Blending Weather Forecasting with Team Science Leads to Advances in Cancer Immunotherapy

March 2, 2022, by Elana J. Fertig, Ph.D.

The March 9 webinar has passed, but a recording is now available.

On March 9, Dr. Elana J. Fertig will present the next Data Science Seminar, “Multi-omics Modeling for Predictive Cancer Immunotherapy.” In this blog, Dr. Fertig describes how she is using artificial intelligence, blended with spatial and single cell technologies, to better understand how cancer will respond to treatment. Predicting the changes that occur in the tumor during treatment may someday enable us to select therapies in advance, essentially stopping the disease in its tracks before it reaches the next stage in its evolution.

You’ll be discussing the topic, “Multi-omics Modeling for Predictive Cancer Immunotherapy,” in the upcoming webinar. Can you tell us what first interested you in this topic area?

I’m a mathematician by training. My early work was in using time-course data for weather prediction. That field has been highly effective in blending data and computational methods to arrive at consistently accurate predictions that truly impact people’s lives. For example, we can use models and high-throughput data to predict the risk for a winter storm or hurricane path, enabling us to issue advanced warning to keep residents safe.

Toward the end of graduate school and at the start of my post-doctorate work, multi-omics analysis through microarrays was just coming into vogue. As a mathematician, you’re trained to look for new ways to apply what you know to other disciplines. I saw a potential for using the multi-dimensional microarray data to develop predictive models in cancer biology similar to how satellite data are used for weather prediction. I soon realized, however, that high-throughput temporal profiling of cancer was a lot more complicated. Then, single cell technology exploded. These data empower temporal profiling in biological systems in a way that we never could before.

It was incredibly exciting to be in the field at the start of this technology boom and to apply what I’ve learned across disciplines to cancer biology.

Who should attend the webinar? What can they expect to learn from this hour with you?

I think this topic has a broad appeal. People in biological or clinical research, computational science, biostatistics, mathematics—really anyone who is interested in using technology and data science to advance cancer research.

I hope to show how powerful analysis of single cell data can be when they’re based on the biological mechanisms underlying a cancerous tumor and its environment, and how those assessments change over time. We’re getting at the very heart of tumor biology, which, in turn, is helping us better understand the basic science of cancer, as well as how the body will respond to treatment. This is powerful, not only because it helps us develop more accurate predictive models, but also because understanding the mechanisms underlying that response will enable us to develop new, more effective therapies.

How would this technology help in identifying effective treatments? Is this similar to precision medicine?

I think of this field as predictive rather than precision medicine. In precision medicine, the idea is to match a medication to a patient’s tumor at a time when the diagnosis is made. The problem with this approach is that it disregards the time element; that is, it ignores the influence of how cancer evolves over time. The tumor and its microenvironment are constantly changing over time.

Our artificial intelligence methods, combined with spatial and single cell technologies, enable us to map changes in cellular phenotypes in a tumor and its microenvironment as a tumor responds to therapy. Combining the properties learned from these data with mathematical models enables us to model how a cancer will respond to treatment. This provides a framework for future work that will enable us to use computational tools to predict the changes associated with therapeutic resistance in cancer so that we can select treatments in advance, essentially stopping the disease in its tracks before it reaches the next stage in its evolution.

Have you been applying this technology to one particular cancer?

We’ve been using a pan-cancer approach, examining the genomic and cellular alterations that occur across a wide variety of tumor types, and looking at how we can develop a computational tool that can be applied broadly across all of these. In my presentation, I’ll be discussing our findings in melanoma, breast, and liver cancer. However, what we found is that many of these basic biological underpinnings of therapeutic response and resistance span cancer types.

You’re using an approach that includes CoGAPS. Can you tell us a bit about that?

CoGAPS is a machine learning method we established to identify transcriptional signatures related to cell type and state. It allows us to distinguish fundamental (i.e., low dimensional) processes that sum up a biological system. My talk will look at how this can be applied to cancer immunotherapy. But the tool is also being applied more broadly to developmental biology and neuroscience. Tools like these are quickly becoming standard practice for single cell data analysis.

Were there any surprises that you encountered in your work on this topic?

I started developing the CoGAPS tool in the early days of microarrays, so I’m surprised how sustainable it’s been across technologies, including modern single cell technologies. That was something I never expected. It’s been very durable.

I’ve also been amazed at how the field of computational cancer biology has grown. I remember in the beginning, when I first started applying this tool to time-course data, my postdoc mentor said, you’re never going to have the same amount of biological data as you do with weather. It’s amazing to see how that’s changed, with so much biological data emerging in this field today.

Not everything has been positive though. When I began working with biological data, I was surprised by how much people tended to “silo” their data. As a mathematician, I came from a field with 100% open data, so it was a new experience to encounter data sets that were “owned” by someone. I was surprised by some researchers’ hesitancy to share data, which are confounded by privacy concerns, making it that much harder to share information.