A patient diagnosed with cancer needs a treatment option that proposes the best prognosis. Most treatment options include chemotherapy, but the prediction for how the chemotherapeutic agents will react with the patient's cancerous cells is based on previous outcomes of patients with similar cancer and conditions. This approach is somewhat flawed, since not every person with the same type of cancer responds to a drug with the same effectiveness in the same way; similarly, there is not one universal drug for the treatment of different cancer types. Often the testing of several treatment plans, or combinations of drugs, is necessary to develop a regimen that an individual responds to and this simultaneously compromises a patient's already unstable health. Chemotherapeutic treatments can be monetarily, psychologically, and physically demanding, introducing a need for a methodology that would accommodate the pre-screening of potential drug treatment options on an individual's diseased cells prior to administration of potentially ineffective and harmful drugs to the body. Presented are efforts toward introducing infrared (IR) spectroscopic means as a tool to develop personalized cancer treatment plans by monitoring the response of live cells to chemotherapeutic agents in vitro.

The methods and results presented utilized Fourier transform infrared (FTIR) microspectroscopy to monitor IR spectroscopic response of live cells. Data presented here demonstrated the feasibility of using an in-lab designed live sample chamber capable of sustaining cellular life for long experimental times, including a 24 hour experiment following the same cells, which represents the first FTIR live cell study to be published for that experimental duration. Also presented are data demonstrating the ability to detect drug induced spectral changes in HeLa cells exposed to a commonly used antineoplastic agent for various cancers, cyclophosphamide monohydrate, in comparison with cells kept under normal culture conditions. Principal component analysis, a multivariate analytical technique, was employed to demonstrate subtle, yet distinct biochemical changes observed for all data collected. Most recent results were obtained after optimization of the methodology, in conjunction with newly developed MATLAB based algorithms, allowing faster data acquisition, better spectral quality and noise reduction.