To our knowledge, this study added evidence to explore the predictors of survival time in patients with non-Hodgkin T/NK lymphoma and presenting their varied impact over different percentile levels using a quantile regression model rather than linear or logistic regression. Our main findings were that the IPI has a consistent negative association with OS (IPI ≥2) and that higher IPI score showed more impact on the survival time. The quantile regression also showed that the IPI score is a highly robust prognostic indicator after adjusting for other factors and statistically significant at 3 quartiles of the curve. In addition, serum ESR is also comparatively stable at the middle 2 quartiles of the curve as a prognostic indicator. The same trend was observed in the coefficients of platelet count, serum Ca+ level, and serum β2MG level, while HSCT and clinical stage without clinical symptoms impacted the survival time only in the middle section of the curve; the lower and upper concentration levels had no significant impact on the survival time. These findings are interesting and are worthy of further observation. Most of the prognostic indicators were validated with Federico et al  and Xu et al, but we presented with a more specified way in term of concrete section of the distribution of the curve.
There is no consensus on the efficacy of HSCT in the treatment of non-Hodgkin T/NK lymphoma. Some studies have focused on small populations characterized by mixed histology, varying disease status at transplantation, and treatments with diverse regimens. Other studies have excluded patients with chemo-refractory or poor-risk disease who were not eligible for HSCT, which may incur selection bias. Studies[27–29] have also shown that HSCT could improve the prognosis of peripheral T cell lymphoma. However, the results of the present study did not show that HSCT could increase the OS rate in the multivariable analysis. These results were consistent with those of Tse,[30,31] showing that allogeneic HSCT should be reserved for patients who are at high risk of relapse; moreover, the role of allogeneic HSCT in NK/T cell lymphoma must be strictly evaluated. These findings might be true or might be due to the limited number of selected research participants, the aggressive nature of these diseases or the frequency at which the disease relapses. However, HSCT might have an impact on OS among the average patients at the central quantile.
The findings of the present study on the correlation between serum ESR and OS are consistent with those of Bien et al ; moreover, these results may be attributed to quantile regression. The correlation of ESR with OS does not present a linear prediction, central 2 quartiles have good predicting roles in OS. In addition, platelet count was an independent prognostic indicator in patients with diffuse large B cell lymphoma, which was consistent with the others’ findings,[26,32] but the impact of platelet count was more significant at extremely low levels according to the percentage curve.
The strength of this study included: We used quantile regression and revealed some interesting findings on the impact of different indicators on the prediction of OS over a quartile distribution. These effects might be underestimated by least squares regression. There were paucity of researches focus on predictors of OS in B cell lymphoma patients, while few studies conducted among non-Hodgkin T/NK cell lymphoma patients. As the largest study center for HLH in China, our hospital admits quite high proportion of HLH patients. In the present study, we included 30 HLH patients. Most HLH patients have really poor prognoses; therefore, to control for the potential confounding factor of HLH, we stratified patients based on HLH status for a clearer understanding of the predictive values of clinical manifestations/indicators on OS than other studies that might mix HLH patients with other participants.
This study had also some limitations. As a retrospective study, we collected data on OS time and prognostic factors but no other information on survival, such as progress-free survival (PFS) or disease-free survival (DFS), in a single institution. There might be bias during patient selection, data collection and even data analysis. These findings may not be generalizable throughout China. Most patients who sought treatment in Beijing, a comparatively mega-metropolitan city, had advanced disease stages or better economic conditions. Discharged patients who did not live in Beijing presented challenges in the follow-up. In addition, patients with different disease types or stages and received heterogeneous treatment undoubtedly had different clinical outcomes and treatment responses; thus, the serum indicators may vary. Therefore, we reported whether the lower bound, middle bound or upper bound of the indicators had greater impact on survival. Furthermore, considering the number of systemic lymphoma patients, the impact of prognostic factors may not be generalizable. However, 183 patients could shed some light on the prediction of prognostic outcomes. We did not consider the impact of the histological subtype on autologous stem cell transplantation (ASCT), and as the DFS or PFS time is much shorter for patients with diseases such as NK/T cell lymphoma, we selected the OS rate as the only indicator to compare with more conventional risk factors, such as the IPI.
In summary, we used an innovative statistical method, quantile regression, to explore the prognostic predictors of OS among systemic lymphoma patients. The IPI score was determined to be a robust indicator of prognosis at 3 quartiles, and serum ESR is stable at the middle 2 quartiles section when adjusted for HLH. In addition, platelet count, and serum β2MG level could predict OS among non-Hodgkin's T/NK cell lymphoma patients at different percentage levels.
This work was supported by grants from the National Natural Science Foundation of China (No. 81673232), the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding (No. ZYLX201702), the Beijing Municipal Science and Technology Plan of Capital Characteristics Project (No. Z151100004015172), and the Capital Health Research and Medical Development Foundation (No. 2016-2-2027).
1. Dhawale TM, Shustov AR. Autologous and allogeneic hematopoietic cell transplantation in peripheral T/NK-cell Lymphomas: a histology-specific review. Hematol Oncol Clin North Am
2017; 31:335–357. doi: 10.1016/j.hoc.2016.11.003.
2. Xu B, Liu P. No survival improvement for patients with angioimmunoblastic T-cell lymphoma over the past two decades: a population-based study of 1207 cases. PloS One
2014; 9:e92585doi: 10.1371/journal.pone.0092585.
3. Kao HW, Lin TL, Shih LY, Dunn P, Kuo MC, Hung YS, et al. Clinical features, outcome and prognostic factors of 87 patients with angioimmunoblastic T cell lymphoma in Taiwan. Int J Hematol
2016; 104:256–265. doi: 10.1007/s12185-016-2010-6.
4. Nakajima Y, Tomita N, Watanabe R, Ishiyama Y, Yamamoto E, Ishibashi D, et al. Prognostic significance of serum beta-2 microglobulin level in Hodgkin lymphoma treated with ABVD-based therapy. Med Oncol
2014; 31:185doi: 10.1007/s12032-014-0185-3.
5. Itoh K, Kinoshita T, Watanabe T, Yoshimura K, Okamoto R, Chou T, et al. Prognostic analysis and a new risk model for Hodgkin lymphoma in Japan. Int J Hematol
2010; 91:446–455. doi: 10.1007/s12185-010-0533-9.
6. Kanemasa Y, Shimoyama T, Sasaki Y, Tamura M, Sawada T, Omuro Y, et al. Beta-2 microglobulin as a significant prognostic factor and a new risk model for patients with diffuse large B-cell lymphoma. Hematol Oncol
2017; 35:440–446. doi: 10.1002/hon.2312.
7. Yoo C, Yoon DH, Suh C. Serum beta-2 microglobulin in malignant lymphomas: an old but powerful prognostic factor. Blood Res
2014; 49:148–153. doi: 10.5045/br.2014.49.3.148.
8. Yoo C, Yoon DH, Jo JC, Yoon S, Kim S, Lee BJ, et al. Prognostic impact of beta-2 microglobulin in patients with extranodal natural killer/T cell lymphoma. Ann Haematol
2014; 93:995–1000. doi: 10.1007/s00277-014-2015-2.
9. Chihara D, Oki Y, Ine S, Yamamoto K, Kato H, Taji H, et al. Analysis of prognostic factors in peripheral T-cell lymphoma: prognostic value of serum albumin and mediastinal lymphadenopathy. Leuk Lymphoma
2009; 50:1999–2004. doi: 10.3109/10428190903318311.
10. Xie W, Hu K, Xu F, Zhou D, Huang W, He J, et al. Significance of clinical factors as prognostic indicators for patients with peripheral T-cell non-Hodgkin lymphoma: A retrospective analysis of 252 cases. Mol Clin Oncol
2013; 1:911–917. doi: 10.3892/mco.2013.146.
11. Andjelic B, Todorovic-Balint M, Antic D, Bila J, Djurasinovic V, Mihaljevic B. Follicular lymphoma patients with a high FLIPI score and a high tumor burden: a risk stratification model. Vojnosanit Pregled
2015; 72:26–32. doi: 10.2298/VSP1501026A.
12. Katsuya H, Shimokawa M, Ishitsuka K, Kawai K, Amano M, Utsunomiya A, et al. Prognostic index for chronic- and smoldering-type adult T-cell leukemia-lymphoma. Blood
2017; 130:39–47. doi: 10.1182/blood-2017-01-757542.
13. Ochi Y, Kazuma Y, Hiramoto N, Ono Y, Yoshioka S, Yonetani N, et al. Utility of a simple prognostic stratification based on platelet counts and serum albumin levels in elderly patients with diffuse large B cell lymphoma. Ann Hemat
2017; 96:1–8. doi: 10.1007/s00277-016-2819-3.
14. Inukai T, Hirose K, Inaba T, Kurosawa H, Hama A, Inada H, et al. Hypercalcemia in childhood acute lymphoblastic leukemia: frequent implication of parathyroid hormone-related peptide and E2A-HLF from translocation 17;19. Leukemia
2007; 21:288–296. doi: 10.1038/sj.leu.2404496.
15. Bien E, Balcerska A. Serum soluble interleukin-2 receptor, beta2-microglobulin, lactate dehydrogenase and erythrocyte sedimentation rate in children with Hodgkin's lymphoma. Scand J Immunol
2009; 70:490–500. doi: 10.1111/j.1365-3083.2009.02313.x.
16. Li YJ, Yi PY, Li JW, Liu XL, Tang T, Zhang PY, et al. Prognostic role of ABO blood type in patients with extranodal natural killer/T cell lymphoma, nasal type: a triple-center study. Chin J Cancer
2017; 36:62doi: 10.1186/s40880-017-0229-0.
17. Moon SH, Lee AY, Kim WS, Kim SJ, Cho YS, Choe YS, et al. Value of interim FDG PET/CT for predicting outcome of patients with angioimmunoblastic T-cell lymphoma. Leuk Lymphoma
2017; 58:1341–1348. doi: 10.1080/10428194.2016.1236380.
18. Cattaneo C, Oberti M, Skert C, Passi A, Farina M, Re A, et al. Adult onset hemophagocytic lymphohistiocytosis prognosis is affected by underlying disease and coexisting viral infection: analysis of a single institution series of 35 patients. Hematol Oncol
2017; 35:828–834. doi: 10.1002/hon.2314.
19. Gebregziabher M, Lynch CP, Mueller M, Gilbert GE, Echols C, Zhao Y, et al. Using quantile regression to investigate racial disparities in medication non-adherence. BMC Med Res Methodol
2011; 11:88doi: 10.1186/1471-2288-11-88.
20. Despa S. Quantile regression. Cornell University, Cornell Statistical Consulting, StatNews. 2007; 70.
21. Yang Y, Thumula V, Pace PF, Banahan BF 3rd, Wilkin NE, Lobb WB. Predictors of medication nonadherence among patients with diabetes in Medicare Part D programs: a retrospective cohort study. Clin Ther
2009; 31:2178–2188. discussion 50–1. doi: 10.1016/j.clinthera.2009.10.002.
22. Hebenstreit K, Iacobelli S, Leiblein S, Eisfeld AK, Pfrepper C, Heyn S, et al. Low tumor burden is associated with early B-cell reconstitution and is a predictor of favorable outcome after non-myeloablative stem cell transplant for chronic lymphocytic leukemia. Leuk Lymphoma
2014; 55:1274–1280. doi: 10.3109/10428194.2013.836598.
23. Koenker R. Quantile Regression (Econometric Society Monographs). 2005; Cambridge:Cambridge University Press, 2. doi: 10.1017/CBO9780511754098.ISBN 9780521845731.
24. Fornaroli R, Cabrini R, Sartori L, Marazzi F, Vracevic D, Mezzanotte V, et al. Predicting the constraint effect of environmental characteristics on macroinvertebrate density and diversity using quantile regression mixed model. Hydrobiologia
2015; 742:153–167. doi: 10.1007/s10750-014-1974-6.
25. Federico M, Rudiger T, Bellei M, Nathwani BN, Luminari S, Coiffier B, et al. Clinicopathologic characteristics of angioimmunoblastic T-cell lymphoma: analysis of the international peripheral T-cell lymphoma project. J Clin Oncol
2013; 31:240–246. doi: 10.1200/JCO.2011.37.3647.
26. Xu P, Yu D, Wang L, Shen Y, Shen Z, Zhao W. Analysis of prognostic factors and comparison of prognostic scores in peripheral T cell lymphoma, not otherwise specified: a single-institution study of 105 Chinese patients. Ann Hematol
2015; 94:239–247. doi: 10.1007/s00277-014-2188-8.
27. Gui L, Shi YK, He XH, Lei YH, Zhang HZ, Han XH, et al. High-dose therapy and autologous stem cell transplantation in peripheral T-cell lymphoma: treatment outcome and prognostic factor analysis. Int J Hematol
2014; 99:69–78. doi: 10.1007/s12185-013-1465-y.
28. Furukawa M, Ikeda K, Ohkawara H, Saito S, Takahashi H, Ueda K, et al. Persistent complete remission of acute leukemic-phase CCR4-positive gamma-delta peripheral T-cell lymphoma by autologous stem cell transplantation with mogamulizumab. Int J Hematol
2015; 102:498–505. doi: 10.1007/s12185-015-1805-1.
29. Katsuya H, Ishitsuka K. Treatment advances and prognosis for patients with adult T-cell leukemia-lymphoma. J Clin Exp Hematop
2017; 57:87–97. doi: 10.3960/jslrt.17008.
30. Tse E, Kwong YL. The diagnosis and management of NK/T-cell lymphomas. J Hematol Oncol
2017; 10:85doi: 10.1186/s13045-017-0452-9.
31. Tse E, Chan TS, Koh LP, Chng WJ, Kim WS, Tang T, et al. Allogeneic haematopoietic SCT for natural killer/T-cell lymphoma: a multicentre analysis from the Asia lymphoma study group. Bone Marrow Transplant
2014; 49:902–906. doi: 10.1038/bmt.2014.65.
32. Zhou S, Ma Y, Shi Y, Tang L, Zheng Z, Fang F, et al. Mean platelet volume predicts prognosis in patients with diffuse large B-cell lymphoma. Hematol Oncol
2018; 36:104–109. doi: 10.1002/hon.2467.
33. Federico M, Bellei M, Marcheselli L, Schwartz M, Manni M, Tarantino V, et al. Peripheral T cell lymphoma, not otherwise specified (PTCL-NOS). A new prognostic model developed by the International T cell Project Network. Br J Haematol
2018; 181:760–769. doi: 10.1111/bjh.15258.