We created a new algorithm based on the expression of CD10, FOXP1, and BCL6 that precisely stratifies the GCB and ABC subtypes of DLBCL. The associations of each marker with GCB or ABC DLBCL and the cutoffs to determine positivity were assessed using ROC curves, which obviated the need to use any arbitrary cutoffs. Our algorithm had strong prognostic power matching that of GEP in R-CHOP-treated patients and was independent of IPI. In an independent cohort of patients treated with either CHOP or R-CHOP, we confirmed the algorithm’s prognostic predictive value. Finally, the algorithm proposed also allowed us to classify patients with DLBCL whose disease had been unclassifiable according to GEP, although survival analysis in this small group of patients did not reach statistical significance.
Our results confirm the reliability of previous findings,33
demonstrating that GEP can be performed by extracting RNA from FFPET instead of frozen tissue, which is often not obtained at diagnosis and is becoming decreasingly available in the current era of small needle biopsy for diagnosis. The immunohistochemical algorithm can be easily performed by most laboratories on paraffin-embedded tissues and allows for the direct visualization of tumor cells.26
Moreover, compared with GEP analysis, the phenotype of the tumor reflects gene expression of the lymphoma cells, revealing which molecules are in fact expressed and functional and could thus be the target of new drugs. Recent studies have shown that some drugs enhance the activity of chemotherapy in ABC- but not GCB-DLBCL, providing a rationale for different therapeutic approaches for distinct DLBCL subtypes.34–38
Malignant B-cells of DLBCL are thought to be “frozen” at particular stages of B-cell development. In the GC microenvironment, specific proteins are up- or down-regulated at any one particular stage. It has been shown that B-cells in the GC can migrate extensively within their respective compartments.39
In this scenario, GCET1 stains positive in rapidly dividing B-cells (i.e., Ki-67+
centroblasts) in the dark zone of the GC. Its expression is enhanced when B-cells are stimulated by CD40 signaling,40
and it is then likely to identify centroblasts that have been rescued from cell death and are prompted to proliferate and undergo somatic hypermutation and class-switch recombination.17,28,41
is an essential transcriptional regulator of B-cell development that influences B-cell development at very early stages,42
and its mRNA expression is also typically elevated in ABC-DLBCL.42
Cell lines that are at an intermediate stage of differentiation between GCB and ABC (i.e., LIB) express CD10, BCL6 and MUM1 as well as FOXP1,19
indicating that this marker could represent a bridge from the GC stage to subsequent B-cell activation. Some preliminary data have suggested that smaller FOXP1 isoforms may have a role in activating the transcription factor MUM1, pushing B-cells toward plasma cell differentiation.6,41–44
Hence, in the construction of our algorithm, we evaluated the expression of CD10, GCET1, FOXP1, and MUM1 in that order to progressively address the steps of B-cell maturation.
Since BCL6 is the marker with the largest variability in its staining interpretation between laboratories, only a minority of patients will need to rely on its staining for subset discrimination. According to our algorithm, the role of BCL6 is confined to patients (less than 20%) that are negative both for CD10 and FOXP1, while the Choi and the Hans algorithms gave strong decisional power to BCL6 (50% and 60% of patients, respectively). We acknowledge that the assignment of these patients to a specific subset might benefit of other GC-specific markers that have been used in other algorithms, but were not analyzed in our study. Among these, human GC-associated lymphoma (HGAL or GCET-1) expression has been shown to correlate with improved survival in CHOP-treated patients with DLBCL.45
This observation was confirmed in our series of cases as well (data not shown). Similarly, LMO2
mRNA expression was reported as a predictors of superior outcome in DLBCL patients in a relatively small series of DLBCL cases, however, the finding has not been confirmed and validated from other groups in a large cohort of cases.46
HGAL is an adaptor protein involved in prevention of lymphocyte migration, thus constraining lymphocytes to the GC.47,48
Double-staining studies have demonstrated that most BCL6+
cells co-express HGAL, although several BCL6+
cells of the proliferating pole or dark zone of GCs lack staining for HGAL. Therefore, it is suggested that HGAL, unlike GCET1, may identify resting cells within the GC.49,50
Other markers that are discriminatory for ABC-DLBCL, such as cyclin D2,9,51
were excluded based on our previous experience and on the absence of data in R-CHOP-treated patients at the time of the approval for the current study. We found that GCET1 and FOXP1 were both predictive of PFS in R-CHOP-treated patients, regardless of the cutoff utilized (). To the best of our knowledge, this is the first report addressing the prognostic predictive value of GCET1 expression as a single marker for R-CHOP-treated patients with DLBCL.
The use of the algorithm we proposed, when applied to patients uniformly treated with R-CHOP, had remarkable prognostic significance and was independent of IPI, as shown in the multivariate analysis. When we combined the IPI score and our algorithm, we could identify cohorts of patients at very low (IPI 0–1 and GCB phenotype, 5-year PFS of 86 ± 1%) or very high risk of relapse (IPI score 4–5 and non-GCB phenotype, 5-year PFS of 28 ± 7%).
The staining algorithm proposed by Choi et al. has shown good concordance with GEP analysis, but had a complicated structure, using five markers with different cutoffs.12
The more recent Tally algorithm16
was also based on the expression of five antibodies, three of which are not commonly used by pathologists, and with arbitrary decisional cutoffs. In both studies, it is not clear whether patients with transformed, primary mediastinal, primary cutaneous, or central nervous system DLBCL were excluded from the analysis, despite the peculiar biological features and clinical behavior of these tumor types.
Unlike any other new marker, CD10 is part of the initial immunophenotypic panel used by hematopathologists. Therefore, our use of CD10 as the first discriminating marker in the new algorithm simplifies the categorization of patients. The predictive value of CD10 positivity alone in identifying patients with GCB according to GEP was 95% in our series, similar to that reported by others,9,12,20
and could not be improved by the addition of any other markers. Thus, although we have maintained the structure of the Hans algorithm for CD10+
patients, our new algorithm improves the discrimination of CD10−
patients, who were correctly assigned in 91.4% of cases, compared with 82.3% when the Hans algorithm was used. However, a very small subset of cases with strong CD10 expression was classified as ABC. Most of these cases had strong FOXP-1 or MUM-1 expression, but rarely expressed GCET-1. On the other hand rare cases lacking CD10 expression were classified as GCB by GEP. Most of these cases had strong BCL6 and FOXP1 expression, while only few expressed GCET1. Morphologically, the first group had polymorphic morphology, whereas cases classified as ABC by IHC algorithm, but GCB by GEP analysis, showed typical centroblastic morphology. The low number of misclassified patients did not allow conclusion on clinical behavior of this particular subset.
We analyzed 296 patients with available material for the presence of C-MYC, and 8% had MYC rearrangements. Patients with C-MYC breaks had a significantly inferior outcome compared to patients without breaks. Although numbers were low, C-MYC breaks had a significant impact on survival of GCB, but not in ABC patients, either when recognized by means of GEP or of our algorithm.
We reviewed 466 patients with available material for morphologic classification using 2008 WHO classification as criteria.53–55
Four hundred and five (87%) had centroblastic morphology (CB). Of them, 251 had cleaved (59) or large non-cleaved (192) cell types. Twenty-four patients had anaplastic morphology (5%), and 37 had immunoblastic morphology (IB, 8%). According to morphologic subtype distribution, CB was significantly more represented in the GCB (54%) than IB (27%, p = 0.001), while anaplastic morphology was GCB in 66%. Large non-cleaved cell type was more represented in GCB (63%), similarly to the medium-sized cells (76%), and differently from cleaved cells (41%) and polymorphic cell type (38%). Patients with CB had significantly better OS (p < 0.0001) and PFS (p = 0.001) compared to IB, or anaplastic morphology (p = 0.0001 and p = 0.004, respectively). According to cell type distribution, large non-cleaved cell type had the better 5-year PFS and OS (65% and 70%, respectively). A significant difference in terms of OS or PFS was observed between large non-cleaved cell type and others (p = 0.04 and p = 0.005, respectively).
In conclusion, we found that the expression of three markers can be combined to divide DLBCL into GCB and non-GCB subgroups with high specificity and that our method can predict an outcome similar to that of GEP analysis in R-CHOP-treated patients. Our findings are currently used in our new clinical trial DLBCL studies. We believe the algorithm presented here will substantially improve upon the performance of the former algorithms, and allow a better stratification of DLBCLs for further characterizing the pathways that identify each of the DLBCL subtypes and for testing the efficacy of new drugs in distinct subgroups.