Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userIn Silico Implementation of Evolutionary Paradigm in Therapy Design: Towards Anti-Cancer Therapy as Darwinian Process
52
0
0.0
(
0
)
Added by
Branislav Brutovsky
Publication date
2018
fields
Biology
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
In here presented in silico study we suggest a way how to implement the evolutionary principles into anti-cancer therapy design. We hypothesize that instead of its ongoing supervised adaptation, the therapy may be constructed as a self-sustaining evolutionary process in a dynamic fitness landscape established implicitly by evolving cancer cells, microenvironment and the therapy itself. For these purposes, we replace a unified therapy with the `therapy species, which is a population of heterogeneous elementary therapies, and propose a way how to turn the toxicity of the elementary therapy into its fitness in a way conforming to evolutionary causation. As a result, not only the therapies govern the evolution of different cell phenotypes, but the cells resistances govern the evolution of the therapies as well. We illustrate the approach by the minimalistic ad hoc evolutionary model. Its results indicate that the resistant cells could bias the evolution towards more toxic elementary therapies by inhibiting the less toxic ones. As the evolutionary causation of cancer drug resistance has been intensively studied for a few decades, we refer to cancer as a special case to illustrate purely theoretical analysis.
rate research
Read More
Research into mechanisms of hematogenous metastasis has largely become genetic in focus, attempting to understand the molecular basis of `seed-soil relationships. Preceeding this biological mechanism is the physical process of dissemination of circulating tumour cells (CTCs). We utilize a `filter-flow paradigm to show that assumptions about CTC dynamics strongly affect metastatic efficiency: without data on CTC dynamics, any attempt to predict metastatic spread in individual patients is impossible.
Cancer and healthy cells have distinct distributions of molecular properties and thus respond differently to drugs. Cancer drugs ideally kill cancer cells while limiting harm to healthy cells. However, the inherent variance among cells in both cancer and healthy cell populations increases the difficulty of selective drug action. Here we propose a classification framework based on the idea that an ideal cancer drug should maximally discriminate between cancer and healthy cells. We first explore how molecular markers can be used to discriminate cancer cells from healthy cells on a single cell basis, and then how the effects of drugs are statistically predicted by these molecular markers. We then combine these two ideas to show how to optimally match drugs to tumor cells. We find that expression levels of a handful of genes suffice to discriminate well between individual cells in cancer and healthy tissue. We also find that gene expression predicts the efficacy of cancer drugs, suggesting that the cancer drugs act as classifiers using gene profiles. In agreement with our first finding, a small number of genes predict drug efficacy well. Finally, we formulate a framework that defines an optimal drug, and predicts drug cocktails that may target cancer more accurately than the individual drugs alone. Conceptualizing cancer drugs as solving a discrimination problem in the high-dimensional space of molecular markers promises to inform the design of new cancer drugs and drug cocktails.
Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.
Cold atmospheric plasma (CAP) was shown to affect cells not only directly, but also indirectly by means of plasma pre-treated solution. This study investigated a new application of CAP generated in deionized (DI) water for the cancer therapy. In our experiments, the CAP solution was generated in DI water using helium as carrier gas. We report on the effects of this plasma solution in breast (MDA-MD-231) and gastric (NCI-N87) cancer cells. The results revealed that apoptosis efficiency was dependent on the plasma exposure time and on the levels of reactive oxygen and nitrogen species (ROS and RNS). The plasma solution that resulted from 30-minute treatment of DI water had the most significant effect in the rate of apoptosis.
This paper reports on the conclusions of a 2013 Joint DOE/NCI Workshop, and translates clinical accelerator facility requirements into accelerator and beam-delivery technical specifications. Available or feasible accelerator technologies are compared, including a new concept for a compact, CW, and variable energy light ion accelerator currently under development. This new light ion accelerator is based on advances in non-scaling Fixed-Field Alternating gradient (FFAG) accelerator design. The new design concepts combine isochronous orbits with long (up to 4m) straight sections in a compact racetrack format allowing inner circulating orbits to be energy selected for low-loss, CW extraction, effectively eliminating the high-loss energy degrader in conventional CW cyclotron designs.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
