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