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With the intent of dissecting the molecular complexity of Philadelphia-negative myeloproliferative neoplasms (MPN), we designed a target enrichment panel to explore, using next-generation sequencing (NGS), the mutational status of an extensive list of 2000 cancer-associated genes and microRNAs. The genomic DNA of granulocytes and in vitro-expanded CD3+T-lymphocytes, as a germline control, was target-enriched and sequenced in a learning cohort of 20 MPN patients using Roche 454 technology. We identified 141 genuine somatic mutations, most of which were not previously described. To test the frequency of the identified variants, a larger validation cohort of 189 MPN patients was additionally screened for these mutations using Ion Torrent AmpliSeq NGS. Excluding the genes already described in MPN, for 8 genes (SCRIB, MIR662, BARD1, TCF12, FAT4, DAP3, POLG and NRAS), we demonstrated a mutation frequency between 3 and 8%. We also found that mutations at codon 12 of NRAS (NRASG12V and NRASG12D) were significantly associated, for primary myelofibrosis (PMF), with highest dynamic international prognostic scoring system (DIPSS)-plus score categories. This association was then confirmed in 66 additional PMF patients composing a final dataset of 168 PMF showing a NRAS mutation frequency of 4.7%, which was associated with a worse outcome, as defined by the DIPSS plus score.

Acute myeloid leukemia (AML) blasts are immature committed myeloid cells unable to spontaneously undergo terminal maturation, and characterized by heterogeneous sensitivity to natural differentiation inducers. Here, we show a molecular signature predicting the resistance or sensitivity of six myeloid cell lines to differentiation induced in vitro with retinoic acid or vitamin D. The identified signature was further validated by TaqMan assay for the prediction of response to an in vitro differentiation assay performed on 28 freshly isolated AML blast populations. The TaqMan assay successfully predicts the in vitro resistance or responsiveness of AML blasts to differentiation inducers. Furthermore, performing a meta-analysis of publicly available microarray data sets, we also show the accuracy of our prediction on known phenotypes and suggest that our signature could become useful for the identification of patients eligible for new therapeutic strategies.