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In the Spotlight: Shaping how genomics is used in the clinic

This In the Spotlight features Helio Costa, a geneticist who has developed an assay currently being used to help cancer patients.

Helio Costa, PhD, is an early career geneticist whose work has led to a diagnostic test that is used at Stanford Health Care. I spoke with him recently to learn more.

How long have you been at Stanford and where were you before?

I've been at Stanford since 2010. I did my PhD training in genetics in the fields of precision medicine and cancer genomics.

I was an undergraduate in genetics at UC Davis.

Why did you decide to go into science?

Ever since I was very young, I was always very inquisitive and interested in how things worked. I would always bug my teachers with obnoxious questions about why things were the way they were, and throughout the years teachers gave me opportunities to explore those interests.

Also, my parents encouraged me to watch a lot of PBS, which had some amazing shows for children that fostered curiosity and fascination with science. Everything from the "Magic School Bus," to "NOVA" really inspired me and made science approachable for me.

What are you working on now?

Currently, I'm really interested in developing DNA sequencing experimental assays and computational tools to interpret clinical genomes (that is, the DNA of patients, who have disorders or diseases such as cancer).

I'm the founding director of a clinical data science fellowship that seeks out physicians and scientists interested in being at the interface of translational medicine and clinical research.

I'm also a geneticist affiliated with the molecular genetic pathology service.

Could you tell me more about the clinical side of genetics?

It's a very rewarding experience. The molecular genetic pathology service does all the in-house tumor profiling for Stanford Health Care and does some Mendelian (heritable) disease testing. I have the opportunity to develop some of my tools from my research into actual clinical practice.

For example, we've launched an RNA-based fusion detection assay (fusion occurs when two genes join together; the resulting proteins may produce cancer) using next-generation sequencing that I developed in the lab that is now a clinical test for cancer patients.

What is most rewarding about your work?

The ability of my work to have an effect in the lives of others is really fulfilling. In my own personal life, I've had loved ones afflicted by many different disorders, cancer being a big one, and so being able to throw my skills, ability, time and effort into trying to make a little dent into that is a huge driver of fulfillment for me.

Equally as important for me is also the training and mentorship I do. I wouldn't be where I am today without the people that provided me with mentorship and opportunities that really paved the way for me.

What is the biggest challenge in your field right now?

A big challenge is trying to understand the clinical significance of genetic variances we identify. We have a very good idea or know exactly what some do, like variants that lead to increased risk for breast cancer, or Alzheimer's, but for a tremendous amount of variants, we have no clue. So, the field is working very hard to develop standards for how to interpret and assess those kinds of variants.

What do you do to unwind from your busy schedule?

I enjoy cooking -- trying new recipes, techniques, and methods. I'm a big foodie in general so I love to go out and try new foods too.

I also enjoy exercising, so it's a nice balance.

I also love to travel.

What are some of your most memorable trips?

I am a sucker for ancient civilizations. I've been to Greece and to Egypt. My next dream destination is Machu Picchu.

What is one thing you're looking forward to?

There's been a lot of really exciting developments in the machine-learning space as it relates to clinical medicine. I have projects starting to use these tools on data sets to aid in patient disease progression, to find where there are gaps in care that we can identify and correct.

I think these machine-learning tools will allows us to transition from a health care approach of "hard facts" -- such as a certain blood lipid level or this genetic alteration -- to a system that allows you to create predictive models that will allow medicine to be very proactive rather than reactive.

Photo by Daphne Sashin

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