How Bioinformatics Can Be Used To Develop Precision Cancer Therapies
The following article is an opinion piece written by Daniel Elgort. The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position of Technology Networks.
Cancer is one of the most ubiquitous, impactful and challenging diseases. It affects every person, whether personally or through family members. Curing cancer has become one of the goals of modern society and precision medicine is one of the advances that promises to help us achieve this goal. Precision medicine is, among other things, a way to prescribe treatment regimens based on the genetics, environment and lifestyle of an individual.
As our ability to develop precision medicines continues to grow, our findings can influence the way we conduct experiments and clinical trials. This is because traditional methods for drug discovery and clinical development involve a great deal of heterogeneity; the same treatment has very different effects on different people and different tumors. These methods do not account for specific genes of the patients and mutations and micro-environment of the tumor cells.
The goal of precision medicine, and precision cancer treatments specifically, is to move away from making blanket statements about the effectiveness of a treatment in the general population, and to move towards identifying the specific details of each individual case and making accurate predictions about how the therapies will perform.
How big data can deliver precision medicines
To continue advancing precision medicines, researchers need a significant quantity of high-quality data. Over time, as sequencing technology and data processing has improved, scientists have developed high-throughput capabilities for sequencing and analyzing large-scale genetic data, such as whole exome and RNA transcriptome data. However, adjacent biomolecular data are equally important for developing precision medicines. We need to identify not just what mutations exist but to what extent the mutations are affecting the expression of different proteins. We need to understand the details of the patient’s immune system and its activity in the tumor microenvironment.
In addition to genetic and physiological data, it’s important for researchers to have access to clinical data. This includes everything from measurements taken during clinical visits to details about treatment regimens and responses. These data are often difficult to obtain because of a lack of standardization in the way the data are observed and documented between centers and even between clinicians.
Collecting these data allow us to optimize the use of our resources and to provide more valuable care to patients. Real-world evidence, or the analysis of clinical data collected outside of a controlled clinical study, can fill in the gaps left by traditional clinical trials, including identifying rare events and side effects. Clinico-genomic data, a combined set of clinical and genomic data, can also be used to streamline the drug discovery process by helping to identify genes to target, potential therapeutics and testing potential therapies using simulations. In the future, clinico-genomic data will be integrated into the clinical trial process, allowing clinical trials to be conducted more efficiently.
This is a difficult undertaking and needs not just to be a collaboration of research institutes and cancer centers. Building and maintaining an effort to harness big data for precision medicine requires a significant infrastructure. Previous academic and government initiatives, such as the Cancer Genome Atlas project, have not been properly structured to accomplish this because of a lack of consistent funding.
Standardization: The challenge of data in drug development
A significant challenge in the field of bioinformatics is that the data need to be collected and organized in a standardized way. The clinical data collected by healthcare systems varies widely, sometimes within the system itself, making it difficult to use the data for statistical purposes. Standardizing data includes developing standard protocols to ensure that collected data is statistically useful and as much information is gathered as possible. To achieve this, the data collected must be constantly monitored and procedures must be updated. It is an evolving system that requires the active participation of all stakeholders.
I work as the chief data and analytics officer for M2GEN, a company that collects, synthesizes and analyzes clinico-genomic data from a network of 18 leading cancer centers called the Oncology Research Information Exchange Network (ORIEN). This partnership gives us the ability to collect and standardize clinico-genomic data using a rigorous protocol that ensures that the data are as clean and homogeneous as possible. We then work with our research partners to analyze the data in ways that are meaningful in the fight against cancer, whether that means sequencing the microbiome of the tumor environment, identifying new molecules that have potential as therapeutics, or determining the best strategies for sequential treatments.
There is still a considerable amount of work to be done to realize the full potential of precision medicine, but health care stakeholders are beginning to recognize the value of standardized data collection and analysis. M2GEN is just one of many organizations working to refine data collection, analytic techniques and effectively utilize the data in both the clinic and in the research laboratory. Additionally, researchers continue to identify opportunities to increase the diversity of patients represented in these datasets. We are committed to ensuring that the development of precision medicines is inclusive of all people, regardless of ethnicity. In doing so, we are not only able to provide more health care resources to under-represented groups, but we can advance precision medicines by studying a wider scope of the human genome and its variations.