One of the holy grails of cancer treatment is predicting a patient’s response to therapy.
This information not only shapes the type of drug to give a patient, but also the particular schedule and dosage it will be administered. The dogma used today is known as the maximum tolerated dose (MTD) approach, where patients receive as much chemotherapy as possible before high grade toxicities are reached.
However, tumor cells display a wide range of heterogeneity and chemoresistance, and the MTD approach can often lead to tumors that are completely chemoresistant, and become difficult to treat when tumors recur.
Recently, there has been exploration in alternate strategies such as metronomic chemotherapy (MCT), which deliver lower doses more frequently. This approach balances the outcome of tumor size reduction and resistant cell growth, and is frequently better tolerated by patients. Although several studies exploring this approach for multiple cancers demonstrated more optimal results than MTD, finding the optimal dose and schedule for MCT has been somewhat empirical.
We developed a 2D experimental model system along with a simple mathematical model to determine the optimal initial dose and frequency for administration for a given tumor. Using breast cancer as an example, we demonstrated that this 2D model system can easily be spatially controlled and dynamically tracked for chemoresistant and sensitive subpopulations via time-lapse fluorescence microscopy. We showed, using a simple mathematical model, that trade-off exists for using MCT vs. MTD, whereby multiple doses can lead to smaller tumor sizes than a single dose, but will inevitably contain a higher fraction of resistant cells. However, the characterization of our simple, easy to optimize 2D co-culture can provide information to predict dose and schedule of chemotherapy to attain the lowest tumor size.
We envision our experimental-computational system as building towards a platform to test patient-specific cell populations and determine what dosage and frequency a particular drug, or combination of drugs, should be administered.
The preprint of an article from this project is here.