Jean Clairambault

Circadian Timing in Cancer Treatments

The circadian timing system is composed of molecular clocks, which drive 24-h changes in xenobiotic metabolism and detoxification, cell cycle events, DNA repair, apoptosis, and angiogenesis. The cellular circadian clocks are coordinated by endogenous physiological rhythms, so that they tick in synchrony in the host tissues that can be damaged by anticancer agents. As a result, circadian timing can modify 2- to 10-fold the tolerability of anticancer medications in experimental models and in cancer patients. Improved efficacy is also seen when drugs are given near their respective times of best tolerability, due to (a) inherently poor circadian entrainment of tumors and (b) persistent circadian entrainment of healthy tissues. Conversely, host clocks are disrupted whenever anticancer drugs are administered at their most toxic time. On the other hand, circadian disruption accelerates experimental and clinical cancer processes. Gender, circadian physiology, clock genes, and cell cycle critically affect outcome on cancer chronotherapeutics. Mathematical and systems biology approaches currently develop and integrate theoretical, experimental, and technological tools in order to further optimize and personalize the circadian administration of cancer treatments.

Linear and non-linear analyses of heart rate variability: a minireview

To complete traditional time- and frequency-domain analyses, new methods derived from non-linear systems analysis have recently been developed for time series studies. A panel of the most widely used methods of heart rate analysis is given with computations on mouse data, before and after a single atropine injection.

Heart rate variability in normal sleeping full-term and preterm neonates

To assess maturation of the Autonomic Nervous System (ANS) and sleep states, Heart Rate Variability (HRV) was studied in 24 healthy sleeping newborns, aged from 31 to 41 weeks, conceptional age (CA). Spectral analysis of the interbeat interval (RR) signal, was performed by Short-Time Fourier Transform, in three frequency bands: high (HF), of purely vagal origin, mid (MF), and low (LF), vagal and sympathetic, thus allowing evaluation of both branches of the ANS, observed in Active Sleep (AS = REM Sleep) and in Quiet Sleep (QS = nREM Sleep). Principal Component Analysis, Discriminant Analysis, and hypothesis tests were used to investigate the evolution of spectral variables and their relation with sleep states. HF, MF, LF, and mean RR all increased with age; the differences from the premature to the full-term group, were more marked, as a whole, in AS than in QS. HF showed the highest increase from the premature (31-36 weeks CA) to the intermediate (37-38) group, whereas LF showed equal differences from the premature to the intermediate, and from the intermediate to the full-term (39-41) groups. These results suggest a steep increase in vagal tone at 37-38 weeks CA, with stability afterwards, and a more regular increase in sympathetic tone from 31 to 41 weeks CA.

Heart rate and heart rate variability during sleep in small-for-gestational age newborns

ABSTRACT: To assess the influence of intrauterine growth retardation on heart rate (HR) and HR variability during sleep, we performed polygraphic recordings in 10 small-forgestational age (SGA) and 16 appropriate-for-gestational age (AGA) newborns. Both groups were clinically and neurologically normal and were at 37 to 41 wk conceptional age. RR intervals were analyzed using the short-time Fourier transform in three frequency bands: 7) high frequency, with a period 3–8 heartbeat; 2) mid frequency, with a period 10–25 heartbeat; and 3) low frequency, with a period 30–100 heartbeat. In both active and quiet sleep, SGA newborns significantly differed from AGA newborns by having a shorter RR interval (p < 0.01) and lower amplitude of HR variability in all bands (p < 0.05) except low frequency in quiet sleep. Quiet sleep differed from active sleep by having a longer RR interval (p < 0.05), higher high-frequency variability (p < 0.02) in both SGA and AGA newborns, and lower low-frequency variability (p < 0.005 for AGA newborns). Our data give evidence of clear modifications of both sympathetic and parasympathetic HR control in the at-risk SGA population. Similarity of between-state characteristics suggests maintained CNS control of HR in SGA as well as in AGA newborns. We speculate that between-group HR and HR variability differences may be related to augmented metabolic rate in SGA compared with AGA newborns.

Heart-rate variability in low-risk prematurely born infants reaching normal term: A comparison with full-term newborns

To investigate the influence of prematurity and postnatal age on the maturation of the autonomic nervous system function, we analysed heart-rate and heart-rate variability in twelve prematurely born infants (< 37 weeks gestational age) reaching the conceptional age of 37-41 weeks. These neonates were compared with sixteen 37-41 week conceptional age newborns (< 10 days postnatal age). Heart-rate variability was analysed by spectral analysis of interbeat intervals using Short-Time Fourier Transform. We found that during both active and quiet sleep, the durations of RR-intervals were shorter and the amplitude of heart-rate variability in different frequency bands was lower in prematures reaching term than in newborns of the same conceptional age (P < 0.001). Between-state comparison showed differences in both groups. In both groups, low-frequency heart-rate variability was higher in active sleep than in quiet sleep. Between-state differences of RR-intervals and high-frequency heart-rate variability were present only in newborns (P < 0.01). Discrimination between newborns and prematures reaching term, based on RR-intervals and heart-rate variability, was correct in both sleep states with errors between 7 to 16%. However, in both newborns and prematures reaching term, between-state discrimination showed less reliable results, especially for quiet sleep discrimination with 24% (in PRT) and 20% (in NB) of errors. Our results, especially information given by factor analysis, suggest that the differences between newborns and prematures reaching term, concerning RR-interval and heart-rate variability, may be related to a changed balance between the sympathetic and parasympathetic nervous systems with a diminished parasympathetic component of heart rate control in prematures reaching term, as compared to newborns.

Implications of circadian clocks for the rhythmic delivery of cancer therapeutics.

The circadian timing system (CTS) controls drug metabolism and cellular proliferation over the 24 hour day through molecular clocks in each cell. These cellular clocks are coordinated by a hypothalamic pacemaker, the suprachiasmatic nuclei, that generates or controls circadian physiology. The CTS plays a role in cancer processes and their treatments through the downregulation of malignant growth and the generation of large and predictable 24 hour changes in toxicity and efficacy of anti-cancer drugs. The tight interactions between circadian clocks, cell division cycle and pharmacology pathways have supported sinusoidal circadian-based delivery of cancer treatments. Such chronotherapeutics have been mostly implemented in patients with metastatic colorectal cancer, the second most common cause of death from cancer. Stochastic and deterministic models of the interactions between circadian clock, cell cycle and pharmacology confirmed the poor therapeutic value of both constant-rate and wrongly timed chronomodulated infusions. An automaton model for the cell cycle revealed the critical roles of variability in circadian entrainment and cell cycle phase durations in healthy tissues and tumours for the success of properly timed circadian delivery schedules. The models showed that additional therapeutic strategy further sets the constraints for the identification of the most effective chronomodulated schedules.

Applying ecological and evolutionary theory to cancer: a long and winding road

Since the mid 1970s, cancer has been described as a process of Darwinian evolution, with somatic cellular selection and evolution being the fundamental processes leading to malignancy and its many manifestations (neoangiogenesis, evasion of the immune system, metastasis, and resistance to therapies). Historically, little attention has been placed on applications of evolutionary biology to understanding and controlling neoplastic progression and to prevent therapeutic failures. This is now beginning to change, and there is a growing international interest in the interface between cancer and evolutionary biology. The objective of this introduction is first to describe the basic ideas and concepts linking evolutionary biology to cancer. We then present four major fronts where the evolutionary perspective is most developed, namely laboratory and clinical models, mathematical models, databases, and techniques and assays. Finally, we discuss several of the most promising challenges and future prospects in this interdisciplinary research direction in the war against cancer.

Modeling the Effects of Space Structure and Combination Therapies on Phenotypic Heterogeneity and Drug Resistance in Solid Tumors

Histopathological evidence supports the idea that the emergence of phenotypic heterogeneity and resistance to cytotoxic drugs can be considered as a process of selection in tumor cell populations. In this framework, can we explain intra-tumor heterogeneity in terms of selection driven by the local cell environment? Can we overcome the emergence of resistance and favor the eradication of cancer cells by using combination therapies? Bearing these questions in mind, we develop a model describing cell dynamics inside a tumor spheroid under the effects of cytotoxic and cytostatic drugs. Cancer cells are assumed to be structured as a population by two real variables standing for space position and the expression level of a phenotype of resistance to cytotoxic drugs. The model takes explicitly into account the dynamics of resources and anticancer drugs as well as their interactions with the cell population under treatment. We analyze the effects of space structure and combination therapies on phenotypic heterogeneity and chemotherapeutic resistance. Furthermore, we study the efficacy of combined therapy protocols based on constant infusion and bang–bang delivery of cytotoxic and cytostatic drugs.

Enabling multiscale modeling in systems medicine.

CITATION: Wolkenhauer, O. et al. 2014. Enabling multiscale modeling in systems medicine. Genome Medicine, 6:21, doi:10.1186/gm538.

The p53 protein and its molecular network: modelling a missing link between DNA damage and cell fate

Various molecular pharmacokinetic-pharmacodynamic (PK-PD) models have been proposed in the last decades to represent and predict drug effects in anticancer chemotherapies. Most of these models are cell population based since clearly measurable effects of drugs can be seen much more easily on populations of cells, healthy and tumour, than in individual cells. The actual targets of drugs are, however, cells themselves. The drugs in use either disrupt genome integrity by causing DNA strand breaks, and consequently initiate programmed cell death, or block cell proliferation mainly by inhibiting factors that enable cells to proceed from one cell cycle phase to the next through checkpoints in the cell division cycle. DNA damage caused by cytotoxic drugs (and also cytostatic drugs at high concentrations) activates, among others, the p53 protein-modulated signalling pathways that directly or indirectly force the cell to make a decision between survival and death. The paper aims to become the first-step in a larger scale enterprise that should bridge the gap between intracellular and population PK-PD models, providing oncologists with a rationale to predict and optimise the effects of anticancer drugs in the clinic. So far, it only sticks at describing p53 activation and regulation in single cells following their exposure to DNA damaging stress agents. We show that p53 oscillations that have been observed in individual cells can be reconstructed and predicted by compartmentalising cellular events occurring after DNA damage, either in the nucleus or in the cytoplasm, and by describing network interactions, using ordinary differential equations (ODEs), between the ATM, p53, Mdm2 and Wip1 proteins, in each compartment, nucleus or cytoplasm, and between the two compartments. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications.