Advancing Patient Care by Integrating New Molecular & Genetic Testing Technologies

Stephen C. Peiper, MD stephen.peiper@jefferson.edu

Peter A. Herbut Professor & Chair Department of Pathology, Anatomy & Cell Biology

Thomas Jefferson University

Disclosures

• No financial interests

• Member of Roche Molecular Advisory Board

• Participated in the early access program for

the 454 Junior

• Purchased a 454 Junior

Disclaimer

• Clinical applications for 454 Jr are not recommended by Roche

• No testing for patient care has been performed using the 454 Jr

• Analyses were performed with IRB approval (expedited review for de-identified specimens)

Today’s Pathology

Standard Therapy Theranostic +

=> Targeted Therapy

Personalized Medicine

All patients with

same diagnosis;

Genetic variation

Gene

Testing

“Genetically Informed Medicine”

The Road to Genetically Informed Medicine

• Yesterday

• Today

• Tomorrow

• Molecular laboratory organization

• Economic considerations

• Genomic oncology trends

• NextGen Sequencing as an emerging technology

• Clinical examples

Molecular Diagnostics: The Territory

Diagnostic Target

• Infectious Disease

• Hematology:

• Solid tumor mutations:

• Cytogenomics/Cytogenetics

Location

• Microbiology Laboratory

• Clinical Pathology

• Anatomic Pathology

• Anatomic Pathology?

Infectious Disease

• Viral load – HIV-1 – HBV – HCV – CMV – BK

• HCV genotyping • Respiratory virus panel • Bacterial & fungal ribosomal genes • MRSA screening & surveillance

Hematology

• IGH gene rearrangement

• BCR-ABL fusion transcript

• JAK2 mutation

• Bone marrow engraftment (PowerPlex-16)

• NPM mutation

• IDH1 & IDH2 mutations

• Also coagulopathy (ie FactorV and Factor II)

Solid Tumor Oncology

• Microsatellite instability

• KRAS mutation (codons 12 & 13)

• BRAF V600 mutation

• NRAS mutation (codon 61)

• EGFR mutation (exons 18, 19, 20, 21)

• IDH1 & IDH2 mutations

Clinical Laboratory Economics

Clinical Laboratory

Personnel Costs

Supply Costs

Routine 60% 40%

Molecular 40% 60%

Applied Genomics

30% 70%

Types of Production

• Assembly line – High volume

– Automation

– Minimal interpretation

• Craft shop – Low volume

– Manual, minimal automation

– Active interpretation/correlation with clinical & pathologic findings

Test Categories

• Viral titers: assembly line

• High volume

• FDA cleared

• Automated

• Minimal specimen processing

• Outpatient

• Low margin

• Minimal interpretation

• Oncology (hematology & solid tumor): craft shop

• Low volume

• Most lab developed

• Minimal automation

• Active specimen processing

• Pathologist selection of tissue

• Inpatient & outpatient

• Some high margin

• Interpretation critical

Oncology Diagnostics

• Companion diagnostics/Theranostics

• Codified guidelines (ie NCCN)

• Medical utility

• Parallel to standard specimen pathways

Oncology Diagnostics (2)

• Performed on FFPE

• Requires pathology review

– Selection of regions for analysis

• Adequacy

– Size

– Tumor content

• Microdissection

Oncology Diagnostics (3)

• Technologies for analysis

– Allele-specific RT-PCR

– Nucleotide sequencing (Sanger)

• Interpretive report

– Correlation with histopathology

– Tumor size & content (required in proficiency testing)

Oncology Mutation Detection

• Nucleotide sequencing (Sanger) preferred for analysis when multiple mutations are possible

• Transition to NextGen sequencing

• Limited sensitivity (Sanger)

• Sequencing & RT-PCR similar sensitivity for BRAF mutation detection in thyroid FNA (n=30)

NextGen Sequencing

Pros

• High throughput

• Barcoded specimens => multiplex analysis

• Quantitative

• Sensitive

Cons

• Error prone (mutations & indels)

• Expensive

• Labor intensive (automation in pipeline)

• Requires bioinformatics expertise

Applications of NextGen Sequencing

• “Deep sequencing” of amplicons

• Targeted sequencing following exon capture

• Total Kinome

• Total Exome

• Total Gnome

(cannot even spell it correctly)

Applications of NextGen Sequencing

• “Deep sequencing” of amplicons

• Targeted sequencing following exon capture

• Total Kinome

• Total Exome

• Total Genome

Metastatic Colorectal Carcinoma

62% Tumor Cells => 31% mutant KRAS

G G T G G C C

G C C A C C G

Deep (Amplicon) NextGen Seq: KRAS Mutations

Genomics & Economics

• Genome = 3,200,000,000 base pairs (bp)

• Exome (1.5% of Genome) = 48,000,000 bp

• Exon capture & targeted NextGen sequencing = 50,000 bp = 0.1% of exome

• $5,000 - $10,000 + $20,000 bioinformatics

• $1,000 - $5,000 + $10,000 bioinformatics

• <$1,000

• Bringing genomic medicine to CLIA-accredited diagnostics = PRICELESS!

Project Design

• Focus: analysis of genes that have diagnostic and/or prognostic value in clinical oncology

• 1° goal: assess mutations in hot spots of 51 genes (275 exons) -> NextGen Seq

• Specimen types: Formalin-fixed & paraffin embedded tissues (FFPE), blood, bone marrow, fresh tissues,

• Technical & bioinformatic analysis performed in TJU/TJUH clinical laboratory

Objective • Goal = translation of data discovered in genome centers to

the diagnostic arena within the workflow for diagnostic pathology

• Identify low frequency mutations and clinicopathologic associations

• “God is not on the side of the big battalions, but on the side of those who shoot best.” Voltaire: Notebooks (c.1735-c.1750)

• “It is said that God is always on the side of the big battalions.” Voltaire: Letter to François-Louis-Henri Leriche (1770-02-06)

Enrichment

Genomic DNA

Exon Capture

Fragmentation / FFPE

Exon

Capture

Gene Exon Gene Exon Gene Exon Gene Exon Gene Exon Gene Exon Gene Exon Gene Exon Gene Exon Gene Exon

ABL Exon 5 BAP1 Exon 10 DMNT3A Exon 13 GNA11 Exon 4 MLH1 Exon 12 MSH2 Exon 6 NF2 Exon 4 PAX6 Exon 8 PMS2 Exon 11 TET2 Exon 5

ABL Exon 6 BAP1 Exon 11 DMNT3A Exon 14 GNA11 Exon 5 MLH1 Exon 13 MSH2 Exon 7 NF2 Exon 5 PAX6 Exon 9 PMS2 Exon 12 TET2 Exon 6

ABL Exon 7 BAP1 Exon 12 DMNT3A Exon 15 GNAQ Exon 4 MLH1 Exon 14 MSH2 Exon 8 NF2 Exon 7 PAX6 Exon 10 PMS2 Exon 13 TET2 Exon 9

AKT1 Exon 4 BAP1 Exon 13 DMNT3A Exon 16 GNAQ Exon 5 MLH1 Exon 15 MSH2 Exon 9 NF2 Exon 8 PAX6 Exon 11 PMS2 Exon 14 TET2 Exon 10

AKT2 Exon 10 BAP1 Exon 14 DMNT3A Exon 17 HSP90 Exon 2 MLH1 Exon 16 MSH2 Exon 10 NF2 Exon 10 PAX6 Exon 12 PMS2 Exon 15 WT1 Exon 1

AKT2 Exon 11 BAP1 Exon 15 DMNT3A Exon 18 HSP90 Exon 11 MLH1 Exon 17 MSH2 Exon 11 NF2 Exon 11 PAX6 Exon 13 PTEN Exon 5 WT1 Exon 2

APC Exon 18 BAP1 Exon 16 DMNT3A Exon 19 IDH1 Exon 4 MLH1 Exon 18 MSH2 Exon 12 NF2 Exon 12 PDGFRA Exon 12 PTEN Exon 6 WT1 Exon 3

ALK Exon 20 BAP1 Exon 17 DMNT3A Exon 20 IDH2 Exon 4 MLH1 Exon 19 MSH2 Exon 13 NF2 Exon 14 PDGFRA Exon 15 PTEN Exon 7 WT1 Exon 4

ALK Exon 21 BRAF Exon 15 DMNT3A Exon 21 IKZF1 Exon 4 MLL Exon 1 MSH2 Exon 14 NF2 Exon 15 PDGFRA Exon 17 PTPN11 Exon 3 WT1 Exon 5

ALK Exon 22 CBL Exon8 DMNT3A Exon 22 IKZF1 Exon 5 MLL Exon 2 MSH2 Exon 15 NOTCH1 Exon 26 PDGFRA Exon 18 PTPN11 Exon 13 WT1 Exon 6

ALK Exon 23 CBL Exon9 DMNT3A Exon 23 IKZF1 Exon 6 MLL Exon 3 MSH2 Exon 16 NPM Exon 12 PDGFRA Exon 19 RET Exon 5 WT1 Exon 7

ALK Exon 24 CEBPA Exon 1 DMNT3A Exon 24 IKZF1 Exon 8 MLL Exon 4 MSH6 Exon 1 NRAS Exon 2 PDGFRA Exon 23 RET Exon 8 WT1 Exon 8

ALK Exon 25 C-Kit Exon 8 DMNT3A Exon 25 JAK2 Exon 12 MLL Exon 5 MSH6 Exon 2 NRAS Exon 3 PIK3CA Exon 2 RET Exon 10 WT1 Exon 9

ALK Exon 26 C-Kit Exon 9 DMNT3A Exon 26 JAK2 Exon 14 MLL Exon 6 MSH6 Exon 3 P53 Exon 2 PIK3CA Exon 5 RET Exon 11 WT1 Exon 10

ALK Exon 27 C-Kit Exon 11 EGFR Exon 18 KRAS Exon 2 MLL Exon 7 MSH6 Exon 4 P53 Exon 3 PIK3CA Exon 8 RET Exon 13 Summary

ALK Exon 28 C-Kit Exon 13 EGFR Exon 19 KRAS Exon 3 MLL Exon 8 MSH6 Exon 5 P53 Exon 4 PIK3CA Exon 10 RET Exon 14 51 Genes

ALK Exon 29 C-Kit Exon 17 EGFR Exon 20 MET Exon 14 MLL Exon 9 MSH6 Exon 6 P53 Exon 5 PIK3CA Exon 14 RET Exon 15 275 Exons

ASXL1 Exon 12 C-Kit Exon 18 EGFR Exon 21 MET Exon 19 MLL Exon 10 MSH6 Exon 7 P53 Exon 6 PIK3CA Exon 19 RET Exon 16 Hematologic CA

B Actin Exon 1 CTNNB1 Exon 3 EGFR Exon 22 MLH1 Exon 1 MLL Exon 11 MSH6 Exon 8 P53 Exon 7 PIK3CA Exon 21 RUNX1 Exon 2 Solid Tumors

B Actin Exon 6 DMNT3A Exon 3 FGFR1 Exon 7 MLH1 Exon 2 MLL Exon 12 MSH6 Exon 9 P53 Exon 8 PMS2 Exon 1 RUNX1 Exon 3 Colon

BAP1 Exon 1 DMNT3A Exon 4 FGFR1 Exon 10 MLH1 Exon 3 MLL Exon 13 MSH6 Exon 10 P53 Exon 9 PMS2 Exon 2 RUNX1 Exon 4 Lung

BAP1 Exon 2 DMNT3A Exon 5 FGFR3 Exon 7 MLH1 Exon 4 MLL Exon 14 NDP Exon 2 P53 Exon 10 PMS2 Exon 3 RUNX1 Exon 5 Uveal Melanoma

BAP1 Exon 3 DMNT3A Exon 6 FGFR3 Exon 10 MLH1 Exon 5 MLL Exon 15 NDP Exon 3 P53 Exon 11 PMS2 Exon 4 RUNX1 Exon 6 Eye Development

BAP1 Exon 4 DMNT3A Exon 7 FGFR3 Exon 15 MLH1 Exon 6 MPL Exon 10 NF1 Exon 4 PAX5 Exon 2 PMS2 Exon 5 RUNX1 Exon 7 Housekeeping

BAP1 Exon 5 DMNT3A Exon 8 FLT3 Exon 14 MLH1 Exon 7 MSH2 Exon 1 NF1 Exon 10 PAX5 Exon 3 PMS2 Exon 6 RUNX1 Exon 8 1 454 Jr Run

BAP1 Exon 6 DMNT3A Exon 9 FLT3 Exon 15 MLH1 Exon 8 MSH2 Exon 2 NF1 Exon 31 PAX6 Exon 4 PMS2 Exon 7 RUNX1 Exon 9 2 Samples

BAP1 Exon 7 DMNT3A Exon 10 FLT3 Exon 20 MLH1 Exon 9 MSH2 Exon 3 NF1 Exon 37 PAX6 Exon 5 PMS2 Exon 8 TET2 Exon 3a ~100 x Coverage

BAP1 Exon 8 DMNT3A Exon 11 GAPDH Exon 2 MLH1 Exon 10 MSH2 Exon 4 NF2 Exon 1 PAX6 Exon 6 PMS2 Exon 9 TET2 Exon 3b 10E5 Reads

BAP1 Exon 9 DMNT3A Exon 12 GAPDH Exon 8 MLH1 Exon 11 MSH2 Exon 5 NF2 Exon 2 PAX6 Exon 7 PMS2 Exon 10 TET2 Exon 4 ~$1000

QPCR: Enrichment for Exon Capture (MID1)

Captured library

Input library

gDNA

QPCR Set 1 QPCR Set 2

QPCR Set 3 QPCR Set 4

1 Fragment => 1 Bead =>1 Read

1) Sample Input: gDNA, PCR products, BAC’s, or cDNA

2) Sample fragmentation: 300 to 800 bp fragments; Short PCR products can skip to step 4

3) Adaptor ligation: purification, amplification, and sequencing

4) One fragment = One bead: the first step in emulsion PCR

5) Clonally Amplified Beads: millions of copies per bead

6) Flowgram: digital output of real time sequencing-by-synthesis

MID 1 Coverage Tracks Chr1: Capture + 1° Targets

Sequencing Summary

Date Seq Sample 1 Sample 2 Raw Wells Pass Filter Average length

1/19/2010 Uveal Melanoma AML-1 76196 46825 395

1/20/2010 HEL cells UKE-1 92585 57349 418

1/21/2010 Uveal Melanoma AML-1 84690 60812 426

1/27/2010 Uveal Melanoma AML-1 122793 82086 401

1/28/2010 Lynch Synd-1 Lynch Synd-2 130643 65656 211

1/29/2010 Lynch Synd-3 JAK2 Mid-P 140145 46576 219

1/31/2010 AML-2 AML-3 170582 102749 432

2/1/2010 JAK2 high-P JAK2 Mid-P 65094 21820 402

2/2/2010 Lynch Synd-1 none 190378 26440 216

2/3/2010 JAK2 high-P none 104690 71927 432

Average # Reads: 105 Pass Filter Expt (From 19)

0.00

50.00

100.00

150.00

200.00

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271

Diseases Selected for Targeted NextGen Seq

• Complex mutational profile (mutations are “hardwired”)

• Mutation menu: – Establish diagnosis – Prognostic indicators – Identify molecular targets – Required for selection of therapy

• Presentation of cases illustrating our experience with – Colorectal Carcinoma – Acute Myelogenous Leukemia – Ocular Melanoma

NCCN Guidelines for Biomarker Testing in Colorectal Carcinoma

• KRAS (c 12,13) mutation (Fall 2008)

• BRAF V600E mutation (Jan 2010)

• Mismatch Repair (Jan 2010)

Non-Responce to anti-EGFR Rx (Met)

Non-Responce to anti-EGFR Rx (Met)

Resistance to 5FU Rx (Stage II)

Companion Molecular Diagnostic for Colorectal Carcinoma

Prediction of poor response to anti-EGFR therapy

–KRAS mutation (30-40%)

–BRAF mutation (5%)

–NRAS mutation (3%)

–PIK3CA mutation, exon 20 (3%)

Familial Colorectal Cancer

• Hereditary non-polyposis colorectal carcinoma (broad clinical diagnosis of familial cases)

• Lynch syndrome (subset of HNPCC)

– ~2-7% of cases (onset at early age)

– Other malignancies, incl endometrial carcinoma

– Loss of DNA mismatch-repair pathway components

– Autosomal dominant

– Microsatellite instability (MSI): functional assay, IHC

– Germline mutations

DNA Mismatch Repair Pathway

Loss in Lynch Syndrome

• MLH1 (~30%)

• PMS2 (<5%)

• MSH2 (~60%)

• MSH6 (7-10%)

IHC

• MLH1-, PMS2-

• PMS2-

• MSH2-, MSH6-

• MSH6-

• In sporadic cases loss may be epigenetic silencing or somatic mutation

Disease Specific Targets

Common Hemepath Colon Eye Lung

KRAS ABL1 IKZF1 RUNX1 APC BAP1 ALK

P53 ASXL1 JAK2 TET2 ALK BRAF CBL

PIK3CA CBL MLL WT1 BRAF GNA11 EGFR

PTEN CEBPA MPL KRAS GNAQ IKZF1

C-Kit NF1 NF1 NDP KRAS

CTNNB1 NOTCH1 PDGFRA PAX6 MET

DMNT3A NPM PIK3CA

FGFR3 NRAS MLH1

Housekeeping FLT3 P53 MSH2

B Actin IDH1 PAX5 MSH6

GAPDH IDH2 PTPN11 PMS2

Illustrated Case: 41 yo ♀ w/ Endometrial Carcinoma

MLH1 PMS2 MSH2 MSH6

IHC: MLH1-/PMS2- => MLH1 mutation MSH2+/MSH6- => MSH6 mutation

MSI: Functional Assay

Normal Normal

Tumor Tumor

INTERESTING MUTATIONS :-

-Lynch Syndrome Sample MID6 (FFPE)

- Commonly have MSH2, MSH6,

MLH1 and PMS2 mutations

MLH1 mutation identical to germline mutation by Sanger seq

MSH6 mutation identical to germline mutation by Sanger seq

Not codon 12 or 13

No SNP

MSH2 mutation identical to germline mutation by Sanger seq

S

S

CRC-AdenoCA: Mutation Signatures

32 12 352 111 333 896

MMR-IHC N/A DEF DEF N/A N/A N/A

MMR-Seq WT WT MUT MUT MUT MUT

MSS/MSI N/A N/A MSI N/A N/A MSI

KRAS^ WT WT WT WT MUT (G13D)`

MUT (G12D)`

BRAF^ V600E` V600E` WT WT WT WT

APC WT MUT WT MUT MUT MUT

P53 WT WT WT MUT MUT* (R282W)

MUT

PI3K-CA WT MUT* WT WT WT WT

Other --- TET2 (conf)

MLL (conf)

NOTCH1 (conf)

--- PTEN*

Colorectal Carcinoma Summary

• Deep / Amplicon Seq can give mutational burden (% mutant KRAS) which can be correlated with tumor content by virtual microscopy

• Exon capture and NextGen Seq performed on archival FFPE tissue (n=8)

• Mutations in DNA mismatch repair enzymes detected, including MLH1, MSH6, & MSH2

• Mutation data identical to germline mutations detected by Sanger sequencing

• Mutations described in CRC were identified • Novel, low frequency mutations were identified

and confirmed by Sanger sequencing

Trends in Molecular Oncology

Diagnostic Trends

• Several tumor types require analysis of multiple mutations for theranostics – Colorectal carcinoma

• MSI

• KRAS

• BRAF

– Adenocarcinoma of lung • EGFR

• ALK

• KRAS

– Acute myelogenous leukemia • NPM

• FLT3

• IDH1 (IDH2)

• Others

Analytic Trends

• Emerging companion diagnostics are individual

• High cost

• Relatively low throughput

The Impact of NextGen Sequencing

• NextGen Sequencing is a powerful tool for diagnostic studies and translation

• The cost is decreasing

• Throughput is increasing

• Accurate pathology selection is critical for analysis of tumor specimens

• Tumor heterogeniety demands thorough interrogation (NEJM 2012;366:883-92)

Ingredients for Molecular Pathology Success

• Consolidation of laboratories into 1 multi-specialty program => stable base for technology advancement

• Seamless interface with Cytogenomic Laboratory, physically contiguous if possible

• Direct engagement with subspecialty diagnostic pathologists and cytopathologists

• Hard-wiring pathways with reflex testing protocols

• Addition of bioinformatics expertise

Ingredients for Molecular Pathology Success (2)

• Subspecialty diagnostic pathologists are a critical bi-directional interface with the lab

– Establishing testing menus

– Case selection

– Tissue evaluation and guiding tumor microdissection

– Clinical and pathological correlation

– Two-way communication with clinicians

Acknowledgements

• Zixuan Wang (genomic pathology)

• Renu Bajaj (cytogenomics)

• Gerald Gong (hematopathology, molecular pathology)

• Jeffrey Baliff (GI pathology)

• Ashlie Burkart (GI pathology, Lynch syndrome screening)

• Translational Genomic Pathology Team

Tomorrow’s Medicine?