The assessment of minimal residual disease versus that of somatic mutations for predicting the outcome of acute myeloid leukemia patients

Background: In addition to morphological and cytogenetic features, acute myeloid leukemias are characterized by mutations that can be used for target-therapy; also the minimal/measurable residual disease (MRD) could be an important prognostic factor. The purpose of this retrospective study was to investigate if somatic mutations could represent an additional prognostic value in respect of MRD alone. Method: At baseline, 98 patients were tested for NPM1, FLT3, and for WT1 expression; 31 for ASXL1, TET2, IDH1, IDH2, N-RAS, WT1, c-KIT, RUNX1, and DNMT3A. The same genes have been also tested after induction and consolidation. Results: Overall, 60.2% of our patients resulted mutated: 24.5% carried mutations of FLT3-ITD, 38.7% of NPM1, 48.4% of c-KIT, 25.8% of N-RAS and 19.3% of IDH2. The probability of achieving a complete response (CR) was higher for younger patients, with low ELN risk score, NPM1-mutated, with low WT1 levels, and without FLT3. The presence of additional mutations represented a poor predictive factor: only 19% of these cases achieved CR in comparison to 43% of subjects without any of it. Concerning survival, it was conditioned by a lower ELN risk score, younger age, reduction > 1 log of the NPM1 mutational burden, disappearance of FLT3 mutations and lower WT1 expression. Regarding the role of the additional mutations, they impaired the outcome of 20% of the already MRD-negative patients. Concerning the possibility of predicting relapse, we observed an increase of the NPM1 mutational burden at the time-point immediately preceding the relapse (about 2 months earlier) in 50% of subjects. Similarly concerning WT1, an increase of its expression anticipated disease recurrence in 64% of cases. Conclusions: We demonstrated that additional somatic mutations are able to impair outcome of the already MRD-negative subjects. About MRD, we suggest a prognostic role also for the WT1 expression. Finally, we considered as relevant the assessment of NPM1 quantity clearance instead of the presence/absence of mutations alone. Still remains in doubt the utility in terms of long-term prognosis of a baseline more complex mutational screening; we could hypothesize that it would be useful for those patients where other markers are not available or who reached the MRD negativity

The hOCT1 and ABCB1 polymorphisms do not influence the pharmacodynamics of nilotinib in chronic myeloid leukemia

First-line nilotinib in chronic myeloid leukemia is more effective than imatinib to achieve early and deep molecular responses, despite poor tolerability or failure observed in one-third of patients. The toxicity and efficacy of tyrosine kinase inhibitors might depend on the activity of transmembrane transporters. However, the impact of transporters genes polymorphisms in nilotinib setting is still debated. We investigated the possible correlation between single nucleotide polymorphisms of hOCT1 (rs683369 [c.480C > G]) and ABCB1 (rs1128503 [c.1236C > T], rs2032582 [c.2677G > T/A], rs1045642 [c.3435C > T]) and nilotinib efficacy and toxicity in a cohort of 78 patients affected by chronic myeloid leukemia in the context of current clinical practice. The early molecular response was achieved by 81% of patients while 64% of them attained deep molecular response (median time, 26 months). The 36-month event-free survival was 86%, whereas 58% of patients experienced toxicities. Interestingly, hOCT1 and ABCB1 polymorphisms alone or in combination did not influence event-free survival or the adverse events rate. Therefore, in contrast to data obtained in patients treated with imatinib, hOCT1 and ABCB1 polymorphisms do not impact on nilotinib efficacy or toxicity. This could be relevant in the choice of the first-line therapy: patients with polymorphisms that negatively condition imatinib efficacy might thus receive nilotinib as first-line therapy

Learning transferable policies for autonomous planetary landing via deep reinforcement learning

The aim of this work is to develop an application for autonomous landing, exploiting the properties of Deep Reinforcement Learning and Transfer Learning in order to tackle the problem of planetary landing on unknown or barely-known extra-terrestrial environments by learning good-performing policies, which are transferable from the training environment to other, new environments, without losing optimality. To this end, we model a real-physics simulator, by means of the Bullet/PyBullet library, composed by a lander, defined through the standard ROS/URDF framework and realistic 3D terrain models, for which we adapt official NASA 3D meshes, reconstructed from the data retrieved during missions. Where such models are not available, we reconstruct the terrain from mission imagery-generally SAR imagery. In this setup, we train a Deep Reinforcement Learning model-using DDPG and SAC, then comparing the outcomes-to autonomously land on the lunar environment. Moreover, we perform transfer learning on Mars and Titan environments. While still preliminary, our results show that DDPG and SAC can learn good landing policies, that can be transferred to other environments. Good policies can be learned by the SAC algorithm also in the case of atmospheric disturbances-e.g. gusts

Treatment and reuse of textile effluents based on a new ultrafiltration and other technologies

none5MARCUCCI M; NOSENZO G; CAPANNELLI G; CORVIERI D; CIARDELLI GMarcucci, M; Nosenzo, G; Capannelli, Gustavo; Corvieri, D; Ciardelli, G