A FHIR-based System for the Generation and Retrieval of Clinical Documents

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Genetic predisposition and induced pro-inflammatory/pro-oxidative status may play a role in increased atherothrombotic events in nilotinib treated chronic myeloid leukemia patients

Several reports described an increased risk of cardiovascular (CV) events, mainly atherothrombotic, in Chronic Myeloid Leukemia (CML) patients receiving nilotinib. However, the underlying mechanism remains elusive. The objective of the current cross-sectional retrospective study is to address a potential correlation between Tyrosine Kinase Inhibitors (TKIs) treatment and CV events. One hundred and 10 chronic phase CML patients in complete cytogenetic response during nilotinib or imatinib, were screened for CV events and evaluated for: traditional CV risk factors, pro/anti-inflammatory biochemical parameters and detrimental ORL1 gene polymorphisms (encoding for altered oxidized LDL receptor-1). Multivariate analysis of the whole cohort showed that the cluster of co-existing nilotinib treatment, dyslipidaemia and G allele of LOX-1 polymorphism was the only significant finding associated with CV events. Furthermore, multivariate analysis according to TKI treatment confirmed IVS4-14 G/G LOX-1 polymorphism as the strongest predictive factor for a higher incidence of CV events in nilotinib patients. Biochemical assessment showed an unbalanced pro-inflammatory cytokines network in nilotinib vs imatinib patients. Surprisingly, pre-existing traditional CV risk factors were not always predictive of CV events. We believe that in nilotinib patients an induced "inflammatory/oxidative status", together with a genetic pro-atherothrombotic predisposition, may favour the increased incidence of CV events. Prospective studies focused on this issue are ongoing

Residual peripheral blood CD26+leukemic stem cells in chronic myeloid leukemia patients during TKI therapy and during treatment-free remission

Chronic myeloid leukemia (CML) patients in sustained “deep molecular response” may stop TKI treatment without disease recurrence; however, half of them lose molecular response shortly after TKI withdrawing. Well-defined eligibility criteria to predict a safe discontinuation up-front are still missing. Relapse is probably due to residual quiescent TKI-resistant leukemic stem cells (LSCs) supposedly transcriptionally low/silent and not easily detectable by BCR-ABL1 qRT-PCR. Bone marrow Ph+ CML CD34+/CD38− LSCs were found to specifically co-express CD26 (dipeptidylpeptidase-IV). We explored feasibility of detecting and quantifying CD26+ LSCs by flow cytometry in peripheral blood (PB). Over 400 CML patients (at diagnosis and during/after therapy) entered this cross-sectional study in which CD26 expression was evaluated by a standardized multiparametric flow cytometry analysis on PB CD45+/CD34+/CD38− stem cell population. All 120 CP-CML patients at diagnosis showed measurable PB CD26+ LSCs (median 19.20/μL, range 0.27–698.6). PB CD26+ LSCs were also detectable in 169/236 (71.6%) CP-CML patients in first-line TKI treatment (median 0.014 cells/μL; range 0.0012–0.66) and in 74/112 (66%), additional patients studied on treatment-free remission (TFR) (median 0.015/μL; range 0.006–0.76). Notably, no correlation between BCR-ABL/ABLIS ratio and number of residual LSCs was found both in patients on or off TKIs. This is the first evidence that “circulating” CML LSCs persist in the majority of CML patients in molecular response while on TKI treatment and even after TKI discontinuation. Prospective studies evaluating the dynamics of PB CD26+ LSCs during TKI treatment and the role of a “stem cell response” threshold to achieve and maintain TFR are ongoing

Cardiovascular toxicity in patients with chronic myeloid leukemia treated with second-generation tyrosine kinase inhibitors in the real-life practice: Identification of risk factors and the role of prophylaxis

No abstract availabl

The Polycomb BMI1 Protein Is Co-expressed With CD26+ in Leukemic Stem Cells of Chronic Myeloid Leukemia

The Polycomb gene BMI1 expression exerts a negative predictive impact on several hematological malignancies, such as acute and chronic myeloid leukemia (CML), myelofibrosis, and follicular lymphoma. As already demonstrated in CML, BMI1 is responsible for the resistance to the tyrosine kinase inhibitors (TKIs) in a BCR-ABL1-independent way. Even if, it is unknown where BMI1 in CML is expressed (in progenitors or more mature cells). We decided, therefore, to evaluate if and where the BMI1 protein is located, focusing mainly on the CD34+/CD38-/CD26+ CML progenitors. To begin we measured, by flow cytometry, the proportion of CD34+/CD26+ cells in 31 bone marrow samples from 20 CML patients, at diagnosis and during treatment with imatinib. After that the bone marrow blood smears were stained with antibodies anti-CD26, BCR-ABL1, and BMI1. These smears were observed by a confocal laser microscope and a 3D reconstruction was then performed. At diagnosis, CD34+/CD26+ cells median value/μL was 0.48; this number increased from diagnosis to the third month of therapy and then reduced during treatment with imatinib. The number and behavior of the CD26+ progenitors were independent from the BCR-ABL1 expression, but they summed up what previously observed about the BMI1 expression modulation. In this work we demonstrate for the first time that in CML the BMI1 protein is co-expressed with BCR-ABL1 only in the cytoplasm of the CD26+ precursors; on the contrary, in other hematological malignancies where BMI1 is commonly expressed (follicular lymphoma, essential thrombocytemia, acute myeloid leukemia), it was not co-localized with CD26 or, obviously, with BCR-ABL1. Once translated into the clinical context, if BMI1 is a marker of stemness, our results would suggest the combination of the BMI1 inhibitors with TKIs as an interesting object of research, and, probably, as a promising way to overcome resistance in CML patients

A Privacy-Preserving and Standard-Based Architecture for Secondary Use of Clinical Data

The heterogeneity of the formats and standards of clinical data, which includes both structured, semi-structured, and unstructured data, in addition to the sensitive information contained in them, require the definition of specific approaches that are able to implement methodologies that can permit the extraction of valuable information buried under such data. Although many challenges and issues that have not been fully addressed still exist when this information must be processed and used for further purposes, the most recent techniques based on machine learning and big data analytics can support the information extraction process for the secondary use of clinical data. In particular, these techniques can facilitate the transformation of heterogeneous data into a common standard format. Moreover, they can also be exploited to define anonymization or pseudonymization approaches, respecting the privacy requirements stated in the General Data Protection Regulation, Health Insurance Portability and Accountability Act and other national and regional laws. In fact, compliance with these laws requires that only de-identified clinical and personal data can be processed for secondary analyses, in particular when data is shared or exchanged across different institutions. This work proposes a modular architecture capable of collecting clinical data from heterogeneous sources and transforming them into useful data for secondary uses, such as research, governance, and medical education purposes. The proposed architecture is able to exploit appropriate modules and algorithms, carry out transformations (pseudonymization and standardization) required to use data for the second purposes, as well as provide efficient tools to facilitate the retrieval and analysis processes. Preliminary experimental tests show good accuracy in terms of quantitative evaluations

A Privacy-Preserving and Standard-Based Architecture for Secondary Use of Clinical Data

The heterogeneity of the formats and standards of clinical data, which includes both structured, semi-structured, and unstructured data, in addition to the sensitive information contained in them, require the definition of specific approaches that are able to implement methodologies that can permit the extraction of valuable information buried under such data. Although many challenges and issues that have not been fully addressed still exist when this information must be processed and used for further purposes, the most recent techniques based on machine learning and big data analytics can support the information extraction process for the secondary use of clinical data. In particular, these techniques can facilitate the transformation of heterogeneous data into a common standard format. Moreover, they can also be exploited to define anonymization or pseudonymization approaches, respecting the privacy requirements stated in the General Data Protection Regulation, Health Insurance Portability and Accountability Act and other national and regional laws. In fact, compliance with these laws requires that only de-identified clinical and personal data can be processed for secondary analyses, in particular when data is shared or exchanged across different institutions. This work proposes a modular architecture capable of collecting clinical data from heterogeneous sources and transforming them into useful data for secondary uses, such as research, governance, and medical education purposes. The proposed architecture is able to exploit appropriate modules and algorithms, carry out transformations (pseudonymization and standardization) required to use data for the second purposes, as well as provide efficient tools to facilitate the retrieval and analysis processes. Preliminary experimental tests show good accuracy in terms of quantitative evaluations