angel janevski - papaya
DESCRIPTION
PAPAyA: Applications in Oncology Decision SupportTRANSCRIPT
A. Janevski, N. Banerjee, S. Kamalakaran, V. Varadan, N. DimitrovaPhilips Research North AmericaMarch 8, 2011
PAPAyA: Applications in Oncology Decision Support
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011
Clinicalprognostic
indices
Tumor Biology
Drug related
toxicities
Co-morbidity
Patient circumstance
Dx & Therapy
Regiment decisions
2
Augmenting Oncology!
Personalized Tx: the keyto cancer management
Sequencing to bring personalization tests
Most favorable clinical
outcome
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011
Motivation
• Capture clinical and (new) molecular data in an integrative fashion for use by the clinician
• Present clinically actionable information in the context of the patient and relevant data (patient populations, knowledge)
• Input from clinicians: • Need for easy and meaningful access to existing data such as
clinical trials, published literature• Tools to access, quantify and visualize new data types: molecular
profiling, sequencing, underlying biology• Clinicians at institutions of different profiles have different needs• Tools to communicate reasoning to the patient
3
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011 4
PAPAyA for the Clinic
• PlAtform for Personalized Analytics Applications (PAPAyA)• Research/rapid-prototyping software framework enabling context-
sensitive access to bioinformatics and other clinical tools• As a system, PAPAyA comprises definition of the flow of the application,
the user interface, and context-specific tools to explore• We aim to provide:
i. access to and presentation of genomic profilesii. effective and seamless combination of clinical and molecular dataiii. unified and effective user interfaceiv. easy design, prototype and testing environmentv. combination of multiple tool implementation platforms to interact with
data and showcase decision support tools to clinicians
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011 5
Design of PAPAyA
• Tool interface enables flexible integration of research code running on a variety of platforms: R, Matlab, Python, Perl, Web
• Metadata-driven tool specifications and flow control• Uniform and consistent user interface• Principal Architecture:
USER INTERFACE
PRESENTATION
FLOW CONTROL
INTERNAL DATABASES
EXTERNAL DATACLINICAL KOWLEDGE
ACTION
TOOL EXECUTION ENGINE
TOOLSANNOTATION
ANALYSIS RESULTS
CONFIG AND
CONTROL
TOOL CTRL
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011 6
Units of User Interaction
• State: current status of all system components
• Context: current patient data, active tools, and accessed data
• Tool: smallest unit of substantial user action – metadata-defined external plug-ins
• Tool controls: metadata-defined possible iterations with a tool
State S/Context C
Execute Tool T
start
User changes context or state
Tool controls
Tool execution changes context
or state
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011 7
Executing and Interacting With Tools in PAPAyA
Available tools and tool groups
(dynamically updated based on current context)
Active tool
Tool control and interaction
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011 8
Tool Instance: Breast Cancer Subtype
• Extension of traditional histopathology based on disease origin cells
• Breast cancer Subtypes have distinct gene expression signatures +Notable survival differences basedon breast cancer subtypes
• Breast cancer subtypes utilization has traction with clinicians and is already part of some molecular profiling products on the market
• Still, very little indication of the kinds of tools will be needed in the clinic to contemplate data from such patient stratification
Sorlie et al 2003
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011 9
Tool Instance: Breast Cancer Subtype
• Multiple presentations of breast cancer subtype classification:1. Basic subtype assignment2. Comparative presentation of subtype calls3. Relative patient subtype “score” in a broader population4. And beyond …
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011 10
Tool Instance: Breast Cancer Subtype
• Link molecular profile to underlying biology (pathway deregulation with links to literature, targets for therapy, etc)
• Link molecular profile to epidemiological/population data
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011 11
Tool Instance: Clinical Trials
• Tools to integrate the context of the current patient with repositories– Identification of open clinical trials of relevance to current patient.
– Extract the latest clinical trial results of relevance to the current patient to help in decision-making
• Clinicaltrials.gov: open and closed studies with (somewhat) structured records with description, recruitment criteria, sites, funding, and additionally study results
• Multiple presentations of repository data:– Direct link to the web view of clinicaltrials.gov driven by PAPAyA
– Customized interpretation/visualization of the data from the repository –map of relevant clinical trials
– ..
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011 12
Tool Instance: Clinical Trials
Philips Research North America, Angel Janevski, March 8, 2011, AMIA TBI 2011
Conclusions
• Need for tools in the clinic to manage introduction of molecular profiling data particularly in the context of clinically-relevant data
• We presented our software framework PAPAyA:– Test variety of analytics workflows for breast cancer– Prototype novel clinical tools for breast cancer diagnosis and management– Two example tools for access and presentation of clinical and molecular
data: breast cancer subtyping and access to clinical trials information• We continue to develop PAPAyA and new tools for clinical workflow
integration• Future: We anticipate PAPAyA and similar solutions will help clinicians
manage new types data overload, bridge gaps between expert knowledge and common practice and make efficacious decisions
13