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This study is focused on therapy-related myeloid neoplasms after the most promising frontline FCR (fludarabine, cyclophosphamide, and rituximab) therapy in previously untreated chronic lymphocytic leukemia patients. A total of 28 therapy-related myeloid neoplasm patients were identified, including 19 patients from 3 well-controlled FCR frontline trials (n ¼ 426 patients), giving an estimated frequency of 4.5% (1.9-8.3%) in a follow-up period of 44 months (range 5-122 months). Clinically, therapy-related myeloid neoplasms could emerge directly from 'prolonged myelosuppression' after FCR (10 patients), or after achieving complete hematological recovery (n ¼ 18). The overall latency was 35 months (range 3-118 months), with the former group of 23 months and the latter 42 months (Po0.001). In all, 10 cases presented as therapy-related acute myeloid leukemia and 18 as therapy-related myelodysplastic syndromes. Abnormal cytogenetics was present in 26 of 27 (96%) patients, with frequent chromosomes 5 and 7 abnormalities. The median survival was 7 months after therapy-related myeloid neoplasms. Our results indicate that the risk of therapy-related myeloid neoplasms secondary to frontline FCR therapy may not be as high as previously reported after removing the confounding factor of previous cytotoxic exposure, but this risk increased with older age and likely growth factor co-administration. Therapy-related myeloid neoplasms after FCR therapy shares clinicopathological features with therapy-related myeloid neoplasms secondary to other alkylating agents, but has a shorter latency interval indicating possible synergetic effects of the nucleotide analog fludarabine. The fact that therapy-related myeloid neoplasms can directly emerge from 'prolonged myelosuppression' warrants a bone marrow examination to rule out therapy-related myeloid neoplasms in this clinical setting.

Plasma cell myeloma (PCM) exhibits immunophenotypic aberrancies that can be used for minimal residual disease (MRD) detection after therapy. The authors sought to determine whether non-neoplastic plasma cells, especially in the bone marrow (BM) post various therapies, would exhibit immunophenotypic variations interfering PCM MRD detection. The authors studied the flow cytometric immunophenotypes of non-neoplastic plasma cells from 50 BM specimens, including 12 untreated BM and 38 BM specimens from patients with non-plasmacytic haematological malignancies undergoing various therapies, and compared with 59 BM specimens positive for PCM MRD. Non-neoplastic plasma cells showed heterogeneous expressions of CD45 (78% (41-100)) and CD19 (80% (52-97)), and were negative for CD20 and CD117. CD56 was observed in a small subset (6% (0-37)) and CD28 in a larger subset (15% (0-59)) of non-neoplastic plasma cells, with the latter more frequently expressed in post-treatment BMs (p=0.01). However, despite a partial immunophenotypic overlap, PCM cells could be reliably discriminated from non-neoplastic plasma cells by the presence of a higher number of aberrancies (3 (1-6) vs 0 (0-2)) and stronger intensity and uniformity of aberrant expression (p<0.001 in each marker using a cut-off value). In addition, simultaneous assessment of cytoplasmic κ/λ with surface markers detected light chain restriction in all 59 PCM cases. In conclusion, non-neoplastic plasma cells in BM are more immunophenotypically heterogeneous than previously understood; however, these immunophenotypic variations differ from those of PCM. With advances in multicolour flow cytometry and application of recently validated markers, PCM MRD may still be reliably distinguished from non-neoplastic plasma cells.

Motivation: Mathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and outperform CPUs in terms of raw computing power. Despite their computational advantages, their adoption by the systems biology community is relatively slow, since differences in hardware architecture between GPUs and CPUs complicate the porting of existing code.Results: We present a Python package, cuda-sim, that provides highly parallelized algorithms for the repeated simulation of biochemical network models on NVIDIA CUDA GPUs. Algorithms are implemented for the three popular types of model formalisms: the LSODA algorithm for ODE integration, the Euler–Maruyama algorithm for SDE simulation and the Gillespie algorithm for MJP simulation. No knowledge of GPU computing is required from the user. Models can be specified in SBML format or provided as CUDA code. For running a large number of simulations in parallel, up to 360-fold decrease in simulation runtime is attained when compared to single CPU implementations.Availability: http://cuda-sim.sourceforge.net/Contact: christopher.barnes@imperial.ac.uk; m.stumpf@imperial.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Serine/threonine phosphatases such as PP1 lack substrate specificity and associate with a large array of targeting subunits to achieve the requisite selectivity. The tumour suppressor ASPP (apoptosis-stimulating protein of p53) proteins associate with PP1 catalytic subunits and are implicated in multiple functions from transcriptional regulation to cell junction remodelling. Here we show that Drosophila ASPP is part of a multiprotein PP1 complex and that PP1 association is necessary for several in vivo functions of Drosophila ASPP. We solve the crystal structure of the human ASPP2/PP1 complex and show that ASPP2 recruits PP1 using both its canonical RVxF motif, which binds the PP1 catalytic domain, and its SH3 domain, which engages the PP1 C-terminal tail. The ASPP2 SH3 domain can discriminate between PP1 isoforms using an acidic specificity pocket in the n-Src domain, providing an exquisite mechanism where multiple motifs are used combinatorially to tune binding affinity to PP1.

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