Status and prognostic nomogram of patients with Burkitt lymphoma
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- Published online on: November 28, 2019 https://doi.org/10.3892/ol.2019.11155
- Pages: 972-984
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Copyright: © Lu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Burkitt lymphoma (BL) is a highly aggressive B-cell non-Hodgkin lymphoma that was discovered by Denis Burkitt in 1958 in Africa (1). This was the first tumor shown to have a chromosomal translocation activating an oncogene (c-MYC) (2), as well as being associated with Epstein-Barr virus (3). Owing to the introduction of dose-intense multi-drug combination chemotherapy, prophylaxis for central nervous system disease and improvements in supportive care, BL has become the aggressive lymphoma subset that is associated with the highest cure rate in both adults and children (4).
However, compared with published single or multi-center clinical studies, the 3-year survival rate according to the 2002–2008 Surveillance, Epidemiology and End Results (SEER) US study, including 3,691 patients with BL, was notably low (56%) (5). In addition, current guidelines are still recommending uniform, aggressive treatment for all patients without considering prognosis and risk categorization. As with other malignancies, identifying the prognostic factors of BL is critical, as these can guide treatment selection. Castillo et al (6) reported that age, race and stage have independent prognostic roles in survival, by analyzing data from 1,428 patients recorded between 1998 and 2009 from the SEER program database. Nevertheless, systematically identifying prognostic factors to arrive at a more precise estimate of prognosis is of importance, as survival is undoubtedly multifactorial. Nomograms, a simple graphical presentation of a multivariate predictive model, show the impact of each included variable on an outcome of interest, thereby providing a numerical probability of the outcome (7). One of the advantages of nomograms is the ability to integrate multiple prognostic factors into the individual survival numerical estimate of a single patient, providing individualized survival predictions. Nomograms have gradually become a useful tool in the field of oncology, for predicting cancer prognosis (8–10). However, to the best of our knowledge, a nomogram for survival prediction in BL has not been reported.
The present study aimed to use data from a large US population dataset, the SEER database, to provide an update on the incidence and evaluate treatment outcomes in patients with BL over the past three decades, between 1983 and 2015. Furthermore, nomograms were developed and validated to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with BL with different values of prognostic factors.
Patients and methods
Data sources and patient selection
The SEER Program that was used to obtain the data for the present study was created in 1973 to collect cancer statistics in the US, to reduce the cancer burden among the US population (http://seer.cancer.gov). The data on patient demographics, primary tumor site, tumor morphology and stage at diagnosis, first course of treatment, and follow-up for vital status (through linkage with the National Center for Health Statistics) were routinely collected in all 18 registries spread across the US. Currently, SEER registries cover 28% of the US population; coverage includes 25% of white Americans, 26% of African Americans, 38% of Hispanics, 44% of Native Americans and Alaska natives, 50% of Asians and 67% of Hawaiian/Pacific islanders. The SEER database was accessed via SEER*Stat software (version 8.3.5; National Cancer Institute).
The inclusion criteria of the study were as follows: i) Diagnosed with BL as the primary malignancy, according to the 3rd edition (ICD-O-3) histology code 9687 (Burkitt lymphoma) (earlier patients coded as ICD-O-1 and ICD-O-2 were previously re-coded as ICD-O-3); ii) only one malignant tumor present, in order to exclude interference from other tumors; and iii) followed up for vital status until December 31st, 2015, to ensure a minimal follow-up length of two years. The exclusion criteria were as follows: i) Diagnosed by autopsy or reported only on a death certificate; and ii) one or more statistical factors unknown. In total, 4,600 patients were included in the analysis.
Definition of variables
Patients were divided into three groups according to the time of diagnosis (1983–1993, 1994–2004 and 2005–2015), in order to evaluate the development of the diagnosis and treatment of BL. The patients were further stratified by other clinicopathological features including age, sex (male or female), race (black, white, American Indian/Alaska native, Chinese and other Asian/Pacific islander) marital status (married, single, or widowed/divorced/separated), Ann Arbor stage (I, II, or IV), primary site (lymph nodes, digestive system, oral cavity and pharynx, bone marrow, nervous system and other), percentage of families with incomes below the poverty line of the county (% families below poverty; ≤5, 5–10, 10–20 or ≥20), median household income, chemotherapy (yes, or no/unknown), outcome at study cutoff (alive or succumbed), and survival time.
To assess the prognostic significance of age and to avoid arbitrary predetermined cutoff points, X-tile plots were constructed to assess age using four groups, and the most effective cutoff points were identified following correction for the use of minimum P statistics by Miller-Siegmund P-value correction, and dividing into four parts. The X-tile program (Yale University) was first introduced to identify the best cutoff values of several prognostic factors in cohorts of patients with breast cancer (11).
Incidence and survival analysis
To assess changes in the incidence of BL, the overall population incidence was estimated by calculating from the patient data in the SEER database. Incidence rates were expressed per 1,000,000 in the population and were age-adjusted to the 2000 US standard population (12).
As improvements in diagnosis and treatment are made over time, the present study intended to assess changes in survival rates, including OS, meaning the time from diagnosis to mortality for any reason, and CSS, defined as the time from diagnosis to mortality attributed to BL, in patients with BL grouped by different diagnostic time periods (1983–1993, 1994–2004 and 2005–2015).
Construction and validation of the nomograms
To improve predictions for the construction of the nomogram plots for BL, 2,669 patients diagnosed during 2005–2015, identified from the SEER database, were randomly assigned to the training cohort (n=1,762, for the construction and internal validation of the nomograms) and validation cohort (n=907, for external validation of the nomograms). Potential prognostic factors including age, sex, race, marital status, % families below poverty, chemotherapy, primary site and stage, that were associated with OS or CSS were included in the Kaplan-Meier analyses and log-rank tests. To eliminate the interactions between various factors, a multivariate Cox proportional hazard model was used to identify independent prognostic factors and, furthermore, hazard ratios were calculated for the significant prognostic factors. To predict whether a patient will experience the event (mortality for any reason or due to BL) at 3 or 5 years, nomograms incorporate prognostic factors and present them in an intuitive way. To validate the nomogram plots internally and externally, bootstrap validation was used with 1,000-iteration resampling. The data of the training cohort were used for internal validation and validation cohort was used for external validation. Harrell's concordance index (C-index), was used to estimate the predictive accuracy of the nomogram, for the consistency of the predicted and actual values (13). The range of the C-index is 0.5 to 1; 0.5 is completely inconsistent, indicating that the model has no predictive effect, while 1 is completely consistent, indicating that the model prediction results are completely consistent with the actual results. It is generally believed that the C-index is less accurate at 0.50–0.70, has moderate accuracy between 0.71–0.90 and has high accuracy at >0.90. In addition, calibration plots were also used to validate the nomograms, comparing predicted values with actual outcomes.
Statistical analysis
OS was defined as the time from diagnosis to death from all possible causes. CSS was defined as the time from diagnosis to death attributed to BL. Patients who were alive at the time of last follow-up were counted as censored observations. Baseline clinicopathological characteristics of the patients were compared between the training and validation cohort and among the different groups of diagnostic time periods (1983–1993, 1994–2004 and 2005–2015). A Chi-square test was used for counting data (age group, sex, race, marital status, % families below poverty, chemotherapy, primary site and stage). An analysis of variance test (followed by Least Significant Difference) was used for analysis of measurement data (age, median household income and survival time). Potential prognostic factors including age, sex, race, marital status, % families below poverty, chemotherapy, primary site and stage were incorporated in the univariate analysis using Kaplan-Meier analyses and log-rank tests for OS and CSS, respectively. Significant variables were further assessed using multivariate Cox proportional hazards model hazard ratios of each variable with corresponding 95% confidence intervals (CI) were calculated regarding OS and CSS.
All statistical analysis was conducted using SPSS version 23.0 (IBM Corp.). R version 3.3.5 (http://www.r-project.org/) was used to construct and validate nomograms. P<0.05 (two-tailed) was considered to indicate a statistically significant difference.
Results
Patient characteristics
Followed the inclusion and exclusion criteria, 4,600 patients diagnosed with BL between 1983 and 2015 were identified. All the patients were classified by their characteristics, including age, sex, race, marital status, Ann Arbor stage, primary site, % families below poverty, median household income, chemotherapy, radiotherapy, outcome at study cutoff and survival time (Table I).
Using the X-tile program, it was possible to confirm the best cutoff values of age as a prognostic factor for BL. The X-tile program divided age into four subgroups by choosing the grouping scheme that valued the highest χ2 record. The X-tile program identified the most appropriate cutoff values of age as 20, 40 and 56 years based on OS (Fig. 1). Thus, age was divided into the following categories: ≤20, 21–40, 41–55 and ≥56 years. The age grouping profile of the patients with BL is described in Table II.
Incidence and survival analysis
The incidence of BL between 1983 and 2015 was calculated by updating the SEER data to explore the recent development trends of the disease (Fig. 2), although the incidence between 1973 and 2008 has been previously reported (5). Incidence gradually increased from 1983 and reached its peak in 2009, with a rate of 0.491 per 100,000 (95% CI, 0.412–0.581). From 2009, the incidence slowly declined year by year and dropped to 0.28 per 100,000 (95% CI, 0.224–0.346).
Among the different groups of diagnostic time periods (1983–1993, 1994–2004 and 2005–2015), the aforementioned characteristics of the patients significantly differed (P<0.01; Table III), revealing the differences in patient composition. The univariate survival analysis showed that an earlier diagnostic time was associated with worse outcomes, while a more recent diagnosis time was associated with better outcomes (P<0.0001; Fig. 3). The OS rates of patients diagnosed during 2005–2015 were increased, with a 3-year OS of 62.6% and a 5-year OS of 61.2%, in contrast to the 3-year OS of 35.2% and 5-year OS of 33.8% of patients diagnosed in the period 1983–1993 (Fig. 4A). Similarly, the CSS rates of patients diagnosed in 2005–2015 were 65.3% for 3-year CSS and 64.4% for 5-year CSS, while the 3-year CSS was 37.0% and the 5-year CSS was 38.0% for patients diagnosed during 1983–1993 (Fig. 4B).
 | Table III.Demographics and clinical characteristics of Burkitt lymphoma patients with different diagnostic time. |
Construction and validation of the nomograms
Demographics and clinical characteristics of the training and validation cohorts of BL patients were collated (Table IV). Using univariate and multivariate analysis, four variables (age, race, chemotherapy and stage) were found to be significant factors associated with OS (Table V) and CSS (Table VI). Though primary site did not produce meaningful results in the multivariate analysis, it was still included in the nomogram construction to verify its precise clinical significance. The nomograms for 3- and 5-year OS and CSS prediction were constructed by integrating the impact of five independent predictors (Figs. 5 and 6). The predicted probabilities of each covariate were mapped on a scale from 0 to 100, where 100 represented the highest effect and 0 the lowest. The total points accumulated were used to predict the probability for the patients. Each definition of the predictors has an accurate value displayed in the digital axis. The exact score of each point of the various covariates was calculated (Table VII). For the practical application of the nomograms, an example is provided: The total number of points of a 25-year old black patient diagnosed with stage II BL, with a lymph node primary site, would be 60 if treated with chemotherapy and the estimated 3-year OS rate would be 85%.
| Table IV.Demographics and clinical characteristics of training and validation cohort for BL patients. |
| Table VI.Univariate analysis and multivariate analysis of cancer-specific survival in the training cohort. |
The nomograms were validated both internally and externally. The C-indexes of the nomograms predicting 3- and 5-year OS in the internal and external validation were 0.777 (95% CI, 0.758–0.796) and 0.76 (95% CI, 0.735–0.786), respectively. The C-indexes of the nomograms predicting 3- and 5-year CSS in the internal and external validation were 0.777 (95% CI, 0.757–0.797) and 0.755 (95% CI, 0.728–0.782), respectively. In conclusion, the nomograms constructed in the present study were quite accurate. The internal and external calibration plots for 3- and 5-year OS and CSS showed good consistency between the nomogram predictions and the observed outcomes, in that the calculated points were close to the diagonal line (Figs. 7 and 8).
Discussion
As a highly malignant disease with a demonstrated propensity for dissemination, the incidence rates of BL had gradually increased and reached 4.91 per 1,000,000 (95% CI, 4.12–5.81) in 2009. Based on the relationship between the occurrence of BL and acquired immune deficiency syndrome (AIDS), it is possible that this incidence peak is related to the AIDS epidemic (14). The incidence has slowly declined year by year and dropped to 2.80 per 1,000,000 (95% CI, 2.24–3.46) by 2015.
For different diagnostic times, the present study showed that the OS and CSS within 5 years of patients being diagnosed in 2005–2015 were increased compared with patients diagnosed in 1994–2004 and 1983–1993. This is likely to be related to improvements in the therapeutic regimens. In the 1990s, the introduction of dose-intense multi-drug combination chemotherapy and prophylaxis for central nervous system disease led to the high cure rate of BL. It has been reported that complete response and OS reached 67–95 and 54–74%, respectively (15–22). Furthermore, the addition of rituximab resulted in another prognostic improvement in BL, both in children and adults. The 5-year survival rates were reportedly >90% in children (23,24) and the 3-year survival rate was close to 80% in adults (25–27). The 5-year OS rate was improved from 60 to 70% when the Hyper-CVAD regimen with rituximab was compared with the Hyper-CVAD alone. Nevertheless, possibly due to selection bias and therapeutic regimens, the OS rate of the population was decreased.
To estimate the prognosis of diverse patient groups, numerous factors, including age, race, advanced stage, poor performance status, central nervous system or bone marrow involvement, anemia, the presence of circulating blasts and elevated lactate dehydrogenase (LDH), were proven to be individual risk factors in previous studies (6,28–31). Nevertheless, no nomograms to comprehensively consider prognostic factors and predict the outcomes of patients with BL had yet been developed. Therefore, in the present study, nomograms for predicting the 3- and 5-year OS and CSS of patients with BL were constructed using data obtained from the SEER database. Nomograms for BL may allow clinicians to implement differential therapy for patients in different situations, similar to practices in other malignancy such as osteosarcoma, prostate cancer, gastric cancer, lung cancer and breast cancer. As mentioned above, the cure rate of Burkitt leukemia has increased considerably in the past three decades (1983–2015). In order to improve the predictive level of nomogram, the patient data diagnosed during 2005–2015 was selected for modeling. By analyzing the clinicopathological features with univariate and multivariate analysis, four of these, including age, race, chemotherapy and stage, were eventually regarded as independent prognostic factors for OS and CSS. In particular, race was identified as a prognostic factor and this may be related to genotypic milieu and gene specificity. Lymphoma at different primary sites has different clinical features and prognosis (32,33), even though primary sites is not significant in the Cox multivariate analysis, it was still imported into the nomogram prediction. The nomograms of OS and CSS were constructed and were used to predict prognosis with exact scores corresponding to different survival rates, which was validated both internally and externally via C-indexes and calibration plots. American Indian single patients of older age, whose primary sites were in the bone marrow or nervous system and who had disease of stage IV without chemotherapy, were identified to have the worst survival outcomes.
There are still several limitations that should be considered with respect to the nomograms for BL. Firstly, it is true, as the reviewer suggests, that lab data, especially that of LDH, is an important prognostic factor in lymphoma. It is regrettable that the data obtained from SEER do not cover lab data, so the present study was unable to assess the potential prognostic factors and incorporate these into the prediction models to make the nomograms more accurate. Secondly, the nomograms predicted the outcome of future patients via SEER data collected previously and these predictions will remain constant over time. This may lead to a lack of accuracy of the nomogram as time passes, due to improvements in the detection and treatment of BL. Thirdly, the data obtained from SEER do not cover specific chemotherapy regimens, as explained by an official statement and the classification is not completely accurate in that ‘No’ and ‘Unknown’ are bracketed together. The authors of the present study may possibly further investigate the effect of different chemotherapy regimens on prognosis in a single-center study at The First Affiliated Hospital of Guangzhou Medical University (Guangzho, China).
Moreover, the present study constructed and validated the nomograms using data from the same retrospective database. Another non-overlapping dataset or a new study is recommended for stricter validation of the predictive ability of the nomograms. Fourthly, even though multivariate analysis was used to control confounding variables, it is still difficult to completely avoid influences among correlating variables, such as the relationship between primary site and stage. Finally, though the present study has already begun to collect cases of diagnosed BL; it is unfortunate that the limited number of cases is insufficient to verify the accuracy of model. In a follow-up study, the authors will solve this problem by collecting more virtual data.
The results of the present study demonstrated that the nomogram plots for BL may be a useful tool for clinicians to establish treatment options that vary according to the patient's situation and to identify different categories of patients for scientific research.
Acknowledgements
Not applicable.
Funding
The present study was supported by the National Natural Science Foundation of China (grant nos. 81672661 and 81870113), the Guangdong Province Natural Science Foundation (grant nos. 2016A030313598 and 2106A03313654), and Guangzhou Science and Technology Project (grant nos. 201804010199 and 20161A011062).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
JL and YZ conceived and designed the study. HT and LX analyzed and interpreted the data and JL, SC and BL analyzed data, were involved in drafting the manuscript and revised it critically for important intellectual content. HT, LX and YZ revised the paper. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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