We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Gene Expression Analysis in Cancer Research

Illustration of DNA double helices with a flame pattern in the background.
Credit: Darwin Laganzon / Pixabay.
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 2 minutes

Gene expression analysis can provide important insights for cancer research and diagnostics. At AACR 2023, Technology Networks spoke with Bio-Rad’s Justin Barker to discover some of the challenges associated with gene expression analysis in oncology and learn about technology developments in this field.

Diversity presents challenges in identifying correlations

Not all of an organism’s genes are expressed in every cell or tissue at a given time. Which genes are expressed and when affects the proteins that are produced and subsequently a cell’s functions. Measuring and analyzing the amount of a specific gene transcript present can provide researchers and clinicians with a range of information that can be used to understand cancer development and prognosis.

However, looking for correlations can be challenging, in part due to diversity, Barker told Technology Networks. This is encouraging researchers to look at various ways to increase their throughput in terms of the numbers of profiles they are generating to improve the identification and reliability of the correlations identified. But this also means that, although new RNA sequencing products are being launched, quantitative polymerase chain reaction (qPCR), which is a gold standard for low to medium throughput, still “has a lot of importance in validation.”

Discussions with clinicians and practitioners have highlighted the lack of easy testing modalities available and the need to have many different ways to look at both throughput and discovery. The vast volume of data that is generated also remains a massive challenge for this field. “There are so many genes with so many relationships, and so many variant types of cancer, that it seems like that's where all of these big data questions are, and we're still trying to identify even a lot of those correlations to try to dive into,” explained Barker.

Bridging the gap between bioinformaticians and scientists in the lab and helping both sides to have an efficient understanding is also an important consideration for gene expression analysis. Bioinformatics tools, such as SeqSense, are being developed to help support scientists in this area. 

A place for multiple technologies

A range of technologies can be used for gene expression analysis, such as qPCR and RNA sequencing, with the most suitable technique dependent on factors including the sample size and complexity. 

“I think every generation continues to be impactful,” Barker stated, highlighting the contribution of amplification technology over the years, from original PCR to qPCR and most recently digital PCR. However, “the hot element is sequencing.”

While there is a lot of interest in sequencing, “it's not quite at the level where it’s routinely used for the majority of patient types that most practitioners are seeing,” explained Barker. Democratizing platforms is a key area of focus, to make them not only cost-accessible but clear and easy to use.

Integration is key

Bio-Rad is also “trying to approach things from an open platform perspective to try to really support the breadth of items,” Barker said, working to build everything on their mainstream line, with automation in mind for all the gene expression products. This should help to ensure that they are easy to integrate and can address data exchanges, as well as keep up with the data “firehouse” of the sequencing area.

Making some of the mature technologies available in the right regulatory frame and with the right kind of competencies that customers need is a worthwhile opportunity. “I think people are intrigued by the shiny new technology or methodology. But really, our goal with some of these really strong platforms is now to be able to support scientists from an early stage of development with both automation and data integrity, and then be able to be a partner as they're looking at validating, deploying or other elements closer to the customer and making sure that all of our systems are GX compliant,” Barker concluded.

Justin Barker was speaking to Dr. Karen Steward, Senior Science Writer for Technology Networks.

About the interviewee

Headshot of Justin Barker.

Justin Barker has been a life science industry professional for 20 years. He has worked to bring new innovations to market across reagents, consumables, instruments and software in various segments for multiple industry leading brands. Justin is currently the senior global product manager at Bio-Rad Laboratories for research use gene expression instruments and software.