DOI QR코드

DOI QR Code

Time-Dependent Effects of Prognostic Factors in Advanced Gastric Cancer Patients

  • Kwon, Jin-Ok (Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences) ;
  • Jin, Sung-Ho (Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences) ;
  • Min, Jae-Seok (Department of Surgery, Dongnam Institute of Radiological and Medical Sciences) ;
  • Kim, Min-Suk (Department of Pathology, Dongnam Institute of Radiological and Medical Sciences) ;
  • Lee, Hae-Won (Department of Thoracic Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences) ;
  • Park, Sunhoo (Department of Pathology, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences) ;
  • Yu, Hang-Jong (Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences) ;
  • Bang, Ho-Yoon (Department of Surgery, Konkuk University School of Medicine) ;
  • Lee, Jong-Inn (Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences)
  • Received : 2015.10.15
  • Accepted : 2015.11.02
  • Published : 2015.12.31

Abstract

Purpose: This study aimed to identify time-dependent prognostic factors and demonstrate the time-dependent effects of important prognostic factors in patients with advanced gastric cancer (AGC). Materials and Methods: We retrospectively evaluated 3,653 patients with AGC who underwent curative standard gastrectomy between 1991 and 2005 at the Korea Cancer Center Hospital. Multivariate survival analysis with Cox proportional hazards regression was used in the analysis. A non-proportionality test based on the Schoenfeld residuals (also known as partial residuals) was performed, and scaled Schoenfeld residuals were plotted over time for each covariate. Results: The multivariate analysis revealed that sex, depth of invasion, metastatic lymph node (LN) ratio, tumor size, and chemotherapy were time-dependent covariates violating the proportional hazards assumption. The prognostic effects (i.e., log of hazard ratio [LHR]) of the time-dependent covariates changed over time during follow-up, and the effects generally diminished with low slope (e.g., depth of invasion and tumor size), with gentle slope (e.g., metastatic LN ratio), or with steep slope (e.g., chemotherapy). Meanwhile, the LHR functions of some covariates (e.g., sex) crossed the zero reference line from positive (i.e., bad prognosis) to negative (i.e., good prognosis). Conclusions: The time-dependent effects of the prognostic factors of AGC are clearly demonstrated in this study. We can suggest that time-dependent effects are not an uncommon phenomenon among prognostic factors of AGC.

Keywords

References

  1. Sobin LH, Gospodarowicz MK, Wittekind C, eds. TNM classification of malignant tumours. New York: John Wiley & Sons, 2011.
  2. Gospodarowicz M, O'Sullivan B. Prognostic factors in cancer. Semin Surg Oncol 2003;21:13-18. https://doi.org/10.1002/ssu.10016
  3. Cox DR. Regression models and life-tables. J R Stat Soc Series B Stat Methodol 1972;34:187-220.
  4. Bradburn MJ, Clark TG, Love SB, Altman DG. Survival analysis part II: multivariate data analysis: an introduction to concepts and methods. Br J Cancer 2003;89:431-436. https://doi.org/10.1038/sj.bjc.6601119
  5. Ahmed FE, Vos PW, Holbert D. Modeling survival in colon cancer: a methodological review. Mol Cancer 2007;6:15. https://doi.org/10.1186/1476-4598-6-15
  6. Hilsenbeck SG, Ravdin PM, de Moor CA, Chamness GC, Osborne CK, Clark GM. Time-dependence of hazard ratios for prognostic factors in primary breast cancer. In: Gasparini G, ed. Prognostic variables in node-negative and node-positive breast cancer. Boston, MA: Springer, 1998:317-327.
  7. Bellera CA, MacGrogan G, Debled M, de Lara CT, Brouste V, Mathoulin-Pelissier S. Variables with time-varying effects and the Cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer. BMC Med Res Methodol 2010;10:20. https://doi.org/10.1186/1471-2288-10-20
  8. Warwick J, Tabar L, Vitak B, Duffy SW. Time-dependent effects on survival in breast carcinoma: results of 20 years of followup from the Swedish two-county study. Cancer 2004;100:1331-1336. https://doi.org/10.1002/cncr.20140
  9. Kattan MW, Karpeh MS, Mazumdar M, Brennan MF. Postoperative nomogram for disease-specific survival after an R0 resection for gastric carcinoma. J Clin Oncol 2003;21:3647-3650. https://doi.org/10.1200/JCO.2003.01.240
  10. Eom BW, Ryu KW, Nam BH, Park Y, Lee HJ, Kim MC, et al. Survival nomogram for curatively resected Korean gastric cancer patients: multicenter retrospective analysis with external validation. PLoS One 2015;10:e0119671. https://doi.org/10.1371/journal.pone.0119671
  11. Park JI, Jin SH, Bang HY, Paik NS, Moon NM, Lee JI. Survival rates after operation for gastric cancer: fifteen-year experience at a Korea Cancer Center Hospital. J Korean Gastric Cancer Assoc 2008;8:9-19. https://doi.org/10.5230/jkgca.2008.8.1.9
  12. Sakuramoto S, Sasako M, Yamaguchi T, Kinoshita T, Fujii M, Nashimoto A, et al; ACTS-GC Group. Adjuvant chemotherapy for gastric cancer with S-1, an oral fluoropyrimidine. N Engl J Med 2007;357:1810-1820. https://doi.org/10.1056/NEJMoa072252
  13. Songun I, Putter H, Kranenbarg EM, Sasako M, van de Velde CJ. Surgical treatment of gastric cancer: 15-year follow-up results of the randomised nationwide Dutch D1D2 trial. Lancet Oncol 2010;11:439-449. https://doi.org/10.1016/S1470-2045(10)70070-X
  14. Tsujimoto H, Ichikura T, Ono S, Sugasawa H, Hiraki S, Sakamoto N, et al. Impact of postoperative infection on long-term survival after potentially curative resection for gastric cancer. Ann Surg Oncol 2009;16:311-318. https://doi.org/10.1245/s10434-008-0249-8
  15. Pietrantonio F, De Braud F, Da Prat V, Perrone F, Pierotti MA, Gariboldi M, et al. A review on biomarkers for prediction of treatment outcome in gastric cancer. Anticancer Res 2013;33:1257-1266.
  16. Takeuchi H, Kakeji Y, Maehara Y. Time-dependent relevance of prognostic factors in patients with gastric cancer. Hepatogastroenterology 2008;55:779-781.
  17. Japanese Gastric Cancer Association. Japanese classification of gastric carcinoma: 2nd English Edition. Gastric Cancer 1998;1:10-24. https://doi.org/10.1007/PL00011681
  18. Sobin LH, Wittekind C, eds. TNM Classification of Malignant Tumors. 6th ed. New York: Wiley-Liss, 2002.
  19. Hermans J, Bonenkamp JJ, Boon MC, Bunt AM, Ohyama S, Sasako M, et al. Adjuvant therapy after curative resection for gastric cancer: meta-analysis of randomized trials. J Clin Oncol 1993;11:1441-1447. https://doi.org/10.1200/JCO.1993.11.8.1441
  20. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361-387. https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
  21. Miller R, Siegmund D. Maximally selected chi square statistics. Biometrics 1982;38:1011-1016. https://doi.org/10.2307/2529881
  22. R Development CORE TEAM. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2010.
  23. Platt RW, Joseph KS, Ananth CV, Grondines J, Abrahamowicz M, Kramer MS. A proportional hazards model with timedependent covariates and time-varying effects for analysis of fetal and infant death. Am J Epidemiol 2004;160:199-206. https://doi.org/10.1093/aje/kwh201
  24. Gilchrist KW, Gray R, Fowble B, Tormey DC, Taylor SG 4th. Tumor necrosis is a prognostic predictor for early recurrence and death in lymph node-positive breast cancer: a 10-year follow-up study of 728 Eastern Cooperative Oncology Group patients. J Clin Oncol 1993;11:1929-1935. https://doi.org/10.1200/JCO.1993.11.10.1929
  25. Bolard P, Quantin C, Esteve J, Faivre J, Abrahamowicz M. Modelling time-dependent hazard ratios in relative survival: application to colon cancer. J Clin Epidemiol 2001;54:986-996. https://doi.org/10.1016/S0895-4356(01)00363-8
  26. Berger U, Schafer J, Ulm K. Dynamic Cox modelling based on fractional polynomials: time-variations in gastric cancer prognosis. Stat Med 2003;22:1163-1180. https://doi.org/10.1002/sim.1411
  27. Ng'andu NH. An empirical comparison of statistical tests for assessing the proportional hazards assumption of Cox's model. Stat Med 1997;16:611-626. https://doi.org/10.1002/(SICI)1097-0258(19970330)16:6<611::AID-SIM437>3.0.CO;2-T
  28. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 1994;81:515-526. https://doi.org/10.1093/biomet/81.3.515
  29. Schoenfeld D. Chi-squared goodness-of-fit tests for the proportional hazards regression model. Biometrika 1980;67:145-153. https://doi.org/10.1093/biomet/67.1.145