The Human Genome Project showed that while mankind’s genetic makeup is 99.1% identical, and only 0.9% of genetic variability creates vast variability that exists within the human species (Novelli 2010). Personalized medicine is the effort to prescribe the most appropriate drug for each individual patient based on their specific biology. Genetics explain some of the variations in responses seen during clinical trials. The variety of cancer mutations means that effective diagnosis and treatment of cancer must take into account a high degree of complexity. By sequencing individual cancer genomes, researchers and physicians may develop more targeted medical solutions. By using interpreted data in routine patient exams, clinicians can analyze how the symptoms of a disease in a patient correlate with their specific biology, resulting in a more effective treatment. Cancer is the second major cause of mortality in the United States, but targeted cancer therapies are bringing about an exponential increase in effectiveness over traditional cancer therapies.The potential exists to identify early indicators of disease, including cancer, in the form of biomarkers for early detection of a disease.
According to the Global Oncology Trend Report, global spending on cancer medications rose 10.3 percent in 2014, bringing the total to $100 billion, up from $75 billion in 2010. The rising cost of cancer treatment is linked to the emergence of precision therapeutics, which are costly to develop and often fail before they reach the market. While more effective, they target a smaller population which is hard to identify. The pharmaceutical industry recently turned to theoretical and computational modelling to improve the drug discovery process, lowering the cost of care in the process.
The cost of next-generation sequencing and other techniques that provide comprehensive whole-patient data is decreasing rapidly, making personalized multi-omics analysis increasingly cost effective and accessible. There is a long and costly effort to introduce precision treatment based on molecular data into both health delivery and pharma. A massive amount of data is available, yet few know how to use the data insights effectively. To be truly effective, the data has to be analyzed effortlessly with an integrative approach. Using multi-omics data in developing an informed, personalized approach to treatment, access to effective clinical trials, and preventive strategies can provide major cost savings in terms of avoiding ineffective treatments, expensive diagnostic regimens, etc.
With affordable whole-patient scale data just around the corner, the challenge has now moved into the realm of extraction of meaningful insights from the data. To get the most value from multi-omics data analysis in clinical applications, Pine Biotech is developing an omics-first machine learning platform, OmicsLogic. The platform goes beyond analytics, integrating clinical knowledge with multi-omics raw data analysis for biomarker discovery and personalized molecular studies. As the field evolves and data continues to become available, algorithmic innovation is poised to be a driving force in solving healthcare ecosystem challenges. The wealth of data that is generated should be exploited – ultimately to improve care, and benefit consumers.