I-Corps: Implementation of Genetic Algorithms for Personalized Chemosensitivity Testing for Cancer Patients

点击翻译按钮获取中文摘要

Researchers are investigating a direct approach to evaluate chemotherapy effectiveness, where tumor tissue that has been extracted from a patient is exposed to clinically relevant treatments in an environment that closely mimics the human body. Through monitoring and analysis methods, researchers are able to determine which chemotherapies appear to be most effective in treating a patient?s cancer. These results can be used synergistically with current gene expression methods to improve predicting patient response to all therapies including chemotherapy. In the proposed process, researchers provide a mechanism for determining the efficacy of a cancer treatment directly on living biopsied tissue. This method is not dependent on historical statistical correlations to predict patient treatment and outcome. Researchers propose to predict therapeutic patient response more accurately by precisely measuring drug efficacy directly on cultured tumor tissue. This method is designed to be a high throughput real-time assay using genetic algorithms for automated data capture. Currently, clinicians choose chemotherapy treatment options for their patients according to the National Comprehensive Cancer Network (NCCN) guidelines and historical clinical outcome results for patients with similar types of breast cancer. Historically this approach has had limited success since less than 10% of breast cancer patients receiving chemotherapy are thought to exhibit absolute improvement. Researchers aim to provide oncologists and pathologists with information to better determine the best FDA approved chemotherapeutic option for their patients. The proposed method, if successful, will be provided as a service requested by an oncologist and pathologist, similar to other current forms of clinical diagnostic testing, such as medical imaging, blood work, etc. This method has the potential to be expanded to all other solid tumor cancers as well as being adapted to testing novel therapeutic agents.