Being Precise about Precision Medicine Jeffrey Golden, M.D. Chair, Department of Pathology Brigham and Women’s Hospital Ramzi S. Cotran Professor of Pathology Harvard Medical School
Being Precise about
Precision Medicine
Jeffrey Golden, M.D.
Chair, Department of Pathology
Brigham and Women’s Hospital
Ramzi S. Cotran Professor of Pathology
Harvard Medical School
Personalized
Medicine
Clinical Value of Molecular Pathology
• History: – 52 year old woman with headaches and cough
• Diagnosis: metastatic lung cancer
• Prognosis:
– Median survival: 16-36 weeks – Likelihood of survival at 18 months: <1%
• Course:
– April: Carboplatin-Paclitaxel chemotherapy • Response for 6 wks • Progression by 8 months
– December: Cetuximab (Erbitux) • Stabilization for 4 months • Progression by 7 months
• Molecular pathology:
– EGFR L858R mutation – Test result guides treatment decision
– 4 years after DX: Gefitinib (Iressa)
• Cancer disappeared • Sustained response for 30 months
DX
2 yrs
4 yrs
• 42 M with “atypical” Small cell carcinoma
• Has not responded to chemotherapy
Tumor doubled, multiple new nodules
• Oncopanel: EWS-ERG fusion!
Diagnostic of Ewing Sarcoma, not SC lung cancer
• Responded to Ewing’s therapy
• Follow up: Mr. S is actually overall doing incredibly well. This
is the best he has felt in months. He has even been running
again, which he and his wife are both pleased with. He has
had an interval decrease in tumor size and no evidence of new
metastatic disease in the thorax.
Exons: ABL1, AKT1, AKT2, AKT3, ALK, ALOX12B, APC, AR, ARAF, ARID1A, ARID1B, ARID2, ASXL1, ATM, ATRX, AURKA, AURKB, AXL, B2M,
BAP1, BCL2, BCL2L1, BCL2L12, BCL6, BCOR, BCORL1, BLM, BMPR1A, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BUB1B, CARD11, CBL, CBLB,
CCND1, CCND2, CCND3, CCNE1, CD274, CD58, CD79B, CDC73, CDH1, CDK1, CDK2, CDK4, CDK5, CDK6, CDK9, CDKN1A, CDKN1B, CDKN1C,
CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK2, CIITA, CREBBP, CRKL, CRLF2, CRTC1, CRTC2, CTNNB1, CUX1, CYLD, DDB2, DDR2, DICER1,
DIS3, DMD, DNMT3A, EGFR, EP300, EPHA3, EPHA5, EPHA7, ERBB2, ERBB3, ERBB4, ERCC2, ERCC3, ERCC4, ERCC5, ESR1, ETV1, ETV4,
ETV5, ETV6, EWSR1, EXT1, EXT2, EZH2, FAM46C, FANCA, FANCC, FANCD2, FANCE, FANCF, FANCG, FAS, FBXW7, FGFR1, FGFR2, FGFR3,
FGFR4, FH, FKBP9, FLCN, FLT1, FLT3, FLT4, GATA3, GATA4, GATA6, GLI1, GLI2, GLI3, GNA11, GNAQ, GNAS, GPC3, GSTM5, H3F3A, HNF1A,
HRAS, ID3, IDH1, IDH2, IGF1R, IKZF1, IKZF3, JAK2, JAK3, KDM6A, KDM6B, KDR, KIT, KRAS, LMO1, LMO2, LMO3, MAP2K1, MAP2K4, MAP3K1,
MAPK1, MCL1, MDM2, MDM4, MECOM, MEF2B, MEN1, MET, MITF, MLH1, MLL, MLL2, MPL, MSH2, MSH6, MTOR, MUTYH, MYB, MYBL1, MYC,
MYCL1, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NFKBIZ, NKX2-1, NOTCH1, NOTCH2, NPM1, NRAS, NTRK1, NTRK2, NTRK3, PALB2,
PARK2, PAX5, PDCD1LG2, PDGFRA, PDGFRB, PHF6, PHOX2B, PIK3C2B, PIK3CA, PIK3R1, PIM1, PMS1, PMS2, PNRC1, PRAME, PRDM1, PRF1,
PRKAR1A, PRKCI, PRKCZ, PRKDC, PRPF40B, PRPF8, PSMD13, PTCH1, PTEN, PTK2, PTPN11, RAD21, RAF1, RARA, RB1, RBL2, REL, RET,
RFWD2, RHPN2, ROS1, RPL26, RUNX1, SBDS, SDHAF2, SDHB, SDHC, SDHD, SETBP1, SETD2, SF1, SF3B1, SH2B3, SMAD2, SMAD4,
SMARCA4, SMARCB1, SMC1A, SMC3, SMO, SOCS1, SOX2, SOX9, SRC, SRSF2, STAG1, STAG2, STAT3, STAT6, STK11, SUFU, SUZ12, SYK,
TCF3, TCF7L1, TCF7L2, TERT, TET2, TNFAIP3, TP53, TSC1, TSC2, U2AF1, VHL, WRN, WT1, XPA, XPC, XPO1, ZNF217, ZNF708, ZRSR2.
Introns: ABL1, AKT3, ALK, BCL2, BCL6, BRAF, CIITA, EGFR, ETV1, EWSR1, FGFR1, FGFR3, FUS, IGH@, IGK@, IGL@, JAK2, MLL, MYC, NPM1,
PAX5, PDGFRA, PDGFRB, RAF1, RARA, RET, ROS1, TRA@, TRB@, TRG@.
Oncopanel
• Finding #1: It works. 96% success sequencing, concordant with “gold standard”, sensitive and precise
• Finding #2: Common mutations and CNVs are common
• Finding #3: Somewhat unexpected findings. Eg. POLE mutations, emerging resistance clones, incipient diseases (hematologic disorders)
• Finding #4: Unexpected findings. Eg. Targetable mutations in unexpected cancers (ERBB2 in ovary) and altered diagnoses (NSCLC to Ewings)
Rapid Heme Panel (RHP) at CAMD
Need: “real time” genomic information for patients with
acute leukemia and aggressive forms of myeloid
neoplasms
Solution: Illumina TruSeq Custom Amplicon + Miseq-
based test (launched in late August, 2014)
Test characteristics (performed on blood or marrow):
• 95 genes (757 coding exons)
• 3-7 day turnaround time from time of
specimen receipt
Example of RHP Impact on Dx and Tx
NOTCH1 R1598P - in 40.4% of 171 reads NOTCH1 S2467fs* - in 24.9% of 185 reads IL7R I241N - in 41.0% of 1221 reads JAK1 S703I - in 34.6% of 52 reads
Bone marrow aspirate RHP Findings (3 days later)
Molecularly informed diagnosis
Early T-cell progenitor (ETP) ALL associated with driver mutations in the Notch and JAK/STAT signaling pathways
Salvage therapy with nelarabine attempted, but no response. Enrolled on a Notch inhibitor trial (BMS-906024), a rational choice for this patient’s disease
Acute leukemia with ambiguous immunophenotype
18 yo male referred to DF/BWCC with relapsed refractory ALL with ambiguous immunophenotype
So What? • Known mutation(s) can be specifically treated
• More precise diagnosis
• Superior Treatment stratification
– Clinical trial eligability
• Targeted therapy: – More effective – Less toxic – Improved outcomes – More cost effective
• New Biomarker Discovery
Kris, JAMA, 2014
Challenges for Clinicians
Common laboratory test results (chem panels, blood counts)
• Clinicians overwhelmed by shear volume of data
• Millions of test results per year
• Abnormal results and trends are easy to miss
Complex and esoteric laboratory test results
• Clinicians often do not understand which tests should be ordered and how to interpret results
• Current laboratory menu over 3000 different tests
Precision
Medicine
BioInformatics
1. Building of
algorithms
2. Hypothesis
generating
3. Hypothesis
testing
4. Correlative
research
5. Data modeling
6. Prediction
modeling
Hardware/software
1. Data acquisition
2. Data storage
3. Data retrieval
4. Data annotation
(including
management and
prediction)
5. Integration of
computer
systems
6. Image
digitalization
Biostatistics
1. Data analysis
2. Data
interpretation
3. Data validation
4. Inference
validation
5. Clinical decision
support
6. Test building
7. Support of clinical
trials
Computational Pathology
Electronic medical record (EPIC)
Laboratory information system
External Databases
Knowledge Pipeline
1. Leverage clinical
data
2. Lead data
interrogation
3. Efferent arm
4. Full collaboration
with clinical
colleagues
5. Biomarker discovery
External data bases Internal databases (e.g., genomic, literature) (e.g., clinical, pharmacy, laboratory)
Data Technologies (e.g., databases, networks, cloud)
“Input”
Informatics
Examples • Clinical (e.g., natural
language processing) • Molecular (e.g., variant
calling) • Image processing (e.g.,
segmentation)
Modeling, inference, and prediction
Customer Interfaces
Examples • Decision Support • Integrated Reporting
Action
Customers 1. Physicians 2. Laboratories 3. Patients 4. Health Care Systems
Data
Molecular Individual Population
Information Knowledge
Arch Pathol Lab Med. 2014 Sep;138(9):1133-8 Arch Pathol Lab Med. 2015 In press
President & CEO Betsy Nabel
Executive Steering Committee Golden, Loscalzo, Bry, Garraway, Kohane, Maas, MacRae, Silverman, Tempany, Rehm,
Philippakis
Computer Science
Hardware /
Software
Advisory Group
Bioinformatics,
Biostatistics
Advisory Group
Bio Banking
Advisory Group
Medical and EHR
Expert
Advisory Group
Clinical Care
Lab/”Omics”
Advisory Group
Clinical Data
Advisory Group
Cores Informatics
Imaging
Metrics/Validation
Data Warehouse
Lab Data Education
Test Dev.
Clinical Support
IRB
Precision Medicine Hub
What Can Be Delivered
1. Leverages electronic integrated health and administrative systems.
2. Provide higher quality, safer, more efficient (cost effective) patient care.
3. Support new ways of caring for individuals and populations.
4. Provide better informatics to support all aspects of healthcare mission.
Thank you and Questions