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The Big Challenge… With Steven Criscione

With the ever-increasing potential of new technology and the exponential growth of the life sciences field, researchers are always running into new problems to solve. In this interview series, we get scientists’ opinions on the ‘Big Challenge’ in their field and the steps being taken to address it. From new and unique hurdles to fresh takes on common problems, we dive into the complexities of the research landscape.

In this interview, we chat to Steven Criscione (Senior Director, Head of TDE Bioinformatics, Oncology Data Science, Oncology R&D, AstraZeneca) about the big challenges related to using data science in cancer research.

Want to hear more from Steven? Come and hear him speak at The Festival of Genomics and Biodata!

Could you introduce yourself and tell us about your background, your role and what you work on?

My name is Steven Criscione.  I lead the global small molecule bioinformatics team as a Senior Director within the Oncology Data Science group at AstraZeneca.  My team delivers actionable bioinformatics insights to advance AstraZeneca’s oncology small molecule drug portfolio.  We investigate cancer drugs targeting diverse mechanisms in the oncology biological pillar areas of tumor drivers and resistance, DNA damage response, and epigenetics.  I am new to the role, recently taking on this new opportunity in March 2024.  Previously, I led our team working to deliver insights to the early oncology epigenetics portfolio. I find the work exhilarating because it provides an opportunity to deepen our understanding of how our drugs impact cancer biology and translate those insights into medicines that can transform the lives of patients.

What would you say is the big challenge in your field?

I find that the study of cancer is at an inflection point where we are now seeing convergence of new technologies and data science.  The task is formidable: translate the recent innovations in omics technologies while leveraging revolutions in data science into better drugs for cancer treatment.  We now have unprecedented resolution in our ability to measure how drugs work in preclinical and clinical settings – owing to our ability to perform dynamic multiomics.  However, we now need to deliver – we need to show that these revolutions can translate into better medicines for cancer patients and a faster ability to anticipate and counteract mechanisms of drug resistance. 

One of the biggest challenges I see is that accomplishing this requires teams with diverse expertise who communicate well and speak each other’s languages.  This often requires different perspectives and outside the box thinking that can challenge traditional team structures and ways of working. The goal is simple and ambitious – to better understand how cancer drugs impact tumor biology and turn those understandings into better medicines for patients.

What is currently being done to help address that challenge?

At AstraZeneca, we are studying how our drugs work earlier in the clinic using single cell methods.  These studies enable us to assess the translatability of preclinical hypotheses for our cancer drugs at an earlier stage. We also gain insights into the impact of our small molecules, designed to kill tumor cells, on the tumor microenvironment and immune cells. This data is a computational scientists dream, but also carries unique challenges, essentially, there are countless questions we can ask of this data. 

We are also tackling similar questions preclinically.  AstraZeneca is co-leading the PERSIST-SEQ consortium, which aims to improve our understanding of therapeutic drug tolerance and resistance.  We hope to answer fundament biological questions on how our cancer drugs impact tumor microenvironments and how tumors adapt to evade death from our drugs.  This initiative should lead to the development of new strategies to address drug tolerance and resistance, including hypotheses for new drug combinations that aim to improve therapeutic outcomes.

If money or resources weren’t a barrier, what would you recommend be done to address the issue, if the above solutions were insufficient?

I do not see money as the largest barrier.  The main limited resource I see is people’s time.  To achieve deep biological insights, and transform cancer care, we need more eyes on data. However, there is only a fixed amount of time a computational biologist or data scientists can work on a problem.  And the number of cancer drugs, drug combinations, and dynamic multiomics datasets is large.  We therefore absolutely require new ways to increase our efficiency at solving problems. 

At AstraZeneca, we are tackling this using a multipronged approach.  We are looking for ways to automate or standardize routine analyses and focus more time on the innovation space.  We’re also leveraging tools that use generative artificial intelligence to provide code-assist to bioinformaticians to make their work easier.  We have courses for data scientists to influence stakeholders and communicate better with our scientists.  Importantly, we are also training our scientists to be additional eyes on data.  We built courses on data science for our wet lab and translational scientist community.  And we developed a suite of tools and resources that enable bench scientists to do bioinformatics, an initiative called bioinformatics for the bench.  Collectively, these efforts help contribute to the convergence I mentioned earlier, uniting multiple disciplines to facilitate a deeper understanding of our cancer drugs.

What advice do you have for somebody who’s up and coming, trying to break into this field and address these problems?

Computational biology, data science, and its application to the study of cancer is evolving.  There is a need for new scientists capable of wearing multiple hats – a bioinformatician with a deep understanding of cancer biology, a data scientist who can explain their models to a clinician.  Similarly, there is a need for biologists who can code or can understand machine learning models.  The way we train and learn needs to evolve as well to prepare a new generation of multimodal scientists. 

We are at a time of exceptional resources and courses online, that we can leverage to build continuous learning plans.  We all need growth and development goals, things to think about yearly, which can help us achieve the multidisciplinary learning plan we need to be successful.  The field needs more scientists who wear multiple hats, who can communicate across disciplines, and can achieve deeper understanding of cancer biology through data.

And finally, you’re speaking at the upcoming Festival of Genomics and Biodata in Boston next month. What are you most looking forward to about the event?

The Festival of Genomics and Biodata is a unique meeting that focuses on some of the things I have discussed including learning and cross-fertilization across disciplines.  I am particularly keen on learning more on how data is leveraged outside of oncology, and whether there are learnings we can translate from other fields of study.  Within a field we tend to have tunnel vision over time, becoming rigid in our approach, and hearing how other fields are gaining insights from data can be eye opening and lead to new ideas.

Want to hear more from Steven? Come and hear him speak at The Festival of Genomics and Biodata!

More on these topics

Bioinformatics / Cancer Research / Oncology