Determination of caspase-3 activation fails to predict chemosensitivity in primary acute myeloid leukemia blasts

BACKGROUND: Ex-vivo chemosensitivity tests that measure cell death induction may predict treatment outcome and, therefore, represent a powerful instrument for clinical decision making in cancer therapy. Such tests are, however, work intensive and, in the case of the DiSC-assay, require at least four days. Induction of apoptosis is the mode of action of anticancer drugs and should, therefore, result in the induction of caspase activation in cells targeted by anticancer therapy. METHODS: To determine, whether caspase activation can predict the chemosensitivity, we investigated enzyme activation of caspase-3, a key executioner caspase and correlated these data with chemosensitivity profiles of acute myeloid leukemia (AML) blasts. RESULTS: There was, however, no correlation between the ex-vivo chemosensitivity assessed by measuring the overall rates of cell death by use of the DiSC-assay and caspase-3 activation. CONCLUSION: Thus, despite a significant reduction of duration of the assay from four to one day, induction of apoptosis evaluated by capase-3 activity does not seem to be a valid surrogate marker for chemosensitivity

Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modelling

International audienceEvidence is mounting that influenza virus interacts with other pathogens colonising or infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. This is particularly true for mathematical modelling studies, which have become critical in public health decision-making. Yet models usually focus on influenza virus acquisition and infection alone, thereby making broad oversimplifications of pathogen ecology. Herein, we report evidence of influenza virus interactions with bacteria and viruses and systematically review the modelling studies that have incorporated interactions. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitidis, respiratory syncytial virus (RSV), human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. The notable exception is the pneumococcus-influenza interaction, for which several recent modelling studies demonstrate the power of dynamic modelling as an approach to test biological hypotheses on interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and possible misinterpretation, and we illustrate the impact of interactions on public health surveillance using simple transmission models. We demonstrate that the development of multipathogen models is essential to assessing the true public health burden of influenza and that it is needed to help improve planning and evaluation of control measures. Finally, we identify the public health, surveillance, modelling, and biological challenges and propose avenues of research for the coming years