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Rapid Learning Precision Oncology

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Page 1: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Rapid Learning Precision Oncology

Page 2: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Rapid Learning Precision Oncology

Part I: Patient’s Perspective

Part II: Industry Perspective

Part III: Aligning Incentives

Page 3: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Part I: Patient’s Perspective

surgery radiation

clinical trials

experimentalmethods

chemotherapy

• Thousands of rare molecular subtypes

• Tens of thousands of treatment combinations

• Traditional RCTs become problematic

Page 4: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Biopsy

Sequence

Compare

TargetTest

Treat

Monitor

Precision Oncology 2.0 (Today)

In silicoIn vivo In vitro

Normal skin cell

Sequencing Machines

Chromosomes

Normal cell

Cancer cell

Treated cell

Scans

Patient

Biomarkers

Original cancer cell

Adapted from NY Times

Page 6: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Molecular Tumor Board

Medical, surgical and radiation oncologists, biostatisticians, radiologists, and pathologists+ clinical geneticists and specialists in cancer pathways, pharmacology, bioinformatics

Page 7: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Case Reports

Page 8: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

No Learning

Page 9: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Rapid Learning Precision OncologyA rapid learning community for

cancer• Help each patient obtain the best possible outcome

• Learn as much as possible

• Disseminate rapidly Learn

Model

AnalyzeTreat

Page 10: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

mTOR

AKT

PI3K

PTEN

NRASBRAF

MEK

ERK

Bcl-2, Bcl-xL, Mcl-1

BAKBAX

NOXA, PUMABIM, BID,

BAD

p53

MDM2

p14ARF

CDK4/6 p16

Cyclin D

MITF

MAPK1

2 NRAS

3 MITF

4 PI3K

5 CDK

6 c-KIT

7 Bcl-2

8 8MAPK/ PI3K

9 9MAPK/ CDK

10

10

10

NRAS/ MAPK/ PI3K

Melanoma Molecular SubtypesSubtypes Cell Signaling Pathways

Responders

Non- Responders

Page 11: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Scientists Clinical Researchers Physicians Patients

Human-Machine Knowledge System

PatientModels

ReferenceModels

Specimens

Labs Clinical Trials

PharmaPayersHospitals

Page 12: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

What It Means To Do Our Best

Tony Blau MD, U. Washington

“Although our ability to exploit knowledge of cancer pathways is in its infancy, we must do our best for today's cancer patients and, in the process, learn as much as possible for the patients of tomorrow.”

NCT01957514: Collecting, Analyzing, and Storing Samples From Patients With Metastatic, Triple Negative Breast Cancer Receiving Cisplatin (ITOMIC)

University of Washington & NCI

ITOMIC: Intensive Trial of OMics in Cancer

Page 13: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Rapid Learning Network

Tony BlauU.

Washington

Andrea Califano

Columbia U.

Lincoln NadauldIntermountain

Health

Keith Flaherty

MGH

George Demetri Dana

Farber

Ravi SalgiaU. Chicago

Mitesh BoradMayo Clinic

Beth KarlanCedars Sinai

Heinz Josef Lenz, USC

Joel NealStanford

Page 14: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Global Cumulative Treatment Analysis

Page 15: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Part II: Industry Perspective

DrugDiscovery

FDAApproval

TrialsPhase 1

TrialsPhase 2

TrialsPhase 3

Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

JMT
Redraw as a Funnel (one reason it takes so long is that there are 1000’s of drugs that need to be eliminated, to find the few that advance to trials, and the one that is finally marketed. )
Marty Tenenbaum
Consider lopping off everything with big Xs and then making a separate 3 mo time line with a single patient and all the world's drugs. Alt. adapt the tree/maze to tell this part of the story but we will need to make the points about multiple drugs and the ability to agregate learnings aboout multiple patients.
Page 16: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Replace Large Trials With…

DrugDiscovery

FDAApproval

TrialsPhase 1

TrialsPhase 2

TrialsPhase 3

Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

JMT
Redraw as a Funnel (one reason it takes so long is that there are 1000’s of drugs that need to be eliminated, to find the few that advance to trials, and the one that is finally marketed. )
Page 17: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

N-of-1 Studies

DrugDiscovery

FDAApproval

Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

JMT
Redraw as a Funnel (one reason it takes so long is that there are 1000’s of drugs that need to be eliminated, to find the few that advance to trials, and the one that is finally marketed. )
Page 18: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Replace Discovery With…

DrugDiscovery

FDAApproval

Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

JMT
Redraw as a Funnel (one reason it takes so long is that there are 1000’s of drugs that need to be eliminated, to find the few that advance to trials, and the one that is finally marketed. )
Page 19: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

All Approved + Investigational Drugs

DrugDiscovery

FDAApproval

Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

JMT
Redraw as a Funnel (one reason it takes so long is that there are 1000’s of drugs that need to be eliminated, to find the few that advance to trials, and the one that is finally marketed. )
Page 20: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Years To Months

Month 1 2 2.5 31.5

Precision Oncology 3.0

JMT
Redraw as a Funnel (one reason it takes so long is that there are 1000’s of drugs that need to be eliminated, to find the few that advance to trials, and the one that is finally marketed. )
Page 21: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

GCTA: Many Parallel N-of-1 Trials

Month 1 2 2.5 31.5

Precision Oncology 3.0

JMT
Redraw as a Funnel (one reason it takes so long is that there are 1000’s of drugs that need to be eliminated, to find the few that advance to trials, and the one that is finally marketed. )
Page 22: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

The Search For Cures

Page 23: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Succeed Slowly

1 yr3 yrs5 yrs9 yrs

Page 24: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Fail Fast

1 yr3 yrs5 yrs

Page 25: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Fail Fast

1 yr3 yrs5 yrs

Page 26: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Bed to Bench

1 yr3 yrs5 yrs

Page 27: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Bed to Bench

1 yr3 yrs5 yrs

Page 28: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Tightly Integrate Research and Care

Tony Blau MD, U. Washington

“Although our ability to exploit knowledge of cancer pathways is in its infancy, we must do our best for today's cancer patients and, in the process, learn as much as possible for the patients of tomorrow.”

“…There are still no curative treatments for castration-resistant prostate cancer (CRPC) and, therefore, it remains fatal….Our findings suggest that dual targeting of the Akt and mTOR signaling pathways using MK-2206 and MK-8669 may be effective for treatment of CRPC, particularly for patients with deregulated Rb pathway activity. “Andrea Califano, PhD, Columbia

Page 29: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Part III. Aligning Incentives

Physician applies for compassionate

use

Pharma provides drug

• Health plan pays• Replicate-small n• Fast track approval

• Lose cost of pills• Save years

Drug Works

Drug Fails

Page 30: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Toolkit Licensing

Page 31: Rapid Learning Precision Oncology. Part I: Patient’s Perspective Part II: Industry Perspective Part III: Aligning Incentives

Rapid Learning Precision Oncology1. Tightly integrating cancer research, drug development, and clinical care will improve outcomes, accelerate research, and slash time to clinic.

2. Trials are for validation, not discovery. GCTA-like studies are the only way to efficiently search the vast space of targeted therapies x subtypes.

3. Managing an individual’s cancer, and then generalizing to other patients, is much more achievable than “Curing Cancer”.

4. Barriers such as drug access and reimbursement can be overcome by aligning industry’s interests with the those of patients.

5. The FDA has a critical role in predictive pharmacology, toolkit licensing, and single subject INDs for testing rational combination therapies.