Follicular Lymphoma in China: Systematic Evaluation of Follicular Lymphoma Prognostic Models

Introduction

Follicular lymphoma (FL) is a low-grade non-Hodgkin lymphoma (NHL) with follicular-like growth derived from B cells.¹ The course of the disease is variable, usually starting from the upper mediastinum or abdominal lymph nodes and gradually involving extranodal tissues or organs such as bone marrow.² Studies have found that many factors may affect the prognosis of the disease. The commonly accepted baseline prognostic models mainly include the Follicular Lymphoma International Prognostic Index (FLIPI) and Follicular Lymphoma International Prognostic Index 2 (FLIPI2) models proposed in the “post-rituximab era”³ and the extremely simplified PRIMA Prognostic Index (PRIMA-PI) score.⁴ The BioFLIPI score developed from the tumour immune microenvironment and biological characteristics are gradually being used for clinical evaluation.⁵ Recent studies on the tumour microenvironment of diffuse large B-cell lymphoma (DLBCL) have revealed the complex regulation of tumour growth and invasion, which involves lymphocytes and monocytes, and facilitate the identification of a more convenient biological prognostic marker.^6,7 However, most of the abovementioned studies on various indicators are based on clinical data from Western countries, and a systematic and comprehensive evaluation of prognostic factors among the characteristics of Chinese patients has not been performed. In this study, we performed a single-centre retrospective analysis to systematically and comprehensively assess the baseline characteristics, immune microenvironment, and treatment response of Chinese FL patients to evaluate prognostic factors.

Materials and Methods

Patients

We retrospectively analysed data for a total of 112 FL patients in the Department of Haematology of Peking Union Medical College Hospital from January 2000 to August 2017. The inclusion criteria were as follows: clinical diagnosis of FL, hospitalization in our hospital, follicular grade 1 to grade 3A, and intact and traceable baseline data and imaging information. Data verification and regular follow-up visits were used to prevent missing data. All patients were followed up by telephone until August 12, 2020. The follow-up rate was 90.2%. Eleven patients were lost to follow-up. The median follow-up time was 64.5 months (3.2–146.3 months).

Data Collection

FL diagnosis is based on clinical manifestations; imaging examinations such as lymph node colour Doppler ultrasound, CT and PET-CT; and histopathology including immunohistochemistry. Antibodies included CD20, CD5, CD21, CD23, BCL2, BCL6, CD10, cyclin D1, MUM-1, and the Ki67 index (Ki67%) was assessed as a cell proliferation marker.

The absolute lymphocyte count (ALC) and absolute monocyte count (AMC) were determined through whole blood cell analysis, and the lymphocyte/monocyte ratio (LMR) was calculated at the same time. Other possible prognostic predictors included baseline/mid/end PET-CT performance, lactate dehydrogenase (LDH), β2 microglobulin (β2-MG) and other indicators. The samples were classified according to the WHO lymphoma classification and diagnostic criteria and staged according to the Ann Arbor staging scheme. The patients’ short-term efficacy evaluations included complete remission (CR), partial response (PR), stable disease (SD) and disease progression (PD). POD24 is defined as progression or relapse within 2 years from the start of chemotherapy. Survival outcomes including overall survival (OS), which is the time from the diagnosis of FL to death from any cause or the end of follow-up, and progression-free survival (PFS), which is the time from the beginning of FL treatment to the end of disease progression or follow-up, were evaluated.

Statistical Analysis

X-tile was used to select the discriminative cut-off values for the LMR, ALC and AMC, and IBM SPSS Statistics 20 was used to combine all quantitative data. For example, LMR, ALC, AMC, sex, age, clinical stage, FLIPI score, FLIPI2 score, β2-MG, LDH, complications, and extranodal involvement were all treated as binary/three-category variables, shown in the form of absolute values and percentages. The χ2 test was used to analyse the correlations between the LMR, ALC, AMC and other prognostic evaluation indicators and to evaluate the impact of the LMR, ALC, and AMC on short-term efficacy. The Spearman correlation analysis was used for the two-variable nonparametric tests, and the Pearson correlation test was used to assess continuous parameters. The Kaplan–Meier method was used to analyse the survival of patients over 10 years, and the Log rank test was used to calculate the effects of all prognostic predictors on PFS and OS. Multivariate analysis relied on the Cox proportional hazards model. P<0.05 was considered to indicate a statistically significant difference.

Results

Clinical Statistics of All Patients

Of the 112 initially treated FL patients assessed in this study, 21.4% had an age of onset >60 years old; 54 patients were males, and 58 were females. 86.6% was diagnosed as Stage III-IV. The rest of the basic clinical information is shown in Table 1 and Supplemental Tables 1-2.

Table 1 Basic Clinical Characteristics of the 112 FL Patients

Extranodal Involvement and Complications

Of the 112 patients, 29.5% (33/112) exhibited extranodal involvement based on imaging findings, pathology, and bone marrow examination. The bone marrow was the most common site for extranodal involvement, accounting for 19.6% (22/112) of cases. Other involved organs and common complications probably affecting the prognosis are listed in Supplemental Tables 1-2.

Prognostic Factor Analysis

Statistics of Patients’ Clinical Baseline Characteristics

The most significant cut-off value of the LMR was determined to be 3.6, and the critical cut-off value of the ALC and AMC was determined to be 0.6*10⁹/L. Supplemental Table 3 shows the correlations between the LMR, ALC, AMC and other baseline clinical characteristics and prognostic factors. There was no significant difference in the clinical baseline data of patients with different LMR levels, suggesting that LMR acted as the immune status of patients could predict independent of other clinical factors.

The Impact of Baseline Characteristics on Survival

Kaplan–Meier survival analysis was performed for indicators that might influence survival (Table 2). The FLIPI score had a significant effect on both PFS and OS (P<0.05), the FLIPI2 score only had a significant effect on PFS (P<0.05), and the PRIMA-PI did not significantly affect survival. In addition, pathological grade 3a and Ki67%>50 could also indicate poor prognosis.

Table 2 General Survival Analysis of Risk Factors for Prognostic Assessment

Multivariate analysis of the above survival factors using the Cox proportional hazards model showed that none of the three indicators could be used as an independent prognostic factor.

Prognostic Evaluation of the LMR, ALC, and AMC

The Kaplan–Meier method was used to analyse the survival of all patients over a 10-year period. Table 2 and Figures 1–3 (detailed in Supplemental Table 4) show that patients with LMR≤3.6, ALC≤0.6*10⁹/L and AMC>0.6*10⁹/L had significantly worse OS than other patients and that the PFS of patients with a high AMC (AMC>0.6*10⁹/L) was also significantly shortened. A subsequent multivariate analysis with the Cox risk model (Table 2) showed that LMR could be used as an independent prognostic predictor for OS.

Figure 1 Kaplan–Meier curve demonstrating differences in PFS and OS based on the cut-off value of the LMR. Abbreviations: PFS, progression-free survival; OS, overall survival; LMR, lymphocyte/monocyte ratio. Notes: (A) Kaplan–Meier curve demonstrating differences in PFS based on the cut-off value of the LMR. Event/total number of PFS: Group”LMR≤3.6” is 18/47, Group”LMR>3.6” is 24/57; (B) Kaplan–Meier curve demonstrating differences in OS based on the cut-off value of the LMR. Event/total number of OS: Group”LMR≤3.6” is 11/45; Group”LMR>3.6” is 4/56.

Figure 2 Kaplan–Meier curve demonstrating differences in PFS and OS based on the cut-off value of AMC. Abbreviations: PFS, progression-free survival; OS, overall survival; AMC, absolute monocyte count. Notes: (A) Kaplan–Meier curve demonstrating differences in PFS based on the cut-off value of AMC. Event/total number of PFS: Group “AMC≤0.6*109/L” is 36/92, Group “AMC>0.6*109/L” is 6/12; (B) Kaplan–Meier curve demonstrating differences in OS based on the cut-off value of AMC. Event/total number of OS: Group “AMC≤0.6*109/L” is 11/91, Group “AMC>0.6*109/L” is 4/10.

Figure 3 Kaplan–Meier curve demonstrating differences in PFS and OS based on the cut-off value of ALC. Abbreviations: PFS, progression-free survival; OS, overall survival; ALC, absolute lymphocyte count. Notes: (A) Kaplan–Meier curve demonstrating differences in PFS based on the cut-off value of ALC. Event/total number of PFS: Group “ALC≤0.6*109/L” is 8/13, Group “ALC>0.6*109/L” is 34/91; (B) Kaplan–Meier curve demonstrating differences in OS based on the cut-off value of ALC. Event/total number of OS: Group “ALC≤0.6*109/L” is 5/13, Group “ALC>0.6*109/L” is 10/88.

Impact of POD24 on Survival

In terms of POD24 evaluation, 26% (27/104) of patients exhibited POD24 after treatment, and there was a higher frequency of patients with high risk according to the FLIPI and FLIPI2 scores in the POD24 subgroup than in the non-POD24 subgroup (63.0% vs 35.1%, P value=0.013 and 18.5% vs 3.9%, P value=0.003) (Supplemental Table 5). The median survival of the POD24 group was 34.5 months, which was significantly different from that of the non-POD24 group (P<0.0001) (Figure 4), suggesting that POD24 has good predictive value for the long-term survival of patients.

Figure 4 Prediction of OS according to POD24 status. Abbreviation: POD24, progression or relapse within 2 years from chemotherapy. Note: Event/total number of OS: Group “non-POD24” is 2/75, Group “POD24” is 13/26.

Discussion

Analysis of the clinical data of this study showed that the median age of onset for FL in China was 52 years old, which was lower than that in foreign countries. Stage III–IV was the most common stage, accounting for 86.8% of cases in China. The incidence of stage III–IV disease was much lower compared with cohort study in Sweden (60%). The immunity levels before treatment were weaker in China than in Western countries (median AMC of 0.35*10⁹/L vs median ALC of 1.24*10⁹/L).^8,9 It has been suggested that the diagnosis of FL in China is relatively late, which may be associated with poor public physical examination awareness. However, with the application of immune-targeted therapeutics such as rituximab, prognosis has improved significantly. During our research, more than 80% of the patients received treatment regimens including rituximab, and their long-term survival was excellent; the median PFS was 74.8 months and the 10-year PFS rate was 43.1%, and these values were slightly better than those in foreign countries in the same period.¹⁰ The 5-year OS rate was 88.1%, and the 10-year OS rate was 75%, and these values were similar to those of studies abroad with the same treatment conditions.⁹

Regarding the prognostic impact of baseline characteristics, due to the long time span of more than 10 years, β2-MG was not commonly used, and the clinical value of FLIPI2 may have been undervalued;¹¹ however, the predictive value of FLIPI2 in the post-rituximab era should be brought to the attention of clinicians. Therefore, we suggest following a standardized diagnosis process by obtaining the baseline data, such as β2-MG, bone marrow examination and even gene rearrangement, before initial assessment to provide the conditions for individual prognosis evaluation. In terms of tumour microenvironment indicators, peripheral blood LMR evaluation is a simple process. Previous studies have found that in patients with DLBCL and FL, OS after chemotherapy was significantly shortened in those with a low LMR (LMR≤2.6).^12–14 Our study showed that the LMR is the only independent predictor of the long-term OS of FL patients in the Chinese population. ALC or AMC may also have some implications for OS. These results suggest that the immune microenvironment, especially cellular immunity, plays an important role in the progression of FL. Especially in recent years, drugs targeting the tumour microenvironment, such as lenalidomide and chidamide, have emerged. The LMR may serve as an important reference for clinical treatment decision making and may become a more popular indicator. In terms of the mechanism, previous literature has reported that peripheral blood lymphocytes can activate a variety of antigen-presenting cells, thus facilitating the recognition, uptake, presentation and immune system activation of tumour surface antigens. However, M2-type macrophages (tumour-associated macrophages [TAMs]) in peripheral blood are enriched around tumours under the guidance of regulatory T cells (Tregs), which directly promote tumour progression by endowing tumour tissues with functional polarity.^15,16 In terms of treatment response prediction, POD24 status demonstrated strong predictive power for long-term survival and strong associations with baseline risk stratification status. Overall, considering the accuracy of prognosis prediction with the M7-FLIPI and health economics concerns, POD24 may be an important inclusion criterion for further gene expression profiling of patients.

This study is a systematic and comprehensive prognostic evaluation based on clinical data of Chinese patients. Prognostic predictors cover three categories: clinical baseline characteristics, tumour microenvironment characteristics and treatment response. All patients were followed up for more than 3 years, and the longest follow-up time was 146.3 months. We found a new factor, the LMR, that could predict prognosis independent of FLIPI or FLIPI2 status. The first drawback of this study is that the PRIMA-PI is affected by β2-MG and the small number of cases with bone marrow involvement, so the stratification results are not accurate. Second, due to the lack of gene expression profiling data for all of our patients, the prognostic assessment of tumour biological characteristics was incomplete. This study can be supplemented and corrected through prospective experiments in future clinical practice. Finally, because the sample size was insufficient, it was difficult to establish a prognostic evaluation model. Perhaps in the future, a more optimized FL prognostic model can be established with a larger cohort of patients from multiple centres with available data on the LMR and other independent prognostic predictors; this optimized model could be a simple, economical, and easy-to-apply evaluation system for guiding individualized treatment.