Machine learning can identify newly diagnosed patients with CLL at high risk of infection

Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors

Genome-wide association study identifies risk loci for progressive chronic lymphocytic leukemia

Prognostication in patients with chronic lymphocytic leukemia (CLL) is challenging due to heterogeneity in clinical course. We hypothesize that constitutional genetic variation affects disease progression and could aid prognostication. Pooling data from seven studies incorporating 842 cases identifies two genomic locations associated with time from diagnosis to treatment, including 10q26.13 (rs736456, hazard ratio (HR) = 1.78, 95% confidence interval (CI) = 1.47–2.15; P = 2.71 × 10−9) and 6p (rs3778076, HR = 1.99, 95% CI = 1.55–2.55; P = 5.08 × 10−8), which are particularly powerful prognostic markers in patients with early stage CLL otherwise characterized by low-risk features. Expression quantitative trait loci analysis identifies putative functional genes implicated in modulating B-cell receptor or innate immune responses, key pathways in CLL pathogenesis. In this work we identify rs736456 and rs3778076 as prognostic in CLL, demonstrating that disease progression is determined by constitutional genetic variation as well as known somatic drivers

Targeting Bcl-2 for the treatment of multiple myeloma

International audienceDespite advances in the treatment of multiple myeloma, the disease still remains incurable for the majority of patients. The overexpression of anti-apoptotic proteins (i.e., Bcl-2, Bcl-X L or Mcl-1) is a hallmark of cancer and favors tumor cell survival and resistance to therapy. The oral drug venetoclax is the first-in-class Bcl-2-specific BH3 mimetic. In myeloma, in vitro sensitivity to venetoclax is mainly observed in plasma cells harboring the t(11;14) translocation, a molecular subgroup associated with high Bcl-2 and low Mcl-1/Bcl-XL expression. In addition with Bcl-2 members expression profile, functional tests as BH3 profiling or in vitro BH3 mimetic drug testing also predict sensitivity to the drug. Phase 1 clinical trials recently confirmed the efficacy of venetoclax monotherapy in heavily pretreated myeloma patients, mostly in patients with t(11;14). In combination with the proteasome inhibitor bortezomib, venetoclax therapy was found to be feasible and allowed promising response rate in relapsed myeloma patients, independent of t(11;14) status. The present review summarizes the current knowledge, "from bench to bedside", about venetoclax for the treatment of multiple myeloma