Cancer data science research teams are most effective when the people on that team have a range of experiences and backgrounds. This means that there’s more than one role you can play! If you’re curious about what types of people contribute to a “dream team” in cancer data science research, keep reading.

We’re talking to three scientists from NCI’s Center for Biomedical Informatics and Information Technology (CBIIT) to see who’s on their ideal cancer data science team.

The Importance of Developers and Biologists

Our first opinion comes from Dr. Granger Sutton, who is a data scientist but also works as a computer scientist, bioinformatician, and computational biologist.

“I focus a lot on artificial intelligence (AI) and imaging in my work, so ensuring my team has both AI developers and a mix of cancer and biology researchers is essential,” says Dr. Sutton.

Who’s on Dr. Sutton’s Team?

• A large language models AI developer
• A deep learning model AI developer
• A radiologist researcher
• An oncologist researcher
• A molecular biologist researcher
• A data scientist/bioinformatician with a genomics and proteomics background

Oncology Know-How Is a Must

Our second opinion comes from Dr. Tanna Nelson, who is a registered nurse working in the cancer and healthcare informatics space. She also develops AI solutions that address the many documentation and data challenges that occur throughout the clinical trial lifecycle. She considers her ideal roles from that perspective. Dr. Nelson says that first, she’d look for clinicians (preferably informaticians) who have both clinical knowledge and expertise from working at the bedside coupled with knowledge and expertise in informatics.

“Since this team is focusing on cancer, oncology-specific knowledge is a must, especially around the complex landscape of clinical trials,” says Dr. Nelson.

Who’s on Dr. Nelson’s Team?

• Machine learning (ML)/AI engineers
  • You design and implement ML and natural language processing architectures. The most important thing is a focus on scalable infrastructure that is reusable.
• Biostatisticians
  • You help with rigorous, experimental design and analysis.
• Computational biologists and bioinformaticians
  • You analyze omics data.
• Software engineers
  • You build scalable solutions platforms to enable wide sharing of the work.
• Communications and data visualization specialists
  • You communicate complex information in a way that is understandable to a broad audience, not just those in the field.
• Cloud computing architects
  • You manage large data sets and high-computational demands associated with data science activities.
• Bioethicists
  • You address privacy and ethical concerns, especially for teams who are pushing the boundaries and function at the “bleeding edge” of new technologies.
• Patient advocates
  • You consult and provide input on what’s important to patients when doing cancer data science work. You share lived experiences, challenges, and opportunities for innovative work. Earnestly working with cultural consultants and community leaders helps to garner trust and support.
• Health policy expert
  • You navigate complex regulatory frameworks, interface with government agencies, and advocate for legislation and funding.
• Industry collaborator
  • You develop and manage strategic partnerships with pharmaceutical and biotechnology companies, manage data sharing and intellectual property agreements, and share availability of end products (if applicable) to a broader community.
• Project manager
  • You keep the team going in the right direction and on track. You coordinate project timelines and deliverables, handle resource allocation, and facilitate communications and meetings.
• Chief data science officer or director
  • You manage the team and develop the strategic direction. You engage executives and organizational leadership to highlight the work, accomplishments, and vision for the future.

Keeping Better Patient Outcomes in Mind

Our third and final opinion comes from Dr. Daoud Meerzaman. He leads the Computational Genomics and Bioinformatics Branch at CBIIT, doing research to advance precision medicine through computational AI and machine learning. He’s working to bridge the gap between computational research and clinical applications by translating complex biological data into actionable insights on patient care. Dr. Meerzaman looks at building his dream team from the angle of, “What special skills can this person bring to better round out the team so that our discoveries can ultimately improve outcomes for patients with cancer?”

Who’s on Dr. Meerzaman’s Team?

• Leadership and project manager
  • You oversee research direction, funding, collaboration, and regulatory compliance. You would lead the interdisciplinary team.
• Core data science team
  • You specialize in bioinformatics, AI, ML, data engineering, and statistical analysis for cancer research. You contribute to developing predictive models for cancer detection, progression, and treatment response.
• Clinical trial data analysts
  • You help integrate and analyze multi-omics and clinical trial data sets to identify biomarkers and optimize therapeutic strategies.
• Multi-omics data integration specialists
  • You bring expertise in genomics, transcriptomics, proteomics, metabolomics, and radiomics to the team. You can leverage multi-modal data to find novel cancer markers and therapeutic targets.
• Communication and grant writing specialist
  • You translate research findings into publications and secure grants for the team. Strong communications skills enable you to share findings with stakeholders like policymakers and clinicians.
• Networking expert
  • You foster partnerships with government agencies, industry leaders, and academic institutions. Your work helps accelerate computational oncology advancements and translational research by making important connections.
• Training and collaboration specialist
  • You facilitate collaboration across this diverse team of data scientists, clinicians, and cancer researchers. Your ability to train non-bioinformatics scientists and enhance data literacy helps ensure the team can communicate effectively with each other.

If you’re interested in data science for cancer research and want to know more about the different ways to get started, head over to our training section. In this section you can:

• watch the interactive video course,
• explore the training guide library, or
• learn about cancer data science through our how-to guides!