Q: What aspect of cancer progression are you trying to study and characterize?
AND: A very common story with cancer is that patients will first respond to therapy and then eventually the treatment will stop working. This is largely because cancers have an amazing and very complex ability to evolve: the ability to change their genetic makeup, protein signaling composition, and cellular dynamics. The tumor as a system also evolves at the structural level. Often, the reason why a patient dies from cancer is because either the tumor has evolved to a condition we can no longer control, or it has evolved in an unpredictable way.
In many respects, cancers can be seen as, on the one hand, extremely dysregulated and disorganized, and, on the other hand, as having their own internal logic that is constantly changing. My lab’s central thesis is that cancers follow stereotyped patterns across space and time, so we hope to exploit computational and experimental technology to decode the molecular processes underlying these transformations.
We focus on one particular way that cancers evolve through a form of DNA amplification called extrachromosomal DNA. Cut from the chromosome, these ecDNAs are circular and exist as a separate pool of DNA particles in the nucleus.
They were first discovered in the 1960s. ecDNA was thought to be a occasional occurrence in cancer. But when researchers began using next-generation sequencing in immense patient cohorts in 2010, it appeared that ecDNA amplifications not only made cancers adapt more quickly to stress and therapy, but that they were much more widespread than initially thought.
We now know that ecDNA amplifications are seen in about 25 percent of cancers, the most aggressive cancers: brain, lung and ovarian cancer. We found that, for a variety of reasons, ecDNA amplifications are able to change the rule book of cancer evolution in a way that allows them to accelerate to more aggressive disease in very surprising ways.
Q: How are you using machine learning and artificial intelligence to study ecDNA amplification and cancer evolution?
AND: I’m commissioned to translate what I do in the lab to improve patients’ lives. I want to start with patient data to discover how different evolutionary pressures drive the diseases and observed mutations.
One of the tools we exploit to study cancer evolution is single-cell lineage tracing technologies. Overall, they allow us to study the origins of individual cells. When we take a sample of a specific cell, we not only know what it looks like, but we can (ideally) determine exactly when aggressive mutations occurred in the tumor’s history. This evolutionary history enables us to study these lively processes that we would otherwise not be able to observe in real time, and helps us understand how we might capture this evolution.
My hope is that we will become better at stratifying patients who will respond to specific drugs, at predicting and overcoming drug resistance, and at identifying fresh therapeutic targets.
Q: What excited you about joining the MIT community?
AND: One of the things that really attracted me was the combination of excellence in both engineering and biological sciences. At the Koch Institute, each floor is designed to promote an interface between engineers and basic scientists, and off campus we can connect with all biomedical research companies in the Boston area.
Another thing that attracted me to MIT was the fact that it places such a mighty emphasis on education, training, and investing in student success. My personal belief is that what distinguishes academic research from industrial research is that academic research is fundamentally a service in nature, training the next generation of scientists.
My mission has always been to ensure excellence in both computational and experimental technology disciplines. I hope to recruit interns who are willing to collaborate and solve large problems that require both disciplines. KI [Koch Institute] is specifically designed for this type of hybrid lab: my droughty lab is located right next to my soggy lab and is a source of collaboration and connection, reflecting the overall vision of KI.