Over 14 million new cancer cases worldwide were reported in 2012, with the rate of occurrence expected to jump by approximately 70% over the next two decades, according to the World Health Organization.

Cancer patients are often treated with chemotherapy and various types of drugs, but the results of these treatments aren’t uniform in effectiveness, which is why it’s imperative for hospitals, clinics, and doctors to make the best drug treatment choices for each individual patient. Getting drug therapies right is an area where digitalized genomics data can help.

SEE: 10 ways tech is improving cancer research

“The technique that we use for this is genomic sequencing,” explained Dr. Jurgi Camblong, cofounder of Sophia Genetics, a provider of artificial intelligence that pinpoints the genomic code mutations behind cancers and rare disorders to assist physicians and healthcare institutions in prescribing optimal drug treatments for their patients. Today, 240 hospitals in 39 countries use the Sophia platform.

“What the technology does is spot variations in different genetic codes so we can use historical data that aids in prescribing the best combination of drugs to treat a particular cancer or condition in an individual patient,” said Camblong. “This is next-generation genomic sequencing, and it is used in two different areas: chronic hereditary disorders and oncology. By using the algorithms that are part of our artificial intelligence, we can spot the origin of a genetic mutation causing a cancer or a particular condition and then give an idea of what the best drug treatment would be to the attending physician.”

An example is lung cancer, where treatment in the past was prescribed based upon the patient’s tissue type instead of on a particular genetic mutation. By using genetic sequencing and mutation detection instead of tissue analysis, physicians can now identify the genetic events that caused the condition in the first place, and not just treat symptoms.

“The more we understand the molecular events at the origin of the disease or disorder, the better we can understand the effects of what certain combinations of drugs are likely to be,” said Camblong.

“The process begins with the extraction of the patient’s DNA via a blood draw or biopsy, said Camblong. “The hospital then uses molecular biology processes to prepare the samples and subsequently digitizes them using a DNA sequencer. The resulting genomic data is then submitted to the company AI on the Sophia DDM Software as a Service (SaaS) platform, which digs around to identify the patient’s genomic mutations. The more hospitals use the analytics platform, the more patients’ genomic profiles are accumulated, and the smarter the AI gets.”

“Without this technology, the process of determining a drug treatment takes about two days work, and in some cases can take several months when using old technologies,” said Camblong. In contrast, healthcare professionals who use Sophia for genetic sequencing and analysis can get drug treatment regime recommendations for individual patients in one day.

When developing the solution, one challenge Sophia and its healthcare customers faced was guaranteeing patient privacy. To ensure privacy, references to individual patients are stripped off of all treatment records so that the data is fully anonymized before it is ever admitted to Sophia’s data repository.

SEE: IBM Watson’s latest gig: Improving cancer treatment with genomic sequencing (TechRepublic)

Two benefits to Sophia being a SaaS-based solution

One benefit to the Sophia system being a SaaS-based solution is that even smaller hospitals and clinics can afford the technology, which on average costs $50-$200 per genetic evaluation.

A second advantage that a SaaS-based platform brings is democratization of information, because drug outcomes for different cancers and conditions can be shared globally.

“This democratization of the data is extremely important,” said Camblong, “because not every hospital has the clinical expertise to prescribe the optimal treatments for different conditions. Because we can share data and encourage collaboration through the platform, these clinics now have access to experts and results from around the world.”