In:
Blood, American Society of Hematology, Vol. 118, No. 21 ( 2011-11-18), p. 3662-3662
Kurzfassung:
Abstract 3662 The differential diagnosis among the commonest peripheral T-cell lymphomas (PTCLs) (i.e. PTCL not otherwise specified, NOS; angioimmunoblastic T-cell lymphoma, AITL; and anaplastic large cell lymphoma, ALCL) is difficult, the morphologic and phenotypic features being largely overlapping. Noteworthy, recent international studies indicated significant differences in their clinical behavior as well as concerning the presence of potential therapeutic targets. We performed whole genome gene expression profiling (GEP) of PTCLs aiming to identify molecular signatures able to improve their diagnosis. We studied 95 PTCLs, including 73 PTCLs/NOS, 12 ALCLs (6 ALK+ and 6 ALK-), and 10 AITLs. All tissue samples were formalin-fixed and paraffin embedded (FFPE). GEP was performed by Illumina Whole Genome DASL Assay. First, we documented the efficiency of GEP from FFPE tissues by comparing the mRNA levels and the presence of the corresponding protein, including expressed (i.e. CD3) and not expressed (i.e. BCL10) molecules. Secondly, we tried to discriminate different PTCLs basing on their GEPs. By dividing a training (N=47) and a test set (N=48), we found 2 signatures able to differentiate PTCL/NOS vs. AITL and PTCL/NOS vs. ALCL ALK-. Specifically, in the test set the sensitivity (ST) and specificity (SP) of the assays were 100% – 80% (PTCL/NOS vs. AITL) and 100% – 100% (PTCL/NOS vs. ALK- ALCL) (Table 1). Accordingly, the positive (PPV) and negative (NPV) predicting values for the identification of PTCL/NOS were 0.92 and 1 (vs. AITL) and 1 and 1 (vs. ALK- ALCL) (Table 1).Table 1.Accuracy of GEP based signature in differentiating PTCL subtypesSTSPPPVNPVTraining setPTCL/NOS vs. AITL100%80%0.921PTCL/NOS vs. ALK-ALCL100%100%11Test setPTCL/NOS vs. AITL92.50%100%10.77PTCL/NOS vs. ALK-ALCL92.50%100%10.67Validation setPTCL/NOS vs. AITL85%86%0.920.76PTCL/NOS vs. ALK-ALCL96%73%0.960.73 Interestingly, the identified genes represented relevant functional pathways differentially regulated in the 3 tumour types, including protein kinase cascade, proliferation, and cell cycle. When applied to the test set of cases, the assay correctly classified 37/40 PTCLs/NOS (92.5%), 5/5 AITLs, and 3/3 ALK- ALCLs. Finally, we tested our signatures on 133 independent PTCL cases (including 78 PTCL/NOS, 43 AITL, and 12 ALK- ALCL) for which GEP data were available on the GEO database and were originally obtained from fresh/frozen tissues. Interestingly, we could efficiently recognize PTCL/NOS cases vs. AITLs (ST, 85%; SP 86%; PPV 0.92; NPV 0.76) and vs. ALK- ALCLs (ST 96%; SP 73%; PPV 0.96; NPV 0.73). In conclusion, we successfully generated for the first time GEP from routinary FFPE PTCL samples, identifying molecular signatures potentially useful for the clinical practice and, specifically, for the differential diagnosis of PTCL types. Disclosures: No relevant conflicts of interest to declare.
Materialart:
Online-Ressource
ISSN:
0006-4971
,
1528-0020
DOI:
10.1182/blood.V118.21.3662.3662
Sprache:
Englisch
Verlag:
American Society of Hematology
Publikationsdatum:
2011
ZDB Id:
1468538-3
ZDB Id:
80069-7
