Abstract
Interferon-alpha 2 (IFN) is able to induce hematological response in about 70-80% of ET patients but some of them could be defined as bad responders.
IFN binding its receptor results in tyrosine cross-phosphorylation and auto-phosphorylation of the JAKs proteins (Tyk2 and Jak1). These phosthyrosines recruit and activate STAT family member such as STAT1 and STAT3. These proteins induce the transcription of SOCSs, whose role is to extinguish cytokine signaling by inhibition of JAK kinase-activity directly through the KIR-domain, and indirectly promoting the proteasomal degradation of Jak2, by SOCS-box-motif.
In summary, IFN induces the expression of SOCSs, which inhibit TPO mediated signaling through Jak2 double inhibition. This allows IFN-α and TPO pathway to cross-talks by means of the JAK-STAT-SOCS cascade.
To identify molecular markers that identify those patients who respond to IFN, we analyzed bone marrow cells transcript levels of specific genes involved in the IFN receptor pathway, whose signal cross-talks with the TPO dependent JAK-STAT pathway. In particular we investigated the mRNA expression of JAK1, TYK2, STAT1, STAT3, SOCS1 and SOCS3.
We analyzed 60 ET patients treated with 3 million units of IFN-α-2b 5 times a week as induction (3 months), and 3 times a week as maintenance. Responses were classified as follow: Good-Responders(R) (n=44), those who achieved complete response according to European Leukemia Net criteria, and Bad-Responders(NR) (n=17) who didn’t reach the criteria.
The mRNA expression of genes of interest was measured in bone marrow samples from ET patients by RTq-PCR and tested for their predictive value using receiver operating characteristics (ROC) curves.
Data were normalized as following: [mRNA normalized copy number (NCN)=mRNA target gene/mRNA GUSB].
An IFN score was calculated as an average in log2 of mRNA levels of genes differently expressed between Good-R and Bad-R.
Main clinical characteristics were similar between the two groups of response. JAK2 V617F mutation was detected in 56,8% of Good-R and 58,8% of Bad-R (p=0,81) and no difference was found in JAK2V617F allele burden (p=0,17) and mRNA expression (p=0,2). Patients showed a median spleen volume of 500 ml in Good-R and 250 ml in Bad-R group (p=0.01).
Bad-R compared with Good-R showed higher mRNA expression of JAK1 (13.4 vs 4.7; p<0.00001), STAT3 (2.7 vs 2.4; p=0.0002) and SOCS3 (1.1 vs 2; p=0,002). The AUC, using the normalized gene expression values, was 0.88 for JAK1, 0.81 for STAT3 and 0.7 for SOCS3.
Average expression in log2 of these three genes was calculated and used as IFN score. The ROC curve AUC analysis for IFN-score revealed an AUC of 0.9 (95% CI:0.8-1.0). The score value with highest combined sensitivity (94.1%, 95% CI: 71.3-99.8) and specificity (88.6%, 95% CI: 75.4-96.2) was 4.74, with a likelihood ratio of 8.28. In our cohort, all Bad-R patients but one, showed IFN-score higher than the established cut off, and this further support the accuracy of our IFN-responsive score for ET patients.
We identified a set of three genes whose expression could be compounded into IFN score that showed a significant correlation with response in ET patients.
The IFN score could represent a predictive biomarker for responsiveness to IFN and may likely become a substantial aid to the physician.
No relevant conflicts of interest to declare.
Author notes
Asterisk with author names denotes non-ASH members.
