ABSTRACT
Objective: To determine the association of red cell blood counts, and liver panel tests to predict outcomes in patients with gastroenteropancreatic neuroendocrine tumors who underwent systemic antineoplastic treatments.
Methods: Patients with gastroenteropancreatic neuroendocrine tumors in systemic treatment were assessed according to laboratory tests within the same period. Progression free survival was determined by the period between the beginning of treatment and the date of progression. We used conditional models (PWP model) to verify the association between laboratory tests and tumor progression. The level of significance used was 5%.
Results: A total of 30 treatments given to 17 patients in the intention-to-treat population were evaluated. Treatment included octreotide, lanreotide, everolimus, lutetium, and chemotherapy. We had statistically significant results in chromogranin A, neutrophils and platelets-to-lymphocyte ratio. The risk of progression increases by 2% with the addition of 100ng/mL of chromogranin A (p=0.034), 4% with the increase of 100 neutrophil units (p=0.006), and 21% with the addition of 10 units in platelets-to-lymphocyte ratio (p=0.002).
Conclusion: Chromogranin A, neutrophils and platelets-to-lymphocyte ratio were associated with disease progression during systemic treatment in gastroenteropancreatic neuroendocrine tumors. Further prospective studies with larger cohorts are necessary to validate our findings.
Keywords: Biomarkers; Chromogranin A; Neuroendocrine tumors; Gastro-enteropancreatic neuroendocrine tumor; Blood platelets; Neutrophils; Lutetium; Intention to treat analysis
INTRODUCTION
Neuroendocrine neoplasms account for only 0.5% of diagnosed neoplasia cases,(1) which is a rare disease with an average incidence of 2/100,000 and that around 60% of cases originate from the digestive system.(2–4) Most gastroenteropancreatic neuroendocrine tumors (GEP-NET) diagnosed are asymptomatic,(2) and treatment in case of advanced disease includes several options that range from somatostatin analogues, tyrosine kinase inhibitors, radioisotopes such as lutetium 177, and even chemotherapy.(5,6)
Part of the difficulty in choosing the systemic treatment rises from the lack of validated indicators to evaluate the response to multiple available treatments. Image workout as computed tomography has difficulty to conclude progression due to their indolent behavior.
Chromogranin A marker used for diagnosis presents conflicting results in the published literature regarding the actual ability to monitor treatment response.(7,8) We still lack validated indicators to evaluate the response in GEP-NET to the multiple treatments available. Moreover, other laboratory tests that usually present association with other tumors of the digestive tract have been poorly studied in neuroendocrine tumors. In addition, there is no current recommendation about evaluating laboratory tests in GEP-NET.(9,10)
OBJECTIVE
To determine the association of red cell blood counts, and liver panel tests to predict outcomes in patients with gastroenteropancreatic neuroendocrine tumors who underwent systemic antineoplastic treatments.
METHODS
Patients
We included patients with GEP-NET who were admitted to the Hospital Israelita Albert Einstein (HIAE) and Americas Oncology Group in São Paulo, Brazil, from January 2006 to December 2018. Patients with incomplete data were excluded from the analysis of the study. Data collected included patient’s sex and age, clinical stage, and if available, the pathologic stage at diagnosis, location of the primary tumor, location of metastasis, hepatic tumor volume, tumor grade, Ki-67, previous treatments, surgery of the primary tumor, metastasectomy, trans-arterial, and radiotherapy treatments.
Each treatment was assessed in the same period individually based on the laboratory findings such as gamma-glutamyltransferase (GGT), alkaline phosphatase (AP), chromogranin A (CgA), neutrophils, lymphocyte and platelets, neutrophil-to-lymphocyte ratio (NLR), and platelets-to-lymphocyte ratio (PLR). Neutrophil-to-lymphocyte ratio higher than 3 and a PLR higher than 150 were considered as cutoff points for analysis that were also associated with worse oncologic outcomes in gastrointestinal cancers.(9,10)
Time to progression was determined by the period between the beginning of treatment to be evaluated, and the date of progression. The study was approved by the research and ethical committees of both institutions in accordance with the existing national standards (CAAE: 81744017.6.0000.0071, protocol: 2.489.784, and CAAE: 64273317.9.0000.5533, protocol: 4.324.677). The consent term was waived since this was a retrospective study. All datasets in which the conclusions of this report were based on are available upon request.
Statistical analysis
The quantitative variables were described using median and interquartile range (IIQ: 1st and 3rd quartiles), and the qualitative variables were described by means of absolute and relative frequencies.(11) The association between laboratory tests and tumor progression were evaluated by conditional models developed by Prentice, Williams and Peterson (PWP model).(12) Such survival models deal with recurrent events such as repetition of the occurrence of progression and time-dependent variables such as the tests that were collected at more than one time and the status of progression.(13) The statistical package R was used for the analyzes.(14) The level of significance adopted was 5%.
RESULTS
Thirty treatments were provided to 17 patients as an intention-to-treat population (median 2 treatment per patient). Table 1 presents the description of the demographic variables, staging, and locations of the primary tumor. The median age of patients was 58 years (ranging 26-91). The majority of them were women (∼65%), and ∼30% of individuals had hepatic tumor volume >50%, ∼90% metastatic cancer at the time of diagnosis, and ∼17% (n=3) died because of the disease progression.
Clinical and pathological characteristics of the patients who underwent systemic treatment of gastroenteropancreatic neuroendocrine tumors
The metastasis locations, previous treatments, and systemic therapies are demonstrated in table 2, and the results of laboratory tests in appendix 1 Appendix 1 Laboratory tests and values in different evaluated periods Variable Total Progression No Yes Baseline chromogranin A (CgA) (ng/mL) Median [IQR] 316.50 [25.50-640.00] 382.00 [138.00-630.00] 312.00 [25.00-535.00] Minimum-Maximum (n) 2.70-11,060.00 (20) 2.70-8,531.00 (9) 4.00-11,060.00 (11) CgA (1-3 months) Median [IQR] 206.00 [52.22-538.50] 90.00 [48.55-458.00] 259.00 [153.00-690.00] Minimum-Maximum (n) 2.00-6,335.00 (12) 17.00-3,159.00 (7) 2.00-6,335.00 (5) CgA (3-6 months) Median [IQR] 139.00 [9.40-493.00] 93.10 [11.00-468.00] 325.50 [40.60-5,710.25] Minimum-Maximum (n) 3.20-8,531.00 (11) 3.20-474.00 (5) 6.40-8,531.00 (6) CgA (6-12 months) Median [IQR] 87.00 [67.00-106.00] 114.00 [106.00-122.00] 58.00 [49.00-67.00] Minimum-Maximum (n) 40.00-130.00 (4) 98.00-130.00 (2) 40.00-76.00 (2) Baseline GGT (U/L) Median [IQR] 72.50 [31.50-217.25] 74.00 [28.50-211.50] 71.00 [40.00-208.50] Minimum-Maximum (n) 15.00-518.00 (26) 15.00-435.00 (11) 21.00-518.00 (15) GGT (1-3 months) Median [IQR] 47.00 [28.00-208.00] 36.50 [23.50-169.50] 74.00 [29.00-411.00] Minimum-Maximum (n) 13.00-435.00 (25) 13.00-359.00 (12) 16.00-435.00 (13) GGT (3-6 months) Median [IQR] 189.00 [66.25-284.00] 123.50 [44.75-198.50] 292.00 [131.25-360.50] Minimum-Maximum (n) 22.00-457.00 (12) 29.00-273.00 (6) 22.00-457.00 (6) GGT (6-12 months) Median [IQR] 75.00 [28.00-221.50] 148.50 [91.25-205.75] 75.00 [22.00-180.00] Minimum-Maximum (n) 17.00-297.00 (7) 34.00-263.00 (2) 17.00-297.00 (5) Baseline alkaline phosphatase (AP-U/L) Median [IQR] 125.00 [99.50-161.00] 129.00 [91.50-159.00] 124.00 [105.50-161.00] Minimum-Maximum (n) 50.00-431.00 (27) 50.00-378.00 (11) 65.00-431.00 (16) AP (1-3 months) Median [IQR] 125.50 [92.00-160.25] 120.00 [86.50-131.00] 129.00 [96.00-190.00] Minimum-Maximum (n) 53.00-667.00 (26) 53.00-161.00 (11) 76.00-667.00 (15) AP (3-6 months) Median [IQR] 144.50 [116.75-171.00] 144.00 [105.50-145.00] 176.00 [119.50-254.50] Minimum-Maximum (n) 91.00-614.00 (14) 91.00-156.00 (7) 99.00-614.00 (7) AP (6-12 months) Median [IQR] 135.00 [93.50-172.00] 177.50 [156.25-198.75] 95.00 [92.00-161.00] Minimum-Maximum (n) 83.00-220.00 (7) 135.00-220.00 (2) 83.00-183.00 (5) Baseline neutrophils Median [IQR] 4,110.50 [2,376.25-4,965.25] 3,447.00 [2,348.75-4,120.00] 4,479.00 [2,769.25-5,656.50] Minimum-Maximum (n) 929.00-10,331.00 (24) 1,749.00-4,283.00 (8) 929.00-10,331.00 (16) Neutrophils (1-3 months) Median [IQR] 3,534.50 [2,242.00-4,075.00] 2,740.00 [2,168.25-3,434.75] 3,848.00 [2,386.75-4,268.00] Minimum-Maximum (n) 1,520.00-11,294.00 (22) 1,520.00-5,026.00 (8) 1,650.00-11,294.00 (14) Neutrophils (3-6 months) Median [IQR] 3,807.00 [2,813.00-4,773.50] 3,267.00 [2,881.25-4,493.50] 4,119.00 [2,431.25-4,621.00] Minimum-Maximum (n) 929.00-5,859.00 (12) 2,693.00-5,007.00 (6) 929.00-5,859.00 (6) Neutrophils (6-12 months) Median [IQR] 3,564.00 [2,700.00-3,928.00] 2,700.00 [2,700.00-2,700.00] 3,746.00 [3,285.50-4,462.00] Minimum-Maximum (n) 2,450.00-6,064.00 (5) 2,700.00-2,700.00 (1) 2,450.00-6,064.00 (4) Baseline lymphocytes Median [IQR] 1,811.00 [1,147.00-2,088.50] 2,088.50 [1,732.75-2,339.75] 1,708.00 [996.50-1,885.50] Minimum-Maximum (n) 579.00-2,880.00 (23) 601.00-2,880.00 (8) 579.00-2,868.00 (15) Lymphocytes (1-3 months) Median [IQR] 1,778.00 [1,187.00-2,049.00] 1,526.50 [1,108.50-1,823.75] 1,903.00 [1,187.00-2,102.00] Minimum-Maximum (n) 519.00-2,726.00 (21) 519.00-2,050.00 (8) 583.00-2,726.00 (13) Lymphocytes (3-6 months) Median [IQR] 1,820.50 [940.25-2,083.00] 1,531.00 [993.50-2,023.50] 1,840.50 [1,140.50-2,459.50] Minimum-Maximum (n) 579.00-2,751.00 (12) 711.00-2,140.00 (6) 579.00-2,751.00 (6) Lymphocytes (6-12 months) Median [IQR] 1,662.00 [1,195.00-2,114.25] 1,241.00 [1,241.00-1,241.00] 2,083.00 [1,570.00-2,145.50] Minimum-Maximum (n) 1,057.00-2,208.00 (4) 1,241.00-1,241.00 (1) 1,057.00-2,208.00 (3) Baseline platelets Median [IQR] 225,000 [196,500-330,500] 219,000 [200,500-300,000] 233,500 [193,000-362,000] Minimum-Maximum (n) 109,000-560,000 (27) 109,000-354,000 (11) 140,000-560,000 (16) Platelets (1-3 months) Median [IQR] 209,000 [168,000-317,000] 197,000 [154,500-270,000] 274,500 [185,250-325,500] Minimum-Maximum (n) 130,000-542,000 (25) 130,000-349,000 (11) 133,000-542,000 (14) Platelets (3-6 months) Median [IQR] 216,500 [173,000-259,500] 210,000 [167,000-248,000] 225,000 [184,000-255,000] Minimum-Maximum (n) 142,000-391,000 (16) 142,000-391,000 (7) 146,000-341,000 (9) Platelets (6-12 months) Median [IQR] 162,000 [149,500-186,500] 170,000 [166,000-174,000] 156,000 [143,000-195,000] Minimum-Maximum (n) 54,000-305,000 (7) 162,000-178,000 (2) 54,000-305,000 (5) NLR (baseline) Median [IQR] 2.18 [1.21-3.42] 1.72 [1.09-2.45] 2.90 [1.42-3.88] Minimum-Maximum (n) 0.66-13.12 (23) 0.66-5.06 (8) 0.80-13.12 (15) NLR (1-3 months) Median [IQR] 1.98 [1.23-2.96] 2.20 [1.56-2.68] 1.98 [1.23-3.13] Minimum-Maximum (n) 0.80-9.51 (21) 1.12-6.12 (8) 0.80-9.51 (13) NLR (3-6 months) Median [IQR] 2.14 [1.53-2.87] 2.44 [1.71-3.49] 1.87 [1.55-2.15] Minimum-Maximum (n) 1.09-7.04 (12) 1.26-7.04 (6) 1.09-5.06 (6) NLR (6-12 months) Median [IQR] 1.98 [1.76-3.07] 2.18 [2.18-2.18] 1.78 [1.74-3.76] Minimum-Maximum (n) 1.71-5.74 (4) 2.18-2.18 (1) 1.71-5.74 (3) PLR (baseline) Median [IQR] 162.42 [114.25-232.58] 133.45 [86.00-187.09] 168.62 [130.12-242.18] Minimum-Maximum (n) 72.22-305.70 (23) 72.22-274.21 (8) 101.55-305.70 (15) PLR (1-3 months) Median [IQR] 154.93 [112.75-215.66] 154.78 [112.26-216.95] 179.35 [136.63-215.66] Minimum-Maximum (n) 87.19-440.00 (21) 100.46-440.00 (8) 87.19-288.33 (13) PLR (3-6 months) Median [IQR] 181.23 [116.45-208.63] 194.58 [130.17-226.44] 148.49 [119.83-189.91] Minimum-Maximum (n) 75.23-252.16 (12) 75.23-237.74 (6) 96.88-252.16 (6) PLR (6-12 months) Median [IQR] 115.87 [79.01-144.18] 143.43 [143.43-143.43] 88.32 [69.70-117.37] Minimum-Maximum (n) 51.09-146.42 (4) 143.43-143.43 (1) 51.09-146.42 (3) N: sample size; IQR: interquartile range; CgA: chromogranin A; GGT: gamma-glutamyltransferase; NLR: neutrophil-to-lymphocyte ratio; PLR: platelets-to-lymphocyte ratio. . Around 80% of patients had liver metastasis, one-third were treated with octreotide, and about 25% (n=8) were treated with lutetium.
The results of the models for the risk of occurrence of progression given the results of the laboratory tests over time are presented in table 3. All factors collected for risk of progression had risk ratio >1, except lymphocytes (risk ratio <1). Statistically meaningful results were observed in CgA, neutrophils, and PLR. The risk of progression increases by 2% with the addition of 100ng/mL of CgA (p=0.034), the risk of progression increases by 4% with the increase of 100 neutrophil units (p=0.006), and the risk of progression increased by 21% with the adding of 10 units in PLR (p=0.002).
Prentice-Williams-Peterson models for progression and laboratory tests as explanatory variables
DISCUSSION
The use of CgA has been extensively studied in the last years. These studies have shown that patients with high levels of CgA have worst prognosis.(15–17) In addition, preoperative CgA levels has also been related to recurrence of disease following resection of the primary tumor.(18) Regarding the ability to predict progression during treatment in advanced disease, the results of studies published in the literature so far present conflicting data, with groups demonstrating positive association of the marker with progression,(19,20) and others demonstrating poor correlation.(21,22)
Concerning patients with GEP-NET, CgA, neutrophils and PLR were correlated with disease progression. These three biomarkers were progressively related. The higher the value the greater the risk of progression. We noticed a tendency to control disease and high levels of lymphocytes, however, without statistical significance. In our study, the risk of progression increased by 2% with each addition of 100ng/mL of CgA (p value=0.034). We must emphasize the limitations of this marker that can directly influence different results found in the literature including, e.g., the lack of assay standardization, and generation of significant variations across different laboratories. Other conditions that can affect CgA levels should be considered such as gastrointestinal and cardiovascular disorders and ultimately, some GEP-NET (30-50%) which do not show elevated CgA levels.(15,23,24)
It is known that high neutrophil counts contribute to the survival of tumor cells due to the inhibition of lymphoid cells.(25) Tumor-associated neutrophils and neutrophils in the bloodstream of patients with advanced cancer are associated with poor prognosis in several tumors.(26–28) One study evaluated patients with neuroendocrine tumors (NET), including GEP-NET, and found that elevated neutrophil counts have association with worse overall survival.(29) In our study, we demonstrated the importance of neutrophil levels in the patient’s bloodstream, with the risk of progression increasing by 4% with the increase of 100 neutrophil units (p=0.006) during systemic treatment. Neutrophil-to-lymphocyte ratio was associated in other studies with worse outcomes in GEP-NET.(30)
An association was not found in this study despite the risk associated with neutrophils at high levels and the apparent protective effect of lymphocytes for progression, although with no statistical significance. We believe that this absence of association between NLR and progression-free survival is probably because of an adequate cutoff point that has not been well defined yet for NLR, given that studies have used several cutoff points ranging from 2-5.(10,30–32)
It seems that platelets also contribute to metastatic mechanisms by protecting circulating tumor cells.(33) In agreement with these evidence, PLR seems to be able to estimate the magnitude of systemic inflammation in cancer patients.(10,34) A meta-analysis with 17 cohorts defined that the PLR ratio is a potential marker in pancreatic adenocarcinoma.(35) Based on our results, each addition of 10 units beyond the cutoff point 150 in PLR increases the risk of progression by 21% (p=0.002). These association were also observed in pancreatic NET by another group who demonstrated that PLR is an independent predictor of disease progression.(36)
There were some limitations in our study. First, our data are limited by the retrospective nature of data analyses. Second, this study was based in a relatively small sample of patients. Third, around 70% of the patients were treated with somatostatin analogs and peptide receptor radionuclide therapy. For this reason, in earlier lines of systemic treatment, therefore, the results of this study should be taken with caution in other settings. However, despite these limitations, our analysis is consistent with previous findings reported by other research groups.(19,29,30,36,37)
CONCLUSION
The laboratory variables chromogranin A, neutrophils, and platelets-to-lymphocyte ratio are associated with disease progression during systemic treatment for gastroenteropancreatic neuroendocrine tumors. Prospective initiatives using these factors could demonstrate the real impact of these biomarkers on different treatment strategies.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the help of statistical support from the Research Support Center (NAP) at Hospital Israelita Albert Einstein.
REFERENCES
- 1 Taal BG, Visser O. Epidemiology of neuroendocrine tumours. Neuroendocrinology. 2004;80(Suppl 1):3-7.
- 2 Oronsky B, Ma PC, Morgensztern D, Carter CA. Nothing but NET: a review of neuroendocrine tumors and carcinomas. Neoplasia. 2017;19(12):991-1002. Review.
- 3 Vinik AI, Chaya C. Clinical presentation and diagnosis of neuroendocrine tumors. Hematol Oncol Clin North Am. 2016;30(1):21-48. Review.
- 4 Abstracts Presented at the 7th Annual Meeting of the North American Neuro Endocrine Tumor Society, October 10-11, 2014, Nashville, Tennessee. Pancreas. 2015;44(2):347-62.
- 5 Strosberg JR, Al-Toubah T, Cives M. Evaluating risks and benefits of evolving systemic treatments of neuroendocrine tumors. JAMA Oncol. JAMA Oncol. 2019;5(4):489-90. Erratum in: JAMA Oncol. 2019;5(4):580.
- 6 Kaderli RM, Spanjol M, Kollár A, Bütikofer L, Gloy V, Dumont RA, et al. Therapeutic options for neuroendocrine tumors: a systematic review and network meta-analysis. JAMA Oncol. 2019;5(4):480-9. Review.
- 7 Walter T, Chardon L, Chopin-laly X, Raverot V, Caffin AG, Chayvialle JA, et al. Is the combination of chromogranin A and pancreatic polypeptide serum determinations of interest in the diagnosis and follow-up of gastro-entero-pancreatic neuroendocrine tumours? Eur J Cancer. 2012;48(12):1766-73.
- 8 Tian T, Gao J, Li N, Li Y, Lu M, Li Z, et al. Circulating chromogranin A as a marker for monitoring clinical response in advanced gastroenteropancreatic neuroendocrine tumors. PloS One. 2016;11(5):e0154679.
- 9 Luo G, Liu C, Cheng H, Jin K, Guo M, Lu Y, et al. Neutrophil lymphocyte ratio predicts survival in pancreatic neuroendocrine tumors. Oncology Letters. 2017;13(4):2454-8.
- 10 Oh D, Pyo JS, Son BK. Prognostic roles of inflammatory markers in pancreatic cancer: comparison between the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio. Gastroenterol Res Pract. 2018;2018:9745601.
- 11 Morettin PA, Hazzan S, Bussab WO. Cálculo: funções de uma e várias variáveis. 2a ed. São Paulo: Saraiva; 2010. p. 219-26.
- 12 Landers TL, Jiang ST, Peek JR. Semi-parametric PWP model robustness for log-linear increasing rates of occurrence of failures. Reliability Engineering & System Safety. 2001;73(2):145-53.
- 13 Colosimo EA, Giolo SR. Análise de sobrevivência aplicada. 1a ed. Blucher; São Paulo; 2006. p. 1-392.
-
14 R Core Team. R: A Language and Environment for Statistical Computing. (R: Foundation for Statistical Computing). 2017 [last accessed May 2019]. Nova Zelândia: R Core Team; 2017 [cited 2021 Aug 30]. Available from: https://cran.r-project.org/mirrors.html
» https://cran.r-project.org/mirrors.html - 15 Marotta V, Zatelli MC, Sciammarella C, Ambrosio MR, Bondanelli M, Colao A, et al. Chromogranin A as circulating marker for diagnosis and management of neuroendocrine neoplasms: more flaws than fame. Endocr Relat Cancer. 2018;25(1):R11-29. Review.
- 16 Pavel ME, Baudin E, Öberg KE, Hainsworth JD, Voi M, Rouyrre N, Peeters M, Gross DJ, Yao JC. Efficacy of everolimus plus octreotide LAR in patients with advanced neuroendocrine tumor and carcinoid syndrome: final overall survival from the randomized, placebo-controlled phase 3 RADIANT-2 study. Ann Oncol. 2017;28(7):1569-1575. Erratum in: Ann Oncol. 2019; 30(12):2010.
- 17 Yao JC, Pavel M, Lombard-Bohas C, Van Cutsem E, Voi M, Brandt U, et al. Everolimus for the treatment of advanced pancreatic neuroendocrine tumors: overall survival and circulating biomarkers from the randomized, phase III RADIANT-3 study. J Clin Oncol. 2016;34(32):3906-13.
- 18 Nanno Y, Toyama H, Matsumoto I, Otani K, Asari S, Goto T, et al. Baseline plasma chromogranin A levels in patients with well-differentiated neuroendocrine tumors of the pancreas: a potential predictor of postoperative recurrence. Pancreatology. 2017;17(2):291-4.
- 19 Bajetta E, Ferrari L, Martinetti A, Celio L, Procopio G, Artale S, et al. Chromogranin A, neuron specific enolase, carcinoembryonic antigen, and hydroxyindole acetic acid evaluation in patients with neuroendocrine tumors. Cancer. 1999;86(5):858-65.
- 20 Nehar D, Lombard-Bohas C, Olivieri S, Claustrat B, Chayvialle JA, Penes MC, et al. Interest of Chromogranin A for diagnosis and follow-up of endocrine tumours. Clin Endocrinol (Oxf). 2004;60(5):644-52.
- 21 Ćwikła JB, Bodei L, Kolasinska-Ćwikła A, Sankowski A, Modlin IM, Kidd M. Circulating transcript analysis (NETest) in GEP-NETs treated with somatostatin analogs defines therapy. J Clin Endocrinol Metab. 2015;100(11):E1437-45.
- 22 Pavel M, Jann H, Prasad V, Drozdov I, Modlin IM, Kidd M. NET blood transcript analysis defines the crossing of the clinical rubicon: when stable disease becomes progressive. Neuroendocrinology. 2017;104(2):170-82.
- 23 Lindholm DP, Oberg K. Biomarkers and molecular imaging in gastroenteropancreatic neuroendocrine tumors. Horm Metab Res. 2011;43(12):832-7. Review.
- 24 Modlin IM, Gustafsson BI, Moss SF, Pavel M, Tsolakis AV, Kidd M. Chromogranin A--biological function and clinical utility in neuro endocrine tumor disease. Ann Surg Oncol. 2010;17(9):2427-43. Review.
- 25 Schmielau J, Finn OJ. Activated granulocytes and granulocyte-derived hydrogen peroxide are the underlying mechanism of suppression of T-cell function in advanced cancer patients. Cancer Res. 2001;61(12):4756-60.
- 26 Schmidt H, Bastholt L, Geertsen P, Christensen IJ, Larsen S, Gehl J, et al. Elevated neutrophil and monocyte counts in peripheral blood are associated with poor survival in patients with metastatic melanoma: a prognostic model. Br J Cancer. 2005;93(3):273-8.
- 27 Jensen HK, Donskov F, Marcussen N, Nordsmark M, Lundbeck F, von der Maase H. Presence of intratumoral neutrophils is an independent prognostic factor in localized renal cell carcinoma. J Clin Oncol. 2009;27(28):4709-17.
- 28 Reid MD, Basturk O, Thirabanjasak D, Hruban RH, Klimstra DS, Bagci P, et al. Tumor-infiltrating neutrophils in pancreatic neoplasia. Mod Pathol. 2011;24(12):1612-9.
- 29 Zou J, Li Q, Kou F, Zhu Y, Lu M, Li J, et al. Prognostic value of inflammation-based markers in advanced or metastatic neuroendocrine tumours. Curr Oncol. 2019;26(1):e30:8.
- 30 Salman T, Kazaz SN, Varol U, Oflazoglu U, Unek IT, Kucukzeybek Y, et al. Prognostic value of the pretreatment neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio for patients with neuroendocrine tumors: an Izmir Oncology Group Study. Chemotherapy. 2016;61(6):281-6.
- 31 Asaoka T, Miyamoto A, Maeda S, Tsujie M, Hama N, Yamamoto K, et al. Prognostic impact of preoperative NLR and CA19-9 in pancreatic cancer. Pancreatology. 2016;16(3):434-40.
- 32 Zhou B, Deng J, Chen L, Zheng S. Preoperative neutrophil-to-lymphocyte ratio and tumor-related factors to predict lymph node metastasis in nonfunctioning pancreatic neuroendocrine tumors. Sci Rep. 2017;7(1):17506.
- 33 Gay LJ, Felding-Habermann B. Contribution of platelets to tumour metastasis. Nat Rev Cancer. 2011;11(2):123-34. Review.
- 34 Li B, Zhou P, Liu Y, Wei H, Yang X, Chen T, et al. Platelet-to-lymphocyte ratio in advanced Cancer: Review and meta-analysis. Clin Chim Acta. 2018;483:48-56. Review.
- 35 Zhou Y, Cheng S, Fathy AH, Qian H, Zhao Y. Prognostic value of platelet-to-lymphocyte ratio in pancreatic cancer: a comprehensive meta-analysis of 17 cohort studies. Onco Targets Ther. 2018;11:1899-908. Review.
- 36 Gaitanidis A, Patel D, Nilubol N, Tirosh A, Sadowski S, Kebebew E. Markers of systemic inflammatory response are prognostic factors in patients with pancreatic neuroendocrine tumors (PNETs): a prospective analysis. Ann Surg Oncol. 2018;25(1):122-30.
- 37 Carmona-Bayonas A, Jiménez-Fonseca P, Lamarca Á, Barriuso J, Castaño Á, Benavent M, et al. Prediction of progression-free survival in patients with advanced, well-differentiated, neuroendocrine tumors being treated with a somatostatin analog: the GETNE-TRASGU study. J Clin Oncol. 2019;37(28):2571-80.
Appendix 1 Laboratory tests and values in different evaluated periods
Publication Dates
-
Publication in this collection
01 June 2022 -
Date of issue
2022
History
-
Received
30 Aug 2021 -
Accepted
21 Jan 2022