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Utility of reverse transcriptase – Multiplex ligation-dependant probe amplification (RT-MLPA) in the molecular classification of Diffuse Large B cell lymphoma (DLBCL) by cell-of-origin (COO)


1 Departments of Pathology, Christian Medical College, Vellore, Tamil Nadu, India
2 Departments of Haematology, Christian Medical College, Vellore, Tamil Nadu, India

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Date of Submission09-Apr-2021
Date of Decision18-Jul-2022
Date of Acceptance20-Jul-2022
Date of Web Publication16-Nov-2022
 

   Abstract 


Classifying diffuse large B cell lymphomas, not otherwise specified (DLBCL, NOS), is based on their cell-of-origin (COO) which is included in the WHO classification (2016), is essential to characterize them better in context of prognostication. While gene expression profiling (GEP) considered the gold standard and more recently, the Nanostring-based approach, classify these tumors accurately, many laboratories with limited resources and instrumentation need an alternate approach that is reliable, inexpensive, and with a reasonable turnaround. The Reverse Transcriptase Multiplex Ligation Dependant Probe Amplification (RT-MLPA) to subtype DLBCL, NOS cases, as designed by CALYM group appears to provide a good alternative but needs to be validated in other centres. Therefore, this study evaluated DLBCL, NOS and compared the results of RT-MLPA to that obtained by immunohistochemistry using the Hans algorithm. Methods: Sixty-five DLBCL, NOS cases were included and the RT-MLPA was set up and standardized using probes that were designed by the CALYM study group. Briefly, RNA was extracted converted to cDNA and the 21-gene expression classifier that also included probes to detect MYD88 mutations and EBER mRNA was performed by MLPA. The results were analyzed by the open home grown software designed by the same group and compared to the results obtained by IHC. Results: Forty-four of the sixty-five cases provided concordant results (k = 0.35) and if the MYD88 results were to be used as a classifier the concordance would have improved from 67.7% to 82%. The 21 discordant cases were divided into five categories to provide a possible explanation for the discordance. Further 26% and 31% of the samples tested were positive for MYD88 mutations and EBER mRNA, respectively. The test had a turnaround of three days. Conclusion: The test provided moderate (67.7%) concordance when compared with IHC and perhaps would have provided higher concordance if compared with GEP. The test also has the advantage of providing information on the MYD88 and EBV infection status. It was found to be reliable, easy to perform and standardize, requiring only routine instruments available in most molecular laboratories. The RT-MLPA assay therefore provides an alternative for laboratories that would require subtyping of DLBCL, NOS cases in the absence of an access to GEP or other instrument intensive methods.

Keywords: ABC, cell-of-origin, DLBCL, GCB, RT-MLPA


How to cite this URL:
Dcunha N, Sakhti D, Sigamani E, Chandramohan J, Korula A, George B, Manipadam MT, Pai R. Utility of reverse transcriptase – Multiplex ligation-dependant probe amplification (RT-MLPA) in the molecular classification of Diffuse Large B cell lymphoma (DLBCL) by cell-of-origin (COO). Indian J Pathol Microbiol [Epub ahead of print] [cited 2022 Dec 7]. Available from: https://www.ijpmonline.org/preprintarticle.asp?id=361294





   Introduction Top


Diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS) is the most frequent non-Hodgkin lymphoma (NHL) accounting of ~30-40% of all NHLs diagnosed.[1],[2] A large number of clinical and genomic studies have helped to highlight the heterogeneity of these tumors.[3],[4],[5],[6],[7]

The advancement in genomic technologies have resulted in the cell-of-origin (COO) classification of DLBCL, NOS.[8] While the early studies were driven by microarray platforms, the currently accepted COO-based classification, extensively used the gene expression classifiers (GEPs), helping to throw light on the biology of each subtype.[5],[9],[10] This COO-based classification now recognizes three subgroups including Germinal Centre B-cell (GCB) like subtype (originating from the centroblasts in the dark zone), activated B-cell (ABC) subtype (includes activated B-cells transitioning to plasmablasts), and an unclassified subtype. Each of these is now known to be biologically a distinct entity with differing prognosis, where the GCBs appear more curable with five-year overall survival (OS) of 75% while the prognosis of the aggressive ABC type is more dismal.[5],[11] A rare subtype called Primary Mediastinal B cell lymphoma (PMBL) with good prognosis is typically described as those arising in the mediastinum among younger patients. However, evidence in recent years shows it can include non-mediastinal sites as well, complicating the accurate diagnosis of this entity.

The COO classifier (ABC Vs GCB) is a now a part of the WHO classification of DLBCL, NOS requiring laboratories to identify these entities that might impact treatment strategies.[12] Conventional histology cannot help to stratify the subtypes, however, immunohistochemistry (IHC)-based Hans algorithm, which is most frequently used, has helped to distinguish the GCB from the non-GCB types.[13] In the absence of high concordance between IHC and GEP and GEP not lending itself to a routine diagnostic format, the focus has shifted to find better alternatives. In the recent years, a 20-gene signature-based assay (Lymph 2Cx) on a nanostring platform has been found to be very promising, making it amenable to work with formalin fixed paraffin embedded tissues, FFPE, while retuning results in ~36 hours.[14] Unfortunately, the high costs associated with the nanostring make it a difficult resource limited settings. Another alternate approach has been described by the CALYM study group that also utilizes FFPE but adopts a strategy that can be easily set up in any routine molecular laboratory with basic instrumentation.[15],[16] This testing system, with a 21-gene signature, has been shown to rapid, reliable, and cost-effective with ~85% concordance to the GEP, making this an attractive assay for molecular labs in resource limited setting. Further, the panel has the additional advantage of detection both the MYD88 mutation (L265P) and EBV infection status. However, there is no clarity on the utility of this system in settings other than in the laboratory that originally described the assay. We, therefore, decided to determine the utility of the RT-MLPA-based assay in our laboratory evaluating 65 consecutive cases of DLBCL, NOS comparing the results to that obtained using the Hans algorithm.


   Materials and Methods Top


Cases: 69 consecutive cases of DLBCL, NOS, obtained over an 18-month period (Jan 2019-June 2020) were included in the study after patient consent. However, only 65 cases were included as four samples did not provide good quality RNA. The approval of the institutional review board was obtained to conduct the study.

IHC: IHC was performed on all cases using the standard IHC markers used in a case of DLBCL, that is, CD20, CD3, MIB1, CD10, BCL6, BCL2, C-MYC and MUM1 and IHC was performed on the Ventana Benchmark system. Additional IHC markers, for example, CD30, EBV-LMP1 were used in some cases to rule out other differential diagnosis. The Hans algorithm was applied to all cases and they were subsequently classified as GCB or Non-GCB subtypes.

RNA extraction: RNA samples were extracted from FFPE tissue using the RecoverAll Total Nucleic Acid Extraction Kit (Ambion, life technologies, USA). About 3-4 sections that are 5 μm in thickness were used for extraction following the manufacturer's instructions. In this study, the extracted RNA was checked for quality based on the 260/280 ratio based on the Nanodrop (Nanodrop technologies Inc, USA) and considered acceptable if the values greater than equal to 1.8. Further, the amplifiability of the RNA was determined by the using the primers for a housekeeping gene (GAPDH).

Reverse Transcription (cDNA conversion): The reverse transcription was performed using the High capacity cDNA reverse transcription kit (Life technologies, USA). Briefly, 500-1000 ng of RNA was reverse transcribe using random hexamers.

RT-MLPA assay: The RT-MLPA test designed by the CALYM study group, looks at the expression of 21 different markers using primers across exon–exon boundaries where all 5' probes have been designed to have a GTGCCAGCAAGATCCAATCTAGA tail at their 5' ends and all 3' probes have a TCCAACCCTTAGGGAACCC tail at their 3' end to allow final amplification.[16] The primers have also been designed with varying length of spacers to allow amplification of different lengths of products.

The MLPA probe mix was be prepared using 10 μmol/L dilution of probes and competitors in the ratio as described in the CALYM study. Then, 6.25 μl of cDNA was mixed with 3 μl of RT-MLPA probe mix containing 1.5 μl of SALSA-MLPA buffer (MRC–Holland, The Netherlands) and 1.5 μl of final dilution probe mix. The reaction contents were then denatured at 95°C for 2 min and hybridized at 60°C for 1 hour. Ligation of the annealed oligonucleotides was performed at 54°C for 15 min, adding 32 μl of ligation mix, and heated for 5 min at 98°C. Next, 7.5 μl of the Salsa PCR master mix containing the labelled forward primer and the unlabelled reverse primer and 2.5 μl of ligation buffer was added to it.

PCR amplification was performed involving 35 cycles of 94°C for 30 seconds, 58°C for 30 seconds, and 72°C for 30 seconds, followed by 72°C for 4 minutes. The resulting MLPA amplicons was analyzed by fragment analysis using an ABI 3500 capillary electrophoresis system (Applied Biosystems, Foster City, CA). For fragment analysis, PCR amplicon was mixed with 19 μl of Hi-di Formamide (Thermo Fisher, USA) and 0.5 μl Genescan-400 HD ROX size standard (Applied Biosystems, Foster City, USA). The mixture was incubated for 3 minutes at 95°C and analyzed. The results of the fragment analysis were used to deduce the molecular subtype based on COO. The RT-MLPA interface https://bioinformatics.ovsa.fr/138/MLPA was used for analysis.[16] The results obtained by RT-MLPA and IHC were compared and only that were GCB by IHC but were either ABC or PMBL by RT-MLPA and those that were non-GCB by IHC but were GCB by RT-MLPA were considered discordant. The ones that were unclassified by RT-MLPA were not considered discordant as this class does not exist by IHC for comparison.

Statistical analysis

The results of IHC and RT-MLPA were compared and the correlation coefficient was calculated using the Cohen's Kappa. Concordant cases were defined as those cases that were GCB on IHC as well as RT-MLPA and the cases that were non-GCB on IHC and were either ABC, PMBL, or unclassified on RT-MLPA. All other combinations were considered discordant.


   Results Top


The testing system was evaluated on 69 cases of DLBCL, but only 65 were included for analysis as four samples did not yield good quality RNA. A typical report generated on analysis of a sample using the home-grown software of the CALYM study group is shown in [Figure 1]. Of the 65 cases, the RT-MLPA classified 42 cases as GCB, 11 as ABC, 3 as PMBL, and 9 were unclassified. A comparison of the results of IHC and RT-MLPA helped to classify 44 cases accurately while 21 cases appeared discordant between the two systems [Figure]/[Table 1] and [Table 2]. Concordance of the two methods was 67.7%. Kappa statistic of 0.35 i.e., the two methods showed moderate agreement (Std error: 0.057 and CI 58.05 -81.80). The 21 discordant cases were reviewed and classified into the following categories:
Figure 1: Sample DLBCL case with RT-MLPA classified as GCB. The algorithm compares the expression of various genes and gives a final result. Blue: ABC-related genes, Yellow: GCB-related genes, Pink: PMBL-related genes, Black: Other miscellaneous genes added to the RT-MLPA (Original)

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Table 1: Table showing the concordant (n=44) and discordant (n=21) cases between IHC and RT-MLPA (Original)

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Table 2: Discordant DLBCL cases with residual follicles or secondary to follicular lymphoma grade 3B (FL3B) and discordant DLBCL cases with MYD88 mutations on RT-MLPA. (Original)

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  1. DLBCL cases with residual follicles or secondary to Follicular lymphoma grade 3B (FL3B).
  2. DLBCL cases with MYD88 mutations on RT-MLPA
  3. Unclassified case with background reactive cells
  4. PMBL cases by RT-MLPA
  5. Discordant cases between RT-MLPA and IHC with no definite explanation.


Category 1 of the discordant cases [Figure/[Table 3]] included four cases that were classified by IHC as non-GCB while RT-MLPA called them GCB subtype. All four samples were also CD21 and CD23 positive, indicating that they had expanded dendritic networks. Category II of discordant cases included seven cases, where six were classified as GCB and one case was unclassified by RT-MLPA. All seven cases were also found to have MYD88 (L265P) mutations. Category III included a discordant case where RT-MLPA fails to classify the case when IHC called it GCB but contained a rich background of reactive cells on morphology.
Table 3: Discordant DLBCL cases classified as PMBL by RT-MLPA and Discordant DLBCL cases between RT-MLPA and IHC with no definite explanation (Original)

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There were three cases classified as PMBL based on RT-MLPA results. Two of these three cases were in variance with IHC results and were classified as category IV. Finally, seven cases with discordance were grouped as category V, where a plausible reason for their discordance could not be explained.

In addition to all these findings RT-MLPA helped to detect MYD88 (L265P) mutation in 26% (n = 17) of all samples tested. Further 31% of all the samples tested were also positive for EBV infections that could be detected because the RT-MLPA also includes a probe detecting EBER mRNA (EBV-encoded small nuclear early region).


   Discussion Top


Classifying DLBCL, NOS into its corresponding subtypes is currently essential not only with the WHO classification including it as diagnostic criteria but also to classify tumors in a prognostically relevant manner.[8] This study has attempted to determine the utility of the RT-MLPA that was described as an alternate method by the CALYM study.[16] The data obtained by RT-MLPA has been compared to that obtained by IHC to determine concordance and therefore define utility.

Of the 65 cases included a concordance was seen in 44 cases (67.7%) with a kappa of 0.35, indicating moderate concordance. The RT-MLPA when evaluated previously had shown lesser concordance with the Hans IHC algorithm (78.8%) while a much higher concordance was seen with GEP (85.0%).[16] Although IHC is widely available and cost effective, there have been studies stating GEP and not immunophenotypic algorithms predicts prognosis in patients with diffuse large B-cell lymphoma treated with immunochemotherapy.[17] Perhaps, the use of GEP would have provided better concordance while evaluating the utility of the RT-MLPA and lesser samples would have appeared discordant than a third as seen now.

The 21 discordant cases in the study were grouped into five categories to determine if a plausible explanation exists for such discordance. Category I included four GCB cases by RT-MLPA that were positive for CD21 and CD23 and classified as non-GCB by IHC. This can probably be explained at the molecular level where a follicular lymphoma is known to be a GCB type and this expression pattern is typically maintained even in the transformed biopsy. Davies et al.[18] classified all cases of Follicular 3B lymphomas by GEP as GCB types and a small percentage were non-classified but none were ABC type DLBCL, NOS. Interestingly, the IHC approach classified some of those cases as non-GCB. Therefore, in light of such findings, it is quite possible that RT-MLPA actually classified these four cases appropriately.

Category II included seven cases all of which were MYD88 (L265P) mutation positive. MYD88 mutation is now known to be highly associated with ABC subtype and the very presence of this mutation indicates bad prognosis.[19],[20],[21] Unfortunately, the CALYM software is by default not meant to classify samples as ABC based on the presence a MYD88 mutation, perhaps because compelling data of the strong association between the mutation and the ABC type is fairly recent. The software classifies DLBCLs as PMBL or no-PMBL and then further classifies the non-PMBL as GCB and ABC types. Therefore, accepting this inherent limitation of the software and reclassifying all MYD88 positive cases as ABC, would have lent a much higher concordance with IHC, that is, a concordance of 82%. This level of concordance is certainly helpful while considering the RT-MLPA as a testing system as part of routine diagnostics.

Category III included a case where the sample could not be classified by RT-MLPA but was a GCB type by IHC. On morphological examination of this case, it had a lot of background reactive lymphocytes which probably have their own expression profiling causing such cases to remain in the unclassified class. This is a disadvantage of not only RT-MLPA but also when the GEP system is used while IHC being a binary concept of GCB or non-GCB, does not encounter the same issue. This raises a pertinent question on how these cases should be regarded as concordant or discordant since IHC can never categorize a sample as unclassified.

Category IV included three cases that were classified as PMBL by RT-MLPA. Of the three, a PMBL diagnosis was established on one case with a mediastinal mass. The remaining two cases would perhaps have to be considered erroneously classified by RT-MLPA if the WHO classification that defines PMBL as a lymphoma with mediastinal involvement is adhered to. However, more recently several investigators and also the seminal work of Lacy et al.[19] to classify DLBCL using an NGS-based approach, have all described the presence of a PMBL-like genetic signature even from nodes outside of the mediastinum.[20],[21] This is a relatively new finding and it might be a while before the therapeutic and prognostic consequences of such a finding is fully understood.

Category V included seven cases where the authors were not able to attribute a definitive explanation to determine the plausible reasons for discordance.

Apart from the fact that many of the discordant cases could be explained providing an opportunity to adopt the system in routine practice, it is also important to note that MLPA had an advantage over IHC in being able to determine not only the MYD88 positivity but also determine the percentage that are EBV positive. Detecting both these features in every DLBCL sample can be of value especially considering the evidence available today. MYD88 is not only strongly associated with the ABC subtype but it also directly puts these cases in a group with poor prognosis.[19] Similarly testing for Epstein–Barr Virus (EBV) in every sample of DLBCL has gained ground with the WHO classification in 2016[12] being revised from 'EBV+ DLBCL in the elderly' to the recognition of a rare entity, the NOS category, which is associated with an aggressive type of tumor with no history of immunosuppression.[22],[23] EBER is an RNA method while EBV-LMP1 is an IHC. EBER is positive in all phases of EBV infection but EBV-LMP1 is negative in some latent phases of the infection. Therefore according to the WHO 2016[12] wherever possible EBER should be done to detect EBV presence rather than only EBV-LMP1. EBER RNA is detected through RT-MLPA method used here which is an added advantage of the method.

In addition to all these aspects, of note is the fact that RT-MLPA has a reasonably quick turnaround, is relatively easy to perform and standardize and set up even in molecular facility with basic instrumentation. Our comparison of the probe mix synthesized for our centre versus that obtained from the CALYM (donated by Dr Philipe Ruminy; data not presented) study group showed comparable performance, lending itself to be set up in smaller molecular laboratories.[16] Finally, the assay is inexpensive making it more attractive for resource limited settings.

The study has standardized and evaluated a testing system keeping in mind the need for subtyping of DLBCL, NOS cases by smaller laboratories that cannot invest on high-end instrumentation. However, the study is limited by the fact that it was evaluated against IHC using the Hans algorithm and should have ideally been compared with the GEP, as the gold standard. Further, the study would have had a better impact if a larger set of DLBCL, NOS cases could have been evaluated. Finally, having limited follow-up of the DLBCL, NOS patients included in the study has also prevented the application of the results in context of prognosis. However, despite all these limitations the study still provides a strong justification of its utility in settings where GEP and the nanostring might not be an immediate possibility.

Acknowledgments

The authors would like to place on record their deep gratitude for all the help lent by Dr Philippe Ruminy, INSERM, Centre Henri Becquerel, University of Normandy, Rouen, France, in standardizing, interpreting results. Thanks are due to Mr Daniel Beno and Ms Rachel Nancy for their efforts in completing the bench work.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Smith A, Crouch S, Lax S, Li J, Painter D, Howell D, et al. Lymphoma incidence, survival and prevalence 2004-2014: Sub-type analyses from the UK's Haematological Malignancy Research Network. Br J Cancer 2015;112:1575-84.  Back to cited text no. 1
    
2.
Teras LR, DeSantis CE, Cerhan JR, Morton LM, Jemal A, Flowers CR.US lymphoid malignancy statistics by World Health Organization subtypes.CA Cancer J Clin 2016;66:443-59.  Back to cited text no. 2
    
3.
Pasqualucci L, Trifonov V, Fabbri G, Ma J, Rossi D, Chiarenza A, et al. Analysis of the coding genome of diffuse large B-cell lymphoma. Nat Genet 2011;43:830-7.  Back to cited text no. 3
    
4.
Xie Y, Pittaluga S, Jaffe ES. The histological classification of diffuse large B-cell lymphomas. Semin Hematol 2015;52:57-66.  Back to cited text no. 4
    
5.
Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000;403:503-11.  Back to cited text no. 5
    
6.
Rosenwald A, Wright G, Leroy K, Yu X, Gaulard P, Gascoyne RD, et al. Molecular diagnosis of primary mediastinal B cell lymphoma identifies a clinically favorable subgroup of diffuse large B cell lymphoma related to Hodgkin lymphoma. J Exp Med 2003;198:851-62.  Back to cited text no. 6
    
7.
Wright G, Tan B, Rosenwald A, Hurt EH, Wiestner A, Staudt LM. A gene expression-based method to diagnose clinically distinct subgroups of diffuselarge B cell lymphoma. Proc Natl Acad Sci U S A 2003;100:9991-6.  Back to cited text no. 7
    
8.
Amin AD, Peters TL, Li L, Rajan SS, Choudhari R, Puvvada SD, et al. Diffuse large B-cell lymphoma: Can genomics improve treatment options for a curable cancer? Cold Spring Harb Mol Case Stud 2017;3:a001719.  Back to cited text no. 8
    
9.
Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, et al. The use of molecular profiling to predict survival after chemotherapy for diffuselarge-B-cell lymphoma. N Engl J Med 2002;346:1937-47.  Back to cited text no. 9
    
10.
Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 2002;8:68-74.  Back to cited text no. 10
    
11.
Shaffer AL 3rd, Young RM, Staudt LM. Pathogenesis of human B cell lymphomas. Annu Rev Immunol 2012;30:565-610.  Back to cited text no. 11
    
12.
Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the World Health Organization (WHO) classification of lymphoid neoplasms. Blood 2016;127:2375-90.  Back to cited text no. 12
    
13.
Hans CP, Weisenburger DD, Greiner TC, Gascoyne RD, Delabie J, Ott G, et al. Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood 2004;103:275-82.  Back to cited text no. 13
    
14.
Scott DW, Wright GW, Williams PM, Lih CJ, Walsh W, Jaffe ES, et al. Determining cell-of-origin subtypes of diffuse large B-cell lymphoma using gene expression in formalin-fixed paraffin-embedded tissue. Blood 2014;123:1214-7.  Back to cited text no. 14
    
15.
Mareschal S, Ruminy P, Bagacean C, Marchand V, Cornic M, Jais JP, et al. Accurate classification of germinal center B-cell–like/activated B-cell–like diffuse large B-cell lymphoma using a simple and rapid reverse transcriptase–multiplex ligation-dependent probe amplification assay: A CALYM study. J Mol Diag 2015;17:273-83.  Back to cited text no. 15
    
16.
Bobée V, Ruminy P, Marchand V, Viailly PJ, Sater AA, Veresezan L, et al. Determination of molecular subtypesof diffuse large B-cell lymphoma using a reverse transcriptase multiplex ligation-dependent probe amplification classifier: A CALYM study. J Mol Diag 2017;19:892-904.  Back to cited text no. 16
    
17.
Coutinho R, Clear AJ, Owen A, Wilson A, Matthews J, Lee A, et al. Poor concordance among nine immunohistochemistry classifiers of cell-of-origin for diffuse large B-cell lymphoma: Implications for therapeutic strategies. Clin Can Res 2013;19:6686-95.  Back to cited text no. 17
    
18.
Davies AJ, Rosenwald A, Wright G, Lee A, Last KW, Weisenburger DD, et al. Transformation of follicular lymphoma to diffuse large B-cell lymphoma proceeds by distinct oncogenic mechanisms. Br J Haemat 2007;36:286-93.  Back to cited text no. 18
    
19.
Lacy SE, Barrans SL, Beer PA, Painter D, Smith AG, Roman E, et al. Targeted sequencing in DLBCL, molecular subtypes, and outcomes: A haematological malignancy research network report. Blood 2020;135:1759-71.  Back to cited text no. 19
    
20.
Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, Redd RA, et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nature Med 2018;24:679-90.  Back to cited text no. 20
    
21.
Schmitz R, Wright GW, Huang DW, Johnson CA, Phelan JD, Wang JQ, et al. Genetics and pathogenesis of diffuse large B-cell lymphoma. N Eng J Med 2018;378:1396-407.  Back to cited text no. 21
    
22.
Zhou Y, Xu Z, Lin W, Duan Y, Lu C, Liu W, et al. Comprehensive genomic profiling of EBV-positive diffuse large B-cell lymphoma and the expression and clinicopathological correlations of some related genes. Front Oncol 2019;9:683.  Back to cited text no. 22
    
23.
Murthy SL, Hitchcock MA, Endicott-Yazdani TR, Watson JT, Krause JR. Epstein-Barr virus-positive diffuse large B-cell lymphoma. Proc (BaylUniv Med Cent) 2017;30:443-4.  Back to cited text no. 23
    

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Correspondence Address:
Rekha Pai,
Department of Pathology, Christian Medical College, Vellore - 632 002, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijpm.ijpm_326_22



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