| By Tré LaRosa NeuLine Health |
Introduction
The sheer volume of data in the world continues to increase at an extremely high rate, making it more important than ever to use the available data in the most effective way possible. Since science comprises many steps with data as a linchpin between those steps, it is of primary importance to ensure that data is not misused. To accomplish this, the world of data should be viewed as a web, where different types of data intersect and add context and information to other types of data and vice versa. In the clinic, with the recent advent of emerging fields such as omics, many patients might be confused as to how the different types of data can be used in conjunction to improve clinical care and support research. Some data points are measured in the clinic using advanced technology or blood tests; others are more basic, capable of being measured at home using cheap devices or even smartphones; others still are subjective, like behaviors and feelings. But how a patient is feeling is not separated from their bloodwork — in fact, the markers measured in the bloodwork might provide helpful information for why a patient is feeling a certain way. Not every data point is even necessarily specific to each individual; individuals that live in the same city will have similar data points for air quality despite variations in the more granular data points related to the processes measured in their bloodwork. Clinicians and researchers take these factors into consideration when evaluating patients, but to best understand every patient’s individual needs and etiology, all data related to them should be considered holistically. With the advent of increasingly complex technology and software including bioinformatic tools and programs, the factors are there to use data holistically and with other types of data. Data, then, is a vague term; at its most basic, data is a quantifiable measurement of information. For research and medicine to produce the best insights possible, no type of data can be considered independently.
Clinical Measures
Objective measurements are critical in clinical research as they reduce the amount of noise in the data. Clinical trials seek to uncover correlative trends between the use of a therapeutic and the effects seen in the patients, but to do so, the signal must be separated from the noise. Clinical outcome measures are used to control for the confounding variables when it comes to therapeutics and whether a patient is responding directly to the medication or some other variable. This is why the gold standard of clinical research is the placebo-controlled, randomized, double-blind clinical trial. In these clinical trials, there are two groups, one of whom receives a compound of interest while the other receives a placebo identical in appearance (placebo-controlled), and members of the groups are assigned at random (randomized); also, neither the clinical trial staff who administer and consult the patient nor the patient know if they are receiving placebo or drug (double-blind). These techniques are utilized to account for the externalities that can influence whether somebody responds to a medication.
It’s difficult to develop consistent clinical measures for many conditions due to a myriad of factors: Patients often present clinically differently from one another despite having the same condition which means they might have different subtypes; not all manifestations of a condition present in the same order; what causes a condition or its mechanism are not always well understood. This doesn’t mean clinical measures aren’t useful or necessary, it just means that clinical measures are constantly improving as research gleans insights and progresses, unveiling previously unknown mechanisms, causes, clinical manifestations, or disease subtypes. And to continuously improve clinical measures, research and medicine should work closely together to advance understanding from the lab to the clinic.
One strong example of the integration of laboratory research driving insights into clinical care is the emergence of “omics” fields.
Omics
First, let’s discuss what “omics” data actually is. Omics as a field considers sets of data in a dynamic, comprehensive manner, where sets of data influence and are influenced by other sets of data. Perhaps, omics is better understood as a way to organize knowledge. As Jong Bhak, a genomics expert at the Korean Bioinformation Center (KOGIC), describes in his history of omics, “The main contribution of omics is to make humans work better with computers by generating well-defined systematic data types, databases, and data representations.” Much of biology presupposes there are normal biological functions, but these functions have ranges — what is ideal for any given person is unlikely to be identical to the next person’s ideal figure for a biological function. This makes sense; we are all genetically different and occupy different environments. Our bodies are actually superb at figuring out what is stable for us, but our environment can continuously shift and modify how our bodies are functioning, so our bodies are constantly adjusting to those conditions. This bidirectional manner of our bodies being affected by our environments just as we exert influence on our environments is similar to how the field of omics considers sets of information against other sets of information. Omics, then, at its most basic, is the discipline of using knowledge frameworks, data, and technology together to yield novel insights. Systems biology, another relatively nascent discipline in biology, takes a similar approach: Systems biology views biological units as parts of a bigger, comprehensive system. The cardiovascular system is not separate from the rest of the body, but it is defined by many biological processes and components that act together for certain functions; systems biology views the cardiovascular system as part of a broad array of systems working in concert, so if a disease impacts the heart, the rest of the patients’ body, including organs that make up the pulmonary, gastrointestinal, and nervous systems, is likely to affect and be affected by the heart. In systems biology and omics, a multi-disciplinary approach advances science and medicine more rapidly. Of course, this approach adds complexity to the research process by adding additional considerations to every research question, but the added complexity at the beginning is intended to reduce complexity in the end, and to make the research and clinical process more effective. The alternate, reductionist approach, which considers biological systems independently, might produce insights in the short term, but these insights are nebulous once considered in the bigger picture. These potential adverse effects are usually observed in the clinical trial process. Omics intends to circumvent these issues by providing basic, preclinical, and clinical researchers with the framework to use large volumes of data with software tools to better map out the interactions between different components of the body.
As the authors of one paper on the importance of considering multiple omics disciplines holistically put it, “the addition of ‘omics’ to a molecular term (such as genome, protein, lipid, transcription, or metabolites) implies a comprehensive, or global, assessment of a set of molecules.” For many conditions, omics provides the research and clinical communities with tools to better understand how impaired biological functions affect other parts of the body. Omics adds quite a bit of useful information especially when omics data is considered in addition to clinical outcome measures.
In every field of science, there are many sub-disciplines; the field of omics exemplifies this. There is genomics, the study of genome-wide associations; then there’s proteomics, or the study of the function and expression of proteins; there’s the study of lipids and their function, known as lipidomics; there’s the study of metabolites: metabolomics; and there’s transcriptomics, which studies the entire complement of RNA molecules in a cell. Many fields of omics can indeed overlap, where one study comprises components that fit within other fields, such as proteomics and transcriptomics since the RNA is what codes for the expression of proteins. What omics does particularly well is it anchors information around a centralized node by which everything else is referenced. This intersectional method of knowledge organization provides much more nuanced and comprehensive information and frames the importance of considering many types of data to develop a holistic picture of a given patient.
Answer ALS: Tying together Clinical Measures and Omics information
A recent, powerful example of the possibilities that can come from the integration of clinical and omics data is the data repository called “Answer ALS,” an innovative, imaginative, and forward-looking resource. These researchers utilized modern technology in the form of smartphones and multi-omics bioinformatic tools with tried-and-true clinical measures to develop a massive database of information for researchers to investigate. Their intention, in their own words, was to create a resource that “provides population-level biological and clinical data that may be employed to identify clinical–molecular–biochemical subtypes of amyotrophic lateral sclerosis (ALS).” The novelty of such a resource can’t be overstated: This resource invites researchers from across the scientific spectrum to utilize omics and clinical data to uncover promising insights and strategies for better understanding ALS, a tragic condition that affects 30,000 people in the United States.
To develop this repository of information, researchers used patient-derived spinal neurons to generate pluripotent (meaning able to become one of many different types of cells) cell lines which could be further investigated. This is where the power of omics comes into play: Since these cells were pluripotent, researchers were able to differentiate the cells to become many different types of specialized cells, so different disciplines of omics — proteomics, epigenomics, and transcriptomics — could be harnessed to broadly deepen the collective understanding of how ALS affects cellular processes. This alone would be a feat, but the researchers went further. They utilized “a unique smartphone-based system” to “collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition” — which could be correlated to the same patients’ differentiated spinal neurons. Altogether, this resource could produce critical insights into the way ALS affects cellular processes as well as inform clinicians of subtypes of ALS. With an increased understanding of ALS pathogenesis, researchers can innovate therapeutic development further. This resource also created a repository of what the researchers call “biopsy-like” cells where therapeutics can be safely tested in a laboratory setting that more closely mimics humans than other available models.
Ultimately, Answer ALS is a profound accomplishment that could very well be a major inflection point in the history of ALS research.
Conclusion
Science is at its best when researchers use lessons from the past — such as the difficulty of developing a deeper understanding of conditions, their etiology, and pathogenesis using clinical measures alone — to imagine a better, healthier, and safer future with new, innovative, and emerging methods and technologies. Answer ALS provides a modern example of the remarkable progress that can come when a bridge is built between clinical measures, technology, and data. It seems reasonable to believe that many, many insights that should translate to better diagnostic tools, prevention strategies, and therapeutics will come from the advent of Answer ALS. Omics, as it continues to become institutionalized in different fields, will be a major contributor to these revelations and more.
Resources
- Yadav, A. K., Banerjee, S. K., Das, B., & Chaudhary, K. (2022). Editorial: Systems Biology and Omics Approaches for Understanding Complex Disease Biology. Frontiers in Genetics, 13. https://www.frontiersin.org/articles/10.3389/fgene.2022.896818
- Hasin, Yehudit, Marcus Seldin, and Aldons Lusis. “Multi-Omics Approaches to Disease.” Genome Biology 18, no. 1 (May 5, 2017): 83. https://doi.org/10.1186/s13059-017-1215-1
- Baxi, E. G., Thompson, T., Li, J., Kaye, J. A., Lim, R. G., Wu, J., Ramamoorthy, D., Lima, L., Vaibhav, V., Matlock, A., Frank, A., Coyne, A. N., Landin, B., Ornelas, L., Mosmiller, E., Thrower, S., Farr, S. M., Panther, L., Gomez, E., … Rothstein, J. D. (2022). Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines. Nature Neuroscience, 25(2), 226–237. https://doi.org/10.1038/s41593-021-01006-0
