- 1 Welcome to the Cancer Deep Phenotype Extraction (DeepPhe) project
- 2 Funding
- 3 Who We Are
- 4 Publications and presentations crediting DeepPhe
- 5 DeepPhe Software
- 6 DeepPhe Gold Set
- 7 Qualitative Interviews
- 8 Project materials/ WIKIs to tasks
- 9 Communication
- 10 Meeting Notes
- 11 Contact
- 12 Getting started
Welcome to the Cancer Deep Phenotype Extraction (DeepPhe) project
Welcome to the Cancer Deep Phenotype Extraction (DeepPhe) project.
Cancer is a genomic disease, with enormous heterogeneity in its behavior. In the past, our methods for categorization, prediction of outcome, and treatment selection have relied largely on a morphologic classification of Cancer. But new technologies are fundamentally reframing our views of cancer initiation, progression, metastasis, and response to treatment; moving us towards a molecular classification of Cancer. This transformation depends not only on our ability to deeply investigate the cancer genome, but also on our ability to link these specific molecular changes to specific tumor behaviors. As sequencing costs continue to decline at a supra-Moore’s law rate, a torrent of cancer genomic data is looming. However, our ability to deeply investigate the cancer genome is outpacing our ability to correlate these changes with the phenotypes that they produce. Translational investigators seeking to associate specific genetic, epigenetic, and systems changes with particular tumor behaviors, lack access to detailed observable traits about the cancer (the so called ‘deep phenotype’), which has now become a major barrier to research.
We propose the advanced development and extension of a software platform for performing deep phenotype extraction directly from medical records of patients with Cancer, with the goal of enabling translational cancer research and precision medicine. The work builds on previous informatics research and software development efforts from Boston Children’s Hospital and University of Pittsburgh groups, both individually and together. Multiple software projects developed by our groups (some initially funded by NCI) that have already passed the initial prototyping and pilot development phase (eMERGE, THYME, TIES, ODIE, Apache cTAKES) will be combined and extended to produce an advanced software platform for accelerating cancer research. Previous work in a number of NIH-funded translational science initiatives has already demonstrated the benefits of these methodologies (e.g. Electronic Medical Record and Genomics (eMERGE), PharmacoGenomics Research Network (PGRN), SHARPn, i2b2). However, to date these initiatives have focused exclusively on select non-cancer phenotypes and have had the goal of dichotomizing patients for a particular phenotype of interest (for example, Type II Diabetes, Rheumatoid Arthritis, or Multiple Sclerosis). In contrast, our proposed work focuses on extracting and representing multiple phenotype features for individual patients, to build a cancer phenotype model, relating observable traits over time for individual patients.
Our first four development specific aims significantly extend the capability of our current software, focusing on challenging problems in biomedical information extraction. These aims support the development and evaluation of novel methods for cancer deep phenotype extraction:
Specific Aim 1: Develop methods for extracting phenotypic profiles. Extract patient’s deep phenotypes, and their attributes such as general modifiers (negation, uncertainty, subject) and cancer specific characteristics (e.g. grade, invasion, lymph node involvement, metastasis, size, stage)
Specific Aim 2: Extract gene/protein mentions and their variants from the clinical narrative
Specific Aim 3: Create longitudinal representation of disease process and its resolution. Link phenotypes, treatments and outcomes in temporal associations to create a longitudinal abstraction of the disease
Specific Aim 4: Extract discourses containing explanations, speculations, and hypotheses, to support explorations of causality
Our last two implementation specific aims focus on the design of the software to support the cancer research community, ensuring the usability and utility of our software. These aims support the design, dissemination and sharing of the products of this work to maximize impact on cancer research:
Specific Aim 5: Design and implement a computational platform for deep phenotype discovery and analytics for translational investigators, including integrative visual analytics.
Specific Aim 6: Advance translational research in driving cancer biology research projects in breast cancer, ovarian cancer, and melanoma. Include research community throughout the design of the platform and its evaluation. Disseminate freely available software.
Impact: The proposed work will produce novel methods for extracting detailed phenotype information directly from the EMR, the major source of such data for patients with cancer. Extracted phenotypes will be used in three ongoing translational studies with a precision medicine focus. Dissemination of the software will enhance the ability of cancer researchers to abstract meaningful clinical data for translational research. If successful, systematic capture and representation of these phenotypes from EMR data could later be used to drive clinical genomic decision support.
The project described is supported by Grant Number 1U24CA184407-01 from the National Cancer Institute at the US National Institutes of Health. This work is part of the NCI's Informatics Technology for Cancer Research (ITCR) Initiative (http://itcr.nci.nih.gov/) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The project period is May 2014 - April, 2019.
Who We Are
- Boston Childrens Hospital/Harvard Medical School
- Guergana Savova (MPI)
- Dmitriy Dligach
- Timothy Miller
- Sameer Pradhan
- Sean Finan
- David Harris
- Pei Chen
- University of Pittburgh
- Rebecca Crowley Jacobson (MPI)
- Harry Hochheiser
- Roger Day
- Adrian Lee
- Robert Edwards
- John Kirkwood
- Kevin Mitchell
- Eugene Tseytlin
- Girish Chavan
- Liz Legowski
Publications and presentations crediting DeepPhe
The DeepPhe system will be available as part of Apache cTAKES at http://ctakes.apache.org/
DeepPhe software components will also be deployed in the TIES Software System for sharing and accessing deidentified NLP-processed data with tissue(http://ties.pitt.edu/) which is deployed as part of the TIES Cancer Tissue Network (TCRN) across multiple US Cancer Centers.
DeepPhe software development will be coordinated as per software development policies.
DeepPhe Gold Set
- Process for Deidentification of Source Documents.
- Process for Selection of Gold Set Source Documents.
Project materials/ WIKIs to tasks
- Liquid Planner link (project management): https://app.liquidplanner.com/space/26220/dashboard
- Templates for describing stakeholders.
- Software development policies and repositories.
- Data Repository and Policies.
- Adopted Standards and Conventions for NLP annotations (task 1.4.2)
- Gold Set Selection
- Cancer phenotype modeling notes
- FHIR modeling
- Validation of models with domain experts
- Competency questions to be used for validation of models.
- Representations of the models.
- Historical pages
- CEM Cancer phenotype models: models describing the original CEM Models
- How to effectively use LiquidPlanner for DeepPhe: https://www.dropbox.com/s/1f6nkhx3yxh4v9q/LiquidPlanner%20for%20Deep-Phe.pptx
- Bi-weekly team meetings
- Tools we use for communication are listed in our Communications Plan .
- January 6, 2015 Bi-weekly team meeting
- December 9, 2014 BCH team meeting
- December 9, 2014 Bi-weekly team meeting
- November 20, 2014 BCH team meeting
- November 11, 2014 Bi-weekly team meeting
- November 11, 2014 BCH team meeting
- November 4, 2014 BCH team meeting
- November 3, 2014 PI meeting
- October 27, 2014 Bi-weekly team meeting: Avillach's presentation on tranSMART, cTAKES and PCORI
- October 14, 2014 Bi-weekly team meeting: agenda and notes
- September 30, 2014 Bi-weekly team meeting: agenda and notes
- September 2, 2014 Bi-weekly team meeting: agenda and notes
- August 19, 2014 Bi-weekly team meeting: agenda and notes
- August 5, 2014 Bi-weekly team meeting: agenda and notes
- July 22, 2014 Bi-weekly team meeting: agenda and notes
- July 15, 2014 Bi-weekly team meeting: agenda and notes
- July 10, 2014 Hochheiser visit to Savova group
- June 24, 2014 Bi-weekly team meeting: agenda and notes
- June 10, 2014 Bi-weekly team meeting: agenda and notes
- June 3, 2014 All hands kick-off meeting
- May 08, 2014 NCIP collaboration with UT (Bermstram/Xu)
If you need assistance and/or if you have questions about the project, feel free to send e-mail to Guergana.Savova at childrens dot harvard dot edu
Consult the User's Guide for information on using the wiki software.