Paclitaxel is a standard adjuvant treatment for breast cancer and several other cancers. Originally isolated from the yew tree Taxus brevifolia, it inhibits mitosis by stabilizing microtubules and preventing tubulin depolymerization (Gornstein and Schwarz, 2014; Hershman et al., 2014; Yang and Horwitz, 2017). A serious dose- and therapy-limiting side effect, which it shares with other commonly used cytostatic drugs, is the chemotherapy-induced peripheral neuropathy and neuropathic pain (CIPN), which affects up to 80% of treated patients (Pachman et al., 2011; Park et al., 2013). Patients report a variety of primarily sensory symptoms, encompassing sensations like numbness, paresthesia, spontaneous pain, and heightened sensitivity to mechanical and/or cold stimuli in their hands and feet. In more severe instances, the loss of vibration sense and joint position sense can further impact their functionality (Cavaletti and Marmiroli, 2010). Engaging in essential daily activities becomes challenging for patients, leading to difficulties in tasks like fine finger movements (such as buttoning clothing). Walking can induce pain due to mechanical hypersensitivity, while handling tasks like retrieving items from a fridge or exposure to cold weather may exacerbate symptoms (cold hypersensitivity). Chemotherapy can also emerge post-treatment in a state termed ‘coasting’ where mild neuropathy can worsen, or new instances of CIPN may develop (Flatters et al., 2017). Currently, there are no pharmacologic treatments for CIPN expert for duloxetine (Pachman et al., 2011; Cavaletti and Marmiroli, 2020; Smith et al., 2013). Therefore, research on the mechanism of paclitaxel induced CIPN with possible identification of novel treatments is an active research topic.

Several genes and neurofilament proteins have been implicated in paclitaxel-induced neuropathy (Diaz et al., 2018; Sisignano et al., 2019; Huehnchen et al., 2022). More recently, lipid mediators have been shown to be produced at high levels in sensory neurons, neuronal tissue and immune cells after chemotherapy due to oxidative stress and have been shown to contribute to chemotherapy-induced neuropathy and neuropathic pain by modulating neuronal ion channels (Hohmann et al., 2017; Piomelli and Sasso, 2014; Sisignano et al., 2016; Stockstill et al., 2018). Therefore, they are of particular interest as signaling molecules and potential markers for chemotherapy-induced neuropathy in patients. In fact, lipids are already considered markers for other neurological diseases such as Alzheimer’s disease and amyotrophic lateral sclerosis (ALS; Jensen et al., 2020; Chen et al., 2018; Igarashi et al., 2011; Kim et al., 2017). However, a systematic approach to investigate lipids in patients with paclitaxel-induced neuropathy has not been performed.

In this prospective clinical cohort study, plasma concentrations of 255 different lipid mediators were evaluated for changes in their concentrations associated with paclitaxel treatment. A comprehensive LC-MS/MS-based targeted and LC-QTOFMS-based untargeted lipidomics screening was performed on plasma samples from paclitaxel-treated patients. Lipid groups measured included eicosanoids, endocannabinoids, oxidized linoleic acid metabolites, sphingolipids, lysophospholipids and free fatty acids, many of which have been previously associated with persistent pain states (Osthues and Sisignano, 2019; Shapiro et al., 2016; Sisignano et al., 2014). A data-driven approach was used to identify lipid mediators whose concentrations could be used to train machine learning algorithms to identify, in new cases, whether a plasma sample was collected before or after therapy, or from a patient with or without post-therapy neuropathy. The biological relevance of the findings was then validated in vitro by applying SA1P to primary sensory neurons using calcium imaging.

All patients received paclitaxel as adjuvant or neoadjuvant therapy, without any other potentially neurotoxic substances. Of the 60 patients from our analysis cohort, two were excluded due to rescheduling of paclitaxel therapy. Blood samples were obtained from 31 patients before and after chemotherapy. Twenty lipid marker variables had >20% missing values. Following exclusion of these patients and variables and imputation, a data matrix for further analyses was obtained, sized 79×255 (79 data set instances, samples, and 255 different lipid mediators Figure 1). These included 48 samples drawn on day 1 before therapy and 31 samples drawn on day 2 after 12 cycles of paclitaxel therapy. On day 2, n=17 of the 31 patients had symptoms of neuropathy (54.9%), which is in line with previous clinical reports on occurrence and severity of paclitaxel-induced neuropathy (Pachman et al., 2011; Scripture et al., 2006). Most patients reported grade 1 neuropathy, although two patients experienced grade 3 neuropathy. Occurrence and degree of neuropathy were monitored 4.5 years after finishing chemotherapy. The neuropathy lasted for several months or, in many cases, still persisted for 4.5 years after chemotherapy at the last examination (Supplementary file 1).

Results of unsupervised analysis of structure in the lipidomics data supporting prior knowledge

PCA yielded d=28 components with eigenvalues >1, which together explained 93.93% of the total variance in the lipid mediators (Figure 2—figure supplement 1). The d=86 lipid mediators that contributed most to the relevant PCs were identified based on the membership to category ‘A’ in the cABC analysis of the weighted variable contributions to each PC (Figure 2—figure supplement 1). This was carried over as one of the several feature-importance measures to the supervised analyses reported in the next chapter. On the emergent self-organizing map (ESOM, Figure 2a), a clear separation of two clusters was observed, which provided support that the lipid mediators contained a data structure contingent with the prior classification into pre- and posttherapy samples (Fisher’s exact test: p=0.01054, odds ratio: 3.57 with 95% confidence interval 1.28–10.52; Figure 2b). The separation of samples on the ESOM also corresponded, but to a lesser extent, with the occurrence of neuropathy observed at the time of the 2nd blood sample (p=0.0328, odds ratio 0.16, 95% confidence interval 0.0137–1.022).

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The results of the unsupervised analysis thus supported that the lipidomics data contained a structure contingent on a known prior classification. This supported the continuation of data analysis with supervised methods to determine which of the lipids carried relevant information to assign a probe to a particular prior class.

Results of supervised analyses identifying lipid mediators relevant to the class structure

Lipid mediators informative for assigning samples to before or after paclitaxel therapy

Based on the majority vote of the different approaches to feature selection including PCA importance and further univariate and multivariate feature selection methods, d=77 lipid mediators were found to provide relevant information on whether a sample was collected before or after paclitaxel therapy (Figure 3). When statistical (logistic regression) and machine learning (random forests, support vector machines - SVM) algorithms were trained with this set of lipid mediators, the assignment of a sample to day 1 or 2 was well above the guessing level (Table 1). By contrast, when the training data were randomly permuted, the performance fell to a balanced accuracy of 0.5, that is guessing level, which established that the obtained class assignment in the non-permuted scenario had not been due to overfitting. In addition, by rerunning the cABC analysis on the mediators assigned to subset ‘A’ in the first run (‘recursive’ cABC analysis Lötsch and Ultsch, 2023b), the informative set of lipid mediators could be further reduced to d=27 (Table 2, Table 3). With these mediators, SVM and random forest were still able to detect whether an instance of a lipidomics dataset was from before or after paclitaxel treatment at a balanced accuracy better than expected from guessing. In summary, with the top hits (‘sparse’ feature set), three different algorithms (logistic regression, random forests, support vector machines) could be trained to identify, in new cases, whether a blood sample was drawn before or after paclitaxel therapy with a median balanced accuracy of up to 90%.

Figure 3

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Table 1

Classifier	Performance measure	Feature set
		Full	Reduced	Reduced permuted	Sparse
	Number of lipid mediators	255	77	77	27
SVM	Balanced accuracy	0.7 (0.48–0.92)	0.78 (0.61–1)	0.48 (0.23–0.76)	0.75 (0.54–0.91)
Random forests		0.7 (0.56–0.83)	0.75 (0.58–0.85)	0.46 (0.24–0.75)	0.74 (0.55–0.88)
Logistic regression		0.7 (0.52–0.85)	0.77 (0.58–0.92)	0.48 (0.29–0.7)	0.7 (0.49–0.89)
SVM	roc-auc	0.88 (0.67–1)	0.95 (0.85–1)	0.48 (0.16–0.77)	0.9 (0.81–0.99)
Random forests		0.86 (0.76–0.95)	0.88 (0.81–0.98)	0.48 (0.21–0.81)	0.9 (0.8–1)
Logistic regression		0.81 (0.64–1)	0.88 (0.75–1)	0.46 (0.16–0.79)	0.86 (0.69–0.98)

Table 2

Sample 1 versus sample 2
SA1P	Sphingomyelin 42:1	Palmitic acid 16:0	Eicosaeinoic acid 20:1	PE 38:5
LacCeramid C16	LPE 22:6	Margaritic acid 17:0	2-AG	LPC 22:4
S1P	LPE 18:0 p	Sphingomyelin 42:3	OEA	Cholesterolsulfate
Sphingomyelin 36:3	LPE 18:0	LPC 18:0	Sphingomyelin 40:1
Ceramide 18:0	LPC 20:1	LPC 18:1	Sphingomyelin 42:2
Ceramide 24:0	Nervoneic acid 24:1	Dehydroepiandrosterone sulfate
Sample 2: neuropathy versus no neuropathy
SA1P	Sphingomyelin 33:1	Sphingomyelin 43:1

Table 3

Classifier	Performance measure
		Sparse	Sparse permuted
	Number of lipid mediators	27	27
SVM	Balanced accuracy	0.6 (0.52–0.68)	0.5 (0.49–0.51)
Random forests		0.62 (0.5–0.68)	0.52 (0.37–0.65)
Logistic regression		0.62 (0.54–0.74)	0.51 (0.31–0.69)
SVM	roc-auc	0.65 (0.54–0.73)	0.51 (0.19–0.72)
Random forests		0.66 (0.55–0.75)	0.52 (0.24–0.76)
Logistic regression		0.69 (0.6–0.78)	0.49 (0.25–0.78)

Lipid mediators informative for assigning post-paclitaxel therapy samples to neuropathy

The n=31 samples from day 2 were probably too small to detect whether a sample was from a patient with neuropathy. Although the three algorithms detected neuropathy in new cases, unseen during training, at balanced accuracy of up to 0.75, while only the guess level of 0.5 was achieved when using permuted data for training, the 95% CI of the performance measures was not separated from guess level. Therefore, multivariate feature selection was not considered a valid approach, since it requires that the algorithms from which the feature importance is read can successful perform their task of class assignment (Lötsch and Ultsch, 2023a). Therefore, univariate methods (Cohen’s d, FPR, FWE) were preferred, as well as a direct hypothesis transfer of the top hits from the above-mentioned day1/2 assessments to neuropathy. Classical statistics consisting of direct group comparisons using Kruskal-Wallis tests (Kruskal and Wallis, 1952) were performed. The small set of d=3 lipid mediators that emerged from all three univariate methods as top hits for neuropathy included sphingolipid sphinganine-1-phosphate (SA1P), also known as dihydrosphingosine-1-phosphate (DH-S1P) sphingomyelin 33:1, and sphingomyelin 43:1. Statistical group comparisons verified that the three mediators differed significantly between samples from neuropathy-positive and neuropathy-negative patients (Figure 4). An overview of the distribution of the entire lipid marker data is shown in Figure 4—figure supplement 1.

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Biological in-vitro validation of the machine learning-based results

The results of the supervised analysis thus established a limited set of lipid mediators to be regulated in association with paclitaxel therapy or with its side effect of inducing neuropathy. The top hit was sphinganine-1-phosphate (SA1P), providing a basis for in vitro validation of its biological effects in the present context of neuropathy.

Calcium imaging measurements were performed on primary sensory neurons obtained from the murine dorsal root ganglia from at least four different animals. We stimulated the neurons with 1 and 10 µM SA1P (ranked as the primary hit) or LPC 24:0 (ranked as one of the least relevant lipids by machine learning analysis). We observed that SA1P caused a direct calcium transient in approximately 11.7% of KCl-responsive sensory neurons (Figure 5a and b). However, LPC 24:0 did not induce any notable activation of sensory neurons at concentration of 1 and 10 µM (Figure 5—figure supplement 1). To further characterize the SA1P-responding neurons, we investigated their responsiveness to agonists of the TRP channels TRPV1 (capsaicin) and TRPA1 (AITC, allyl isothiocyanate), both of which are hallmarks of subpopulations of primary sensory neurons (Julius, 2013). Stimulating SA1P-responsive neurons with capsaicin and AITC revealed that 73% of these neurons also responded to capsaicin and 25% of them responded to AITC, whereas only 9.6% responded to both stimuli. Neurons were identified as responders to KCl (50 mM, 1 min; Figure 5c and d).

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To identify the receptors or channels responsible for SA1P-mediated calcium transients in sensory neurons, the selective TRPV1 antagonist AMG9810 was used. Neurons were stimulated twice with SA1P (1 µM, 1 min) and AMG9810 (1 µM or vehicle) was added two minutes prior to the second SA1P stimulus. The second SA1P response was entirely abolished when the neurons were treated with AMG9810, but not with the vehicle (Figure 6a–c). The potency of AMG9810 was validated using the same measurement protocol as before but with capsaicin (200 nM, 20 s) instead of SA1P, which is the gold standard agonist of TRPV1. AMG9810 completely blocked the second capsaicin response (Figure 6d). The involvement of S1P-receptors previously suggested to be the receptors for SA1P (Magaye et al., 2019), was evident by studying S1PR1 and S1PR3 as the most highly expressed S1P receptors in sensory neurons (Quarta et al., 2017), which are also targets of the approved drug fingolimod. Sensory neurons were incubated with fingolimod or vehicle for one hour and stimulated the neurons with SA1P (Figure 6e and f). Comparing fingolimod- and vehicle-treated neurons, we observed that the response intensity to SA1P was similar (Figure 6g), while the number of neurons responding to SA1P was significantly decreased after fingolimod treatment (Figure 6h).

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Support of the main results in an independent second patient cohort

The study lacked a separate validation cohort with similar data to the main cohort. However, an independent second cohort was available from another hospital (Oncological Center in Offenbach, Germany). This cohort consisted of 28 patients treated with the ‘paclitaxel weekly’ regimen (paclitaxel 80 mg/m², once weekly) as adjuvant or neoadjuvant therapy for breast or ovarian cancer. All patients provided informed consent into study participation and publication of the results. Blood samples were available from routine collections and were analyzed by LC-MS/MS (Figure 7—figure supplement 1, Supplementary file 3). In contrast to the main cohort, plasma from patients in the second cohort was collected after six cycles of paclitaxel treatment due to local routines. Therefore, the second cohort cannot be considered a state-of-the-art validation cohort. In addition, only six patients in this cohort had neuropathy after chemotherapy (26.5%), all with grade 1 neuropathy (Supplementary file 3). Despite these limitations, algorithms trained with lipid information from cohort 1 were able to successfully identify whether a probe was taken before or after paclitaxel therapy in the second cohort at a better than guessing level. Furthermore, a trend toward different SA1P concentrations in the plasma of patients after paclitaxel treatment was observed (p=0.086, Figure 7).

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For paclitaxel, therapeutic doses range from 80 to 225 mg/m². As CIPN symptoms are dose-dependent, the number of PIPN patients that receive a high paclitaxel dose is higher than the number of PIPN patients receiving a low dose. The cumulative threshold dose above which paclitaxel causes sensory neuropathy is 300 mg/m² (Park et al., 2013). In our study, we mainly used a low dose paclitaxel, because this therapeutic regimen is the most widely used paclitaxel monotherapy. Previous studies report an occurrence of neuropathy with this therapeutic regimen is around 50–70%, and most patients (80–90%) are expected to experience Grade 1 neuropathy after 12 weeks (Gornstein and Schwarz, 2014; Hershman et al., 2014; Yang and Horwitz, 2017). Our results are within the range reported by these previous studies.

SA1P induced a direct calcium transient in sensory neurons dependent on sphingosine 1-phosphate receptors (S1PR) and the transient receptor potential vanilloid 1 (TRPV1) channel. The results suggest that lipids are altered during paclitaxel treatment and that alterations in sphingolipid metabolism may be critical for the development of paclitaxel-induced peripheral neuropathy in patients. The final (‘sparse’) set of lipids regulated between sampling days (before and after paclitaxel treatment) was enriched for sphingolipids. Specifically, sphingolipids were significantly overrepresented among the top hits of lipids regulated between sampling days (Fisher’s exact test: p=0.01), that is while 46 of the original 255 lipid mediators were sphingolipids (18%) (Supplementary file 2), 11 of the 27 members of the final sparse marker set (40.7%) belonged to the group of sphingolipids. The main pathway in which the top hits are involved is shown in Figure 8. The classification of patients with regard to neuropathy after paclitaxel treatment was reflected in lipidomics in another sphingolipid, that is dihydrosphingosine sphinganine-1-phosphate (SA1P), which was elevated in patients with neuropathy.

Figure 8

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The results from an unbiased machine-learning-based analysis are in line with those of previous reports that had used classical statistics mainly. For example, S1P was elevated in the spinal cord of mice after bortezomib treatment and during bortezomib-induced neuropathic pain. Blocking S1P1 receptor S1P1R with fingolimod effectively reduced bortezomib-induced mechanical hypersensitivity in vivo (Stockstill et al., 2018). Interestingly, targeting the S1P-S1P1R-axis was also found to reduce paclitaxel-induced neuropathic pain in vivo in a preclinical study (Janes et al., 2014). Further agreements of the present results relate to previous preclinical reports highlighting the significance of the sphingolipid pathway in persistent and neuropathic pain states (Stockstill et al., 2018; Squillace et al., 2020; Chen et al., 2019). In addition, the S1P signaling axis was observed to be relevant in neuropathy and chemotherapy-induced neuropathic pain, which led to the suggestion of targeting S1P receptors as a novel approach to ameliorate chemotherapy-induced neuropathy and neuropathic pain (Janes et al., 2014; Wang et al., 2020; Salvemini et al., 2013; Becker et al., 2020; Chua et al., 2020). Taken together, present results from lipid screening and unbiased machine-learning approach point towards a relevant contribution of sphingolipid signaling in paclitaxel-induced neuropathy in patients, mainly via sphinganine-1-pshophate and sphingomyelins 33:1 and 43:1.

Sphinganine is a key branching point in the sphingolipid pathway, where it can either be acylated to form dihydroceramides or phosphorylated to SA1P by sphingosine kinases (Merrill, 2011). Accumulation of sphinganine has been previously associated with reduced activity of ceramide synthase CerS2 (Pewzner-Jung et al., 2010). Interestingly, low CerS2 expression is a hallmark of various tumors (Zhang et al., 2019). It is conceivable that a subgroup of the patient cohort still exhibits low CerS2 expression after paclitaxel treatment, which is associated with higher SA1P levels and a higher occurrence of neuropathy. We also identified SA1P as a potential proalgesic lipid mediator, as it causes direct calcium transients in approximately 10% of sensory neurons. This effect is mediated, at least in part, by S1P receptors S1P1 and S1P3 and the TRPV1 channel. The TRPV1 channel has previously been identified as an important mediator of neuropathic pain and is associated with exacerbated activity of sensory neurons during paclitaxel-induced neuropathic pain (Jardín et al., 2017; Hara et al., 2013; Kamata et al., 2020). Our data indicate that an S1P receptor modulator, such as fingolimod may be a potential treatment strategy for reducing the proalgesic effect of SA1P and possibly for reducing paclitaxel-induced neuropathy in patients.

Several other lipids were found in the extended list of hits previously associated with chemotherapy-induced neuropathy or acute pain in preclinical studies, such as LPC 18:1, sphingosin-1-phosphate (S1P), and 9,10-EpOME. LPC 18:1 has previously been identified as an endogenous activator of TRPV1 and TRM8 and was found at elevated levels in murine DRGs 24 hr after oxaliplatin treatment. This may contribute to oxaliplatin-induced acute pain (Rimola et al., 2020). Similarly, 9,10-EpOME was elevated in the DRGs of paclitaxel-treated mice. locking its synthesis with the CYP2J2-inhibitor and the approved drug telmisartan was shown to reduce acute paclitaxel-induced mechanical hypersensitivity in vivo and prevent paclitaxel-induced mechanical allodynia by pretreatment (Sisignano et al., 2016). In addition, the direct TRPV1 agonist LPA 18:1 was found in the extended list of hits. Lipid was shown to bind to the C-terminal binding site of TRPV1 to increase the opening probability of the channel (Nieto-Posadas et al., 2012). Other signaling lipids with potential proalgesic effects and potential TRP channel activators that have been described previously have also been identified as crucial for group separation by our unbiased machine-learning approach, which strengthens the presumption that the identified lipids may indeed be connected with paclitaxel neurotoxicity and paclitaxel-induced neuropathy in patients. Additionally, we found several precursor fatty acids in the extended list of hits, including arachidonic acid, linoleic acid, and palmitic acid. These results imply a major dysregulation of lipids after paclitaxel treatment, leading to enhanced plasma levels of precursors for eicosanoids and oxidized linoleic acid metabolites, which may explain the observed enhanced concentrations of the eicosanoids of 5-HETE, 5,6-DHET, and the oxidized linoleic acid metabolites 9,10-EpOME, 9- and 13-HODE.

Several limitations need to be addressed. First, the size of our patient cohort (n=60 patients, n=31 patients who provided blood samples before and after paclitaxel chemotherapy) allowed for errors due to interindividual differences. Second, the paclitaxel treatment regimen differed in some patients included in this study. While the majority of the patients received paclitaxel as ‘pacli weekly’ regime consisting of a weekly dose of 80 mg/m² for 12 consecutive weeks in, some few patients received paclitaxel doses up to 220 mg/m² or a combination chemotherapy consisting of carboplatin/paclitaxel. These variabilities within the patient cohort hamper interindividual correlations between plasma lipid mediator concentrations and paclitaxel-induced neuropathy throughout the treatment course. Third, the assessment of peripheral neuropathy in patients was performed according to the guidelines of the NCI Common Terminology Criteria for Adverse Events (CTCAE) v5.0, which ranks the severity of neuropathy into five grades but is rather focused on general adverse events of chemotherapy rather than specifically assessing peripheral neuropathy in detail. We did not perform any neurological testing of sensory parameters, such as quantitative sensory testing (QST) (Maier et al., 2010), to determine the sensory status quo of the patients.

The identification of sphinganine-1-phosphate (SA1P) as a key lipid marker delineating the effects of paclitaxel was based on a comprehensive analysis performed by a ‘mixture of experts’ of machine learning and classical statistical methods, coupled with in vitro molecular experiments performed on sensory neurons. A rigorous validation process was applied to ensure that the results did not depend on the properties of any single statistical or AI model. This included verifying that the lipidomics data intrinsically supported the existing class structure (days 1/2) using two different data projection methods (PCA, neural network). In addition, the identification of the most relevant lipids associated with past exposure to paclitaxel relied on univariate and multivariate feature selection, and the results were verified by testing three different algorithms that finally could successfully identify from the concentrations of the top hits whether a blood sample was drawn before or after paclitaxel therapy. In addition, all data analyses were subjected to rigorous cross-validation. Results were first validated on a sample split from cohort #1 prior to any feature selection and classifier training, and again on the data from cohort #2. Then, the leading hit from machine learning, SA1P, was validated as a highly active mediator in a neuronal cell model, while a lipid maker deemed irrelevant by machine learning actually showed ineffectiveness in the cell model. The mechanistical details of altered sphingolipid metabolism reflected in their plasma concentrations of paclitaxel patients need to be investigated by further studies.

The patients' informed consent did not cover the publication of their data, even in deidentified form, on a public platform. Researchers who require access to this data for scientific purposes may contact the senior author (Marco.Sisignano@med.uni-frankfurt.de), who will then refer the request to the department's ethics committee. Relevant Python and R code used for data analysis is available at https://github.com/JornLotsch/PaclitaxelNeuropathyProject (copy archived at Lötsch, 2024).

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Author details

1. Jörn Lötsch

   1. Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany
   2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Frankfurt, Germany

   Contribution

   Resources, Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Khayal Gasimli

   Competing interests

   No competing interests declared

2. Khayal Gasimli

   Goethe University, Department of Gynecology and Obstetrics, Frankfurt, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Writing – original draft, Writing – review and editing

   Contributed equally with

   Jörn Lötsch

   Competing interests

   No competing interests declared

3. Sebastian Malkusch

   1. Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany
   2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Frankfurt, Germany

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Lisa Hahnefeld

   1. Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany
   2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Frankfurt, Germany

   Contribution

   Resources, Formal analysis, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Carlo Angioni

   Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany

   Contribution

   Formal analysis, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Yannick Schreiber

   Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Frankfurt, Germany

   Contribution

   Formal analysis, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

7. Sandra Trautmann

   1. Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany
   2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Frankfurt, Germany

   Contribution

   Formal analysis, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

8. Saskia Wedel

   Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

9. Dominique Thomas

   1. Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany
   2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Frankfurt, Germany

   Contribution

   Formal analysis, Supervision, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

10. Nerea Ferreiros Bouzas

    1. Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany
    2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Frankfurt, Germany

    Contribution

    Formal analysis, Supervision, Validation, Investigation, Methodology

    Competing interests

    No competing interests declared

11. Christian H Brandts

    1. German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
    2. Goethe University, University Cancer Center Frankfurt (UCT), Goethe University Hospital, Frankfurt, Germany

    Contribution

    Resources, Supervision, Funding acquisition, Project administration

    Competing interests

    No competing interests declared

12. Benjamin Schnappauf

    Oncology Center, Sana-Klinikum Offenbach, Starkenburgring, Germany

    Contribution

    Formal analysis, Validation, Investigation, Methodology

    Competing interests

    No competing interests declared

13. Christine Solbach

    Goethe University, Department of Gynecology and Obstetrics, Frankfurt, Germany

    Contribution

    Resources, Supervision, Project administration

    Competing interests

    No competing interests declared

14. Gerd Geisslinger

    1. Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany
    2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Frankfurt, Germany

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration

    Competing interests

    No competing interests declared

15. Marco Sisignano

    1. Institute of Clinical Pharmacology, Goethe - University, Frankfurt, Germany
    2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Frankfurt, Germany

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    Marco.Sisignano@med.uni-frankfurt.de

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7581-0951

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