Big data is enabling a new understanding of the molecular biology of cancer. Using personal medical and population genomics data, clinicians now have tools.
How data can improve cancer treatment
Big data is enabling a new understanding of the molecular biology of cancer. The focus has changed over the last 20 years from the location of the tumor in the body (e.g., breast, colon or blood), to the effect of the individual’s genetics, especially the genetics of that individual’s cancer cells, on her response to treatment and sensitivity to side effects. For example, researchers have to date identified four distinct cell genotypes of breast cancer; identifying the cancer genotype allows the oncologist to prescribe the most effective available drug first.
Herceptin, the first drug developed to target a particular cancer genotype (HER2), rapidly demonstrated both the promise and the limitations of this approach. (Among the limitations, HER2 is only one of four known and many unknown breast cancer genotypes, and treatment selects for populations of resistant cancer cells, so the cancer can return in a more virulent form.)
How data can improve clinical trials
As with treatment, progress in developing better cancer drugs has been hindered by a lack of genomic and metabolic understanding. The historical approach to cancer drug clinical trials is to recruit uncharacterized (without any genomic, metabolic or other differentiators that may affect response to the candidate treatment) subjects to test one-size-fits-all drugs. Given what we know now about cancer genotypes and individual response to drugs, it’s amazing any drugs were able to show statistically significant efficacy and reach the market.
Using personal medical and population genomics data, clinicians now have tools to design more targeted clinical trials by matching cancer cell types and individual metabolic response to the drug candidate, recruiting subjects who will be more likely to respond and excluding those likely to have treatment-limiting side effects.
Using new data to address unmet medical needs
In addition to cancer, there are many diseases with wide individual variability and a dearth of effective treatments: e.g., Alzheimer’s, depression, diabetes, asthma, and arthritis. A flood of new data streams in health care (from digitized medical records, genomics, pharmaceutical data, and data from trackers and sensors) may enable clinicians to make better diagnoses and prognoses that can give patients better prevention and treatment choices. Furthermore, aggregated health data can enable researchers to determine which patients are good candidates for particular clinical trials or treatment protocols.
Using sensors, at-home monitors, and smartphone device trackers, clinicians can capture clinical data in real time to monitor patients’ progress outside of the hospital between visits. This new approach is becoming possible through a combination of data sources and improved data management and analytics to move toward more effective treatments–and ultimately, personalized medicine.
by Shane Turner via NursingFacultyJobs.com
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