Independent Jackson Laboratory and Microsoft Corp. announced a partnership based on artificial intelligence to accelerate the implementation of cancer treatments that target patients’ specific genomic profiles, a type of precision medicine that in some cases is more effective than traditional chemotherapy and has fewer side effects.
Who is Jackson Laboratory?
Jackson Laboratory (JAX) is an independent biomedical research institution headquartered in Bar Harbor, Maine. With over 2,100 employees, their mission is to discover precise genomic solutions for disease and empower the global biomedical community in the shared quest to improve human health. They are a National Cancer Institute-designated Cancer Center and has NIH centers of excellence in aging and systems genetics. They have been a source for more than 8,000 strains of genetically defined mice and home to the Mouse Genomics Informatics database not to mention an international hub for scientific courses, conferences, training and education.
With more than 60 active laboratories, JAX focus areas include Cancer, Development/Reproductive Biology, Immunology, Metabolic diseases, Neurobiology and Neurobehavioral disorders.
AI-based Genomic Mutation Discovery Tool
To harness the potential of the new cancer research era—stemming from advances in genomic sequencing technology enabling efficient and effective discovery of genomic mutations driving cancer, not to mention an explosion of new drugs targeting those mutations—JAX developed a tool to help the global medical and scientific communities stay on top of the continuously growing volume of clinical data generated by advances in genomic research.
Clinical Knowledgebase (CKB)
The tool is called Clinical Knowledgebase or CKB, a searchable database where subject matter experts store, sort, and interpret complex genomic data to improve patient outcomes and share information about clinical trials and treatment options. The challenge is to find the most relevant cancer-related information from the 4,000 or so biomedical research papers published each day. They needed help with Project Hanover.
What is Project Hanover?
JAX partnered with computer scientists working on Microsoft’s Project Hanover who are developing AI technology enabling machines to read complex medical and research documents and highlight the important information they contain.
Microsoft notes medicine today is imprecise. For the top 20 prescription drugs in the U.S. 80% of patients are non-responders. The advent of big data heralds a new era of precision medicine, where treatments become increasingly effective by tailoring to individual patients.
The problem: big data leads to information overload, making it extremely difficult to separate signal from noise and discern knowledge from data. For example today a molecular tumor board will take hours the highly trained specialists to review a patient’s genomics data and make decisions. With 1.7 million new cancer cases and 600,000 deaths in the U.S. each year, this is not scalable.
Biomedical texts contains valuable structured information that facilitate harnessing big data for precision medicine. Examples include oncology knowledge in biomedical literature and patient treatment outcomes in electronic medical records (EMRs). These opportunities have triggered the growth of “Curation-as-a-Service (CaaS). Current CaaS vendors must rely on manual curation by human experts and face steep challenges in scalability.
Jackson Laboratory researchers are utilizing Microsoft’s machine reading technology to curate CKB, which stores structured information about genomic mutations that drive cancer, drugs that target cancer genes and the response of patients to those drugs.
Making Curators ‘SuperPowered’
In one case an app allows oncologists to discover matches between a patient’s known cancer-related genomic mutations and drugs that target them as they explore and weigh options for treatment, including enrollment in clinical trials for drugs in development. In another case, translational and clinical researchers face tremendous bottlenecks filtering through more than 4,000 papers published every day in biomedical journals to find the subset of about 200 realted to cancer.
Auro Nair, executive vice president of JAX reports, “The core of Microsoft’s Project Hanover is the capability to comb through the thousands of documents published each day in the biomedical literature and flag and rank all that are potentially relevant to cancer researchers, highlighting, for example, information on gene, mutation, drug and patient response. Hoifung Poon, director of precision health natural language processing with Microsoft’s research organization in Redmond and the lead researcher on Project Hanover reported “Our goal is to make the human curators superpowered.”
Clinical Trials Matching
Over 20% of clinical trials fail due to insufficient patients. Patient recruitment is largely done by word of mouth, placing the burden on physicians and patients to keep track of thousands of open trials and match elaborate eligibility criteria to a given patient’s case. For drug development, matching efficiency could determine success or failure of a trial. For a patient, it can be life-or-death. Machine reading can speed up clinical trial matching by extracting patient attributes from both EMRs and eligibility criteria to facilitate matching. We are in discussion with various stake holders to explore potential opportunities for assisted curation in clinical trial matching.
Call to Action: Want to learn more about Project Hanover? Contact them here.Source: Microsoft