A distinct four-value blood signature of pyrexia under combination therapy of malignant melanoma with dabrafenib and trametinib evidenced by an algorithm-defined pyrexia score.

Pyrexia is a frequent adverse event of BRAF/MEK-inhibitor combination therapy in patients with metastasized malignant melanoma (MM). The study's objective was to identify laboratory changes which might correlate with the appearance of pyrexia. Initially, data of 38 MM patients treated with dabrafenib plus trametinib, of which 14 patients developed pyrexia, were analysed retrospectively. Graphical visualization of time series of laboratory values suggested that a rise in C-reactive-protein, in parallel with a fall of leukocytes and thrombocytes, were indicative of pyrexia. Additionally, statistical analysis showed a significant correlation between lactate dehydrogenase (LDH) and pyrexia. An algorithm based on these observations was designed using a deductive and heuristic approach in order to calculate a pyrexia score (PS) for each laboratory assessment in treated patients. A second independent data set of 28 MM patients, 8 with pyrexia, was used for the validation of the algorithm. PS based on the four parameters CRP, LDH, leukocyte and thrombocyte numbers, were statistically significantly higher in pyrexia patients, differentiated between groups (F = 20.8; p = <0.0001) and showed a significant predictive value for the diagnosis of pyrexia (F = 6.24; p = 0.013). We provide first evidence that pyrexia in patients treated with BRAF/MEK-blockade can be identified by an algorithm that calculates a score.

Introduction

Within the last decade, cutaneous melanoma incidence has been rising steadily worldwide [1, 2]. 40–60% of all patients with malignant melanoma show an activating mutation of the serine-threonine kinase B-RAF (BRAF) [3, 4], the most common BRAF mutation being the V600E point mutation (T→ A nucleotide change). Activating mutations at BRAF V600 lead to a constitutive activation of the MAP kinase signaling pathway (RAS-RAF-MEK-ERK), making these proteins an attractive target for therapies [3].

Patients treated with the combination of the selective BRAF inhibitor dabrafenib and the MEK inhibitor trametinib show a longer progression-free survival (11 months vs 8,8 months), and an improvement of the overall survival (25,1 months vs 18,7 months), as compared to a monotherapy with a BRAF inhibitor [2, 5, 6]. In the US and in Europe, combination therapy with a BRAF-inhibitor and a MEK-inhibitor is approved for patients with unresectable BRAF-V600-mutated melanoma as well as for adjuvant treatment in stage III melanoma [2, 5–7]. Other combinations of approved BRAF/MEK inhibitors such as vemurafenib and cobimetinib or encorafenib and binimetinib have demonstrated similar efficacies [4].

Mostly similar negative side effects can be observed in mono- and combination therapies with BRAF/MEK inhibitors, but pyrexia seems to be particularly frequent when using the combination of dabrafenib and trametinib. Considering all grades, it has been reported in up to 59% of patients treated with dabrafenib and trametinib [8, 9]. Despite these pioneering efforts to develop a therapy for melanoma patients, pyrexia is one of the most common reasons, not only for dose reduction and interruption, but also for a complete therapy discontinuation [7, 10]. Furthermore, under BRAF/MEK inhibition, myelosuppression has been observed [11-13]. Some patients may also experience more than one pyrexia episode and the episodes might be accompanied by hypotension or complicated by neutropenic sepsis [14].

The mechanisms inducing pyrexia under BRAF/MEK-inhibition are not fully understood yet and no clinical marker seems to be available which reliably predicts which patients will develop pyrexia [15]. Still, in previous publications it could be demonstrated that pyrexia under BRAF/MEK inhibition is associated with an increase of acute phase proteins such as CRP and procalcitonin, with an increase of proinflammatory cytokines such as interleukin-1beta (IL-1beta) and interleukin-6, a decrease of leukocytes and granulocytes and with aberrations of the coagulation system [11–13, 16, 17].

In several patients treated at the Department of Dermatology at the University Hospital of the RWTH Aachen, Germany, we observed an increase of C-reactive protein (CRP) in parallel with a decrease of leukocyte and thrombocyte counts at the time of pyrexia and sometimes already before pyrexia (Fig 1). Starting from this observation which suggested that the synchrony of observed laboratory value changes might be predictive as well, we initiated a retrospective study to identify pattern changes within routine laboratory values which could diagnose as well as predict the onset of pyrexia in order to improve management of melanoma patients. Besides traditional statistics used for detecting associations between laboratory values before pyrexia and at the time around pyrexia, an algorithm-based approach was developed for the detection of pyrexia and its results were compared to the statistical analysis of individual laboratory values.

Fig 1

Time series of a patient with pyrexia at day 79 from treatment start, semi-logarithmic scale.

CRP was normalized to 100 = 2.5 mg/l (value CRP patient/2.5 * 100), leukocyte count was normalized to 100 = 6.5 /nl (for male) & 6 /nl (for female), thrombocyte count was normalized to 100 = 275 /nl. Triangle indicates onset of pyrexia.

Materials and methods

Ethics statement

This retrospective study was conducted in accordance with the guidelines of the Declaration of Helsinki. Ethical review and approval were obtained and confirmed in advance by the local ethics committee of the University Hospital of the RWTH Aachen (ethic vote 257/19). The ethics committee did not require informed consent of the patients as the study was strictly retrospective and only analysed available routine laboratory data of the patients without assessing the efficacy of treatments and without contacting of patients or relatives. Prior to statistical data analysis, all anamnestic information was anonymized and time date indications were expressed without a specific calendar date.

Study population

We conducted a retrospective analysis of medical records of 38 patients (27–84 years) including 24 (65%) non-pyrexic (17 male & 7 female) and 14 pyrexic patients (8 male & 6 female), who received combination therapy with dabrafenib plus trametinib at the Department of Dermatology of the University Hospital of the RWTH Aachen from 02/2015 until 04/2020.

Therapy consisted of oral dabrafenib (150 mg twice daily (BID)) and oral trametinib (2mg once a day (QD)). In some patients, the initial dabrafenib dose was reduced to 150 mg per day during the first three days of treatment followed by an evaluation of serum amylase and/or lipase in order to avoid severe pancreatitis which can be associated with this treatment. Start of trametinib treatment was delayed by up to one week in some patients when cardiologic evaluations were not available but inhibition therapy with dabrafenib could not be postponed. In all patients, pyrexia developed under combination therapy. Data from this study population was used for initial statistical analysis and the development of the pyrexia score algorithm.

A second independent data set was generated from a retrospective analysis of 28 patients including 8 additional patients with pyrexia and 20 additional patients without pyrexia from the Department of Dermatology of the University Hospital of the RWTH Aachen (n = 5) and from the Department of Dermatology and Venereology, Cologne University Hospital, Germany (n = 23) who were treated with dabrafenib and trametinib from 09/2015-05/2022. This second data set was used to validate the pyrexia score algorithm developed by the above-described retrospective analysis and was, therefore, blinded and not adjusted for age or sex and consisted only of laboratory data.

The patient’s case histories and laboratory values were obtained from the institution’s digital patient information system of the University Hospital of the RWTH Aachen and from the Department of Dermatology and Venereology, Cologne University Hospital, Germany. Data acquisition by the treating physician and statistical data analysis were strictly separated in order to ensure patients’ data protection.

Inclusion criteria for all patients were age (>18 years) and a confirmed diagnosis of metastatic cutaneous melanoma (stage III or stage IV) treated with dabrafenib and trametinib. Pyrexia was defined as an oral temperature of 38.5°C (≥101.3°F) or higher in the absence of any clinical or microbiological evidence of infection. A pyrexic event was deemed to have resolved after a 24h period of temperature of 37.5°C or less. Patients who had missing data in our institution’s records regarding their treatment regimen and/or side effects were excluded from the statistical analysis.

Laboratory parameters

Biochemical and haematological parameters obtained from the routine laboratory evaluations of all 38 patients were examined for the initial statistical analysis and for the development of the pyrexia score algorithm. Parameters included differential blood count (leukocytes, neutrophils, basophils and eosinophils, lymphocytes, monocytes), erythrocytes, haemoglobin, haematocrit, mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), thrombocytes, glucose, lipase, aspartate transaminase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (gamma-GT), lactate dehydrogenase (LDH), creatine kinase (CK), uric acid, urea, urea-creatinine quotient, Creatinine, glomerular filtration rate (GFR), C-reactive protein (CRP), protein S100, interleukin 6 (IL6) and thyroid stimulating hormone (TSH). For the validation data set, only laboratory values for leukocyte and thrombocyte counts, LDH and CRP during BRAF/MEK-inhibitor treatment were obtained from 28 patients. For normalized data (percentages), the original laboratory data was divided by the sex-specific mean value of the respective parameter and the result was multiplied by 100.

Database

Using Microsoft Excel (MS Excel 2016, Version 15.11), anonymized data sets were created. All data relating to the respective patient history were compiled in an anamnestic table. The extracted variables included age, sex, tumor stage (at time of initial diagnosis and initiation of therapy), tumor classification (according to the TNM system), time of initial diagnosis and initiation of therapy, location of the primary tumor, metastases, secondary diagnoses, medication duration and side effects of therapy. Oncology progress notes were reviewed to identify any inflammatory side effects, time to their onset, and associated management.

The patient’s laboratory data were recorded in a different database and assigned to a time axis, starting from the first day of therapy initiation (T0 = initiation of therapy). Each pyrexic event was retrospectively analysed and assigned to the start of treatment (or to the restart of treatment if it was discontinued). The total number of pyrexic events was recorded and each event was analysed separately. The result of the occurrence was binary dichotomized with regard to its expression whether there were inflammatory side effects (pyrexia) or they were absent (no pyrexia). In patients with recurrence of a certain side effect, time to the first presentation was used as time to side effect onset.

Time cluster analysis

Since it was a retrospective study, laboratory data were determined irregularly and with varying frequency for each patient. To avoid the problem of heterogeneous observation units and to create a better comparability within the cohort, a time cluster consisting of four individual data points was created. The time clusters were then treated as a time dependent covariate in the statistical analyses of the laboratory data. Every cluster included exactly one data point for every patient, in order to have four different data values at comparable time points to include in our statistics.

Laboratory values of patients who did not develop pyrexia were also assigned to the four time clusters based on the determination of the mean time value of the occurrence of pyrexia in our data set, thereby assuming a time at which a pyrexia might have occurred in these patients. This strategy was used in order to rule out that the suspected laboratory values might change by a similar mode in time regardless of pyrexia.

The four time clusters covered data points within the time phases, i.e., last laboratory evaluation before the start of therapy (1st cluster: pre-T), first data after the start of therapy (2nd cluster: post-T), last laboratory evaluation before the onset of pyrexia (3rd cluster: pre- P) and first laboratory values after the onset of pyrexia but before any treatment of pyrexia (4th cluster: post-P). Cluster 1 covered 33 days (from T-1 to T-33), cluster 2 covered 21 days (from T1-T21), cluster 3 covered 23 days (from P-1 to P-23) and cluster 4 covered 28 days from (P0 to P28). Laboratory values for cluster 1 were missing in 4 patients, laboratory values for cluster 2 were missing in 4 patients, laboratory values for cluster 3 missing in 3 patients and laboratory values for cluster 4 in 2 patients.

Statistical analysis

All analyses were conducted with the program SAS (SAS 9.4, SAS Institute, Cary, N.C., USA). The distribution of the independent parameters was classified through histogram comparison. Due to the results, as a first step a non-parametric correlation analysis (Spearman) between the development of pyrexia and laboratory data was conducted.

Descriptive statistics included percentage, frequency, median, and standard deviation were evaluated. General characteristics and tests of between-group homogeneity of the variables were analysed using the chi-square test, Wilcoxon test, ANOVA and t-test. The limit of significance was set for α at p = 0.05.

The impact of the individual blood parameters on the development of pyrexia was tested using a nested generalized mixed effects model. Due to the fact that the blood parameters as continuous variables revealed no saturated models, we utilized the quartiles of the particular blood parameter as a predictor. The time cluster was set as a random factor and the individual patient’s ID was set as a nesting variable in dependence of the time cluster. Sex, age and the time cluster were included as covariates in the model, and we applied a post hoc Tukey test to adjust for repeated measurements. Additionally, we ran all models once more, including a term for the interaction of the blood parameter and the time cluster to detect if the effect of the blood parameter was time dependent.

The predictive value of the pyrexia score was also tested by a nested generalized linear mixed effects model. The individual patient’s ID was set as a nesting variable, and the time span from onset of therapy until the blood sample was obtained was set as a random factor. We established a binomial regression with the pyrexia score as predictor, the time span variable as covariate and the development of the pyrexia as a dichotomous outcome. We applied this model to the respective scores.

To determine score thresholds for the development of a pyrexia, we ran a logistic regression model. We built a dummy variable and set this variable to zero for all participants without developing a pyrexia. The pyrexia score value of the other participants, who developed a pyrexia, was divided into quartiles and the particular number of the quartiles was applied for the dummy variable. The logistic regression model with developing pyrexia y/n as outcome and the dummy variable as predictor revealed the odds ratio and their confidence intervals for developing pyrexia in the particular quartiles of the score in comparison to the participants without developing pyrexia. The lower bounds were then adopted as thresholds, if the odds ratio was significantly increased. The same procedure was applied to the external validation cohort after the respective pyrexia scores were calculated.

Development of a pyrexia score algorithm

Identification of previously unknown associations in medicine may be initiated by a single observation suggesting that a patient’s characteristics, symptoms, clinical and laboratory data, side effects or treatment outcome in an individual patient or in a few treated patients could be correlated in a hitherto unreported fashion.

The starting point of the presented retrospective study was the observation in single patients of an increase in C-reactive protein (CRP) in parallel with a decrease of leukocyte and thrombocyte counts at the time of pyrexia and sometimes already before pyrexia (Fig 1). Graphical time-series visualization also demonstrated that available clinical routine data were sampled at uneven intervals ranging from single days to several weeks. In general, blood was drawn more often at the beginning of the treatment in order to exclude side effects such as pancreatitis and at the time of pyrexia as a direct consequence of the need to differentiate pyrexia from infection.

An algorithm is a process or set of rules that need to be followed in calculations or other problem-solving operations. In medicine, algorithms are widely used to define calculations of scores that may then be used for diagnosis, for objective evaluation of disease severity or for risk prediction. While some scores may be directly derived from statistical data, many scores in medicine have been developed following heuristic techniques.

We have adopted the idea of calculating a score by an algorithm for detection and prediction of pyrexia based on the following heuristic assumptions [18]:

The data describe clinical time-series, but uneven temporal spacing of blood sampling in patients as well as missing values will probably limit the use of traditional mathematical methods for analysis of time series data [19].

The visualization of laboratory data suggested that parallel changes of laboratory values during pyrexia were more important than single absolute values.

From a clinician’s point of view, laboratory values that demonstrate a trend like a continuous rise or fall over subsequent samplings, are considered more reliable than changes observed at a single time point.

An algorithm may be optimized by an iterative and incremental optimization process, which by itself constitutes an algorithmic approach.

An algorithm can integrate different values as well as temporal changes of values into one single decision or value. A single value, such as a score, may be subsequently analysed independently by traditional statistical methods.

Fig 2 describes the general structure of the algorithm developed for the calculation of the pyrexia score. Based on the results obtained by our statistical analysis (see results section), lactate dehydrogenase (LDH) was added to the other three laboratory values. Value 1 (V1) represents leukocyte count, V2 = thrombocyte count 2), V3 = LDH and V4 represents C-reactive protein (CRP).

Fig 2

Design and optimization of the pyrexia score algorithm.

The key heuristic assumption, that changes of laboratory values are probably more important than absolute values, was integrated into the algorithm by calculating the pyrexia score at the time of laboratory assessment (= tx) by comparing the individual values (V1, V2, V3, V4) at tx with the value of the previous blood sampling date at tx-1. The initial algorithm also contained threshold variables for a minimum change (F1-F4). For each value (V1-4), the first calculation (S1-S4) equals either 1 or 0. This basic algorithm was refined through a five-level iterative development following established Agile methodologies [20]. The heuristic assumption, that a constant trend over subsequent blood samplings is a stronger indication than values determined at a single time point, was integrated into the algorithm by repeating the above calculations in version 2 of the algorithm for corresponding values V1-V4 at tx-1 and tx-2. In versions 1–2 of the algorithm, the assumption that pyrexia is characterized by a parallel change of these four values is reflected by the simplest possible calculation which consists of building a sum out of the eight calculations regarding V1 to V4 at tx as well as at tx-1, thus following Occam’s Razor principle. In version 3, further threshold values for minimal (T1, T2) or maximum (T3, T4) values were introduced. Moreover, as it was unknown a priori which laboratory value would prove most important, initial calculations S1-S8 derived from values V1-V4 at tx as well as at tx-1 were multiplied by variables FS1-FS8 in version 3.

In version 4 of the algorithm, in order to explore whether combination of individual laboratory changes might be more indicative of pyrexia, further variables OA1-OA6 were introduced. At tx, in case of simultaneous decrease of leukocytes and thrombocytes (OA1), simultaneous decrease of leukocytes and increase of LDH (OA2), simultaneous decrease of leukocytes and increase of CRP (OA3), simultaneous decrease of thrombocytes and increase of LDH (OA4), simultaneous decrease of thrombocytes and increase of CRP (OA5) and simultaneous increase of CRP and LDH (OA6), the respective variables were attributed a value and the sum obtained out of the eight calculations regarding V1 to V4 at tx as well as at tx-1 was multiplied by OA1-OA6.

Version 5 of the algorithm introduced two additional calculations, one for a bacterial infection signature and one for a CRP-threshold. It was assumed that bacterial infection is characterized by a simultaneous increase of CRP and an increase of leukocytes. Albeit with reduced sensitivity, an increase of LDH and thrombocytes has also been observed during bacterial infection [21]. For each leukocyte, CRP, thrombocyte and LDH value at t0, threshold variables were added for a minimum change IC1-IC4, as well as further threshold variables (IM1-IM4) were introduced for minimal values of leukocytes, CRP, thrombocytes and LDH. In case of a positive infection signature, PS were multiplied with variable IS (<<1), thus reducing the pyrexia score. It is further assumed that pyrexia is always associated with a CRP increase. If CRP at t0 does not exceed CRP value CT, PS is multiplied with variable GC (<<1), thus reducing pyrexia score. If CRP at t0 was equal or higher than CT or if no CRP value was recorded, this calculation reported 1. Supplementary file calculation-matrix-PS-5.0 contains an excel spreadsheet for calculation of PS-scores that also allows modification of variable values (see DYRAD repository: https://doi.org/10.5061/dryad.xpnvx0kjj.

In order to define the values of all the variables that would allow the strongest differentiation between patients not developing pyrexia and pyrexia patients (which would therefore also be able to best determine the onset of pyrexia), a heuristic, iterative and incremental optimization approach was chosen in versions 1.0–5.0 (Fig 2). This approach consisted in calculating pyrexia scores (PS) for all patients at all time points. Mean values of PS were then determined for all patients without pyrexia at all time points = M0P, for all patients with pyrexia at all time points = MWP as well as for all patients with pyrexia at time points of +/- one week around pyrexia = MWP14. These values were used to calculate the ratios MWP/(M0P+MWP) and MWP14/(M0P+ MWP). Besides, graphic displays of time series of patients without and with pyrexia were used to visualize the discriminative potential of the PS-algorithm. In an iterative process, one of the variables F1-8, T1-8, FS1-8, OA1-OA6, IS, CG, IC1-IC4, IM1-IM4 was increased or decreased by one increment and the above calculations were repeated. If ratios MWP/M0P and MWP14/M0P demonstrated an increase or graphic displays of time series demonstrated a better separation of patients without and with pyrexia, the same variable was altered by one additional increment and ratios and graphic displays were again determined. If the ratios decreased, the increment was reversed, and the procedure was repeated. The value of this variable was retained, and the next variable was chosen for the same procedure. Only one round of optimization was performed. Calculations and graphic display were conducted within an excel spreadsheet. Patients’ data used for calculating the pyrexia scores as well an EXCEL-sheet that calculates the pyrexia score and which allows modification of the algorithm have been deposited as open access at the DYRAD repository under https://doi.org/10.5061/dryad.xpnvx0kjj.

Results

Descriptive statistics, first data set

We conducted a retrospective analysis of medical records of 38 patients (27–84 years) including 24 (65%) non-pyrexic (17 male & 7 female) and 14 pyrexic patients (8 male & 6 female), who received combination therapy with dabrafenib plus trametinib at the Department of Dermatology of the University Hospital of the RWTH Aachen, from 02/2015 until 04/2020. Patient sex was not correlated with development of pyrexia (chi-square test: p = 0.39).

During the observation period, 17 pyrexic events occurred in total. One patient developed two independent pyrexia episodes at intervals of 2 years, both of which were included in the statistics, because they had no causal connection and were regarded as independent events. One patient developed 3 pyrexic events, but episodes correlated with each other and were not independent, therefore only the first episode was included in the statistics. 86% (12 patients) of all pyrexic patients had only one event, 7% (1 patient) had 2 events and 7% (1 patient) had 3 events of pyrexia. The mean and median time from the beginning of therapy to onset of the first pyrexic event were 31 days and 26.5 days (range 5 days—79 days) and the mean and median time of duration of the pyrexic event were 2 days (range 1 day—4 days). Pyrexic events led to either temporary dose reduction or complete therapy interruption. When pyrexia had subsided, dabrafenib/trametinib therapy was either re-introduced or, in severe cases of recurrent pyrexia, therapy was finally discontinued. 8 out of 13 pyrexia patients (57%) with severe symptoms were treated with glucocorticoids.

The median age of all patients was 63 years. Patients who developed pyrexia were slightly older (median: 64 years) than those without any pyrexic event (median: 62 years). 28 patients (74%) had a stage IV MM, when starting therapy, 7 patients (18%) stage IIIC MM, 2 patients (5%) stage IIIB MM and 1 patient (3%) stage IIIA MM (Table 1). There were relatively more patients with pyrexia in stage III compared to stage IV, still the difference was not statistically significant (chi-square test: p = 0.077). The median and the mean pre-treatment values for LDH were slightly higher in patients without pyrexia (205 and 263 U/l) compared to patients who developed pyrexia (194 and 217 U/l). This difference was not statistically significant (t-test: p = 0.14). The study was not intended to evaluate the impact of pyrexia on the outcome of BRAF/MEK inhibitor combination therapy.

Table 1

Descriptive statistics of the first data set, patient´s tumor stage at time of therapy initiation.

	IIIA	IIIB	IIIC	IV
Pyrexia	0	1	5	8
no pyrexia	1	1	2	20
Total	1	2	7	28

Using mixed model analysis, we developed a stepwise model for statistical analysis of the laboratory data. Model 1 was adjusted for time cluster, sex, and age. Model 2 (M2) was then adjusted only for time cluster and age and showed results that were statistically more significant. Model 3 (M3) contained the same covariates as model 2, but an additional term for the interaction between time clusters and the quartiles of the laboratory values was added to the equation. The results of the M3 analysis reached significance and showed also that the laboratory values have a time-dependent influence (Table 2). The interaction analysis of M3 then further showed the strength and significance of interaction between laboratory values and time cluster. These model analyses served to identify the parameters relevant for a pyrexia score, which were conspicuous in addition to the clinical experience.

Table 2

Mixed model analysis of laboratory parameters: Model 1 with adjustment for time cluster, age and sex; Model 2 with adjustment for time cluster and age; Model 3 with adjustment for time cluster, age and interaction of laboratory value quartile with time cluster; Interaction term model M3 indicates strengths and significance of interaction between laboratory values and time cluster.

Value Quartiles	Model 1		Model 2		Model 3		Interaction Term Model for Model 3
	F	p	F	p	F	p	F	p
Leukocytes↓	4.08	0.045	5.74	0.018	5.27	0.023	0.15	0.927
CRP↑	2.90	0.091	1.43	0.234	0.24	0.624	4.11	0.008
LDH↑	14.38	< 0.001	16.32	< 0.001	10.05	0.002	4.96	0.003
γ-GT↑	0.91	0.342	0.01	0.939	0.11	0.742	1.18	0.320
AST↑	2.15	0.145	1.28	0.259	0.13	0.722	1.37	0.256
ALT↑	3.49	0.064	1.34	0.249	1.18	0.281	2.21	0.090
S 100↑	0.04	0.839	0.58	0.449	0.55	0.461	1.81	0.151
Erythrocytes↓	2.99	0.086	4.88	0.029	3.85	0.052	0.91	0.439
Thrombocytes↓	12.64	0.0005	11.96	0.0007	7.18	0.0085	2.33	0.061

-- = model did not reach convergence; p = level of significance; F = test statistic of mixed models

↓ Decrease of absolute laboratory values

↑ Increase of absolute laboratory values

When testing the quartiles of laboratory parameters (adjusted for time cluster and age) for significance, using mixed model analysis, the following results were obtained (Table 2). The analysis revealed a significant effect for elevated LDH (Model 1: F = 14.38; p< 0.0001; Model 2: F = 16.32; p < 0.0001; Model 3: F = 10.05; p = 0.002; Interaction M3: F = 4.96; P = 0.003). This effect was statistically significant for all 3 models, regardless of the time cluster. Furthermore, reduced leukocyte counts exerted an increasing influence as well (Model 1: F = 4.08; p = 0.045; Model 2: F = 5.74; p = 0.018; Model 3: F = 5.27; p = 0.023). For an elevated CRP, only the interaction between the quartiles of CRP and the fourth time cluster reached significance in the course of time (Interaction M3: F = 4.11; p = 0.008). A reduction of erythrocyte counts (Model 2: F = 4.88; p = 0.029) as well as reduced thrombocytes counts (Model 2: F = 11.96; p = 0.0007) demonstrated significance only in the model with adjustment for time cluster and age. Analysis of y-GT, AST, ALT, and S100 did not reveal statistically significant results.

Pyrexia scores determined by the algorithm approach 5.0

Based on the results obtained by our statistical analysis, lactate dehydrogenase (LDH) was added to the other three laboratory values: C-reactive protein CRP, leukocyte count and thrombocyte count. Fig 2 describes the general structure of the algorithm developed for the calculation of the pyrexia score.

Fig 3 demonstrates graphically the transformation of normalized patient’s laboratory values into pyrexia scores. For this patient, laboratory values were determined at three clinical visits after start of therapy and till development of pyrexia. In the semi-logarithmic display, CRP and LDH values rise nearly linearly with time (Fig 3 and 3A). Likewise, in this patient leukocyte counts seem to fall linearly as well on a semi-logarithmic scale. After diagnosis of pyrexia and treatment interruption, values returned to normal range within 10 days. The pyrexia score primarily assesses changes of analysed laboratory values only in the expected direction, i.e., rise of CRP and LDH and fall of thrombocytes and leukocytes. Absolute values are only used for defining thresholds (see materials and methods section). For the displayed patient, calculated pyrexia scores rise and obtain a maximum value after the onset of pyrexia (Fig 3 and 3B). Although CRP and LDH levels remain elevated and leukocyte and thrombocyte counts remain depressed shortly after the onset of pyrexia, the pyrexia score returns to low values immediately after the maximum as no further increase of CRP or LDH and no further decrease of leukocytes and thrombocytes occur in the patient after the onset of pyrexia and discontinuation of BRAF/MEK-inhibition.

Fig 3

Transformation of individual and normalized laboratory values (A) of one patient with pyrexia into pyrexia scores (B) by algorithm 5.0. Inversed triangles indicate start of pyrexia. CRP was normalized to 100 = 2.5 mg/l (value CRP patient/2.5 * 100), LDH was normalized to 100 = 125 U/l, leukocyte count was normalized to 100 = 6.5 /nl (for male) & 6 /nl (for female), thrombocyte count was normalized to 100 = 275 /nl.

Fig 4 shows a graphic display of pyrexia score time series using algorithm version 5.0 for all patients at all available data points. For patients without pyrexia, day 0 indicates the start of BRAF/MEK-inhibitor therapy, while for patients with pyrexia, day 0 indicates the start of pyrexia symptoms. The graphic display demonstrates that in most pyrexia patients, maximum pyrexia scores are obtained at or after the onset of pyrexia while most patients without pyrexia only show low score values at laboratory assessment dates. The observation that maximum PS score values in most pyrexia patients are found around the documented date of pyrexia and not at the exact date of pyrexia can be explained by both, the retrospective nature of the study and the calculation of the pyrexia score. The retrospective nature of the study implies that laboratory values most often were obtained after the onset of pyrexia while some laboratory assessments were obtained some days before the onset of pyrexia. Moreover, as the pyrexia score is based on changes of laboratory values in the expected direction, scores determined during pyrexia symptoms but after interruption of treatment may be lower than scores obtained shortly before onset of symptoms.

Fig 4

Representative graphic displays of pyrexia scores by algorithm 5.0 of all analyzed patients over time; First data set, used for optimization of the pyrexia score algorithm.

All patients without pyrexia, day 0 indicates start of treatment. All patients with pyrexia, day 0 indicates start of pyrexia.

As described by material and methods, the pyrexia score algorithm was optimized through an iterative approach by calculation of ratios MWP/(M0P+MWP) and MWP14/(M0P+ MWP) and by visualization of all pyrexia scores as depicted by Fig 4. The algorithm is based on heuristic assumptions and the optimization process can only be considered approximative. The resulting normalized values of variables for algorithm 5.0 are shown by Table 3. It is of note that variables FS which denote a weighting parameter for laboratory values at t0 and t-1 display highest values for the thrombocyte count, followed by values for LDH. Both laboratory values have also demonstrated highest F-values and highest levels of significance in the mixed model analysis using time clusters. Variables OA for simultaneous change demonstrated the highest value for simultaneous reduction of thrombocyte and leukocyte counts. The addition of additive factors OA in versions 4.0 and 5.0 adds a stronger spread between patients with and without pyrexia in the graphic display and addition of a CRP threshold and introduction of an infection signature into the algorithm (see materials and methods section) leads to a smoothing of pyrexia scores not associated with pyrexia in the graphic display. The intention of the pyrexia score algorithm was not only to provide an additional approach for statistical validation of the putative laboratory signature but also to provide a tool for visualization of laboratory changes associated with pyrexia for the treating clinician as a future clinical decision support system. In this regard, version 5.0 demonstrated best graphical visualization and was further analysed with binary logistic regression. The processing of the pyrexia score as well as the anonymized datasets containing time series of leukocyte and thrombocyte counts as well as of CRP and LDH serum levels of all patients have been deposited as open access in the datadryad.org data repository as an excel file (https://doi.org/10.5061/dryad.xpnvx0kjj).

Table 3

Values of variables, normalized for pyrexia score algorithm 5.0.

Values at t0
Leukocytes		Thrombocytes		LDH		CRP
F1	T1	F2	T2	F3	T3	F4	T4
1.05	100	1.02	90	0.80	80	0.70	800
FS1		FS2		FS3		FS4
1.69		3.24		2.25		2.25
Values at t-1
Leukocytes		Thrombocytes		LDH		CRP
F5	T5	F6	T6	F7	T7	F8	T8
1.10	120	1.10	90	0.80	80	0.70	500
FS5		FS6		FS7		FS8
1.44		4.84		2.25		1.44
Factors for simultaneous change							CRP-gate at t0
Values at t0
Leu.-	Leu.-	Leu.-	Thr.-	Thr.-	LDH+		CT300
Thr.-	LDH+	CRP+	LDH+	CRP+	CRP+
OA1	OA2	OA3	OA4	OA5	OA6		GC0.1
2.0	1.4	1.2	1.4	1.0	1.1
Infection signature at t = 0
Leukocytes		CRP		Thrombocytes		LDH
IC1	IM1	IC2	IM2	IC3	IM3	IC4	IM4
0.90	100	0.90	200	0.90	100	0.90	120

Association of pyrexia-score and pyrexia

ANOVA analysis showed that there was a significant difference between the two groups (pyrexia/no pyrexia) regarding the pyrexia-score (Table 4). The pyrexia-score differentiated well between patients with and without pyrexia (F = 20,8; p = <0,0001) and was significantly higher in patients with pyrexia. In this regard, the pyrexia score outpaced single laboratory values. The pyrexia-score displays a better variance clarification than the single-value analysis and has, therefore, a significantly better predictive power (in the sense of diagnosing pyrexia) than individual values, due to the specific weighting of the individual elements through the algorithm.

Table 4

ANOVA-analysis of pyrexia-score 5.0.

Pyrexia score 5.0	Values	Mean	Median	Min	Max	Std.error	Std.dev.	IQR
Non-pyrexia patients	255	2.151	0.270	0.000	56.246	0.331	5.285	1.690
Pyrexia patients	162	6.604	0.651	0.000	88.741	1.110	14.113	6.280

Pyrexia scores of pyrexia patients were divided into quartiles and the odds ratios were determined using logistic regression in order to define a putative cut-off value for pyrexia. The 4th quartile of PS5.0 of patients with pyrexia demonstrated an odds-ratio of 3.446 (95% CI: 1.907–6.227) for development of pyrexia at a value > = 6.280 (Tables 4 and 5). Binary logistic regression analysis (generalized, mixed model) showed a significant predictive value of the pyrexia-score on the diagnosis of pyrexia (fixed effects pyrexia-score: F = 6.24; p = 0.013). ROC analysis showed a good relationship between sensitivity and specificity of the pyrexia-score (AUC ROC-curve = 0.9480). Through threshold analysis for each score of each patient, it could be predicted when there was a significant increase in the risk of diagnosing pyrexia and by how much the risk increased compared to patients with a lower score than the respective threshold.

Table 5

Odds-ratio of Pyrexia-score 5.0; P0 = Patients without pyrexia, Quartile = Quartile of pyrexia score of patients with pyrexia.

Score	Odds-ratio	95% Wald CI	Pyrexia Score
Quartile 1 vs P0	1.423	0.778–2.604	0.014 < 0.468
Quartile 2 vs P0	1.324	0.709–2.469	0.468 < 1.440
Quartile 3 vs P0	1.220	0.669–2.224	1.440 < 6.280
Quartile 4 vs P0	3.446	1.907–6.227	> = 6.280

Validation of the pyrexia score algorithm with an independent data set

We tested the predictive (diagnostic) power of the pyrexia score on an additional data set from the University Hospital of Cologne and from the University Hospital of the RWTH Aachen (Fig 5). Of the 28 cases, the pyrexia score algorithm 5.0 correctly diagnosed the development of pyrexia or no pyrexia in 23 cases using the threshold value of > = 6.280. In 5 cases, the score led to a prediction that did not match the clinical course. In the sensitivity/specificity analysis (ROC), the score reached an area under curve (AUC) of 0.92. In 16 cases, the absence of pyrexia and in 7 cases, the occurrence of pyrexia were correctly diagnosed. 1 case of pyrexia could not be diagnosed by the score and in 4 cases the score indicated the development of pyrexia, which then did not occur.

Fig 5

Representative graphic displays of pyrexia scores by algorithm 5.0 of all analyzed patients over time; second independent data set.

All patients without pyrexia, day 0 indicates start of treatment. All patients with pyrexia, day 0 indicates start of pyrexia.

Discussion

Pyrexia is by far the most common of all adverse events in patients with malignant melanoma who receive combination treatment with the BRAF-inhibitor dabrafenib and the MEK-inhibitor trametinib [5, 11]. Pyrexia may also occur during treatment with BRAF/MEK combinations vemurafenib/cobimetinib as well as encorafenib/binimetinib, albeit at a considerably lower frequency. Median time of onset is 19 days (range 1–82 days), and median duration is 9 days [11]. In multiple studies, combination therapy with dabrafenib and trametinib has shown improved survival rates and progression-free survival for patients afflicted with BRAF-V600-mutant metastatic malignant melanoma [6, 7, 11] as well as for other patients with non-melanoma cancers [22, 23].

Although life-threatening toxicities associated with BRAFi and MEKi toxicities are rare and uninterrupted treatment is advised in case of mild toxicities, in case of moderate to severe side effects, treatment interruption is advised. Till date, early management of pyrexia has been performed by patient education for the prodromes as well as by interruption or dose reduction of BRAF/MEK inhibitors at the very first symptoms [11]. Recent studies demonstrated that the incidence of pyrexia is particularly high in the early stages of therapy and that temporary interruption of dabrafenib or trametinib is the most effective way to manage pyrexia [24-26]. Adapted pyrexia management seems to reduce the severity of pyrexia and to enhance treatment adherence [26]. Early and precise confirmation of pyrexia and differentiation of pyrexia from other febrile conditions is, therefore, mandatory.

By visual analysis of routine laboratory values of melanoma patients developing pyrexia under BRAF/MEK inhibition, we observed that CRP rises and leukocyte as well as thrombocyte counts drop simultaneously before and during pyrexia. Our retrospective statistical analysis of a cohort with and without pyrexia under dabrafenib plus trametinib therapy using time clusters further revealed that elevated LDH levels under treatment were also associated with pyrexia. In order to prove whether a putative four laboratory value signature of pyrexia, i.e., the simultaneous rise of CRP and LDH as well as drop of leukocyte and thrombocyte counts, correlated with the development of pyrexia, we designed an algorithmic approach that calculates a pyrexia score based on the predicted laboratory value changes. The algorithm itself was designed by a heuristic approach combined with an iterative optimization procedure [20].

Statistical analysis demonstrated that the individual parameters by themselves displayed only modest significance and had low predictive power compared to the pyrexia scores calculated by the proposed algorithm. This underlines the importance of the interaction between the laboratory values as well as of the time-dependent changes. The score’s predictive performance with regard to the diagnosis of pyrexia derives from the specific weighting of the individual elements through the algorithm and its course over time. Maximum pyrexia score values in patients correlated with pyrexia and statistical analysis demonstrated that the score outplays each single laboratory value with regards to its diagnostic performance. Although elevated pyrexia scores could be measured in few patients already before the onset of pyrexia, highest score values were found mainly after the onset of pyrexia. Therefore, the basic use of the pyrexia score seems to be the confirmation of drug-induced pyrexia under dabrafenib plus trametinib therapy. True prediction of pyrexia in the clinical sense of being able to foresee the development of pyrexia by the score seems to be impaired, on the one hand, by missing laboratory values determined only few days before the onset of pyrexia. On the other hand, graphical displays of laboratory time series suggest that leukocyte and thrombocyte counts might fall only shortly before the onset of fever (see Figs 1 and 3), thus limiting the diagnostic window during which the pyrexia score might clinically predict the onset of pyrexia.

Until now the exact cause of pyrexia development is still unclear but the release of proinflammatory proteins and cytokines seem to play a central role as it has been noted before [11–13, 15–17]. In patients under therapy with BRAF/MEK inhibitors but also by in-vitro experiments it could be demonstrated that the BRAF inhibitor dabrafenib induces IL-1beta (IL-1β) to a greater extent than the other BRAF inhibitors which are associated less frequently with pyrexia. Moreover, it could be demonstrated that the degree of IL-1beta release under dabrafenib displays individual variability which might explain why only a subset of patients develop pyrexia [15, 17]. Other side effects of BRAF inhibition include conditions with hyperproliferation of keratinocytes leading to the development of a spectrum of hyperkeratotic conditions including actinic keratosis, Grover’s disease, cutaneous squamous cell carcinoma and acneiform eruptions. A peak in these conditions is seen between weeks 8 and 36 of treatment [27]. A paradoxical activation of the MAPK pathway by BRAF inhibitors was proposed as a causative factor. It was hypothesized that in BRAF wild-type myeloid cells, paradoxical MAPK signaling activation might be induced by BRAF-inhibitors and that this mechanism might cause pyrexic events [28]. Although it has been shown that the BRAF-inhibitor dabrafenib induces inflammasome activation and IL-1beta release [17], the role of MAPK signaling in inflammasome activation is not clear. The addition of a MEK-inhibitor to BRAF inhibition does not seem to reduce the frequency of pyrexia which might suggest additional molecular mechanisms linking BRAF inhibition to immune cell activation. It has been demonstrated that KRAS overexpression may induce inflammasome activation in myeloid cells by the KRAS/RAC1/ROS/NLRP3/IL-1β-axis [29] which might suggest a MEK-independent pathway.

Although the observed laboratory signature of pyrexia is not able to identify the cause of treatment-induced pyrexia by itself, the presented data and the analysis by the pyrexia score algorithm suggest that thrombocytes might play an additional role during pyrexia and that the induction of pyrexia might follow a self-amplifying process leading to a strong increase of proinflammatory factors. It could be shown that thrombocytes are able to boost IL-1beta production in inflammasome-activated monocytes and that platelets are an additional source of IL-1beta [30]. The simultaneous drop of leukocytes and thrombocytes might be induced by aggregation of thrombocytes and granulocytes followed by sequestration in the liver and spleen as described in virus-induced hemorrhagic fever [31, 32]. Interestingly in hemorrhagic fever, LDH serum levels seem to correlate with severity of disease [33].

Main limitations of the presented study are the small number of patients involved and the inhomogeneous times of laboratory data assessments, which have to be attributed to the retrospective nature of the study. In many pyrexic patients, CRP values were only determined after pyrexia had occurred, so we often lacked data points for the pre-pyrexic time cluster. Our study did not identify a laboratory value which could clinically predict pyrexia before treatment start as no predictive laboratory value or combination of values were identified for the 1st time cluster. In order to address this important issue which would greatly impact on treatment decisions in melanoma patients, it might be necessary to focus more on cytokines and on polymorphisms of cytokines associated with the inflammasome function.

Another limitation of the presented study which also stems from its retrospective nature lies in the diagnosis of pyrexia which was only based on the physician’s clinical documentation but did not follow a structured diagnostic algorithm that would have been able to exclude other causes of fever besides pyrexia and infection. Tumor fever, sarcoidosis-like reactions as well as pancreatitis may occur under dabrafenib plus trametinib treatment and might have induce fever in the analysed patients.

The structure of the data only allows a prediction regarding the diagnosis of pyrexia but does not allow a more precise temporal differentiation of the exact time of onset of pyrexia in individual patients. We recommend performing a prospective study with a larger number of patients using fixed time clusters that can be set based on our preliminary results. The analysis of inflammatory parameters should also be expanded including IL-1β. Furthermore, as the biomarker LDH correlates with the tumor burden in melanoma patients, it might be necessary quantifying tumor burden independently by quantitative liquid biopsy based on the BRAF mutation. This would help identifying response to the treatment as a confounding factor on LDH levels.

It must be taken into account that the pyrexia score was optimized in order to discriminate between patients with and without pyrexia and to identify the onset of pyrexia. As a consequence, high score values indicate development of pyrexia but do not necessarily provide a measurement of the severity of pyrexia. When leukocyte and thrombocyte numbers do not diminish further or when CRP and LDH do not increase any more, the pyrexia score quickly falls although the associated symptoms of pyrexia, such as fever, leukopenia or thrombocytopenia, might still be severe. Therefore, the pyrexia score may not be regarded as a decision tool for corticosteroid treatment of pyrexia. In this respect, fever CTCAE grading as well as absolute leukocyte and thrombocyte counts together with their dynamics might be more informative.

Still, by opting for an algorithmic approach based on heuristic assumptions [18] we were able to demonstrate the statistical significance of the observed and postulated four-value laboratory signature of pyrexia. From a methodological standpoint, defining algorithms based on heuristic assumptions may be very useful to assess clinical hypotheses with real world patient data which, unfortunately, are often incomplete and inhomogeneous. Moreover, laboratory values of patients in the routine practice will be influenced by many factors that treating physicians normally take into account when assessing the patient’s laboratory results, but which might reduce the validity of any traditional statistical evaluation. An algorithm may integrate information from different sources, thus offering a much-needed standardized metric. To the best of our knowledge, this work is the first study proposing a pyrexia score for diagnosis of pyrexia. The proposed algorithm, possibly in a more elaborate version, may help clinicians monitor BRAF/MEK-inhibitor treatment more efficiently and thus optimize treatment outcome and increase patients’ quality of life.

21 Jun 2022

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4 Aug 2022

Response to Reviewers’ comments and suggestions:

Manuscript: PONE-D-22-11421

Reviewer 1:

Schaefer et al. submitted an original manuscript dealing with a biomarker-model to Plos ONE. This model can predict pyrexia in BRAF/MEK inhibitor-treated melanoma patients when analysing on-treatment changes in standard lab values. The content and conclusions are sound and interesting, but the manuscript needs to be improved in regards of layout and data presentation. Figures legends appear at random places, and tables do not hat a footnote explaining abbreviation. Design of Table 1 is horrible. Usage, language and style are ok but could be better.

Answer: Thank you very much for this helpful comment. We agree that our manuscript needs improvement in terms of layout and data presentation and, therefore, we redesigned the figures with a uniform layout and simplified the tables as well as the data presentation within the text, referring especially to table 1.

Reviewer 2

1. The authors analyzed laboratory changes to predict combi-DT-induced pyrexia and showed that a pyrexia score calculated from CRP, LDH, leukocyte and thrombocyte numbers correlated with the appearance of pyrexia. Although this kind of study may potentially be interesting, the statistical analysis in this paper is difficult to follow and I am not confident that I am evaluating this paper correctly. I have a fear that most readers will be not able to fully understand the content as well as I did not.

• Answer: Thank you for your valuable comment. We tried to clarify ambiguous formulations within the statistical description and discussion, especially with regard to time clusters and the predictive value (see also answers to comments no. 2 and 5).

The design of the algorithm has been described in detail in order to facilitate similar approaches by other researchers. In addition, in the revised manuscript we will provide not only the normalized laboratory values of all patients with respect to the pyrexia signature, but we will also include the calculations of the algorithm within an excel spreadsheet. This approach should enable the interested reader to reproduce the statistical analysis and to better understand the algorithmic approach.

2. Considering that this retrospective study involves a very small number of patients, although the authors made their own settings with respect to the time clusters established in this study, it is possible that this just happened to make a significant difference in the analysis of the present data set.

• Thank you very much for this comment.

We are well aware of the relatively low number of patients and, hence, recommended a prospective study with more participants.

The enclosed post hoc power analysis indicates that to obtain a significantly stronger statistical impact, we would need approx. 100 patients (see graph in the uploaded file).

Nevertheless, the evaluation of the algorithm with an additional independent data set of 28 patients confirmed the usefulness and the statistical power of the algorithmic approach. The total sample size including the second data set is 64 patients.

With respect to the time cluster analysis, we would like to place emphasis on the step-wise evolution of the study: As described in the manuscript, the initial observation in single patients suggested a concurrent decrease in leukocyte and thrombocyte counts together with an elevation of serum C-reactive protein during pyrexia.

In order to verify or falsify the hypothesis that this might represent a laboratory signature of pyrexia, our PhD-student was instructed to analyse patient’s laboratory data together with our statistician from the Department of Occupational, Social and Environmental Medicine in the classical way. They conceived the time cluster approach in order to control for the temporal dependence of pyrexia. As stated in the manuscript, this time cluster approach was also used for patients without pyrexia based on the determination of the mean time value of the occurrence of pyrexia in our data set, thereby assuming a time at which a pyrexia might have occurred in these patients. We have performed this approach as it could be that the suspected laboratory values might change by a similar mode in time regardless of pyrexia.

Out of this analysis, unexpectedly, elevation of serum lactate dehydrogenase appeared as the strongest laboratory value associated with pyrexia. LDH was included in the quest for a pyrexia signature as time series in patients with pyrexia strongly suggested that during pyrexia LDH rises concurrently with CRP. The finding of an unexpected laboratory value associated with pyrexia, which subsequently also proved useful for pyrexia score analysis, demonstrates that time clusters were not chosen and adapted in order to gain significance within the statistical analysis. The design of the pyrexia score algorithm was preconceived only later as we still assumed that the performed statistical analysis did not prove that the synchrony of the laboratory changes by itself was a characteristic of pyrexia.

We understand that the way by which we analysed the available laboratory data, first by statistics with time clusters and then with an algorithm leading to a score, might appear complicated and non-orthodox. On the other hand, it reflects the gain in comprehension derived from a stepwise approach.

3. Of the 28-patient data set used to validate the pyrexia score algorithm, patients who received treatment other than combi-DT should be excluded.

• Answer: Thank you very much for your feedback. We totally agree that it is preferable to omit patients treated with other BRAF/MEK inhibitors as pyrexia occurs more frequently with dabrafenib treatment compared to other BRAF inhibitors and as the first data set included only patients treated with dabrafenib plus trametinib.

The second data set used for validation contained four patients treated with encorafenib plus binimetinib. For the revised manuscript we have omitted these four patients and we have recalculated the statistics with a dataset without these four patients and with a data set where these four patients were replaced by four patients which were treated with dabrafenib plus trametinib. In order to avoid any bias, three patients without pyrexia were selected chronologically from our patient treatment list and the next first patient with pyrexia was included in order to substitute for the one pyrexia patient treated with encorafenib plus binimetinib.

In the sensitivity/specificity analysis (ROC) with the original second data set which contained the four patients treated with encorafenib plus binimetinib, the pyrexia score algorithm reached an area under curve (AUC) of 0.83. Recalculation of the area under the curve with the second data set without the four patients demonstrated an area under curve of 0.85 (thus a better fit) and recalculation with the second data set without the four patients treated with encorafenib plus binimetinib but with four additional patients treated with dabrafenib plus trametinib even revealed an area under curve of 0.92. Although statistics already improved by omitting patients treated with encorafenib plus binimetinib, we have submitted the revised manuscript with the new dataset with four new patients in order to maintain the patient numbers. Moreover, together with the revised manuscript we have deposed all anonymized and normalized patient laboratory value time series for leukocyte counts, thrombocyte counts, CRP and LDH. Thereby we not only allow independent verification of our algorithm but also give the possibility to modify or optimize the algorithm or to analyse the data by other statistical procedures. Exchanging the four patients treated with encorafenib plus binimetinib by four new patients treated with dabrafenib plus trametinib provides a larger dataset for independent verification.

Accordingly, we also modified the title to “A distinct four-value blood signature of pyrexia under combination therapy of malignant melanoma with dabrafenib and trametinib evidenced by an algorithm-defined pyrexia score‘‘ replacing “BRAF/MEK inhibitors” with “dabrafenib and trametinib” as the four-value blood signature might be only correlated to dabrafenib/trametinib-induced pyrexia.

4. Although the authors described that “most patients without pyrexia only show low score values at laboratory assessment dates“, can the author provide the cut-off value for pyrexia score?

• Answer: A cut-off value for the pyrexia score is reported by tables 4 and 5. We have clarified this point in the revised manuscript (lines 481-484).

5. As shown in the limitation, figures 4 and 5 show that the PS reaches its maximum value in the blood collection immediately “after” the pyrexia events, while the PS is low before the pyrexia events in the most cases. Therefore, it is difficult to say that the score is predictive of fever.

• Answer: Thank you very much for this important comment. Predictive in a statistical sense means the probability that a person has a condition given a positive test result. Therefore, the pyrexia score is predictive as it is able to correctly diagnose pyrexia. A predictive test in the clinical sense allows identifying patients with an enhanced risk for development of a condition. In our study we did not detect laboratory values obtained for the time cluster 1 and 2 which might have demonstrated a correlation with subsequent development of pyrexia. In future studies it might be helpful to focus more on cytokines and on polymorphisms of cytokines associated with the inflammasome function in order to identify clinically predictive factors. In the revised manuscript we have clarified that “predictive” refers to the power of diagnosing pyrexia and that true clinical prediction of pyrexia before pyrexia occurs is not possible with the score (see lines 477-490 and 553-562).

6. Is there a correlation between the severity of pyrexia and PS?

7. Could the severity of pyrexia be classified according to some criterion such as CTCAE?

• Answer: Thank you very much for these two important comments.

As stated, pyrexia in our retrospective study was defined as an oral temperature of 38.5°C (≥101.3°F) or higher, in the absence of any clinical or microbiological evidence of infection. Due to the retrospective nature of the study, we could not classify the severity of pyrexia as we had to rely on the clinical documentation which was often based on patients’ personal accounts which did not allow for a reliable distinction between different CTCAE grades.

The pyrexia score itself does not evaluate the severity or the duration of fever but only analyses laboratory changes. Still, on the basis of the putative molecular mechanisms, as hypothesized in lines 556-586, we would expect a positive correlation between the severity of pyrexia and the maximal score values. This could be assessed in a future prospective study.

8. If the pyrexia is more severe in patients with high PS, it may allow for the early administration of corticosteroids to such patients.

Answer: As to the previous comments, we could not reliably grade pyrexia due to the retrospective nature of the study. Moreover, it must be taken into account that the score was optimized in order to discriminate between patients with and without pyrexia and to identify the onset of pyrexia. As a consequence, high score values indicate development of pyrexia but do not necessarily reflect severity of pyrexia. Moreover, when leukocyte and thrombocyte numbers do not diminish further or when CRP and LDH do not increase any more, the pyrexia score quickly falls although the associated symptoms of pyrexia, such as fever, leukopenia or thrombocytopenia, might still be severe.

Nevertheless, the question itself, when to administer corticosteroids to pyrexia patients, is of great clinical significance. Our score should help to better identify pyrexia in the first place. Once pyrexia is very likely, we would suggest relying on absolute leukocyte and thrombocyte counts as well as on their dynamics for deciding whether to initiate corticosteroid treatment. This is what we currently do in our clinical routine, but we may not provide any specific cut-off values as we have no reliable data for such a claim. We have included a discussion on this point in the revised manuscript (see lines 618-626).

9. I guess that in cases with high baseline LDH levels, whose tumor volume is assumed to be high, it may be difficult to differentiate combi-DT-induced pyrexia from tumor fever. Were the authors able to exclude tumor fever from the differential diagnosis?

• Answer: We totally agree that tumor fever might have led to the erroneous diagnosis of treatment-induced pyrexia. We have discussed this possibility in our revised manuscript (see lines 603-608). In addition, fever and elevated LDH or CRP under treatment might also be associated with a sarcoidosis-like reaction as well as with pancreatitis, both representing known side effects of BRAF/MEK inhibitor treatment. In our experience with malignant melanoma, both conditions seem to occur even more frequently as compared to tumor fever. As our study was retrospective, we have scored patients as having pyrexia only by the clinical assessment of the treating physician as documented in the clinical documentation or by the discharge summary. We have not reassessed each case for possible competing causes of fever as we did not want to introduce an additional bias by only selecting patients which fulfilled our personal speculations. We strongly believe that in a future prospective study where competing sources of fever in treated patients would be ruled out through a standardized approach, the association of the described laboratory signature with pyrexia should be even stronger.

10. Was there any difference in baseline LDH or Stage at the treatment initiation between the pyrexia and non-pyrexia groups?

• Answer: Thank you very much for your question and pointing this out. In effect, the median and the mean normalized pre-treatment values for LDH were slightly higher in patients without pyrexia (163.9 and 210.6) compared to patients who developed pyrexia (155.0 and 173.9). This difference was not statistically significant (T-test: p=0.14). There were relatively more patients with pyrexia in stage III compared to stage IV, still the difference was not statistically significant (Chi2-test: p=0.077). We have included this information in the revised manuscript (see lines 354-362).

11. As described in the manuscript, more than half of the patients used corticosteroids as treatment for pyrexia. Since corticosteroids have a significant impact on white blood cell counts, I think it would not be appropriate to analyze patients who did and did not receive this treatment together.

• Answer: Thank you for your valuable input. Yes, severe pyrexia events led to treatment with glucocorticoids in 8 cases, but steroid treatment was only conducted after the clinical development of pyrexia and not before pyrexia. Therefore, bloodwork parameters, especially regarding white blood cell counts, were only modified by steroids after the beginning of pyrexia and thus were not included in the statistical analysis. Time clusters 1-4 only contain values before administration of glucocorticoids. Time cluster 4 contains the first laboratory value after pyrexia occurred for each patient which was obtained when the patient presented in the clinic with symptoms of pyrexia. At this time the patient has not yet received corticosteroids. In figures 4 and 5, highest pyrexia score values are often found after the onset of pyrexia, which is explained by the fact that all patients developed the symptoms at home and only rarely presented at the clinic on the same day of first symptoms. In the revised manuscript we have clarified this point (line 190).

In addition to the changes suggested by the reviewers we have deposited the processing of the pyrexia score as well as the anonymized datasets containing time series of leukocyte and thrombocyte counts as well as of CRP and LDH serum levels of all patients in the open access datadryad.org data repository as an excel file (https://doi.org/10.5061/dryad.xpnvx0kjj).

We have further omitted statements with “data not shown”.

The authors declare no conflict of interest.

10 Aug 2022

A distinct four-value blood signature of pyrexia under combination therapy of malignant melanoma with dabrafenib and trametinib evidenced by an algorithm defined pyrexia score

PONE-D-22-11421R1

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15 Aug 2022

PONE-D-22-11421R1

A distinct four-value blood signature of pyrexia under combination therapy of malignant melanoma with dabrafenib and trametinib evidenced by an algorithm-defined pyrexia score

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