Predictive significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and eosinophil levels in advanced renal cell carcinoma patients treated with nivolumab and ipilimumab – a Polish multicenter, observational study

doi:10.21203/rs.3.rs-5313520/v1

Introduction

Immune checkpoint inhibitors have improved the treatment of metastatic renal cell carcinoma (RCC), with the combination of nivolumab (NIVO) and ipilimumab (IPI) showing promising results. However, not all patients benefit from these therapies, emphasizing the need for reliable, easily assessable biomarkers.

Methods

This multicenter study involved 116 advanced RCC patients treated with NIVO+IPI across nine oncology centers in Poland. Blood markers such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), eosinophils and monocytes were assessed at baseline, after three months, and before disease progression (PD). The predictive significance of these parameters was analyzed using linear regression, Kaplan-Meier survival analysis, and Cox regression models.

Results

After a median follow-up of 11.8 months, the progression-free survival (PFS) was 12.8 months (95% confidence interval [CI]: 5.7-28.1), while the overall survival (OS) was 27.3 months (95% CI:16-not reached). Patients with an NLR increase of ≥25% had a PFS of 8.2 (3.1-24.7) months compared to 17.5 (8.6-28.1) months in those with a rise in <25% (p=0.015). Similarly, a ≥25% increase in PLR was linked to a PFS of 6.8 (2.8-8.3) months compared to 17.4 (8.4-28.1) months (p<0.001). Multivariate analysis confirmed PLR as an independent predictor of PFS (HR 2.9, 95% CI:1.5-5.6, p=0.001), while elevated eosinophil levels were associated with a reduced risk of death (HR 0.2, 95% CI: 0.04-0.9, p=0.05).

Conclusions

NLR, PLR, and eosinophil levels may serve as valuable biomarkers for predicting treatment response in RCC patients receiving NIVO+IPI.

Renal cell carcinoma

immune checkpoint inhibitors

predictive factors

neutrophil-to-lymphocyte ratio

platelet-to-lymphocyte ratio

lymphocyte-to-monocyte ratio

eosinophils

With the advent of immune checkpoint inhibitors (ICIs), the treatment landscape for metastatic renal cell carcinoma (RCC) has significantly shifted. Commonly used agents from the group include programmed death-1 (PD-1) inhibitors (nivolumab, pembrolizumab), programmed death ligand-1 (PD-L1) inhibitors (avelumab, atezolizumab), cytotoxic T lymphocyte antigen-4 (CTLA-4) inhibitors (ipilimumab). They are used as monotherapy or in combination with antiangiogenic agents targeting vascular endothelial growth factor (VEGF: axitinib, sunitinib, pazopanib, lenvatinib). Additionally, mammalian target of rapamycin (mTOR) inhibitors like everolimus are also utilized [1–3].

The choice of therapy is influenced by risk stratification based on prognostic models, mainly the International Metastatic RCC Database Consortium (IMDC) criteria [4], which categorizes patients into favourable, intermediate, or poor risk groups. For patients with a favourable risk and limited disease burden, options may include close surveillance, single-agent therapy, or combination treatments. Those with intermediate, or poor risk groups with substantial disease burden or more aggressive disease typically receive combination therapies including anti-PD-1 and anti-CTLA-4 antibodies or ICIs with antiangiogenic agents. Such combinations offer better outcomes, though these may come with increased toxicity [1–3]. Of them, the combination of nivolumab with ipilimumab is a widely accepted regimen with manageable toxicity, especially advised in intermediate- and high-risk groups. In these groups in the extended, 4-year follow-up in the pivotal Checkmate-214 study, the overall response rate (ORR) increased from 26.8% in the sunitinib group to 42.9% in nivolumab + ipilimumab, complete responses (CR) were observed in 10.4% of all patients (vs. 1.4%). Patients had a 35% reduced risk of death (hazard ratio [HR] 0.65, 95% confidence interval [CI] 0.54–0.78) [5].

ICIs have shown promising results in improving survival outcomes, yet a substantial proportion of patients do not derive benefit from these therapies. This has increased interest in identifying reliable biomarkers to better predict patient response to ICIs and guide therapeutic decision-making.

Emerging evidence highlights the role of systemic inflammation in cancer progression and patient survival, with specific inflammatory markers being associated with prognosis in various cancers [6–9]. Indeed, compared to the previous Motzer criteria [10], in the currently valid IMDC-risk score [4] neutrophils and platelets concentrations were incorporated as they reveal an ongoing systemic inflammation. Platelet-to-lymphocyte ratio (PLR) and other peripheral blood markers, such as neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocytes ratio (LMR) and C-reactive protein (CRP), have been increasingly recognized for their prognostic significance in predicting responses to immunotherapy [6, 11, 12]. The evidence suggests that NLR can effectively predict patient response to immunotherapy. Similarly, elevated levels of PLR or CRP have been linked to poorer outcomes, indicating that patients with higher ratios may respond less favourably to ICIs [13].

Data on patients with RCC treated with combined immunotherapy remains limited. Therefore, we aimed to assess the prognostic significance of peripheral blood marker levels in RCC patients undergoing nivolumab and ipilimumab therapy. We evaluated whether the pre-treatment level or their change during the treatment may predict disease progression (PD), progression-free survival (PFS), and overall survival (OS).

Patients and data collection

This observational study involved a cohort of patients diagnosed with metastatic RCC who were treated with first-line combined immunotherapy under the regulation of a national health program [14]. Specifically, the study included patients qualified for the treatment with nivolumab (NIVO) in combination with ipilimumab (IPI) between May 1, 2022, and July 2, 2024, at nine oncology centers across Poland. The data collection was finalized on September 15, 2024.

Inclusion criteria for the study required that patients either had at least one computed tomography (CT) scan performed after starting treatment that allowed to assess the response to the treatment, developed significant toxicity leading to discontinuation, experienced PD, or died- whichever occurred first. Details regarding the national reimbursement criteria for the first-line NIVO+IPI modality for the treatment of metastatic RCC in Poland can be found in the Supplementary Materials.

Data including initial diagnosis, primary treatment modalities, metastatic sites, and baseline patient characteristics were collected retrospectively, whereas records of NIVO+IPI treatment such as the number of cycles, duration of treatment, adverse events occurrence and patient responses with outcomes were recorded prospectively.

Study objectives

The primary objective of this study was to evaluate the prognostic value of peripheral blood markers in advanced RCC patients treated with NIVO+IPI. Specifically, we assessed changes in these markers at three key time points: baseline, after three months of treatment, and prior to PD, defined as the last complete blood count before the CT scan confirming PD. Additionally, we aimed to determine whether the pre-treatment levels or changes in these markers during treatment could predict disease progression and overall treatment outcomes.

Treatment Protocol

Patients received treatment according to the European Union's approved product information for these drugs [15, 16]. The treatment protocol consisted of four cycles of ipilimumab (1 mg/kg) combined with nivolumab (3 mg/kg), administered intravenously every three weeks. Following this induction phase, patients were maintained on nivolumab either at 240 mg biweekly or 480 mg every four weeks, continuing until PD, unacceptable toxicity, or withdrawal of consent. No modifications to the dosing regimen or premedication were permitted.

Efficacy Assessment

Treatment efficacy was monitored through CT scans, evaluated based on Response Evaluation Criteria in Solid Tumours version 1.1 (RECIST) criteria, and conducted every 12 weeks or earlier if clinical signs indicated potential progression. Efficacy was determined by analyzing OS and PFS, ORR and disease control rate (DCR), per RECIST 1.1 guidelines [17]. Detailed definitions of these survival parameters are provided in the Supplementary Materials.

Laboratory Assessments

Laboratory assessments were performed before each treatment infusion, three months after treatment began, and prior to documented PD. These assessments involved calculating the NLR, PLR, LMR, and total counts of eosinophils and monocytes. The ratios were determined by dividing the corresponding cell counts. Changes in these levels were calculated by comparing the values at three months to the baseline levels, then multiplying the result by 100 to express the percentage change:

We investigated whether a 25% increase in these parameters after three months of treatment was associated with worse outcomes and whether such increases could predict PD. This hypothesis was based on findings from a previous study [18].

Ethical Approval

The study protocol received approval from the Bioethics Committee of Jagiellonian University Medical College (approval number 118.0043.1.115.2024, dated April 19, 2024). Informed consent was obtained from all patients before starting the NIVO+IPI treatment regimen.

Statistical Analysis

Statistical analysis was performed using PS Imago Pro 9 (SPSS). Comparisons of categorical variables were made using Fisher’s exact test or chi-square tests, while Wilcoxon’s test was used for continuous variables. Kaplan-Meier methods were employed to estimate OS and PFS, with Cox proportional hazards models and log-rank tests used to identify prognostic factors affecting survival outcomes. Variables with p-values < 0.05 in univariate analysis were further evaluated in multivariate models. The optimal cutoff point for NLR, PLR and LMR was determined using receiver operating characteristic (ROC) curve analysis. The Youden's Index was used to identify the cutoff that maximized the balance between sensitivity and specificity for predicting PFS. The final cutoff values were derived by taking the average of the optimal cutoff points calculated from three different time points: baseline, after 3 months, and before PD. For the remaining parameters, we used the reference ranges from local laboratories, and we determined whether the values were upper normal limits (unl). Linear regression analysis was employed to evaluate the relationships between PFS in months and several key hematological parameters: NLR, PLR, LMR, monocytes, and eosinophils at different time points. The regression models were visualized with scatter plots and regression lines. A p-value < 0.05 was considered statistically significant.

Baseline Characteristics

The baseline clinical characteristics of the enrolled patients are summarized in Table 1. The median age was 64 years, with males comprising the majority (76%). Approximately 82% of the patients had undergone nephrectomy, though nearly half presented with primary metastatic disease, requiring systemic treatment after a median of 3.3 months from initial diagnosis. Following IMDC stratification, 72.2% of the patients were classified in the intermediate-risk group. The most common sites of distant metastases were the lungs, followed by non-regional lymph nodes and bones.

Blood biomarkers and their change during treatment

Based on the ROC curve analysis (see Supplementary Materials, Tables 1S, 2S, and 3S for detailed analysis), the cut-off values were determined as follows: 2.44 for NLR, 181 for PLR, and 2.83 for LMR. At the initiation of treatment, 56.5% (n=65) of patients had elevated NLR levels, 46.1% (n=53) had elevated PLR, 48.7% (n=56) had elevated LMR, and 9.6% (n=11) had elevated eosinophil and monocyte counts. The medians, interquartile ranges (IQR), and distributions of these biomarkers are presented in Table 2.

After three months of treatment, significant increases were observed in eosinophil and monocyte levels, while PLR levels decreased. Before PD, a substantial increase in NLR and a decrease in LMR were noted. These trends are visualized through box plots, which can be found in the Supplementary Materials (Figure 4S).

Treatment efficacy according to blood biomarkers

During a median follow-up of 11.8 months (IQR: 5.1-19.8), patients received a median of 7 cycles of the NIVO+IPI regimen (IQR: 4-14), over a median treatment duration of 6.5 months (IQR: 2.5-12 months). The best overall response according to CT scans indicated CR or partial response (PR) in 40% of patients (n=46), stable disease (SD) in 34.8% (n=40), and PD in 25.2% (n=29), leading to a DCR of 76.5% (n=88) and an ORR of 42.6% (n=49). The median PFS was 12.8 months (95% CI: 5.7-28.1), while the median OS reached 27.3 months (95% CI: 16-not reached).

Patients who experienced a ≥25% increase in NLR had a significantly shorter median PFS compared to those with an NLR increase of <25% (8.2 months, IQR: 3.1-24.7 vs. 17.5 months, IQR: 8.6-28.1, p=0.015, Figure 1A). A similar trend was observed for patients with a ≥25% increase in PLR (6.8 months, IQR: 2.8-8.3 vs. 17.4 months, IQR: 8.4-28.1, p<0.001, Figure 1B).

In a linear regression analysis, the values of NLR and PLR after three months were significantly correlated with PFS (β = -0.2, p=0.02 for NLR; β = -0.2, p=0.04 for PLR), indicating that an increase in these biomarkers was associated with shorter PFS (Figure 1C and 1D). No significant relationship was observed for other laboratory parameters (details provided in the Supplementary Materials, Figure 5S).

Factors Influencing Survival

In the univariate Cox regression analysis, age > 65 years was associated with a significantly lower risk of PD (HR = 0.5, 95% CI: 0.3-0.8, p = 0.01), while elevated PLR ³ 181 (Figure 2D), assessed 3 months after initiating NIVO+IPI, was linked to a higher risk of PD (HR = 2.4, 95% CI: 1.3-4.5, p = 0.008). In multivariate analysis, both factors remained significant: age > 65 years showed an even stronger protective effect on PFS (HR = 0.3, 95% CI: 0.2-0.7, p = 0.002), and PLR 2 further increased the risk of progression (HR = 2.9, 95% CI: 1.5-5.6, p = 0.001). These findings suggest that both advanced age and elevated PLR after 3 months are independent predictors of PFS in this cohort.

For OS, univariate Cox regression analysis identified several significant factors. Karnofsky performance status (KPS) and metastases to the central nervous system (CNS) were associated with an increased risk of mortality (HR = 2.7, 95% CI: 1.2-6, p = 0.02; HR = 3.4, 95% CI: 1.2-11.3, p = 0.05, respectively). Elevated NLR ³2.44 and PLR ³181 (Figure 1A and 2C), both measured 3 months after treatment initiation were also associated with worse OS (HR = 2.6, 95% CI: 1.1-6.4, p = 0.04; HR = 3.9, 95% CI: 1.6-9.4, p = 0.003, respectively). Interestingly, elevated eosinophils after 3 months (Figure 2B) were linked to better OS (HR = 0.2, 95% CI: 0.04-0.7, p = 0.01).

In the multivariate Cox regression analysis, KPS remained a significant predictor of worse OS (HR = 3.3, 95% CI: 1.1-8.2, p = 0.04), as did CNS metastases (HR = 5.9, 95% CI: 1.2-27.7, p = 0.03). However, the effects of NLR and PLR were no longer statistically significant (HR = 0.8, 95% CI: 0.2-2.7, p = 0.07; HR = 3.0, 95% CI: 0.9-9.3, p = 0.07). Elevated eosinophils after 3 months continued to be associated with a significantly reduced risk of mortality (HR = 0.2, 95% CI: 0.04-0.9, p = 0.05). These findings indicate that KPS, CNS metastases, and eosinophil levels are key independent predictors of OS in patients treated with NIVO+IPI in RCC.

No associations were observed between other clinical parameters and PFS or OS (see Table 1S in the Supplementary Materials for details).

In this study, we found that routine blood tests, performed before each treatment infusion, can serve as predictive biomarkers in RCC patients treated with the NIVO+IPI regimen. Specifically, an increase of ≥ 25% in NLR and PLR after 3 months of therapy may indicate a poorer prognosis and shorter PFS, with larger increases in these parameters correlating with even shorter PFS. Furthermore, patients with a PLR ≥ 181 after 3 months have a threefold higher risk of disease progression and a fourfold higher risk of death. Conversely, an increase in total eosinophil count after 3 months of treatment reduces the risk of death by 80%. Our findings suggest that these biomarkers may help clinicians assess early treatment response, as a decrease in NLR or PLR, or an increase in eosinophil levels may offer reassurance of a more favorable prognosis.

The tumor microenvironment is a dynamic and complex ecosystem in which various immune cells interact with cancer cells and surrounding stromal components. Systemic inflammation is a key driver of cancer development, and interestingly, cancer patients often exhibit similar changes in blood test results to those seen in inflammatory diseases [19]. Neutrophils can either promote or suppress tumor growth, depending on their activation state and the signals they receive [20]. Among lymphocytes, T cells are crucial regulators of the immune response to cancer, though their role in tumor progression is highly context-dependent. CD8+ cytotoxic T lymphocytes (CTLs) and Th1 cells are typically involved in directly attacking cancer cells and are associated with a better prognosis in several cancers. Conversely, other T cell subsets, such as Th2 and Th17, can contribute to tumor progression by creating an immunosuppressive environment that shields the tumor from immune attack [21]. However, tumor-infiltrating lymphocytes (TILs) play a significant role in generating antitumor responses. For example, in breast cancer patients, TILs have been linked to improved survival [22] [23]. Tumor-associated macrophages (TAMs) within the tumor microenvironment are frequently skewed toward an M2-like phenotype, which supports tumor growth, angiogenesis, and metastasis. These macrophages release factors that help the tumor evade immune detection and promote tissue remodelling, essential for tumor expansion [24]. Platelets also facilitate tumor growth and metastasis by secreting various growth factors that stimulate cancer cells [25]. Eosinophils play a dual role in cancer progression, acting either as promoters or inhibitors of tumor growth, depending on the context. In some cancers, such as colorectal and prostate cancer, eosinophil infiltration is associated with a favorable prognosis, as they contribute to antitumor immunity through the release of cytotoxic granules and cytokines. However, in cancers like Hodgkin's lymphoma, eosinophils are linked to poor outcomes, possibly due to their role in promoting a pro-tumorigenic microenvironment [26, 27]. This intricate interplay between immune cells is depicted in Figure 3. Based on this information, we may conclude that elevated NLR, PLR, and monocyte levels are strongly associated with cancer progression, as they reflect an ongoing state of systemic inflammation that favours tumor growth and metastasis. In contrast, elevated LMR and eosinophil counts are generally regarded as good prognostic factors as indicate a stronger immune surveillance capability.

The cutoff values of NLR, PLR, and LMR are not well established and remain a subject of debate. In the systematic review of NLR in solid tumors [7], which included 1704 RCC patients, the median cutoff for NLR was 4.0 (range 1.9-7.2). In other RCC-specific studies [28, 29] the reference value was set at 3.0. Similarly, for PLR, cutoff values ranged from 150 to 300 [11], though one RCC study [30] established a cutoff at 160. LMR data were more consistent, with an accepted cutoff value of 3.0 [6, 30]. The most common method for determining cutoff values is ROC curve analysis, which varies depending on the specific patient cohort and treatment context. These custom cutoffs are used to optimize the stratification of patients based on survival outcomes. Therefore, we employed ROC curve analysis, and the cutoff values we obtained (2.44 for NLR, 181 for PLR, and 2.83 for LMR) align with the reference ranges provided in the aforementioned systematic reviews.

Our findings are consistent with those reported in other studies. Studies in RCC patients treated with nivolumab have shown that elevated NLR levels before treatment are associated with poorer outcomes, including reduced OS and PFS [12, 18, 31–34]. Most studies on patients treated with the NIVO+IPI regimen have focused on Asian populations. In the study by Iinuma et al. [35], elevated NLR, PLR, and the systemic inflammatory index were associated with poorer PFS. Similarly, Yano et al. [36] identified CRP as a significant predictor of OS, with a CRP level > 1.0 mg/dL correlating with worse outcomes. Furthermore, Nukamura et al. [37] highlighted the importance of the LMR, demonstrating that a low LMR (≤ 3) was an independent predictor of shorter OS.

Monitoring changes in the NLR during treatment has proven to be a valuable tool for predicting outcomes in metastatic RCC. Studies by Templeton et al. [38] and Suzuki et al. [18] demonstrated that a reduction in NLR is associated with improved patient outcomes and serves as a positive prognostic indicator. A decrease of ≥25% in NLR following the initiation of nivolumab treatment was linked to a significantly better response to the therapy. Patients exhibiting this reduction had a higher ORR compared to those without such a decrease [18]. Collectively, these findings highlight the importance of tracking NLR as a predictor of cancer progression and treatment response in RCC patients. Such studies related specifically to changes in PLR and eosinophils in patients treated with ICIs have not been reported.

Study limitations

The main limitation of our study is the relatively small sample size, which may limit the statistical power and affect the robustness of the findings. To mitigate this, we plan to continue monitoring the enrolled patients over a longer period. Despite this limitation, our study represents the first analysis of peripheral blood biomarkers in RCC patients treated with the NIVO+IPI regimen in the Caucasian population. Additionally, the retrospective nature of data collection for certain variables may introduce bias. Blood biomarker levels were measured at multiple oncology centers, potentially introducing variability due to differences in laboratory techniques and reference ranges. This could lead to inconsistencies in the data and affect the interpretation of results. Although the study provides important insights into PFS and OS, the follow-up period may be insufficient to capture long-term outcomes and delayed effects of the NIVO+IPI regimen. The study was conducted exclusively in Poland, and the results may not be fully generalizable to RCC populations in other regions with different genetic backgrounds, environmental factors, or healthcare practices. Despite multivariate analyses, residual confounding factors, such as differences in baseline health status or comorbidities, may have influenced the outcomes. Future prospective studies are needed to confirm the utility of NLR, PLR, and LMR as predictive markers for patient outcomes.

These data suggest that NLR, PLR, and eosinophil levels measured after 3 months of the NIVO+IPI regimen could serve as valuable biomarkers of response. A decrease in initially high NLR, PLR, or an increase in eosinophils may indicate to both physicians and patients that the therapy is effective and associated with improved survival outcomes. For example, even if tumor measurements show stable or slightly increasing disease, a reduction in NLR, PLR can offer reassurance and support the decision to continue therapy. However, an increase in NLR, PLR, or a lack of elevated eosinophils alone should not prompt treatment discontinuation, though it may provide important information when the balance between benefit and risk is unclear. Future prospective studies with larger patient populations and longer follow-ups are warranted to confirm these results and better understand the clinical utility of these biomarkers in everyday practice.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing interests

RPM has received travel grants from Accord, BMS, MSD and lecture fees from Astra Zeneca, BMS, GSK, Novartis, Roche, MSD; AD has received travel grants and lecture fees from Roche, BMS, Janssen, Molteni, MSD, Accord; AGW has received travel grants and lecture fees from Roche, Novaris, Lili, Astellas, Pfizer, Amgen, Swixx, Pierre Fabre, Egis, BMS, MSD; JC has received lecture fees from BMS; NVV has no financial interests; DTM has received travel grants and lecture fees from MSD, Pierre Fabre, BMS, Novartis, Pfizer; JD has received travel grants and lecture fees from Amgen, AstraZeneca, BMS, MSD, Novartis, Nutricia; AR has received travel grants and lecture fees from Roche, BMS, Ipsen, Pfizer, Novartis, Gilead, MSD;

AB has received travel grants and lecture fees from BMS, MSD, Astellas, Merck, Servier, Astra Zeneca, Pfizer; MS has received travel grants and lecture fees from Roche, BMS, Janssen, MSD, Amgen, Astellas. , Ipsen, Pfizer, Novartis, Gilead; AGU has received travel grants from GSK, Lilly, Astra Zeneca, Gilead, Accord, and lecture fees from Astra Zeneca. JW has no financial interests; PB has received travel grants, lecture fees, and advisory boards from AstraZeneca, MSD, GSK, Pharma; ŁS has received travel grants from BMS, Accord; MP has received travel grants and lecture fees from AstraZeneca, Roche, Novartis, Elli Lilly, Janssen, Gilead and Amgen;

Author contributions

RPM collectively designed and conceptualized the research. All the authors actively organized the database and collected patient data. RPM led all the statistical analyses, prepared figures and tables. Ethical approval was obtained through the collaborative efforts of RPM, AD, and ŁS. RPM wrote the initial manuscript draft, with specific contributions from MP. The other authors actively participated in revising the manuscript. All the authors approved the final version and reviewed it critically.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Ethical approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Jagiellonian University Medical College (approval number 118.0043.1.115.2024, dated April 19, 2024

Consent to participate

Informed, institutional consent was obtained from all individual participants included in the study before starting treatment.

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Table 1. Baseline clinical characteristics of the enrolled patients (n=116).

Age			64 (57-71.5)
Males, n(%)			87 (75.7%)
BMI			26.49 (23.45-32.44)
Time of follow-up (months)			11.8 (5.1-19.8)
Performance status, n(%)		0	13 (11.3)
		1	87 (75.7)
		2	15 (13)
Nephrectomy, n(%)		Yes	94 (81.7)
Nephrectomy, n(%)		No	21 (8.3)
Time from nephrectomy to treatment initiation (months)			3.3 (1.7-8.1)
T stage after nephrectomy*, n(%)		T1	12 (10.4)
		T2	8 (7.8)
		T3	66 (57.4)
		T4	5 (4.3)
		No data	23 (20)
Histologic subtype, n(%)		Clear cell	90 (78.3)
		Sarcomatous components	16 (13.9)
		No data	9 (7.8)
Primary metastatic, n(%)	Yes		64 (55.7)
Primary metastatic, n(%)	No		51 (44.3)
Site of metastasis at the baseline computer tomography scan, n(%)		Nonregional Lymph nodes	51 (44.3)
		Liver	26 (22.6)
		Central nervous system	6 (5.2)
		Lungs	78 (67.8)
		Bones	31 (27)
IMDC risk group, n(%)		Intermediate	83 (72.2)
IMDC risk group, n(%)		Poor	32 (27.8)
Number of risk factors according to IMDC score, n(%)		1	44 (38.3)
		2	40 (4.8)
		3	4 (20.9)
		4	7 (6.1)
No of patients with risk categories according to IMDC score, n(%)		Time from the diagnosis to treatment onset	99 (86.1)
		Karnofsky Score <80%	22 (19.1)
		Hemoglobin level <unl	68 (59.1)
		Corrected calcium >unl	9 (7.8)
		Neutrophils >unl	9 (7.8)
		Platelets >unl	23 (20)

Categorical variables are presented as numbers (percentages), and continuous variables are presented as medians and interquartile ranges.

Abbreviations: AJCC, American Joint Committee on Cancer; CT, computed tomography; IMDC, International Metastatic Renal Cell Carcinoma Database Consortium; n, number; T, tumor; unl, upper normal limit

Table 2. Summary of selected laboratory parameters at three different checkpoints.

Parameter	Baseline (1)	After 3 months (2)	p-value (1 vs. 2)	Before progressive disease (3)	p-value (1 vs. 3)
Neutrophil–to-lymphocyte ratio	2.65 (1.87-3.49)	2.35 (1.66-3.32)	0.17	3.05 (2.29-4.75)	0.001*
Platelet–to-lymphocyte ratio	169.95 (120.92- 247.02)	139.76 (100.39-181.65)	0.0005*	169.86 (107.98-224.45)	0.63
Lymphocyte–to-monocyte ratio	2.82 (2.1-3.61)	2.88 (2.02-3.88)	0.62	2.51 (1.77-3.54)	0.02*
Eosinophils (10³/ul)	0.16 (0.1-0.23)	0.24 (0.09- 0.5)	0.00003*	0.14 (0.04-0.43)	0.25
Monocytes (10³/ul)	0.67 (0.55-.86)	0.75 (0.58-0.96)	0.05*	0.71 (0.52-0.9)	0.53

Variables are presented as medians and interquartile ranges.

Statistically significant values are marked as *.

Competing interest reported. RPM has received travel grants from Accord, BMS, MSD and lecture fees from Astra Zeneca, BMS, GSK, Novartis, Roche, MSD; AD has received travel grants and lecture fees from Roche, BMS, Janssen, Molteni, MSD, Accord; AGW has received travel grants and lecture fees from Roche, Novaris, Lili, Astellas, Pfizer, Amgen, Swixx, Pierre Fabre, Egis, BMS, MSD; JC has received lecture fees from BMS; NVV has no financial interests; DTM has received travel grants and lecture fees from MSD, Pierre Fabre, BMS, Novartis, Pfizer; JD has received travel grants and lecture fees from Amgen, AstraZeneca, BMS, MSD, Novartis, Nutricia; AR has received travel grants and lecture fees from Roche, BMS, Ipsen, Pfizer, Novartis, Gilead, MSD; AB has received travel grants and lecture fees from BMS, MSD, Astellas, Merck, Servier, Astra Zeneca, Pfizer; MS has received travel grants and lecture fees from Roche, BMS, Janssen, MSD, Amgen, Astellas. , Ipsen, Pfizer, Novartis, Gilead; AGU has received travel grants from GSK, Lilly, Astra Zeneca, Gilead, Accord, and lecture fees from Astra Zeneca. JW has no financial interests; PB has received travel grants, lecture fees, and advisory boards from AstraZeneca, MSD, GSK, Pharma; ŁS has received travel grants from BMS, Accord; MP has received travel grants and lecture fees from AstraZeneca, Roche, Novartis, Elli Lilly, Janssen, Gilead and Amgen;

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Predictive significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and eosinophil levels in advanced renal cell carcinoma patients treated with nivolumab and ipilimumab – a Polish multicenter, observational study

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