The role of pathologic testing in the diagnosis and management of patients with diffuse large B cell lymphoma: a narrative review

Introduction

Diffuse large B cell lymphoma (DLBCL) is the most common form of non-Hodgkin lymphoma with about 150,000 new cases annually worldwide (1). The majority of patients newly diagnosed with this disease are cured with standard immunochemotherapy consisting of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) (2). However, about 40% of patients fail to respond to or relapse after treatment with R-CHOP (3).

Patients who are not cured with frontline therapies are more likely to have a poor outcome, although some can be cured with subsequent lines of therapy (4). Historically, fit patients with relapsed or refractory disease are treated with high dose chemotherapy followed by autologous stem cell transplant with cure rates of about 30% (5,6). However, patients with primary refractory disease or early relapse after front line therapy have particularly poor outcomes with this approach. Recently, CAR T cell therapies have been approved in this setting with superior outcomes to autologous stem cell transplant (7,8). However, still more than half of these patients ultimately relapse (7,8).

There is importantly also a growing armamentarium that has become available for subsequent, palliative lines of therapy for DLBCL patients. Drugs including targeted small molecule inhibitors (9,10), antibody-drug conjugates (11,12), bispecific antibodies (13,14), cellular therapies (15-17), and others can extend the lives and palliate the symptoms of patients who have relapsed after or are not candidates for curative intent therapies. However, which patients are most likely to benefit from which therapies remains incompletely understood.

Despite substantial improvements to later lines of therapy for DLBCL patients, the best chance of cure remains with front line treatments. Standard clinical assays are able to risk stratify patients, but to this point have had only very limited implications in therapeutic decision making. Variability in patient responses reflect biologic heterogeneity across cases of DLBCL (18-20). There remains an important need for better approaches to risk stratify patients with DLBCL and to better target intensified treatment regimens to those most likely to benefit from them. There is also an important need to tailor later lines of therapy to individual patients. More recently, molecular techniques, such as comprehensive genomic analyses or clinical laboratory mutation analysis, may have utility in achieving these goals. In this narrative review, we will discuss recent work in this area and how it might be applied in the future to better treat patients in the clinic. We present this article in accordance with the Narrative Review reporting checklist (available at https://aol.amegroups.com/article/view/10.21037/aol-23-19/rc).

Methods

This narrative review was intended to discuss the role of pathologic, molecular and genetic testing in DLBCL. The analysis was performed in the PubMed and Google Scholar databases, which were searched for studies discussing molecular testing in patients with DLBCL (Table 1). There was no specified time frame. All international peer-reviewed papers in English language including retrospective, observational, prospective, randomized, and real world studies were included. No exclusion criteria were used. All the authors were involved in the selection and reviewing of the relevant publications.

Molecular testing in DLBCL

Cell of origin (COO) classifications

One of the established classification systems for DLBCLs is based on their COO with prognostic and therapeutic implications. Well accepted immunohistochemistry (IHC) based protocols, including the Hans (21) and Tally (22) algorithms, can be used to determine a germinal center B cell (GCB) or activated B cell (ABC)/non-GCB COO classification for these lymphomas. Gene expression profiling strategies can also be used to determine COO classification (23-25) with enhanced prognostic value (26), but are not commonly used in clinical practice. GCB DLBCLs appear to develop from normal GCBs, while ABC DLBCLs arise from post-GCBs in a phase between activation and plasmacytic differentiation (27).

Lymphomas classified as GCB type have superior outcomes when compared with ABC or non-GCB type DLBCL in patients treated with R-CHOP (25). Efforts to improve outcomes for patients with non-GCB DLBCL by adding non-cytotoxic agents to R-CHOP therapy has been largely unsuccessful (28-31), although recent studies suggest potential benefit of some modified treatment regimens (Table 2). A subgroup analysis of the REMoDL-B trial, suggests improved survival outcomes with the addition of bortezomib to R-CHOP in patients with ABC COO (32). The POLARIX trial also showed an early progression-free survival (PFS) advantage can be achieved by replacing vincristine with polatuzumab (pola-R-CHP) in this subgroup (33,34).

Despite no clear benefit in the front line (29,31), some therapies did appear to be more effective in certain COO subgroups in patients with relapsed disease. For example, lenalidomide was shown to be more effective than standard of care therapies in multiply relapsed non-GCB lymphomas, but not in GCB lymphomas (35). Similarly, patients with relapsed ABC lymphomas were significantly more likely to respond to ibrutinib than patients with GCB lymphomas (36). Additionally, using gene expression profiling to classify DLBCL specimens by COO, bortezomib sensitized patients with ABC type lymphoma to chemotherapy, but did not benefit patients with GCB type DLBCL (37).

Gene expression profiling has been used to identify distinct genetic pathways that are associated with each COO subtype including established oncogenes and tumor suppressors (38). In preclinical models, targeting the transcription factor encoding gene SBIP was able to slow the growth of cells derived from ABC type DLBCL, but not GCB type DLBCL (38). Another study identified upregulation of nuclear factor (NF)-κB pathway signaling in ABC, but not GCB DLBCL. Repression of the NF-κB pathway was toxic to ABC derived DLBCL cell lines, but not GCB derived cell lines in preclinical models (39). Activating NF-κB pathway mutations were associated with inferior outcomes among DLBCL patients treated with R-CHOP (40). Patients with relapsed or refractory ABC type DLBCL who were treated with bortezomib, which inhibits the NF-κB pathway in vitro, in combination with chemotherapy appeared to derive a benefit that was not seen in patients with GCB type DLBCL (37), as above. Treatment with ibrutinib, which blocks B cell receptor signaling upstream of the NF-κB pathway generated significantly higher response rates in patients with ABC type lymphomas than in patients with GCB lymphomas (36). Further studies like these will help to identify potential actionable targets that may improve outcomes for future patients.

Other IHC based testing in DLBCL

While cell of origin testing is currently the most clinically useful IHC based test, other markers have historically been found to have prognostic utility. About 5-10% of patients diagnosed with DLBCL have IHC positive for CD5 at the time of diagnosis and the significance of this marker has been reviewed in detail previously (41). While some of these patients may have transformed disease from occult chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL) or mantle cell lymphoma (MCL), which almost always express CD5 (42), there appears to be a histologically distinct subset with de novo CD5 expression (43). These are typically associated with an ABC phenotype (44,45) and high international prognostic index (IPI) scores (46). As a result, patients with CD5 positive disease have relatively poor prognosis (46-48). Some studies have suggested that the negative prognostic impact of CD5 expression may be independent of other factors including COO and IPI (45,49,50), however there remains very limited data from prospective clinical trials to support the use of escalated therapies in these patients (41). Due to an absence of strong data to drive treatment strategies and the fact that CD5 positivity is often confounded by other poor prognostic signals, it is not frequently used in clinical decision making.

Another commonly utilized immunohistochemical stain in DLBCL is MIB-1 or Ki-67, which is expressed in actively cycling cells and is used as a marker of tumor proliferation. Higher Ki-67 levels are associated with poor prognosis in DLBCL, but did not add additional prognostic value to IPI scores (51,52). Interestingly, a high Ki-67 proliferation index was more predictive of poor outcomes in patients with GCB type lymphoma than in those with ABC type (53). Broadly, high Ki-67 correlates with more aggressive tumors, but remains limited in its clinical utility for DLBCL patients as it has not been shown to predict response to specific therapies.

MYC, BCL2, and BCL6 abnormalities

Cytogenetic abnormalities in MYC, BCL2, and BCL6 detected by fluorescence in situ hybridization (FISH) also have prognostic utility in patients with newly diagnosed DLBCL. Translocations in the MYC gene, seen in about 10% of DLBCL patients, are associated with poor outcomes (54,55), particularly when found together with either BCL2 or BCL6 rearrangements (56,57) colloquially referred to as “double hit” or “triple hit” lymphomas. In one large cohort study, this negative prognostic impact of the MYC translocation was only present when the translocation partner was the immunoglobulin gene and BCL2 and/or BCL6 translocation was also present (58). More recent work has suggested that patients with MYC and BCL6 rearrangements have more heterogenous disease (59), which has led some to no longer classify tumors with these alterations as “double hit” lymphomas (60). Strategies using intensified chemotherapy regimens in this subset of patients have shown improved PFS, but not overall survival (OS) (61,62) and remain controversial.

One group used RNA sequencing to establish a high grade B cell lymphoma double hit signature that included 104 unique genes. Using an independent validation cohort, they were able to demonstrate that their gene signature was in fact prognostic for patients with DLBCL receiving R-CHOP independent of FISH translocation status (63). Further, this gene signature was able to identify a cohort of patients with high risk of disease relapse after R-CHOP who tested negative for MYC translocations (64). This work suggests that FISH testing may not capture the complete subset of patients at increased risk of relapse.

Others have evaluated patient samples for overexpression of MYC and BCL2 proteins in the presence or absence of translocations using IHC. This captures a larger group of patients as nearly all patients with translocations express high levels of protein. It also captures patients with ABC COO disease, which very rarely presents with translocations in MYC, but can present with elevated expression of these proteins. This “double expressor” status has also been associated with poor outcomes with front line R-CHOP (65,66), but intensified therapy is not typically recommended in this cohort of patients (in the absence of gene translocations) due to high levels of biological heterogeneity among them (67).

High levels of MYC and BCL2 protein expression have been linked to increased signaling through the B cell receptor pathway (68), which has led to interest in using Bruton’s tyrosine kinase inhibitors in these patients. Interestingly, patients with relapsed or refractory double expressor DLBCL had high response rates to ibrutinib monotherapy (69). Further, in a subgroup analysis of the PHOENIX trial evaluating the addition of ibrutinib to front line R-CHOP in patients with non-GCB DLBCL (28), patients in the ibrutinib arm who were double expressors (as measured by RNA sequencing) had improved event-free, but not OS in the ibrutinib arm (30). When MYC and BCL2 expression status were elevated in an RNA-sequencing analysis both event-free and OS were significantly improved. Additionally, increased MYC/BCL2 co-expression was correlated with increased rates of MYD88^L265P and CD79B mutations (30).

These data demonstrate that alterations in MYC and BCL2 expression are imperfect prognostic markers and may function only as a surrogate for changes in the expression patters of other genes. Perhaps more precise tools, including NGS and gene expression panels (63,64), can more directly detect the alterations affecting prognosis and responsiveness to different/escalated therapies. These types of tools will likely be useful in directing frontline and subsequent lines of therapy in DLBCL patients.

NGS and DLBCL clusters

More recently, several groups have done work seeking to use more comprehensive genomic analyses to more effectively identify clinically relevant DLBCL subgroups. Early work in this area used large gene expression panels to develop a 13 gene microarray that effectively segregated patients with a high risk of relapse (12% 5-year OS) and low risk of relapse (70% 5-year OS) in response to CHOP based therapy (70). More recent work utilized an unbiased CRISPR screen to identify genetic drivers of DLBCL and generated a prognostic model that outperformed COO, IPI, and MYC/BCL2 expression levels (71).

Chapuy et al. used whole exome sequencing to classify tumors into five different clusters (72). Clusters 1 (associated with NOTCH2 activating mutations and NF-κB pathway mutations) and 5 (associated with BCL2 gain, MYD88^L265P, and CD79B mutations) were composed of predominantly ABC-type DLBCL. Patients with cluster 1 tumors had significantly better prognosis compared with patients in cluster 5 when treated with front line R-CHOP. Cluster 3 (associated with BCL2 translocation and mutations in epigenetic modifiers including EZH2) and 4 (associated with mutations in NF-κB, B cell receptor pathway, and immune evasion molecules) tumors were associated with GCB type. Patients with cluster 4 tumors had superior PFS compared with those in cluster 3. Cluster 2 tumors harbored frequent TP53 inactivating mutations and were also associated with poor prognosis (Table 3). Over 95% of DLBCL specimens in this cohort could be classified into one of these five clusters suggesting potential real world feasibility as a tool to risk stratify patients within a known COO subtype (72).

A similar study from Schmitz et al. developed a clustering algorithm that divided tumor samples into four distinct clusters characterized by MYD88^L265P and CD79B mutations (MCD); BCL6 fusions and NOTCH2 mutations (BN2); EZH2 mutations and BCL2 translocations (EZB); and NOTCH1 mutations (N1) (73). The MCD and BN2 clusters were both associated with ABC COO with those in the MCD cluster having significantly worse PFS after treatment with R-CHOP. The EZB cluster was associated with GCB COO and these patients had the best prognosis after treatment with R-CHOP. Those in the N1 cluster had poor prognosis (73) (Table 3). Intriguingly, when these clusters were applied to patient samples from the PHOENIX trial, patients in the poor prognosis MCD and N1 clusters had dramatically improved OS with the addition of ibrutinib to front line therapy suggesting valuable therapeutic implications (76). Unfortunately, only 47% of tumor samples fit within these cluster definitions limiting its clinical potential (73).

One group used targeted mutational analysis only to define clusters similar to those described in the studies above. Unfortunately, these clusters had only limited prognostic value (74). Another study using targeted mutational analysis was able to define a subset of patients with features of a Burkitt lymphoma-like gene expression pattern enriched for mutations in KMT2D, BCL2, MYC, and DDX3X. Specimens in this cluster made up 9% of all cases evaluated, were all of GCB COO, and were associated with poor prognosis (75) (Table 3). Additionally, work has shown that somatic hypermutation (77) and clonal evolution/mutation (78) patterns can also be used to divide DLBCL specimens into distinct subtypes with prognostic significance (77). Others have used microenvironmental signatures (79) and groupings based on host immune response (80) to group DLBCL subtypes. These approaches theoretically could have a role in determining which patients are likely to respond to newly approved immunotherapies like CAR T cells and bispecific antibodies.

In an effort to enhance clinical applicability of these techniques, Wright et al. developed the LymphGen assay, which assigns probabilistic values to tumor samples based on mutations identified (81). This publicly available tool allows for sequencing analysis on any patient specimen to be used to categorize it into one or more groups. As more work is completed, this tool will likely have increasing utility in therapeutic decision making.

The utility of circulating tumor DNA (ctDNA) in DLBCL

Another emerging tool for use in DLBCL management is ctDNA testing or “liquid biopsies”. These tests allow for the detection of DNA shed from tumors through peripheral blood draws bypassing the need for more invasive tumor biopsies (82) and are already widely clinically utilized in some solid malignancies (83). Early work in DLBCL has shown that ctDNA is a sensitive and specific tool useful for identifying and surveilling disease. Pretreatment ctDNA levels and their trends in response to therapy can be utilized as independent prognostic markers for patients (84,85). Early work has also shown that liquid biopsies can be used to stratify GCB and ABC COO (85,86). Further, ctDNA testing can detect specific somatic mutations that may help direct targeted therapies or could be used to help classify patients into distinct clusters (as above) (87-89). In some cases, ctDNA testing was found to be more sensitive than testing on standard biopsies (88).

Liquid biopsies may also have utility in disease monitoring and surveillance after treatment. Several studies have demonstrated that ctDNA is sensitive and specific for detecting occult malignancy (90,91). These tests may detect disease earlier and less ambiguously than standard surveillance methodologies like imaging scans (90,91). While not yet approved or widely utilized clinically, these technologies continue to evolve and may play an important role in disease monitoring and treatment decision making for DLBCL in the future (92,93).

Discussion

A variety of standard-of-care and investigational pathologic techniques are available to characterize DLBCL specimens. Established methodologies including IHC for COO classification and FISH for identification of MYC, BCL2 and BCL6 translocations have important prognostic significance for patients with these malignancies (Figure 1). While these strategies are readily performed in a timely manner in most clinical labs, they have significant limitations and may not accurately risk stratify all patients with DLBCL. How the results of these tests should be utilized in clinical decision making remains uncertain and controversial.

Figure 1 Summary of molecular testing in DLBCL. Fluorescence in situ hybridization testing and immunohistochemical tests for cell of origin, MYC/BCL2 expression, and other markers are standard of care in the workup of DLBCL. Genetic sequencing methodologies are emerging as potentially important and useful tools to more effectively characterize and treat patients with DLBCL. DLBCL, diffuse large B cell lymphoma; ctDNA, circulating tumor DNA.

The aforementioned studies using comprehensive genomic analysis to define clusters or subgroups of DLBCL/HGBL tumors certainly provide encouragement that it is possible to improve upon our ability to risk-stratify and potentially individualize therapy for newly diagnosed patients with these diseases. The challenge remains to determine how best to deploy these tests clinically and how to incorporate them into recommendations made to patients in need of treatment.

Tumor sample mutational analysis is available in most clinical labs. At our institution, we regularly perform these tests on patient DLBCL specimens with high success rate (>92%) and short median turnaround time (18 days) (94). Results of these tests can be used to group patients into clusters using LymphGen (81) or similar platforms that can be used to direct therapeutic decisions in challenging cases (Figure 1). While not yet available, it stands to reason that commercial laboratories could soon develop versions of a lymphoma mutation panel with a short turnaround time as is done for other types of tumors.

Another issue to explore is whether the goal of clinical laboratory mutation analysis should be to replicate cluster assignment as experimentally defined in the work described above (72-74) or to identify relevant individual mutations associated with the clusters without as strong a focus on definitive subgroup assignment. Individual “high-risk” mutations including TP53 (95,96), MYD88 (97), MYC (98), and EZH2 (99) reported in these studies are reported to be predictive of patient outcomes after treatment with R-CHOP. These types of results would be more easily incorporated into patient care than cluster assignment. For example, based upon the results of the aforementioned Wilson study (76), one could use mutations in any of MYD88^L265P, CD79B or NOTCH1 as a surrogate for the MCD or N1 subgroups and offer R-CHOP with ibrutinib in the front line setting to fit patients in these categories. Finally, there may be significant variability of both mutations detected and signaling pathways affected within a genetic subgroup, which may have implications for efforts to offer a single targeted therapy to all patients whose tumors are classified within a specific subgroup. For example, EZH2 mutations are almost exclusively detected in EZB tumors, but <50% of EZB tumors actually harbor an EZH2 mutation (81), so it is not clear that all patients with EZB tumors would benefit from treatment with EZH2 inhibitor therapy.

A very recent study took advantage of this idea to design a basket trial (100). In this very exciting trial, patients were screened using a 20 gene panel to classify them into one of six genetic subgroups based on the clustering systems and individual mutations described above. All patients received one cycle of R-CHOP while awaiting their sequencing results and then were randomized into a control group that would receive an additional 5 cycles of R-CHOP or an experimental group to receive R-CHOP plus an additional agent tailored to their genetic classification (100). The addition of targeted agents based on genetic subtype classification impressively improved OS outcomes (100) further suggesting an important role for the incorporation of genomic data into front line treatment decisions. Future work will help us to better understand specific genetic subgroups and more effectively cluster patients into groups that might benefit from personalized escalated therapies.

Conclusions

We now have an embarrassment of riches of pathologic data available to help guide the risk-stratification and management of patients with newly diagnosed DLBCL. Moving forward, it would be a worthwhile effort to create a standardized targeted lymphoma mutation panel, guided by the findings from the investigations discussed above (63,72-75,100) and others, which can be routinely performed by clinical laboratories with a short turnaround time sufficient to direct patient care (101). Such a panel could be utilized by clinical trials in this patient population so results obtained in the experimental setting could be easily translated. While results of pathologic testing have not translated into major advances in clinical management of newly diagnosed DLBCL patients over the past several years, they have the potential to do so in the years to come.

Acknowledgments

Funding: This work was supported in part by Abramson Cancer Center’s Ruth L. Kirschstein National Research Service Award T32 Cancer Center Research Training Program Training Grant (T32-CA009615).

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://aol.amegroups.com/article/view/10.21037/aol-23-19/rc

Peer Review File: Available at https://aol.amegroups.com/article/view/10.21037/aol-23-19/prf

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://aol.amegroups.com/article/view/10.21037/aol-23-19/coif). M.S. is supported by Abramson Cancer Center’s Ruth L. Kirschstein National Research Service Award T32 Cancer Center Research Training Program Training Grant (T32-CA009615). D.L. serves as an unpaid editorial board member of Annals of Lymphoma from June 2023 to May 2025. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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