Cell of Origin (COO) determination
The cell of origin of DLBCL explains part of the heterogeneity of the
disease. Using gene expression profiling, DLBCL NOS was initially
classified into germinal center B cell (GCB), activated B-cell (ABC),
and non-classifiable types (20). GCB type DLBCL is thought to derive
from centroblasts while the ABC type has features reminiscent of a
B-cell committed to terminal B-cell differentiation (21) (see Figure 1).
The COO classification could predict overall survival of DLBCL patients
and response to R-CHOP therapy. The GCB subtype has a more favorable
prognosis compared to the ABC subtype. (22,23,24,25). Determination of
cell of origin also has implications on drug therapy in
relapsed/refractory disease. Recently, ibrutinib, a BCR inhibitor was
found to be effective in relapsed and refractory ABC type DLBCL (26).
The gold standard method for determining COO has been gene expression
profiling (GEP) (27). This modality, however, is not available at all
centers and many assays require fresh tissue, which may not always be
available. In routine practice, various immunohistochemistry (IHC) based
algorithms (see Figure 2) such as Hans, Tally, Choi and Visco-Young are
used to determine COO for DLBCL (28,29,30,31). IHC methods for COO
assignment are more practical and widely available, but are plagued by
inter-observer variability that can result in discordant classification
compared with the gold standard. Gutiérrez-García et al demonstrated
that when compared with GEP, different IHC algorithms; Colomo, Hans,
Muris, Choi, and Tally, misclassified cases at a higher rate when
defining the GCB subset: 41%, 48%, 30%, 60%, and 40%, respectively
(32). Gene-expression profiling and not immunophenotypic algorithms
predicts prognosis in patients with diffuse large B-cell lymphoma
treated with immunochemotherapy. In this study, while the GEP-defined
groups showed significantly different 5-year progression-free survival
(76% vs 31% for GCB- and ABC- DLBCL) and overall survival (80% vs
45%), none of the IHC algorithms retained the prognostic impact of the
COO groups (GCB vs non-GCB). Other studies have also suggested that IHC
algorithms may not have the same prognostic impact as other methods of
COO determination (33,34,35). These results underscore the unmet
clinical need for a methodology that uses readily available biopsy
material for accurate COO classification, concordant with GEP while
maintaining prognostic utility.
The COO classification is not without limitations however. COO does not
explain all the heterogeneity in the behavior and prognosis of DLBCL.
Using various approaches, subgroups within ABC showing favorable
prognosis have been identified and adverse prognostic groups have also
been identified for the GCB type. In a study involving 574 DLBCL biopsy
samples using exome and transcriptome sequencing, array-based DNA
copy-number analysis, and targeted amplicon resequencing of 372 genes to
identify genes with recurrent aberrations, Schmitz et al. identified
four DLBCL genetic subtypes: BN2, EZB, N1, and MCD, with the former two
determined to have favorable prognosis and the latter two associated
with poor prognosis (36). Progression free survival and overall survival
varied significantly within these groups. Of note, heterogeneity in
behavior within the ABC group could be identified, with inferior
survival in the MCD and N1 subtypes of ABC and favorable survival in the
BN2 subtype. In GCBs, EZB subtype had a worse predicted 5-year survival
compared with other GCBs non-classifiable by this system. Significant
independent and additive contributions to survival by gene expression
profiling (ABC vs GCB) were noted in this genetic subtyping model.
Chapuy et al used a combination of recurrent mutations, somatic copy
number alterations
and structural variants (SV) to identify five distinct groups of DLBCL
including a hitherto unappreciated group of ABC-DLBCL with favorable
prognosis (37). They also identified a subtype of GCB-DLBCL with poor
prognosis characterized by mutations and structural variants ofBCL2, mutations in PTEN and chromatin modifiers such asKMT2D , CREBBP , and EZH2 and focal10q23.31/PTEN loss. These subgroups within the different COO
types provide insight not only for prognosis but also suggest possible
mechanisms for therapeutic intervention. In spite of the usefulness of
these multiparametric approaches for classification, it will be
challenging to apply these genetic classification models in the clinical
settings due to limitations in the amount of diagnostic material
available, resources and bioinformatics expertise.
Disease Monitoring During Therapy
The rationale of using multiple agent chemotherapies in the treatment of
cancers is to avoid development of resistance, but resistance does
develop regardless of the type and combination of therapy. The
neoplastic cells may develop adaptive resistance to chemotherapy in the
course of treatment. Serial tumor profiling can provide insight into the
pathways mediating adaptive resistance and identify targets for novel
therapeutic drug development to overcome the resistance mechanisms (38).
Real time analysis may also allow prompt detection of resistance and
suggest alteration of therapy if signatures of resistance portending
refractoriness or relapse are detected. In DLBCL patients, it is not
usually feasible to obtain tissue regularly for such profiling without
invasive measures, which cause patient discomfort and increase the risk
of infections from multiple biopsy procedures. Thus, a non-invasive
method for tissue sampling at multiple time points would be of great
benefit.
Post Treatment Surveillance
The role of post-therapy or remission imaging surveillance in the
management of DLBCL patients is controversial. While the National
Comprehensive Cancer Network (NCCN) and European Society of Medical
Oncology (ESMO) guidelines recommend surveillance imaging for stage
III/IV disease for the first two years after completion of front-line
therapy (39), the 2014 Lugano classification system advises against
routine surveillance imaging (40). The positive predictive value of post
treatment PET is low, resulting in patient anxiety and increased costs
for these unnecessary medical procedures (41,42). Studies have also
failed to establish a survival advantage in imaging detected disease
relapse (43,44). Realization of the lack of survival benefit of
surveillance imaging has resulted in decreased rates of surveillance,
although this practice is still quite frequent, with over half of DLBCL
patients diagnosed in 2014-2016 undergoing surveillance imaging (45).
Thus, there is a pressing need for more specific and sensitive methods
for detecting recurrence of disease while also avoiding risks of
radiation exposure (46).
Novel Methodologies in DLBCL Management- Liquid Biopsy Based Approaches
Liquid biopsy techniques which involve assessment of cancer related
biomarkers in bodily fluid samples, have potential to resolve the unmet
clinical needs in management of DLBCL. Common liquid biopsy techniques
include circulating tumor cells (CTC), circulating tumor DNA (ctDNA) and
exosomes.