UCSF Informatics Day 2014 - Sue Dubman and Alexandra Solomon, "Innovative informatics at the UCSF Breast Care Center"

UCSF INFORMATICS DAY10 June 2014 1

UCSF INFORMATICS DAY: INNOVATIVE INFORMATICS AT THE UCSF BREAST CARE

CENTER

10 June 2014

Sue Dubman, Director IT & InformaticsCarol Franc Buck Breast Care Center

Alexandra Solomon, Athena & Applied Genomics Program Manager


Outline

The UCSF Breast Care Centero Leadershipo Mission and Visiono Key Programso Organizational Framework

UCSF Breast Care Center Integrated Platform

Use Caseso I-SPY2: Accelerating new treatments to marketo Athena: Integrating research and care

Q&A


UCSF Breast Care Center Leadership

Laura Esserman, M.D., M.B.A.

Director, UCSF Carol Franc Buck Breast

Care Center

Internationally-recognized Surgeon and

Breast Cancer Oncology Specialist

Visionary behind Informatics-enabled

research and care programs

Identified by San Francisco Chronicle as

a “Bay Area Person to Watch” in 2014

“A force of nature”

Laura Van’t Veer, Ph.D.

World renowned Molecular Biologist

Called by Cancer World, the “Person Behind

Personalized Medicine”

Inventor of MammaPrint, a diagnostic test that

foretells the risk of recurrence for breast

cancer patients

Winner, 2014 EU Prize for Women Innovators

Co-PI with Dr. Laura Esserman on one of key

BCC Programs enabled by Informatics


Mission and Vision for the UCSF’s BCC

• Total Quality Management (TQM)• Improve outcomes and quality of life for

Breast Cancer Patients• While reducing overall costs

Mission

• Transform• The way we do research• The way care is provided• How patients, providers, payers and other

stakeholders interact with each other and the health care system

Vision


EMREHR

Patient reported data

Clinical trial management & adaptation

Clinical trial matching

Individualized Connected

Care

Clinician entered and

verified

UCSF Breast Care Integrated Platform


Achieving the Promise of Total Quality Management:Requires lots of coordination at each juncture through development, discovery and delivery, from bench to bedside and back

UCSF INFORMATICS DAY10 June 2014 7UCSF INFORMATICS DAY10 June 2014 7

I-SPY 2 CLINICAL TRIALA Replicable Model for

Accelerating Drug Development and Approval


What problem are we solving?

New oncology drugs take 10-15 years to reach patients

Price tag is $1+ billion

Absence of innovation in trial design/data collection tools

Cancer is a subset of diseases

Blockbuster approach won’t work

Current path is UN-SUSTAINABLE


I-SPY’s Primary Aim: Accelerate Pace of Progress

Key Goals include:

Implement efficient trial designs with use of biomarkers and/or surrogate endpoints to drive knowledge turns

Increase therapeutic agents tested with a standing trial and extensive network of clinical sites

Integrate the processes of clinical care and research, both technologically and culturally with team approach


I-SPY’s Acceleration ofKnowledge Turns

Promising qualifying biomarker

I-SPY 2 TRIAL amendment

approved

Continuous enrollment

Drug graduates or

is dropped Full Approval

AcceleratedApproval for

Agent/Approval for

biomarker/PMA

SCREENING PHASE

Adapts on drugs(~60 patients)

Agent EntersNew agent/combination qualifies and

is approved for I-SPY 2

pCR not confirmed

Agreement on candidate

marker

File IDE

Analysis of biomarker data

Feedback to consortium

BIOMARKER PHASE

3 YR RFS confirms pCR

result

pCR signal confirmed

Surgical Therapy to

Confirm pCR

Enroll, randomize on

qualifying biomarkers

Identify next agent combination

CONFIRMATORY PHASE

Adapts on biomarker(~300 patients)


22 Participating Trial Sites, Expanding to CanadaScreening 40+ patients per month


Clinical Trial Data Capture – Advances with TRANSCENDAn integrated modular platform to support adaptive clinical

trials like I-SPY 2 TRIAL

Structured, coded eCRFs with source documents attached to CRF in Electronic Data Capture system o Enable real-time, remote source data verification within EDC

Randomization as an automated web serviceo Using data that has been source data verified

Combining evaluation of drugs and biomarkers togethero Scientists need access to data early and in an integrated fashion

(one stop shopping)• Clinical, Pathology, Imaging data along with biomarker

data of various types (microarray, sequencing, etc.)


TRANSCEND PlatformData Flow

Key Features of TRANSCEND Scalability with Salesforce, cloud-based environment Modular, can securely integrate with other applications as well

RE App

Quartz Scheduler

THE Force on SalesForce (Electronic Data Capture)

Data Coordinating Center

Study SitesCase Report

Forms

Agendia

caIntegrator (Role based access

to data)

caArray

Research Labs

AutomatedManual

Integration Engine(MirthConnect)

SMART Randomization

Engine

-Single Sign On-Pulls data from caArray

NBIA (Imaging with AIM tool)

Reporting (Pentaho)


Advantages of Adaptive Design

Learn if the drug works better or worse than you think, as the trial progresses

Act earlyo Drop drugs quickly if they are ineffective or harmfulo Graduate sooner if they are clearly beneficial

Learn, for each drug, which biomarkers are optimal

Phase 2 conclusions will be more accurate, better treatment of patients in the trial

Follow on 3 trials can be smaller (usually)


I-SPY 2 Major Accomplishments

Demonstrated that endpoints work better by subtype

Enlisted multiple pharma companies into same trial

Developed I SPY 2 infrastructureo IT systems to support adaptive learningo New methods to distribute credit

Accelerated Approval guidance issued by FDA

Next Step: I-SPY 3 international confirmatory trial


I-SPY 2/3 Partnership Opportunities

Collaboration with other therapeutic areas to propagate I-SPY methodologyo Assist in setting up research networks o Sharing of systems & technologies

Seeking partners for I-SPY 3 Trialo Investorso Research networkso Delivery partners


R&D, Product to Implementation: Linear Inefficiency


...TO A MODEL THAT LEARNS BY RE-USE OF INFORMATION

Source: Buetow, BIO

• New biomarkers identified• Validation conducted in silico

against clinical outcomes

Discovery

• Drug candidates targeted to defined sub-groups

• Clinical trials enriched with appropriate sub-group

• Clinical trial recruitment from “standing” cohort of pre-enrolled volunteers

• NDA includes genomic data

Product Development • All patients genotyped• Clinical trial protocols used as treatment

plans, following regulatory approval• Clinical decision support tools assist

physician by comparing the patient vs.. the Outcomes Database

• Each patient’s outcome de-identified and fed back to the database

Clinical Care

• All clinical outcomes captured and fed to Outcomes Database

• “Sentinel” function sounds alarm for safety or opportunity for expanded indications

Outcomes andSurveillance

• Outcomes linked to molecular profiles

• Algorithms identify linkages and trends

• New hypothesis generated

Analysis and Learning

A Learning Health Care System


Athena

On mission to save lives by transforming how we deliver care today, learn from our patients, create life-changing science, and improve prevention and treatment options

today and tomorrow.


Athena Platform

Web services (algorithms, risk

models, thresholds)

Athena Breast Health NetworkInformation Exchange

Breast Health Specialist

Supportive cancer services; primary

providers; care team

Patient


Athena Breast Health Network

On a mission to achieve transformational change

Capture patient stories and preferences, tumor biology, clinical performance

Deliver personalized prevention, screening, and treatment

Deliver better care tomorrow by enabling care to be an engine for discovery and improvement

Data in – knowledge out A UC-wide and affiliate program


Automated delivery and patient submission of targeted electronic intake forms based on appointment

Athena Platform


models, thresholds)

Real-time assessment of risk, provider and specialist communication, referral to personalized resources and services




Patient


Athena Technology Platform


Increasing efficiency, reducing costs and improving care

Athena Platform

Referral thresholds

met

Pended in APeX for clinician sign off

Revenue for services

Better care for patients


Athena Platform


models, thresholds)

Personalized Screening“one size does not fit all”




Patient

Genomic Profiling

Breast Density


Strength In Numbers and Team Science

Dr. Barbara Parker, UCSD Dr. Robert Cardiff, UCD

Dr. Hoda Anton-Culver, UCI Dr. Laura van ‘t Veer , UCSF


Athena wouldn’t be possible without many of you UCSF IT Teams (Radiology,

Interface, Clinical Inpatient, HIMSS, Leadership)

Athena IT Team and Program Management Office (PMO)

Athena UCSF Site team

UCDavis Mirth Interface Team

Molecular Biologists

Epidemiologists

Radiologists

Genetic Counselors / Breast Health Specialist

Geneticists

Surgeons

Radiation Oncologists

Oncologists

Anthropologists

Statisticians

Social Workers

Psycho-Oncologists

Psychologists

Nutrition and exercise specialists

Pathologists

Reconstructive surgeons

Mammography Technologists

Administrators and Leadership

Clinical Coordinators

Laboratory Technicians

Clinical staff

Call center staff

Patients


“Never doubt that a small group of committed people can change the world. Indeed it is the only thing that ever has.”

-Margaret Mead

“The greater danger for most of us lies not in setting our aim too high and falling short; but in setting our aim too low, and achieving our mark.”

- Michelangelo

The Challenges are Hard but...


Athena UCSF Interfaces

*Athena platform collects discrete data; opportunities to integrate discrete data mapped to health history etc. back into Epic.


Data in, Knowledge out

Capture patient stories and preferences, tumor biology, clinical performance

Deliver personalized prevention, screening, and treatment

Deliver better care tomorrow by enabling care to be an engine for discovery and improvement