Big Data Part I: Data-Driven Life Sciences Innovation, Personalized Medicine and Research

Transcription

1 Big Data Part I: Data-Driven Life Sciences Innovation, Personalized Medicine and Research December 9, Boston Brussels Chicago Düsseldorf Frankfurt Houston London Los Angeles Miami Milan Munich New York Orange County Paris Rome Seoul Silicon Valley Washington, D.C. Strategic alliance with MWE China Law Offices (Shanghai) 2014 McDermott Will & Emery. The following legal entities are collectively referred to as "McDermott Will & Emery," "McDermott" or "the Firm": McDermott Will & Emery LLP, McDermott Will & Emery AARPI, McDermott Will & Emery Belgium LLP, McDermott Will & Emery Rechtsanwälte Steuerberater LLP, McDermott Will & Emery Studio Legale Associato and McDermott Will & Emery UK LLP. These entities coordinate their activities through service agreements. McDermott has a strategic alliance with MWE China Law Offices, a separate law firm. This communication may be considered attorney advertising. Prior results do not guarantee a similar outcome.

2 Faculty Matthew Hawryluk, Ph.D., Senior Director, Corporate & Business Development, Foundation Medicine Inc. Jennifer C. King, Ph.D., Director of Data Governance for CancerLinQ, American Society of Clinical Oncology Terence Hogan, Head of Law & Compliance, Digital Health, Amgen Amy Hooper Kearbey, Partner, McDermott Will & Emery LLP Moderator Jennifer S. Geetter, Partner, McDermott Will & Emery LLP 2

3 Big Data Part I: Data-Driven Life Sciences Innovation, Personalized Medicine and Research Matthew Hawryluk, Ph.D. Senior Director, Corporate & Business Development Foundation Medicine Inc. 3

4 Molecularly Targeted Therapy Is Evolving 4

5 From Discovery To Clinic In ~1 Year From Test (2/2012) To Treatment (3/2014) Matching the correct targeted therapy to the correct patient is diagnostically challenging as the number of clinically relevant genomic alterations increases 5

6 Diagnostic Challenge: The Number Of Clinically Relevant Cancer Genes Across Solid Tumors Is High Clinically relevant genes in non-small cell lung cancer Base Substitution: ALK, AKT1, AKT2, AKT3, ATM, BRAF, BRCA1, BRCA2, CDKN2A, EGFR, ERBB2, FGFR1, FGFR2, GNA11, GNAS, KRAS, MAP2K1, MAP2K2, MET, NF1, NOTCH1, NRAS, PIK3CA, PIK3R1, PIK3R2, PTCH, PTEN, STK11, TSC1, TSC2 Short Insertion/Deletion: ATM, BRCA1, BRCA2, CDKN2A, EGFR, ERBB2, MET, NF1, NOTCH1, PIK3R1, PIK3R2, PTCH, PTEN, STK11, TSC1, TSC2 Focal Amplification: AKT1, AKT2, AKT3, CDK4, CCND1, CCND2, CCNE1, EGFR, ERBB2, FGFR1, FGFR2, KRAS, MDM2, MET Homozygous Deletion: BRCA1, BRCA2, CDKN2A, NF1, NOTCH1, PIK3R1, PIK3R2, PTCH, PTEN, STK11, TSC1, TSC2 Gene Fusion: ALK, RET, ROS1 6

7 Diagnostic Challenge: Low tumor purity in many clinical specimens requires diagnostic tests with high accuracy Number of Mutations Mutant allele frequencies N=107 clinically relevant somatic mutations in FFPE non-small cell lung cancer specimens *Purity = relative proportion of extracted DNA originating from tumor cells Capillary sequencing would have missed over half the mutations in this study as 20% allele frequency is the lower limit of detection Mutant Allele Frequency Mutant Allele frequency spectrum of known mutations found in a series of clinical samples Fraction of mutations <5% Fraction of mutations <10% Fraction of mutations <20% Fraction of mutations <25% Fraction of mutations <50% Fraction of mutations <100% 11% 32% 55% 67% 93% 100% 7

8 Diagnostic Challenge: Many clinical cancer specimens are small needle biopsies, FNAs and cell blocks Formalin fixation and subsequent storage can damage nucleic acids Percutaneous needle biopsy of lung nodules under CT fluoroscopic guidance Sample preparation needs be optimized to maximize accuracy and isolate sufficient material for diagnostic testing from tiny specimens 8

9 Number Of Targeted Therapeutics Rising ROS1 RET KRAS FBXW7 VEGF/VEGFR STK11 AURKA CDK4 DDR2 CCND1 ERBB3 DNMT3A GNAQ BRCA1 BRAF Target Markers CDK6 TSC1/2 NOTCH1 MET HER2 PTEN PIK3CA FGFR1 FLT3 TSC2 CDKN2A NF1 IGF1R AKT1 ALK GATA3 RAF1 TNF IGF/IGFR MAP2K1 KDR IDH1/2 Coming Soon ~700 compounds hitting ~150 targets in development Years 2025 Knowing which tests to order for a tumor type increasingly challenging 9

10 FMI Solution: FoundationOne & FoundationOne Heme Breakthrough Breadth and Sensitivity Less Time, Cost and Tissue Ordered by Oncologist Sent by Pathologist/Hematologist Laboratory developed tests Work in routine, realworld setting Require small amounts of routine tissue Turnaround time of ~12-14 days for FoundationOne and ~15-18 days for FoundationOne Heme Launched Updated Version August 2014: 314 genes, all 4 classes of alterations: Base substitutions Insertions and deletions Copy number alterations Rearrangements Launched December 2013 with genes relevant to target hematologic malignancies: 405 genes for DNA Seq 265 genes for RNA Seq Same DNA performance Enhanced fusion detection Results delivered through our Interactive Cancer Explorer >99% sensitivity and specificity to call mutant alleles at 5-10% frequency for each test >99% sensitivity and >95% specificity to detect gene fusions at 20% frequency for FoundationOne Heme 10

11 FMI Value Thesis: Three Significant Opportunities Molecular Information Platform Clinical Every Dataset Adds to Value Sticky experience for physicians/pharma User interface to translate vast amount of data BioPharma Ordered by physicians across more than 40 countries Medical Literature Data External Clinical Data Future opportunities in cancer management >20 partnerships >100 clinical trials in 2014 Value delivered through testing and molecular information delivery 11

12 ICE: Creating and Maintaining Network Effect Interactive Cancer Explorer - the FMI physician portal developed in consultation with Google Ventures Delivers FoundationOne results, including click-throughs to associated scientific references and clinical trials Enables physicians to integrate our solutions into routine clinical practice Developing expanding functionality to enable sharing of treatment and clinical outcomes data Provides additional valuable information to inform physician decision making Promotes physician interaction and creates a community of oncologists Building dedicated Product Development team Interactive Cancer Explorer creates a dynamic network effect, affording optimal care decisions while fostering a collaborative peer environment 12

13 Big Data Part I: Data-Driven Life Sciences Innovation, Personalized Medicine and Research Jennifer S. King, Ph.D. Director, Data Governance and Data Services for CancerLinQ American Society of Clinical Oncology 13

14 Not enough data 1.7 MM people diagnosed with cancer in the US Only3% 3% enroll in clinical trials. 14

15 Data is inaccessible 1.7 MM people diagnosed with cancer in the US 97% of patient data locked away in unconnected files and servers 15

16 Clinical trial patients tend to be younger healthier and less diverse less diverse than most of the patients oncologists care for every day. 16

17 New understanding = more complexity 1986 One disease molecular drivers and more to be discovered 17

18 Information Overload In 2013, Medline added 734,052 citations Assume just 1% of that new literature is relevant to a doctor's practice If a doctor reads 2 articles per night..they would still be 10+ years behind Credit: Dan Masys 18

19 will unlock a universe of practical insights to improve the care of every patient with cancer. 19

20 Quality Improvement System: High-quality, personalized care for every patient 20

21 Improving Quality for All Stakeholders The primary purpose of CancerLinQ is to improve the QUALITY of care and to enhance outcomes; additional benefits include: For Patients: Improved outcomes Clinical Trial matching Safety Monitoring Real time side effect management Patient Reported Outcomes For Providers: Real time second opinions Observational and guideline-driven Clinical Decision Support Real time access to resources at the point of care Quality reporting and benchmarking For Research/Public Health: Mining big data for correlations Comparative Effectiveness Research Hypothesis generating exploration of data Identifying early signals for adverse events and effectiveness in off label use 21

22 HIPAA & Healthcare Operations The HIPAA definition of healthcare operations includes quality assessment and improvement activities This is only when NOT obtaining generalizable knowledge as a primary purpose Business Associate Agreement required, the organization is a covered entity under the agreement. 22

23 Example: QI Report from CancerLinQ Prototype Overall practice score Can drill down by patients, see parameters Can view scores by provider to determine an intervention 23

24 Example: ESA Usage in 8,300 Breast Cancer Cases (using de-identified data) Percentage of Cycles Median Hb level 24

25 Data Integrity Using the data appropriately is critical Different use cases have different data quality needs and need to be addressed differently Clinical Quality Reports: specific field attributes need to be as clean and complete as possible Generation/Research: big data view, if you have a huge amount of data, not everything has to be correct This is a challenge but needs to be carefully addressed for each use case 25

26 Protecting Privacy Physicians and practices will be able to access PHI from their patients only Learning, trend-analysis, research, and guideline development will only be done on redacted data sets 26

27 Data Stewardship There are multiple other stewardship considerations to take into account in this type of system Physician Privacy Practice-level data identifying competitors Proper usage of Quality Improvement reports Patients who see multiple providers at different locations 27

28 Big Data Part I: Data-Driven Life Sciences Innovation, Personalized Medicine and Research Terence Hogan Head of Law and Compliance, Digital Health Amgen E: 28

29 29

30 What Exactly is Privacy? Right to control what and how personal information is processed Includes collection, use, sharing, storage and destruction 30

31 What is Personal Information? Personal Information Any information relating to an individual whose identity is apparent, or can be ascertained from the information, by direct or indirect means. Here are some examples: Name Telephone numbers Government Identification Information Home or Business Address Address Bank account information 31

32 Foundation of Privacy: Fair Information Practices Eight Fundamental Principles for the Collection of Personal Information (PI) 1. Collection Limitation 2. Data Quality 3. Purpose Specification 4. Use Limitation 5. Security Safeguards 6. Openness 7. Individual Participation 8. Accountability 32

33 Genomic Data s Impact on Privacy Analysis 33

34 Applicable Laws, Regulations and Standards 34

35 Big Data Part I: Data-Driven Life Sciences Innovation, Personalized Medicine and Research Amy Hooper Kearbey Partner McDermott Will & Emery LLP E: 35

36 Federal Regulation of Research HIPAA (OCR) Common Rule (OHRP) NIH Research FDA But don t forget about States! Federal Privacy Act v

37 Definition of Research Common Rule: Research means a systematic investigation, including research development, testing and evaluation, designed to develop or contribute to generalizable knowledge HIPAA: Research means a systematic investigation, including research development, testing, and evaluation, designed to develop or contribute to generalizable knowledge 37

38 Research under the Common Rule Common Rule applies to research involving human subjects Research means a systematic investigation, including research development, testing and evaluation, designed to develop or contribute to generalizable knowledge Human Subject means a living individual about whom an investigator (whether professional or student) conducting research obtains (1) data through intervention or interaction with the individual, or (2) identifiable private information Private Information is identifiable if the identity of the subject is or may readily be ascertained by the investigator or associated with the information 38

39 Research Under HIPAA HIPAA recognizes research but provides special pathways for: Activities preparatory to research Activities that are quality improvement, not research Activities using limited data sets 39

40 Research Under HIPAA Special Pathway: Prep to Research No patient authorization is required for a review of PHI by a researcher if the review is necessary to prepare a research protocol or for similar purposes preparatory to research Requires Covered Entity to obtain representations from the researcher that the review is for activities that are preparatory to research, no PHI will be removed from the Covered Entity, and the PHI is necessary for the research purposes 40

41 Research Under HIPAA Special Pathway: Quality Improvement Activities Defined as health care operations when primary purpose is not obtaining generalizable knowledge Permits a Covered Entity to use/disclose PHI for quality improvement activities without patient authorization Business Associate can be engaged by Covered Entity to support these activities by engaging in a compliant Business Associate Agreement 41

42 Research Under HIPAA Special Pathway: Limited Data Sets HIPAA permits research using a Limited Data Set without patient authorization Requires compliant Data Use Agreement Limited Data Set must exclude the following identifiers Names Postal Street Address Telephone/Fax numbers addresses SSN Medical record number Health plan number Account number Certificate/license # Vehicle identifier/serial # Device identifier/serial # URL IP address Biometric identifiers (finger/voice prints) Photos Derivatives (like initials) 42

43 Consent and Authorization for Future Use Common Rule Consent for future, unspecified uses permitted HIPAA Requirement: valid authorization must establish the purpose for any use or disclosure of PHI Prior to 2013, OCR interpreted purpose to require that authorizations be study-specific As of 2013, OCR modified interpretation to permit authorization for future research uses Application: Authorization for future research purposes must adequately describe such purposes such that it would be reasonable for the individual to expect that his/her PHI could be used/disclosed for future research. 43

44 Strategy: Finding the Right Balance RESEARCH PARTICIPANT S WILLINGNESS TO AUTHORIZE Balance Point SPECIFICITY AS TO FUTURE USES 44 44

45 Deceased Persons Common Rule does not apply to deceased persons Human subject is defined as a living individual HIPAA regulates use and disclosure of PHI belonging to a deceased person Information is PHI until 50 years after person s death Covered Entity must obtain from researcher Representation that use/disclosure is sought solely for research on PHI of decedents and that the PHI is necessary for research purposes Documentation of death (if requested) 45

46 Example: Research Repositories Step 1: Create the Repository Key Research Moments Use and disclosure of data to create a repository for future research studies Consent/Authorization needed (or waiver) if fully-identifiable Can cover future, unspecified uses (i.e. Step 3) Step 2: Manage the Repository Step 3: Withdrawal for Research Key Research Moments Querying the repository to determine whether there is sufficient data to support a particular research study Using data to create data sets for specific research study Key Research Moments Disclosure of data set from repository to the researcher Under HIPAA, these activities may qualify as prep to research or health care operations Under Common Rule, would likely be considered research activities Under both HIPAA and the Common Rule, this may be research depends on the identifiability of the data disclosed 46

47 Prep to Research Covered Entity may disclose PHI to a researcher without an Authorization or Waiver for a review of PHI necessary to prepare a research protocol or for similar purposes preparatory to research if the Covered Entity obtains certain representations from researcher: Use of PHI is sought solely for prep to research activities PHI sought is necessary for the research purposes PHI will not be removed from the Covered Entity during or after the review 47

48 Prep to Research in a Digital Environment What does removal mean in a digital environment? 48

49 Consumer-Generated Health Information What is it? Data generated by a consumer s use of a mobile app, website, or another digital service that relates to his/her health. How is it regulated? Typically not HIPAA Federal Trade Commission (FTC) 49

50 Consumer-Generated Health Information Section 5 of FTC Act: FTC has broad power to enjoin unfair and deceptive business practices FTC has used this power to bring actions against businesses operating digital services that were not transparent about how and what information is collected from consumers and how the collected information is used, shared, and secured. Similar statutes in many statutes 50

51 Future of De-Identification: Common Rule Advance Notice of Proposed Rulemaking published June 26, 2011 by OHRP Proposal: Research involving biospecimens requires written informed consent, even if de-identified Rationale: Advances in genetics may undermine the concept of de-identification as it relates to biospecimens 51

52 Future of De-Identification: HIPAA Two methods for De-Identification under HIPAA Statistical Method: Statistician determines risk is very small that information could be used, alone or in combination with other reasonable available information, to identify an individual Safe Harbor: Remove 18 specified identifiers 52

53 Future of De-Identification: HIPAA 18 Identifiers Removed Under Safe Harbor Method Names Geographic subdivisions smaller than a state (address, zip code) Elements of dates except year (birth date, service date) directly related to individual Telephone/Fax numbers addresses SSN Medical record number Health plan number Account number Certificate/license # Vehicle identifier/serial # Device identifier/serial # URL IP address Biometric identifiers Photos Other unique characteristics 53

54 Future of De-Identification: FDA Regulation does not turn on identifiability De-identified biological specimens used in FDAregulated research are still considered human subjects Exception: FDA exercises enforcement discretion for informed consent for in vitro diagnostic device studies involving de-identified human specimens, but still requires IRB oversight and other protections 54

55 Future of De-Identification Consumer-Generated Health Information Is true anonymization possible? White House Big Data report (May 2014): Anonymization of a data record might seem easy to implement. Unfortunately, it is increasingly easy to defeat anonymization by the very techniques that are being developed for many legitimate applications of big data. In general, as the size and diversity of available data grows, the likelihood of being able to re identify individuals (that is, re associate their records with their names) grows substantially. What can businesses promise consumers? 55

56 Questions & Answers 56

57 Digital Health: The New Dynamics Save the Date Big Data Part 2: Data-Driven Changes to Payment Models, January 13, 2015 Mobile Health & Telehealth: Mobile and Telehealth Technology Create New Business Opportunities, February 10,

58 Speaker Biographies 58

59 Speaker Biography: Matthew Hawryluk, Ph.D. Dr. Matthew Hawryluk contributes to the Foundation Medicine team as the Senior Director of Corporate & Business Development. In this role, Dr. Hawryluk negotiates strategic corporate & business development transactions, manages the operational partnerships with pharmaceutical companies and academic medical centers, co-leads the companion diagnostic and regulated products strategy and also leads the molecular information strategy. Matthew has been with Foundation Medicine for almost 4 years. Senior Director, Corporate & Business Development Foundation Medicine Inc. E: mhawryluk@foundationmedicine.com Dr. Hawryluk completed his undergraduate training in biochemistry at the University of Notre Dame. As a doctoral researcher, Dr. Hawryluk studied Cell Biology and Protein Biochemistry and earned his Ph.D. from the University of Pittsburgh School of Medicine. He completed his M.B.A. at Carnegie Mellon University s Tepper School of Business, as a Swartz Entrepreneurial Fellow with academic concentrations in finance, marketing, strategy, and organizational behavior. 59

60 Speaker Biography: Jennifer C. King, Ph.D. Jennifer C. King, PhD, is the Director of Data Governance and Data Services for CancerLinQ, at the American Society of Clinical Oncology (ASCO). Jennifer leads the data governance programs for CancerLinQ, a learning health care system for oncology, and she is establishing the direction and managing the implementation of the CancerLinQ s data request and utilization process. Director, Data Governance and Data Services for CancerLinQ American Society of Clinical Oncology E: jennifer.king@asco.org Jennifer has a Ph.D. in Molecular Biology from Massachusetts Institute of Technology and a Bachelor of Science degree in Biology from Duke University. She spent five years as a postdoctoral research fellow in translational cancer research studying targeted therapeutics and mouse models of cancer, at both University of California, Los Angeles, and Memorial Sloan-Kettering Cancer Center. After that, Jennifer spent five years at the Conquer Cancer Foundation of ASCO, acting as the Associate Director and Scientific Reviewer for the Grants and Awards Division. She is a member of ASCO, AACR, and AAAS, and serves as an Officer of the Board for a nonprofit childcare center. 60

61 Speaker Biography: Terence Hogan Head of Law and Compliance, Digital Health Amgen E: Terence Hogan is the Head of Law and Compliance for Amgen s Digital Health business unit. In this role, he advises his clients on matters of US FDA regulatory concepts in the medical devices, digital applications and Human Factors, as well as counsel on market research and appropriate online interactions with HCPs and/or patients. In addition, he helps the team develop sound privacy and data protection strategies for projects utilizing large-scope medical or personal data. Over the course of his career, he has helped several multi-national company develop strong health-care compliance programs and policies, Part 11 compliance programs and has assisted in developing programs to protect electronic clinical data. Terence has a Juris Doctorate from Seton Hall University School of Law and a Masters of Science in Biotechnology from Johns Hopkins. 61

62 Speaker Biography: Amy Hooper Kearbey Amy Kearbey is a partner in the law firm of McDermott Will & Emery LLP and is based in the Firm s Washington, D.C. office. She focuses her practice on complex regulatory counseling to health care organizations including providers, manufacturers, suppliers, and professional societies. Amy s regulatory practice covers a range of health care topics, including fraud and abuse laws, Medicare reimbursement, and data privacy. Partner McDermott Will & Emery LLP T: E: akearbey@mwe.com Amy counsels clients on compliance with Federal health care program requirements, including the Stark Law and the Anti-Kickback Statute. Her work includes prospective counseling in connection with proposed transactions and arrangements as well as defending clients in enforcement actions, including False Claims Act litigation. Amy also counsels providers and pharmaceutical, biotechnology and device manufacturers and suppliers on Medicare coverage, coding, and reimbursement issues. She has experience advising on compliance issues as well as developing and implementing strategies to address specific issues in administrative rulemaking processes, including legal challenges to administrative actions through litigation. She has also represented clients in Provider Reimbursement Review Board proceedings. Amy also has extensive experience advising clients on data privacy issues, with a particular emphasis on issues associated with data registries. She counsels clients on all aspects of the development and operation of data registries, including requirements under federal and state privacy laws and data governance policies and procedures. Amy has experience with a range of federal and state privacy laws, including the Health Insurance Portability and Accountability Act (HIPAA) and the Patient Safety Act. 62

63 Speaker Biography: Jennifer S. Geetter Jennifer S. Geetter is a partner in the law firm of McDermott Will & Emery LLP and is based in Firm s Washington, D.C., office. She has been ranked by Chambers USA as a leader in her field. Jennifer is a frequent speaker and author on areas within her practice, including life sciences and biomedical innovation, financial relationships and aggregate spend, data sharing strategies and data privacy and security. Partner McDermott Will & Emery LLP T: E: jgeetter@mwe.com Jennifer routinely advises global life sciences and health care clients on legal issues attendant to biomedical innovation. Her clients include a broad range of pharmaceutical, device, health plan, institutional health care provider, and others. Her work on behalf of these clients focuses her practice on emerging biotechnology and safety issues, research design and compliance, research program structure and operational and compliance infrastructure, personalized medicine, formulary compliance, scientific review and research misconduct proceedings, and emerging issues in the future, unspecified use of biospecimens and data. Jennifer advises companies on global privacy and data security laws. She regularly advises health care companies regarding compliance with HIPAA, the Privacy Act of 1974, and other federal and state health information privacy laws. She prepares enterprise-wide privacy and data security programs and policies for multinational businesses, including businesses with portfolio companies spanning multi-regulatory environments, and regularly counsels businesses regarding the collection, use, retention, disclosure, transfer and disposal of personal information. Jennifer helps companies proactively protect private information and, in the event of a breach, she helps clients respond and remediate. 63