precision medicine in oncology informatics
Post on 13-Apr-2017
593 Views
Preview:
TRANSCRIPT
9/28/15
1
Precision Medicine in Oncology – an Informatics
Perspective
September 22nd, 2015
Warren Kibbe, PhD
NCI Center for Biomedical Informatics
2
1. Precision Medicine
2. Pre-clinical models
3. TCGA
4. Genomic Data Commons
5. Cloud Pilots
Slides are from many sources, but special thanks to Drs. Harold Varmus, Doug Lowy, Jim Doroshow, Lou Staudt
9/28/15
2
Photo by F. Collins
President Obama Announces the Precision Medicine Initiative
The East Room, January 30, 2015
4
TOWARDS PRECISION MEDICINE (IoM REPORT, NOVEMBER 2011)
9/28/15
3
5
Definition of Precision Oncology
§ Interventions to prevent, diagnose, or treat cancer, based on a molecular and/or mechanistic understanding of the causes, pathogenesis, and/or pathology of the disease. Where the individual characteristics of the patient are sufficiently distinct, interventions can be concentrated on those who will benefit, sparing expense and side effects for those who will not.
Modified by D. Lowy, M.D., from IoM’s Toward Precision Medicine report, 2011
6
Understanding Cancer
§ Precision medicine will lead to fundamental understanding of the complex interplay between genetics, epigenetics, nutrition, environment and clinical presentation and direct effective, evidence-based prevention and treatment.
9/28/15
4
7
What is Cancer?
§ Cancer is a disease where cells ‘lose’ the normal controls that enable them to participate as productive, stable members of tissues, organs and organ systems. The process of developing cancer is usually viewed as having three phases § Initiation, where some of the normal control processes define the cell
type, inter-cellular communication with other cells, and normal responses to cell-cell signaling are altered
§ Proliferation, where cancerous cells ‘take over’ normal processes and begin to proliferate
§ Metastasis, where the cancerous cell leave their normal location and invade other tissues
8
Cancer Statistics
In 2015 there will be an estimated
1,700,000 new cancer cases and
600,000 cancer deaths - American Cancer Society 2015
Cancer remains the second most common cause of death in the U.S.
- Centers for Disease Control and Prevention 2015
9/28/15
5
9
Survivorship is on the rise
Today there are 14 million Americans alive with a history of cancer.
It is estimated that by 2024, the population of cancer survivors will increase to almost 19 million: 9.3 million males and 9.6 million females
- American Cancer Society 2015
10
The hope
§ We need to do what the HIV/AIDS research community did in the last 30 years, turn a certain death sentence into a managed, chronic disease.
§ Like the HIV/AIDS story for the course of the HIV infection, we believe that cancer has multiple pathways that are altered during the course of the disease, and that targeted therapies hitting multiple involved signaling pathways, metabolic pathways, receptors can block and prevent disease progression
§ Unlike HIV, the course of cancer is more individualized and involves different molecular actors depending on the cancer and the individual
9/28/15
6
11
The frustration
We already know how to remove >50% of the cancer burden in the U.S.
Consistent nutrition, exercise & sleep – estimate(10-30%)
Tobacco control (smoking cessation) – ~25%
HPV and HCV immunization – ~20%
Behavior change is hard. Impacting the behavior of a broad population is harder.
12
Precision Medicine is not new: An early example
First disease for which molecular defect identified Single substitution at position 6 of ß-globin chain
Abnormal Hb polymerization upon deoxygenation “I believe medicine is just now entering into a new era when progress will be much more rapid than before, when scientists will have discovered the molecular basis of diseases, and will have discovered why molecules of certain drugs are effective in treatment, and others are not effective.” Linus Pauling 1952 2015: still no good treatment
9/28/15
7
13
The NCI Mission & the PMI for
Oncology
• NCI Mission: To help people live longer, healthier lives by supporting research to reduce the incidence of cancer and to improve the outlook for patients who develop cancer
• Precision Medicine Initiative for
Oncology: Significantly expand our efforts to improve cancer treatment through genomics
14
What Problems are We Trying to Solve?
Precision Medicine Ini5a5ve in Oncology
• For most of its 70-year history, systemic cancer treatment has relied on therapies that are marginally more toxic to malignant cells than to normal tissues
• Molecular molecules that predict benefit, response, or resistance in the clinic have been lacking for many cancers
9/28/15
8
15
Proposed Solution to these Problems
Use genomics and other high throughput technologies including
imaging to identify, create predictive signatures, and
target molecular vulnerabilities of individual cancers
Precision Medicine Ini5a5ve in Oncology
16
Precision Oncology in Practice
Nature Rev. Clin. Oncol. 11:649-662 (2014)
9/28/15
9
17
Precision Oncology Trials Launched 2014: MPACT Lung MAP ALCHEMIST Exceptional Responders 2015: NCI-MATCH ALK Inhibitor MET Inhibitor
NCI-MATCH: Features [Molecular Analysis for Therapy Choice]
• Foundational treatment/discovery trial; assigns therapy based on molecular abnormalities, not site of tumor origin for patients without available standard therapy • Regulatory umbrella for phase II drugs/studies from > 20 companies; single agents or combinations
• Available nationwide (2400 sites)
• Accrual began mid-August 2015
NCI MATCH
• Conduct across 2400 NCI-‐supported sites • Pay for on-‐study and at progression biopsies • Ini5al es5mate: screen 3000 pa5ents to complete
20 phase II trials
1CR, PR, SD, and PD as defined by RECIST 2Stable disease is assessed relaSve to tumor status at re-‐iniSaSon of study agent 3Rebiopsy; if addiSonal mutaSons, offer new targeted therapy
,2
9/28/15
10
19
NCI-MATCH: Initial Ten Studies
Agents and targets below grey line are pending final regulatory review; economies of scale—larger number of agents/genes, fewer overall patients to screen
Agent(s) Molecular Target(s) Estimated Prevalence
Crizotinib ALK Rearrangement (non-lung adenocarcinoma) 4% Crizotinib ROS1 Translocations (non-lung adenocarcinoma) 5% Dabrafenib and Trametinib BRAF V600E or V600K Mutations (non-melanoma) 7%
Trametinib BRAF Fusions, or Non-V600E, Non-V600K BRAF Mutations (non-melanoma)
2.8%
Afatinib EGFR Activating Mutations (non-lung adenoca) 1 – 4% Afatinib HER2 Activating Mutations (non-lung adenoca) 2 – 5% AZD9291 EGFR T790M Mutations and Rare EGFR Activating Mutations (non-
lung adenocarcinoma) 1 – 2%
TDM1 HER2 Amplification (non breast cancer) 5% VS6063 NF2 Loss 2% Sunitnib cKIT Mutations (non GIST) 4%
≈ 35%
20
MATCH Assay: Workflow for 10-12 Day Turnaround
Tissue Fixation Path Review
Nucleic Acid Extraction
Library/Template Prep
Sequencing , QC Checks
Clinical Laboratory aMOI
Verification
Biopsy Received at Quality Control Center
1 DAY
1 DAY
1 DAY 1 DAY
3 DAYS
10-12 days
Tumor content >70%
Centralized Data Analysis
DNA/RNA yields >20 ng
Library yield >20 pM Test fragments Total read Reads per BC Coverage NTC, Positive, Negative Controls aMOIs Identified
Rules Engine Treatment Selection
3-5 DAYS
9/28/15
11
21
The Components of the Precision Medicine Initiative for Oncology
• Dramatically expand the NCI-MATCH
umbrella to include new trials, new agents, new genes, and new drug combinations
• Understand and overcome resistance to therapy through molecular analysis and development of new cancer models
• Increase genomics-based preclinical studies, especially in the area of immunotherapy, through the creation of patient-derived pre-clinical models and non-invasive tumor profiling
• Establish the first national cancer database integrating genomic information with clinical response and outcome: to accelerate understanding of cancer and improve its treatment
22
PMI-O: Expanding Genomically-Based Cancer Trials (FY16-FY20)
• Accelerate Launch of NCI-Pediatric MATCH • Broaden the NCI-MATCH Umbrella:
ü Expand/add new Phase II trials to explore novel clinical signals—mutation/disease context
ü Add new agents for new trials, and add new genes to panel based on evolving evidence
ü Add combination targeted agent studies ü Perform Whole Exome Sequencing, RNAseq,
and proteomic studies on quality-controlled biopsy specimens—extent of research based on resource availability
ü Add broader range of hematologic malignancies • Perform randomized Phase II studies or hand-off to
NCTN where appropriate signals observed • Apply genomics resources to define new predictive
markers in novel immunotherapy trials • Expand approach to ‘exceptional responders’: focus
on mechanisms of response/resistance in pilot studies
9/28/15
12
23
PMI-O: Understanding and overcoming resistance to therapy (FY16-FY20)
• Create a repository of molecularly analyzed
samples of resistant disease • Expand the use of tumor profiling methods
such as circulating tumor cells (CTCs) and fragments of tumor DNA in blood to understand and monitor disease progression
• Develop new cancer models to identify the heterogeneity of resistance mechanisms
• Use preclinical modeling to determine the effectiveness of new combinations of novel molecularly targeted investigational agents
24
Mechanisms of Resistance
To Targeted Cancer Therapeutics
Primary or Acquired
Resistance
Epigenetic DNA Damage Repair
Drug Efflux
Drug Inactivation
EMT: Micro-
environment
Cell Death Inhibition
Drug Target Alteration
• Broad range of mechanisms • Until recently, tools to interrogate possibilities in
vivo quite limited • Resistance to single agents inevitable: 1º or
acquired; requires combinations but data to provide molecular rationale for the combination (both therapy & toxicity) not often available
9/28/15
13
25
Develop a Cancer Knowledge System. Establish a national database that integrates genomic information with clinical response and outcomes as a resource.
PMI-O: Informa5cs Goal
26
Develop molecular, imaging, pathology, and clinical signatures that predict therapeutic response, outcomes, and tumor resistance
PMI-O: Informa5cs Goal
9/28/15
14
27
Build multi-scale, predictive computational biology models for understanding cancer biology and informing therapy. Develop detailed cancer pathway models to create targeted combination therapies in cancer. This approach has transformed HIV therapy and has the potential to do the same in cancer
PMI-O: Informa5cs Goal
28
The Cancer Genomic Data Commons (GDC) is an existing effort to standardize and simplify submission of genomic data to NCI and follow the principles of FAIR -Findable, Accessible, Interoperable, Reusable. The GDC is part of the NIH Big Data to Knowledge (BD2K) initiative and an example of the NIH Data Commons
Genomic Data Commons
9/28/15
15
The Genomic Data Commons
FacilitaSng the idenSficaSon of molecular subtypes of cancer and potenSal drug targets
NCI Cancer Genomic Data Commons (GDC)
9/28/15
16
31
Genomic Data Commons (GDC) – Rationale § TCGA and many other NCI funded cancer genomics projects each
currently have their own Data Coordinating Centers (DCCs) § BAM data and results stored in many different repositories; confusing
to users, inefficient, barrier to research § GDC will be a single repository for all NCI cancer genomics data
§ Will include new, upcoming NCI cancer genomics efforts § Store all data including BAMs § Harmonize the data as appropriate
§ Realignment to newest human genome standard § Recall all variants using a standard calling method § Define data sharing standards and common data elements
§ Will be the authoritative reference data set § Will need to scale to 200+ petabytes
32
Genomic Data Commons (GDC)
§ First step towards development of a knowledge system for cancer § Foundation for a genomic precision medicine platform § Consolidate all genomic and clinical data from:
§ TCGA, TARGET, CGCI, Genomic NCTN trials, future projects
§ Project initiated Spring of 2014 § Contract awarded to University of Chicago § PI: Dr. Robert Grossman § Go live date: Mid 2016 § Not a commercial cloud
§ Data will be freely available for download subject to data access requirements
9/28/15
17
The NCI Cancer Genomics Cloud Pilots
Understanding how to meet the research
community’s need to analyze large-scale cancer genomic and clinical data
Slide courtesy of Deniz Kural, Seven Bridges Genomics
9/28/15
18
NCI Cloud Pilots
The Broad PI: Gad Getz
InsStute for Systems Biology PI: Ilya Shmulevich
Seven Bridges Genomics PI: Deniz Kural
36
NCI GDC and the Cloud Pilots
§ Working together to build common APIs § Working with the Global Alliance for Genomics and Health (GA4GH) to
define the next generation of secure, flexible, meaningful, interoperable, lightweight interfaces
§ Competing on the implementation, collaborating on the interface § Aligned with BD2K and serving as a part of the NIH Commons and
working toward shared goals of FAIR (Findable, Accessible, Interoperable, Reusable)
§ Exploring and defining sustainable precision medicine information infrastructure
9/28/15
19
37
Information problem(s) we intend to solve with the Precision Medicine Initiative for Oncology § Establish a sustainable infrastructure for cancer genomic
data – through the GDC § Provide a data integration platform to allow multiple data
types, multi-scalar data, temporal data from cancer models and patients § Under evaluation, but it is likely to include the GDC, TCIA,
Cloud Pilots, tools from the ITCR program, and activities underway at the Global Alliance for Genomics and Health
§ Support precision medicine-focused clinical research
38
NCI Precision Medicine Informatics Activities
§ As we receive additional funding for Precision Medicine, we plan to: § Expand the GDC to handle additional data types
§ Include the learning from the Cloud Pilots into the GDC
§ Scale the GDC from 10PB to hundreds of petabytes
§ Include imaging by interoperating between the GDC and the Quantitative Imaging Network TCIA repository
§ Expand clinical trials tooling from NCI-MATCH to NCI-MATCH Plus
§ Strengthen the ITCR grant program to explicitly include precision medicine-relevant proposals
9/28/15
20
39
Bridging Cancer Research and Cancer Care
§ Making clinical research relevant in the clinic § Supporting the virtuous cycle of clinical research informing
care, and back again § Providing decision support tools for precision medicine
CGC Pilot Team Principal Inves5gators • Gad Getz, Ph.D -‐ Broad InsStute -‐ h_p://firecloud.org • Ilya Shmulevich, Ph.D -‐ ISB -‐ h_p://cgc.systemsbiology.net/ • Deniz Kural, Ph.D -‐ Seven Bridges – h_p://www.cancergenomicscloud.org
NCI Project Officer & CORs • Anthony Kerlavage, Ph.D –Project Officer • Juli Klemm, Ph.D – COR, Broad InsStute • Tanja Davidsen, Ph.D – COR, InsStute for Systems Biology • Ishwar Chandramouliswaran, MS, MBA – COR, Seven Bridges Genomics
GDC Principal Inves5gator • Robert Grossman, Ph.D -‐ University of Chicago
Cancer Genomics Project Teams
NCI Leadership Team • Doug Lowy, M.D. • Lou Staudt, M.D., Ph.D. • Stephen Chanock, M.D. • George Komatsoulis, Ph.D. • Warren Kibbe, Ph.D.
Center for Cancer Genomics Partners • JC Zenklusen, Ph.D. • Daniela Gerhard, Ph.D. • Zhining Wang, Ph.D. • Liming Yang, Ph.D. • MarSn Ferguson, Ph.D.
9/28/15
21
41
QuesSons?
Warren A. Kibbe
warren.kibbe@nih.gov
Thank you
www.cancer.gov www.cancer.gov/espanol
top related