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Researcher Interview: Aviv Regev

, by Amy E. Blum, M.A.

Aviv Regev, Ph.D., Core Member at the Broad Institute, Associate Professor of Biology at MIT, and Early Career Scientist at the Howard Hughes Medical Institute.

For this Researcher Interview, CCG Communications Manager Amy E. Blum, M.A., spoke to Aviv Regev, Ph.D. Dr. Regev is a Core Member at the Broad Institute, Associate Professor of Biology at MIT, and an Early Career Scientist of the Howard Hughes Medical Institute.

Amy E. Blum: Your research focuses on molecular circuitry. For starters, what are molecular circuits?

Aviv Regev: Cells receive all kinds of signals from the outside – molecules bind, hormones enter. They need to process these signals, understand what they mean, and react to them. This happens through networks of molecular interactions both inside the cell and between cells.

As genomic scientists, we are discovering many more components of these interactions in a general way. But in order to really understand a system, we have to be able to predict what would happen if we were to perturb it. If you can begin to predict what happens to the cell if you knock out a gene, or two or more genes, then you not only have an understanding of the system, but you can intervene in a way that produces desirable results like killing cancer cells, reinvigorating exhausted T-cells, or diminishing inflammation.

AEB: Everything in the cell is interconnected. How do you zoom in one just one circuit?

AR: It’s really important to look at physiological responses. For example, we study T-cells by looking at their phenotypes on multiple levels. We study the genes they express, the cytokines they produce, and by putting them into a mouse or other experimental system, we look at their effect on models of autoimmune diseases like multiple sclerosis and inflammatory bowel disease. These are outputs that we can measure.

To study a single circuit, you need to have clear inputs and outputs and let the biology lead the way. If you are looking at the retina tissue, measure its ability to process light! There must always be something physiological –outside of the circuit itself – to which you peg your interpretation.

AEB: As a computational biologist by training, how do you organize your lab to study complex biological problems?

AR: I’ve always had the same focus, which is how molecular circuitry allows cells to understand the world and respond appropriately. I started off as a computational biologist, but I found that for the types of questions I wanted to ask, I would need biological experimental data to get good answers. Our lab is a multi-talented team that has a wide range of skills, and we also forge deep partnerships with other biologists who are experts in specific systems.

The lab that I run at the Broad Institute is a continuum on multiple levels. Some people come from computer science and mathematics backgrounds while others are purely biological experimentalists. Even within those two backgrounds there is a spectrum between people who are experts in a certain system and those who focus on developing new techniques. We are a very diverse group and I think that tackling such complex problems requires this diversity, a lot of mutual respect, and a lot of collaboration both within the lab and with other labs. It’s a slightly different model from other labs, but it’s great because everyone is challenged to learn things outside of their skillset, and the different perspectives lead to new solutions and new insights.

AEB: What do you see as the most important developments in the field of cancer genomics in the next ten years?

AR: Right now, one way of studying tumors focuses on looking at things under a microscope and another way focuses on molecules analyzed by sequencing, but in the body these views are of course coupled to each other. Putting genomics and histology together into one scheme is an important next step and I think it will happen sooner rather than later. We already have the ability to study the genomics of single cells and the ability to do multiplex imaging to visualize several types of information at once. Next we will start looking at all of this information together and computation will be at the nexus of this effort.

We are also moving toward an understanding of tumors as ecosystems comprised both cancer cells and different non-malignant cells, and this gives us new opportunities to intervene. While targeted therapy focuses on intracellular circuitry and managing interactions between molecules in the cell, immunotherapy relies on intercellular circuitry and interactions between extracellular molecules. Cancer cells in tumors interact with many other cell types, giving us a whole new circuit that spans the tissue of interactions between cells. Understanding the ecosystem level of tumors will lead us to new and better therapies, and immunotherapy is the current example of that.

AEB: What work are you most proud of?

AR: Our recent work on melanoma is very exciting because it brought together two things that we’re very passionate about (more on this work from CCG, here). First, we applied tools that we had previously only used on cells in tissue culture and model organisms, to clinical samples. We worked alongside clinicians and brought our genomics and computational expertise to real patient samples. Second, we were able to glean high resolution data from a small number of samples. There were nineteen individuals in this study. With computational tools, we were able to work through the basic question of how different cells interact with each other in real melanoma tissue. This was a very initial study, but it shows a lot of promise. It proved to us that answering questions about how a tissue works on a systematic basis is not a hopeless ambition. With the right tools and the right collaborators, we can do it. That was a big turning point in our thinking and we are excited to pursue that into the future.

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