Abstract
Background
Venous thromboembolism is a common cause of morbidity and mortality in patients with cancer, with chemotherapy being an established risk factor. Emerging data suggest elevated venous thromboembolism risk with immune checkpoint inhibitors (ICIs), yet the association between ICIs and hypercoagulability remains unclear.
Objectives
We aimed to evaluate longitudinal levels of hemostatic biomarkers in patients during chemotherapy and ICI monotherapy.
Methods
Patients were matched by age, sex, cancer type, and stage. Biomarker levels were measured at baseline, 3 weeks and 3 months after treatment and compared using Wilcoxon rank-sum tests with a mixed-effects model used to assess the effects of therapy and time. Spearman’s correlation analyzed biomarker relationships.
Results
Thirty patients (20 with non–small cell lung cancer and 10 with head and neck cancer) were included. Eighteen patients (60%) were females, with a median age of 66 years (IQR, 57-78). Levels of soluble P-selectin, D-dimer, extracellular vesicle tissue factor activity, peak thrombin, and neutrophil extracellular trap formation (via citrullinated histone H3 in complex with extracellular DNA) showed an increasing tendency over time, with comparable biomarker levels between treatment modalities. Mixed-effects analysis revealed weak differences in the longitudinal dynamics of biomarker levels within the first 3 months of treatment, with ICI therapy (compared with chemotherapy) being associated with higher levels of soluble P-selectin (+3.03 ng/mL; P = .61), D-dimer (+0.01 mg/mL; P = .99), extracellular vesicle tissue factor activity (+0.05 pg/mL; P = .58), and citrullinated histone H3 in complex with extracellular DNA (+15.22 ng/mL; P = .06), and lower peak thrombin levels (−34.34 nM; P = .29).
Conclusion
Hemostatic biomarker levels were largely comparable between the 2 treatment modalities and showed increasing trends over time.
Keywords: antioneoplastic agents, biomarkers, immune checkpoint inhibitors, neoplasms, venous thromboembolism
Essentials
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Chemotherapy induces hypercoagulability and raises biomarkers; immunotherapy’s impact is unclear.
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Patients on both treatment types were matched, and hemostatic biomarkers were measured over time.
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Biomarkers showed positive trends with minor differences, remaining largely comparable between groups.
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Immunotherapy appears to have a similar effect as chemotherapy on hemostatic biomarker levels.
1. Introduction
Cancer is a well-established risk factor for venous thromboembolism (VTE) [1,2]. Cancer-associated VTE can result in a multitude of complications, including increased mortality, morbidity, prolonged hospital stays, increased healthcare costs, and a heightened psychological burden [[3], [4], [5], [6]].
Hemostatic biomarkers, including D-dimer (a breakdown product of fibrin), soluble P-selectin (sP-selectin; a marker of platelet activation), extracellular vesicle tissue factor (EV-TF) activity, thrombin generation potential, and markers of neutrophil extracellular trap (NET) formation, are implied to reflect hypercoagulability and have been associated with the risk of VTE in patients with cancer [[7], [8], [9], [10], [11]]. These associations were described in populations receiving chemotherapy; however, the modern landscape of cancer treatment is rapidly evolving [[12], [13], [14], [15], [16]]. Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment and are increasingly used across various malignancies [17,18]. However, by amplifying immune activation, they can trigger adverse events that are predominantly inflammatory in nature [19]. Given the established interplay between inflammation and coagulation, ICIs are implied to carry a risk of both VTE and arterial thromboembolism (ATE) [20,21]. Despite these implications, there is no data on the impact of ICI therapy on hemostatic biomarker levels.
A deeper understanding of longitudinal dynamics in hemostatic biomarker levels over time could offer insights into a potential association between ICI therapy and VTE risk. As ICIs are known to induce a proinflammatory state, and inflammation and coagulation are tightly intertwined, it is plausible that the mechanisms leading to a procoagulant state in ICI-treated patients differ from those induced by chemotherapy [22,23]. This difference could result in distinct and altered biomarker levels over time, revealing distinct biomarker patterns between the 2 treatment modalities.
Therefore, the aim of this exploratory study was to investigate the levels and changes in hemostatic biomarkers over time, including sP-selectin, D-dimer, EV-TF activity, thrombin generation via peak thrombin, and citrullinated histone H3 in complex with extracellular DNA (citH3-DNA), a marker of NET formation, in patients initiating ICI monotherapy compared with those initiating chemotherapy.
2. Methods
2.1. Study population
This study was conducted within the framework of the Vienna Cancer Thrombosis and Bleeding (CAT-BLED) study, an ongoing, prospective, single-center, observational cohort study initiated in July 2019. CAT-BLED enrolls patients with histologically confirmed cancer, initiating a novel systemic anticancer therapy. The study design and procedures have been described in detail in previous publications [24,25]. The study was approved by the local ethics committee (EC number: 1533/2019; ethik-kom@meduniwien.ac.at) and conducted in accordance with the Declaration of Helsinki and its amendments. Written informed consent was obtained from all participants at study inclusion.
Exclusion criteria for this analysis were defined to avoid potential inferences with biomarker measurements, comprising therapeutic anticoagulation at study inclusion and prior systemic anticancer therapies (excluding prior surgery and/or radiation therapy). To control for confounding, the analysis was restricted to patients with non–small cell lung cancer (NSCLC) and head and neck cancer, with additional matching performed for cancer stage, sex, and age. Among 284 NSCLC patients included in the CAT-BLED study, 150 were treatment-naive and nonanticoagulated at baseline. Forty-four patients initiated ICI monotherapy and 27 chemotherapies, allowing 10 matched pairs. Of 143 head and neck cancer patients, 48 met the same criteria. Twenty-two patients initiated ICI monotherapy and 11 chemotherapies, resulting in 5 matched pairs.
2.2. Laboratory analysis
Within the CAT-BLED study, citrated blood samples were collected at study inclusion before the start of anticancer therapy, 3 weeks after treatment initiation, and subsequently every 3 months.
Blood samples were drawn into Vacutainer citrate tubes (Vacuette; Greiner-Bio One). Samples were immediately centrifuged for 10 minutes at 3000 × g at 18 °C, aliquoted, and then stored at −80 °C for measurements. Platelet-free plasma was obtained by centrifugation at 1500 × g for 15 minutes, followed by an additional centrifugation at 13,400 × g for 2 minutes. Biomaterial was processed and stored according to standard operating procedures at the MedUni Wien Biobank in an ISO 9001-certified environment until analysis [26].
sP-selectin levels were measured using a commercially available immunoassay (R and D Systems) according to the manufacturer’s instructions and as previously described [8]. D-dimer was assessed using the Tina-quant D-Dimer Gen2 Assay (Roche Diagnostics) on the cobas t 711 coagulation analyzer (Roche Diagnostics) at the Department of Laboratory Medicine, Medical University of Vienna, following previously described protocols and measured in fibrinogen equivalent units [25]. EV-TF activity was measured using a chromogenic assay and a standardized protocol described in the original work [27]. Thrombin generation potential via peak thrombin was assessed using a commercially available kit (Technothrombin, Technoclone) following the manufacturer’s instructions and as previously reported [28]. NETs were quantified via an ELISA detecting circulating cell-free DNA-citH3 complexes, as described in the original publication [29].
2.3. Statistical analysis
The R package MatchIt (developed by Ho et al. [30]) was employed to pair patients initiating ICI monotherapy with chemotherapy, matched (1:1) by sex, cancer type, and stage, while nearest neighbor matching was utilized for age. Standard descriptive statistics were used to report patient baseline characteristics (absolute frequencies, percentages, medians, and IQRs [indicating the 25th and the 75th percentile]). Quantitative variables, including biomarker levels, were analyzed as continuous variables throughout all analyses. Numerical variables between 2 groups were compared by applying a Mann–Whitney U-test, while categorical variables were compared with a chi-squared test. A mixed-effects model was used to examine the effects of treatment type and time on biomarker levels. Differences between the 2 treatment groups were assessed using serial Wilcoxon signed-rank tests. Correlations between the 5 biomarker levels were evaluated using Spearman’s rank correlation. Overall survival was plotted using Kaplan–Meier estimates. All statistical analyses were conducted using R (version 4.4.1; R Core Team). An alpha level of 0.05 was applied to all statistical tests. Due to the exploratory and hypothesis-generating nature of this study, no correction for multiple testing was performed. Missing values were not imputed, as analyses were conducted using a complete case approach.
3. Results
3.1. Patient characteristics
Thirty patients were included in the present analysis, including 15 patients treated with ICIs and 15 treated with chemotherapy. The median follow-up time was 400 days (IQR, 283-1036). Of the 30 patients, 18 (60%) were female, 20 (66.6%) had NSCLC, and 10 (33.3%) had head and neck cancer. The median age was 66 years (IQR, 57-78), and 22 (73%) patients presented with metastatic disease at inclusion. The matching process resulted in 15 matched patient pairs (matched for the same cancer type, sex, and stage). Nearest neighbor age matching resulted in a mean age difference of 9 years (SD, 5.5; range, 0-16). Matched patient pairs, including the variables used for matching, are provided in Supplementary Table S1. Overall baseline patient characteristics, as well as stratification by treatment type, are presented in Table. Baseline citrate samples were available for all 30 patients (100%). Three-week and 3-month samples were available for 25 patients (83.3%), resulting in a total of 80 samples. Regarding the 10 missing samples, 4 were in 2 patients who died within the first 3 weeks of inclusion, and 6 were the result of insufficient blood volume or unsuccessful blood draws. During the first 12 months of observation, 12 patients died (12-month overall survival estimate: 65.8%; 95% CI, 48.5%-83.1%; Supplementary Figure S1).
Table.
Baseline patient characteristics.
| Patients (N) | Overall n (%)a |
ICI therapy n (%)a |
Chemotherapy n (%)a |
|---|---|---|---|
| 30 | 15 | 15 | |
| Age (y), median (IQR) | 66 (57-78) | 75 (56-78) | 65 (62-78) |
| Sex, female | 18 (60) | 9 (60) | 9 (60) |
| BMI, kg/m2, median (IQR) | 23.3 (20.4-26.7) | 23.1 (20.4-26) | 23.8 (20.4-26.9) |
| ECOG score | |||
| 0 | 11 (36.7) | 6 (40.0) | 5 (33.3) |
| 1 | 14 (46.7) | 7 (46.7) | 7 (46.7) |
| 2 | 5 (16.6) | 2 (13.3) | 3 (20) |
| Cancer parameters | |||
| Cancer type | |||
| NSCLC | 20 (66) | 10 (66) | 10 (66) |
| Head and neck | 10 (33) | 5 (33) | 5 (33) |
| Cancer stage | |||
| Localized | 0 (0) | 0 (0) | 0 (0) |
| Lymph node involvement | 8 (27) | 4 (27) | 4 (27) |
| Metastatic disease | 22 (73) | 11 (73) | 11 (73) |
| Treatment parameters | |||
| Antiplatelet therapy | 8 (27) | 4 (27) | 4 (27) |
| Treatment approach | |||
| Palliative | 27 (90) | 15 (100) | 12 (80) |
| Curative | 3 (10) | 0 (0) | 3 (20) |
| ICI type | |||
| Pembrolizumab | 13 (43) | 13 (87) | − |
| Atezolizumab | 1 (3) | 1 (7) | − |
| Cemiplimab | 1 (3) | 1 (7) | − |
| Chemotherapy type | |||
| Carboplatin/vinorelbine | 1 (3) | − | 1 (7) |
| Carboplatin/etoposide | 1 (3) | − | 1 (7) |
| Carboplatin/gemcitabine | 1 (3) | − | 1 (7) |
| Carboplatin/pemetrexed | 1 (3) | − | 1 (7) |
| Carboplatin/paclitaxel | 3 (10) | − | 3 (20) |
| Cisplatin/vinorelbine | 4 (13) | − | 4 (27) |
| Cisplatin/gemcitabine | 1 (3) | − | 1 (7) |
| Cisplatin/pemetrexed | 1 (3) | − | 1 (7) |
| Pemetrexed | 2 (7) | − | 2 (13) |
| Laboratory parameters | |||
| Hemoglobin, g/dL, median (IQR) | 13.0 (11.4-14.3) | 12.7 (11.5-14.5) | 13.1 (10.9-14) |
| Thrombocytes, 109/L, median (IQR) | 279 (251-354) | 289 (247-390) | 267 (252-333) |
| Leukocytes, 109/L, median (IQR) | 8.1 (6.5-11.3) | 8.1 (7.4-10.2) | 8.0 (6.5-12.2) |
BMI, body mass index; ECOG, Eastern Cooperative Oncology Group; ICI, immune checkpoint inhibitor; NSCLC, non–small cell lung cancer.
Unless otherwise specified.
3.2. Distribution of biomarker levels
When stratified by treatment type, median biomarker levels were largely comparable between the ICI and chemotherapy cohorts at baseline and 3 weeks and 3 months after treatment (Figure 1A–E, Supplementary Table S2). A linear mixed-effects model indicated some weak differences in the effect of ICI therapy compared with chemotherapy on sP-selectin (estimate = +3.03 ng/mL; P = .61), D-dimer (estimate = +0.01 mg/mL; P = .99), EV-TF activity (estimate = +0.05 pg/mL; P = .58), peak thrombin (estimate = −34.34 nM; P = .29), or citH3-DNA levels (estimate = +15.22 ng/mL; P = .06; Supplementary Table S3). sP-selectin levels exhibited an upward trend in both treatment groups, with an increase of +4.75 ng/mL (P = .04) per time point in the ICI cohort and +4.19 ng/mL (P = .11) per time point in the chemotherapy cohort (Supplementary Table S3). Similarly, D-dimer and EV-TF activity showed slight upward trends over time. In the ICI cohort, citH3-DNA levels peaked at 3 weeks before returning to baseline, whereas they remained stable in the chemotherapy group (Supplementary Figure S2A–E, Supplementary Tables S2 and S3).
Figure 1.
(A–E) Levels of biomarkers dichotomized by therapy type and their longitudinal changes (N = 80, per biomarker). Each panel displays 2 boxplots, one for each treatment modality: immune checkpoint inhibitor (ICI) therapy (red) and chemotherapy (blue) at each time point for the 5 biomarker levels (soluble P-selectin [sP-selectin], D-dimer, extracellular vesicle tissue factor [EV-TF] activity, peak thrombin, and citrullinated histone H3 in complex with extracellular DNA [citH3]-DNA). The central line within each boxplot indicates the median, while the whiskers represent the 25th and 75th percentiles. Individual patient values are shown as dots. Statistical significance was assessed using the Wilcoxon rank-sum test. ns, not significant. Figure created in https://BioRender.com.
When dichotomized by metastatic status, sP-selectin levels were consistently higher in metastatic patients across all time points, while no other biomarker was consistently higher or lower between metastatic and nonmetastatic patients (Supplementary Table S4).
Among cancer types, NSCLC patients exhibited higher baseline sP-selectin levels compared with those with head and neck cancer, a difference that remained consistent over time. No differences by cancer type were observed for the other biomarkers (Supplementary Table S5).
When stratified based on the 12-month survival, the median levels of sP-selectin and D-dimer remained consistently higher in patients who died within the first 12 months compared with those who survived (Supplementary Table S6).
In a sensitivity analysis, we compared biomarker levels between patients receiving pembrolizumab (86.7%) and those treated with other ICIs (cemiplimab or atezolizumab; 13.3%). Overall, biomarker levels were comparable between the groups, except for higher EV-TF activity and citH3-DNA levels in 2 nonpembrolizumab patients (Supplementary Table S7).
3.3. Correlations between biomarkers
Although positive correlations emerged over time as several biomarkers exhibited increasing trends, most of these correlations did not reach statistical significance. Notably, at 3 weeks, sP-selectin and EV-TF activity levels showed a modest positive correlation (r = .44; P = .02), as well as sP-selectin and D-dimer at 3 months (r = .38; P = .06; Figure 2A–C).
Figure 2.
(A–C) Correlation matrices between biomarkers at all 3 time points (N = 80, per biomarker). The figure presents correlation matrices assessing Spearman’s correlation between 5 biomarker levels (soluble P-selectin [sP-selectin], D-dimer, extracellular vesicle tissue factor [EV-TF] activity, peak thrombin, and citrullinated histone H3 in complex with extracellular DNA [citH3-DNA]). The color and shape of each cell indicate the strength and direction of the correlation: blue represents negative correlations, white indicates no correlation, and yellow represents positive correlations. The shape varies, with ellipsoids denoting stronger correlations and circles indicating weaker or no correlation. The Spearman’s correlation coefficient is displayed within each shape. (A) Correlations at baseline, (B) at 3 weeks, and (C) at 3 months. Figure created using R.
4. Discussion
In this exploratory study, we assessed the levels and changes in hemostatic biomarkers in patients initiating and receiving ICIs compared with those initiating and receiving chemotherapy. Although slight differences were observed between the treatment modalities, our findings suggest overall comparable biomarker levels, indicating a similar impact on hemostatic biomarkers and their trajectories.
Chemotherapy is a known risk factor for VTE, which is believed to promote coagulation through multiple mechanisms, such as endothelial damage, platelet activation, elevation of procoagulant molecules, and concurrent decrease in endogenous anticoagulants [[31], [32], [33], [34]].
In contrast, less is known about the mechanisms of VTE development in patients treated with ICIs. Previous studies have reported that ICIs lead to T-cell activation and endothelial inflammation, which potentially leads to both ATE and VTE [22,35,36]. Additionally, immunotherapy is known to induce a proinflammatory state, and since inflammation and coagulation are tightly linked, it can be hypothesized that ICIs might promote a procoagulatory state through immune activation and the release of cytokines and NETs [22,37,38]. Consistently, in our current study, citH3-DNA complexes, a marker of NET formation, were higher in the ICI group at 3 weeks. This increase in NET formation may not only indicate a procoagulant state with a heightened risk of VTE but also an elevated risk of ATE, which is arguably even more well-established in the context of ICIs than the risk of VTE [21,[39], [40], [41]].
While temporal biomarker changes suggest a potential influence of therapy on biomarker dynamics, it is important to recognize that therapy is not the only factor affecting these biomarkers. Tumor stage and activity, including tumor angiogenesis, invasion, and disease progression, likely exert a substantial impact on biomarker levels [42,43]. These factors may outweigh the influence of the treatment modality itself. Additionally, the aggressive nature of certain cancers may be reflected in biomarker levels [43]. For instance, higher levels of sP-selectin and D-dimer in patients who died within the first 12 months suggest that these biomarkers may correlate with more aggressive or advanced disease. This aligns with previous studies showing that elevated D-dimer and sP-selectin levels are often indicative of poor prognosis in patients with cancer [[44], [45], [46]]. sP-selectin seemed to be the most robust biomarker in our analysis, as it was consistently elevated in metastatic patients, patients with NSCLC compared with head and neck cancer patients, and in those who died within the first 12 months at all 3 time points. These findings highlight the potential of sP-selectin as a useful biomarker for monitoring disease progression and potentially prothrombotic risk in patients with cancer, irrespective of the treatment modality. Positive correlations between biomarkers emerged after treatment initiation, likely reflecting the impact of systemic anticancer therapy and the tumor response on coagulation activation. At 3 weeks, sP-selectin correlated with EV-TF activity, possibly indicating platelet activation triggered by treatment (a phenomenon described in chemotherapy, though less established in ICI therapy) [33,47], as well as the release of EV-TF from stressed or dying cancer cells [48]. At 3 months, sP-selectin correlated with D-dimer, suggesting ongoing platelet activation in parallel with increased procoagulant and fibrinolytic activity, as reflected by elevated D-dimer, potentially due to disease progression or increased tumor activity [49].
Monitoring of biomarker levels could provide an advantage in predicting VTE risk by capturing dynamic changes in a patient’s procoagulatory state over time. As VTE risk in patients with cancer represents a dynamic process, particularly peaking in the first 6 months of cancer diagnosis, tracking these biomarkers could help clinicians to better understand evolving thromboembolic risk [50]. Additionally, dynamic D-dimer monitoring has been repeatedly shown to predict VTE risk and might be more accurate than baseline measurements alone [25,51]. Moreover, biomarker remeasurement facilitates repeated risk assessments as treatment regimens and patient conditions change, addressing a significant gap in current risk assessment models, most of which are designed to assess only the initial 6-month VTE risk after cancer diagnosis.
Finally, several limitations of this study must be considered when interpreting our findings. First, the exploratory nature of the study and the small sample size have limited the ability to detect small differences between the 2 treatment groups. The sample size was chosen based on the best matched pairs available. A larger sample would have yielded more definitive results; however, it would have introduced a myriad of potential confounders, and therefore, we opted for a smaller but well-matched cohort to elucidate the effect of specifically ICIs and chemotherapy on biomarker levels. Second, the strict matching process itself may have introduced bias, as it matched patients on multiple characteristics, namely cancer type, cancer stage, sex, and age. While this helped create comparable groups, it may have masked potential differences in biomarker levels due to treatment type. Third, the heterogeneity of therapeutic agents within the chemotherapy and ICI treatment groups may have influenced biomarker levels, as drugs within the same class can exhibit distinct effects. Fourth, while a lower concentration of the thrombin generation trigger might have revealed subtler differences between groups, we chose to follow the manufacturer’s standardized protocol to ensure reproducibility and comparability of results. sP-selectin, while primarily considered a marker of platelet activation, can also be released by activated endothelial cells. The assay utilized in this study does not distinguish between these 2 sources. Finally, patient characteristics, such as age, comorbidities, and frailty, often influence the decision to treat with ICIs or chemotherapy, which further complicates direct comparisons between the 2 groups. Patients who were deemed unfit for chemotherapy, for example, were more likely to receive ICI monotherapy, and this likely introduced confounding that affects biomarker levels as well as limiting the generalizability of our findings. As treatment guidelines are constantly changing and ICIs are implemented rapidly in anticancer care, indications may change, further limiting generalizability. All participants in this study were of European descent, which limits the generalizability of the findings to other racial and ethnic groups.
In conclusion, this exploratory study showed comparable levels of hemostatic biomarkers in patients receiving ICIs vs chemotherapy longitudinally, implying that both treatment modalities have a similar impact on these biomarkers. In future studies, it will be important to explore other hemostatic biomarkers, such as those involved in the contact system, and investigate inflammatory biomarkers in ICI-treated patients. Additionally, prospective studies with larger sample size are necessary to determine the predictive value of biomarkers in VTE risk assessment and in patients receiving ICIs.
Acknowledgments
We sincerely thank Lynn Gottmann, Josef Fürst, Martin Korpan, and Markus Kleinberger for their invaluable assistance in patient enrolment for the Vienna Cancer Thrombosis and Bleeding study. Additionally, we extend our gratitude to Christine Brostjan, Theresa Schramm, and Carla Tripisciano for their valuable input and support in conducting the laboratory analyses.
Funding
This work was supported by funding obtained by F.M. via the Early Career Research Grant of the “Gesellschaft für Thrombose- und Hämostaseforschung” (GTH) 2021 (Society for Thrombosis and Haemostasis Research). The financial support by the Christian Doppler Research Association is gratefully acknowledged.
Author contributions
N.V., C.E., and C.A. conceptualized the study. N.V. performed the data analysis. J.M.B., C.E., F.M., and N.V. conducted patient recruitment. C.A. and I.P. supervised the project. H.H. provided significant help in method development. N.V. performed the laboratory analysis. J.T., A.S.B., T.F., and M.P. provided vital input and contributed to the manuscript preparation. All authors reviewed and approved the final manuscript.
Relationship Disclosure
N.V. has no conflict of interest to declare. C.E. has no conflict of interest to declare. J.M.B. has no conflict of interest to declare. F.M. has received travel/congress support from Novartis and Bayer, honoraria for lectures from Servier and Bristol Myers Squibb, honoraria for consulting from Johnson and Johnson, and participated in advisory boards for MSD, Merck, and Servier. H.H. has no conflict of interest to declare. J.T. received honoraria for lectures and/or participation in advisory boards from Daiichi Sankyo, LFP, and Novo Nordisk. A.S.B. has received research support from Daiichi Sankyo and Roche, honoraria for lectures, consultation, or advisory board participation from Roche, Bristol Myers Squibb, Merck, Daiichi Sankyo, AstraZeneca, CeCaVa, and Seagen, and travel support from Roche, Amgen, and AbbVie. T.F. has received honoraria from and/or was an advisor for MSD, Merck KGaA, Bristol Myers Squibb, Boehringer Ingelheim, Roche, Sanofi, Amgen, Takeda, Invios, Janssen, and Ely Lilly. M.P. has received honoraria for lectures, consultation, or advisory board participation from the following for-profit companies: Bayer, Bristol Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, AstraZeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dohme, Tocagen, Adastra, Gan and Lee Pharmaceuticals, Janssen, Servier, Miltenyi, Böhringer-Ingelheim, Telix, Medscape, OncLive, Medac, Nerviano Medical Sciences, and ITM Oncologics GmbH. I.P. has no conflict of interest to declare. C.A. has received personal fees for lectures and/or participation in advisory boards from Bayer, Daiichi Sankyo, BMS/Pfizer alliance, and Sanofi.
Data availability
The data presented in this study are available on request from the corresponding author.
Footnotes
Handling Editor: Dr Kristen Sanfilippo
The online version contains supplementary material available at https://doi.org/10.1016/j.rpth.2025.102933
Supplementary material
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data presented in this study are available on request from the corresponding author.