Cancer Therapy Targeting the HER2-PI3K Pathway: Potential Impact on the Heart

The HER2-PI3K pathway is the one of the most mutated pathways in cancer. Several drugs targeting the major kinases of this pathway have been approved by the Food and Drug Administration and many are being tested in clinical trials for the treatment of various cancers. However, the HER2-PI3K pathway is also pivotal for maintaining the physiological function of the heart, especially in the presence of cardiac stress. Clinical studies have shown that in patients treated with doxorubicin concurrently with Trastuzumab, a monoclonal antibody that blocks the HER2 receptor, the New York Heart Association class III/IV heart failure was significantly increased compared to those who were treated with doxorubicin alone (16 vs. 3%). Studies in transgenic mice have also shown that other key kinases of this pathway, such as PI3Kα, PDK1, Akt, and mTOR, are important for protecting the heart from ischemia-reperfusion and aortic stenosis induced cardiac dysfunction. Studies, however, have also shown that inhibition of PI3Kγ improve cardiac function of a failing heart. In addition, results from transgenic mouse models are not always consistent with the outcome of the pharmacological inhibition of this pathway. Here, we will review these findings and discuss how we can address the cardiac side-effects caused by inhibition of this important pathway in both cancer and cardiac biology

Addition of platelet concentrate to Dermo-Epidermal Skin Graft in deep burn trauma reduces scarring and need for revision surgeries

Backround. Deep skin burn injuries, especially those on the face, hands, feet, genitalia and perineum represent significant therapeutic challenges. Autologous dermo-epidermal skin grafts (DESG) have become standard of care for treating deep burns. Additionally, human autologous thrombin activated autologous platelet concentrate (APC) has gained acceptance in the setting of wounds. While each of these interventions has been independently shown to accelerate healing, the combination of the two has never been evaluated. We hypothesized that the addition of platelets (source of growth factors and inhibitors necessary for tissue repair) to the DESG (source of progenitor cells and of tissue proteases necessary for spatial and temporal control of growth regulators released from platelets) would create the optimal environment for the reciprocal interaction of cells within the healing tissues. Methods: We used clinical examination (digital photography), standardised scales for evaluating pain and scarring, in combination with blood perfusion (laser Doppler imaging), as well as molecular and laboratory analyses. Results: We show for the first time that the combination of APC and DESG leads to earlier relief of pain, and decreased use of analgesics, antipruritics and orthotic devices. Most importantly, this treatment is associated with earlier discharges from hospital and significant cost savings. Conclusions: Our findings indicate that DESG engraftment is facilitated by the local addition of platelets and by systemic thrombocytosis. This local interaction leads to the physiological revascularization at 1-3 months. We observed significant elevation of circulating platelets in early stages of engraftment (1-7 days), which normalized over the subsequent 7 and 90 days.Web of Science158225824

Future paradigms for precision oncology

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Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks

International audienceBackground: The ever-increasing expanse of online bioinformatics data is enabling new ways to, not only explore the visualization of these data, but also to apply novel mathematical methods to extract meaningful information for clinically relevant analysis of pathways and treatment decisions. One of the methods used for computing topological characteristics of a space at different spatial resolutions is persistent homology. This concept can also be applied to network theory, and more specifically to protein-protein interaction networks, where the number of rings in an individual cancer network represents a measure of complexity. Results: We observed a linear correlation of R = −0.55 between persistent homology and 5-year survival of patients with a variety of cancers. This relationship was used to predict the proteins within a protein-protein interaction network with the most impact on cancer progression. By re-computing the persistent homology after computationally removing an individual node (protein) from the protein-protein interaction network, we were able to evaluate whether such an inhibition would lead to improvement in patient survival. The power of this approach lied in its ability to identify the effects of inhibition of multiple proteins and in the ability to expose whether the effect of a single inhibition may be amplified by inhibition of other proteins. More importantly, we illustrate specific examples of persistent homology calculations, which correctly predict the survival benefit observed effects in clinical trials using inhibitors of the identified molecular target. Conclusions: We propose that computational approaches such as persistent homology may be used in the future for selection of molecular therapies in clinic. The technique uses a mathematical algorithm to evaluate the node (protein) whose inhibition has the highest potential to reduce network complexity. The greater the drop in persistent homology, the greater reduction in network complexity, and thus a larger potential for survival benefit. We hope that the use of advanced mathematics in medicine will provide timely information about the best drug combination for patients, and avoid the expense associated with an unsuccessful clinical trial, where drug(s) did not show a survival benefit. Reviewers: This article was reviewed by Nathan J. Bowen (nominated by I. King Jordan), Tomasz Lipniacki, and Merek Kimmel

Normal Wound Healing and Tumor Angiogenesis as a Game of Competitive Inhibition.

Both normal wound healing and tumor angiogenesis are mitigated by the sequential, carefully orchestrated release of growth stimulators and inhibitors. These regulators are released from platelet clots formed at the sites of activated endothelium in a temporally and spatially controlled manner, and the order of their release depends on their affinity to glycosaminoglycans (GAG) such as heparan sulfate (HS) within the extracellular matrix, and platelet open canallicular system. The formation of vessel sprouts, triggered by angiogenesis regulating factors with lowest affinities for heparan sulfate (e.g. VEGF), is followed by vessel-stabilizing PDGF-B or bFGF with medium affinity for HS, and by inhibitors such as PF-4 and TSP-1 with the highest affinities for HS. The invasive wound-like edge of growing tumors has an overabundance of angiogenesis stimulators, and we propose that their abundance out-competes angiogenesis inhibitors, effectively preventing inhibition of angiogenesis and vessel maturation. We evaluate this hypothesis using an experimentally motivated agent-based model, and propose a general theoretical framework for understanding mechanistic similarities and differences between the processes of normal wound healing and pathological angiogenesis from the point of view of competitive inhibition

Gibbs Free Energy, a Thermodynamic Measure of Protein–Protein Interactions, Correlates with Neurologic Disability

Modern network science has been used to reveal new and often fundamental aspects of brain network organization in physiological as well as pathological conditions. As a consequence, these discoveries, which relate to network hierarchy, hubs and network interactions, have begun to change the paradigms of neurodegenerative disorders. In this paper, we explore the use of thermodynamics for protein–protein network interactions in Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), traumatic brain injury and epilepsy. To assess the validity of using network interactions in neurological diseases, we investigated the relationship between network thermodynamics and molecular systems biology for these neurological disorders. In order to uncover whether there was a correlation between network organization and biological outcomes, we used publicly available RNA transcription data from individual patients with these neurological conditions, and correlated these molecular profiles with their respective individual disability scores. We found a linear correlation (Pearson correlation of −0.828) between disease disability (a clinically validated measurement of a person’s functional status) and Gibbs free energy (a thermodynamic measure of protein–protein interactions). In other words, we found an inverse relationship between disease disability and thermodynamic energy. Because a larger degree of disability correlated with a larger negative drop in Gibbs free energy in a linear disability-dependent fashion, it could be presumed that the progression of neuropathology such as is seen in Alzheimer’s disease could potentially be prevented by therapeutically correcting the changes in Gibbs free energy

Personalized therapy design for systemic lupus erythematosus based on the analysis of protein-protein interaction networks.

We analyzed protein expression data for Lupus patients, which have been obtained from publicly available databases. A combination of systems biology and statistical thermodynamics approaches was used to extract topological properties of the associated protein-protein interaction networks for each of the 291 patients whose samples were used to provide the molecular data. We have concluded that among the many proteins that appear to play critical roles in this pathology, most of them are either ribosomal proteins, ubiquitination pathway proteins or heat shock proteins. We propose some of the proteins identified in this study to be considered for drug targeting

A snapshot of the proposed agent-based model.

<p>(A) Model initialization. The grid is populated by cells, which are characterized by pre-determined number of binding sites. The color changes in the microenvironments represent heparinase concentration. Darker patches correspond to higher concentrations of heparinase; lighter patches correspond to lower levels of heparinase. (B) After the simulation has begun, the growth factors LA, MA and HA mode randomly throughout the grid. Once they encounter a cell with free binding sites (red crosses), they occupy them, and thereby decreasing the number of available sites (numbers by the cell agents decreases, reflecting the number of available sites). Growth factors become cleaved depending on the current concentration of heparinase in the corresponding microenvironment. The NetLogo code for this model is attached in Supporting Information.</p