Voltar
Artigo Acesso aberto Revisado por pares
BIBTEX RIS

Evaluation of Plasma IL-6 in Patients with Melanoma as a Prognostic and Checkpoint Immunotherapy Predictive Biomarker

2021; Elsevier BV; Volume: 142; Issue: 7 Linguagem: Inglês

10.1016/j.jid.2021.12.012

ISSN

1523-1747

Autores

Yuling Wang, Vijaya Ramachandran, Dawen Sui, Kejing Xu, Lauren E. Haydu, Shenying Fang, Jennifer L. McQuade, Sarah B. Fisher, Anthony Lucci, Emily Z. Keung, Jennifer A. Wargo, Jeffrey E. Gershenwald, Merrick I. Ross, Jeffrey E. Lee,

Tópico(s)

Cancer Cells and Metastasis

Resumo

Treatment of advanced melanoma has been revolutionized by targeted therapy (TT) and immune checkpoint blockade (ICB), but not all patients respond; identification of predictive biomarkers remains a critical need. Our previous investigations showed the circulating level of CRP as an independent melanoma prognostic biomarker (Fang et al., 2017Fang S. Wang Y. Dang Y. Gagel A. Ross M.I. Gershenwald J.E. et al.Association between body mass index, C-reactive protein levels, and melanoma patient outcomes.J Invest Dermatol. 2017; 137: 1792-1795Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar, Fang et al., 2016Fang S. Sui D. Wang Y. Liu H. Chiang Y.J. Ross M.I. et al.Association of vitamin D levels with outcome in patients with melanoma after adjustment for C-reactive protein.J Clin Oncol. 2016; 34: 1741-1747Crossref PubMed Scopus (51) Google Scholar, Fang et al., 2015Fang S. Wang Y. Sui D. Liu H. Ross M.I. Gershenwald J.E. et al.C-reactive protein as a marker of melanoma progression.J Clin Oncol. 2015; 33: 1389-1396Crossref PubMed Scopus (56) Google Scholar). IL-6 is upstream of CRP, induces hepatocyte release of CRP, and is a pleiotropic inflammatory cytokine involved in progression of melanoma (Hoejberg et al., 2012Hoejberg L. Bastholt L. Schmidt H. Interleukin-6 and melanoma.Melanoma Res. 2012; 22: 327-333Crossref PubMed Scopus (62) Google Scholar). Higher circulating IL-6 levels are associated with a poorer prognosis in multiple cancers (Guo et al., 2012Guo Y. Xu F. Lu T. Duan Z. Zhang Z. Interleukin-6 signaling pathway in targeted therapy for cancer.Cancer Treat Rev. 2012; 38: 904-910Abstract Full Text Full Text PDF PubMed Scopus (525) Google Scholar). We therefore evaluated IL-6 as a prognostic and predictive biomarker of TT and ICB response in melanoma. This study was conducted under protocols LAB00-063 and PA11-0957 approved by The University of Texas MD Anderson Cancer Center Institutional Review Board. All patients provided written informed consent before participation. Demographic and clinical factors for study cohorts are summarized in Table 1. Analysis of the exploratory cohort found plasma IL-6 levels associated with age (correlation coefficient: 0.21), sex, disease stage, tumor thickness, ulceration, tumor burden, BRAF mutation status, body mass index, and CRP (Supplementary Table S1). Elevated CRP (≥10 μg/ml) and elevated IL-6 (≥4 pg/ml), dichotomized by recursive partitioning, showed association with poorer overall survival and melanoma-specific survival (Supplementary Table S2 and Supplementary Figure S1). Previously proposed cut-offs for IL-6 have varied from 2 to 20 pg/ml (Soubrane et al., 2005Soubrane C. Rixe O. Meric J.B. Khayat D. Mouawad R. Pretreatment serum interleukin-6 concentration as a prognostic factor of overall survival in metastatic malignant melanoma patients treated with biochemotherapy: a retrospective study.Melanoma Res. 2005; 15: 199-204Crossref PubMed Scopus (45) Google Scholar); our cut-off (≥4 pg/ml) is preliminary, requiring confirmation. Multivariable analysis identified IL-6 (≥4 pg/ml) as an independent predictor of poorer overall survival and melanoma-specific survival, whereas CRP was not (Supplementary Table S2). These results suggest that IL-6, upstream in the inflammatory cascade to CRP, could influence the association of CRP with poorer outcomes in patients with melanoma. Comparing change in IL-6 with change in disease status (responding disease/stable disease/progressive disease) using sequential samples identified an increase in IL-6 level with disease progression (Supplementary Table S3).Table 1Patient Demographics and Clinical ParametersPatient Demographics and Clinical Parametersn (%)Median (Interquartile Range)Exploratory CohortAge at blood draw (y)46258.6 (47.3–68.0)Sex462— Female176 (38) Male286 (62)Disease stage at blood draw462— I152 (33) II34 (7) III155 (34) IV121 (26)BRAF status251— Wild type149 (59) Mutant (V600E/K)102 (41) Not available211Vital status462— Alive390 (84) Dead (any cause death)72 (16) Dead (melanoma-specific death)48/72 (67)BMI (kg/m2)45529.0 (25.4–32.6)IL-6 (pg/ml)4622.0 (1.2–3.7)CRP (μg/ml)4592.0 (0.6–4.9)Validation Cohort (for ICB response)Age at blood draw (y)6260 (49–69)Sex62— Female22 (35) Male40 (65)Disease stage at blood draw62— III28 (45) IV34 (55)Abbreviations: BMI, body mass index; ICB, immune checkpoint blockade. Open table in a new tab Abbreviations: BMI, body mass index; ICB, immune checkpoint blockade. We further evaluated the potential associations between IL-6 and response to treatment in the exploratory cohort (Table 2). Treatment included TT (dabrafenib with or without trametinib) alone, ICB (ipilimumab and/or nivolumab and/or pembrolizumab) alone, or the combination of ICB and TT (ICB + TT). Treatment response was evaluated using both anytime-treatment samples (Any-Tx, samples collected before, during, or after treatment) and pre-treatment samples (Pre-Tx, samples collected ≤90 days before treatment). Among patients who received ICB alone, 3–8-fold higher IL-6 levels were observed in nonresponders than in responders using Any-Tx or Pre-Tx samples, respectively (Any-Tx samples, 10.68 ± 17.34 pg/ml vs. 3.20 ± 2.98 pg/ml, P = 0.001; Pre-Tx samples, 19.97 ± 26.51 pg/ml vs. 2.39 ± 1.48 pg/ml, P = 0.01). Similarly, among patients who received ICB + TT, 3–8-fold higher levels of IL-6 were observed among nonresponders than among responders using Any-Tx or Pre-Tx samples, respectively. No significant correlation was observed between IL-6 levels and nonresponse among patients who received TT alone; however, the relatively small subset of TT-only patients raises the possibility of a type II error in this group.Table 2IL-6 Level as a Predictive Biomarker of Response to ICBSetsTreatmentResponseStatusIL-6 Level (pg/ml)Any-Tx Sample, (Before, during, or after Treatment Samples—Collected at Any Time Point)Pre-Tx Sample, (Before Treatment Samples Only—Collected within 90 Days Before Treatment)nMean ± SDP-ValuenMean ± SDP-ValueExploratory setTTResponders293.69 ± 3.650.4052.54 ± 1.230.57Nonresponders1111.08 ± 23.2533.38 ± 4.22ICB + TTResponders1103.09 ± 2.81<0.00011P<0.05182.30 ± 1.490.0051P<0.05Nonresponders6810.02 ± 16.191219.27 ± 25.39ICBResponders803.20 ± 2.98<0.0011P<0.05172.39 ± 1.480.011P<0.05Nonresponders5810.68 ± 17.341119.97 ± 26.51ICB - stage IIIResponders313.15 ± 3.040.4162.43 ± 2.00.39Nonresponders194.08 ± 4.2138.43 ± 9.42ICB - stage IVResponders463.30 ± 3.050.00011P<0.05112.38 ± 1.230.031P<0.05Nonresponders3515.16 ± 21.03824.29 ± 30.00Validation setICB - stage IIIRespondersNA171.66 ± 0.970.65Nonresponders111.95 ± 2.21ICB - stage IVRespondersNA173.59 ± 4.050.031P<0.05Nonresponders177.58 ± 8.60Combination of both setsICB - stage IVRespondersNA283.11 ± 3.260.0021P<0.05Nonresponders2512.93 ± 19.37Abbreviations: Any-Tx, anytime-treatment; ICB, immune checkpoint blockade; NA, not available; Pre-Tx, pretreatment; TT, targeted therapy.Responders include complete response + partial response; Nonresponders include stable disease + progressive disease.1 P<0.05 Open table in a new tab Abbreviations: Any-Tx, anytime-treatment; ICB, immune checkpoint blockade; NA, not available; Pre-Tx, pretreatment; TT, targeted therapy. Responders include complete response + partial response; Nonresponders include stable disease + progressive disease. To further explore the relationship between IL-6 and response to ICB with disease stage, subset analysis of patients at stages III and IV was conducted. Elevated IL-6 was correlated with nonresponse to ICB in stage IV patients (stage IV Any-Tx nonresponders vs. responders, 15.16 ± 21.03 pg/ml vs. 3.30 ± 3.05 pg/ml, P = 0.0001; stage IV Pre-Tx nonresponders vs. responders, 24.29 ± 30.00 pg/ml vs. 2.38 ± 1.23 pg/ml, P = 0.03). In contrast, no association was observed between IL-6 and ICB response among stage III patients. These data suggested that elevated IL-6 levels could be a stratification marker for stage IV ICB treatment resistance. To validate this finding, we evaluated Pre-Tx plasma IL-6 levels and response to ICB in an independent cohort (Table 2). Among stage IV patients in the validation cohort, Pre-Tx elevated IL-6 was again associated with ICB nonresponse (nonresponders vs. responders, 7.58 ± 8.60 pg/ml vs. 3.59 ± 4.05 pg/ml, P = 0.03); no association was identified among stage III patients. Analysis of the combined cohorts yielded similar findings. We evaluated the potential confounders of tumor mutation status and tumor burden. Although BRAF tumor mutation status was correlated with elevated IL-6, we did not find BRAF status to be correlated with ICB response (data not shown). Tumor burden may predict response to immunotherapy (Kim et al., 2021Kim S.I. Cassella C.R. Byrne K.T. Tumor burden and immunotherapy: impact on immune infiltration and therapeutic outcomes.Front Immunol. 2021; 11: 629722Crossref PubMed Scopus (52) Google Scholar); we found increased tumor burden associated with elevated IL-6 and with Pre-Tx ICB nonresponse (Supplementary Table S4), thus suggesting that tumor burden could contribute to the IL-6 correlation with ICB nonresponse. Because elevated IL-6 could provide at least one mechanism explaining the association between tumor burden and ICB resistance, further investigation of these complex associations is indicated. IL-6 levels have been reported higher in patients with melanoma not responding to chemotherapy or IFN-α (Mouawad et al., 1999Mouawad R. Khayat D. Merle S. Antoine E.C. Gil-Delgado M. Soubrane C. Is there any relationship between interleukin-6/interleukin-6 receptor modulation and endogenous interleukin-6 release in metastatic malignant melanoma patients treated by biochemotherapy?.Melanoma Res. 1999; 9: 181-188Crossref PubMed Scopus (24) Google Scholar, Mouawad et al., 1996Mouawad R. Benhammouda A. Rixe O. Antoine E.C. Borel C. Weil M. et al.Endogenous interleukin 6 levels in patients with metastatic malignant melanoma: correlation with tumor burden.Clin Cancer Res. 1996; 2: 1405-1409PubMed Google Scholar). In a report of patients with melanoma treated with ipilimumab, IL-6 levels tended to be higher in those with treatment failure, but the association did not reach statistical significance (Bjoern et al., 2016Bjoern J. Juul Nitschke N. Zeeberg Iversen T. Schmidt H. Fode K. Svane I.M. Immunological correlates of treatment and response in stage IV malignant melanoma patients treated with ipilimumab.Oncoimmunology. 2016; 5e1100788Crossref PubMed Scopus (63) Google Scholar). In a report of patients treated with ICB, elevated IL-6 was associated with poorer overall survival; treatment response was not evaluated (Laino et al., 2020Laino A.S. Woods D. Vassallo M. Qian X. Tang H. Wind-Rotolo M. et al.Serum interleukin-6 and C-reactive protein are associated with survival in melanoma patients receiving immune checkpoint inhibition.J Immunother Cancer. 2020; 8e000842Crossref Scopus (99) Google Scholar). Our study specifically identifies IL-6 level as a possible predictor of ICB response in patients with melanoma. IL-6‒mediated immune suppression has been linked to myeloid-derived suppressor cells (Chen et al., 2014Chen M.F. Kuan F.C. Yen T.C. Lu M.S. Lin P.Y. Chung Y.H. et al.IL-6-stimulated CD11b+ CD14+ HLA-DR- myeloid-derived suppressor cells, are associated with progression and poor prognosis in squamous cell carcinoma of the esophagus.Oncotarget. 2014; 5: 8716-8728Crossref PubMed Scopus (127) Google Scholar) and tumor-associated macrophages (Tsukamoto et al., 2018Tsukamoto H. Fujieda K. Senju S. Ikeda T. Oshiumi H. Nishimura Y. Immune-suppressive effects of interleukin-6 on T-cell-mediated anti-tumor immunity.Cancer Sci. 2018; 109: 523-530Crossref PubMed Scopus (90) Google Scholar); these mechanisms could contribute to resistance to ICB. Our results indirectly suggest that targeting IL-6 either alone or in combination with ICB could be beneficial in advanced melanoma. Murine models suggest that combined IL-6 and PDL-1 blockade can reduce melanoma metastasis (Tsukamoto et al., 2018Tsukamoto H. Fujieda K. Senju S. Ikeda T. Oshiumi H. Nishimura Y. Immune-suppressive effects of interleukin-6 on T-cell-mediated anti-tumor immunity.Cancer Sci. 2018; 109: 523-530Crossref PubMed Scopus (90) Google Scholar). Interestingly, tocilizumab, a humanized mAb against IL-6 receptor, may improve immune-related adverse events without affecting the therapeutic efficacy of ICB in melanoma (Hopkins et al., 2017Hopkins A.M. Rowland A. Kichenadasse G. Wiese M.D. Gurney H. McKinnon R.A. et al.Predicting response and toxicity to immune checkpoint inhibitors using routinely available blood and clinical markers.Br J Cancer. 2017; 117: 913-920Crossref PubMed Scopus (131) Google Scholar) and lung adenocarcinoma (Horisberger et al., 2018Horisberger A. La Rosa S. Zurcher J.P. Zimmermann S. Spertini F. Coukos G. et al.A severe case of refractory esophageal stenosis induced by nivolumab and responding to tocilizumab therapy.J Immunother Cancer. 2018; 6: 156Crossref PubMed Scopus (45) Google Scholar). Thus, the results reported in this paper in context with other evidence supports continued evaluation of the role of IL-6 as a predictive biomarker of ICB response and a potential therapeutic target. All data relevant to the study are included in the article or uploaded as supplementary information. Yuling Wang: http://orcid.org/0000-0002-9579-9067 Vijaya Ramachandran: http://orcid.org/0000-0002-9717-9662 Dawen Sui: http://orcid.org/0000-0002-8956-478X Kejing Xu: http://orcid.org/0000-0001-7613-9066 Lauren E. Haydu: http://orcid.org/0000-0002-6360-0199 Shenying Fang: http://orcid.org/0000-0002-0604-793X Jennifer L. McQuade: http://orcid.org/0000-0002-2393-2172 Sarah B. Fisher: http://orcid.org/0000-0002-6881-2633 Anthony Lucci: http://orcid.org/0000-0003-4039-174X Emily Z. Keung: http://orcid.org/0000-0002-8783-8484 Jennifer Wargo: http://orcid.org/0000-0003-3438-7576 Jeffrey E. Gershenwald: http://orcid.org/0000-0003-4519-5369 Merrick I. Ross: http://orcid.org/0000-0002-7787-0421 Jeffrey E. Lee: http://orcid.org/0000-0001-6010-5590 JW serves as a consultant/advisory board member for Roche/Genentech, Novartis, AstraZeneca, GlaxoSmithKline, Bristol-Myers Squibb, Merck, Biothera Pharmaceuticals, and Ella Therapeutics. JW reports compensation for speaker's bureau and honoraria from Imedex, Dava Oncology, Omniprex, Illumina, Gilead, PeerView, Physician Education Resource, MedImmune, and Bristol-Myers Squibb. JW receives research support from GlaxoSmithKline, Roche/Genentech, Bristol-Myers Squibb, and Novartis. JLM is a consultant for Roche, BMS, Merck, and Novartis. JLM reports honoraria from BMS and Roche. JEG serves as a consultant and/or as an advisory board member for Merck, Syndax, Regeneron, Novartis, and Bristol-Myers Squibb. MIR is a consultant for Amgen and a consultant and advisory board member for Merck. The remaining authors state no conflict of interest. This work was supported by the National Cancer Institute Specialized Programs of Research Excellence grant P50 CA093459 and the University of Texas M. D. Anderson Cancer Center Support Grant P30 CA016672 (Clinical Trials Support Resource), philanthropic contributions to the University of Texas M. D. Anderson Cancer Center (Houston, TX) Moon Shots Program, University of Texas M. D. Anderson Cancer Center Various Donors Melanoma and Skin Cancers Priority Program Fund; Miriam and Jim Mulva Research Fund; McCarthy Skin Cancer Research Fund, and Marit Peterson Fund for Melanoma Research. Conceptualization: JEL; Data Curation: YW, DS, KX, LEH, SF, VR; Formal Analysis: DS, YW; Funding Acquisition: JEL; Investigation: YW, KX; Methodology: YW, JEL; Project Administration: YW; Resources: JEL; Software: YW, DS, SF; Supervision: JEL; Validation: YW, VR, JEL; Visualization: VR, YW; Writing - Original Draft Preparation: VR; Writing - Review and Editing: VR, YW, DS, SF, JLM, SBF, AL, EZK, JW, JEG, MIR, JEL Patients included a retrospectively defined consecutive cohort of patients with cutaneous melanoma evaluated at The University of Texas MD Anderson Cancer Center (Houston, TX). The time frame of the cohort was chosen to include those treated with modern systemic therapies, for whom substantial follow-up was available. The exploratory cohort included 176 female and 286 male patients with a median age at blood draw of 59 years (21–89 years); 186 patients were stage I/II, and 276 were stage III/IV; median follow-up was 31 months (range: 0–43 months). There were 72 deaths, including 48 melanoma-specific deaths. The validation cohort included 22 females and 40 males with a median age of 60 years (21–80 years). Peripheral blood samples were collected in EDTA-containing tubes, and plasma was separated and kept at –80 °C until use. The exploratory cohort included 462 patients with melanoma stages I–IV at primary blood draw, including 178 patients who presented with or developed the advanced disease, who were treated with targeted therapy (TT) and/or immune checkpoint blockade (ICB), and whose data on treatment response were available. The exploratory dataset was used for an initial evaluation of the measured biomarkers. For patients in whom more than one blood sample was collected, the first sample collected between January 2015 and July 2016 was designated as the primary blood sample. A total of 67 patients in the exploratory cohort had an additional 1‒4 blood samples (n = 106 samples) obtained from 2007 to 2016 available for sequential analysis. The validation cohort consisted of 62 patients with melanoma stages III/IV with blood samples collected from August 2016 to December 2020 and treated with ICB within 90 days of blood draw. Clinical data were obtained from patient records and maintained in the Melanoma Informatics, Tissue Resource, and Pathology Core, supplemented by a review of the individual patient electronic medical record. Melanoma stages were coded according to the American Joint Committee on Cancer 8th edition staging system (Gershenwald and Scolyer, 2018Gershenwald J.E. Scolyer R.A. Melanoma Staging: American Joint Committee on Cancer (AJCC) 8th Edition and Beyond.Ann Surg Oncol. 2018; 25: 2105-2110Crossref PubMed Scopus (270) Google Scholar). Melanoma-specific survival was defined as death due to melanoma; deaths due to other causes or unclear causes were not included. For sequential blood analysis, change in disease status was coded as a response (if disease status improved, including if the disease was removed by surgery), stable (if disease status did not change), or progression (if disease status progressed) between sequential blood draws (Fang et al., 2015Fang S. Wang Y. Sui D. Liu H. Ross M.I. Gershenwald J.E. et al.C-reactive protein as a marker of melanoma progression.J Clin Oncol. 2015; 33: 1389-1396Crossref PubMed Scopus (60) Google Scholar). There were no patients who showed a mixed response to treatment between blood draws. For treatment response analysis, stage III/IV patients were identified as treated with TT alone (dabrafenib with or without trametinib) (TT, exploratory cohort n = 40), ICB (ipilimumab and/or nivolumab and/or pembrolizumab) plus TT (ICB + TT, exploratory cohort n = 178), or ICB alone (ICB, exploratory cohort n = 138; validation cohort n = 62). Plasma samples were coded separately as pretreatment samples (if collected within 90 days of initiation of treatment) or as anytime-treatment samples (if collected at any time before, during, or after initiation of treatment). Response to therapy was assessed according to Response Evaluation Criteria in Solid Tumors (version 1.1) (Eisenhauer et al., 2009Eisenhauer E.A. Therasse P. Bogaerts J. Schwartz L.H. Sargent D. Ford R. et al.New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).Eur J Cancer. 2009; 45: 228-247Abstract Full Text Full Text PDF PubMed Scopus (19552) Google Scholar); coded as complete response, partial response, stable disease, or progressive disease; and grouped as responders (complete response + partial response) or nonresponders (stable disease + progressive disease). Biomarker assessment, clinical data acquisition, and data analysis were performed blinded and independently. ELISA kits were used to measure plasma CRP and IL-6 (catalog numbers DCRP00 and HS600B, respectively, R&D Systems, Minneapolis, MN) Associations between clinical parameters and biomarker levels were assessed using Spearman test (for continuous variables) and Wilcoxon two-sample test (for dichotomized variables) or Kruskal‒Wallis test (for greater than two-level variables). To determine the best cut-off for association analysis and outcome prediction, recursive partitioning was performed, and IL-6 was dichotomized at ≥4 pg/ml. A CRP cut-off ≥10 μg/ml was used on the basis of our previous analysis (Fang et al., 2015Fang S. Wang Y. Sui D. Liu H. Ross M.I. Gershenwald J.E. et al.C-reactive protein as a marker of melanoma progression.J Clin Oncol. 2015; 33: 1389-1396Crossref PubMed Scopus (60) Google Scholar). Standard definitions from World Health Organization for body mass index were used, including categorizing 25–29.9 kg/m2 as overweight and ≥30 kg/m2 as obese. A general linear model was used for sequential blood data analysis. Each consecutive blood draw from an individual patient was considered independent. Changes in disease status and changes in IL-6 levels were recorded between consecutive blood draws. Model evaluation indicated that the general linear model fitted the data well. Survival duration was measured from the date of blood draw to the date of death or the date of the last follow-up. Survival curves were plotted by Kaplan–Meier method and compared by log-rank test. Cox proportional hazards regression model was used for univariate and multivariable analysis. All statistical analyses were performed using SAS 9.4 for Windows (SAS Institute, Cary, NC), Stata (version 11.2, StataCorp, College Station, TX), and R package (version 3.6.3). P < 0.05 (two-sided) were considered significant.Supplementary Table S1Association of IL-6 Level with Clinical ParametersClinicalParametersLevelnIL-6 (pg/ml)P-ValueMean ± SDMedianAge (y)—46257.31 ± 14.5058.63<0.00011P<0.05.SexFemale1763.46 ± 8.661.710.0011P<0.05.Male2864.73 ± 8.402.30Disease stageI1522.32 ± 3.351.53<0.00011P<0.05.II342.70 ± 1.862.12III1553.73 ± 5.262.05IV1217.75 ± 14.483.08BRAF statusWild type1494.79 ± 10.222.250.011P<0.05.Mutant1026.93 ± 11.832.96BMI (kg/m2)<302644.22 ± 9.531.66<0.00011P<0.05.≥301914.34 ± 7.052.63CRP (μg/ml)<10.04052.64 ± 2.981.77<0.00011P<0.05.≥10.05415.96 ± 19.987.39Tumor thickness (mm)<=11402.88 ± 5.081.45<=21082.51 ± 2.401.75<0.00011P<0.05.<=4615.28 ± 10.852.25>4584.36 ± 5.462.74UlcerationAbsent2493.44 ± 6.751.710.041P<0.05.Present1013.37 ± 4.112.25Tumor burden (cm)—431.10 ± 1.191.10<0.0051P<0.05.Abbreviations: BMI, body mass index; RECIST, Response Evaluation Criteria in Solid Tumors.Tumor burden is calculated as the sum of the diameters of all lesions measured according to RECIST.1 P<0.05. Open table in a new tab Supplementary Table S2Association of IL-6 Level with the Survival Measures of Patients with MelanomaClinical ParametersUnivariate AnalysisMultivariable AnalysisP-ValueHR (95% CI)P-ValueHR (95% CI)OSStage (III/IV vs. I/II)<0.00011P<0.05.7.27 (3.15–16.8)<0.00011P<0.05.5.46 (2.35–12.72)BMI (≥30 vs. <30)0.0061P<0.05.0.48 (0.28–0.80)0.0091P<0.05.0.49 (0.29–0.84)IL-6 (≥4 vs. <4)<0.00011P<0.05.3.53 (2.22–5.61)<0.0011P<0.05.2.77 (1.59–4.81)CRP (≥10 vs. <10)<0.00011P<0.05.3.53 (2.10–5.93)0.121.60 (0.88–2.92)MSSStage (III/IV vs. I/II)<0.0011P<0.05.15.3 (3.73–63.2)<0.0011P<0.05.11.23 (2.71–46.64)BMI (≥30 vs. <30)0.031P<0.05.0.49 (0.26–0.92)0.021P<0.05.0.46 (0.24–0.88)IL-6 (≥4 vs. <4)<0.00011P<0.05.4.24 (2.41–7.48)0.0021P<0.05.2.97 (1.52–5.80)CRP (≥10 vs. <10)<0.00011P<0.05.3.78 (2.02–7.06)0.241.55 (0.75–3.22)Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazard ratio; MSS, melanoma-specific survival; OS, overall survival.1 P<0.05. Open table in a new tab Supplementary Table S3Association of Increase in IL-6 Level with the Disease Progression of Patients with MelanomaChange in Disease StatusnChange in IL-6Change in IL-6 (Adjusted2Adjusted for sex, age and stage at first blood draw, and time between two consecutive blood draws.)P-ValueMean ± SDMedianP-ValueResponse35–1.31 ± 11.37–0.160.021P<0.05.0.011P<0.05.Stable450.27 ± 9.500.310.050.041P<0.05.Progression265.37 ± 10.760.98RefRefAbbreviation: Ref, reference.1 P<0.05.2 Adjusted for sex, age and stage at first blood draw, and time between two consecutive blood draws. Open table in a new tab Supplementary Table S4Association of ICB Treatment Response with Tumor BurdenResponse Status (Pretreatment ICB Patients from Exploratory Cohort)Tumor Burden (cm)nMean ± SDP-ValueResponders103.34 ± 2.640.051P<0.05.Nonresponders712.60 ± 10.89Abbreviation: ICB, immune checkpoint blockade.1 P<0.05. Open table in a new tab Abbreviations: BMI, body mass index; RECIST, Response Evaluation Criteria in Solid Tumors. Tumor burden is calculated as the sum of the diameters of all lesions measured according to RECIST. Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazard ratio; MSS, melanoma-specific survival; OS, overall survival. Abbreviation: Ref, reference. Abbreviation: ICB, immune checkpoint blockade.

Referência(s)