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Leveraging Molecular Technologies to Stay on Top of the Scientific Game

Joel Pearson discusses how emerging genomic technologies are shaping his cancer research.

The Scientist Staff

As soon as he solved his first Punnett square during a high school biology class, Joel Pearson knew he wanted to become a geneticist. Realizing that technological advances drive big discoveries, he familiarized himself along the way with emerging methods and instruments to better understand how genetic variation promotes cancer growth. Pearson’s interest in novel technologies also led him on unexpected forays into other scientific areas, including developmental biology, infectious disease, and fertility. Upon joining Rod Bremner’s research group at the Lunenfeld-Tanenbaum Research Institute as a postdoctoral fellow, Pearson embarked on a functional genomic journey to molecularly connect seemingly unrelated cancer types.1,2 Pearson recently started his own laboratory at the University of Manitoba, where he will continue this search for common molecular cancer signatures between distinct cancer types and discover novel therapeutic avenues for this devastating disease.

Joel Pearson, PhDAssistant Professor, Department of Pharmacology and Therapeutics, University of ManitobaScientist, CancerCare Manitoba Research Institute
Joel Pearson, PhD
Assistant Professor, Department of Pharmacology and Therapeutics, University of Manitoba
Scientist, CancerCare Manitoba Research Institute
What are the major questions that you tackle in your laboratory?  Cancer is a complex and heterogeneous disease. So far, the field has studied every cancer type, sequencing and profiling individual cell lines and tumor tissues to develop highly targeted therapies for each cancer. But we have mostly studied one cancer type at a time. Our research group takes a broader view, comparing various cancers to see if we can identify common molecular signatures in tumor cells. We are trying to identify the molecular mechanisms that underlie Hanahan and Weinberg’s general cancer hallmarks3—six biological capabilities that cells acquire to become malignant—to identify common mechanisms that drive cancer growth. Identifying such overarching rules may help us look beyond a cancer cell’s tissue preference to reveal novel cancer classification models that will change how we treat the disease.

How do you identify such overarching molecular cancer signatures? We first stumbled on it rather unexpectedly. We were studying retinoblastoma, an eye cancer that starts in the retina, and discovered two transcriptional coactivators, YAP and TAZ, thatfunction as tumor suppressors in this cancer type.1,2 This was very surprising because these proteins act as oncogenes that promote tumor growth in breast, lung, prostate, skin, and many other solid cancers. We wondered if there were other cancer types in which YAP and TAZ behave as tumor suppressors and took a bioinformatic approach to compare these other cancers’ transcriptomes.

This is when we realized that there are a lot of cancers, including small-cell lung cancer and nearly all blood cancers, that share this molecular signature in which YAP and TAZ, along with about 80 co-regulated genes, are silenced.

Which techniques do you use to verify a newly identified molecular signature’s biological relevance for cancer growth? We take a multifaceted approach, using various methods and technologies to ask the same question in a lot of different ways so that we can look for commonalities. In this case, we studied multiple in vitro and mouse models and turned to functional genomics to validate the molecular signature we discovered in various ways. We performed structure-function experiments, chromatin immunoprecipitation (ChIP) sequencing, and RNA sequencing to find out why YAP and TAZ are tumor suppressors in some cancers but oncogenes in others. We found that it all comes down to their interaction partner, TEAD, which binds DNA and recruits the entire complex with YAP and TAZ to specific target genes. This was surprising because TEAD also mediates the oncogenic effect in solid cancers. It took us a long time to convince ourselves that this was real, but the data was so black and white: in tumors where YAP and TAZ act as oncogenes (YAPon), TEAD binds to cell cycle genes to promote growth, whereas the protein binds an entirely different group of genes in this second group of cancers (YAPoff). We did a lot of quantitative real-time PCR (qPCR) to validate these RNA and ChIP sequencing experiments with the Bio-Rad CFX384 Touch Real-Time PCR System. We have two of those instruments, and we were running plate after plate—as soon as one set came off of the machines, we were loading them up again. We then performed CRISPR screens on these target genes to identify which ones modify tumor growth in YAPoff versus YAPon cancers. Finally, we mined through drug databases to identify specific inhibitors that might suppress growth for each cancer subgroup and we are now performing follow-up studies with these molecules with the hope that we can one day bring them into the clinic to treat patients. 

How do technological advances drive cancer research? Technology is what moves science forward. As a researcher, I think it is really important to keep up with emerging technologies because they will allow you to come up with cool new questions and answer them. I try to stay on top of developments in the field in various ways: I check the latest issues of most popular journals, I have PubMed alerts in place for several search terms, I read scientific blogs, and, most importantly, I often talk to my colleagues because they may have read about something that I missed. I also often try out new technologies when I learn about them. For example, I think that emerging spatial and single-cell technologies will help us get to the heart of cancer heterogeneity. These technologies will be really powerful for helping us

better understand how cancer heterogeneity contributes to disease progression, how cellular interactions in the tumor microenvironment affect growth, and more. Another benefit of being technology-savvy is that it can lead you in unexpected directions. In the early days of the pandemic, our province’s biggest diagnostic lab, which was located within our institution, was running out of qPCR reagents for their SARS-CoV-2 tests. The entire world was looking for these reagents and they were in short supply, so I found myself taking a step back and wondering how I could help. Because I had run so many qPCRs for my cancer project, I joined a molecular diagnostics working group to optimize COVID testing. We had diverse scientific backgrounds, so we each looked at the problem in a different way and came up with a lot of different ideas—it was so exciting. qPCR was, and still is, the gold standard for COVID testing, but extracting RNA from patient samples requires several reagents and is quite labor-intensive, especially back then because most laboratories did not have automated workflows in place. So, we compared a lot of different protocols and optimized every step and reagent to develop a qPCR protocol that does not require RNA extraction.4 Many other tests came out of these efforts, such as a next-generation sequencing (NGS)-based diagnostic test that can detect all SARS-CoV-2 variants,but mostly it was a great experience to see how we can work together as scientists and leverage our diverse backgrounds and expertise to quickly find solutions to emerging problems.

References
1. J.D. Pearson et al., “Binary pan-cancer classes with distinct vulnerabilities defined by pro- or anti-cancer YAP/TEAD activity,” Cancer Cell, 39(8):1115-34. e12, 2021.
2. J.D. Pearson, R. Bremner, “Simplifying cancer: binary pan-cancer superclasses stratified by opposite YAP/TEAD effects,” Mol Cell Oncol, 8(5):1981111, 2021.
3. D. Hanahan, R.A. Weinberg, “Hallmarks of cancer: the next generation,” Cell, 44(5):646-74, 2011.
4. J.D. Pearson et al., “Comparison of SARS-CoV-2 indirect and direct RT-qPCR detection methods.” Virol J, 18(1):99, 2021.
5. M. Aynaud et al., “A multiplexed, next generation sequencing platform for high- throughput detection of SARS-CoV-2,” Nat Commun, 12(1):1405, 2021.

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