The WNT Pathway Is Relevant for the BCR-ABL1-Independent Resistance in Chronic Myeloid Leukemia.

Notwithstanding the introduction of Tyrosine Kinase Inhibitors (TKIs) revolutionized the outcome of Chronic Myeloid Leukemia (CML), one third of patients still suspends treatment for failure response. Recent research demonstrated that several BCR/ABL1-independent mechanisms can sustain resistance, but the relationship between these mechanisms and the outcome has not yet been fully understood. This study was designed to evaluate in a "real-life" setting if a change of expression of several genes involved in the WNT/BETA-CATENIN, JAK-STAT, and POLYCOMB pathways might condition the outcome of CML patients receiving TKIs. Thus, the expression of 255 genes, related to the aforementioned pathways, was measured by quantitative PCR after 6 months of therapy and compared with levels observed at diagnosis in 11 CML patients, in order to find possible correlations with quality of response to treatment and event-free-survival (EFS). These results were then re-analyzed by the principal component method (PCA) for tempting to better cluster resistant cases. After 12 months of therapy, 6 patients achieved an optimal response and 5 were "resistant;" after application of both statistical methods, it was evident that in all pathways a significant overall up-regulation occurred, and that WNT was the pathway mostly responsible for the TKIs resistance. Indeed, 100% of patients with a "low" up-regulation of this pathway achieved an optimal response vs. 33% of those who showed a "high" gene over-expression (p = 0.016). Analogously, the 24-months EFS resulted significantly influenced by the degree of up-regulation of the WNT signaling: all patients with a "low" up-regulation were event-free vs. 33% of those who presented a "high" gene expression (p = 0.05). In particular, the PCA analysis confirmed the role of WNT pathway and showed that the most significantly up-regulated genes with negative prognostic value were DKK, WNT6, WISP1, and FZD8. In conclusion, our results sustain the need of a wide and multitasking approach in order to understand the resistance mechanisms in CML.

Regulation of hSos1 activity is a system-level property generated by its multi-domain structure.

The multi-domain protein hSos1 plays a major role in cell growth and differentiation through its Ras-specific guanine nucleotide exchange domain whose complex regulation involves intra-molecular, inter-domain rearrangements. We present a stochastic mathematical model describing intra-molecular regulation of hSos1 activity. The population macroscopic effect is reproduced through a Monte-Carlo approach. Key model parameters have been experimentally determined by BIAcore analysis. Complementation experiments of a Saccharomyces cerevisiae cdc25(ts) strain with Sos deletion mutants provided a comprehensive data set for estimation of unknown parameters and model validation. The model is robust against parameter alteration and describes both the behavior of Sos deletion mutants and modulation of activity of the full length molecule under physiological conditions. By incorporating the calculated effect of amino acid changes at an inter-domain interface, the behavior of a mutant correlating with a developmental syndrome could be simulated, further validating the model. The activation state of Ras-specific guanine nucleotide exchange domain of hSos1 arises as an "emergent property" of its multi-domain structure that allows multi-level integration of a complex network of intra- and inter-molecular signals.

Biomedical signal and image processing.

Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain­computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.

Biomedical applications of Piece-Wise Affine identification for hybrid systems.

Modeling switching processes for control purpose takes advantage from the Piece-Wise Affine identification of hybrid dynamical systems briefly recalled in this paper. A couple of applications are addressed, namely to discriminate hormone pulses from background noise, in a physiologically switching process, and to identify sleep apneas, as pathological switching among healthy and potentially risky states. Other potential applications are proposed.

Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity.

BACKGROUND: Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns. METHODOLOGY/PRINCIPAL FINDINGS: Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences), whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R) to evaluate the average pattern complexity, the structure of essential classes and their stability in time. CONCLUSIONS/SIGNIFICANCE: We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population oscillations with multisite discharge patterns.

An ARX model-based approach to trial by trial identification of fMRI-BOLD responses.

Being able to estimate the fMRI-BOLD response following a single task or stimulus is certainly of value, since it allows to characterize its relationship to different aspects either of the stimulus, or of the subject's performance. In order to detect and characterize BOLD responses in single trials, we developed and validated a procedure based on an AutoRegressive model with eXogenous Input (ARX). The use of an individual exogenous input for each voxel makes the modeling sensitive enough to reveal differences across regions, avoiding any a priori assumption about the reference signal. The detection of variability across trials is ensured by a suitable choice, for each voxel, of the order of the moving average, which in our implementation determines the relative delay between the recorded and the reference signal. This is a quality useful in finding different time profiles of activation from high temporal resolution fMRI data. The results obtained from simulated fMRI data resulting from synthetic activations in actual noise indicate that such approach allows to evaluate important features of the response, such as the time to onset, and time to peak. Moreover, the results obtained from real high temporal resolution fMRI data acquired at l.5 T during a motor task are consistent with previous knowledge about the responses of different cortical areas in motor programming and execution. The proposed procedure should also prove useful as a pre-processing step in different approaches to the analysis of fMRI data.

An improved computational method to assess pituitary responsiveness to secretagogue stimuli.

OBJECTIVE: The quantitative assessment of gland responsiveness to exogenous stimuli is typically carried out using the peak value of the hormone concentrations in plasma, the area under its curve (AUC), or through deconvolution analysis. However, none of these methods is satisfactory, due to either sensitivity to measurement errors or various sources of bias. The objective was to introduce and validate an easy-to-compute responsiveness index, robust in the face of measurement errors and interindividual variability of kinetics parameters. DESIGN: The new method has been tested on responsiveness tests for the six pituitary hormones (using GH-releasing hormone, thyrotrophin-releasing hormone, gonadotrophin-releasing hormone and corticotrophin-releasing hormone as secretagogues), for a total of 174 tests. Hormone concentrations were assayed in six to eight samples between -30 min and 120 min from the stimulus. METHODS: An easy-to-compute direct formula has been worked out to assess the 'stimulated AUC', that is the part of the AUC of the response curve depending on the stimulus, as opposed to pre- and post-stimulus spontaneous secretion. The weights of the formula have been reported for the six pituitary hormones and some popular sampling protocols. RESULTS AND CONCLUSIONS: The new index is less sensitive to measurement error than the peak value. Moreover, it provides results that cannot be obtained from a simple scaling of either the peak value or the standard AUC. Future studies are needed to show whether the reduced sensitivity to measurement error and the proportionality to the amount of released hormone render the stimulated AUC indeed a valid alternative to the peak value for the diagnosis of the different pathophysiological states, such as, for instance, GH deficits.

Forecasting the performance status of head and neck cancer patient treatment by an interval arithmetic pruned perceptron.

The integration of chemotherapy and radiotherapy for the treatment of advanced head and neck cancer is still a matter of clinical investigation. An important limitation is that the concomitant administration of chemotherapy and radiotherapy still induces severe toxicity. In this paper, a simple artificial neural network is used to predict, on the basis of biological and clinical data, if the cumulative toxicity of the combined chemo-radiation treatment itself would be tolerated. The resulting method, tested on clinical data from a phase II trial, proved to be able to forecast which patients will tolerate a combined chemo-radiotherapeutic approach. This result should open a new perspective in the clinical approach, by supplying a potential predictive indicator for toxicity.