Download Using Medbook evidence streams to guide medical decisions

Using Medbook evidence streams to guide medical decisions
Robert Baertsch, Ph.D., SU2C/PCF West Coast Dream Team, Eric Small, Jack
Youngren, Ph.D., Joshua M. Stuart, Ph.D., and Theodore Goldstein, Ph.D.
UC Santa Cruz and UC San Francisco
Abstract:
Medicine is often called both an art and a science. The “art” of medicine works at an
individual (n of 1) level to provide compassionate care while a physician employs medical
science to treat a patient. Medical “science” works best for diseases whose etiology are
pathogens or common genes because large cohorts (n > 100) of patients provide the statistical
power necessary to distinguish the important clinical signals from the noise inherent in all
biological systems. Unfortunately, cancer and many other diseases caused by rare genetic
variations confound current scientific and statistical methods. For example, every individual may
have a unique set of mutations that alone drives their cancer. This creates the Information Gap
of Genomic Medicine. To fill this gap, we must aggregate even larger cohorts than previously
done for medicine in an effort to find common clinical signals, and to develop new tools and
machine learning approaches that will allow us to separate the low signal from the noise caused
by cancer. Without this information, cancer treatment depends more on the art of medicine
rather than the science. We estimate that a million patient database of cancer will provide the
level of breakthrough improvement needed to understand cancer and other genomic diseases.
Our approach is to build a system called MedBook based on social network principles as part of
the world wide collaboration called the Global Alliance for Genomic Health in the context of the
Prostate Care Foundation / SU2C West Coast Dream Team.
Background:
Clinical datasets typically have small sample size limiting the ability to generate
inference with strong statistical significance. By combining: 1) existing datasets; 2)integrated
analysis of different data types 3) curated pathways and 4) state of the art machine learning
classifiers into one analysis pipeline, clinicians and researchers may be able to combine
multiple observations to spot a pattern in an individual patient sample to base a critical decision
about therapy. However, few tools exist to enable a diverse set of analytical results to be
coherently pooled and disseminated to a research team. New web-based modalities are
urgently needed to meet the demands of new genomics-based oncology use cases.
Methods:
Genomics-based oncology typically relies on the integration of the following types of
data: Import public datasets from NCBI, Wrangle data into standard format, Import clinical from
OnCore, RNA Seq analysis, Sharing Data with collaborators in a secure manner, integration
with online portals such as MSKCC’s cBioPortal and UCSC’s Cancer Genomics Browser for
visualization and analysis, differential gene expression analysis, pathway enrichment analysis,
training classifiers to recognize events on existing datasets, applying classifiers to new datasets
to infer molecular events. We have created a new Medical Information System called MedBook
to give a context for integrating multiple observations about patients and their biopsies into a
unified social network. Borrowing terminology from systems such as FaceBook and Google
Plus, we describe an organization of collaborative analyses as evidence “streams” that allow
sharing, annotation, and nucleation points for further analyses. Streams are composed of
“evidence cards” that encapsulate figures and/or tables. Discussion threads allow interpretation
and commenting on findings associated with each evidence card.
Results:
We demonstrate the utility of the stream concept using a gene expression based
signature that predicts small cell disease in castration resistant prostate cancer. Importantly,
the stream concept allows 1) redefinition of the signature to incorporate additional patient
samples and clinical definitions on-the-fly, 2) the application of the signature to query samples,
3) viewing the predictions in cBioPortal to assist clinicians judgement of samples in the context
of other relevant genomics events, 3) extend the results by leveraging additional bioinformatics
apps, 4) the recording of the provenance, and 5) creates a focal point for deliberation about
patients, signatures, genes, mutations, pathways, and clinical inferences.
Conclusions:
Prior to MedBook, there was a information gap between individual doctor
observations and large research paper driven cohort meta-analysis. MedBook is able to fill the
gap by facilitating the online integration necessary for data analysis in a dynamic clinical
environment.