Optimizing genomic-driven precision medicine at Duke · 2019-02-25 · HSTL overview •First...
Transcript of Optimizing genomic-driven precision medicine at Duke · 2019-02-25 · HSTL overview •First...
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Optimizing genomic-driven precision medicine at Duke
Matt McKinney (Co-leader, Duke Molecular Tumor Board)
Assistant Professor of Medicine
Division of Hematologic Malignancies
Department of Medicine, Duke University School of Medicine
February 14, 2019
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Key points
1) We are reaching the end of initial genomic discovery of alterations driving cancers.
2) Next generative sequencing has arrived as a clinical diagnostic
3) There are huge obstacles to connecting data generation to treatment decisions and improved outcomes
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Massively parallel sequencing and the growth of cancer
genomic information
Science 2005; 309(5741): 1728-1732.
See: The Economist June 17th 2010
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Nature 2014; 507: 294-5.
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Opportunity for genomic discovery:
T cell lymphomas • T cell lymphomas comprise only 15% of lymphoma
cases
• Dozens of subtypes of T cell lymphomas exist so
each subtype is a rare disease
• We know very little about the biology of these
diseases
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HSTL overview
• First described by Farcet and Gaulard in 1990 based on report of similar cases
• Usually gd –TCR phenotype with sinusoidal infiltration of liver/spleen/bone marrow
• Isochromosome 7q frequently present
• Occurs more frequently in young males and associated with anti-TNF agents for IBD
• No standard treatment approach exists
Blood. 1990;75(11):2213-9. Leukemia 1996; 10(9): 1453-5. J Pediatr Gastroenterol Nutr 2007; 44(2): 265-7.
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HSTL is a rare disease with dire
outcomes
International T cell lymphoma project. J Clin Oncol. 2008 ;26(25):4124-30.
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Approach to understand HSTL
biology
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The genomic landscape of HSTL
Cancer Discov. 2017 7(4):369-379.
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Enhancement of clonogenic potential and in vitro
proliferation with SETD2 loss
shRNA #1
SETD2 Knockdown Non-
Silencing
Control
#1 #2
SETD2 shRNAs
NS
shRNA #2
Relative
Colony
Formation
0 50 1000
2
4
6SETD2 shRNA #1
SETD2 shRNA #2
Non-Silencing
Control
Time (hours)
Normalized
Proliferation
0
1
2
3
4 ***
Cancer Discov. 2017 7(4):369-379.
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Evolution of understanding cancer types
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Proliferation of anti-cancer therapies
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Clinical “next gen” sequencing
• A plethora of commercial CLIAA-certified genomic assays now exist
• Generally focus on sequencing of tumor biopsy tissue for somatic mutations
• Similarly, numerous start-ups offer analysis/treatment pathway pipelines
• Molecular testing offers potential genomic “basket” approach versus drug approved by anatomic site of cancer
• 1st FDA approval based on molecular test occurred May 2017 (pembrolizumab for MSI-high solid tumors)
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Molecular analysis drives development of precision
medicine strategies
Patients
A
B
No treatment selection
based on molecular profile
of tumor
Clinical trial
A
B
Personalized medicine model
Patients Molecular analysis of tumor
A
B
C
D
Choice of treatment
dependent upon molecular
profile of tumor
Traditional model of drug development
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Tumor genetics ≠ Normal genetics
Tumors have somatic changes in genetic/genomics plus germline variation
Tumors acquire:
Chromosomal rearrangements
Gene deletion/amplification
Mutations
Gene expression changes and epigenetic dysregulation
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FoundationOne (solid tumor) panel genes
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FoundationOne (solid tumor)
panel example results
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Duke Clinical NGS panelsPanel Genes
assayed
Disease
type(s)
Sample
collection
FoundationOne ~500
(mutations/fusions/
amplifications)
Solid tumors Tissue block
FoundationOne
Heme
~500
(mutations/fusions/
amplifications)
Myeloid/lymphoid/
sarcomas
Tissue block
Guardant360 72 genes Solid tumors Blood/(cell free
DNA)
Duke Lung Hotspot ~50 genes Non small cell lung
cancer
Tissue block
Duke Colon Hotspot ~50 genes Colorectal cancer Tissue block
Duke Melanoma Hotspot ~50 genes Melanoma Tissue block
Duke Myeloid ~200 genes Myelodysplasia/
leukemia
Bone
marrow/blood
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Lung cancer: Targeting driver mutations improves survival
Kris et al., JAMA. 2014;311(19):1998-2006.
Patients (N) Median survival 95% C.I. (years)
Oncogenic driver treated with targeted therapy
280 3.5 years3.0-4.3
(P <0.001)
Oncogenic driver not treated with targeted therapy
318 2.4 years 1.8-2.9
No driver mutations identified
360 2.1 years 1.8-2.5
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Biomarker testing in lung cancer
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Example of precision medicine: NTRK gene fusions
Kummar and Lassen, Targeted Oncology, October 2018, Volume 13, Issue 5, pp 545–556
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Larotrectinib is a selective TRK inhibitor
Presented By Ulrick Lassen at 2018 ESMO Meeting
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Frequency of NTRK Fusions Across Histologies
Gatalica et al., Modern Pathology, 2018.Cocco et al., Nature Reviews, 2018.
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Larotrectinib for NTRK+ tumors: Tumor response
Presented By Ulrick Lassen at 2018 ESMO Meeting
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Larotrectinib for NTRK+ tumors: Substantial benefit
Presented By Ulrick Lassen at 2018 ESMO Meeting
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The future is here…• Precision cancer medicine improves patient outcomes
• A basic (?expert) understanding of tumor biomarkers is critical to management of cancer patients (and part of the guidelines)
• Resources are needed to assist clinicians understand key biomarkers and provide decision-making support
• Infrastructure is needed to consume and handle results of multiplexed clinical genomic assays
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Duke Molecular Tumor Board (V2.0)• First meeting: 1/29/2018
• Multi-disciplinary group of oncologists, surgeons, pathologists, geneticists
• ~ 1208 cases reviewed in 2018
• 20-30 cases per week
• ~20% - recommend targeted therapy
• ~10% - recommend genetic counseling
• ~50% cases have discussion re: diagnostic/prognostic implications
• QC issues identified
• Multiple research concepts generated
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Duke MTB Mission
• Foster precision medicine within the DCI– Support patient enrollment/treatment on
molecular/biomarker directed therapies/trials
– Deliver high impact genomic information to scientists/ clinicians/ pathologists/ clinical trial staff and our patients
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Case review breakdown
0
20
40
0 5 10 15 20week (q1-2 2018)
# c
ase
s
testColon_hotspotFoundationGuardant_360Lung_hotspotMelanoma_hotspotMyeloid_hotspotOtherSolid_tumor_hotspot
Most cases reviewed are based on Foundation Medicine and Myeloid NGS testing
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Detection of germline alterations – referral
to genetic counseling discussed
alterations with germline HRD implications
ATMBRCA1
BRCA2CHEK2
FANCA
nonePALB2PMS2
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Case example
• 61 yo M with metastatic pancreatic cancer,
progression on frontline chemotherapy.• FoundationOne assay revealed BRCA2 mutation,
confirmed to be germline by separate testing
• Important implications for patient re: genetic counseling and PARP inhibition as option.
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Why MTB database is needed: MyPathway trialEGFR activating mutation
BRAF V600E
SMO activating mutation
PTCH1 loss of function
ALK mutation, fusion, CNV
PD-L1 gain, MSI-high,
TMB > 10 muts/mb, dMMR
ERBB2 amplification
erlotinib
vemurafenib/cobimetinib
vismodegib
alectinib
atezolizumab
trastuzumab/pertuzumab
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10-15% of patients have an actionable
alteration for My Pathway
myPathway alteration cases
ALKBRAFEGFR
ERBB2multiple
none
PTCH1SMO
TMBTMB−high−lung
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Why MTB database is needed: MyPathway trial
• 6/2016: Duke Phase I clinical trial team opened a multi-cohort “basket” clinical trial to pair molecular alterations with targeted therapies
• 6 molecular targets/ all solid tumors eligible
• 49 sites nationally
• Over 1,000 Duke patients received NGS profiling
• Total enrollment over 2 years: 6 patients– Initial plan: “Word of mouth” to match patients with trials
– No mechanism to alert MD that pt has an actionable alteration
– No mechanism to alert the clinical trial team that there are patients potentially eligible for trial
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Where is the disconnect?
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1st line treatment
Timeline(months)
0 6 12
2nd line Treatment
18
3rd line treatment
24
4th line treatment
5th line treatment
30
Supportive care
Foundation Medicine G360
A
B
C
D
E
Trials Available for Gene
Genes: A, B, D Genes: B, D, E
Window for trial
Clinical trial schematic
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Presented By Jessica Tao at 2018 ASCO Annual Meeting
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Presented By Jessica Tao at 2018 ASCO Annual Meeting
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Presented By Jessica Tao at 2018 ASCO Annual Meeting
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Presented By Jessica Tao at 2018 ASCO Annual Meeting
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Molecular r.egisTryhttps://mrt.dhe.duke.edu
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Molecular r.egisTry searching
I pity the fool who doesn’t know how many SETD2 mutations his institution has seen in the last 6 months!
Mr. T
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Mr. T continuously updates Foundation Medicine & Guardant360 data in a curated form using standards-compliant nomenclature in a DHTS-secured environment. PHI is protected by program-specific roles. Robust APIs allow for data consumption from any source.Clinical trial/treatment matching code can alert clinicians to actionable mutations and potential clinical trials
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WEE1/dNTP depletion and
synthetic lethality with SETD2 loss
SETD2(-)
SETD2(+)
Cancer Cell. 2015; 28(5): 557–568.
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UM1 group study of AZD 1775 in SETD2
advanced malignancies
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Other research initiatives
1. BRPC Alignment– Mr. T born in Pathology with Clinical
Laboratory Data
– Genomic data links to BRPC blood, tissues, cell lines and PDX models
– Many ongoing projects requiring biospecimen cohorts with specific molecular variants, 2 published 2019
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Research Initiatives
2. AACR (American Association for
Cancer Research) GENIE (Genomics
Evidence Neoplasia Information
Exchange) Project
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AACR GENIE Overview• International pan-cancer registry built through data sharing
– Driven by openness, transparency, and inclusion
GOAL: improve clinical decision making
• Linking clinical genotype to clinical outcomes
8 19
Eight founding participants, now 19
• North America & Europe
• Plans for future expansion
Sponsored research
Collaborative projects
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AACR GENIE, 19 Participants
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Institution-onlyaccess
6 months
Consortium-onlyaccess
6 months
● Data mapped to common ontology and harmonized
● Limited PHI removed● Data governance,
provenance, and versioning in a secure, HIPAA-compliant environment.
clinical queries are posed based on registry content
clinical data required to answer the question are manually abstracted
regular data uploads
genomic and clinical data linked
Consortium/sponsor-only access6 months to time of publication
A
B
DFCIDUKEJHUMDAMSKCHOPWFUVICC
Clinical Sequencing
How the Registry Operates
www.aacr.org/genie/data
The AACR Project GENIE Consortium, Cancer Discovery, 2017
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Define Virtual Cohorts of Interest Cancer Type Gene(s) of interest
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Long-term Outcomes ExampleImportant clinical dates (metastatic Dx, XRT, etc.)
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Research Initiatives
3. “NGS Experience” IRB Protocol; QA
projects in Pathology
– Multiplatform QA correlation study for MSI
testing (PCR vs. IHC vs. NGS tumor vs.
NGS cfDNA)
– Correlations with TMB and PD-L1
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Future Initiatives• Integrating NGS data as discrete elements in Epic,
continued development of innovative Pathology tools
• Launching GenomOncology for enhanced bioinformatics, decision-making support and clinical trials matching
• Improving clinical annotation of molecular results
• We are looking for collaborators in the space of AI/EMR data extraction
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Early research projects• Assess negative predictive value of multiplexed genomic
testing on cancer biopsies for germline alterations
• “Real world” analysis of use of myeloid targeted sequencing panels in acute myeloid leukemia patients
• Explore genomic predictors of primary/ acquired treatment resistance in patients with MSI-H cancers treated with anti-PD-1 antibodies
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Conclusions• Comprehensive genomic profiling vastly improves our ability to
detect cancer alterations.
• We are already significantly impacting patient care.
• We are piloting systems to better characterize cancers with genomics and assign molecularly directed therapies (& trials).
• We have made substantial progress and we are learning more all the time!