Computational biology Archives - Sanford Burnham Prebys
Institute News

Is cloud computing a game changer in cancer research? Three big questions for Lukas Chavez

AuthorMiles Martin
Date

February 22, 2023

As an assistant professor at Sanford Burnham Prebys and director of the Neuro-Oncology Molecular Tumor Board at Rady Children’s Hospital, Lukas Chavez, PhD, leverages modern technology for precision diagnostics and for uncovering new treatment options for the most aggressive childhood brain cancers.

We spoke to Chavez about his work and asked him how modern technology—particularly cloud computing—is shifting the approach to cancer research.

How are you using new technologies to advance your research?

New technologies are helping us generate a huge amount of data as well as many new types of data. All this new information at our disposal has created a pressing need for tools to make sense of it and maximize their benefits. That’s where computational biology and bioinformatics come into play. The childhood brain cancers I work on are very rare, which has historically made it difficult to study large numbers of cases and identify patterns.

Now, data for thousands of cases can be stored in the cloud. By creating data analysis tools, we can reveal insights that we would never have seen otherwise. For example, we’ve developed tools that can use patient data in the cloud to categorize brain cancers into subtypes we’ve never identified before, and we’re learning that there are many more types of brain tumors than we’ve previously understood. We’re basically transforming the classic histo-pathological approach that people have studied for decades by looking at tumor tissues under the microscope and turning that into data science.

How is cloud computing improving cancer research in general?

Assembling big datasets delays everything, so I believe the main idea of cloud computing is really to store data in the cloud, then bring the computational tools to the data, not the other way around.

My team did one study where we assembled publicly available data, and basically downloaded everything locally. The data assembly process alone took at least two to three years because of all the data access agreements and legal offices that were involved.

And that is the burden that cloud computing infrastructures remove. All of this personalized cancer data can be centrally stored in the cloud, which makes it available to more researchers while keeping it secure to protect patient privacy. Researchers can get access without downloading the data, so they are not responsible for data protection anymore. It’s both faster and more secure to just bring your tools to the data.

Are there any risks we need to be aware of?

Like any new technology, we need to be clear about how we use it. The technology is another tool in the toolbox of patient care. It will never entirely replace physicians and researchers, but it can complement and assist them.

Also, because we use costly and sophisticated tools that are being built and trained on very specific patient groups, we need to be careful that these tools are not only helping wealthier segments of society. Ideally, these tools will be expanded worldwide to help everybody affected by cancer.

Institute News

Three big questions for cutting-edge biologist Will Wang

AuthorMiles Martin
Date

January 26, 2023

Will Wang’s spatial omics approach to studying neuromuscular diseases is unique.

He works at the intersection of biology and computer science to study how complex systems of cells interact, specifically focusing on the connections between nerves, muscles, and the immune response and their role in neuromuscular diseases.

We sat down with Wang, who recently joined the Institute as an assistant professor, to discuss his work and how computer technology is shaping the landscape of biomedical research.

How is your team taking advantage of computer technology to study neuromuscular diseases?

No cell exists in isolation. All our cells are organized into complex tissues with different types of cells interacting with each other. We study what happens at these points of interaction, such as where nerves connect to muscle cells. Combining many different types of data such as single cell sequencing, spatial proteomics, and measures of cell-cell signaling helps us get a more holistic look at how interactions between cells determine tissue function, as well as how these interactions are disrupted in injury and disease. Artificial neural networks help us make sense of these different types of data by finding patterns and insights the human brain can’t see on its own. And because computers can learn from the vast modality of data that we gather, we can also use them to help predict how biological systems will behave in the lab. The process goes both ways – from biology to computers and from computers to biology. 

How will these technologies shape the future of biomedical research?

Biology and computer programming are two different languages. There are a lot of mathematicians and programmers who are great at coming up with solutions to process data, but biological questions can get lost in translation and it’s easy to miss the bigger picture. And pure biologists don’t necessarily understand the full scope of what computers can do for them. If we’re going to get the most out of this technology in biomedical research, we need people with enough expertise in both areas that they can bridge the gap, which is what our lab is trying to do. Over time we’re going to see more and more labs that combine traditional biological experiments and data analysis approaches with artificial intelligence and machine learning. 

Are there any potential risks to these new technologies?

Artifical intelligence is here to accelerate discovery. Mundane tasks and measurements that took me weeks to carry out as a graduate student can be automated to a matter of minutes. We can now find patterns in high dimensional images that the human brain can’t easily visualize. However, any kind of artificial intelligence comes with a certain amount of risk if people don’t understand when and how to use the tools. If you just take the absolute word of the algorithm, there will inevitably be times where it’s not correct. As scientists, we use artificial intelligence as a cutting-edge discovery tool, but need to validate the findings in terms of the biology. At the end of the day, it is us, scientists, who are here to drive the discovery process and design real life experiments to make sure our therapies are safe and efficacious.