3 Questions: Constructing predictive models to characterize tumor progression

Q: What aspect of tumor progression are you working to explore and characterize? 

A: A quite common story with cancer is that patients will reply to a therapy at first, after which eventually that treatment will stop working. The rationale this largely happens is that tumors have an incredible, and really difficult, ability to evolve: the power to alter their genetic makeup, protein signaling composition, and cellular dynamics. The tumor as a system also evolves at a structural level. Oftentimes, the explanation why a patient succumbs to a tumor is because either the tumor has evolved to a state we will not control, or it evolves in an unpredictable manner. 

In some ways, cancers will be considered, on the one hand, incredibly dysregulated and disorganized, and alternatively, as having their very own internal logic, which is always changing. The central thesis of my lab is that tumors follow stereotypical patterns in space and time, and we’re hoping to make use of computation and experimental technology to decode the molecular processes underlying these transformations.  

We’re focused on one specific way tumors are evolving through a type of DNA amplification called extrachromosomal DNA. Excised from the chromosome, these ecDNAs are circularized and exist as their very own separate pool of DNA particles within the nucleus. 

Initially discovered within the Sixties, ecDNA were regarded as a rare event in cancer. Nevertheless, as researchers began applying next-generation sequencing to large patient cohorts within the 2010s, it appeared like not only were these ecDNA amplifications conferring the power of tumors to adapt to stresses, and therapies, faster, but that they were much more prevalent than initially thought.

We now know these ecDNA amplifications are apparent in about 25 percent of cancers, in essentially the most aggressive cancers: brain, lung, and ovarian cancers. We’ve found that, for a wide range of reasons, ecDNA amplifications are capable of change the rule book by which tumors evolve in ways in which allow them to speed up to a more aggressive disease in very surprising ways. 

Q: How are you using machine learning and artificial intelligence to check ecDNA amplifications and tumor evolution? 

A: There’s a mandate to translate what I’m doing within the lab to enhance patients’ lives. I need to start out with patient data to find how various evolutionary pressures are driving disease and the mutations we observe. 

Considered one of the tools we use to check tumor evolution is single-cell lineage tracing technologies. Broadly, they permit us to check the lineages of individual cells. After we sample a selected cell, not only can we know what that cell looks like, but we will (ideally) pinpoint exactly when aggressive mutations appeared within the tumor’s history. That evolutionary history gives us a way of studying these dynamic processes that we otherwise wouldn’t have the ability to watch in real time, and helps us make sense of how we would have the ability to intercept that evolution. 

I hope we’re going to recuperate at stratifying patients who will reply to certain drugs, to anticipate and overcome drug resistance, and to discover latest therapeutic targets.

Q: What excited you about joining the MIT community?

A: Considered one of the things that I used to be really drawn to was the mixing of excellence in each engineering and biological sciences. On the Koch Institute, every floor is structured to advertise this interface between engineers and basic scientists, and beyond campus, we will connect with all of the biomedical research enterprises within the greater Boston area. 

One other thing that drew me to MIT was the indisputable fact that it places such a robust emphasis on education, training, and investing in student success. I’m a private believer that what distinguishes academic research from industry research is that academic research is fundamentally a service job, in that we’re training the following generation of scientists. 

It was at all times a mission of mine to bring excellence to each computational and experimental technology disciplines. The sorts of trainees I’m hoping to recruit are those that are desperate to collaborate and solve big problems that require each disciplines. The KI [Koch Institute] is uniquely arrange for any such hybrid lab: my dry lab is correct next to my wet lab, and it’s a source of collaboration and connection, and that reflects the KI’s general vision.