‘Scientifically Speaking’ highlights up-and-coming researchers

Scientifically Speaking title image from The Jackson Laboratory.

New virtual series features JAX postdoctoral associates focused on cancer research

The Jackson Laboratory will kick off “Scientifically Speaking,” a new twice-yearly virtual event featuring JAX leaders and scientists striving to change the future of human health.

The series, which highlights JAX’s distinctive combination of research, educational programs and scientific resources, launches Nov. 8 with Brittany Angarola, Ph.D., and Aaron Taylor, Ph.D., both early-career scientists working to make a difference in the field of cancer research. The conversation will be moderated by JAX faculty member Jeff Chuang, Ph.D., a computational biologist focused on cancer research. We sat down with each of them for a preview of the conversation to come.

The intersection of cancer and aging

Brittany Angarola

Angarola is a recipient of the Brooks Scholar Award for postdoctoral trainees bridging the fields of cancer and aging. She investigates how age-related changes in alternative RNA splicing – a key mechanism by which genes are read to produce different proteins – contribute to breast cancer development and progression.

What’s been most exciting to you about pursuing this research at JAX?

I’ve been able to use incredible state-of-the art splicing technology to answer challenging questions about gene and isoform expression, and how those discoveries might help us better understand other markers of aging that could predispose individuals to breast cancer. Breast cancer gene mutations 1 and 2 (BRCA 1 and 2) are two well-known variations, but we know there are other genetic risk factors that could help identify people at a higher risk of developing cancer with age. We’re hoping this research helps to fill that need, and I’m grateful to have been part of that process.

What’s next for you, and what will you remember most about JAX?

I’m moving on to an associate director role at Yale University helping to design the molecular biology curriculum for graduate students. Before I started at JAX, I had no experience with next-generation gene sequencing techniques. The exposure to a broad range of methods has broadened my understanding of science, and I’ll carry that knowledge with me into all my future ventures. I’ve gained a better understanding of how to approach big data sets, how to establish great scientific collaborations and how to quickly learn different computational tools. So, I’ll remember gathering an amazing skill set, but also the many wonderful people I’ve met along the way. I hope to keep in touch and remain part of important scientific conversations happening at JAX and beyond.

Seeking breakthroughs in pediatric cancer
Aaron Taylor

Aaron Taylor, Ph.D. is a computational postdoctoral associate studying rare pediatric cancers – specifically brain tumors, osteosarcoma (bone cancer) and pediatric acute myeloid leukemia. He works closely with several clinical partners on finding potential biomarkers for these types of tumors.

What are some of the unique challenges of studying pediatric cancer compared to cancer in adults?

Pediatric cancers in general can be difficult to study for a range of reasons. You have to consider the long-term effects of any treatment. These patients are still growing and developing, so you have to weigh the impact of treatment for the rest of their lives. These cancers are also rarer, so the ability to perform large studies is very difficult. Sample size is one of the various challenges with studying pediatric osteosarcoma. Clinicians have tried immunotherapy-based treatments for osteosarcoma, but these haven’t been designed as an osteosarcoma-specific targeted therapy, and therefore haven’t worked particularly well for the patients.

We theorize that these tumors contain immunosuppressive mechanisms that cause the immunotherapies to fail. I work with single-cell transcriptomics that allows us to peel apart the cellular layers of the tumor and dissect its microenvironment. If we can better understand the key cellular components in the tumor, we might be able to find a way to overcome different types of immunosuppression. The treatment outcomes for osteosarcoma have not improved much in the past 40 years. If I can do anything in my entire career to change that, it will be hugely fulfilling for me.

What would you most want people to know about this field of study?

Collaboration with clinical partners is crucial in all cancer research, but especially for pediatric cancers. It truly “takes a village” of different institutions working together to produce a large number of tissue samples for study. For our osteosarcoma project, we receive samples from multiple institutions. These biopsy samples need to first be used for diagnosis. Once clinical needs are met, only a small number of those samples can move on to single-cell analysis. Without that collaborative effort from other institutions, the scientific research simply wouldn’t be possible. Pediatric research is ripe for such partnerships with the clinic.

Harnessing technology for cancer research
Jeff Chuang

Jeff Chuang, Ph.D., uses computational, mathematical and high-throughput data generation approaches to study how cancer ecosystems function, evolve and respond to therapeutic treatment. His team explores challenges in cancer sequence and image analysis across a range of cancer types.

How have new technologies such as machine learning and artificial intelligence changed our approach to cancer research?

A major technological revolution is taking place in the field of tissue imaging, enabling us to take fantastically improved pictures of cancer. For example, we can now visualize genes throughout a tissue to see how cancers grow. My lab uses machine learning and artificial intelligence approaches to determine treatments based on these new pictures of cancer.

When doctors want to see pictures of a patient’s cancer, they typically make do with images that have only two colors. However, we can now track the behavior of every gene. We can see which cancer cells have genes that have been activated, and then we can use machine learning to determine how such activity depends on where the cancer cells are. With past methods, these important spatial clues to the tumor’s development were lost. Now, these new technologies allow us to draw maps of cancerous cells in relation to healthy cells. We can see how the cancer cells are moving, how they’re responding to treatment, and how they’re interacting with the immune system. We’re breaking patients’ cancers down into their component parts, learning how those components interact, and then using this knowledge and artificial intelligence to find new weaknesses that can be targeted to eradicate the cancer.

To which types of cancer are you applying these methods?

My team is now using these approaches to study colorectal cancer and melanoma, with the goal of expanding to other systems. Our goal is to interpret tumor images faster and more precisely so that cancer patients will benefit from more effective treatment approaches that the cancer cannot overcome.

Register here for Scientifically Speaking or visit the event website to learn more.