Article Text

Original research
Favorable impact of PD1/PD-L1 antagonists on bone remodeling: an exploratory prospective clinical study and ex vivo validation
  1. Tamara Gassner1,
  2. Christina Chittilappilly1,
  3. Theo Pirich1,
  4. Benjamin Neuditschko2,
  5. Klaus Hackner3,4,
  6. Judith Lind1,
  7. Osman Aksoy1,
  8. Uwe Graichen5,
  9. Sascha Klee5,
  10. Franz Herzog2,
  11. Christoph Wiesner6,
  12. Peter Errhalt3,4,
  13. Martin Pecherstorfer7,8,
  14. Klaus Podar1,7 and
  15. Sonia Vallet1,7,8
  1. 1Department of Basic and Translational Oncology and Hematology, Division of Molecular Oncology and Hematology, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
  2. 2Institute Krems Bioanalytics, IMC University of Applied Sciences, Krems an der Donau, Austria
  3. 3Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
  4. 4Division of Pneumology, University Hospital Krems, Krems an der Donau, Austria
  5. 5Department of General Health Studies, Division Biostatistics and Data Sciences, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
  6. 6Department of Medical and Pharmaceutical Biotechnology, IMC University of Applied Sciences, Krems an der Donau, Austria
  7. 7Division of Internal Medicine 2, University Hospital Krems, Krems an der Donau, Austria
  8. 8Karl Landsteiner Institute of Supportive Cancer Therapy, Karl Landsteiner Gesellschaft, St. Poelten, Austria
  1. Correspondence to Dr Sonia Vallet; sonia.vallet{at}


Background Skeletal morbidity in patients with cancer has a major impact on the quality of life, and preserving bone health while improving outcomes is an important goal of modern antitumor treatment strategies. Despite their widespread use in early disease stages, the effects of immune checkpoint inhibitors (ICIs) on the skeleton are still poorly defined. Here, we initiated a comprehensive investigation of the impact of ICIs on bone health by longitudinal assessment of bone turnover markers in patients with cancer and by validation in a novel bioengineered 3D model of bone remodeling.

Methods An exploratory longitudinal study was conducted to assess serum markers of bone resorption (C-terminal telopeptide, CTX) and formation (procollagen type I N-terminal propeptide, PINP, and osteocalcin, OCN) before each ICI application (programmed cell death 1 (PD1) inhibitor or programmed death-ligand 1 (PD-L1) inhibitor) for 6 months or until disease progression in patients with advanced cancer and no evidence of bone metastases. To validate the in vivo results, we evaluated osteoclast (OC) and osteoblast (OB) differentiation on treatment with ICIs. In addition, their effect on bone remodeling was assessed by immunohistochemistry, confocal microscopy, and proteomics analysis in a dynamic 3D bone model.

Results During the first month of treatment, CTX levels decreased sharply but transiently. In contrast, we observed a delayed increase of serum levels of PINP and OCN after 4 months of therapy. In vitro, ICIs impaired the maturation of preosteoclasts by inhibiting STAT3/NFATc1 signaling but not JNK, ERK, and AKT while lacking any direct effect on osteogenesis. However, using our bioengineered 3D bone model, which enables the simultaneous differentiation of OB and OC precursor cells, we confirmed the uncoupling of the OC/OB activity on exposure to ICIs by demonstrating impaired OC maturation along with increased OB differentiation.

Conclusion Our study indicates that the inhibition of the PD1/PD-L1 signaling axis interferes with bone turnover and may exert a protective effect on bone by indirectly promoting osteogenesis.

  • Immune Checkpoint Inhibitor
  • Treatment related adverse event - trAE
  • Solid tumor
  • Stem cell
  • Monocyte

Data availability statement

Data are available in a public, open access repository. The mass spectrometry data are deposited at the ProteomeXchange Consortium via the PRIDE partner repository 23 with the dataset identifier PXD047604.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See

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  • Preserving bone health while improving the outcome is an important goal of modern cancer therapy. Despite their widespread use across many indications, the effects of immune checkpoint inhibitors (ICIs) on the skeleton are still poorly defined.


  • Our findings highlight the dual effect of ICIs on bone turnover through the impaired differentiation of mature osteoclasts, which indirectly leads to increased osteogenesis.


  • This study indicates that ICIs may have a protective effect on bone and supports their use in patients with advanced cancer at risk for bone loss.


Major therapeutic advancements in the last decade have significantly increased life expectancy for most patients with cancer, shifting the focus of routine clinical care toward the improvement of life quality.1 2 A healthy skeletal system is critical to a high quality of life for patients with cancer. However, metastatic disease, age, and antitumor agents have a negative impact on bone health by accelerating bone loss and increasing the risk of fractures.3 Therefore, preserving bone health while improving the outcome is an important goal of therapy for patients with cancer.

Skeletal health is maintained by the regular remodeling of the bone structure, which relies on the concerted activity of bone-resorbing cells, osteoclasts (OCs), and bone-forming cells, osteoblasts (OBs). These functions are tightly coupled in time and space through mutual interactions between mature bone cells and their precursors, which constitute the basic multicellular unit (BMU).4 In addition, bone homeostasis is dynamically regulated by the crosstalk with immune cells, which not only secrete bone-modulating cytokines but also represent a reservoir of OC progenitor cells.5 6

The successful therapeutic modulation of the immune response has led to a paradigm shift in the treatment of cancer. In particular, immune checkpoint inhibitors (ICIs) targeting programmed cell death 1 (PD1) receptor and its ligand, PD-L1, represent a standard of care across many cancer indications.7 Despite their increasing application in early disease stages, the effects of PD1 and PD-L1 inhibitors (PD1i and PDL1i) on bone health are still under debate.

Given the limited and inconsistent data, we initiated a comprehensive assessment of the effect of ICIs on bone remodeling by investigating longitudinal changes in markers of bone turnover in patients with cancer and validating them in an ex vivo 3D model of the BMU of bone remodeling.


Exploratory longitudinal cohort study

Study design

We prospectively collected serum samples of patients with advanced solid cancer treated with ICIs for up to 6 months or until disease progression and assessed markers of bone turnover and bone metabolism.

Study population

From July 2020 to June 2021, nine patients were recruited from the Division of Pneumology and Internal Medicine 2 of the University Hospital Krems. All subjects had to meet the following inclusion criteria: age ≥18 years, advanced cancer, and treatment with PD1i or PD-L1i as monotherapy. Prior systemic treatment was permitted if completed at least 3 months before ICI initiation. Patients were excluded if they had evidence of bone metastases assessed per bone scintigraphy or fluorodeoxyglucose positron emission tomography scan, had a diagnosis of osteoporosis or primary hyperparathyroidism, or had received bone-modifying agents (bisphosphonates or denosumab).

Serum sample collection and analysis

Serum samples were collected in the morning in fasting condition prior to ICI administration at prespecified timepoints: baseline, month 1, month 2, month 4, and month 6 of ICI treatment. All biospecimens were processed within 1 hour of collection and stored at −80°C before assessment with commercially available ELISA kits. Standard clinical laboratory tests were performed to evaluate C-terminal telopeptide (CTX) (marker of bone resorption), procollagen type I N-terminal propeptide (PINP) and osteocalcin (OCN) (markers of bone formation), and parathyroid hormone (PTH), 25-hydroxy vitamin D (25(OH) Vit D), and calcium (markers of bone metabolism).

Statistical analysis

Descriptive statistics were provided for demographic and laboratory variables. Data are presented as median and range. Differences in concentrations of markers of bone turnover and metabolism between baseline and month 1 or month 4 measurements were evaluated using the Wilcoxon signed-rank test. P≤0.05 indicated statistical significance. Effect size r was calculated using the formula r=Z/√N and classified as large (r ≥0.50), moderate (r ≥0.3), or small (r ≥0.1).8 Statistical analyses were performed using SPSS V.28 (SPSS, Chicago, IL, USA), and plots were generated using R (V.4.3.2).9

In vitro and ex vivo studies

Media and reagents

Cell culture medium α-Modified Essential Medium+GlutaMAX (α-MEM), L-glutamine, and fetal bovine serum (FBS) were purchased from Gibco, Life Technologies Limited, UK. Dulbecco phosphate-buffered saline and media supplements (including penicillin/streptomycin, β-glycerol phosphate, ascorbic acid, and dexamethasone) were purchased from Sigma Aldrich, USA. The cytokines human macrophage colony-stimulating factor (M-CSF) and human receptor activator of nuclear factor kappa B ligand (RANKL) were obtained from Peprotech, UK. The PD1i pembrolizumab and the PD-L1i atezolizumab were purchased from Selleck Chemicals, TX, USA; as an antibody control, we used the human IgG1 from Biolegend, San Diego, CA, USA (cat. number 403501). In line with most in vitro studies,10 11 experiments were performed with ICI concentrations of 0.05–1 µg/mL, which reflect the poor biodistribution of ICIs in the bone.12–14

Cell culture

OCs were generated from healthy donor peripheral blood mononuclear cells (HD-PBMCs) as previously described.15 HD-PBMCs were seeded into 6-well or 96-well plates at the density of 2×106 cells/cm2. After 2 hours, non-adherent cells were removed to enrich for OC progenitors, which were then cultured in α-MEM containing 10% FBS, 1% penicillin-streptomycin, RANKL 50 ng/mL, and M-CSF 50 ng/mL. OBs were differentiated from healthy donor mesenchymal stem cells (hMSCs, LONZA, Switzerland) as previously described.16 Briefly, hMSCs were seeded in 96-well plates at a density of 6×103 cells/cm2 and cultured in osteogenic media, consisting of α-MEM with 20% FBS, 1% penicillin/streptomycin, 2,5% L-glutamine, 2.16 mg/mL β-glycerol phosphate, 0.05 mg/mL ascorbic acid, and 10 nM dexamethasone.

Dynamic 3D model of BMU

To generate the dynamic 3D bone model, OC progenitors and hMSCs were mixed in 1:2 ratio (2.5×104 and 5×104, respectively) and seeded onto scaffolds of poly-ε-caprolactone (PCL) (3D BioTek, NJ, USA). After overnight incubation, the cell-loaded scaffolds were transferred into disposable High Aspect Ratio Vessels of a RCCS-8 bioreactor (Synthecon Inc, TX, USA) and cultured at a rotation speed of 24–26 rpm in the absence (undifferentiated scaffolds: HD-PBMC/hMSC) or presence of osteogenic medium (differentiated scaffolds: OC/OB).17 18 After 3 weeks, scaffolds were assessed for cell viability and OB differentiation or fixed in paraformaldehyde and processed for imaging studies.

Viability assay

Bone cells or scaffolds were pulsed with alamarBlue (Invitrogen, Germany) at a concentration of 1:100 for 4 hours at 37°C, according to the manufacturer’s instructions. Absorbance was detected at 570 nm and 600 nm using the Tecan plate reader (Infinite M Nano+, Tecan, Switzerland).

Immunohistochemistry and immunofluorescence

For immunohistochemistry analysis, bone cells or scaffolds were stained using chromogenic substrates for tartrate-resistant acid phosphatase (TRAP) and/or alkaline phosphatase (ALP) (Takara Bio, France) according to the manufacturer’s instructions. Scaffolds were processed for cryosections at a thickness of 6 µm and imaged with the Leica DMIL LED microscope (Vienna, Austria). For immunofluorescence, scaffolds were incubated with antibodies against cathepsin K, DAPI (Santa Cruz Biotechnology, TX, USA), and phalloidin (Cell Signaling Technology, MA, USA). Images were taken with the Leica SP8 confocal microscope (Vienna, Austria). Fiji software was used to quantify TRAP+cells and cathepsin K+ areas.19

Resorption pit assay

OC function was assayed by measuring resorption pit areas on dentine slices. Briefly, OCs were differentiated on dentin slices (Immunodiagnostic Systems Holdings Ltd, Boldon Business Park, UK) for 3 weeks. After gently scraping off adherent cells with 0.1% Triton X-100, dentine slices were stained with 1% toluidine solution and resorption pits quantified by light microscopy using the Fiji software.19

ALP expression and alizarin red staining

OB differentiation was assessed by measuring ALP activity with the p-nitrophenyl phosphate substrate (Sigma-Aldrich, Schnelldorf, Germany) according to the manufacturer’s protocol. OB function was evaluated by alizarin red (Sigma-Aldrich) staining of mineralized areas. Briefly, the dye was extracted with 10% acetic acid and read at 405 nm in a Tecan plate reader (Infinite M Nano+).

Western blot analysis

Cells were lysed in radioimmune precipitation assay buffer (ThermoFisher Scientific, MA, USA). Lysates were separated and transferred to nitrocellulose membranes (Bio-Rad, CA, USA). Primary antibodies against pSTAT3/STAT3, pAKT/AKT, pJNK/JNK (Cell Signaling Technology), PD-L1, cathepsin K, pERK, ERK, actin, and vinculin (Santa Cruz Biotechnology) were used. Blots were imaged on a ChemiDoc system (BioRad, CA, USA). Densitometric analysis was performed using the Fiji software.19

Quantitative real-time PCR

Total mRNA was isolated using the RNeasy Kit (Qiagen, Germany), according to the manufacturer’s protocol. cDNA was synthesized with iScript cDNA kit (BioRad) and processed by quantitative real-time PCR (qPCR) using SYBR Green Master Mix (BioRad) on a LightCycler 96 (Roche Diagnostics GmbH, Germany). Transcript levels were normalized to GAPDH. Primers for GAPDH, PD-L1, and NFATc1 were purchased from Eurofins Scientific (Luxembourg). The primer sequences were as follows: GAPDH (forward, 5’- TCGCTGTTGAAGTCAGAGGAGA-3’; reverse, 5’-GTCTTCACCACCATGGAGAAGG-3’); PD-L1 (forward, 5’-TGCAGGGCATTCCAGAAAGA-3’; reverse, 5’-TAGGTCCTTGGGAACCGTGA-3’); NFATc1 (forward, 5’-CACCAAAGTCCTGGAGATCCCA-3’; reverse, 5’-TTCTTCCTCCCGATGTCCGTCT- 3’).

Liquid chromatography-mass spectrometry analysis

For the profiling of the secretome of the dynamic 3D model, scaffolds were incubated in starving media (0.1% FBS) for 24 hours and supernatant processed and stored at −20°C until further use, as previously described.20 A total of 12 individual proteomic analyses were performed corresponding to starving media and supernatant of HD-PBMC/hMSC, OC/OB, OC/OB+IgG, OC/OB+PD1i, and OC/OB+PD-L1i at day 21 of differentiation using 2 different healthy donors for HD-PBMCs and hMSCs. The analysis of secreted proteins was performed with a nano-Ultra High Performance Liquid Chromatography (UltiMate 3000) coupled to an Orbitrap Eclipse Tribrid mass spectrometer (both Thermo Fisher Scientific, Vienna, Austria) applying data-independent acquisition (DIA) method with settings described in Colleselli et al.21 To deepen the analysis, a pool of all samples was created and used for gas phase fractionation (GPF).22 For protein identification and quantification, Spectronaut (17.7.230531.55965) was used in direct DIA mode running the BGS Factory settings using the human (Uniprot V.10.2021, 20 386 entries) and bovine (Uniprot V.06.2022, 6391 entries) protein database to predict the spectral library. GPF measurements were used as Library Extension Runs to maximize protein identification. Protein lists and quantities were exported and used for further statistical evaluations. The mass spectrometry data are deposited at the ProteomeXchange Consortium via the PRIDE partner repository23 with the dataset identifier PXD047604.

Bioinformatic analysis

To minimize false identification of residual FBS-derived proteins as human proteins, we excluded all proteins classified as bovine. In addition, proteins identified in fractions of cell-free starving medium were considered serum-derived contaminants and were removed. Bioinformatic analysis of the proteomics data was done with Perseus software V. Only proteins with two valid values in at least one condition were included, resulting in a total of 757 proteins identified from all samples. After filtering, log2 transformed values were normalized by subtraction of the median. The identified proteins were assessed for the presence of a signal peptide or their extracellular localization based on gene ontology (GO) cellular compartment annotations using UniProt database. We classified proteins as uniquely expressed in a specific sample if the expression level was greater than zero in both biological replicates and not expressed if the expression levels were zero in all replicates.

To assess differential protein abundance between conditions, the dataset was filtered to include only proteins identified in all groups, and any missing values were imputed using the normal distribution with a width of 0.3 and a down-shift of 1.8. Group comparisons were made using a two-sample t-test with p<0.05, unless otherwise specified. GO (biological process and molecular function) and reactome pathway analysis of the differentially expressed proteins were performed using DAVID (V.2021)25 against the Homo sapiens background using a count threshold of 2 and an EASE value of 0.1.

Statistical analysis

Each experiment was performed in triplicate using primary cells from at least three different donors. Results are displayed as the mean±SE unless otherwise specified. For statistical comparison of in vitro experiments, we used two-tailed Student’s t-test. P<0.05 indicates statistical significance. Venn diagrams, enrichment bubble plot, and heat map were plotted using, an online platform for data analysis and visualization.


In vivo longitudinal assessment of serum markers of bone turnover during treatment with ICIs

Bone remodeling is a dynamic process with each cycle of bone resorption and formation lasting about 4–8 months. Therefore, to determine the effects of PD1i and PD-L1i, we initiated an exploratory analysis of the changes in markers of bone turnover and bone metabolism during treatment for up to 6 months or until disease progression (figure 1A). Patient clinical characteristics at study entry are shown in online supplemental table 1. The median age was 71 years, the most frequent tumor types were lung, and head and neck cancer. Five patients were treated with a PD1i, and four received a PD-L1i. Overall, seven patients were still evaluable after 4 months of therapy and three patients after 6 months.

Supplemental material

Figure 1

Long-term ICI treatment favorably modulates bone turnover. (A) Swimmer plot showing duration of treatment, type of ICI, and reason for discontinuation. (B–D) Individual percent change in bone turnover markers from baseline by treatment month, representing (B) the bone resorption marker C-terminal telopeptide (CTX) and the bone formation markers (C) procollagen type I N-terminal propeptide (PINP) and (D) osteocalcin (OCN). Dark line indicates the median, and dotted line indicates the baseline marker level. ICI, immune checkpoint inhibitor.

A total of 37 serum samples were collected prior to each drug administration at the prespecified timepoints, and bone marker concentrations were assessed (table 1, figure 1). In eight out of nine patients, we observed a rapid but transient decrease in serum levels of the bone resorption marker CTX after 1 month of treatment (median change (range) −23% (−37, 20%), r=0,8, p=0.01). At later timepoints, CTX levels returned to baseline in most patients (median change (range) 6.7% (−35, 65%), r=0,03, p=NS) (figure 1B). In contrast, serum levels of the bone formation markers PINP and OCN gradually increased from baseline after 4 months of ICI treatment in six of the seven evaluable patients (median change (range) +8.4% (0, 53%), r=0,8, p=0.02) and five of the seven patients (median change (range) +30% (−71, 139%), r=0, 3 p=NS), respectively (figure 1C,D). No relevant changes in the levels of bone metabolism markers (PTH, 25(OH) Vit D, and calcium) were observed.

Table 1

Levels of bone biomarkers at prespecified timepoints

Taken together, the gradual increase of the markers of bone formation after the transient decrease of the bone resorption marker suggests a potentially favorable impact of ICIs on bone turnover.

In vitro studies of ICI impact on bone cell differentiation

PD1 and PD-L1 inhibitors impair the formation of osteoclasts but do not affect osteoblast differentiation

To investigate the effect of ICIs on bone turnover, we assessed their impact on OC and OB differentiation in a cancer-free environment. By differentiating HD-PBMCs in the presence of PD1 or PD-L1 antagonists, we observed a dose-dependent reduction in OC number (fold change of TRAP+multinucleated cells of 0.5±0.06, p<0.05, and 0.3±0.06, p<0.01, in the presence of ICI 100 and 1000 ng/mL, respectively). Importantly, OC function was almost completely abrogated already at doses of 100 ng/mL (fold change of resorption pit area 0.16±0.11, p<0.05, and 0.07±0.03, p<0.01, in the presence of 100 ng/mL PD1i and PD-L1i, respectively) compared with control (figure 2A,B). In contrast, as shown in figure 2C,D, neither the number of OBs nor their activity was influenced by ICIs (fold change of ALP activity 1.06±0.12 and 0.8±0.04 in the presence of 1000 ng/mL PD1i and PD-L1i, respectively, and fold change of calcium deposits 1±0.03 and 0.98±0.06 in the presence of 1000 ng/mL PD1i and PD-L1i, respectively). While confirming that even in the absence of exogenous or cancer-derived PD-L1, ICIs inhibit osteoclastogenesis already at doses of 100 ng/mL, these results clearly indicate the absence of a direct effect of PD1i or PD-L1i at doses as high as 1 µg/mL on OB differentiation.

Figure 2

PD1 and PD-L1 antagonists impair osteoclast differentiation, without a direct effect on osteoblasts. (A, B) Osteoclast (OC) number and activity on treatment with IgG control, PD1i, or PD-L1i (concentrations are expressed as ng/mL); PBMCs indicate peripheral blood mononuclear cells. Representative images of TRAP+multinucleated cells (A, right panel) and resorption pits (B, right panel) are shown. (C, D) Osteoblast (OB) number and activity on treatment with IgG control, PD1i, or PD-L1i; MSCs indicate mesenchymal stem cells. Representative images of alkaline phosphatase (ALP)+cells (C, right panel) and calcium deposits (D, right panel) are shown. Expression levels are represented as the fold change relative to the positive control; results are expressed as average and SE; * p<0.05, ** p<0.01. PD1i, programmed cell death 1 inhibitor; PD-L1i, programmed death-ligand 1 inhibitor; TRAP, tartrate-resistant acid phosphatase.

PD1 and PD-L1 inhibitors arrest mononuclear cell differentiation at the preosteoclast stage

We further explored the mechanism by which ICIs inhibit OC differentiation. In line with previous studies,26 27 our data revealed differential expression patterns of PD1 and PD-L1 during OC differentiation. Specifically, on stimulation of HD-PBMCs with M-CSF and RANKL for 1 week, expression levels of PD-L1 are upregulated, whereas PD1 expression is downregulated (online supplemental figure 1). We therefore hypothesized that ICIs may target an early OC differentiation stage characterized by the coexistence of subpopulations of precursor cells expressing both PD1 and PD-L1. During osteoclastogenesis, mononuclear cells of the monocyte/macrophage lineage first acquire TRAP activity to generate preosteoclasts (POCs), and then through cell-cell fusion, they become gradually multinucleated ultimately giving rise to mature OCs.28 After 1 week of differentiation, POCs can be identified as small TRAP+cells, containing three or less nuclei, whereas after 3 weeks of differentiation, mainly mature OCs are present as large, multinucleated TRAP+cells with bone-resorbing ability.29 To determine the specific effects of ICIs on POCs, we induced osteoclastogenesis from HD-PBMCs and assessed cell viability as well as expression of the OC markers TRAP and cathepsin K, a lysosomal cysteine protease responsible for bone resorption and a marker of functionally active OCs,30 in the presence of PD1i/PD-L1i. Compared with untreated and IgG-treated cells, PD1i and PD-L1i significantly increased the number of POCs after 1 and 2 weeks of differentiation (% of POCs 67±8.1 in ICI-treated cells vs 44.8±5.7 in control cells, p<0.05, at day 7, and 53±4.5 vs 34.5±2.6, p<0.01, at day 14, respectively), without affecting viability (figure 3A,B). Importantly, the expression of cathepsin K was inhibited by ICIs (figure 3C), thus confirming the arrest of the osteoclastogenesis process at an early stage of cell differentiation.

Supplemental material

Figure 3

PD1 and PD-L1 inhibitors block mononuclear cell differentiation at the preosteoclast stage. (A,B) Number and viability of preosteoclasts (POCs) on treatment with IgG control, PD1i, or PD-L1i 100 ng/mL for 1 and 2 weeks; PC indicates positive control. Representative images of TRAP+cells indicating POCs (arrowhead) and OCs (arrow) (A, right panel) are shown. Results are expressed as average and SE; * p<0.05, ** p<0.01. (C) Immunoblot of cathepsin K in POCs treated with IgG control, PDi or PD-L1i, 100 ng/mL for 1 week. OCs, osteoclasts; PD1i, programmed cell death 1 inhibitor; PD-L1i, programmed cell death-ligand 1 inhibitor; TRAP, tartrate-resistant acid phosphatase

PD1 and PD-L1 inhibitors modulate STAT3 and NFATc1 activation in OC progenitor cells

Next, we investigated how ICIs modulate the signaling cascades in POCs. M-CSF-mediated and RANKL-mediated differentiation of progenitor cells into active OCs requires the activation of several signaling pathways, including MAPK, JNK, AKT, and STAT3, which ultimately promote the expression of the transcription factor NFATc1.28 29 31 In the presence of RANKL and M-CSF, PD1i and PD-L1i (100 ng/mL, 24 hours) inhibited the activation of STAT3, without affecting JNK, ERK, and AKT signaling (figure 4A,B). Importantly, we observed the concomitant downregulation of NFATc1 on treatment with ICIs (figure 4C). Thus, ICIs may impair OC differentiation via the STAT3/NFATc1 signaling pathway.

Figure 4

PD1 and PD-L1 inhibitors modulate STAT3/NFATc1 signaling in OC progenitor cells. (A,B) Immunoblot analysis of the indicated signaling pathways in POCs treated with IgG control or ICIs 100 ng/mL for 24 hours. Quantification of STAT3 signaling as fold change of positive control is shown (A, lower panel). (C) qPCR of NFATc1 in POCs treated with IgG control or ICIs 100 ng/mL for 24 hours. Results are expressed as average and SE; * p<0.05 . (D) Schematic representation of PD1i and PD-L1i-mediated inhibition of STAT3 and NFATc1 signaling. ICIs, immune checkpoint inhibitors; OC, osteoclast; PD1i, programmed cell death 1 inhibitor; PD-L1i, programmed cell death-ligand 1 inhibitor; POCs, preosteoclasts; qPCR, quantitative PCR.

Evaluation of the effect of ICIs on a bioengineered bone remodeling model

Generation and characterization of the dynamic 3D BMU model

The discordant in vivo and in vitro results on the impact of ICIs on markers of bone formation and osteoblastogenesis, respectively, suggest that their effect may be indirectly mediated by the increase in POCs and the consequent changes in the OB/OC coupling. Indeed, bone remodeling relies on the timely and spatially coordinated interactions of all bone cells at different differentiation stages within the BMU.4 To test our hypothesis, we developed a dynamic culture of bone precursor cells in 3D scaffolds mimicking the BMU. Specifically, we seeded hMSCs and HD-PBMCs on PCL scaffolds and cultured them for 3 weeks in the presence (OC/OB scaffold) or absence (HD-PBMC/hMSC scaffold) of osteogenic conditions in culture vessels of an RCCS-8 bioreactor (Synthecon Inc) (figure 5A). As shown in figure 5B–D, compared with undifferentiated control cells, we observed an increase in ALP activity per viable cells (2.3±0.2-fold increase, p<0.01) and cathepsin K expression (3.3±0.5-fold increase, p<0.05) in the OC/OB scaffolds, indicating the simultaneous differentiation of OBs and OCs in our 3D model. Next, we compared the secretomes of OC/OB and HD-PBMC/MSC scaffolds using liquid chromatography-mass spectrometry (LC-MS) proteomic analysis. We identified 32 proteins uniquely expressed and 16 proteins significantly upregulated in the OC/OB secretome (figure 5E). Manual inspection of the OC/OB secretome signature revealed proteins linked to OB and OC activity, such as periostin, elastin and collagen, and CSF1 and metalloproteases, respectively, as well as immune-related proteins, such as cytokine receptors and serum amyloid. GO enrichment analysis of the 48 proteins characterizing the OC/OB secretome confirmed an enrichment of terms closely associated with OB function, including collagen biosynthesis, collagen binding, and extracellular matrix organization, as well as OC activity, such as proteolysis and degradation of extracellular matrix (figure 5F). Interestingly, ontology terms related to immune response and immune system were over-represented in the OC/OB secretome, which is in line with the established immunomodulatory role of bone cells. In summary, these data indicate that our bioengineered 3D model mimics the coupling of bone cells, thus enabling the study of the cellular basis of bone remodeling.

Figure 5

Characterization of the dynamic 3D bone remodeling model. (A) Schematic representation of the bioengineered bone model generation. (B) Quantification of ALP activity corrected for cell viability (API) of undifferentiated (HD-PBMC/hMSC) and osteogenic differentiated (OC/OB) scaffolds. (C) Quantification of cathepsin K+area of scaffolds cultured with or without osteogenic media. (D, upper panel) Microscopic bright field image showing TRAP+ (arrow) and ALP+ (arrowhead) cells within the regular porous structure of the PCL scaffold (10× and 40×). (D, lower panel) Microscopic immunofluorescence image showing scaffolds cultured with (OC/OB) or without (HD-PBMC/MSC) osteogenic differentiation media (actin cytoskeleton, green; cathepsin K, red; DAPI, blue). Image depicts maximal projection of Z-stack areas of the osteogenic-treated scaffold (467 µm×210 µm×68 µm and 290 µm×290 µm×44 µm, respectively), scale bars as indicated. (E, upper panel) Venn diagram of proteins detected in the secretome of undifferentiated and osteogenic differentiated scaffolds. (E, lower panel) Volcano plot comparing differentially expressed proteins in the two secretomes. Dashed horizontal line indicates p=0.05. (F) Gene ontology (GO) for the biological process and molecular function category as well as reactome pathway enrichment analysis of the 48 differentially expressed proteins in the OB/OC secretome. The gene count indicates the number of genes from the input list found on each pathway. The p value indicates the significance of the enrichment. ALP, alkaline phosphatase; HD-PBMC/hMSC, healthy donor peripheral blood mononuclear cell/mesenchymal cell; OC/OB, osteoclast/osteoblast; TRAP, tartrate-resistant acid phosphatase.

ICIs enhance osteogenesis in a dynamic 3D model of bone remodeling

To address the hypothesis that ICIs may alter the OB/OC coupling during the bone remodeling process, we evaluated the impact of PD1i and PD-L1i on osteoclastogenesis and osteoblastogenesis in our dynamic 3D bone model. As shown in figure 6A, we confirmed the lack of toxicity of ICIs on bone cells. Confocal immunofluorescence imaging revealed a decrease in cathepsin K expression on ICI treatment (2.9±1.16 vs 27.9±5.3, p<0.01) (figure 6B), confirming impaired terminal OC differentiation. Importantly, we also observed a significant increase in ALP activity (3.3±0.6-fold increase, p≤0.05), which indicates enhanced OB formation (figure 6C). Next, we characterized the secretomes of the 3D bone models on exposure to ICIs by LC-MS. To gain specific insight into the ICI-induced changes, we compared the secretomes of PD1i-treated and PD-L1i-treated scaffolds with those of OC/OB-untreated and IgG-treated scaffolds. As shown in figure 6D, we observed a high degree of overlap between treated and control samples with only 6 and 3 proteins uniquely identified in each group, respectively. The assessment of differentially expressed proteins revealed that 15 proteins were significantly upregulated by ICI treatment, whereas 3 were downregulated (figure 6E). Importantly, the proteins characterizing the ICI-treated secretomes are involved in bone cell differentiation, function, and coupling, such as afamin (AFM), laminin 4 (LAMA4), dipeptidyl peptidase (DPP3), prolyl 4-hydroxylase subunit alpha-2 (P4HA2), sushi-repeat-containing protein (SRPX), and hippo signaling pathways (FAT4 and MOB1A).32–37 Taken together, our in vitro data corroborate the in vivo results by showing the uncoupling of the OC/OB activity on ICI treatment with impaired OC differentiation and increased OB activity.

Figure 6

ICIs induce the uncoupling of bone remodeling. (A) Cell viability of osteogenic differentiated scaffolds treated with IgG control, PD1i, or PD-L1i for 3 weeks. (B) Quantification of cathepsin K+areas of osteogenic differentiated scaffolds treated with IgG control, PD1i, or PD-L1i. Representative microscopic immunofluorescence images showing cathepsin K expression (cathepsin K, red; DAPI, blue). The image represents maximum intensity projection of the Z-stack. Scale bars as indicated. *, p<0.05; **, p<0.01. (C) Quantification of ALP activity of osteogenic differentiated scaffolds treated with IgG control, PD1i, or PD-L1i. #, p=0.05; *, p<0.05. (D) Venn diagram of proteins identified in the secretome of scaffolds differentiated with or without IgG control (controls) or ICIs (treated). (E) Heatmap representing log2 expression values of the 18 differentially regulated proteins by treatment with ICIs (p≤0.05). ALP, alkaline phosphatase; ICIs, immune checkpoint inhibitors; OC, osteoclast; PD1i, programmed cell death 1 inhibitor; PD-L1i, programmed cell death-ligand 1 inhibitor.


Preserving bone health in patients with cancer is becoming increasingly relevant in light of the growing incidence rates, the aging population, and the availability of life-prolonging therapies.3 ICIs targeting the PD1 and PD-L1 axes have transformed the treatment landscape of several cancers.7 Despite their widespread use, the influence of ICIs on bone health is still controversial due to the discordance between preclinical evidence and patient data. Specifically, preclinical studies suggest that modulating the PD1 and PD-L1 axis has an anticatabolic effect.26 27 38 In contrast, ICIs have been shown to increase bone resorption and thus trigger skeletal adverse events in patients with cancer.39–41 In this study, we investigated the long-term effect of ICIs on levels of bone metabolism biomarkers in patients with cancer and elucidated their impact on bone remodeling using a novel, bioengineered 3D model of the BMU. To the best of our knowledge, our findings highlight for the first time the dual effect of ICIs on bone metabolism through the impaired differentiation of mature OCs, which indirectly leads to increased osteogenesis.

The exploratory longitudinal analysis of bone turnover markers (BTMs) in a population of patients with advanced cancer receiving single-agent PD1i or PD-L1i provides strong evidence for a rapid but transient decline in bone resorption marker with a delayed increase in formation markers. Patients included in this trial had no sign of skeletal metastasis, suggesting a direct effect of ICIs on bone turnover. In contrast to previous studies indicating a catabolic action of ICIs on the skeleton,39 we observed a rapid decrease of CTX levels after the first administration, which slowly returned to baseline at later timepoints. The longitudinal follow-up of BTM may account for the difference with the study of Pantano et al, who focused their analysis on one timepoint. Indeed, serial BTM assessments are critical to capture the dynamic changes of bone turnover during treatment with bone-modifying agents.42 43

These in vivo findings are supported by our own, and other preclinical correlation studies demonstrating an OC inhibitory effect of ICIs in the absence of tumor cells or exogenous PD-L1.26 27 38 Specifically, our data suggest that ICIs, at concentrations reflecting their biodistribution in the bone,12–14 hamper differentiation of POCs into OCs by inhibiting STAT3 signaling and NFATc1 expression, which is in line with previous studies showing that activation of STAT3 in bone marrow macrophages is required for OC differentiation.31 Importantly, POCs are considered important players in the bone remodeling balance by stimulating OB formation and targeting POCs represents a novel promising approach against osteoporosis.44–47

Our results suggest that ICIs may indirectly enhance bone formation by interfering with the differentiation of non-resorbing POCs into mature OCs. In patients, we observed a delayed increase in markers of bone formation (PINP and OCN), which was evident after 4 months of treatment with single-agent PD1i or PD-L1i. Preclinically, we confirmed enhanced OB activity on treatment of a BMU model with ICIs, despite the lack of a direct effect on osteogenesis. By culturing bone precursor cells within a rigid and porous, bone-like PCL scaffold in a horizontally rotating system, our innovative 3D model enables the simultaneous differentiation of OBs and OCs in a bone-like microenvironment, thus capturing the complex spatial and temporal interactions among bone cells at different differentiation stages within the BMU. Proteomics analysis of the secretome supports these findings and gives further insight into the ICI-induced changes of bone remodeling. Interestingly, we observed expression of POC-derived proteins, such as afamin involved in the OC/OB coupling, as well as proteins regulating differentiation of POCs, such as phospholipase A2-activating protein and laminin.48–50

A major limitation of our study is the small number of participants with a predominance of male patients. Due to its exploratory nature, the in vivo analysis had stringent exclusion criteria, which made the results more accurate due to the lack of confounding factors. Overall, we found compelling evidence for a rapid but transient decline in bone resorption and a delayed increase in bone formation markers. The observed BTM changes may be unrelated to the treatment; however, the consistent in vitro results strengthen our hypothesis of a favorable effect of ICI on bone turnover. Importantly, the lack of diversity affects the study’s generalizability since gender differences in immune response and pharmacokinetics may influence ICI activity.51–53

Another key question to be addressed in future work is the effect of ICIs on bone turnover in patients with skeletal metastases. Several studies indicate that bone metastases negatively impact the prognosis of patients treated with ICI,54–56 partly due to the low PD-L1 expression characterizing this metastatic site.57 Notably, better outcomes have been reported in combination with bone-targeted agents,58–60 whose immunomodulatory function may enhance ICI activity.61–63

Taken together, our study indicates that the inhibition of the PD1/PD-L1 signaling axis interferes with the OB/OC coupling of bone turnover and may potentially exert a protective effect on bone. Further research in a larger and more diverse population is needed to better delineate the effects of ICIs alone and in combination with bone-targeted agents on the bone health of patients with cancer.

Data availability statement

Data are available in a public, open access repository. The mass spectrometry data are deposited at the ProteomeXchange Consortium via the PRIDE partner repository 23 with the dataset identifier PXD047604.

Ethics statements

Patient consent for publication

Ethics approval

This study involves human participants and was approved by the Ethics Boards of Lower Austria (GS4-EK-4/606-2019). Participants gave informed consent to participate in the study before taking part.


The authors want to appreciate the contribution of NÖ Landesgesundheitsagentur, the legal entity of University Hospitals in Lower Austria, for providing the organizational framework to conduct this research. The authors also would like to acknowledge the support of Open Access Publishing Fund of Karl Landsteiner University of Health Sciences, Krems, Austria. We are especially grateful to our study nurses Mag. Elisabeth Zwickl-Traxler and Ms Michaela Scherb for their great help and support in organizing and coordinating the clinical study.


Supplementary materials

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  • Contributors TG and CC performed experiments and analyzed the data. TP collected and analyzed the data. BN performed experiments. KH and PE monitored data collection. JL and OA performed experiments. UG and SK provided statistical support. KP supervised the project. SV designed the study, supervised the project, analyzed and interpreted the data, and wrote the manuscript and edited by MP, FH, and CW. SV acts as guarantor for the study. All authors read and approved the final manuscript.

  • Funding This work was supported by the Lower Austria Research Promotion Agency (Gesellschaft fuer Forschungsfoerderung Niederoesterreich) (grant number LFS18-010) and by Forschungsimpulse (project ID: SF_0039 and RTO_0002), a program of Karl Landsteiner University of Health Sciences funded by the Federal Government of Lower Austria.

  • Competing interests KP has received speaker’s honoraria from Celgene, Amgen Inc, and Janssen Pharmaceuticals; consultancy fees from Celgene, Takeda, Janssen Pharmaceuticals, and Amgen; and research support from Roche Pharmaceuticals. SV has received speaker's honoraria from Bristol Myers Squibb, Pfizer, MSD, and Merck; consultancy fees from Roche, MSD, EUSA Pharma, and Merck; and travel support from Pfizer, Roche, Pierre Fabre, and Angelini.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.