Mutated JAK kinases and deregulated STAT activity are potential therapeutic targets in cutaneous T-cell lymphomaThe malignant mechanisms that control the development of cutaneous T-cell lymphoma (CTCL) are starting to be identified. Recent evidence suggests that disturbances in specific intracellular signaling pathways, such as RAS-MAPK, TCR-PLCG1-NFAT and JAK-STAT, can play an essential role in the pathogenesis of CTCL.1,2 Our group previously reported a network of somatic mutations affecting genes with potential to affect critical Tcell signaling pathways in CTCL patients. 1 As part of our findings we detected a number of mutations potentially affecting JAK/STAT signaling. These findings were recently confirmed by an independent group, suggesting that mutations in this pathway may contribute as disease mechanisms in CTCL.3 Deregulated JAK/STAT signaling is involved in many types of cancer. In fact, somatically acquired genetic alterations of JAK or STAT genes that induce aberrant activation of downstream signaling, via STAT phosphorylation, have been reported in some human hematologic malignancies including T-cell lymphomas. 4,5 We decided to explore JAK/STAT signaling as part of an intricate network of malignant signaling that controls the pathogenesis of CTCL, on the basis of the following evidence: (i) we had detected mutations in the pseudokinase domain of JAK1 and JAK3 in two of 11 patients and one cell line; (ii) we had also found several mutations that can directly (i.e., IL6S/T) or indirectly (i.e., TRAF6, RELB and CARD11) activate JAK/STAT signaling; and (iii) activated STAT3 had been detected in a large proportion of patients with advanced CTCL. 6,7 To explore the mutational status of JAK genes in a larger cohort of human CTCL patients' samples and cell lines, two independent state-of-the-art ultrasequencing approaches were used: a targeted gene-enrichment kit (HaloPlex) coupled to Ion-PGM (Life Technologies) sequencing, and a specific polymerase chain reactionbased amplification protocol targeting the pseudokinase domains of JAK1, JAK2 and JAK3 genes (hereafter, referred to as PsTKd-PCR), followed by specific indexing and sequencing with MiSeq (Illumina; see the Online Supplementary Methods for details). These are two highly sensitive methods that can enable the detection of mutations even present at low frequencies in neoplastic cells or in minority clones which may be found in CTCL samples. Thus, taken together, the data from our series (including those already described by Vaqué et al. © F e r r a t a S t o r t i F o u n d a t i o nthe pseudokinase domain of JAK proteins, a finding that is consistent with the results of other research groups that have found somatic mutations in the same domain of JAK1 and JAK3 kinases in prolymphocytic leukemia, other T-cell leukemias including CTCL and various human malignancies. 3,4,[8][9][10] Thus, it has been shown that JAK pseudokinase domains are auto-inhibitory and keep the kinase domain inactive until receptor dimerization stimulates transition to an a...

Peripheral T-cell lymphomas (PTCLs) are a group of nonHodgkin lymphomas (NHLs) with heterogeneous clinical presentation, histology, response to treatment and outcome, whose genetic background is still poorly understood. Patients with PTCL are usually treated with CHOP or more intensive regimens, generally with minimal effectiveness, thus highlighting the need for new therapeutic strategies.

Follicular lymphoma (FL) is an indolent but largely incurable disease. Some patients suffer histological transformation to a more aggressive subtype with poorer prognosis. This study aimed to improve our understanding of the genetics underlying FL histological transformation, and to identify genetic drivers or promoters of the transformation by elucidating the differences between FL samples from patients who did and did not transform. We conducted targeted massive parallel sequencing of 22 pre-transformed FL/transformed diffuse large B-cell lymphoma pairs and 20 diagnostic samples from non-transformed FL patients. Additionally, 22 matched samples from 11 transformed FL patients (pre-transformed FL and diffuse large B-cell lymphoma) and 9 non-transformed FLs were studied for copy number variation using SNP arrays. We identified recurrently mutated genes that were enriched at transformation, most notably LRP1B , GNA13 and POU2AF1 , which have roles in B-cell differentiation, GC architecture and migration. Mutations in POU2AF1 might be associated with lower levels of expression, were more frequent in transformed FLs, and seemed to be specific to transformed- compared with de novo- diffuse large B-cell lymphomas. Pre-transformed FLs carried more mutations per sample and had greater subclonal heterogeneity than non-transformed FLs. Finally, we identified four mutated genes in FL samples that differed between patients who did and did not transform: NOTCH2 , DTX1 , UBE2A and HIST1H1E . The presence of mutations in these genes was associated with shorter time to transformation when mutated in the FL biopsies. This information might be useful for identifying patients at higher risk of transformation.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease whose prognosis is associated with clinical features, cell-of-origin and genetic aberrations. Recent integrative, multi-omic analyses had led to identifying overlapping genetic DLBCL subtypes. We used targeted massive sequencing to analyze 84 diagnostic samples from a multicenter cohort of patients with DLBCL treated with rituximab-containing therapies and a median follow-up of 6 years. The most frequently mutated genes were IGLL5 (43%), KMT2D (33.3%), CREBBP (28.6%), PIM1 (26.2%), and CARD11 (22.6%). Mutations in CD79B were associated with a higher risk of relapse after treatment, whereas patients with mutations in CD79B, ETS1, and CD58 had a significantly shorter survival. Based on the new genetic DLBCL classifications, we tested and validated a simplified method to classify samples in five genetic subtypes analyzing the mutational status of 26 genes and BCL2 and BCL6 translocations. We propose a two-step genetic DLBCL classifier (2-S), integrating the most significant features from previous algorithms, to classify the samples as N12-S, EZB2-S, MCD2-S, BN22-S, and ST22-S groups. We determined its sensitivity and specificity, compared with the other established algorithms, and evaluated its clinical impact. The results showed that ST22-S is the group with the best clinical outcome and N12-S, the more aggressive one. EZB2-S identified a subgroup with a worse prognosis among GCB-DLBLC cases.