Inhibition of inflammatory signaling in Pax5 mutant cells mitigates B-cell leukemogenesis

© The Author(s) 2020.PAX5 is one of the most frequently mutated genes in B-cell acute lymphoblastic leukemia (B-ALL), and children with inherited preleukemic PAX5 mutations are at a higher risk of developing the disease. Abnormal profiles of inflammatory markers have been detected in neonatal blood spot samples of children who later developed B-ALL. However, how inflammatory signals contribute to B-ALL development is unclear. Here, we demonstrate that Pax5 heterozygosis, in the presence of infections, results in the enhanced production of the inflammatory cytokine interleukin-6 (IL-6), which appears to act in an autocrine fashion to promote leukemia growth. Furthermore, in vivo genetic downregulation of IL-6 in these Pax5 heterozygous mice retards B-cell leukemogenesis, and in vivo pharmacologic inhibition of IL-6 with a neutralizing antibody in Pax5 mutant mice with B-ALL clears leukemic cells. Additionally, this novel IL–6 signaling paradigm identified in mice was also substantiated in humans. Altogether, our studies establish aberrant IL6 expression caused by Pax5 loss as a hallmark of Pax5-dependent B-ALL and the IL6 as a therapeutic vulnerability for B-ALL characterized by PAX5 loss.We would also like to thank all members of our groups for useful suggestions and for their critical reading of the manuscript. Research at G.C.’s laboratory was supported by Italian Association for Cancer Research (grant IG-17593 to GC) and Fondazione Cariplo (grant 2018-0339 to CP). Research at CC’s laboratory was partially supported by FEDER, EU, MINECO (SAF2017-83061-R), the “Fundación Ramón Areces,” a Research Contract with the “Fundación Síndrome de Wolf-Hirschhorn o 4p-”, and institutional grants from the “Fundación Ramón Areces” and “Banco de Santander” to the CBMSO. Research in the CVD group is partially supported by FEDER, “Miguel Servet” Grant (CPII19/00024—AES 2017-2020) from the Instituto de Salud Carlos III (Ministerio de Economía y Competitividad), “Fondo de Investigaciones Sanitarias/Instituto de Salud Carlos III” (PI17/00167). Research in the ISG group is partially supported by FEDER and by SAF2015-64420-R MINECO/FEDER, UE, RTI2018-093314-B-I00 MCIU/AEI/FEDER, UE, by Junta de Castilla y León (UIC-017, CSI001U16, and CSI234P18), and by the German Jose Carreras Foundation (DJCLS R13/26; DJCLS 07R/2019). CVD, and ISG have been supported by the German Federal Office for Radiation Protection (BfS)-Germany (FKZ: 3618S32274). M.R.O., and ISG have been supported by the Fundacion Unoentrecienmil (CUNINA project). Research in the A.O. group is partially supported by "Fondo de Investigaciones Sanitarias/Instituto de Salud Carlos III" - FEDER-Ministerio de Economía y Competitividad (PI19/01183). AC-G and M.I.-H. are supported by FSE-Conserjería de Educación de la Junta de Castilla y León 2019 and 2020 (ESF- European Social Fund) fellowship, respectively. J.R.-G. is supported by a scholarship from University of Salamanca co-financed by Banco Santander and ESF

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Peer reviewed: TrueObjectiveEarly detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.MethodData were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic “F” fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores.ResultsMean logit algorithm scores were significantly different across groups in the testing sample (p &amp;lt; 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively.ConclusionFindings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.</jats:sec