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Personalized healthcare tailors the right treatments to the right patients
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Challenge: Speed advances in biomarker research by deploying statistical methods to exploit increasingly sophisticated omics data.
Solution: Use interactive features in JMP Genomics to perform robust statistical analyses in less time.
Results: By improving statistical rigor and reproducibility, biomarker researchers speed critical innovations in the emerging field of personalized healthcare.
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Recent biomarker research has led to the creation of increasingly targeted drugs and diagnostic assays. This personalized approach to healthcare is largely due to the proliferation of new genomics technologies that generate larger and more complex data sets. With access to increasingly sophisticated omics data, scientists can investigate diagnostic and drug response questions that until recently, were beyond the capabilities of available technology. However, the quantity of data makes statistical analysis demanding, creating a need for analytical tools that can simultaneously manage a high throughput of data while providing an interactive, user-friendly interface for researchers who may have limited statistical training. JMP Genomics gives scientists at Genentech the flexibility to manipulate data quickly and identify promising biomarkers for personalized patient treatments.
Biomarkers promote personalization in healthcare
As technology and health research advance, researchers seek new ways to leverage biological information to predict health outcomes and tailor patient regimens. Personalized healthcare, as this movement is broadly known, includes such activities as:
- Developing diagnostic tests to identify the root cause of symptoms.
- Categorizing patients into sub-groups that may respond differently to treatment.
- Identifying biomarkers that can give healthcare providers key information about a patient’s disease.
Biomarker development offers an exciting window into the unique impacts of technology and analytics on the ability to provide a truly personalized healthcare experience. Biomarker scientists evaluate various biological interactions between drugs and patient sub-groups to better understand treatment efficacy and outcomes, allowing healthcare providers to prescribe the ideal treatment for each patient.
To both effectively deploy these patient-specific drugs and gain regulatory approval, researchers must concurrently develop companion diagnostic (CDx) assays to identify biomarker-positive patients. Although this “one drug/one test” model has improved outcomes for many patients, some still do not respond to treatment as expected, leaving researchers with more questions that often remain unanswered due to the limited tools at their disposal.
In the last few years, however, next-generation sequencing technologies and massive parallel sequencing have opened up the field. Rapid development in the areas of microfluidics and multiplexing technologies have expanded investigational capabilities, letting researchers evaluate multiple genes in a single assay, as well as collect more data from smaller tissue samples. These technologies have supplied researchers with the means to comprehensively genotype and assess transcriptive wide gene expression to develop a more comprehensive view of a patient’s biology. Armed with this information, biomarker researchers can continue to evaluate treatments for more and more refined sub-groups of patients, increasing the likelihood of successful treatment outcomes. Still, with an ever increasing amount of available omics data, researchers require sophisticated statistical tools to evaluate them.
Harnessing the power of big data to develop oncological biomarkers
Matt Wongchenko, Principal Scientific Researcher at Genentech, studies biomarkers in the context of oncology drug development. “Genentech and Roche have a very strong interest in the future of personalized medicine. That’s the background for the existence of the oncology biomarker development group,” says Wongchenko. At such a large company, there are myriad drugs under development for oncology indications; Wongchenko’s job is to identify specific biomarkers that can be used to predict or measure patient response to each drug, then create reliable diagnostic assays to identify these biomarkers. Through this process, Genentech tailors available treatments to various patient sub-populations, pushing the frontier of personalized healthcare.
The scale of this research has grown by magnitudes in a relatively short period. “When I first started in research, it was very much answering small questions, having assays to look at individual genes, individual proteins, and having to approach the data with very specific questions,” says Wongchenko. The expanded capabilities offered by this volume of data presents new opportunities for understanding the molecular foundation of each indication and patient sub-group, thereby increasing the value of using biomarkers in patient treatment decisions.
Growing data volume brings new challenges
As the volume and complexity of omics data grows, so do the demands on biomarker scientists like Wongchenko. Although technology has advanced to give biomarker scientists the tools to conduct multi-omic experiments and improve their overall statistical power and reproducibility, these scientists often lack the statistical expertise and training to understand and take full advantage of the available resources. While Genentech maintains highly trained statistical staff to support teams like Wongchenko’s, they often cannot support the full spectrum of analytical needs in the desired timeframes.
“We work with our biomathematicians, and they have conflicting priorities and limited time,” Wongchenko says. Relying on outside staff for statistical support often constrains the biomarker team temporally and limits their ability to adapt their analyses to their ongoing observations, preventing them from maximizing the output of the available data. “Sometimes there’s a meeting the next day that I need to have an analysis ready [for] to help inform decision-making and I can turn that around [using JMP] rather than having to wait weeks, months, for someone else to do the analysis.”
Tools capable of analyzing these data in a timely and nimble manner are highly valuable, especially user-friendly tools like JMP Genomics that do not require that users possess a robust backgrounds in statistics. Although he initially began using JMP Genomics because it was Genentech’s companywide software offering, Wongchenko soon recognized the advantages of JMP; he could adapt his previous programming experience, in combination with additional training, to apply JMP to his research. “I didn’t necessarily get into as high throughput of analysis as I did until I knew how to use JMP Genomics,” he shares. “Back before I used JMP...it was definitely annoying and time consuming to try to get statistics.”
Matching the right tools with the right needs
So what makes JMP Genomics such a useful tool for this type of robust computational analysis?
“The biggest driver is the interactive analyses,” says Wongchenko. “It’s hard to beat the ability to just open up a data table and grab my variables that I want to look at. In five seconds I can see what the association between two different things is, or the association between one variable and survival, etc. It’s really the ease of analyzing the data and looking at it interactively.”
With the flexibility afforded by JMP Genomics, scientists can explore the data more thoroughly, allowing them to use their genomic expertise to tailor analyses and identify issues or outcomes more readily than with fully automated analytical tools. This flexibility also facilitates effective decision making; quick turnaround times let the team evaluate multiple scenarios and provide broader statistical justification than previously available.
With the expansion of analytical capabilities, scientists at Genentech and across the biomarker industry are finally able to begin unlocking the critical information hiding in patient sequencing data. Understanding how to customize treatments to individual patients will become more and more common as scientists like Wongchenko develop biomarkers to shed light on the interactions between drugs and patients.
“The process of drug approval is always going to be a long one,” says Wongchenko. But with the right tools, researchers can extract previously unknown associations that will change the way treatments are delivered, bringing oncology research closer to achieving personalized healthcare.
