Aaron Meyer Ph.D

Assistant Professor
Department of Bioengineering

4121G Engineering V
310-794-4821
310-794-5956 fax
 | Meyer Lab Website

Education

  • B.S., University of California, Los Angeles, 2009
  • Ph.D., Massachusetts Institute of Technology, 2014

Awards and Recognitions

  • Fellowship Grant, Terri Brodeur Breast Cancer Foundation, 2017
  • Ten to Watch, Amgen Scholars Foundation, 2016
  • AMIGOS Program Award, Jayne Koskinas Ted Giovanis Foundation and Breast Cancer Research Foundation, 2016
  • Frontier Research Program Initiator Award, Koch Institute, 2015
  • NIH Director’s Early Independence Award, 2014
  • Siebel Scholar, Class of 2014
  • Whitaker Health Sciences Fund Fellowship, MIT, 2013
  • Repligen Fellowship in Cancer Research, Koch Institute, 2012
  • Frontier Research Program Initiator Award, Koch Institute, 2011
  • Breast Cancer Research Predoctoral Fellowship, Department of Defense, 2010
  • Graduate Research Fellowship, National Science Foundation, 2009
  • Momenta Presidential Fellowship, MIT, 2009

Research Interests

We pair experiments with data-driven modeling to learn about cancer biology and innate immune signaling. This combination is necessary and synergistic: models in systems biology are only as good as the information used to assemble them, and as our understanding of biology assembles from information about single proteins, we need quantitative models to understand and communicate these complex processes.

Systems Approaches for Rationally Designing TAM and Other Innate Immune Therapies

TAM (Tyro3, AXL, MerTK) receptors are implicated in resistance to targeted therapies and metastasis via tumor cell-intrinsic effects, while more recent evidence has implicated the same receptors expressed on immune cells as potentially effective therapeutic targets in many cancers. Outside of cancer, these receptors have been implicated in a number of diseases involving immune dysregulation including lupus and viral infection. Rationally targeting these receptors, and even understanding how existing therapies function, has been limited by poor understanding of how the receptors are activated.

We are using activation models of the receptor family to understand their coordinated function across cell populations. This, in turn, helps us learn about the pleiotropic effects of inhibitors in vivo and identify better therapeutic strategies. We are similarly interested in applying this general approach—of receptor activation models tied to data-driven inference—across other innate immune families such as Fc, interferon, and cytokine receptors.

Identifying Shared Features Among Resistance Mechanisms to Help Predict Effective Combination Therapies for Individual Patients

Targeted therapies extend many cancer patient’s lives but are limited in efficacy to a subset of patients and by the development of resistance. Enormous efforts undertaken to identify mechanisms of resistance have uncovered numerous changes involving gene expression, post-translational regulation, and even tumor-extrinsic factors such as host-derived growth factors. Combination therapy can effectively combat resistance but requires accurate identification of the relevant resistance mechanism. Precision therapy must account for many genetic and non-genetic intrinsic and adaptive resistance mechanisms if it will accurately select these combinations.

Rather than focus on single molecular changes causing resistance, we are studying sets of these changes to reveal the essential commonalities. This approach helps pinpoint measurements that predict whether individual tumor cells will respond to therapy and design therapeutic combinations less susceptible to resistance development.

Improving model parameterization and interpretation 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.


Publications