What we learned from 190 cancer models that is helping us better target our cancer drugs
types of available cancer cell line models have only increased. The breadth of findings gleaned from studying cancer lines has been huge.
Thirty years later it became possible to transplant human tumor tissue into immune compromised mice, starting the success of patient derived xenograft (PDX) models. Human cancer models have made important contributions to the development of chemotherapies, particularly immunotherapies driving cancer drug development. New tools that allow us to analyze the complete genetic and molecular profiles of humans have also elucidated the profiles of PDX and cell lines. We are learning volumes about their genetic and epigenetic profiles.
Today, many studies rely on the power of large PDX panels to provide predictive biomarkers for new cancer drugs. They also provide an unprecedented opportunity to identify tomorrow’s blockbuster drugs for cancer. My company (Charles River Labs) recently developed a systematic collection of more than 700 tumor models that includes PDX, human cell lines, syngeneic and genetically engineered mouse models. These models have been extensively profiled, allowing us to make a precise selection of suitable tumor models for preclinical anti-cancer agent testing.
In short, the compendium of tumor models is allowing us to take drug discovery to the next level, and to create customized therapies that exquisitely target certain cancer types. While it is still a work in progress we already have good evidence of how important these models can play in helping us identify the best compounds.
Recently we selected models bearing a specific protein present on the surface of cancer cells and normal cells to see whether the expression of the protein correlates with the sensitivity toward treatment. The epithelial growth factor receptor (EGFR) is a protein found on a multitude of different solid cancer types. About 15% of non-small cell lung cancers harbor a mutation, and rely heavily on the corresponding downstream signaling cascade.
For our study, high and low expressing tumor models were selected; the tumor panel consisting of 196 models was screened for sensitivity against 13 different compounds that target the EGFR signaling pathway. The compounds were analyzed in vitro 2D and 3D, as well as in vivo. A bioinformatics analysis of the data revealed a positive correlation and sensitivity not only to the expression of EGFR but the pathways involved in cell proliferation, ERBB2 signaling and chemokine induction. In other words, not only the expression level of the protein which was modulated by the treatment was predictive for anticancer activity, but also other proteins not directly linked to EGFR. This kind of knowledge is important to define selection criteria for patients which might benefit most from these anticancer compounds.
These finding underscore the value of large, well characterized panels of tumor models. With the advent of next-generation sequencing data, it is not only possible to select specifically sensitive or non-sensitive tumor model but also to perform subsequent analyses for the identification of possible biomarkers in clinical cohorts. The compendium makes this data easily accessible thereby improving hit to lead times, which ultimately helps us develop more effective therapies faster. The combination of genomic and phenotypic screening enhances the efficacy of the drug development process specifically during those crucial phases of target discovery and validation of a cancer compound, as well as pharmacology and biomarker development.
The use of PDX models instead of conventional cell line-based models adds an additional layer of complexity to our EGFR findings. The value of PDX models in oncology drug development and tumor biology research is becoming more and more evident. A database of PDX-related data has the potential to enhance our ability to better translate preclinical findings into clinical outcomes, which ultimately benefits patients.
At the end of the day, cancer is not one disease but hundreds of diseases. That the technology is finally catching up to this and helping us understand cancer diversity is truly exciting.