (C) Representative flow cytometry profile of DC expression of CD103 x XCR1 and Sirpα x CD11b analysis based on fluorescence minus one (FMO) controls. (D) Percentage of CD103+XCR1+ DC1 and CD11b+Sirpα+ DC2 of MHC IIhiCD11c+ cells (DCs). (E) Percentage of CD103+XCR1+ DC1 at 8 and 15 days after the i.v. injection of B16F10 cells. (A-D) Data are expressed as mean ± SEM. n = 5–8 mice per group and are representative of 2–6 independent experiments. * = p < 0.05 using (A-D) an unpaired t-test. (E) Data are presented as individual points with means.
Lung tumor development decreases the proportions of CD103+XCR1+ DC1s.
Analysis of lung tumors and DC populations following i.v. injection of B16F10 or LLC cancer cells. (A) Lung index (lung weight/mice weight) and total lung cell number. (B) Gating strategy for the identification of total DCs. DCs were gated on auto‐fluorescence-, NK1.1-, CD90.2-, CD19-, MHC IIHi and CD11c+. (C) Representative flow cytometry profile of DC expression of CD103 x XCR1 and Sirpα x CD11b analysis based on fluorescence minus one (FMO) controls. (D) Percentage of CD103+XCR1+ DC1 and CD11b+Sirpα+ DC2 of MHC IIhiCD11c+ cells (DCs). (E) Percentage of CD103+XCR1+ DC1 at 8 and 15 days after the i.v. injection of B16F10 cells. (A-D) Data are expressed as mean ± SEM. n = 5–8 mice per group and are representative of 2–6 independent experiments. * = p < 0.05 using (A-D) an unpaired t-test. (E) Data are presented as individual points with means.
Cancer cells/antigens directly inhibit CD103+XCR1+ DC1 differentiation
We previously demonstrated that antigens or inflammatory molecules such as lipopolysaccharide (LPS) and TNF interfere with CD103 expression on DC1 and alter DC1 differentiation [8]. We thus tested whether cancer cells (alive or dead) directly impact the capacity of DC precursors to differentiate into CD103+ DC1s, which could in part explain the altered DC populations observed in Fig 1. FLT3L-BMDCs were stimulated with GM-CSF to induce DC1 CD103 expression [8, 23], and exposed to LLC or B16F10 cells (live or an antigenic preparation of cancer cells). As expected, GM-CSF alone increased the percentage of CD103+XCR1+ DCs in cultures (Fig 2A and 2B). However, the addition of live LLC or B16F10 cells, or exposure to an antigenic preparation during GM-CSF stimulation significantly decreased the percentage of CD103+XCR1+ DC compared to GM-CSF alone (Fig 2A and 2B). Additionally, this phenomenon was significantly reversed when DCs were segregated from LLC and B16F10 using 0.4 μm inserts (Fig 2C), indicating that the contact between DCs and cancer cells is necessary to prevent CD103+ DC1 differentiation. These results suggest that cancer cells, or an antigenic mix of dead cancer cells could directly alter the proportions of CD103+ DC1s in vivo.
Fig 2
Cancer cells prevent the differentiation of bone marrow precursors into CD103+XCR1+ DC1s.
(A-C) FLT3L-BMDCs were exposed to GM-CSF ± live B16F10/LLC cells or a B16F10/LLC antigenic preparation (A and B). In C), DCs were segregated (or not) from cancer cells with a 0.4 μm insert. (A) Representative flow cytometry profiles of CD103 and XCR1 expression on DCs. (B-C) Percentage of CD103+XCR1+ of MHC IIHiCD11c+ DCs. Data are expressed as mean ± SEM. n = 5, pooled from two independent experiments. ϕ = p < 0.05 compared to GM-CSF alone condition, * = p < 0.05 when conditions with and without inserts are compared. P-values were determined using repeated measures one‐way ANOVA, with the Geisser-Greenhouse correction followed by Tukey’s multiple comparisons test. (D to I) Lung DC populations were analyzed by flow cytometry following an i.v. injection of B16F10 cancer cells. (D) Gating strategy for the identification of CD103+XCR1+ and CD103-XCR1+ populations from previously gated autofluorescent-, CD19-, CD90.2-, CD20-, MHC II+, CD11c+ DCs and representative histogram of PD-L1, PD-L2, CD200, MHC II within these two populations. (E) The ratio of the number of CD103-XCR1+ DCs over CD103+XCR1+ DCs. Percentage and Δ MFI of (F) PD-L1, (G) PD-L2, (H) CD200 and (I) MHC II of CD103+XCR1+ DCs and CD103-XCR1+ in mice injected with B16F10 cells. Data are expressed as mean ± SEM. n = 11 pooled from two independent experiments. * = p < 0.05 using two-way ANOVA with Šídák’s multiple comparisons test.
Cancer cells prevent the differentiation of bone marrow precursors into CD103+XCR1+ DC1s.
(A-C) FLT3L-BMDCs were exposed to GM-CSF ± live B16F10/LLC cells or a B16F10/LLC antigenic preparation (A and B). In C), DCs were segregated (or not) from cancer cells with a 0.4 μm insert. (A) Representative flow cytometry profiles of CD103 and XCR1 expression on DCs. (B-C) Percentage of CD103+XCR1+ of MHC IIHiCD11c+ DCs. Data are expressed as mean ± SEM. n = 5, pooled from two independent experiments. ϕ = p < 0.05 compared to GM-CSF alone condition, * = p < 0.05 when conditions with and without inserts are compared. P-values were determined using repeated measures one‐way ANOVA, with the Geisser-Greenhouse correction followed by Tukey’s multiple comparisons test. (D to I) Lung DC populations were analyzed by flow cytometry following an i.v. injection of B16F10 cancer cells. (D) Gating strategy for the identification of CD103+XCR1+ and CD103-XCR1+ populations from previously gated autofluorescent-, CD19-, CD90.2-, CD20-, MHC II+, CD11c+ DCs and representative histogram of PD-L1, PD-L2, CD200, MHC II within these two populations. (E) The ratio of the number of CD103-XCR1+ DCs over CD103+XCR1+ DCs. Percentage and Δ MFI of (F) PD-L1, (G) PD-L2, (H) CD200 and (I) MHC II of CD103+XCR1+ DCs and CD103-XCR1+ in mice injected with B16F10 cells. Data are expressed as mean ± SEM. n = 11 pooled from two independent experiments. * = p < 0.05 using two-way ANOVA with Šídák’s multiple comparisons test.In the lung, CD103 is one of the main markers used to identify DC1s. Recently, other markers such as XCR1 have also been used to identify this population [10, 11]. Using XCR1 to stain the DC1 population, we observed that following B16F10 lung metastasis development, the ratio of lung CD103-XCR1+ over CD103+XCR1+ DCs was significantly increased compared to naïve mice, suggesting an accumulation of DC1s that do not express CD103 (Fig 2E). As CD103 is normally used to identify DC1s, the difference of function between CD103+XCR1+ DCs and CD103-XCR1+ DCs in cancer is not well-established. We therefore analyzed the expression of PD-L1 and PD-L2, two regulatory molecules that induce the inhibition of T cell proliferation, survival and effector functions through their binding with PD-1 on T cells [5]. While both populations express PD-L1 and PD-L2, the percentage of positive cells and the MFI for these two inhibitory molecules were significantly higher within the CD103- population following tumor development (Fig 2D, 2F and 2G). Conversely, the expression (percentage and MFI) of CD200, another regulatory molecule, was significantly higher on CD103+ cells compared to CD103-XCR1+ DCs (Fig 2D and 2H) [24]. Finally, the MFI of MHC II, which is a marker of DC maturation and activation, was found at a high level in both populations, with MFIs over 10 000 units, but slightly higher in CD103+XCR1+ compared to CD103- DCs (Fig 2D and 2I) [25]. This demonstrates that following cancer development, the lung DC signature is skewed towards CD103- DC1s with high regulatory and activation potential.
Tumor development leads to the accumulation of CD103loCD11b+ DCs in the lung
Through the thorough dissection of the local DC population signature in cancer, we observed that the CD103+ DC1 (population circled in green, Fig 3A) and CD11b+ DC2 (population circled in blue, Fig 3A) are well segregated in naïve mice. However, following the injection of either LLC or B16F10 cells, a third DC population (circled in red) co-expressing low to intermediate levels of CD103 and CD11b was also observed (Fig 3A). We termed this population CD103loCD11b+ DCs. This population was significantly increased in the lung for both cancer models (Fig 3B). The ratio of anticancer DC1s to CD103loCD11b+ DCs was reduced from approximately 4: 1 in naïve mice to 1: 1 in both cancer models (Fig 3C), indicating that the number of CD103loCD11b+ DCs is similar to that of anticancer CD103+ DC1s following tumor development in the lung.
Fig 3
Accumulation of CD103loCD11b+ DCs following lung cancer development.
Lung DC populations were analyzed by flow cytometry following an i.v. injection of LLC and B16F10 cancer cells. (A) Representative flow cytometry profiles of CD103 and CD11b expression on MHC IIhiCD11c+ DCs, showing conventional DC1 population in green, DC2 population in blue and CD103loCD11b+ DCs in red. (B) Percentage and number of CD103loCD11b+ DC. (C) Ratio of CD103+CD11b-/lo DC1 (green) on CD103loCD11b+ (red) DCs. Data are expressed as mean ± SEM. n = 5–8 mice per group and are representative of two independent experiments. * = p < 0.05 using an unpaired t-test with Welch’s correction.
Accumulation of CD103loCD11b+ DCs following lung cancer development.
Lung DC populations were analyzed by flow cytometry following an i.v. injection of LLC and B16F10 cancer cells. (A) Representative flow cytometry profiles of CD103 and CD11b expression on MHC IIhiCD11c+ DCs, showing conventional DC1 population in green, DC2 population in blue and CD103loCD11b+ DCs in red. (B) Percentage and number of CD103loCD11b+ DC. (C) Ratio of CD103+CD11b-/lo DC1 (green) on CD103loCD11b+ (red) DCs. Data are expressed as mean ± SEM. n = 5–8 mice per group and are representative of two independent experiments. * = p < 0.05 using an unpaired t-test with Welch’s correction.
CD103loCD11b+ DCs express surface markers and transcription factors that are characteristic of a DC2 population
CD103+CD11b+ DC2 populations were reported in the gut in various models [26, 27], but whether the CD103loCD11b+ DCs we observed following cancer development were functionally similar to gut CD103+ DC2s remained unclear. Additionally, since lung MHC II+CD11c+ DCs expressing CD11b can originate from bone marrow pre-DCs (conventional DCs), but also blood monocytes (monocyte-derived DCs (mo-DCs)), the origin of this CD103loCD11b+ DC population was ambiguous [11, 28]. Thus, two surface markers, CCR2 and Ly-6C, which are respectively associated with the monocyte lineage and mo-DCs, were analyzed on total MHC II+CD11c+ DCs, and compared between the CD103+ DC1, CD103loCD11b+ DC and total CD11b+ DC2 (which include mo-DCs) populations (the gating strategy specific to this section is presented in S1 Fig) [28, 29]. The percentage of CD103loCD11b+ DCs expressing CCR2 was similar to CD11b+ DC2s following LLC injection (Fig 4A). In contrast, in response to B16F10 injection, the percentage of CD103loCD11b+ DCs expressing CCR2 was similar to DC1s, i.e. fairly low (Fig 4A). However, in both cancer models, the percentage of CD103loCD11b+ DCs expressing Ly-6C, a robust marker of mo-DCs, was significantly lower than CD11b+ DC2s (Fig 4B). This suggests that a significant proportion of CD103loCD11b+ DCs is derived from pre-DCs and does not originate from the monocyte lineage. To confirm the pre-DC origin, we analyzed the expression of ZBTB46, a transcription factor expressed by pre-DCs and conventional DC populations, which was found in the vast majority of CD103loCD11b+ DCs (Fig 4C) [30].
Fig 4
CD103loCD11b+ DCs express markers of the DC2 population.
Surface markers and transcription factors expression were analyzed by flow cytometry and compared between lung DC1 (green), CD103loCD11b+ DC (red) and DC2 (blue) populations following the i.v. injection of LLC or B16F10 cells. Percentage of (A) CCR2+, (B) Ly-6C+ and (C) ZBTB46+ cells for each subpopulation of DCs. (D) Representative contour plots of Sirpα and XCR1 expression of each DC subpopulation, as well as the percentage of Sirpα+ cells in CD103loCD11b+ DCs (red) and DC2s (blue), and the percentage of XCR1+ cells in DC1s (green) and CD103loCD11b+ DCs (red). (E) Representative contour plots of IRF4 and IRF8 expression, as well as Δ IRF4 MFI in CD103loCD11b+ DCs (red) and DC2s (blue), and Δ IRF8 MFI in DC1s (green) and CD103loCD11b+ DCs (red). Data are expressed as mean ± SEM. (A-B-E) n = 5–8 mice per group and are representative of two independent experiments. (C-D) n = 11–14 pooled from two independent experiments. * = p < 0.05, (A-B-C) using repeated measures one‐way ANOVA, with the Geisser-Greenhouse correction followed by Tukey’s multiple comparisons test and (D-E) using a paired t-test.
CD103loCD11b+ DCs express markers of the DC2 population.
Surface markers and transcription factors expression were analyzed by flow cytometry and compared between lung DC1 (green), CD103loCD11b+ DC (red) and DC2 (blue) populations following the i.v. injection of LLC or B16F10 cells. Percentage of (A) CCR2+, (B) Ly-6C+ and (C) ZBTB46+ cells for each subpopulation of DCs. (D) Representative contour plots of Sirpα and XCR1 expression of each DC subpopulation, as well as the percentage of Sirpα+ cells in CD103loCD11b+ DCs (red) and DC2s (blue), and the percentage of XCR1+ cells in DC1s (green) and CD103loCD11b+ DCs (red). (E) Representative contour plots of IRF4 and IRF8 expression, as well as Δ IRF4 MFI in CD103loCD11b+ DCs (red) and DC2s (blue), and Δ IRF8 MFI in DC1s (green) and CD103loCD11b+ DCs (red). Data are expressed as mean ± SEM. (A-B-E) n = 5–8 mice per group and are representative of two independent experiments. (C-D) n = 11–14 pooled from two independent experiments. * = p < 0.05, (A-B-C) using repeated measures one‐way ANOVA, with the Geisser-Greenhouse correction followed by Tukey’s multiple comparisons test and (D-E) using a paired t-test.To further assess whether CD103loCD11b+ DCs are associated with the DC1 or DC2 conventional DC subsets, XCR1 and Sirpα surface expression was analyzed and compared to DC1 or DC2 conventional DC populations. We observed that the vast majority of the CD103loCD11b+ DC population (Fig 4D; red population), co-distributes with CD11b+ DC2s on the XCR1 vs Sirpα contour plots. Also, CD103loCD11b+ DCs express Sirpα at a similar level to DC2s, and XCR1 at a significantly lower level than DC1s. To deepen the characterization of CD103loCD11b+ DCs, IRF8 (DC1) and IRF4 (DC2) transcription factors expression were also compared between DC populations. As observed in the IRF4 and IRF8 contour plots (Fig 4E), CD103loCD11b+ DCs co-distributes with the DC2 population. The MFI of IRF4 and IRF8 was compared between CD103loCD11b+ DCs and DC1s/DC2s. The IRF4 MFI in CD103loCD11b+ DCs was significantly higher than conventional DC2s. Additionally, IRF8 expression was significantly lower than DC1s in this population (Fig 4E). Therefore, the surface markers and transcription factors analyzes indicate that CD103loCD11b+ DCs are likely associated with the DC2 population in these models.
CD103loCD11b+ DCs express high levels of migratory, co-stimulatory and antigen-presenting molecules
With a presence in the lung that is quantitatively comparable to that of anticancer CD103+ DC1s, we set out to address the capacity of CD103loCD11b+ DCs to present antigen and migrate, as indicators of their functional potential. To do so, we verified the expression of antigen presentation molecules, chemokine receptors involved in DC migration as well as co-stimulatory molecules.MHC I participates in the cross-presentation of tumor antigens by DCs to CD8 T cells, while MHC II is involved in antigen presentation to CD4 T cells [7]. We observed that CD103loCD11b+ DCs strongly express MHC I and MHC II in both models, to a level similar or higher than other DC subpopulations (Fig 5A). CCR7 is involved in DC trafficking from the lung to the draining lymph nodes [7]. We observed that the CD103loCD11b+ population expresses higher CCR7 levels than the other DC populations in both cancer models (Fig 5B), suggesting a strong potential for trafficking to the lymph nodes and T cell interactions. Additionally, in LLC and B16F10 models, the co-stimulatory molecule CD80 surface expression was higher in CD103loCD11b+ DCs compared to the CD103+ DC1 population, (Fig 5C). Alternatively, the co-stimulatory molecule CD86 expression was significantly higher in CD103loCD11b+ DCs than in the DC2 population (Fig 5C).
Fig 5
CD103loCD11b+ DCs are activated and show strong potential for T cell interactions.
Expression of surface markers and cytokines production were analyzed by flow cytometry and compared between lung DC subpopulations following the i.v. injection of LLC or B16F10. Upper panels in (A-B-C): representative flow cytometry histograms showing the normalized number of cells (unit area) on the Y-axis and fluorescence intensity on the X-axis. FMO controls appear in grey in each histogram. Lower panels in (A-B-C): comparison of (A) ΔMHC I, MHC II, (B) ΔCCR7, (C) ΔCD80 and ΔCD86 MFI between DC1 (green), CD103loCD11b+ DC (red) and DC2 (blue) populations. Data are expressed as mean ± SEM. n = 10–17 pooled from two independent experiments. * = p < 0.05 using repeated measures one‐way ANOVA, with the Geisser-Greenhouse correction followed by Tukey’s multiple comparisons test.
CD103loCD11b+ DCs are activated and show strong potential for T cell interactions.
Expression of surface markers and cytokines production were analyzed by flow cytometry and compared between lung DC subpopulations following the i.v. injection of LLC or B16F10. Upper panels in (A-B-C): representative flow cytometry histograms showing the normalized number of cells (unit area) on the Y-axis and fluorescence intensity on the X-axis. FMO controls appear in grey in each histogram. Lower panels in (A-B-C): comparison of (A) ΔMHC I, MHC II, (B) ΔCCR7, (C) ΔCD80 and ΔCD86 MFI between DC1 (green), CD103loCD11b+ DC (red) and DC2 (blue) populations. Data are expressed as mean ± SEM. n = 10–17 pooled from two independent experiments. * = p < 0.05 using repeated measures one‐way ANOVA, with the Geisser-Greenhouse correction followed by Tukey’s multiple comparisons test.All and all, this cluster of results suggests that in cancer, CD103loCD11b+ DCs are overall profiled to present antigen, co-stimulate T cells upon antigen presentation and migrate to lymph nodes compared to CD103+ DC1s and DC2s.
The CD103loCD11b+ DC population expresses high levels of regulatory molecules and produces low levels of IL-12
The regulatory processes allowing tumor progression are linked to the induction/ production of regulatory molecules by DCs, such as TGF-β, PD-L1, PD-L2 and CD200 [5, 24]. Indeed, the interaction of PD-L1/2 with PD-1 on T cells negatively impacts immune responses through the inhibition of T cell proliferation, survival and effector functions [5]. DCs can also produce the regulatory cytokine TGF-β while CD200 interacts with its receptor to induce an inhibitory signal preventing activation [31]. Finally, TIM-3 was recently shown to exert regulatory functions when expressed by CD103+ DC1 [32]. These regulatory markers were therefore evaluated in CD103loCD11b+ DCs to determine the regulatory potential of this population.Following lung tumor development, all three DC populations expressed PD-L1, but CD103loCD11b+ DCs expressed the highest levels of PD-L1 (Fig 6A). We also noted that an important proportion of CD103loCD11b+ DCs expressed PD-L2 (whereas few CD103+ DC1s and DC2s did), with a significantly higher MFI than DC1 and DC2s (Fig 6B). Therefore, both PD-L1 and PD-L2 are highly expressed on CD103loCD11b+ DCs in lung cancer. While we observed a higher CD200 expression on CD103loCD11b+ DCs compared to DC1s and DC2s in both models (Fig 6C), TGF-β expression was significantly higher in CD103loCD11b+ DCs compared to DC1s following LLC injection only (Fig 6D). Finally, TIM-3 expression was significantly higher in the CD103+ DC1 population compared to CD103loCD11b+ DCs and DC2s (Fig 6E). Independently of the TIM-3 expression, the significantly higher expression of PD-L1, PD-L2 and CD200 combined with a higher TGF-β production by CD103loCD11b+ DCs compared to other DC populations bestow a convincing immunoregulatory potential to this population.
Fig 6
CD103loCD11b+ DCs express regulatory molecules.
Surface marker expression and cytokines production were analyzed by flow cytometry and compared between lung DC subpopulations following the i.v. injection of LLC or B16F10. Left panels on (A-B-C): representative flow cytometry histograms showing the normalized numbers of cells (unit area) on the Y-axis and fluorescence intensity on the X-axis for (A) PD-L1, (B) PD-L2 and (C) CD200. FMO control appears in grey in each histogram. Right panels on (A-B-C) and (D-E-F): comparison of (A) ΔPD-L1, (B) ΔPD-L2, (C) ΔCD200, (D) ΔTGFβ, (E) ΔTIM-3 and (F) ΔIL-12 p40 MFI MFI between DC1 (green), CD103loCD11b+ DC (red) and DC2 (blue) populations. Data are expressed as mean ± SEM. n = 10–17 pooled from two independent experiments. * = p < 0.05 using repeated measures one‐way ANOVA, with the Geisser-Greenhouse correction followed by Tukey’s multiple comparisons test.
CD103loCD11b+ DCs express regulatory molecules.
Surface marker expression and cytokines production were analyzed by flow cytometry and compared between lung DC subpopulations following the i.v. injection of LLC or B16F10. Left panels on (A-B-C): representative flow cytometry histograms showing the normalized numbers of cells (unit area) on the Y-axis and fluorescence intensity on the X-axis for (A) PD-L1, (B) PD-L2 and (C) CD200. FMO control appears in grey in each histogram. Right panels on (A-B-C) and (D-E-F): comparison of (A) ΔPD-L1, (B) ΔPD-L2, (C) ΔCD200, (D) ΔTGFβ, (E) ΔTIM-3 and (F) ΔIL-12 p40 MFI MFI between DC1 (green), CD103loCD11b+ DC (red) and DC2 (blue) populations. Data are expressed as mean ± SEM. n = 10–17 pooled from two independent experiments. * = p < 0.05 using repeated measures one‐way ANOVA, with the Geisser-Greenhouse correction followed by Tukey’s multiple comparisons test.Previous studies demonstrated that CD103+ DC1s are the main producer of IL-12, an important step in cancer management by immune cells [12, 13]. We observed that IL-12 p40 production in CD103loCD11b+ DCs was significantly lower than CD103+ DC1s, but slightly higher than DC2s (Fig 6F). These results, combined with the high expression of regulatory molecules, further supports the idea that CD103loCD11b+ DCs do not exert an effective anticancer immune response and rather likely contribute to the immunoregulatory environment in cancer.
Enrichment with CD103+ DC1s improves anti-PD-1 sensitivity
Most current immunotherapy strategies focus on improving T cell function by targeting ICI pathways [3]. However, we demonstrate that lung tumors influence the local DC population signature by decreasing the anticancer CD103+ DC1s proportions and inducing an accumulation of CD103loCD11b+ DCs with regulatory potential. These two phenomena likely cooperate to blunt anticancer immunity. However, we also propose that local DC populations in cancer are ill-equipped to present cancer antigen, which could explain the mitigated success of ICIs, which relies on the assumption of efficient interactions between DCs and T cells.Therefore, we wondered whether enriching the local DC population with CD103+ DC1s, specialized in presenting cancer antigen, would enhance the response to ICIs. To address this question, we first set out to generate a large amount of anticancer FLT3L-BMDCs, and prime these cells with tumor antigen. Our first technical challenge was to counteract the downregulating impact of cancer cell exposure on CD103+ DC1 differentiation. We therefore determined that adding cancer cells two days following GM-CSF allowed for maximal differentiation of CD103+XCR1+ DC1s in the presence of cancer cells (S2A Fig). Critically, the anticancer cytokine IL-12 was strongly induced when DCs were stimulated with live cancer cells (not with the antigenic preparation) two days after GM-CSF stimulation (Fig 7A). This condition was therefore used to produce the maximal level of cancer-primed CD103+ DC1s in cultures. The phagocytosis of cancer cells by DCs was confirmed in a phagocytosis assay, where B16F10 cells were stained with CFSE prior to the co-stimulation. The CFSE signal was then detected in FLT3L-BMDCs as an indication of phagocytosis. The CFSE MFI signal was increased in DCs exposed to B16F10-CFSE, and was higher in CD103+XCR1+ DCs compared to CD11b+Sirpα+ DCs (Fig 7B and S2B Fig). Therefore we concluded that the stimulation of FLT3L-BMDCs with B16F10 prior injections allows DC1 to be primed with tumor antigens, and used these cells in the injections described in the next section.
Injection of XCR1+ DC1 improves sensitivity to anti-PD-1 treatment.
(A) FLT3L-BMDCs were stimulated with GM-CSF (Day 0). Either live B16F10/ LLC cells or a B16F10/ LLC antigenic preparation was added on day 0 or day 2 and ΔIL-12 p40 MFI was measured by flow cytometry on day 3. (B) Two days following GM-CSF stimulation FLT3L-BMDCs were stimulated for 24h with CFSE-treated B16F10 cells and CFSE MFI was measured by flow cytometry in CD11c+MHC II+ DC ± stimulation with CFSE-B16F10. (C) Schematic representation of the timeline treatment with the anti-PD-1 and XCR1+ DC1 injections (D-E-F) Analysis of mice lungs (D) 9 days or (E-F) 18 days after B16F10 injections. (D) The total number of lung CD103+XCR1+ DC1 24h after the last DC injection. (E) Lung index (lung weight/mice weight), cancer score (indicative of the number and size of tumors) and lung total cell number. (F) Lung ratios of CD8 T cell and NK cell numbers relative to the number and size of tumors (cancer score). CD8 T cells were identified as CD45+, CD19-, CD90.2+, CD4-, CD8+ and NK cells were identified as CD19-, CD3e-, B220-, CD49b+, NK1.1+ by flow cytometry analysis. (A) Data are presented as individual dots with means. (B to F) Data are expressed as mean ± SEM. (A-B-E-F, except E panel 3) n = 5–11 pooled from two independent experiments. (D, E panel 3) n = 5 representative of two independent experiments.* = p < 0.05, compared as indicated in the graph; ϕ = p < 0.05 compared to naïve mice. P-values were determined using (B) paired t-test, (D) unpaired t-test and (E-F) one-way ANOVA followed by Tukey’s multiple comparisons test.Then, DCs were injected alone or in combination with a commonly-used anti-PD-1 [3] in the B16F10 model (Fig 7C), which is reportedly resistant to anti-PD-1 therapies [20]. The injection of XCR1+ DC1s led to a significant increase in the total number of lung CD103+XCR1+ DCs 24h later (Fig 7D). The lung index and cancer score, indicators of the quantity and size of tumors, were significantly increased in all groups injected with B16F10 compared to naïve mice (Fig 7E). The total number of lung cells was significantly increased compared to naïve mice in all groups except for the mice treated with anti-PD-1 in combination with DC injection (Fig 7E). As reported, the treatment with the anti-PD-1 did not impact cancer severity when administered alone. However, combining the anti-PD-1 treatment with DC1 injections significantly decreased the lung index and cancer score compared to the B16F10 control group, and showed a strong tendency to decrease total lung cells, restoring sensitivity to the ICI treatment (Fig 7E). As an indicator of an overall impact of DC transfers on the anticancer response, the number of CD8 T cells and NK cells relative to the number and size of tumors was calculated by dividing the total number of CD8 T cells or NK cells (S2C Fig) by the cancer score, and is presented in Fig 7F as CD8 T cell and NK cell ratios. While the injection of the DC1 alone induced trends towards higher ratios of CD8 T cells and NK cells, the combination of treatments significantly increased the CD8 T cell and NK cell ratios compared to baseline. This suggests that restoring potent anticancer DC populations can not only restore the capacity of anticancer DCs to locally interact with CD8 T cells in response to anti-PD-1s, but also supports the accumulation of CD8 T cells in this context, conferring an even higher potential to this approach.
Discussion
The clinical efficacy of ICIs is currently undermined by the development of treatment resistance in the long run, in addition to limited patient responsiveness [3, 6]. The presence and activity of pre-existing CD8 T cells that are specific to tumor antigens is an essential condition for the success of ICIs. DCs, particularly DC1s, play a crucial role in priming antigen-specific CD8 T cells [7]. However, the influence of cancer on the local DC lung signature and DC function remains poorly studied. In this report, we precisely report the influence of tumor development on DC populations, with alterations that are likely detrimental to the anticancer immune response.Our observation that lung tumor development leads to decreased proportions of the anticancer CD103+ DC1 population is supported by various other studies. Indeed, a predominance of the CD11b+ DC2 subset was reported in an orthotopic lung tumor model [33]. Studies using human samples from breast, pancreatic and lung tumors also described decreased proportions of DC1s [34, 35]. Additionally, a decreased DC1 frequency in bone marrow and blood was previously observed in cancer patients [34, 36]. Also, proportions of blood DC1s and the DC1/DC2 ratio were reportedly decreased with cancer severity in lung cancer patients [36]. While supporting our observations, these data also support a high potential to translate this study to human cancer.Additionally, the association between decreased DC1 proportions and cancer severity suggests that reduced DC1 proportions could be detrimental to the successful activation of anticancer immune responses. While the impairment of the anticancer immune response caused by the genetic abrogation of DC1s was previously described (such as in Batf3 mouse models) [12-14], the impact of the naturally-occurring decrease in DC1 proportions provoked by lung tumor development (reported here) on the anticancer immune response and cancer severity remained unaddressed. Furthermore, we observed that tumor development skewed the DC response towards DC1s that do not express CD103, which could be due to a decreased CD103 expression on XCR1+ cells; this phenomenon was previously described by us in response to LPS-induced lung inflammation [8]. In the particular case of live cancer cell interaction, we observed that direct contact with DCs was necessary to influence CD103 expression, suggesting that surface molecules expressed by tumor cells are involved. For instance, the interaction between PD-L1 expressed by tumor cells and PD-1 on DCs or the binding of galectin-9 present on tumor cells with TIM-3 expressed on some DC populations, which are known to influence DC functions, could be involved [32, 37]. Further analysis will be necessary to identify the exact mechanism. Additionally, this result suggests that using CD103 as a marker of DC1s could lead to an underestimation of DC1 numbers. Also, and in accordance with our previous publications, this demonstrates that CD103 expression is highly modulable in the lung and should not be used to identify DC1s. Furthermore, the higher expression of regulatory markers PD-L1 and PD-L2 by CD103-XCR1+ DCs likely supports the immunoregulatory tumor microenvironment. Finally, these observations also complement recent reports demonstrating the accumulation of regulatory DC1s in lung cancer [38].Our analysis of DC populations in lung tumor models also highlighted a CD103loCD11b+ DC population that is not usually observed at these proportions in the lung. Interestingly, most intestine and mesenteric lymph nodes CD11b+ DC2 typically express CD103 under homeostatic and inflammatory conditions [26, 27, 39]. Of note, our results suggest that this population stems from a CD103-expressing DC2 population, similar to CD103+CD11b+ DC populations found in the gut and mesenteric lymph nodes [26, 27]. The only identification marker analyzed in this study that was differentially expressed by CD103loCD11b+ DCs and regular lung CD11b+CD103- DC2s was the transcription factor IRF4, which was expressed at a higher level in CD103loCD11b+ DCs. This may be due to differential expression with regards to the maturation status of these two populations. A study by Schlitzer et al. also observed that in the small intestinal lamina propria, the CD103+CD11b+ DC population expressed higher levels of IRF4 than the CD103-CD11b+ DC populations [40], which strengthens the idea of similarity between lung and gut CD103+CD11b+ DC populations. Several studies demonstrated that intestinal CD103+CD11b+ DCs are crucial in TH17 responses [26, 27]. The development of different cancer types, including NSCLC, is associated with an important increase of TH17 cells in tumors and peripheral blood, but there remains a current lack of consensus in terms of the role of TH17 cells in cancer, as they were alternatively deemed to exert antitumor activity or promote tumor development [41, 42].While few studies reported a CD103+/loCD11b+ DC population in the lung, Sharma et al. observed a CD103+Ly-6C+ DC population that expressed CD11b in tumor extracts from mice treated with chemotherapy. However, this DC population was different from the CD103loCD11b+ observed here, as it expressed DC1-associated markers and no PD-L1. [43]. Another study also reported an accumulation of a CD103+CD11b+ DC population in the lung, in a mouse model of infection with H. capsulatum treated with an anti-TNF. While they did not fully characterize this DC population, authors report its involvement in regulatory T cell amplification, similar to what was previously reported for CD103+CD11b+ gut DCs [44, 45]. An interesting study published by Maier et al. using single-cell RNA sequencing in a murine model of lung adenocarcinoma lesions recently identified a DC population with high levels of maturation markers such as CD80, CD86 and MHC II, and also immunoregulatory genes Pdcd1lg2 (PD-L2 gene) and Cd200. They consequently named this population ‘mature DCs enriched in immunoregulatory molecules’ (mregDCs). In addition, a large proportion of their mregDC population expresses both CD103 and CD11b. While in our hands the CD103loCD11b+ DC population is mainly associated with DC2s, their mregDCs include cells from both DC1 and DC2 populations, which suggest that the population we identified might be a subpopulation of total mregDCs as identified in this study [38].Lung DCs originate from either bone marrow-derived pre-DCs or monocytes [11, 28]. Our analysis of CCR2 and Ly-6C expression, both associated with the monocyte-derived DC lineage [28, 29], and our analysis of ZBTB46, a marker of conventional DCs and pre-DCs [30] suggests that this population originates from pre-DCs. Of note, intestinal CD103+CD11b+ DCs also originate from pre-DCs [39]. Several mechanisms could explain the accumulation of lung CD103loCD11b+ DC following tumor development. Pre-DCs could be directly recruited from bone marrow to the lung and then differentiated into CD103loCD11b+ DCs. As GM-CSF induces CD103, high levels of GM-CSF in the lung in cancer could also induce local CD103 expression on CD11b+ DC2s [8, 23, 46]. As the fate of DCs (DC1 vs DC2 lineage) is determined at the progenitor stage [47], it would be interesting to determine whether this population is associated with a specific bone marrow progenitor. Another hypothesis to explain the presence of CD103loCD11b+ DCs is that lung tumor development could induce the migration of CD103+CD11b+ DCs from the gut to the lung, as was suggested in an H. capsulatum infection model [44].Our analysis of several surface proteins involved in DC functions revealed that lung CD103loCD11b+ DCs could potentially influence T cell responses and ultimately the efficiency of anticancer immune response. Indeed, the interaction between PD-1 and its ligands PD-L1 and PD-L2 leads to the inhibition of T cells [5]. Furthermore, the blockade or silencing of PD-L1 or PD-L2 on DCs results in higher production of IL-12 and enhanced DC maturation combined with improved T cell antitumor function [48, 49]. Also, PD-L1 expression on antigen-presenting cells correlated with clinical efficacy of PD-L1 and PD-1 blockade in a cohort of melanoma patients [50]. These results suggest that the observed high expression of both PD-L1 and PD-L2 on CD103loCD11b+ DCs could restrain T cell anticancer responses. This is of major importance to the field, as we have identified a DC population that could counteract the positive impact of DC1s and that may explain the failure of DC1s to naturally control tumor development in the lung in cancer. A better understanding of the origin of this population could lead to strategies controlling their recruitment to ultimately modulate anticancer immune responses.Several studies report the presence of regulatory DCs in tumor environment [38, 51–53]. Yet, there is actually no consensus on what defines “regulatory DC” in cancer. Some suggest that immature DCs possess regulatory or tolerogenic functions, since they inhibit innate and adaptive immune responses [51, 52]. Others claimed that “regulatory DCs” produce high levels of anti-inflammatory cytokines, and are involved in regulatory T cell development [54]. Finally, high expression of regulatory molecules like PD-1 or PD-L1 is also attributed to “regulatory DCs” [38, 51, 53]. It is very likely that currently, cells under the “regulatory DCs” label comprise different subtypes of DCs at different developmental or maturation stages. Importantly, we and others demonstrate that the expression of some surface proteins like CD103 and PD-L1 can be modulated in various contexts [8, 53]. Therefore, an approach based on the analysis of transcription factors involved in DC development like BATF3, IRF8 and IRF4, combined to surface markers stably expressed from the progenitor to mature stage of DCs would help better characterize regulatory DC populations. In any case, we feel the strong expression of regulatory molecules on CD103loCD11b+ DCs reported in our cancer models allows the classification of this DC population under the “regulatory DC” scope, as one of the best-characterized regulatory DC populations in cancer to date, and a possible new explanation of the failure of antitumor DC1s to control cancer spread.It is widely recognized that CD103+ DC1s are important to the anticancer T cell response [13, 14]. In this report, we observed that lung tumors development leads to altered proportions of CD103+ DC1s populations. This may, in combination with the accumulation of CD103loCD11b+ regulatory DCs, explain the mitigated impact of current immunotherapies. Indeed, even if ICIs therapies re-establish the capacity of T cells to induce effective antitumor responses, it remains that the local DC population is ill-equipped to present cancer antigen, which could be circumvented by injection of ex-vivo conditioned autologous DCs in humans. Interestingly, the vast majority of previous clinical trials using DCs as a cancer vaccination approach used mo-DCs, which are functionally different from CD103+ DC1s [55]. Our study strongly argues that the injection of purified DC1s could improve the success of DC vaccination therapies in cancer. In both mice and humans, in vitro differentiation of DCs in a media containing FLT3L generates a large number of DC1s [23, 56]. To date, few studies have tested the anticancer efficiency of FLT3L-derived DCs. Interestingly, FLT3L injections, which strongly increase the number of circulating DC, improve ICIs efficacy in different mouse models of cancer supporting an important anticancer potential for FLT3L-DCs. [17, 57]. Furthermore, in models of subcutaneous cancer, the injection of FLT3L-BMDCs reduced the progression of tumor volume [21, 58]. These results propose an interesting therapeutic potential for FLT3L-BMDCs. However, in our hands, the DC-alone treatment in the B16F10 model of lung metastasis did not prove effective, which likely relates to the aggressiveness of the model and the consequent strong in vivo immunosuppressive environment altering the anticancer potential of local DC populations. Therefore, tackling the presence of immunosuppressive molecules and cells (such as CD103loCD11b+ DC2s reported here) should remain a priority to fully address the potential of DC transfers to treat cancer.One technique commonly used to improve the efficacy of DC injections is to stimulate DCs with tumor antigens [20, 55]. Here, despite an important production of IL-12 following the stimulation of DCs with live B16F10 cells, the injection of DCs alone did not reduce cancer severity. Several studies have demonstrated that DC stimulation with TLR agonists improves their anticancer efficiency [20, 21]. It therefore might be interesting to use this method to improve anticancer function of FLT3L-BMDCs.New trends in cancer treatment combine two or more immunotherapies [3]. Based on this approach, and considering the fact that the B16F10 melanoma model is resistant to anti-PD-1 alone [20] and that we observed a deficiency in potent anticancer DC1s in this model, we combined FLT3L-BMDC injections to the anti-PD-1, to restore, at least partially, a potent DC1 population in the lung at the time of PD-1/PD-L1 axis blockade. Our results suggest that the optimal ICIs response required the presence of efficient anticancer DC populations. This is of high interest to the field, as DC populations could be evaluated in patients and restored whenever needed to improve the response to ICI therapies.In conclusion, using mouse models of lung cancer and lung melanoma metastasis, we demonstrate that lung tumor development significantly modulates DC populations at the expense of antitumor DC1s, favouring an unusual accumulation of CD103loCD11b+ DC2s that express regulatory molecules. We also demonstrate that enriching the local DC population with CD103+ DC1s restores the efficacy of anti-PD-1 therapy. These results suggest that, despite mitigated previous clinical trials using DC vaccination, targeting DC populations remains a valid therapeutic approach to favour the anticancer immune response or to improve existent ICI therapies in lung cancer.
Gating strategy for the identification of lung DC populations.
Gating strategy for the identification of DCs, associated with Fig 4. Total DCs were gated on auto‐fluorescence-, NK1.1-, CD90.2-, CD19-, MHC IIHi and CD11c+. For this Fig, three populations were segregated prior to the further analysis of DC1 and DC2 markers, i,e CD103+CD11b-/lo (green), CD11b+CD103- (blue) and CD103loCD11b+ DCs (red). A representative flow cytometry histogram showing the normalized number of cells (unit area) on the Y-axis and fluorescence intensity on the X-axis is presented for the XCR1, Sirpα, IRF8, IRF4, CCR2, Ly-6C and ZBTB46 expression for each of these three DC populations. FMO controls appear in grey in each histogram.(TIF)Click here for additional data file.
In vitro and in vivo characterization of FLT3L-BMDCs generated for transfer experiments.
(A) FLT3L-BMDCs were stimulated with GM-CSF (Day 0). Either live B16F10/ LLC cells or a B16F10/ LLC antigenic preparation was added on day 0 or day 2 and the percentage of CD103+XCR1+ DCs was measured by flow cytometry on day 3. Data are presented as individual dots with means. n = 4 pooled from two independent experiments. (B) Two days following GM-CSF stimulation FLT3L-BMDCs were stimulated for 24h with CFSE-treated B16F10 cells and Δ CFSE MFI (CFSE MFI of DC exposed to CFSE-B16F10 –CFSE MFI of unexposed DC (negative control)) in CD103+XCR1+ DC1 and CD11b+Sirpα+DC2 and the percentage of CD103+XCR1+ DC1 and CD11b+Sirpα+DC2 of the total CFSE+ population was measured by flow cytometry. Data are expressed as mean ± SEM. n = 6, pooled from two independent experiments. * = p < 0.05 using a paired t-test. (C) The total number of lung CD8 T cells and NK cells 18 days following B16F10 injections. CD8 T cells were identified as CD45+, CD19-, CD90.2+, CD4-, CD8+ and NK cells were identified as CD19-, CD3e-, B220-, CD49b+, NK1.1+ by flow cytometry analysis. Data are expressed as mean ± SEM. n = 9–11 pooled from two independent experiments. ϕ = p < 0.05 compared to naïve mice. P-values were determined using a one-way ANOVA followed by Tukey’s multiple comparisons test.(TIF)Click here for additional data file.16 Aug 2021Bélatelep, HungaryAugust 11, 2021PONE-D-21-20655Countering the advert effects of lung cancer on the anticancer potential of dendritic cell populations reinstates sensitivity to anti-PD-1 therapy.PLOS ONEDear Dr. Blanchet,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. 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(Please upload your review as an attachment if it exceeds 20,000 characters)Reviewer #1: In the manuscript “Countering the advert effects of lung cancer on the anticancer potential of dendritic cell populations reinstates sensitivity to anti-PD-1 therapy.”, two mice models have been used to show the impact of cancer on dendritic cells (DC) in the lung. The authors conclude that cancer can change the composition of DC, with the generation of a CD103low DC population with DC2 markers and high levels of T-cell inhibitory molecules. Injection of DC1 in combination with anti-PD-1 checkpoint inhibitor could restore sensitivity to immunotherapy. The manuscript is well organized and written. The authors should consider the following comments:1. The authors describe the relative proportions of DC subpopulations. The proportion of CD103+ DC with anticancer functions is falling whereas the proportion of CD11b+ DC with a low CD103 expression is rising. However, what does this mean for absolute DC numbers in the lung? Could the authors use microscopy or another method to get a score of total DC/lung?2. What happens in the blood of the tumor-exposed mice? The authors should show whether one can find CD103low DC in the blood. This would have the advantage that one can give absolute numbers of DC subpopulations over time. Could one use the time course of CD103low DC as a biomarker?3. The authors have to explain their flow cytometry experiments in more detail. Which channel was used for “autofluorescence”? Which viability marker was used? How many cells were measured to get enough DC? Did you identify and exclude doublets?4. If direct contact between tumor cells and DC changes the DC subpopulations of the lung, which tumor molecules could be involved in the process?Reviewer #2: Below are some questions and remarks to the authorsGeneralThe manuscript should be carefully proofread and adapted with special attention to English grammar. Several sentences require reformulation to bring the message more clear and scientifically correct. The choice of words and the lack of punctuations makes reading more difficult.IntroductionIn the introduction the authors write about lung cancer, the different DC subsets and their findings. Although it is a coherent introduction, the authors should elaborate more on immune checkpoints and their relevance in lung cancer especially, the PD-1/PD-L1 and PD-L2 axis.Material and MethodsThe phagocytosis assay (line 132) should be described under a separate subtitle as it is a read-out (proof of phagocytosis by DC) and not a production method of the FLT3L-BMDCs.ResultsThe DC1s, CD103LoCD11b+ DCs and DC2s are phenotypically characterized by the authors. Based on the phenotype, various assumptions about their functionality are made. These assumptions could be confirmed by performing functional assays comparing the different subsets of the DCs. Do the authors have functional data or could they perform some functional assays, such as T cell activation and proliferation assays or a migration assay?In 4.2 the authors refer to a figure with “(Figure 2A and B)” in other cases the authors refer with “(Figure 2DH )”. Use consistency in your references.In the caption of figure 2 line 254-256, the authors write that figure 2F, G, H and I represent the percentage and ΔMFI of PD-L1, PD-L2, CD200 and MHC II of DC subtypes in naïve mice and mice injected with B16F10 cells. The graphs F, G, H and I only show data of DC subtypes in mice injected with B16F10 cells. Please add the data of naïve mice or correct the caption.**********6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.Reviewer #1: NoReviewer #2: No[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.6 Oct 2021Reviewer 1 comments:Reviewer #1: In the manuscript “Countering the advert effects of lung cancer on the anticancer potential of dendritic cell populations reinstates sensitivity to anti-PD-1 therapy.”, two mice models have been used to show the impact of cancer on dendritic cells (DC) in the lung. The authors conclude that cancer can change the composition of DC, with the generation of a CD103low DC population with DC2 markers and high levels of T-cell inhibitory molecules. Injection of DC1 in combination with anti-PD-1 checkpoint inhibitor could restore sensitivity to immunotherapy. The manuscript is well organized and written. The authors should consider the following comments:1. The authors describe the relative proportions of DC subpopulations. The proportion of CD103+ DC with anticancer functions is falling whereas the proportion of CD11b+ DC with a low CD103 expression is rising. However, what does this mean for absolute DC numbers in the lung? Could the authors use microscopy or another method to get a score of total DC/lung?2. What happens in the blood of the tumor-exposed mice? The authors should show whether one can find CD103low DC in the blood. This would have the advantage that one can give absolute numbers of DC subpopulations over time. Could one use the time course of CD103low DC as a biomarker?3. The authors have to explain their flow cytometry experiments in more detail. Which channel was used for “autofluorescence”? Which viability marker was used? How many cells were measured to get enough DC? Did you identify and exclude doublets?4. If direct contact between tumor cells and DC changes the DC subpopulations of the lung, which tumor molecules could be involved in the process?Response to reviewer 1 comments:1. We thank R1 for this pertinent question. All DC populations are increased in cancer, but the proportions of each population are, as reported, very different. Therefore, even if DC1s increase in numbers following cancer development, they are dramatically overtaken by DC2s in this context, which also increase in numbers, but un much higher proportions. Also, anticancer DC1s are found in a 1:1 ratio with regulatory CD103lo/CD11b+ DCs following tumour development. Taking this into consideration, it becomes clear that the data which speaks the most as to the nature of the DC response in the lung remains DC proportions. We hope this clarifies our reasoning for not including total numbers in figures.2. This is an interesting question. Quickly, precursors do not express CD103 in the blood, so CD103 low DCs as a biomarker could be used, but would need to come from the lung.3. We thank R1 for this important question, and have now provided the information in the text.4. We thank the reviewer for this interesting suggestion and have provided additional information with regards to potential tumour molecules interactions in the text.Reviewer 2 comments:GeneralThe manuscript should be carefully proofread and adapted with special attention to English grammar. Several sentences require reformulation to bring the message more clear and scientifically correct. The choice of words and the lack of punctuations makes reading more difficult.IntroductionIn the introduction the authors write about lung cancer, the different DC subsets and their findings. Although it is a coherent introduction, the authors should elaborate more on immune checkpoints and their relevance in lung cancer especially, the PD-1/PD-L1 and PD-L2 axis.Material and MethodsThe phagocytosis assay (line 132) should be described under a separate subtitle as it is a read-out (proof of phagocytosis by DC) and not a production method of the FLT3L-BMDCs.ResultsThe DC1s, CD103LoCD11b+ DCs and DC2s are phenotypically characterized by the authors. Based on the phenotype, various assumptions about their functionality are made. These assumptions could be confirmed by performing functional assays comparing the different subsets of the DCs. Do the authors have functional data or could they perform some functional assays, such as T cell activation and proliferation assays or a migration assay?In 4.2 the authors refer to a figure with “(Figure 2A and B)” in other cases the authors refer with “(Figure 2DH )”. Use consistency in your references.In the caption of figure 2 line 254-256, the authors write that figure 2F, G, H and I represent the percentage and ΔMFI of PD-L1, PD-L2, CD200 and MHC II of DC subtypes in naïve mice and mice injected with B16F10 cells. The graphs F, G, H and I only show data of DC subtypes in mice injected with B16F10 cells. Please add the data of naïve mice or correct the caption.Response to reviewer 2 comments:General: we appreciate R2’s feedback on the quality of grammar in the text, and have consequently carefully reviewed the text.Introduction: We have added information of PD-L1 therapy as proposed by the reviewer.Methods: we thank R2 for this useful suggestion and have provided the information in a separate paragraph as suggested.Results:- We agree with R2 that performing functional assays on CD103lo CD11b DCs would be interesting. We have considered this often, and are unfortunately limited by the low total number of DCs which can be isolated from lung. We have evaluated that we would need over 10 mice per condition to have enough cells to perform functional assays, which, ethically, is not acceptable for our local committee. We therefore try to circumvent this technical limitation by adjusting our message and insuring that we discuss the regulatory “potential” of this population rather than affirming they possess a regulatory function. We hope this explanation will satisfy the reviewer but understand their feedback. Of note, many publications seem limited by this technical limitation with regards to regulatory DCs, and publish regulatory molecule expression as a marker of DC regulatory potential.- We thank R2 for the feedback on consistency in figure naming and have adjusted the text accordingly.- The caption was corrected as suggested by R2.Submitted filename: RESPONSE TO REVIEWERS.docxClick here for additional data file.15 Nov 2021Countering the advert effects of lung cancer on the anticancer potential of dendritic cell populations reinstates sensitivity to anti-PD-1 therapy.PONE-D-21-20655R1Dear Dr. Blanchet,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Please correct a very minor typo, in Materials and. Methods, you indicate that you are using "Tukey's multiple comparison tests" which is correct. However, in the mansucript you write Turkey's multiple comparisons tests in many figure legends.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.Kind regards,Jean Kanellopoulos, M.D., Ph.D.Academic EditorPLOS ONEAdditional Editor Comments (optional):Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #2: All comments have been addressed**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.Reviewer #2: Yes**********3. Has the statistical analysis been performed appropriately and rigorously?Reviewer #2: Yes**********4. Have the authors made all data underlying the findings in their manuscript fully available?The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.Reviewer #2: Yes**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #2: Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)Reviewer #2: (No Response)**********7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.Reviewer #2: No18 Nov 2021PONE-D-21-20655R1Countering the advert effects of lung cancer on the anticancer potential of dendritic cell populations reinstates sensitivity to anti-PD-1 therapy.Dear Dr. Blanchet:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.If we can help with anything else, please email us at plosone@plos.org.Thank you for submitting your work to PLOS ONE and supporting open access.Kind regards,PLOS ONE Editorial Office Staffon behalf ofDr. Jean KanellopoulosAcademic EditorPLOS ONE
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