|Year : 2023 | Volume
| Issue : 3 | Page : 549-555
|Primary central nervous system lymphoma: Comprehension of cell-of-origin subtypes
Shruti Rao1, Sridhar Epari1, Tanuja M Shet1, Sumeet Gujral1, Hasmukh Jain2, Bhausaheb Bagal2, Manju Senagar2, Prakash Shetty3, Aliasgar Moiyadi3, Jayant Sastri Goda4, Tejpal Gupta4
1 Department of Pathology, Tata Memorial Hospital and ACTREC, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
2 Department of Medical Oncology, Tata Memorial Hospital and ACTREC, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
3 Department of Surgical Oncology, Tata Memorial Hospital and ACTREC, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
4 Department of Radiation Oncology, Tata Memorial Hospital and ACTREC, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
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|Date of Submission||03-Apr-2021|
|Date of Decision||03-Feb-2022|
|Date of Acceptance||04-Feb-2022|
|Date of Web Publication||05-May-2023|
| Abstract|| |
Primary central nervous system diffuse large B-cell lymphoma (PCNS-DLBCL) is an uncommon extranodal lymphoma that accounts for more than 95% of all the CNS lymphomas. Unlike its systemic/nodal counterpart, which is currently subtyped into cell-of origin (COO) subtypes, its feasibility and utility are largely debatable in PCNS-DLBCL. Objectives: To classify PCNS-DLBCL into COO-subtypes based on immunohistochemical algorithms by Hans and Choi and evaluate concordance between the two. A further aim is to investigate the clinicoradiological and histomorphological parameters of the subtypes thus obtained. Materials and Methods: As many as 143 cases of primary CNS lymphoma were evaluated by immunohistochemistry for CD10, BCL6, MUM1, GCET, and FOXP1 and based on which the said 143 cases were further classified into COO subtypes using Hans and Choi algorithms. Results: Mean age was 53.8 years with marginal male preponderance and predominantly centroblastic morphology (75.5%). CD 10 was positive in 8.9% of the cases, BCL6 in 58.6%, MUM1 in 89.9%, GCET in 32.9%, and FOXP1 in 79.5%. As much as 84.9% cases were of non-germinal center B-cell (GCB) subtype and 15.1% cases were of GCB subtype as determined based on Hans algorithm. Furthermore, 90.7% cases were of activated B-cell (ABC) subtype and 9.3% cases were of GCB subtype according to Choi algorithm. A 91.8% concordance was observed between Hans and Choi algorithms. Among the 6 discordant cases, 5 cases were subtyped as GCB by Hans and ABC by Choi and 1 case as ABC by Hans and GCB by Choi. Conclusion: Most of PCNS-DLBCLs are of non-GCB/ABC COO subtype, but inconsistences abound in the utility of IHC algorithms in PCNS-DLBCL COO subtypes.
Keywords: Activated B-cell, cell-of-origin, diffuse large B-cell lymphoma, germinal center B-cell, primary central nervous system lymphoma
|How to cite this article:|
Rao S, Epari S, Shet TM, Gujral S, Jain H, Bagal B, Senagar M, Shetty P, Moiyadi A, Goda JS, Gupta T. Primary central nervous system lymphoma: Comprehension of cell-of-origin subtypes. Indian J Pathol Microbiol 2023;66:549-55
|How to cite this URL:|
Rao S, Epari S, Shet TM, Gujral S, Jain H, Bagal B, Senagar M, Shetty P, Moiyadi A, Goda JS, Gupta T. Primary central nervous system lymphoma: Comprehension of cell-of-origin subtypes. Indian J Pathol Microbiol [serial online] 2023 [cited 2023 Sep 27];66:549-55. Available from: https://www.ijpmonline.org/text.asp?2023/66/3/549/375890
| Introduction|| |
Primary central nervous system (PCNS) diffuse large B-cell lymphoma (DLBCL) accounts for 2.4–3% of all brain tumors and 4–6% of all extranodal non-Hodgkin lymphomas (NHLs). It is confined to the brain, spinal cord, leptomeninges, or eyeball without any underlying immunosuppression. The current WHO classification classifies nodal/systemic DLBCL NOS into germinal center B-cell (GCB subtype) and activated B-cell (ABC/non-GCB subtype) cell-of-origin (COO) subtypes as they are clinicobiologically distinct. The genesis of these was based on gene expression profiling (GEP) and its resemblance to the normal lymph nodal areas, but GEP has its own limitations when applied in routine practice. Different immunohistochemistry (IHC)–based algorithms have emerged and showed equivalence to GEP., However, the COO-based studies are limited on PCNS-DLBCL. This study is undertaken to classify PCNS-DLBCL into COO-subtypes based on two commonly used Hans and Choi IHC algorithms and to further evaluate the concordance between the obtained subtypes.
| Materials and Methods|| |
This institutional review board–approved retrospective study included all the diagnosed cases of PCNS-DLBCL between 2010 and 2018. Dural, skull/orbital-based pediatric cases, systemic involvement on follow-up and immunosuppression-associated ambiguity in histological diagnosis, and non-DLBCL type of lymphomas were all excluded, and a final cohort of 143 cases was obtained. All the clinical, radiological, treatment, and follow-up details were extracted from medical records.
Histomorphology and immunohistochemistry
Histologically, all the cases were morphologically classified into centroblastic/immunoblastic/anaplastic morphological types. In addition, the presence of angiocentricity and necrosis was also noted.
IHC for CD3, CD20, MIB1/Ki-67, CD10, BCL6, and MUM1 was performed by an autoimmunostainer (Ventana Benchmark XT autoimmunostainer) and GCET and FOXP1 were performed using manual methods. The related antibody details are depicted in [Table 1]. IHC was interpreted as positive if more than 30% cells showed immunostaining of any intensity. Hans and Choi algorithms were applied for further subtyping into different COO subtypes.,
Statistical analysis was performed for correlating histomorphology, clinical parameters, and IHC markers using IBM SPSS Software 23 version. Kappa statistics were applied to measure concordance between the Hans and Choi COO subtypes.
| Results|| |
Age range was 20-78 years (mean: 53.8, median: 55, and interquartile range: 47-62 years) with a marginal male preponderance (M:F = 1.2:1). Cerebral hemispheric location (n = 105; frontal: 50, frontoparietal: 11, frontotemporal: 4, cerebellum: 15, corpus callosum: 5, thalamus and basal ganglia: 6, intraventricular: 8, periventricular: 5, and hypothalamus: 1) was the commonest. Radiologically deep brain involvement was seen in 69 cases (76.7%). As many as 11 cases were positive on CSF cytology (one showed radiological involvement, while 10 other cases [16.6%] showed no radiological involvement of leptomeninges or the spine).
Histologically, centroblastic variant (n = 108, 75.5%) was more common than the immunoblastic variant (n = 35, 24.5%). Angiocentricity and tumor necrosis were noted in 107 and 49 cases, respectively. MIB-1 labeling index ranged from 40% to 95%. [Figure 1]
|Figure 1: Histomorphological features of PCNSL: (a. 400 x H and E) Sheets of intermediate-sized lymphoid cells of centroblastic morphology with (b. 100 x H and E) areas of necrosis and (c. 400x) high Ki-67 proliferative index. In d. (400x H and E) the tumor shows angiocentricity with neoplastic cells infiltrating the layers of the vessel wall (e. 400x) as highlighted by immunohistochemistry for CD20 and (f. 400x reticulin stain) a concentric network of reticulin fibers|
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Immounohistochemistry-based COO subtyping
Hans algorithm could be applied in 106 cases (74.1%). As many as 90 (84.9%) were of the non-GCB subtype, and 16 (15.1%) were of the GCB COO subtype. While Choi algorithm was applied in 75 cases (52.4%), 7 (9.3%) and 68 (90.7%) cases were of GCB and ABC subtypes respectively [see [Figure 2]].
|Figure 2: Immunohistochemical evaluation for cell-of-origin subtyping in PCNS-DLBCL: Representative photomicrographs of H and E (400x) and B-cell differentiation markers (400x) are shown. (a) A case with immunoblastic morphology and CD10+/BCL6–/MUM1–/GCET+/FOXP1– categorized as GCB by both Hans and Choi algorithms. (b) A case with centroblastic morphology and CD10+/BCL6+/MUM1+/GCET+/FOXP1 + categorized as GCB by Hans algorithm and ABC by Choi algorithm|
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Hans subtypes (n = 106)
GCB subtype (n = 16, 15.1%) was seen most frequently in the age range of 40-60 years, with male preponderance (M: F = 2.2:1) and predominant centroblastic histomorphology (n = 11, 68.8%). Necrosis and angiocentricity were seen in 56.3% and 62.5% of the cases, respectively. As much as 50% (n = 7) of the patients were of ECOG score 3, and 80% presented (n = 4) presented with elevated serum LDH levels. Radiologically 57.1% (n = 8) were multifocal with deep brain involvement.
Non-GCB subtype (n = 90, 84.9%) was also common in the 40-60 years age range with no sex predilection, and 58.5% (n = 65) of the cases were of centroblastic histomorphology. Angiocentricity and necrosis were seen in 76.7% (n = 69) and 31.1% (n = 28) of the cases, respectively.
However, these clinico-radio-pathological features did not show any correlation for the Hans subtypes.
Choi subtypes (n = 75)
As many as 7 (9.3%) and 68 (90.7%) cases belonged to the GCB and ABC subtypes, respectively. Both GCB and ABC subtypes were frequent in the age group of 40-60 years with slight male preponderance. Centroblastic morphology (GCB: n = 6 [85.7%]; ABC: n = 49 [27.9%]) was predominant over immunoblastic morphology (GCB: n = 1 [14.3%]; ABC: n = 19 [72.1%]). Angiocentricity was seen in 57.1% (n = 4) GCB and 72.1% (n = 49) ABC subtypes. Necrosis was seen in 42.9% (n = 3) GCB and 20.6% (n = 19) ABC subtypes. The predominant ECOG score in GCB cases was 3 (n = 3, 42.9%), while the ABC subtype frequently presented with an ECOG score 4 (n = 16, 32.7%). As much as 66.7% of the GCB subtypes presented with multifocality and deep brain involvement and 50% with elevated serum LDH levels. ABC subtype presented with elevated serum LDH levels (59.5%) and single mass lesion (52.9%) involving the deeper structures of the brain (74.5%). However, no correlation was obtained between these parameters for the Choi GCB/ABC subtypes.
Correlation between Hans and Choi algorithms [Figure 3]
|Figure 3: Pictorial comparison of Hans and Choi COO subtypes in a cohort of 73 cases. GCB marked as yellow and non-GCB/ABC as green (ID: identifier that represents a single patient; that is, each row represents one patient who is assigned to either of the subtypes: GCB—germinal center B-cell; ABC—activated B-cell)|
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Both Hans and Choi algorithms could be applied in a total of 73 cases (50.3%). Eleven (15.1%) of these were classified as GCB subtype by Hans algorithm and 62 cases (84.9%) as non-GCB subtype. A total of 7 cases (9.6%) were of the GCB subtype, and 66 cases (90.4%) were classified as the ABC subtype based on Choi algorithm.
Of the 7 Choi GCB cases, 6 of them were determined as the GCB subtype based on Hans algorithm as well, but out of the 66 Choi algorithm-based ABC subtype cases only 61 of them were found to be non-GCB subtypes based on the Hans algorithm. On the contrary, of the 11 Hans GCB cases, only 6 were found to be GCB based on Choi, and, except for one case, all Hans non-GCB cases were of the ABC subtype as determined based on Choi. [Table 3]
A 91.8% concordance was observed between Hans and Choi algorithms (67 cases; 6: GCB, 61: non-GCB/ABC). Using the Kappa statistical analysis, we found there was a 0.62 agreement between the results of both the algorithms (moderate agreement). Confidence interval was 0.35-0.90, where the Z value was 1.96 and standard error 0.14.
| Discussion|| |
The study investigated PCNS-DLBCL for its clinical-histomorphological features and IHC-acquired COO subtypes. The original method used to define these entities was GEP using RNA microarray on frozen tissue samples (e.g., Lymphochip microarray or Affymetrix array). Whereas COO subtyping based on IHC has gained wide applicability for ease of adoption in the routine practice, different IHC algorithms are used to derive binary subdivision into GCB and non-GCB/ABC subtypes.,, The Hans algorithm used three immunostains (CD10, BCL6, and MUM-1/IRF4), while the Choi algorithm used five immunostains (GCET1, also called centerin or serpin A9, and FOXP1 antibodies, in addition to the three antibodies of the Hans algorithm)., Unlike these two algorithms, Chang and Robert's IHC-based algorithm for DLBCL-COO subtyping was designed to classify into three categories using two GCB markers—CD 10 and BCL6—and two activation markers—MUM1 and CD138. Their GCB pattern (called pattern A) is characterized by GCB markers' positivity without activation markers. Unlike the Hans and Choi algorithms, Chang and Robert's subdivided the second group into further subgroups: the activated GCB pattern (called pattern B), expressing at least one GCB marker and one activation marker, and activated non-GCB pattern (called pattern C), expressing both or one of the activation markers but without any of the GCB markers. Other algorithms include Muris (CD10, BCL2, MUM1, FOXP1, and LMO2), Natkunam (LMO2), and Nyman (MUM1, GCET, CD10, FOXP1 and LMO2) algorithms.,, Meyer et al. observed the highest concordance for Choi algorithm with GEP (87%) followed by the Hans algorithm, and the least concordance for Natkunam algorithm.
The median age of 55 years of the participants in the present study is similar to those in the prior studies.,,,, M: F ratio varied from 1.6:1 to 1:1.8 across different studies.,,,,,,,,,,, The present study shows slight male preponderance in the overall cohort.
Preferential location for cerebral hemisphere, especially the frontal region, is noted across different studies, including in the present study; however, the possibility of this being secondary to the bias derived from easier surgical accessibility rather than the true disease preferential location cannot be ruled out. Few other radiology studies reported supratentorial as the most common location.,,,, Interestingly, one was of hypothalamic location in the present study. PCNSL in hypothalamus is extremely rare and is described in isolated case reports. Spinal cord (1%) and corpus callosum (5%) are some of the other uncommon sites. Camilleri-Broet et al. and Raoux et al. reported the involvement of deep brain structures in 54.9% and 51.3% cases, respectively; in the present study it is approximately 72.7%; however, Aki et al. reported the involvement of deep brain structures as uncommon, that is, 31.4%. ECOG performance status was more than 1, and elevated baseline LDH levels were noted.,,
Histologically, PCNS-DLBCL showed diffuse infiltration by sheets of intermediate to large-size neoplastic lymphoid cells with irregular nuclear membrane and prominent nucleoli along with diffuse immunoreactivity for CD20. Based on the histomorphology of the tumor cells, DLBCL was classified into centroblastic and immunoblastic subtypes. Most PCNS-DLBCLs are predominantly of centroblastic morphology and rarely immunoblastic.,,,,,,,, Characteristic angiocentric pattern and areas of necrosis were also reported in the present study. Both angiocentricity and necrosis were expectedly more evident in surgical debulking specimens than in stereotactic biopsies. They were more frequent in the immunoblastic morphological subtype, but their association was statistically insignificant.
|Table 4: Summary of criteria used in few recent studies for PCNSL-COO subtyping|
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Immunohistochemical stains for the diagnostic work-up include LCA (CD45), CD20, and CD3. The neoplastic lymphoid cells are positive for CD20, while CD3 highlights admixed reactive lymphoid cells. Most tumors are positive for BCL2; 48 of 69 (69.6%) cases of the study cohort were also positive for BCL2; however, this was not included in the study, as it was not in the panel of COO subtyping. Thus, assessment was done on IHC markers for germinal center (CD10, BCL6, and GCET) and post-germinal center (MUM1 and FOXP1). Most PCNS-DLBCL are positive (59-97%) for MUM1 and BCL6 (35.9-90.5%) but are negative for CD10 (81-100%).,,,,,,,,,,, Thus, the study findings showed that MUM1 and BCL-6 were positive in 89.9% and 58.6% of the cases, respectively, whereas CD10 was negative in 91.1%. However, none of the prior seminal studies on PCNS-DLBCL had evaluated for GCET and FOXP1, and this has been done in the present study where findings showed that most cases were positive for FOXP1 (n = 62, 79.5%) than for GCET (n = 25, 32.9%).
Hans et al. in 2004 and Choi et al. in 2009 reported a set panel of IHC markers for COO subtyping of DLBCL., However, due to the ease of use with Hans algorithm, most of the PCNSL studies were with the same.,,,,,,,,,. PCNS-DLBCL with IHC-based COO subtyping appears to be a relatively more homogenous disease as compared to its systemic counterpart with a marked predominance of non-GCB subtype.,,,,,,,,,,,, Camilleri-Broet et al. studied by far the largest cohort with 83 cases of PCNSL; by application of Chang IHC algorithm, they found ABC phenotype to be the most frequent (96.3%) with the expression of BCL6 in one-third of their study cohort. In another study, Raoux et al. applied the IHC algorithms by Hans and Muris to a cohort of 39 cases and obtained contradictory results (Hans: 66.7% non-GCB; Muris: 59% GCB). As CD138 expression (used in Chang and Robert's algorithm) is extremely rare in PCNS-DLBCL, raising doubts on the utility of this in PCNS-DLBCL, it was not evaluated in this study. However, the present study evaluated both Hans and Choi algorithms and showed non-GCB/ABC as the predominant subtype in both. In the present study as well as in previous studies, few discordances were identified with BCL6 (GC marker) positivity in non-GCB/ABC cases; however, such positivity in the presence of MUM1 positivity had the same clinical outcome as that of the non-GCB subtype; MUM1 positivity, as post-GC marker, superseded BCL6 positivity as a GC marker, resulting in its final characterization as a non-GCB subtype in these co-positive cases. Choi et al. used a hierarchical sequence of IHC markers based on the sensitivity and frequency of their association with GCB or ABC and the cut-offs were determined based on the specificity for predicting COO-subytpe. It did not include BCL6 expression as a GCB marker due to poor inter-laboratory agreement in interpretation. Cheng et al. proposed a pattern of “activated GC type” and “activated non-GC type” probably to demonstrate the histogenesis of BCL6 co-expression more accurately. Mahadevan et al., Camilleri-Broet et al., Momota et al., and Bhagavathi et al. reported 8.3%, 13.3%, 14.8%, and 9.5% cases as unclassifiable, respectively, because of the negativity found for all the IHC markers. However, in the present study, only 2 cases (2.7%) were negative for all the markers; however, these cases were not excluded and were classified rather as a non-GCB/ABC subtype considering a pan-negative expression of all the markers in a setting where external control was positive.
In the present study, a moderate concordance (kappa = 0.62) was derived between the two methods. The discordant cases (6 cases—8.2%) were further analyzed for the cause of discrepancies. Two cases expressed only BCL6 with the negative GC marker—CD10 and the non-GCB marker—MUM1, leading to their classification as GCB based on Hans algorithm due to the positivity of a single GC marker in the absence of others. On the other hand, with Choi algorithm, this was classified as the ABC subtype because the GC marker—GCET was negative with corresponding positivity of FOXP1. Evidently, according to Hans, a single GC marker was positive, and, according to Choi, a single post-GCB marker positivity leading to discrepancy. In three of the cases, all the IHC markers for Hans and Choi algorithms were positive. In the presence of CD10 positivity, and irrespective of the expression of BCL6 or MUM1, the Hans COO subtype was GCB. On the contrary, according to Choi algorithm, positivity of MUM1 even in the presence of GCET expression was classified as the ABC subtype. In a single discrepant case, a co-expression of BCL6 and MUM1 was seen, with the expression absent for any of the Hans or Choi markers. According to Hans algorithm, the co-expression cases are of the ABC subtype due to MUM1 positivity, but, according to Choi algorithm, MUM1 had no role in cases that lacked the expression of GCET; thus, FOXP1's negativity in this case had led to its classification as the GCB subtype. It is clearly evident that Hans algorithm is trending towards the GCB subtype, while Choi algorithm inclines towards the ABC subtype. This discordance is probably accentuated by the high frequency of MUM1 expression in PCNS-DLBCL, and, thus, the application of these algorithms for COO subtyping in PCNS-DLBCL needs to be further validated without equating it with the nodal counterparts. It also gives rise to the possibility of fluidity in the COO subtyping of PCNS-DLBCL, probably reflecting a lack of fixed stage of maturation of the COO subtypes of PCNS-DLBCL.
None of the earlier PCNSL studies have evaluated the Choi algorithm, though most comparative studies on nodal DLBCL showed excellent concordance. Meyer et al. showed the Choi algorithm to be the most predictive (concordance of 87%) based on the GEP results. Countinho et al. reported excellent concordance (kappa = 0.80-0.90) between Hans and Choi algorithms, whereas Peng et al.'s study reported moderate concordance between Hans and Choi algorithms (kappa = 0.66) where 83% cases showed no discrepancy in COO subtyping. Though the present study is on PCNS-DLBCL, it showed findings similar to that of Peng et al.'s.
The present study is limited by lack of follow-up data in spite of it utilizing a relatively large cohort, which, therefore, prevented the present study from demonstrating prognosis and survival in PCNS-DLBCL cases. The present study did not include any molecular-based method to cross-check and confirm the IHC-based COO subtypes. The concordance between the IHC COO subtypes across all the algorithms used is relatively poor for PCNSL, which, in turn, raises doubts about the efficacy of using the IHC algorithm-based COO to classify the PCNS-DLBCL cases into appropriate subtypes.
In conclusion, a larger prospective study with adequate clinical information would be a more robust and effective option to ascertain the future clinical utility of COO subtyping in PCNSL. To conclude, the present study can be best defined as a hypothesis-generating comparative study whose findings can be used by similar future studies to help sharpen their analyses and derive more robust findings.
Financial support and sponsorship
TMC intramural grant.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4]