Publications by authors named "R Letestu"

Introduction: Measurable residual disease (MRD) is becoming increasingly important in the chronic lymphocytic leukemia (CLL) context. It is of independent prognostic significance in terms of favorable progression-free and overall survival. The standardized methods used to assess CLL MRD are based on flow cytometry and real-time quantitative PCR.

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In previously untreated, medically fit patients with chronic lymphocytic leukemia (CLL), research is focused on developing fixed-duration strategies to improve long-term outcomes while sparing patients from serious toxicities. The ICLL-07 trial evaluated a fixed-duration (15-month) immunochemotherapy approach in which after obinutuzumab-ibrutinib induction for 9 months, patients (n = 10) in complete remission (CR) with bone marrow (BM) measurable residual disease (MRD) <0.01% continued only ibrutinib 420 mg/day for 6 additional months (I arm), whereas the majority (n = 115) received up to 4 cycles of fludarabine/cyclophosphamide-obinutuzumab 1000 mg alongside the ibrutinib (I-FCG arm).

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Article Synopsis
  • Chronic Lymphocytic Leukemia (CLL) is a type of cancer that affects B cells and can vary from slow-growing to fast-growing.
  • The CLL B cells talk to immune cells and help create special cells (Treg cells) that can avoid the immune system's attacks.
  • Studying certain proteins (IL10 and TGFβ1) showed they help in the development of these immune cells and can help doctors better understand patient differences and disease progression.
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Article Synopsis
  • FCR has been the standard treatment for B-chronic lymphocytic leukemia (CLL) but targeted therapies are now taking over, highlighting a need for predictive biomarkers for treatment success.
  • A study focused on identifying specific microRNAs in the blood of untreated CLL patients that could predict whether they would achieve complete remission (CR) with undetectable minimal residual disease (uMRD) post-treatment.
  • The study found 25 differentially expressed miRNAs, with a decision tree model predicting treatment outcomes based on 5 miRNAs, identifying distinct patient groups with varying probabilities of achieving CR, where high levels of certain miRNAs correlated with better outcomes.
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