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During her highly unusual college experience traveling the world, Wolu Chukwu saw the power of communities. She attended Minerva University, a San Francisco-based college that rotates its students to a different country each semester while they take online classes, work together on projects and experiments in the field, and learn how to live in a new environment. 

Through her time in San Francisco; London, UK; Seoul, South Korea; and Hyderabad, India, Chukwu, who grew up in Nigeria, quickly learned how to balance her academics with navigating new cultures. She also learned the importance of community — how to build it, how to celebrate differences, and now through her work at the Ó³»­´«Ã½ of MIT and Harvard, how to make sure research is representative of and beneficial to everyone in a community. 

As an associate computational biologist in the lab of Rameen Beroukhim in the Cancer Program at Ó³»­´«Ã½ and Dana-Farber Cancer Institute, Chukwu is developing tools to analyze cancer genomes and find mutation patterns that can be used to improve cancer diagnosis and treatment. She hopes to become a physician-scientist conducting translational research.

In this WhyIScience Q&A, we spoke with Chukwu about her career journey so far, her research, and how she brings a community mindset to her work.

Tell me about your research and a project you’ve been working on.

My work involves doing a lot of data analysis, trying to find patterns of recurrent genomic amplifications or deletions across a patient's genome to figure out how we can use that information to better diagnose or treat cancers.

I’ve been working on is trying to find a way to classify rearrangements in the genome, which typically affect up to over 50 base pairs. These types of arrangements are important because they affect so much of the genome at once and can cause a lot of damage. They’re also the most prevalent type of mutation across different kinds of cancers. 

In our cells, sometimes we have this random rearrangement in our genomes that doesn’t contribute to disease. We call these germline genomic rearrangements. These are inherited from your parents and contribute to what makes you unique. And then you have the ones that arise and cause cancer. When you’re studying someone’s genomic data, it can be hard to tell which is which. If you have DNA from blood or another tissue that hasn’t been affected by cancer, you can compare normal and tumor tissue and you can tell: these mutations are not cancer-related but these ones are. But oftentimes, we don't have that normal tissue or blood sample from a patient or we don't have a benchmark for comparing a patient's tumors. 

So we decided to build a machine learning algorithm that's able to differentiate between the two types of genome rearrangements when given just the patient's tumor. Is there something particular about how these rearrangements look that tell us ‘this is likely inherited’ versus ‘this is actually contributing to their cancer and to the evolution of their tumor’? There hasn't been a consistent way to do this before in the field, and it's really exciting that that's something a team of us have been able to work on.

Your undergraduate experience was pretty unusual. What was the biggest thing you learned from that?

It was a very challenging journey, but it was well worth it for sure. I got exposure to different cultures, learned how to relate to people from different backgrounds, how to build community in all of these spaces where you don't speak the language, and how to celebrate our differences and to come together. We learned a lot about how to build community, and that's something I really value and I've been able to bring into my work and into every space that I am in.

Did that experience affect how you do science?

Yes. I found that there are a lot of ways that we can go into research thinking community first. For example, in the projects I've been working on, one of our main pillars is to think about the diversity in the people and the genomes we are studying. Are we making sure that the things that we are discovering are based on a representative set of data? Are our findings applicable to patients of different demographics? In clinical research, are we recruiting a diverse community of patients into our studies so that our findings translate to diverse communities?

What is your proudest accomplishment so far? 

Getting a co-first author out within my second year of working here is something I’m really proud of. I would give the accolades to the lab environment that I'm in. I think Rameen is an extremely good mentor, and the postdocs in my lab are also really good, very supportive people. I could not have done it without them. So it's been the best introduction to research. Everyone here has been pushing us to move forward and saying ‘How can I best support you?’ So there's been a lot of training and exposure, and I really love that and I really appreciate it.

What advice would you give to someone who might be walking in your path, such as someone from Nigeria or another country looking to study abroad in the US?

Seek out the opportunities that will set you up to be in the best place that you want to be. Don’t be defeated by maybe not having all the best resources, but persevere and embrace challenges. If I was too scared to embrace the challenge, I would not have made it to where I am today. But be wise about the challenges you pick and figure out whether this is actually a worthwhile challenge to take on. 

For an undergraduate student, my advice would be to advocate for yourself and seek out opportunities. For a lot of my undergraduate career I was reaching out to professors and seeking out these opportunities to do research. So pursue your passions and don't relent. I think that's the motivation that has kept me going.

This conversation was edited for length and clarity.