| Abstract|| |
Background and Aim: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma in Indian population. The disease could be divided into the prognostically important subtypes, germinal center B-cell (GCB)-like and activated B-cell-like, using gene expression profiling (GEP). The molecular subtype as defined by GEP could also be predicted by using immunohistochemistry (IHC) based algorithms using three biomarkers CD10, BCL-6, and multiple myeloma oncogene-1 (MUM1). It has been confirmed that patients belonging to the GCB subtype have a better outcome and survival than those belonging to the second subtype. The present study was conducted to study the prevalence of these two subgroups and their correlation with survival of the patients. Materials and Methods: A total of 83 patients of DLBCL were included in the study. Hematoxylin- and eosin-stained sections were prepared from the paraffin-embedded tissue blocks. The staining for all the three antibodies was considered positive when more than 30% cells were stained with the respective antibody.
Results: The results showed that 44 patients (53%) had GCB immunophenotype and 39 patients (47%) had non-GCB phenotype. However, no statistically significant difference in overall and disease-free survival was noted between the subgroups. Conclusion: This study demonstrated that frequency of GCB subtype of DLBCL is significantly higher than the non-GCB subtype, and the non-GCB immunophenotype has better relapse-free survival 78% (standard error = 0.10) at the end of 3 years, while GCB has 56% (standard error = 0.23). Further studies should be performed with larger number of patients to show difference in clinical outcome between GCB and non-GCB subgroups.
Keywords: Diffuse large B-cell lymphoma, germinal center B-cell like, survival
|How to cite this article:|
Dwivedi A, Mehta A, Solanki P. Evaluation of immunohistochemical subtypes in diffuse large B-cell lymphoma and its impact on survival. Indian J Pathol Microbiol 2015;58:453-8
|How to cite this URL:|
Dwivedi A, Mehta A, Solanki P. Evaluation of immunohistochemical subtypes in diffuse large B-cell lymphoma and its impact on survival. Indian J Pathol Microbiol [serial online] 2015 [cited 2020 Jun 4];58:453-8. Available from: http://www.ijpmonline.org/text.asp?2015/58/4/453/168886
| Introduction|| |
Diffuse large B-cell lymphoma (DLBCL) is a neoplasm of large B-cells and is the most common type of non-Hodgkin lymphoma (NHL) which accounts for 55% of all NHL in Indian population. DLBCL is characterized by the proliferation of large neoplastic B-cells, with nuclear size equal to or exceeding normal macrophage nuclei or more than twice the size of a normal lymphocyte that has a diffuse growth pattern and comprises centroblastic, immunoblastic, T-cell/histiocyte-rich, and anaplastic morphological variants. It occurs over a broad range of ages, manifests at nodal or extranodal sites, and exhibits distinct biological heterogeneity. Unlike indolent lymphomas, DLBCL is an aggressive lymphoma, and if left untreated, survival may be measured in mere weeks to months. Bone marrow involvement is not commonly seen at diagnosis, and only 20–30% of patients show the evidence of disease in the marrow. The prognosis of DLBCL is dissimilar among patients, and the overall survival (OS) rates ranged from 30% to 50% over 5 years. However, in the era of rituximab, the OS range has been increased from 80% to 90% in the patients with low-risk disease.
The disease has been recognized as heterogeneous at clinical, pathological, morphological, molecular, and at immunophenotypic levels. To identify the heterogeneity at molecular level, gene expression profiling (GEP) study has been performed by Alizadeh et al. and it was revealed that DLBCL could be divided into the prognostically important subtypes of germinal center B-cell-like (GCB), activated B-cell-like (ABC), or type 3 GEPs. The GCB and ABC groups have been identified according to their gene expression patterns resembling normal GCBs or ABCs, whereas type 3 group is not well defined. The ABC group and the type 3 entities have later been grouped together as the non-GCB, as type 3 group have similar prognostic outcomes as the ABC group. It has been demonstrated that patients belonging to the GCB subgroup show a better outcome and survival than those belonging to the ABC subgroup.,
Application of these molecular approaches in daily practice is difficult as GEP technology is expensive and not generally available; therefore, an attempt was made to transfer this molecular classification into more user-friendly method using IHC based algorithm that is feasible at any pathology laboratory. In the year 2004, Hans et al. for the first time associated the IHC findings with the cDNA microarray data using three antibodies that is CD10, polyclonal B-cell lymphoma 6 (BCL-6), and multiple myeloma oncogene-1 (MUM1) to classify DLBCL into GCB and non-GCB. The immunostain panel reproduced the gene expression results in 71% of GCB and 88% of non-GCB.
Following the algorithm of Hans et al., several studies examined the proportion of GCB and non-GCB in different population but yielded conflicting results regarding its prognostic significance. A number of studies have found a significantly better survival for the GCB subgroup, whereas others have found no difference in survival between the two groups.,,, Most of these studies have been performed in the Western countries; a few studies have also been reported from the countries of Asia, but there is paucity of data from India with respect to immunophenotypes of DLBCL. Therefore, the present study was designed to know the frequency of GCB and non-GCB in the Indian population and their prognostic significance.
| Materials and Methods|| |
The patients were selected and analyzed retrospectively diagnosed with DLBCL at Rajiv Gandhi Cancer Institute and Research Centre, between the years 2009 and 2012 and follow-up had been maintained until July 2014. A total of 83 patients of DLBCL were immunohistochemically classified into two subgroups. The inclusion criteria of subject selection were cases with confirmed diagnosis of DLBCL, availability of complete information (stage, International Prognostic Index [IPI] score, treatment, follow-up), and having received chemotherapy, rituximab, cyclophosphamide, vincristine, and prednisone (RCHOP). Of 83 patients, total 70 befitted the criteria of patient selection. The clinical records were reviewed in all patients with particular reference to age at diagnosis, site of initial involvement, Ann Arbor stage at presentation, response to treatment, achievement of complete response (CR), occurrence of relapse or progression, and survival according to the predesigned performa of the study. Ethical clearance was obtained from the Institutional Review Board of the Institute.
Hematoxylin- and eosin-stained sections were prepared from the paraffin-embedded tissue blocks to identify diagnostic area. Formalin-fixed paraffin sections of 3 µm thick were used for IHC staining. The specifications of the antibodies used in this study are shown in [Table 1]. Staining of tissue sections for BCL-6 was done manually, whereas immunoreaction for CD10 and MUM1 was performed in an automated Ventana Autostainer. The staining for all the three antibodies was considered positive when more than 30% cells were stained with the respective antibody. For each case, the center with the highest percentage of tumor cells stained was used for analysis. The morphology of the tumor cells was also evaluated.
Hans's algorithm was used for the classification of subjects into two IHC subgroups. According to the algorithm [Figure 1], cases were allocated to the GCB subgroup if CD10 alone was positive; CD10 negative cases were further tested for BCL-6. If BCL-6 was negative, then the case was assigned to the non-GCB subgroup. Cases with BCL-6 positivity were further stained with MUM1; if MUM1 was negative, then the case was assigned to the GCB subgroup [Figure 2] and positive cases were grouped under the non-GCB subgroup [Figure 3].
|Figure 1: The three-marker model (Hans's algorithm) applied for the classification of diffuse large B-cell lymphoma|
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|Figure 2: Staining of germinal center B-cell subtype (×400) that is positive for CD10 and B-cell lymphoma 6, but negative for multiple myeloma oncogene-1|
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|Figure 3: Staining of nongerminal center B-cell subtype (×400) that is negative for CD10 and B-cell lymphoma 6, but positive for multiple myeloma oncogene-1|
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Statistical analysis was done using the Software Statistical Package for the Social Sciences (IBM SPSS statistics version 22 IBM Corp, Armonk, NY). The qualitative data were presented in frequencies and percentages, and quantitative data were presented by mean or median. The Chi-square test was used to compare the patient characteristics, according to the immunophenotypes. The Kaplan–Meier method was used to estimate the overall and relapse-free survival (RFS). The log-rank test was used to compare survival distributions. OS was calculated as the time of diagnosis to the date of death or last follow-up (clinical or demographic). The RFS was calculated from the time of diagnosis to relapse or last contact. The results were considered significant if the p value was < 0.05.
| Results|| |
A total of 83 patients were included in the study: 47 males and 36 females with a median age of 56 (15–83) years; however, the clinicopathological analysis was done in 70 patients who had received complete treatment at the institute. [Table 2] summarizes the background clinical data of the patients. According to the modified Ann Arbor classification, most of the patients, i.e., 70% were diagnosed at a late stage (III and IV) (n = 49), whereas only 30% were diagnosed at an early stage (I and II) (n = 21). Patients with 0, 1, or 2 risk factors (n = 45) according to the IPI showed a significantly better survival as compared to those with 3, 4, or 5 (n = 25) factors (p = 0.012).
It was observed that after the completion of treatment, CR was attained in 81% (57/70) of patients, whereas in 19% (13/70) of patients, disease was residual or progressive [Table 3]. Of the 57 patients who had CR, disease got relapsed in 9 (13%) patients. Among all relapsed cases, recurrence occurred within a year in 3 patients and after 1 year of diagnosis in 5 patients. The type of recurrence in most of the cases was generally nodal, whereas disease failure occurred in the brain in 2 patients [Table 4]. The median follow-up time was 24 (1–62) months. At the last follow-up, 59 (84%) were alive and among all the alive patients, 14 (20%) were surviving with disease either residual or progressive. A total of 11 (16%) patients were found to be expired at last follow-up. The cause of death was observed as stable disease in 4 (36%) patients, recurrent disease in 4 (36%), second primary disease in 1 (9%), unrelated to disease in 1 (9%), and unknown in 1 (9%) of all dead patients. The median OS and RFS were 24 and 18 months, respectively.
|Table 3: Detailed information of the patients showed partial/no response to the treatment|
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|Table 4: Site distribution of disease relapse including clinical characteristics and survival|
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The results showed that 44 patients (53%) had GCB immunophenotype and 39 patients (47%) had non-GCB phenotype. The majority of GCB cases (n = 26) were classified based on CD10 positivity, whereas CD10 negative cases (n = 18) were further classified as the GCB subtype based on combination of BCL-6 positivity and MUM1 negativity [Table 5]. The expression of CD10 was demonstrated in 31% of the cases (n = 26/83), BCL-6 in 63% (39/62), and MUM1 in 52% (26/50). No significant difference in RFS and OS was seen as per the expression of the three antibodies included in the study. In this set of patients, when both the subtypes were compared with respect to the survival, no statistically significant difference was observed, either in OS (p = 0.51) or in RFS (p = 0.95) [Figure 4]. However, in the non-GCB immunophenotype, RFS was found to be 78% (standard error = 0.10) at the end of 3 years, while 56% (standard error = 0.23) in GCB subtype.
|Table 5: Distribution of cases into immunophenotypes according to the expression (+) or absence (−) of the three markers|
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|Figure 4: (a) Kaplan–Meier curves for recurrence-free survival, (b) Kaplan–Meier curves for overall survival|
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| Discussion|| |
Different studies have been carried out in the last decade for identification of the molecular subtypes of the DLBCL ,, that are of clinical and prognostic significance. In the year 2004, Hans et al. found that the molecular subtypes could also be predicted using panel of only three immunostains, that is CD10, BCL-6, and MUM1, and the panel reproduced the gene expression results in 71% of GCB and 88% of non-GCB. The present study was performed in the uniform series of DLBCL patients treated with RCHOP. The classification had been done according to the Hans algorithm, in view of the fact that Hans's algorithm had maintained its prognostic value in the majority of the studies, performed using different algorithms in the same patient population.,
The current study showed that frequency of GCB subtype of DLBCL is significantly higher than the non-GCB subtype, similar to the results of western countries,,, whereas in the Asian countries, GCB subtype is less frequent.,,, The differences in the relative proportions of subtypes among countries may possibly be due to differences in environmental and genetic factors that influence lymphoma genesis and strongly suggest that more research toward the pathogenesis of lymphoid neoplasm is needed. Another reason for variation could be due to the difference in selection criteria of patients in the studies. Several studies have examined the immmunophenotypes in extranodal cases only and have shown that most extranodal DLBCL belong to the non-GCB phenotype.,, Therefore, inclusion of patients with extranodal disease could be the reason for higher percentage of non-GCB in Asian countries.
It has been confirmed through various studies that GCB subtype has better survival than non-GCB subtype;,, nevertheless, no significant difference was found between the two subtypes in our study with respect to the response to the treatment and survival that is similar to a study  of Benesova et al. The cause of nonsignificance could be owing to the small sample size and treatment of patients through RCHOP because it has been revealed through some studies that RCHOP significantly improves the clinical outcome in DLBCL patients., A study done by Nyman et al. has shown that the additional benefit of RCHOP had been mainly observed among the patients with non-GCB DLBCL.
Various authors have studied the significance of these markers individually as well as along with putting up them together for subtyping. CD10 is a membrane metalloproteinase that is found in a variety of lymphoid cells as well as in stromal and epithelial cells. The prognostic significance of CD10 expression has been evaluated in many studies with controversial results. In some studies, the longer survival was found in patients with tumors that expressed CD10,, whereas few studies had reported worse survival., In our study, OS and RFS were not significantly different according to CD10 expression (p = 0.44 and p= 0.34, respectively), which is in concurrence with the study of Fabiani et al. and Hwang et al.,
BCL-6 is a zinc-finger protein and is expressed in GCB cells and subset of CD4+ T cells. IHC studies with BCL-6 expression and its impact on OS have similar results as CD10, however study done by Colomo et al. had shown no difference in OS, whereas few studies have reported that BCL-6 expression predicts for better OS,, and study done by Peh et al. had showed that its expression is associated with poorer prognosis. The present study has also not found any difference in survival with respect to expression of BCL-6 (p = 0.861). MUM1, a member of the interferon regulatory factor family of transcriptional factors, is expressed in plasma cells and in a small percentage of GCBs. Its expression in tumor cells is associated with significantly worse OS;, in contrast, the present study did not find any difference in survival with respect to its expression (p = 0.550), which is in accordance with the study by Berglund et al.
Using the algorithm of Hans, numerous studies had been performed in patients to find the prognostic significance of the subtypes during the preretuximab era and following the use of RCHOP in treatment as well but the results are conflicting. The addition of anti CD20 antibody, rituximab has improved the survival among patients of DLBCL, predicting patient prognosis difficult. Targeted therapies such as bortezomib or dose-modified etoposide, doxorubicin, cincristine, predinosone, cyclophospahmide, and rituxan have also been recommended for DLBCL patients., Recently, many clinical trials are also in progress to determine whether novel targeted agents may be useful in these immunohistochemically defined subgroups of patients. For example, a clinical trial using Genasense with rituximab plus CHOP in non-GCB type DLBCL determined by the Hans algorithm is underway. It is expected that results of such trials would help in developing new strategies of treatment based on individual IHC subtypes
| Conclusions|| |
Different algorithms have been created, combining the expressions of well-known antigens for IHC classification of DLBCL.,,,, However, the replacement of GEP data with these algorithms is still controversial because a higher proportion of cases could not be allocated correctly into the two subtypes using IHC in each algorithm. The present study showed higher percentage of GCB subtype than non-GCB but these subtypes could not be correlated with survival. Extensive efforts are required for sub-classifying DLBCL into its immunophenotypes so that clinical approach to the disease could take into account the distinct biology of these immunophenotypes.
We thank Ms. Sangeeta and Mr. Surendra for their proficient technical assistance.
Financial support and sponsorship
Rajiv Gandhi Cancer Institute and Research Centre.
Conflicts of interest
There are no conflicts of interest.
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Rajiv Gandhi Cancer Institute and Research Centre, Sector 5, Rohini, New Delhi
Source of Support: None, Conflict of Interest: None
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]