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West, J.

Publications and source records attributed to West, J..

76 records · Page 5Linked to original sources

Osteoclasts control sensory neurons axonal growth through epidermal growth factor receptor signaling

The patterning of peripheral innervation is accomplished through the tissue expression, in specific space and timeframe, of attractive or repulsive axonal guidance cues. At the bone microenvironment, neurotrophic factors such as nerve growth factor, brain-derived neurotrophic factor, vascular endothelial growth factor, netrin-1 and others were described to regulate the nerve ingrowth towards the bone compartment, by acting directly on receptors expressed at the nerve terminals. Interestingly, besides the gradient of soluble factors, neurons were described to be responsive to extracellular vesicles (EV) derived from myelinating cells and mesenchymal stem cells. Here we provide evidence on a new mechanism by which peripheral innervation can be coordinated. We show that sensory nerves outgrowth and electric signal propagation are dependent on the EV secreted by osteoclasts, the bone resorbing cells. Furthermore, we demonstrate that the axonal sprouting is achieved through the activation of epidermal-growth factor receptor (EGFR) family signaling pathway. We proved that the EV-depleted osteoclast secretome leads to a significant decrease of neurons firing rate and axonal sprouting, concomitant with a decrease of EGFR/ErbB2 activation levels. Excitingly, the proteomic analysis of the osteoclast-derived EV cargo shows a high correlation with synaptic components reinforcing the role on sensory neurons/osteoclast crosstalk. Our findings that osteoclast-derived EV hold effect in axonal outgrowth, contributing actively to the dynamics of the sensory neurons sprouting and electrophysiology, is a step toward unraveling target mechanisms to control electrical signal propagation and nerve fibers sprouting and consequently open new avenues for the development of innovative therapies to control bone pain. Significance StatementSensory nerve fibers sprouting in bone pathologies is highly associated with pain. Thus, understanding the mechanisms behind sensory nerves ingrowth, sprouting and electrical activity, within the bone compartment, is essential for improving the strategies to overcome pain in bone disorders. We provide a new mechanism on the sensory nerves sprouting, indicating that the effect is dependent on the extracellular vesicles (EV) released by osteoclasts, through the epidermal growth factor receptor family targeting, by integrin independent pathways. We show different electrophysiology patterns being triggered in the presence of osteoclasts secretome and the abolishment of sensory neurons firing rate in EV-depleted conditions. Overall, our results elucidate novel mechanisms on the peripheral nerves sprouting, essential for pursuing new targets for bone pain therapies.

cell biology↗

Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata

De novo resistance is a major issue for the use of targeted anticancer drugs in the clinic. By integrating experimental data we have created a hybrid neural network/agent-based model to simulate the evolution and spread of resistance to the drug Pramlintide in cutaneous squamous cell carcinoma. Our model can eventually be used to predict patient responses to the drug and thus enable clinicians to make decisions regarding personalized, precision treatment regimes for patients.

cancer biology↗

Nonlinear dynamical shaping of the fitness landscape of an evolving tumor to combat competitive release

The development of chemotherapeutic resistance resulting in tumor relapse is largely the consequence of the mechanism of competitive release of pre-existing resistant tumor cells selected for regrowth after chemotherapeutic agents attack the previously dominant chemo-sensitive population. We introduce a prisoners dilemma mathematical model based on the replicator of three competing cell populations: healthy (cooperators), sensitive (defectors), and resistant (defectors) cells. The model is shown to recapitulate prostate-specific antigen measurement data from three clinical trials for metastatic castration-resistant prostate cancer patients treated with 1) prednisone, 2) mitoxantrone and prednisone and 3) docetaxel and prednisone. Continuous maximum tolerated dose schedules reduce the sensitive cell population, initially shrinking tumor volume, but subsequently \"release\" the resistant cells to re-populate and re-grow the tumor in a resistant form. Importantly, a model fit of prostate data shows the emergence of a positive fitness cost associated with a majority of patients for each drug, without predetermining a cost in the model a priori. While the specific mechanism associated with this cost may be very different for each of the drugs, a measurable fitness cost emerges in each. The evolutionary model allows us to quantify responses to conventional therapeutic strategies as well as to design adaptive strategies.

cancer biology↗

Chemotherapeutic dose scheduling based on tumor growth rates: the case for low dose metronomic high entropy therapies

We extend classical tumor regression models, such as the Norton-Simon hypothesis, from instantaneous regression rates (i.e. the derivative) to the cumulative effect (i.e. the integral) over one (or many) cycles of chemotherapy. To achieve this end, we use a stochastic Moran process model of tumor cell kinetics, coupled with a prisoners dilemma game-theoretic cell-cell interaction model to design chemotherapeutic strategies tailored to different tumor growth characteristics. Using the Shannon entropy as a novel tool to quantify the success of dosing strategies, we contrast maximum tolerated dose (MTD) strategies as compared with low dose, high density metronomic strategies (LDM) for tumors with different growth rates. Our results show that LDM strategies can outperform MTD strategies in total tumor cell reduction (TCR). The advantage is magnified for fast growing tumors that thrive on long periods of unhindered growth without chemotherapy drugs present and is not evident after a single cycle of chemotherapy, but grows after each subsequent cycle of repeated chemotherapy. The model supports the concept of designing different chemotherapeutic schedules for tumors with different growth rates and develops quantitative tools to optimize these schedules for maintaining low volume tumors. The evolutionary model we introduce in this paper is compared with regression data from murine models and shown to be in good agreement.\n\nMajor FindingsModel simulations show that metronomic (low dose, high density) therapies can outperform maximum tolerated dose (high dose, low density) therapies. This is due to the fact that tumor cell reduction is more sensitive to changes in dose density than changes in dose concentration, especially for faster growing tumors. This effect is negligible after a single cycle of chemotherapy, but magnified after many cycles. The model also allows for novel chemotherapeutic schedules and quantifies their performance according to tumor growth rate.

cancer biology↗