Quantitative PCR on 5 genes reliably identifies CTCL patients with 5% to 99% circulating tumor cells with 90% accuracy

Abstract

We previously identified a small number of genes using cDNA arrays that accurately diagnosed patients with Sé zary Syndrome (SS), the erythrodermic and leukemic form of cutaneous T-cell lymphoma (CTCL). We now report the development of a quantitative real-time polymerase chain reaction (qRT-PCR) assay that uses expression values for just 5 of those genes: STAT4, GATA-3, PLS3, CD1D, and TRAIL. qRT-PCR data from peripheral blood mononuclear cells (PBMCs) accurately classified 88% of 17 patients with high blood tumor burden and 100% of 12 healthy controls in the training set using Fisher linear discriminant analysis (FLDA). The same 5 genes were then assayed on 56 new samples from 49 SS patients with blood tumor burdens of 5% to 99% and 69 samples from 65 new healthy controls. The average accuracy over 1000 resamplings was 90% using FLDA and 88% using support vector machine (SVM). We also tested the classifier on 14 samples from patients with CTCL with no detectable peripheral involvement and 3 patients with atopic dermatitis with severe erythroderma. The accuracy was 100% in identifying these samples as non-SS patients. These results are the first to demonstrate that gene expression profiling by quantitative PCR on a selected number of critical genes can be employed to molecularly diagnosis SS. (Blood. 2006;107:3189-3196)

Sézary syndrome is a unique cutaneous T-cell lymphoma as identified by an expanded gene signature including diagnostic marker molecules CDO1 and DNM3

“…Real-time PCR analysis of these five genes in our patient samples as compared to HBD or ID confirmed the strong reduction in expression of STAT4 and the strong overexpression of PLS3 in SS while expression of TRAIL and GATA-3 was not significantly altered in contrast to the results of Nebozhyn et al 7 Also in strong contrast to Nebozhyn et al, CD1D was even significantly downregulated in our SS patient samples (Table 2a). Statistical evaluation using ROC methodology based on logistic regression showed an excellent discriminative capacity for STAT4 (AUC ¼ 0.906) and a medium value for CD1D (AUC ¼ 0.796) (Table 2b).…”

“…Comparison of CDO1/DNM3 expression with the 5 gene qRT-PCR assay proposed by Nebozhyn et al 7 Recently, Nebozhyn et al 7 proposed a qRT-PCR assay analyzing STAT4 (kk), CD1D (m), PLS3 (mm), TRAIL (m) and GATA-3 (m) in PBMC as a highly reliable test for the diagnosis of SS. Real-time PCR analysis of these five genes in our patient samples as compared to HBD or ID confirmed the strong reduction in expression of STAT4 and the strong overexpression of PLS3 in SS while expression of TRAIL and GATA-3 was not significantly altered in contrast to the results of Nebozhyn et al 7 Also in strong contrast to Nebozhyn et al, CD1D was even significantly downregulated in our SS patient samples (Table 2a).…”

“…Recently, multi-gene qRT-PCR was proposed to improve the molecular diagnosis of SS cells in the peripheral blood, but the method still awaits confirmation by other groups and validation using independent patient samples. 7 In SS and MF, chemotherapy does not improve survival, but impairs quality of life; due to frequent recurrences, allogeneic bone marrow transplantation is no successful treatment option either. Stage-adapted therapy as pursued currently by most groups aims at mitigating the course of the disease, but a potentially curative treatment regimen for SS or MF does not exist, indicating the urgent necessity to identify novel therapeutic targets.…”

Cutaneous T‐cell lymphoma: 2011 update on diagnosis, risk‐stratification, and management

“…Of note, MF/SS is associated with the expression of Th2-associated genes (e.g., GATA-3) and the production of Th2-associated cytokines (e.g., IL-4, IL-5, and IL-10), thus raising the possibility that a significant subset of patients may harbor Th2-derived clones [32][33][34][35][36]. As the cell of origin is further defined in subsets of CTCL, including MF/SS, one may anticipate that this data may have a significant impact on the classification, risk-stratification, and treatment of these diseases.…”

“…Cyclin upregulation, including cyclinD1, and loss of RB1 have also been described [94]. As gene-expression profiling and nextgeneration sequencing technologies are used, additional pathogenic pathways, including those involving transcription factors regulating T-cell differentiation [35,36], c-MYC [95,96], RAS/RAF/MEK signaling [97], among others [90,98], may be identified in subsets of CTCL.…”