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Peripheral T cell lymphomas (PTCL), are a heterogeneous group of non-Hodgkin diseases with broad morphological and immunophenotypic characteristics, and a poor prognosis.1,2 Despite the recently updated World Health Organisation (WHO) classification, many of the subsets remain undefined and are grouped into a PTCL-not otherwise specified (PTCL-NOS) subtype.1 Better characterisation of this subgroup would guide more targeted treatment and could improve patient outcomes. Therefore, researchers aim to identify the clinical and pathologic features that could offer prognostic value to the different entities within PTCL-NOS.
Recently, gene expression profiling (GEP) has identified two novel subtypes with different clinical outcomes, likely derived from two distinct subtypes of T-cells.3,4,5 The PTCL-GATA3 subtype, representing 33% of PTCL-NOS, is characterised by overexpression of GATA3 and downstream genes, and is associated with a worse prognosis.3 The PTCL-TBX21 subtype, representing 49% of PTCL-NOS cases, has a better prognosis and is characterised by overexpression of TBX21 and its target genes.3 Moreover, these subtypes were found to be associated with enrichment of distinct oncogenic pathways, PI3K-mTOR activation in PTCL-GATA3 and NF-κβ in PTCL-TBX21, and are therefore vulnerable to distinct targeted therapies.3,6,7
Although the stratification of PTCL-NOS patients has clinical utility, routine use of GEP in clinical practice is not considered feasible due to high costs and a lack of accessibility. Catalina Amador from the University of Nebraska Medical Center, US, and colleagues investigated whether using standard immunohistochemistry (IHC) on formalin-fixed, paraffin-embedded (FFPE) tissue could replicate the gene expression diagnostic signatures for routine use in clinical practice.8 The study results were recently published in Blood.
In total, 173 cases of PTCL-NOS from multiple institutions were included in the study (49 cases in the training cohort previously evaluated by the GEP and 124 cases in the validation cohort without GEP data). All cases had data on at least two of the T-follicular helper (TFH) markers such as PD1, CXL13, ICOS, CD10, and BCL6. Cases expressing CD4 and at least two TFH markers were excluded from the study.
In order to develop the IHC algorithm, staining was performed on tissue microarrays (TMAs) using the following markers:
Pathologists blinded to the GEP results assessed immunostaining and set the threshold of positivity. Based on multiple covariates of a percentage of positive tumor cells, an algorithm was used to assign cases to GEP-defined subtypes.
Additionally, 57 cases were also assessed for morphological features and positivity for CD4, CD8, CD30, cytotoxic markers staining and Epstein-Barr encoding region (EBER) in situ hybridisation.
IPI, International Prognostic Index | |||
|
Training cohort |
Validation cohort |
p-value |
---|---|---|---|
IHC classification PTLC-GATA3 PTLC-TBX21 Unclassified |
n=49 15 (31%) 31 (63%) 3 (6%) |
n=124 46 (37%) 69 (56%) 9 (7%) |
0.66
|
Gender Female Male |
n=42 17 (40%) 25 (60%) |
n=100 35 (35%) 65 (65%) |
0.54 |
Age (years) ≤ 60 > 60 |
n=39 13 (33%) 26 (67%) |
n=98 52 (53%) 46 (47%) |
0.04 |
Stage I/II III/IV |
n=17 2 (12%) 15 (88%) |
n=73 20 (27%) 53 (73%) |
0.22 |
Extra-nodal sites ≤ 1 > 1 |
n=6 4 (67%) 2 (33%) |
n=74 54 (73%) 20 (27%) |
0.66 |
IPI score Low (0–2) High (3–5) |
n=13 5 (38%) 8 (62%) |
n=63 36 (57%) 27 (43%) |
0.24 |
Treatment CHOP/CHOP-like Other None |
n=7 4 (57%) 2 (29%) 1 (14%) |
n=63 55 (87%) 7 (11%) 1 (2%) |
0.07
|
|
Univariate analysis |
Multivariate analysis |
||||
---|---|---|---|---|---|---|
|
n |
HR (95% CI) |
p-value |
n |
HR (95% CI) |
p-value |
PTCL-GATA3 vs. PTCL-TBX21 by IHC |
128 |
2.39 (1.55–3.7) |
<0.0001 |
67 |
2.75 (1.51–5.01) |
0.0009 |
High IPI vs. low IPI |
74 |
2.13 (1.21–3.74) |
0.0089 |
67 |
1.8 (1.0–3.24) |
0.05 |
Age > 60 vs. ≤ 60 years |
124 |
1.95 (1.27–3.01) |
0.0025 |
|
|
|
Extra-nodal sites > 1 vs. ≤ 1 |
77 |
1.85 (1.04–3.29) |
0.035 |
|
|
|
Using four commercially available antibodies, the IHC algorithm reliably predicted GATA3 and TBX21 subtypes of PTCL-NOS. Such subclassification of patients could be incorporated into patient management and guide decisions on the treatment regimen according to the underlying biology. Based on current knowledge, PI3K inhibitors may show efficacy in patients with a PTLC-GATA3 subtype due to the predominance of PI3K-mTOR activation pathways, while immunomodulators and NF-κβ inhibitors may be recommended for patients with PTCL-TBX1 due to an enrichment of NF-κβ activation pathways in this subtype.
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