Ph.D. 2014, Massachusetts Institute of Technology
“Our work lies at the intersection of diverse computational approaches including machine learning and mechanistic modeling, combined with translating new methods of assessing biology to systems approaches. Critically, this combination is synergistic; models in systems biology will always be only as good as the information used to assemble them, and as our understanding of biology begins to assemble from information about single proteins, we need quantitative models to understand and even communicate these complex processes.”
Dr. Meyer earned his B.S. in bioengineering from the University of California, Los Angeles in 2009, followed by his Ph.D. in biological engineering from MIT. During his doctoral thesis work in the labs of Douglas Lauffenburger and Frank Gertler, he identified new ways to screen for processes that drive metastasis and improved our understanding of how to target signaling from TAM receptors. Dr. Meyer has received awards from the Siebel Scholars Foundation, National Science Foundation, and Department of Defense, to support his work. In 2014, he received an NIH Director’s Early Independence Award, a NIH Common Fund grant aimed at accelerating the independent careers of promising young scientists.
Targeted therapy resistance
RTK-targeted therapies have been applied successfully in cancer treatment, though with limited effectiveness as activity of non-targeted RTKs can enable cells to become resistant. While redundant signaling is now appreciated as a common mechanism of acquired and innate resistance, exactly what signaling is essential to resistance and whether it is conserved or varies across cancer contexts has not been addressed. RTKs lead to a common set of downstream signals, but in vastly different quantitative combinations, and differ in their ability to confer resistance in a context-dependent manner. A fundamental, rigorous understanding of resistance is necessary if we are to develop better therapies to overcome this redundancy.
Thus, we plan to develop techniques to measure RTK-adapter interaction quantitatively and across the multiple potential interactions within a cell simultaneously with the intention of completely capturing signaling from these receptors. We will use these techniques combined with quantitative modeling to examine interactions during receptor activation to understand how different RTKs can provide redundant signaling leading to RTK-targeted cancer therapy resistance.
Engineering TAM receptor tyrosine kinase therapies
TAM receptors are a family of RTKs, represented by AXL, Tyro3, and MerTK, and promising therapeutic targets in a wide range of cancers. While genetic interventions have identified these receptors as having important roles in cancer cell therapeutic resistance, immune cell-mediated clearance, and metastasis, very little is understood about their post-translational regulation.
Therapeutic targeting of the TAM receptors was initially motivated in part by relatively mild effects upon knocking out expression. Further study has uncovered important function of the receptors in diverse processes and cautions against overly broad therapeutic targeting, but it suggests promise for TAM-targeted cancer therapy. Indeed, ablation of AXL and MerTK increases susceptibility to DSS-induced colitis on one hand, while having potent tumor-extrinsic anti-metastatic effects on the other.
For these reasons, TAM receptor targeting represents a uniquely interesting context to investigate how we might develop fundamentally new approaches to cancer therapy and to use engineering methods to accelerate reverse-engineering molecular systems. These receptors highlight the etiological importance of rare cells to tumor progression, since their preliminary effectiveness has been specific to metastasis. At the same time, the importance of host signaling will limit design of therapies but also presents new possibilities for modulating immune response which may not suffer from the characteristic acquired resistance limitations of previous targeted therapies.
Improving model parameterization and understanding in systems biology
Models in systems biology are frequently evaluated by their overall predictive ability but are then interpreted on a component-by-component basis. This can lead to spurious conclusions when experimental and model uncertainty is not taken into account. We aim to borrow methods from other fields in which model uncertainty is handled more rigorously to improve the process of model decomposition and interpretation.
AS Meyer, MA Miller, FB Gertler and DA Lauffenburger. (2013). The receptor AXL diversifies EGFR signaling and limits the response to EGFR-targeted inhibitors in triple-negative breast cancer cells. Science Signaling.
MA Miller*, AS Meyer*, MT Beste, Z Lasisia, S Reddya, KW Jenga, C-H Chen, J Han, K Isaacson, LG Griffith and DA Lauffenburger. (2013) ADAM-10 and -17 regulate endometriotic cell migration via concerted ligand and receptor shedding feedback on kinase signaling. PNAS.
AS Meyer, SK Hughes-Alford, JE Kay, A Castillo, A Wells, FB Gertler and DA Lauffenburger. (2012). 2D protrusion but not motility predicts growth factor–induced cancer cell migration in 3D collagen. Journal of Cell Biology.
H-D Kim, AS Meyer, JP Wagner, SK Alford, A Wells, FB Gertler and DA Lauffenburger. (2011). Signaling network state predicts Twist-mediated effects on breast cell migration across diverse growth factor contexts. Molecular and Cellular Proteomics.
F Mashayekhi, AS Meyer, SA Shiigi, V Nguyen and DT Kamei. (2008). Concentration of mammalian genomic DNA using two-phase aqueous micellar systems. Biotechnology and Bioengineering.