Characterization of MED12, HMGA2, and FH alterations reveals molecular variability in uterine smooth muscle tumors

Uterine smooth muscle tumors range from benign leiomyomas to malignant leiomyosarcomas. Based on numerous molecular studies, leiomyomas and leiomyosarcomas mostly lack shared mutations and the majority of tumors are believed to develop through distinct mechanisms. To further characterize the molecular variability among uterine smooth muscle tumors, and simultaneously insinuate their potential malignant progression, we examined the frequency of known genetic leiomyoma driver alterations (MED12 mutations, HMGA2 overexpression, biallelic FH inactivation) in 65 conventional leiomyomas, 94 histopathological leiomyoma variants (18 leiomyomas with bizarre nuclei, 22 cellular, 29 highly cellular, and 25 mitotically active leiomyomas), and 51 leiomyosarcomas. Of the 210 tumors analyzed, 107 had mutations in one of the three driver genes. No tumor had more than one mutation confirming that all alterations are mutually exclusive. MED12 mutations were the most common alterations in conventional and mitotically active leiomyomas and leiomyosarcomas, while leiomyomas with bizarre nuclei were most often FH deficient and cellular tumors showed frequent HMGA2 overexpression. Highly cellular leiomyomas displayed the least amount of alterations leaving the majority of tumors with no known driver aberration. Our results indicate that based on the molecular background, histopathological leiomyoma subtypes do not only differ from conventional leiomyomas, but also from each other. The presence of leiomyoma driver alterations in nearly one third of leiomyosarcomas suggests that some tumors arise through leiomyoma precursor lesion or that these mutations provide growth advantage also to highly aggressive cancers. It is clinically relevant to understand the molecular background of various smooth muscle tumor subtypes, as it may lead to improved diagnosis and personalized treatments in the future.Peer reviewe

Somatic MED12 mutations are associated with poor prognosis markers in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is the most common leukemia in adults. We performed systematic database search and identified highly specific MED12 mutations in CLL patients. To study this further, we collected three independent sample series comprising over 700 CLL samples and screened MED12 exons 1 and 2 by direct sequencing. Mutations were identified at significant frequency in all three series with a combined mutation frequency of 5.2% (37/709). Positive mutation status was found to be associated with unmutated IGHV and ZAP70 expression, which are well-known poor prognosis markers in CLL. Our results recognize CLL as the first extrauterine cancer type where 5'terminus of MED12 is mutated at significant frequency. Functional analyses have shown that these mutations lead to dissociation of Cyclin C-CDK8/19 from the core Mediator and to the loss of Mediator-associated CDK kinase activity. Additional studies on the role of MED12 mutation status as a putative prognostic factor as well as mutations' exact tumorigenic mechanism in CLL are warranted.Peer reviewe

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Uterine Leiomyoma-Linked MED12 Mutations Disrupt Mediator-Associated CDK Activity

Peer reviewe

Evaluation of Fault Tolerant System against Actuators Aging applied to Flotation Circuit

International audienceThe main contribution of this paper concerns the reconfigurability evaluation of fault tolerant control system with respect of the reliability requirements and actuators aging. Based on the relationship between the energy consumption defined through the Gramian controllability and the actuators aging under degraded functional conditions, a new reconfigurability index for a reliable design is proposed. A flotation circuit is considered to highlight the performance of the proposed method. Flotation circuit require dependability conditions which can be guaranteed with the proposed reliable fault tolerance approach

Genomic prediction of relapse in recipients of allogeneic haematopoietic stem cell transplantation

Allogeneic haematopoietic stem cell transplantation currently represents the primary potentially curative treatment for cancers of the blood and bone marrow. While relapse occurs in approximately 30% of patients, few risk-modifying genetic variants have been identified. The present study evaluates the predictive potential of patient genetics on relapse risk in a genome-wide manner. We studied 151 graft recipients with HLA-matched sibling donors by sequencing the whole-exome, active immunoregulatory regions, and the full MHC region. To assess the predictive capability and contributions of SNPs and INDELs, we employed machine learning and a feature selection approach in a cross-validation framework to discover the most informative variants while controlling against overfitting. Our results show that germline genetic polymorphisms in patients entail a significant contribution to relapse risk, as judged by the predictive performance of the model (AUC = 0.72 [95% CI: 0.63-0.81]). Furthermore, the top contributing variants were predictive in two independent replication cohorts (n = 258 and n = 125) from the same population. The results can help elucidate relapse mechanisms and suggest novel therapeutic targets. A computational genomic model could provide a step toward individualized prognostic risk assessment, particularly when accompanied by other data modalities.Peer reviewe