Early prediction of disease progression in small cell lung cancer: Toward model-based personalized medicine in oncology

Cancer Research

Volumn 75, Issue 12, 2015, Pages 2416-2425

  Buil Bruna, Núria ^a   Sahota, Tarjinder ^b   López Picazo, José María ^a   Moreno Jiménez, Marta ^a   Martín Algarra, Salvador ^a   Ribba, Benjamin ^c   Trocóniz, Iñaki F ^a  

^a IdiSNA Navarra Institute for Health Research   (Spain)

^b GLAXOSMITHKLINE   (United Kingdom)

^c UNIVERSITÉ DE LYON   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; CARBOPLATIN; CISPLATIN; ETOPOSIDE; LACTATE DEHYDROGENASE; TUMOR MARKER;

ADULT; AGED; ARTICLE; CANCER GROWTH; CANCER RADIOTHERAPY; CANCER RISK; CLINICAL ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; FEMALE; FOLLOW UP; HUMAN; MALE; OUTCOME ASSESSMENT; OVERALL SURVIVAL; PATIENT MONITORING; PERSONALIZED MEDICINE; POPULATION MODEL; PREDICTION; PRIORITY JOURNAL; PROSPECTIVE STUDY; RETROSPECTIVE STUDY; SMALL CELL LUNG CANCER; BIOLOGICAL MODEL; DISEASE COURSE; LUNG NEOPLASMS; MIDDLE AGED; PATHOLOGY; PREDICTIVE VALUE; PROCEDURES; SMALL CELL LUNG CARCINOMA; STATISTICAL MODEL;

AGED; DISEASE PROGRESSION; FEMALE; HUMANS; INDIVIDUALIZED MEDICINE; LUNG NEOPLASMS; MALE; MIDDLE AGED; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PREDICTIVE VALUE OF TESTS; SMALL CELL LUNG CARCINOMA; TUMOR MARKERS, BIOLOGICAL;

EID: 84941738723     PISSN: 00085472     EISSN: 15387445     Source Type: Journal    
DOI: 10.1158/0008-5472.CAN-14-2584     Document Type: Article

References (48)

1. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh J, Comber H, et al. Cancer incidence and mortality patterns in europe: estimates for 40 countries in 2012. Eur J Cancer 2013;49:1374-403.
2. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015;136:E359-86.
3. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin 2013;63:11-30.
4. Govindan R, Page N, Morgensztern D, Read W, Tierney R, Vlahiotis A, et al. Changing epidemiology of small-cell lung cancer in the united states over the last 30 years: analysis of the surveillance, epidemiologic, and end results database. J Clin Oncol 2006;24:4539-44.
5. Janssen-Heijnen ML, Coebergh JW. The changing epidemiology of lung cancer in Europe. Lung Cancer 2003;41:245-58.
6. Lally BE, Urbanic JJ, Blackstock AW, Miller AA, Perry MC. Small cell lung cancer: have we made any progress over the last 25 years? Oncologist 2007;12:1096-104.
7. Small cell lung cancer treatment [Internet]. [updated 2014 Aug. 6; cited 2014 Dec. 11]. Available from: http://www.cancer.gov/cancertopics/pdq/treatment/small-cell-lung/healthprofessional/page6.
8. Sgambato A, Casaluce F, Maione P, Rossi A, Sacco PC, Panzone F, et al. Medical treatment of small cell lung cancer: state of the art and new development. Expert Opin Pharmacother 2013;14:2019-31.
9. Fruh M, De Ruysscher D, Popat S, Crino L, Peters S, Felip E, et al. Small-cell lung cancer (SCLC): ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 2013;24(Suppl 6): vi99-105.
10. Paesmans M, Sculier JP, Lecomte J, Thiriaux J, Libert P, Sergysels R, et al. Prognostic factors for patients with small cell lung carcinoma: analysis of a series of 763 patients included in 4 consecutive prospective trials with a minimum follow-up of 5 years. Cancer 2000;89:523-33.
11. Zelen M. Keynote address on biostatistics and data retrieval. Cancer Chemother Rep 1973;4:31-42.
12. Spiegelman D, Maurer LH, Ware JH, Perry MC, Chahinian AP, Comis R, et al. Prognostic factors in small-cell carcinoma of the lung: an analysis of 1, 521 patients. J Clin Oncol 1989;7:344-54.
13. Gronowitz JS, Bergström R, Noù E, Påhlman S, Brodin O, Nilsson S, et al. Clinical and serologic markers of stage and prognosis in small cell lung cancer. a multivariate analysis. Cancer 1990;66:722-32.
14. Siu LL, Shepherd FA, Murray N, Feld R, Pater J, Zee B. Influence of age on the treatment of limited-stage small-cell lung cancer. J Clin Oncol 1996;14:821-8.
15. 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;45:228-47.
16. National comprehensive cancer network. Practice guidelines for small-cell lung cancer. Rockledge, PA: National Comprehensive Cancer Network; 2000.
17. Sugiyama T, Hirose T, Hosaka T, Kusumoto S, Nakashima M, Yamaoka T, et al. Effectiveness of intensive follow-up after response in patients with small cell lung cancer. Lung Cancer 2008;59:255-61.
18. Diamandis EP. Cancer biomarkers: can we turn recent failures into success? J Natl Cancer Inst 2010;102:1462-7.
19. Cerny T, Anderson H, Bramwell V, Thatcher N, Blair V. Pretreatment prognostic factors and scoring system in 407 small-cell lung cancer patients. Int J Cancer 1987;39:146-9.
20. Molina R, Filella X, Auge JM. ProGRP: a new biomarker for small cell lung cancer. Clin Biochem 2004;37:505-11.
21. 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;14:7813-21.
22. 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;76:263-8.
23. Harmsma M, Schutte B, Ramaekers F. Serum markers in small cell lung cancer: opportunities for improvement. Biochim Biophys Acta 2013;1836:255-72.
24. Agur Z, Elishmereni M, Kheifetz Y. Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration. Wiley Interdiscip Rev Syst Biol Med 2014;6:239-53.
25. 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;25:41-56.
26. Keizer RJ, Schellens JH, Beijnen J, Huitema AD. Pharmacodynamic biomarkers in model-based drug development in oncology. Curr Clin Pharmacol 2011;6:30-40.
27. Buil-Bruna N, López-Picazo J, Moreno-Jiménez M, Martín-Algarra S, Ribba B, Trocóniz IF. A population pharmacodynamic model for lactate dehydrogenase and neuron specific enolase to predict tumor progression in small cell lung cancer patients. AAPS J 2014;16:609-19.
28. Bauer R. NONMEM users guide introduction to NONMEM 7.2. 0. ICON Development Solutions Ellicott City, MD; 2011.
29. Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J 2011;13:143-51.
30. 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;79:241-57.
31. Chan IS, Ginsburg GS. Personalized medicine: progress and promise. Annu Rev Genomics Hum Genet 2011;12:217-44.
32. Crews KR, Hicks JK, Pui C, Relling MV, Evans WE. Pharmacogenomics and individualized medicine: translating science into practice. Clin Pharmacol Ther 2012;92:467-75.
33. Evans WE, Relling MV. Moving towards individualized medicine with pharmacogenomics. Nature 2004;429:464-8.
34. Kalia M. Personalized oncology: recent advances and future challenges. Metab Clin Exp 2013;62:S11-4.
35. Duffy MJ, O'Donovan N, Crown J. Use of molecular markers for predicting therapy response in cancer patients. Cancer Treat Rev 2011;37:151-9.
36. Van Meerbeeck JP, Fennell DA, De Ruysscher DK. Small-cell lung cancer. Lancet 2011;378:1741-55.
37. Kogan Y, Halevi-Tobias K, Elishmereni M, Vuk-Pavlović S, Agur Z. Reconsidering the paradigmof cancer immunotherapy by computationally aided real-time personalization. Cancer Res 2012;72:2218-27.
38. Kronik N, Kogan Y, Elishmereni M, Halevi-Tobias K, Vuk-Pavlović S, Agur Z. Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models. PLoS ONE 2010;5:e15482.
39. Wilbaux M, Hénin E, Oza A, Colomban O, Pujade-Lauraine E, Freyer G, et al. Dynamic modeling in ovarian cancer: an original approach linking early changes inmodeled longitudinal CA-125 kinetics and survival to help decisions in early drug development. Gynecol Oncol 2014;133:460-6.
40. You B, Harvey R, Henin E, Mitchell H, Golfier F, Savage P, et al. Early prediction of treatment resistance in low-risk gestational trophoblastic neoplasia using population kinetic modelling of hCG measurements. Br J Cancer 2013;108:1810-6.
41. Hansson E, Ma G, Amantea M, French J, Milligan P, Friberg L, et al. PKPD modeling of predictors for adverse effects and overall survival in sunitinibtreated patients with GIST. CPT Pharmacometrics Syst Pharmacol 2013;2:e85.
42. Hansson E, Amantea M, Westwood P, Milligan P, Houk B, French J, et al. PKPD modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as predictors of tumor dynamics and overall survival following sunitinib treatment in GIST. CPT Pharmacometrics Syst Pharmacol 2013;2:e84.
43. Davidian M, Giltinan DM. Nonlinear models for repeated measurement data. New York: Chapman & Hall; 1995.
44. Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian data analysis. 2nd ed. New York: Chapman & Hall; 2004.
45. Feinstein AR, Sosin DM, Wells CK. The Will Rogers phenomenon: stage migration and new diagnostic techniques as a source of misleading statistics for survival in cancer. N Engl J Med 1985;312:1604-8.
46. Kalemkerian GP. Staging and imaging of small cell lung cancer. Cancer Imaging 2012;11:253-8.
47. Ribba B, Holford N, Magni P, Trocóniz I, Gueorguieva I, Girard P, et al. A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis. CPT Pharmacometrics Syst Pharmacol 2014;3:e113.
48. Fedorov VV. Theory of optimal experiments. New York: Academic Press; 1972.