Computational Pathology and the Role of Pathology Informatics

Transcription

1 Pathology Informatics Summit 2015 Computational Pathology and the Role of Pathology Informatics Michael J. Becich, MD PhD - [email protected] Chairman, Department of Biomedical Informatics University of Pittsburgh School of Medicine

2 Disclosures of COI for 2015 for MJB Corporate Sponsored Research ZERO (1 st time in 15 years!!!) Startup/Public Companies (Consulting, Royalties/Licensing, Stock - MJB): De-ID Data Corp de-identification software (licensing agreement) Empire Genomics - Scientific Advisory Board ( Omnyx Joint Venture with UPMC and GE ( NinePoint Medical Scientific Advisory Board ( Consultancy (honoraria) Cancer Center Consulting Baylor, CINJ, MD Anderson, Moffitt Cancer Center, Roswell Park Cancer Institute, U Colorado, VCU CTSA Consulting Duke, Emory, MCW, Northwestern, U AK, UC Davis, UCLA, U Chicago, U Cincinnati, U IN, U KY, UC Davis, UMN, UNC, UNM, U WI and Wash U

3 Goals for today s discussion Defining computation pathology Outlining the teaching, research, clinical and work force aspects of comp path Discussing the approach the University of Pittsburgh has taken to embrace comp path Pathology Informatics approach to support computational pathology growth

4 Computational Pathology What is it? Computational Path = Big Data Science Opportunity!!! Volume of Digital Data Generated by Lab think whole slide imaging and genomic sequencing data (terabytes to petabytes) heralds the rise of computational pathology Variety in Pathology it is gross and microscopic imaging, image analysis, genomic testing and lab records produced across the domains of CP and AP and tweeners (e.g. Molecular, Heme Path, Cytogenetics and Microbiomics) Validation Pathology (both AP and CP) is some of the most prized yet hard to get at data in Medicine: Critical for Personalized Medicine, Learning Health Systems, Basic Research and Big Data/Data Science

5 Louis, et. al. 2014, Arch Path Lab Med

6 Definition of Computational Pathology An approach to diagnosis that incorporates multiple sources of data (pathology big data) Presents clinically actionable knowledge (big data to knowledge) Advanced decision support for precision (personalized) medicine Helps to redefine Pathology from an observational to knowledge engineering discipline hence critical to healthcare data science (Louis et al Arch Path Lab Med 2014)

7 Why comp path and why now? The value proposition (the opportunity): Pathology data are increasingly digital and the most detailed and structured data in EMR LIS should be increasingly powerful, flexible and integrated allowing more complex analyses Pathologists must have access to EMR data to practice effectively and should drive decision support in partnership with oncology/medicine Large scale clinical phenotyping data are a major clinical/population health asset and it s action can be fueled by Pathology (Louis et al Arch Path Lab Med 2014)

8 (from Louis PPT at July 2014 meeting)

9 (from Louis PPT at July 2014 meeting)

10 (Louis et al Arch Path Lab Med 2014)

11 (from Louis PPT at July 2014 meeting)

12 (from Louis PPT at July 2014 meeting)

13 (from Louis PPT at July 2014 meeting)

14 (in press, Arch Path Lab Med 2015)

15 Primer for Pathology Informatics Growth Level 1 Priming the Clinical Practice Engine Supporting the Anatomic Pathology Laboratory Information System Supporting Clinical Pathology Laboratory Info System integration w/ EMR Level 2 Clinical Practice Connectivity and Quality Triad Interfaces and Integration Issues with EMR, QA/QC/QI programs, etc Synoptics, report generation, remote computing, imaging and NGS support. Level 3 Supporting the Instructional Mission Path Info Fellowships, MOOCs, Clinical Informatics Boards, Biomed Info Level 4 Supporting Research Information Services Tissue banking, imaging, computable phenotyping and -omics Level 5 Developing an Innovative Research Program Bioinformatics, imaging, decision support, cancer informatics, outcomes

16 The Value of Pathology Data Pathology is uniquely situated in medicine and provides objective/quantifiable data via electronic repositories 70/70 Rule 70% of Data in Electronic Medical Records 70% of Critical Decisions involve a Blood/Fluid Test or Biopsy Genomic/Personalized Medicine trends indicate the time spent on actual testing will decrease and the amount spent on data analysis will increase This will shift the emphasis from reporting up to dozens of individual values to analyzing a profile in the context of thousands of values This will place tremendous value on warehousing data and being able to efficiently datamine archives of Pathology Big Data Tremendous research opportunity across the NIH (and not just cancer pay attention to microbiomics)

17 Comp Path for Bioinformatics & Translational Research Tissue Banking Pathologic Analysis Bioinformatics (cabig!!!) De-ID Microarrays & Proteomics Quantitative Image Analysis Cancer Outcomes Comp Path is critical in Bioinformatics for: Genomic/Microbiomic Analysis Critical Linkages to Outcomes TMA production for Biomarkers Therefore the importance of tissue/ serum banks has never been higher for Genomics, Proteomics and Bioinformatics and Comp Path Research resources development successes thus far include: NHGRI Big Data to Knowledge NCATS CTSA Program NCI Cancer Center Support Grants NCI SPOREs NHLBI SCCORs

18 The Role of Comp Path in Omics/Biomarker R&D Comp Path Pathology = Computable Phenotype Computational Pathology (Comp Path) Pathologic, Genomic, Proteomic Analysis on Patient s Tumors Clinical Data Extraction & Deep Phenotyping (eg. TIES, TCRN, CDP) Biomarker Bioinformatics: Understanding and mapping DNA, RNA and proteins in the context of disease to create new biomarkers Comp Path Genomics/NGS Personalized Medicine Proteomics (and Microbiomics)

19 Text Information Extraction System (TIES) TIES Cancer Research Network (TCRN) Funded in first round of NCI Informatics Program U Grants Builds on success of TIES natural language processing system for supporting translational research Extends system to develop a data sharing and tissue sharing network among cancer centers Potential for developing a national collaborative cancer research network

20 TIES System Architecture TIES System Architecture 20

21 TCRN U24 Specific Aims Specific Aim 1. Enhance the informatics technology to support inter-institutional trust, paraffin registry development, tissue microarray (TMA) development, and nondestructive tissue use Specific Aim 2. Establish the TIES Cancer Research Network (TCRN) with four founding member institutions. Develop governance, network agreements, and policies for operating the TCRN Specific Aim 3. Recruit and support pilot scientific collaborations across the network, especially focused on personalized medicine Specific Aim 4. Disseminate the software/measure impact.

22 TCRN Workflow

23 API - TMA Data Exchange and TCRN TMA slide set contains 1) Cancer tissue from radical prostatectomy specimens of 299 patients 2) Control non-neoplastic tissue from benign prostatic hyperplasia (BPH) 3) Control non-diseased tissue from organ donor prostates 4) Cores from prostate cancer cell lines 5) TMA XML Data Exchange Format TMA XML Data Exchange Format for Core N52 <record> <IMS_Case_Identifier> </IMS_Case_Identifier> <Location_Code>N52</Location_Code> <Race>Caucasian</Race> <Year_of_Birth>1923</Year_of_Birth> <Year_of_Diagnosis>1991</Year_of_Diagnosis> <Year_of_Prostatectomy>1992</Year_of_Prostatectomy> <Is_Residual_Carcinoma_Present>Yes</Is_Residual_Carcinoma _Present> <Most_Prominent_Histologic_Type>adenocarcinoma NOS aka acinar</most_prominent_histologic_type> <Gleason_Primary_Grade>4</Gleason_Primary_Grade> <Gleason_Secondary_Grade>3</Gleason_Secondary_Grade> <Gleason_Sum_Score>7</Gleason_Sum_Score> <Number_of_Nodes_Examined>11</Number_of_Nodes_Examine d> <Number_of_Nodes_Positive>0</Number_of_Nodes_Positive> <Distant_Mets 1_at_Time_of_Diagn>None</Distant_Mets 1_at _Time_of_Diagn> <pt_stage>pt3a</pt_stage> <pn_stage>pn0</pn_stage> <pm_stage>pm0</pm_stage> <Vital_Status>Alive</Vital_Status> <Year_of_PSA_Recurrence></Year_of_PSA_Recurrence> <PSA_Recurrence_Status>Unknown</PSA_Recurrence_Status> <Recurrence_Free_Year></Recurrence_Free_Year> </record>

24 Cancer Deep Phenotyping NCI U24 Funded in second round of NCI Informatics Program U grants Collaboration between UPCI (Crowley) and Harvard Boston Children s (Savova) with cross-upci collaboration (Melanoma and Ovarian SPOREs) Open source software built on foundation of two mature products Develop new methods for extracting cancer phenotype information from electronic medical records using Natural Language Processing Focus on extracting variables needed for Personalized Medicine research teams and future decision support PLUS = CDP for EHR

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29 Causal Network Discovery = Computational Pathology A probabilistic network approach to uncover genetic drivers of melanoma using data on copy number variation and gene expression* Akavia UD, et al. Cell 143 (2010) (The figure above appears in this paper)

30 U of Pitt NGS integration by Path Info w/ Molec Path Roy S, Durso MB, Wald A, Nikiforov YE, Nikiforova MN. SeqReporter: automating next-generation sequencing result interpretation and reporting workflow in a clinical laboratory. J Mol Diagn Jan;16(1): Progress to date: Implemented targeted NGS sequencing system to manage the workflow for Molec UPMC Doing targeted panels on solid tumor - >450 cases completed and reported Not Using a Commercial Solution - SeqReporter Feeds 3 rd party system and customized reporting content to CoPath Supported by UPMC ISD and UPP/U Pitt Pathology Informatics Division On Path Info Summit website Wednesday Selected Abstract Session

31 Computational Pathology Algorithms like those in CCD for imaging and genomics are key enablers!!! 1000 Facts per Decision 100 Proteomics and other effector molecules Functional Genetics: Gene expression profiles 10 Human Cognitive Capacity 5 Structural Genetics: e.g. SNPs, haplotypes Decisions by Clinical Phenotype From William Stead: &

32 U.S. Healthcare: Missed Opportunities, Waste, and Harm (from Dighe PPT at July 2014 meeting)

33 Conclusions - Value of Path Data & Comp Path Pathology is uniquely situated in medicine and provides objective and quantifiable data via electronic repositories Leverage the position we have (70/70 Rule) into leadership role in enterprise clinical datamining and datawarehousing Partner with EMR (text repository) & Personalized Medicine/EDW efforts Develop a strategy for funding efforts (Extra- and Intramural support as well as corporate sponsorship are all critical - resource intensive) As we enter the Era of Genome/Microbiomic Medicine, the time spent on actual testing will decrease and the amount spent on data analysis will increase This will shift the emphasis from reporting up to dozens of individual values to analyzing a profile in the context of hundreds to thousands of values THIS IS A REAL RESEARCH OPPORTUNITY!!! This will place tremendous value on warehousing data and being able to efficiently datamine archives of Pathology data. Focus on changing system architectures and research IT support to unlock this value for your Pathology Department.

34 Computational Pathology Fellowships Why this new fellowship is key! Pathology Informatics is now established as a division or subspecialty in may practices This is perceived as a service component to Pathology Practice the Information Technology component Academic Path Info is emerging as Comp Path Pathology Departmentts can have a greater impact through defined research, data science and software development focus to enable precision (personalized) medicine This is the true Informatics component of Path Info These fellows will help build the research and data science leaders Job future extremely bright!!!

35 End of Talk me at if you have questions/clarifications not covered in the discussion. NOTE: me if you want PDFs of articles or presentation. Thank you for attending Pathology Informatics 2015 Please support Path Info, API & JPI See you in 2016!!!

36 Pathology Informatics th Annual Conference May th 2016 Pittsburgh, PA Anatomic Pathology, Imaging & Internet AP and CP Informatics Oncology & Bioinformatics Imaging Informatics Radiology and Pathology Personalized Medicine and Pathology Informatics Future LIS for Pathology Computational Pathology and the Path Ahead?

37 Association for Pathology Informatics (API) to advance the field of pathology informatics as an academic and a clinical subspecialty of pathology Slide 37

38 Journal of Pathology Informatics Co-Editors Liron Pantanowitz, MD PhD and Anil Parwani, MD PhD Please support JPI, API and Pathology Informatics as the Home for Digital Pathology - Great Academic and Strategic Partnership with Multiple Benefits!!!