How New Practices in Biomedical Research are Changing the Future of Healthcare

Biomedical Research Changing Healthcare

Five years ago, we were ushering in the ‘era of precision medicine,’ with researchers and clinicians alike embracing advances in genomics to help tailor treatments to individual patients, especially in cancer care. 

How has biomedical research and clinical care changed since then, and what does the future hold?

Precision medicine continues to garner attention, with its applications expanding into other areas, such as neuroscience, immunology, women’s health, and rare diseases. 

Patients are not only the focus of targeted therapies; they are also being placed front and center of many aspects of healthcare. 

Researchers are eager to incorporate diverse data from more representative patient populations, and registries from large-scale public sequencing efforts and patient advocacy groups are proving to be valuable resources in these endeavours.   

They are also more willing than ever to work together to accelerate discovery, thanks in part to the recent (and ongoing) global COVID-19 pandemic. 

How prepared are we to not only cope with these changes, but also harness their enormous potential? 

From Real-World Data to Real-World Evidence to Real-World Action

Advancements in technology supporting genomics, proteomics, metabolomics (and all the other ‘omics) have generated great insights into the biology of many diseases — and an enormous amount of information. Managing the vast ocean of data and fishing for answers within it are two of the primary challenges in precision medicine.

City of Hope is tackling the data challenge by creating a system that pools a wide variety of data from multiple sources in a way that can be easily accessed by bioinformaticians and physicians alike. 

Created with the help of DNAnexus, the system integrates DNA sequencing information with other data affiliated with the patient journey, from disease registries to pathologies, molecular characterization of the tumor, medical record data, and clinical trials information.

The POSEIDON platform has become more than just a static repository of data. It’s helped inform City of Hope’s unique in-house drug development. It’s helped place patients into clinical trials. It’s assisted tumor boards, where clinicians, researchers, and technical curators come together to make decisions on tricky cases. And it has led to new research ideas, new methods, and new translational projects. 

As precision medicine expands, so will the hunt for new biomarkers and the use of companion diagnostic tools. Laboratories will need an informatics environment that can flexibly scale to meet the demand for increased test volume. Cloud-based systems enable labs to optimize analysis pipelines for quality, speed, runtime, and cost, in a secure, compliant way.

Myriad Genetics uses the DNAnexus Titan platform to power its computationally intensive AI and machine learning methods. The company uses the technology for biomarker discovery, improvement of its current molecular diagnostic test portfolio, and disease risk prediction. 

The Apollo Platform and its Cohort Browser can also be used in pharmacogenomics to predict an individual’s risk to adverse drug reactions, another area that is likely to be in high demand as precision medicine becomes the norm.

Harnessing Rare Resources 

When researching a rare disease with many subtypes driven by diverse and distinct genetic alterations, data sharing is key. Samples acquired by a single institute, a single research initiative, or even a single nation may lack sufficient statistical power for genomic discovery and clinical correlative analysis.

St. Jude Children’s Research Hospital was an early adopter of cloud-based collaboration,  partnering with DNAnexus and Microsoft in 2018 to create a data-sharing ecosystem that has proved to be a model for harmonized genetic data across the pediatric cancer community. Since then, more than 1.25 petabytes of data have been incorporated into the St. Jude Cloud, and several research studies have been published about scientific discoveries made using the data.

The Muscular Dystrophy Association (MDA) is harnessing its patient registry to improve current and future patient care. Its neuroMuscular ObserVational Research (MOVR) data hub collects longitudinal data in seven neuromuscular disease indications, and a new visualization and analysis platform powered by DNAnexus is enabling 37 MDA Care Centers to easily access and analyze the information.

The MOVR Visualization and Reporting Platform (VRP) allows different levels of analysis, from overviews of disease progression and outcomes across sites, to in-depth dives into clinical parameters across large cohorts of neuromuscular patients. This level of correlative analyses could ultimately stimulate new drug, biologics and gene therapy discoveries. Exploration of deeply curated neuromuscular disease cohorts could also help in clinical trial design, by enabling clinical researchers to rapidly identify populations that meet specific clinical criteria.  

DNAnexus platforms are also being used by the Children’s Tumor Foundation to delve deeply into gene expression and transcriptome data to identify elusive therapeutic options for three forms of neurofibromatosis.

Going Global

Progress in science and medicine accelerates when researchers collaborate around responsibly shared datasets. As the complexity and scale of data increases, collaboration becomes more difficult to manage.

Tools developed on DNAnexus Apollo, such as the cloud-based UK Biobank Research Analysis Platform, have helped harmonize and democratize sequencing data by making it easily accessible to any scientist, from an individual field researcher accessing the database from her laptop, to pharma companies like Biogen, which is using the UKB information to rank candidate compounds in its drug portfolio as well as identify novel gene targets.  

The COVID-19 pandemic has underscored the importance of real-time data sharing, and an emerging focus on global pathogen surveillance will require even more companies to scale up their operations, from vaccine discovery and production, to rapid sample sequencing and diagnostics. 

The DNAnexus Apollo Platform was designed to seamlessly integrate and analyze diverse datasets, including multi-omic & clinical data, driving actionable insights in real time. We’ve compiled some tips for diagnostics businesses looking to scale their operations, as well as pointers for drug discovery companies

As we race towards a more interconnected, interpersonal future in healthcare, we need to ensure the industry is moving at the velocity of technological innovation. At DNAnexus, we’re proud to set the pace and provide solutions that allow all types of users to board the big data train.

How Multi-Omics is Changing Biomedical Research

Multi-Omics Changing Biomedical Research

Genomics, transcriptomics, proteomics, metagenomics… biology now seems to revolve around the “omics.” What exactly are they, why are they so crucial, and where is the field heading?

First off, a definition. When added to a biological word, “omics” refers to a global study of a system. So, for those of you who have always secretly wondered what the difference is between genetics and genomics: genetics is the study of individual genes, while genomics is the study of the entire genome. You’re welcome!

As the definition suggests, ‘omics research is interested in uncovering the often complex systems and molecular mechanisms underpinning diseases and other biological functions. 

Increasingly, scientists are combining ‘omics approaches for a more holistic, systems biology “the whole is greater than the sum of its parts” approach. 

Cancer provides a good example of why this approach is an attractive one. Oncogenic signalling is a multi-layered problem encompassing multiple molecular layers, from initial somatic mutations, to altered protein networks, to downstream activation of transcriptional programs. Combining genomics, proteomics, and transcriptomics can help elucidate activity along entire pathways. The simultaneous measurement of DNA and cell-surface protein, often called proteogenomic profiling, can also be used to characterize clonal diversity and evolution within tumors. And spatial ‘omics is providing yet another valuable layer of in situ insight. By using several tools in the ‘omics toolkit, researchers can piece together the most in-depth molecular pictures possible.

This multi-omics approach comes with challenges, however. Foremost among them is making sense of data that are in different formats and piecing them together to make meaningful conclusions. 

To make multi-omics analysis easier and more accessible to researchers, there must be well curated, publically accessible datasets that enable scientists to build upon already existing molecular knowledge. Scalable infrastructures to store and manage large quantities of data are also needed.

Ideally, there should also be interoperable tools that allow scientists to integrate data from different molecular layers, and to add their own data to study relationships between them. Even better: A streamlined process in which tools are moved into pipelines and accessible via interactive, intuitive graphical interfaces

Luckily, these are all areas in which DNAnexus excels.

DNAnexus Apollo™ is designed to handle the scale and scope of multi-omics research. It is data model agnostic, allowing for interrogation of any structured or unstructured data type.

Biologists can dive into thousands of phenotype fields and millions of variants in seconds, or query multi-omic characteristics to build cohorts for in-depth analysis, exploring associations between genomic and linked clinical data. Its built-in interactive data visualization tools and secure, cloud-based collaborative workspace makes it an even more attractive option for multi-disciplinary teams scattered around campus — or the globe. And it’s as flexible as your research.

The new multi-dimensional, multi-omics scientific queries may be complex, but the computational solutions used to harness them need not be. We aim to simplify the infrastructure needed to support these new approaches, so that their full potential can be realized.

An Introduction to Population Genomics Studies with DNAnexus Apollo

Population Genomics

While the amount and variety of genomics data available to scientists is greater now than ever before, large-scale bioinformatics studies can still seem daunting to researchers interested in exploring the applications of population genomics. Reducing the barrier to entry through software tools and open data access is essential to accelerate the path from genomic discovery to tailored patient care. In this light, we provide an introduction to a few fundamental types of genomic based studies.

Researchers leverage population-scale genomics in their studies to better understand how genetics contribute to an individual’s health. These large-scale studies start with the creation of cohorts — a group of individuals with a particular set of genotypes, phenotypes, and/or combination of the two — that the researcher is interested in studying. Traditional cohort creation for bioinformaticians is a process that involves using the command-line interface (CLI) and a variety of different genotype/phenotype specific tools to sort through datasets and parse hundreds of traits. DNAnexus Apollo enables researchers to work either within a JupyterLab/CLI environment or an intuitive user-interface to easily and quickly explore a multitude of phenotypes and genomic traits in the Cohort Browser, filter for desired traits, and visualize results using built-in charts and visualizations such as Manhattan plots.

DNAnexus Apollo Cohort Browser
Figure 1. DNAnexus Apollo Cohort Browser
DNAnexus Apollo Association Browser
Figure 2. DNAnexus Apollo Association Browser 

Once a cohort has been identified, two common analysis methods are  genome- and phenome-wide association studies.  A genome-wide association study (GWAS) is used to find associations between single-nucleotide polymorphisms (SNPs) and a certain trait or disease. Phenome-wide association studies (PheWAS) operate on a similar premise, and test genetic variants for associations with a set of phenotypes. Both types of studies provide information that researchers can use to uncover genetic risk factors for a variety of diseases and health conditions by surveying phenotype to  SNP correlation. The insights gained into the disease associations provide a better understanding of underlying disease origins, and can aid in the discovery and validation of new targeted drug targets and/or preventative strategies. 

SAIGE and PLATO are two common software algorithms that can be used to execute GWAS analysis.  There are publicly available resources for how to conduct a GWAS using SAIGE, including this repository created by the Neale lab that details the results of a GWAS conducted on the UK Biobank dataset. Many of these guides provide specific details on how quality control was conducted, and which statistical analyses were used, so that those new to these types of studies can replicate their findings. PLATO offers features to those interested in analyzing phenotypes as well, and enables users to run both a GWAS or PheWAS using a single unified tool. Both of these algorithms are available on DNAnexus, wrapped in the scalable application framework accessible, allowing researchers to quickly create cohorts and analyze them in the same place.

Both of these types of studies will process large quantities of data when run on large populations, a challenge for most homegrown informatics systems. DNAnexus Apollo offers Jupyter notebooks that enable researchers to easily analyze the large scale data with actions like annotation of GWAS results, a critical step  for making bioinformatics data more actionable  for all researchers. Apollo is purpose-built to handle the scale and type of computations that population genomics studies use, allowing researchers to work with genomics and complex multi-omics data in exciting new ways. 

More and more tools are becoming available that help enable researchers to conduct innovative and more involved analyses as well. Using the results from a GWAS, researchers can compare their variant sets with other databases online, and incorporate additional omics data types, such as RNA-seq, to better understand how variants affect gene expression. Or, by utilizing machine-learning, researchers can conduct fine-tuned analyses by combining GWAS and functional data to bring clarity to previously noisy results. 

Population genomics studies hold the key to unlocking the potential of precision medicine. With tools like DNAnexus Apollo, researchers can more quickly utilize large and complex datasets for use in the identification of biological mechanisms of disease, the discovery and application of biomarkers, or omics-guided therapeutic target discovery. 

Interested in learning more? Check out the recorded webinar with Ben Busby, Scientific Director, Research Platforms Outreach, and see how Apollo unlocks population-scale omics datasets for accelerated discovery.