Feldman, Andrew L

Recent advances in follicular lymphoma (FL) have resulted in prolongation of overall survival (OS). Here we assessed if early events as defined by event‐free survival (EFS) at 12 and 24 months from diagnosis (EFS12/EFS24) can inform subsequent OS in FL. 920 newly diagnosed grade 1‐3A FL patients enrolled on the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource (MER) from 2002‐2012 were initially evaluated. EFS was defined as time from diagnosis to progression, relapse, re‐treatment, or death due to any cause. OS was compared to age‐and‐sex‐matched survival in the general US population using standardized mortality ratios (SMR) and 95% confidence intervals (CI). We used a cohort of 412 FL patients from two Lyon, France hospital registries for independent replication. Patients who failed to achieve EFS12 had poor subsequent OS (MER SMR = 3.72, 95%CI: 2.78‐4.88; Lyon SMR = 8.74, 95%CI: 5.41‐13.36). Conversely, patients achieving EFS12 had no added mortality beyond the background population (MER SMR = 0.73, 95%CI: 0.56‐0.94, Lyon SMR = 1.02, 95%CI: 0.58‐1.65). Patients with early events after immunochemotherapy had especially poor outcomes (EFS12 failure: MER SMR = 17.63, 95%CI:11.97‐25.02, Lyon SMR = 19.10, 95%CI:9.86‐33.36; EFS24 failure: MER SMR = 13.02, 95%CI:9.31‐17.74, Lyon SMR = 7.22, 95%CI:4.13‐11.74). In a combined dataset of all patients from both cohorts, baseline FLIPI was no longer informative in EFS12 achievers. Reassessment of patient status at 12 months from diagnosis in follicular lymphoma patients, or at 24 months in patients treated with immunochemotherapy, is a strong predictor of subsequent overall survival in FL. Early event status provides a simple, clinically relevant endpoint for studies assessing outcome in FL. Am. J. Hematol. 91:1096–1101, 2016. © 2016 Wiley Periodicals, Inc.

Abstract Peripheral T-cell lymphomas (PTCLs) represent a heterogeneous group of T-cell malignancies that generally demonstrate aggressive clinical behavior, often are refractory to standard therapy, and remain significantly understudied. The most common World Health Organization subtype is PTCL, not otherwise specified (NOS), essentially a “wastebasket” category because of inadequate understanding to assign cases to a more specific diagnostic entity. Identification of novel fusion genes has contributed significantly to improving the classification, biologic understanding, and therapeutic targeting of PTCLs. Here, we integrated mate-pair DNA and RNA next-generation sequencing to identify chromosomal rearrangements encoding expressed fusion transcripts in PTCL, NOS. Two of 11 cases had novel fusions involving VAV1, encoding a truncated form of the VAV1 guanine nucleotide exchange factor important in T-cell receptor signaling. Fluorescence in situ hybridization studies identified VAV1 rearrangements in 10 of 148 PTCLs (7%). These were observed exclusively in PTCL, NOS (11%) and anaplastic large cell lymphoma (11%). In vitro, ectopic expression of a VAV1 fusion promoted cell growth and migration in a RAC1-dependent manner. This growth was inhibited by azathioprine, a clinically available RAC1 inhibitor. We also identified novel kinase gene fusions, ITK-FER and IKZF2-ERBB4, as candidate therapeutic targets that show similarities to known recurrent oncogenic ITK-SYK fusions and ERBB4 transcript variants in PTCLs, respectively. Additional novel and potentially clinically relevant fusions also were discovered. Together, these findings identify VAV1 fusions as recurrent and targetable events in PTCLs and highlight the potential for clinical sequencing to guide individualized therapy approaches for this group of aggressive malignancies.

Key Points The neuronal marker UCH-L1 is induced in, and specifically augments the oncogene-induced transformation of, GCB cells. High levels of UCHL1 identify patients with GC DLBCL with an increased risk for poor outcomes.

We recently defined event‐free survival at 24 months (EFS24) as a clinically relevant outcome for patients with DLBCL. Patients who fail EFS24 have very poor overall survival, while those who achieve EFS24 have a subsequent overall survival equivalent to that of the age‐ and sex‐matched general population. Here, we develop and validate a clinical risk calculator (IPI24) for EFS24. Model building was performed on a discovery dataset of 1,348 patients with DLBCL and treated with anthracycline‐based immunochemotherapy. A multivariable model containing age, Ann Arbor stage, normalized serum LDH, ALC, ECOG performance status, bulky disease, and sex was identified. The model was then applied to an independent validation dataset of 1,177 DLBCL patients. The IPI24 score estimates the probability of failing to achieve the EFS24 endpoint for an individual patient. The IPI24 model showed superior discriminatory ability (c‐statistic = 0.671) in the validation dataset compared to the IPI (c‐statistic = 0.649) or the NCCN‐IPI (c‐statistic = 0.657). After recalibration of the model on the combined dataset, the median predicted probability of failing to achieve EFS24 was 36% (range, 12–88%), and the IPI24 showed an EFS24 gradient in all IPI groups. The IPI24 also identified a significant percentage of patients with high risk disease, with over 20% of patients having a 50% or higher risk of failing to achieve EFS24. The IPI24 provides an individual patient level probability of achieving the clinically relevant EFS24 endpoint. It can be used via electronic apps. Am. J. Hematol. 91:179–184, 2016. © 2015 Wiley Periodicals, Inc.

Key PointsThe NF-κB subunits p52 and RelB increase IRF4 promoter activity and expression in PTCL cells. A positive feedback loop involving CD30, NF-κB, and IRF4 drives PTCL cell proliferation and can be blocked by NF-κB inhibitors.

Key Points BCL2 mutations in FL correlate with activation-induced cytidine deaminase expression and frequently alter the amino acid sequence of the protein. Mutations in the BCL2 coding sequence at diagnosis are associated with shortened time to transformation and earlier death due to lymphoma.

Abstract Motivation: RNA-seq has become the method of choice to quantify genes and exons, discover novel transcripts and detect fusion genes. However, reliable variant identification from RNA-seq data remains challenging because of the complexities of the transcriptome, the challenges of accurately mapping exon boundary spanning reads and the bias introduced during the sequencing library preparation. Method: We developed RVboost, a novel method specific for RNA variant prioritization. RVboost uses several attributes unique in the process of RNA library preparation, sequencing and RNA-seq data analyses. It uses a boosting method to train a model of ‘good quality’ variants using common variants from HapMap, and prioritizes and calls the RNA variants based on the trained model. We packaged RVboost in a comprehensive workflow, which integrates tools of variant calling, annotation and filtering. Results: RVboost consistently outperforms the variant quality score recalibration from the Genome Analysis Tool Kit and the RNA-seq variant-calling pipeline SNPiR in 12 RNA-seq samples using ground-truth variants from paired exome sequencing data. Several RNA-seq–specific attributes were identified as critical to differentiate true and false variants, including the distance of the variant positions to exon boundaries, and the percent of the reads supporting the variant in the first six base pairs. The latter identifies false variants introduced by the random hexamer priming during the library construction. Availability and implementation: The RVboost package is implemented to readily run in Mac or Linux environments. The software and user manual are available at http://bioinformaticstools.mayo.edu/research/rvboost/. Supplementary information: Supplementary data are available at Bioinformatics online.