A Population Pharmacodynamic Model for Lactate Dehydrogenase and Neuron Specific Enolase to Predict Tumor Progression in Small Cell Lung Cancer Patients
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The development of individualized therapies poses a major challenge in oncology. Significant hurdles to overcome include better disease monitoring and early prediction of clinical outcome. Current clinical practice consists of using Response Evaluation Criteria in Solid Tumors (RECIST) to categorize response to treatment. However, the utility of RECIST is restricted due to limitations on the frequency of measurement and its categorical rather than continuous nature. We propose a population modeling framework that relates circulating biomarkers in plasma, easily obtained from patients, to tumor progression levels assessed by imaging scans (i.e., RECIST categories). We successfully applied this framework to data regarding lactate dehydrogenase (LDH) and neuron specific enolase (NSE) concentrations in patients diagnosed with small cell lung cancer (SCLC). LDH and NSE have been proposed as independent prognostic factors for SCLC. However, their prognostic and predictive value has not been demonstrated in the context of standard clinical practice. Our model incorporates an underlying latent variable (“disease level”) representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment; these assumptions are in agreement with the known physiology of SCLC and these biomarkers. Our model predictions of unobserved disease level are strongly correlated with disease progression measured by RECIST criteria. In conclusion, the proposed framework enables prediction of treatment outcome based on circulating biomarkers and therefore can be a powerful tool to help clinicians monitor disease in SCLC.
KEY WORDSbiomarkers lung cancer mixed-effect model pharmacodynamics population model
We thank MC Martínez-Cosgaya, I Echenique-Gubía, and MJ Martínez Alvarez-Nava for the kind help in obtaining data from Clinica Universitaria Navarra and Karen Marron for the help with the manuscript. NB-B was supported by a predoctoral fellowship from Asociación de Amigos de la Universidad de Navarra and a grant from INRIA. This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115156, resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in-kind contribution. The DDMoRe project is also supported by financial contribution from academic and SME partners. This work does not necessarily represent the view of all DDMoRe partners.
- 1.Eisenhauer E, Therasse P, Bogaerts J, Schwartz L, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–47.CrossRef
- 2.Ratain MJ, Eckhardt SG. Phase II studies of modern drugs directed against new targets: if you are fazed, too, then resist RECIST. J Clin Oncol. 2004;22(22):4442–5.CrossRef
- 3.Villaruz LC, Socinski MA. The clinical viewpoint: definitions, limitations of RECIST, practical considerations of measurement. Clin Cancer Res. 2013;19(10):2629–36.CrossRef
- 4.Bender B, Schindler E, Friberg L. Population pharmacokinetic pharmacodynamic modelling in oncology: a tool for predicting clinical response. Br J Clin Pharmacol. 2013;18(2):e86.
- 5.Danhof M, Alvan G, Dahl SG, Kuhlmann J, Paintaud G. Mechanism-based pharmacokinetic–pharmacodynamic modeling—a new classification of biomarkers. Pharm Res. 2005;22(9):1432–7.CrossRef
- 6.Romero E, de Mendizabal NV, Cendrós J, Peraire C, Bascompta E, Obach R, et al. Pharmacokinetic/pharmacodynamic model of the testosterone effects of triptorelin administered in sustained release formulations in patients with prostate cancer. J Pharmacol Exp Ther. 2012;342(3):788–98.CrossRef
- 7.Kogan Y, Halevi–Tobias K, Elishmereni M, Vuk-Pavlović S, Agur Z. Reconsidering the paradigm of cancer immunotherapy by computationally aided real-time personalization. Cancer Res. 2012;72(9):2218–27.CrossRef
- 8.Stovold R, Blackhall F, Meredith S, Hou J, Dive C, White A. Biomarkers for small cell lung cancer: neuroendocrine, epithelial and circulating tumour cells. Lung Cancer. 2012;76(3):263–8.CrossRef
- 9.Yip D, Harper PG. Predictive and prognostic factors in small cell lung cancer: current status. Lung Cancer. 2000;28(3):173–85.CrossRef
- 10.Wójcik E, Kulpa J, Sas-Korczyńska B, Korzeniowski S, Jakubowicz J. ProGRP and NSE in therapy monitoring in patients with small cell lung cancer. Anticancer Res. 2008;28(5B):3027–33.
- 11.Holdenrieder S, von Pawel J, Dankelmann E, Duell T, Faderl B, Markus A, et al. Nucleosomes, ProGRP, NSE, CYFRA 21–1, and CEA in monitoring first-line chemotherapy of small cell lung cancer. Clin Cancer Res. 2008;14(23):7813–21.CrossRef
- 12.Lippert MC, Javadpour N. Lactic dehydrogenase in the monitoring and prognosis of testicular cancer. Cancer. 1981;48(10):2274–8.CrossRef
- 13.Scartozzi M, Faloppi L, Bianconi M, Giampieri R, Maccaroni E, Bittoni A, et al. The role of LDH serum levels in predicting global outcome in HCC patients undergoing TACE: implications for clinical management. PLoS One. 2012;7(3):e32653.CrossRef
- 14.Zelen M. Keynote address on biostatistics and data retrieval. Cancer Chemother Rep. 1973;4(2):31–42.
- 15.Lindstrom MJ, Bates DM. Nonlinear mixed effects models for repeated measures data. Biometrics. 1990;46:673–87.CrossRef
- 16.Bauer R. NONMEM users guide introduction to NONMEM 7.2. 0. ICON Development Solutions Ellicott City, MD 2011.
- 17.Lavielle M. MONOLIX (MOdèles NOn LInéaires à effets miXtes). Orsay, France: MONOLIX group; 2005.
- 18.Jusko WJ, Ko HC, Ebling WF. Convergence of direct and indirect pharmacodynamic response models. J Pharmacokinet Pharmacodyn. 1995;23(1):5–8.CrossRef
- 19.Jacqmin P, Snoeck E, Van Schaick E, Gieschke R, Pillai P, Steimer J, et al. Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K–PD model. J Pharmacokinet Pharmacodyn. 2007;34(1):57–85.CrossRef
- 20.Lindbom L, Pihlgren P, Jonsson N. PsN-Toolkit—a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed. 2005;79(3):241–57.CrossRef
- 21.Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011;13(2):143–51.CrossRef
- 22.Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39(4):561–77.
- 23.Go RS, Adjei AA. Review of the comparative pharmacology and clinical activity of cisplatin and carboplatin. J Clin Oncol. 1999;17(1):409–422.
- 24.Savic RM, Karlsson MO. Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions. AAPS J. 2009;11(3):558–69.CrossRef
- 25.Lesko LJ, Atkinson Jr A. Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies 1. Annu Rev Pharmacol Toxicol. 2001;41(1):347–66.CrossRef
- 26.Almufti R, Wilbaux M, Oza A, Henin E, Freyer G, Tod M, et al. A critical review of the analytical approaches for circulating tumor biomarker kinetics during treatment. Ann Oncol. 2014;25(1):41–56.CrossRef
- 27.Fizazi K, Cojean I, Pignon J, Rixe O, Gatineau M, Hadef S, et al. Normal serum neuron specific enolase (NSE) value after the first cycle of chemotherapy. Cancer. 1998;82(6):1049–55.CrossRef
- 28.Jørgensen L, Osterlind K, Hansen H, Cooper E. The prognostic influence of serum neuron specific enolase in small cell lung cancer. Br J Cancer. 1988;58(6):805.CrossRef
- 29.Jørgensen L, Osterlind K, Genolla J, Gomm S, Hernandez J, Johnson P, et al. Serum neuron-specific enolase (S-NSE) and the prognosis in small-cell lung cancer (SCLC): a combined multivariable analysis on data from nine centres. Br J Cancer. 1996;74(3):463.CrossRef
- 30.Lassen U, Østerlind K, Hirsch F, Bergman B, Dombernowsky P, Hansen H. Early death during chemotherapy in patients with small-cell lung cancer: derivation of a prognostic index for toxic death and progression. Br J Cancer. 1999;79(3/4):515.CrossRef
- 31.Østerlind K, Andersen PK. Prognostic factors in small cell lung cancer: multivariate model based on 778 patients treated with chemotherapy with or without irradiation. Cancer Res. 1986;46(8):4189.
- 32.Osterlind K. LDH or NSE or LDH and NSE as pretreatment prognostic factors in small cell lung cancer? A commentary. Lung Cancer. 2000;30(1):51–3.CrossRef
- 33.Pinson P, Joos G, Watripont P, Brusselle G, Pauwels R. Serum neuron-specific enolase as a tumor marker in the diagnosis and follow-up of small-cell lung cancer. Respiration. 2009;64(1):102–7.CrossRef
- 34.Quoix E, Purohit A, Faller-Beau M, Moreau L, Oster J, Pauli G. Comparative prognostic value of lactate dehydrogenase and neuron-specific enolase in small-cell lung cancer patients treated with platinum-based chemotherapy. Lung Cancer. 2000;30(2):127–34.CrossRef
- 35.You B, Tranchand B, Girard P, Falandry C, Ribba B, Chabaud S, et al. Etoposide pharmacokinetics and survival in patients with small cell lung cancer: a multicentre study. Lung Cancer. 2008;62(2):261–72.CrossRef
- 36.Molina R, Filella X, Auge JM. ProGRP: a new biomarker for small cell lung cancer. Clin Biochem. 2004;37(7):505–11.CrossRef
- 37.Warburg O. On the origin of cancer cells. Science. 1956;123(3191):309–14.CrossRef
- 38.Gambhir SS. Molecular imaging of cancer with positron emission tomography. Nat Rev Cancer. 2002;2(9):683–93.CrossRef
- 39.Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324(5930):1029–33.CrossRef
- 40.Zhuang L, Scolyer RA, Murali R, McCarthy SW, Zhang XD, Thompson JF, et al. Lactate dehydrogenase 5 expression in melanoma increases with disease progression and is associated with expression of Bcl-XL and Mcl-1, but not Bcl-2 proteins. Mod Pathol. 2010;23(1):45–53.CrossRef
- 41.Scatena R, Bottoni P, Pontoglio A, Mastrototaro L, Giardina B. Glycolytic enzyme inhibitors in cancer treatment. 2008.
- 42.Zhou W, Capello M, Fredolini C, Racanicchi L, Piemonti L, Liotta LA, et al. Proteomic analysis reveals Warburg effect and anomalous metabolism of glutamine in pancreatic cancer cells. J Proteome Res. 2011;11(2):554–63.
- 43.Giaccone G. Clinical perspectives on platinum resistance. Drugs. 2000;59(4):9–17.CrossRef
- 44.Anderlini P, Przepiorka D, Champlin R, Korbling M. Biologic and clinical effects of granulocyte colony-stimulating factor in normal individuals. Blood. 1996;88(8):2819–25.
- 45.Zohlnhöfer D, Ott I, Mehilli J, Schömig K, Michalk F, Ibrahim T, et al. Stem cell mobilization by granulocyte colony-stimulating factor in patients with acute myocardial infarction. JAMA:J Am Med Assoc. 2006;295(9):1003–10.CrossRef
- 46.Lindemann A, Herrmann F, Oster W, Haffner G, Meyenburg W, Souza LM, et al. Hematologic effects of recombinant human granulocyte colony-stimulating factor in patients with malignancy. Blood. 1989;74(8):2644–51.
- 47.Anderlini P, Przepiorka D, Seong D, Miller P, Sundberg J, Lichtiger B, et al. Clinical toxicity and laboratory effects of granulocyte‐colony‐stimulating factor (filgrastim) mobilization and blood stem cell apheresis from normal donors, and analysis of charges for the procedures. Transfusion. 1996;36(7):590–5.CrossRef