Understanding drug-drug interactions: how to use ex vivo drug sensitivity screening to improve cancer treatment

Cancer is a heterogeneous group of diseases characterized by the uncontrolled and abnormal growth of cells. Cancer is heterogeneous in its genetics, its response and adaptation to the environment, in its metabolic rewirement to adapt to this environment and in its interaction with the immune system. It is often considered that each tumor in each patient, and even different tumors within the same patient are unique. As an example of a very heterogeneous cancer type, we have melanoma of the skin, classified in four genomic subtypes depending on the mutational state of BRAF, NRAS, and NF1, all of them contributing to deregulation of the MAPK/ERK pathway, leading to uncontrolled cell growth.

This cancer heterogeneity is a problem for treatment-decision making. The future of cancer treatment is in precision medicine, or moving from a scenario where patients get the same standard treatment to other where they get a treatment adapted to their individual characteristics. But this is not the only problem. Most of cancer deaths are due to development of treatment resistance, and this resistance development is associated with tumor heterogeneity and cancer´s evolution capability.

During cancer development and due to its heterogeneity, there will be different subclones inside a tumor. These subclones can show different sensitivities to cancer treatment, which creates a selective pressure for the resistant clones, causing treatment resistance development, tumor progression and patient relapse. In order to overcome resistance, we need to block cancer’s evolutionary escape routes using treatment combinations that target multiple oncogenic pathways at once.

In our study we are focused in understanding the drug combination effects when treating cancer. We have set up a system to screen large amounts of drug combinations in a high-throughput format using ex-vivo drug sensitivity screening. We have analyzed the response of 22 well characterized melanoma cell lines to the effect of 61 drugs that target major cancer pathways and their pair wise drug combinations.

We have identified synergistic and antagonistic drug combinations for each of the cell lines. But more importantly, we have crossed the results with information of the expression patterns and mutational profile for all the cell lines, identifying biomarkers that predict drug combination and treatment efficacy. After validation of these biomarkers in-vivo we expect to implement new drug combinations to be used in clinic, that will improve the treatment of cancer patients. Our long-term goal is to identify personalized synergistic drug combinations for each patient that comes to our clinic.