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Page 4 of 25 Lue et al. J Cancer Metastasis Treat 2022;8:11 https://dx.doi.org/10.20517/2394-4722.2021.193

While extremely informative and prognostic, GEP is rarely used in the real world setting due to several
limitations, including the ability to rapidly produce these results in an informative way and, most
importantly, the reproducibility of these classifications at the community level. As such, other methods have
[17]
[16]
[15]
been developed using immunohistochemistry (IHC), including Hans et al. , Choi et al. and Tally et al.
algorithms in an attempt to rapidly replicate GEP classifications with an additional benefit of significantly
lower cost. The most frequently used IHC method, Hans’ algorithm, proposes utilizing CD10, BCL-6 and
MUM-1 expression to differentiate between GCB vs. Non-GCB DLBCLs , whereas Choi uses GCET1,
[15]
[16]
MUM1, CD10, BCL6 and FOXP1 in order to do so. The Tally method uses a similar antibody panel as
Choi with the notable difference that antibody expression is not reviewed in a sequential manner but
denoted by a score of 0-2, reserving the evaluation of LMO2 if an equal number of GCB vs. ABC
genes/score are present . Interestingly, these three IHC methods incorporate CD10, echoing earlier studies
[17]
in the 1990s supporting the use of CD10 to broadly differentiate DLBCL subtypes . Although quite rapid,
[7-9]
IHC methods are riddled with limitations including inter-user inconsistencies and datasets suggesting
inaccurate classifications using IHC as compared to GEP. For instance, Gutiérrez-Garcia and colleagues
discovered that approximately 30-50% of GCB-DLBCLs and 15-25% of ABC-DLBCLs were incorrectly
classified by IHC . In a separate study, Hans’ algorithm failed to demonstrate a difference in OS between
[18]
GCB-DLBCL and non-GCB DLBCL, whereas classification of subtypes using Lymph2Cx assay, a GEP
platform comprising of a 20 gene panel that can be applied to FFPE tissue samples , was able to
[19]
demonstrate both a 5-year OS and disease-free survival difference (96.6% vs. 77.1%, 96.6% vs. 79.2%,
[20]
respectively) in patients with GCB- vs. ABC-DLBCLs . In fact, the Lymph2Cx assay misidentified 2%
DLBCL tumor samples compared to assignments made by the gold standard GEP . To put this in context,
[21]
the COO assignment assessed by Tally, Hans, and Choi IHC-methods led to a misassignment rate of 6%,
9%, and 17%, respectively [15-17] . Subsequently, the Lymph2x assay was validated in a large cohort of DLBCL
patients (n = 335) treated with R-CHOP therapy and confirmed that COO was associated with clinical
outcomes independent of MYC/BCL2 expression and IPI score . Thus, given its improved accuracy
[22]
compared to IHC, faster turn-over compared to gold standard GEP and the ability to predict prognostic
outcomes, Lymph2Cx was thought to be a more applicable diagnostic tool. Along those lines, the ROBUST
clinical trial [23,24] , a phase III clinical trial that investigated the merits of combining lenalidomide to R-CHOP
in ABC-DLBCL patients, utilized the Lymph2Cx assay as a companion diagnostic in order to rapidly
identify COO. Despite theoretically serving as a real-time GEP assay, the adaption of Lymph2Cx to the
ROBUST study led to a delay in treatment initiation due to logistical hindrances such as central review of
tumor specimens resulting in an inadvertent introduction of selection bias for patients with lower risk
disease that ultimately may not have benefitted from the addition of lenalidomide to R-CHOP [24,25] .
Therefore, although less robust, IHC categorization is still in universal use, with GEP assays often reserved
for clinical trial studies or academic institution applications.

Novel classifications beyond cell of origin
In the era of precision medicine, on-going attempts to better target specific mutations and aberrations have
led to additional sub-classifications that extend beyond COO. GEP, next-generation sequencing and copy
number variation evaluations have made this possible, permitting an increasingly detailed understanding of
DLBCL genomic profiles.

In an integrative analysis utilizing whole-exome sequencing and transcriptome sequencing of 1001 newly-
diagnosed DLBCL patients, Reddy et al. identified 150 genetic drivers of the disease and, in turn,
[26]
characterized the functional impact of these genes using an unbiased in vitro CRISPR screen . CRISPR
[26]
screening of a panel of DLBCL cell lines led to the identification of 35 oncogenes, with 9 genes identified in
a subtype-specific pattern: EBF1, IRF4, CARD11, MYD88 and IKBKB were essential in ABC-DLBCL
lymphomagenesis, whereas ZBTB7A, XPO1, TGFBR2 and PTPN6 were critical for the survival of GCB-