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Advancing Cancer Genomics: The Impact of Personalized Genome SequencingAdvancing Cancer Genomics: The Impact of Personalized Genome Sequencing

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25 February, 2013

Elaine Mardis, Ph.D.Washington University in St. LouisSt. Louis, MO

Michael Snyder, Ph.D.Stanford UniversityStanford, CA

Philip Stephens, Ph.D.Foundation MedicineCambridge, MA

Next-Generation Sequencing: Translation to Cancer Prognosis, Diagnosis and Care

Elaine R. Mardis, Ph.D.Co-director, The Genome InstituteProfessor of GeneticsWashington University School of Medicine

Science/AAAS webinar February 2013

Why is cancer WGS analysis “easy”?

• The comparison of a patient’s tumor to their normal genome • Provides an individualized comparison of what is truly somatic vs.

what is truly inherited (germline)• Existence of online information about frequently mutated genes in

cancer samples (COSMIC)• Large-scale efforts using NGS methods to catalogue mutated genes

(e.g. TCGA, ICGC)

Why is cancer genome analysis challenging?

• In solid tumors, normal cells are present to differing degrees. Certain tumor types are quite diffuse (prostate, pancreas) and may require specific tumor cell isolation by LCM or flow sorting

• FFPE preparation from pathology (DNA/RNA degradation)• Aneuploidy and amplification of chromosomal segments

impacts the coverage model• Cellular heterogeneity: not all cells contain all mutations

and “druggable” mutations may be present at low levels (in a minor subclone)

• In blood or “liquid” tumors, a skin biopsy is taken for the normal but may contain high circulating tumor cells

• A cheek swab or mouthwash normal can be substituted, but lots of bacterial genomes get sequenced too…

Every cancer genome is different…

EGFR mutations in lung cancer

K DFG R Y

Tyrosine kinase

745 Y869

K DFG Y Y Y YTM

718 964

EGF ligand binding autophos

GXGXXG

835

R

776

H

858 947

MLREA

By directed PCR and capillary sequencing, we determined that ~80% of Iressaresponders have EGFR mutations in the tyrosine kinase domain

W. Pao et al., PNAS 2004

Comprehensive Cancer Sequencing

Read Alignment Enables Discovery and Integration

Whole genome sequencing

Exome or targeted sequencing

Whole transcriptome sequencing

Annotating Somatic Alterations

Tier 1

Tier 2

Tier 3

Tier 4

8.6% (conserved/regulatory)1.3% (“the exome”)

41.4% other unique48.7%

(repetitive)

Welch et al., JAMA 2011: 305(15): 1577-1584.

Disease progression as an evolutionary process

Diagnosis: Multiple leukemic clones

present

Clinical remission: loss of most

leukemic clones

Relapse: Acquisition of new mutations in a pre-existing clone

• Multi-clonal or mono-clonal primary disease, wherein relapse always involvesnew mutations, some with unknown relevance

Ding et al., Nature 2012

SNVs

Indels

SVs

CNVs

Fusions

DE genes

DE isoforms

Somatic/Germline Cancer Events

(DNA+RNA)

Drug-Gene interaction database-DGIdb

(24 database sources)

Filtered (activating/drivers)

Candidate genes/pathways

Clinically actionable events

Clinically actionable events

Functional annotation

DrugBank

TTD

clinicaltrials.gov

PharmGKB

STICH2

Kinases

RTKs

NetMHCPan

Clinical prioritization and reporting

Clinical Genome Analysis/Interpretation Pipeline

Malachi & Obi Griffith, Scott Smith

**

****

**

Therapeutic Predictions for NSCLC Mutations

Govindan et al., Cell 2012

Before AfterMost NSCLC patients with mutations in the epidermal growth factor receptor (EGFR) gene are exquisitely sensitive to tyrosine kinase inhibitors (TKIs) These patients often achieve remarkable relief from their tumor burden, but most progress to metastatic disease while on therapy, due to acquired resistance.

Targeted Therapies Often Lead to Therapy Resistance

A reality for targeted therapy prediction: Off-label FDA

We predicted Lukas’ 2nd relapse ALL would respond to Sutent due to FLT3 over-expressionSutent is not FDA approved for use in ALL treatmentPfizer twice rejected compassionate use requests from Lukas

Personalized Immunotherapy

• Identifying the most highly expressed tumor‐specific neo‐antigens present in each patient’s tumor requires an algorithmic evaluation of their genic missense mutations with the HLA class 1 subtype • This approach generates a prioritized list of neo‐antigens that can be used either to generate a DNA‐based vaccine or a mature dendritic cell‐based vaccine that is highly personalized to their tumor cell antigens• Our early work in this approach is establishing a workflow and tractable clinical timeframe for neo‐antigen identification, vaccine production and in vitro functional evaluation prior to the patient’s receipt of the vaccine


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25 February, 2013




Cancer Genomics

Michael Snyder

February 25, 2013

Conflicts: Personalis, Genapsys, Illumina

• Whole genome sequencing from archival cancer samples (formalin fixation and paraffin embedding).

• Illumina sequencing with 100 base paired end reads using a HiSeq 2000 system.

• Analysis of copy number aberrations and other genetic aberrations

Two Examples Metastatic Colorectal Cancer Genome Sequencing Analysis

Patient 2382 – Metastatic colorectal cancerChromosome 7: Two amplification regions

Chr 7p arm Chr 7q armLog 2 ratio‐

genomic copy

number

Chromosome 7 coordinates (Mb) NCBI 37

• Both loci > 10 copy number showing statistical significance.

• EGFR amplification is in ~3% of colorectal cancers (Cancer Genome Atlas)

CEN

Normal diploid copy numberEGFR CDK6

• FFPE 5 micron sections of primary tumor

• Gene amplification containing a CDK8 on Chr 13.

Patient 2222 – metastatic colorectal cancer

CDK8

Identifying novel driving variations in complex cancer genomes remains

challenging, especially in a personalized manner?

Integrative Profiling of Esophageal Cancer

• Used laser‐capture microdissection to isolate tumor and paired normal tissue• LCM critical for transcriptome analysis of normal tissue

• Complete Genomics whole genome sequencing• HiSeq 2x101 bp paired‐end RNA‐sequencing• Nimblegen 2.1M CNV array

How to find potential driver mutations‐ RNA Sequencing

Deep genome sequencing>20,000 SNVs and Indels>1000 Structural Variants

Copy number is concordant with gene expression changes

Could refine CNV list by looking for enrichment of concordant gene expressionOutliers could be interesting cases

CNVs with concordant gene expression

(number of overlapping genes)

Amplifications containing more up regulated genes are enriched in genes annotated with GO terms related to cell cycle and division

GO Enrichment

UpregulatedGenes

GO enrichment on specifically induced genes: Metastasis

OFF ON genes

GO enrichment on down regulated genes:Cell differentiation

≥2x Down genes

Expression can also be used to prioritize single nucleotide variant lists

• Examined RNA‐seq data to look for evidence of genomically called somatic SNVs

• As others have observed about a third of nonsynonymous (NS) variants are expressed

• Known TP53 (Y220C) loss‐of‐function, mutation that destabilizes protein

Conclusions:

1) Druggable targets can be readily found by WGS

2) Transcriptome sequencing of both matched normal and tumor samples can be use as an effective filter for novel somatic variation discovered by cancer genome sequencing

3) Given the low recurrence rates of most novel variants, functional characterization of these variants in relevant models will become increasing important

Acknowledgements

CRC: Hanlee Ji, Hassan Chaib, George Fisher

Esophageal Cancer: Jason Reuter


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25 February, 2013




‹›

Advancing Cancer Genomics: The Impact of Personalized Genome Sequencing

Phil Stephens, Head of R&D, Foundation Medicine Inc, Cambridge, MA

‹›

Therapies targeting the alterations that drive an individual patient’s disease can be very effective

Matching the correct targeted therapy to the correct patient is diagnosticallychallenging as the number of “clinically relevant” genomic alterations increases

Pre‐therapy Post‐therapy

Patient with BRAF V600E mutated malignant melanoma treated with selective inhibitor of BRAF (Vemurafenib)

‹›

The complexity of solid cancer genomes poses significant diagnostic challenges

Pre-cancerous In situ Invasive Metastaticlesion cancer cancer cancer

Mutagenic exposureGenetic InstabilityGenomic instability

Genomic alterations NumberTotal 10,000sClinically relevant 1‐2

Number of “clinically relevant” alterations in a single patient is LOW, buried amongst 1,000s of total genomic alterations

‹›

• Number of clinically relevant genomic alterations across a disease indication (e.g. Lung cancer) is HIGH

• The five clinically relevant types of genomic alterations (base sub, indel, amplification, homozygous deletion and gene fusion) each pose different diagnostic challenges

• Low tumoral purity in many clinical specimens requires diagnostic tests with extremely high accuracy

• Many clinical cancer specimens are FFPE which can damage DNA and include needle biopsies which are tiny

• The clinical report must be comprehensive but easily interpretable by a busy community oncologist

Diagnostic challenge: Translating NGS from the research setting into the clinic

‹›







‹›







‹›







‹›







‹›

FoundationOne™: Comprehensive NGS‐based cancer genomic profiling assay specifications

• One comprehensive genomic profile to detect all clinically relevant genomic alterations in a single assay

• Focused on the 236 known clinically & biologically relevant cancer genes

• Validated high accuracy from routine clinical specimens with ≥20% tumor nuclei

• Requires small amounts of tissue from routine FFPE samples including needle biopsies (≥50ng of DNA)

• Customized computational biology algorithms validated for high sensitivity and specificity in samples with low purity

‹›







‹›







‹›







‹›







‹›

FoundationOne™: Comprehensive NGS‐based genomic profiling assay workflow (14‐21 days)

Sample preparation:

Analysispipeline:

Pre‐Analytic Process(Pre‐Sequencing)

Post‐Analytic Process(Post‐Sequencing)

Translating research grade NGS to a clinical cancer diagnostic assayrequires extensive optimization and investment

Library Construction, Hybrid Capture: Clinical report:

Extensive optimization

Resource intensive

Extensive optimization

Advanced computational biology

‹›

Rigorous analytical validation essential prior to clinical use: Approach used for base substitutions

Mutant Allele Frequency

Number of Subs

Pool 1 Pool 2 Total

<5 % 206 201 407

5 ‐10% 314 300 614

10‐15% 130 103 233

15‐20% 75 77 152

20 ‐ 100 % 332 318 650

Total 1,057 999 2,056Pool 2

Pool 1

Pooling HapMap cell lines generates 2,056 base substitutions (subs) at a range of allele frequencies across the entire assay

‹›

Analytical validation results demonstrate the high accuracy and reproducibility required for clinical use

Sample prep and algorithms optimized to detect genomic alterations with high accuracy from clinical specimens with ≥20% tumor nuclei

Base substitutions (MAF 5-100%)

Sensitivity: >99% PPV: >99%

Insertions/deletions (1-40bp, MAF 10-100%)


Copy number alterations (zero or ≥8 copies)


‹›









‹›









‹›

Overview statistics of first consecutive 2,221 cases profiled in our CLIA certified, CAP accredited lab

Number of samples 2,221*Number of failed samples 109 (4.9%)

Samples with at least one actionable alteration 1,614 (76.4%)

Mean number of alterations per sample 3.06 [0-23]Mean number of actionable alterations per sample 1.57 [0-16]*Number excludes the ~10% of samples with insufficient material

Actionable definitionFDA approved targeted therapy in tumor typeFDA approved targeted therapy in another tumor typeOpen clinical trial of therapy targeting alteration

‹›

Overview statistics of first consecutive 2,221 cases profiled in our CLIA certified, CAP accredited lab

Number of samples 2,221*Number of failed samples 109 (4.9%)

Samples with at least one actionable alteration 1,614 (76.4%)

Mean number of alterations per sample 3.06 [0-23]Mean number of actionable alterations per sample 1.57 [0-16]*Number excludes the ~10% of samples with insufficient material

Actionable definitionFDA approved targeted therapy in tumor typeFDA approved targeted therapy in another tumor typeOpen clinical trial of therapy targeting alteration

‹›Tumor types that comprise the 2,221 clinical cases

‹›

Actionable alterations identified in the 2,221 clinical cases

Hotspot includes alterations detectable by a hypothetical test

combining SNaPshot, OncoCarta, OncoMap and HER2, EML4-ALK,

(assumes 100% sensitivity)

Identifies nearly four times the number of actionable alterations than a hypothetical panel combining multiple Hotspot assays

‹›

0

500

1000

TP53

KRAS

PIK3

CACD

KN2A APC

MYC

MCL1

CDKN

2BEG

FRPTEN

ARID1A RB1

CCND1 NF1

ERBB

2MDM

2FG

FR1

BRAF

CDK4

SMAD

4BR

CA2

PTPR

DAT

MSTK1

1CC

NE1

FBXW

7CT

NNB1

BRCA

1NOTC

H1RICT

OR

LRP1

BNRA

SDN

MT3A

KDM6A

FGFR3

SMAR

C…SO

X2AK

T2PIK3

R1CD

H1RP

TOR

IDH1 NF2

AKT1

BAP1

CCND3

CDK6

MET

CCND2

TET2

AURK

AFG

FR2

MAP

2K4

NKX

2_1

PDGFRA

VHL

TSC2

RUNX1 ALK

KIT

MSH

6EW

SR1

Most frequently altered genes

76.4% of specimens harbored ≥1 actionable alteration*62/155 most commonly altered genes displayed

Long tail of somatically altered genes accounts for majority of actionable alterations

Num

ber o

f alte

red

case

s

‹›

• KIF5B‐RET expression led to oncogenic transformation

• KIF5B‐RET transformed cells are sensitive to RET inhibitors

RET fusions identified in 8/367 (2.2%) NSCLC patients, TWO NSCLC patients with RET fusions demonstrated good response to RET inhibitors

KIF5B RETExons 1-15 Exons 12-20

Kinesin Coiled coil Tyrosine kinase

KIF5B-RET fusion TKI sensitivity

• Screening ~600 patients revealed KIF5B‐RET fusions ~1% in Caucasians and 6.3% in Asian patients without known driver mutations

Translation

Novel KIF5B‐RET fusion identified in first clinical case

‹›

ERBB2 amplification and overexpression routinely tested for in breast and gastroeosphageal cancers

% T

umor

s

0%

5%

10%

15%

20%

ERBB2 alterations by type and site of origin

‹›

0%

5%

10%

15%

20%

• ERBB2 alterations identified in 110 specimens across 13 tumor types (4.8% total cases)

ERBB2 alterations by type and site of origin

• 42.3% of ERBB2 alterations were mutation/fusion (in non‐ERBB2 amplified specimens)

% T

umor

sERBB2 amplification and overexpression routinely tested for in breast and gastroeosphageal cancers

‹›

ERBB2mutations in breast cancer cluster in the kinase domain

Distribution of ERBB2 mutations on ERBB2 protein

‹›



ERBB2 GRB7

ERBB2 mutation/fusion enriched in CDH1 mutated in 5/22 invasive lobular cancer (22.7%) vs CDH1 WT invasive breast cancer 8/286 (2.8%) p=0.0006

ERBB2-GRB7 fusion identified invasive lobular breast cancer

‹›



Invasive lobular breast cancer patient with ERBB2 mutation known to have had a good response to ERBB2 targeted therapy

‹›

0%10%20%30%40%50%60%

EGFR alterations by site of origin

EGFR alterations were identified in 151 cases across 13 different tumor types (6.8% total cases)

% T

umor

s

‹›

6/32 NSCLCs harbored EGFR alterations missed by other diagnostic assaysBreast cancer patient with EGFR mutation had good response to EGFR TKI

Distribution of EGFR mutations on EGFR protein

EGFR alterations were identified in 151 cases across 13 different tumor types (6.8% total cases)

‹›Conclusions

• Translating research grade NGS to a clinical cancer diagnostic assay requires extensive optimization and investment

• Rigorous analytical validation to determine test accuracy and reproducibility is required prior to clinical use

• Complex information must be conveyed in an easily interpretable, comprehensive report to maximize utility for busy oncologists

• Initial clinical results are encouraging, 76.4% of clinical cases harbored at least one actionable genomic alteration

• Comprehensive profiling can identify four times the number of targeted treatment options compared to “Hotspot” tests

• Patients who have exhausted treatment options may show dramatic responses to targeted therapies identified as rational candidates in clinical reports

‹›Conclusions







‹›Conclusions







‹›Conclusions







‹›Conclusions







‹›Conclusions







‹›Acknowledgements

www.foundationmedicine.com, www.foundationone.com

Foundation Medicine Foundation Medicine AlumniAmy Donahue Kai Wang Maureen CroninAlex Parker Kiel Iwanik Mirna JaroszAlex Fichtenholtz Kristen MahoneyChristine Hiemstra Lazaro Garcia

Christine Vietz Mandy Zhao Dana Faber Cancer InstituteDoron Lipson Mario Alfano Pasi A. JänneEmily White Matt Hawryluk Marzia CapellettiFrank Juhn Michelle Nahas Dalia ErcanJames Sun Norma PalmaJared White Roman YelenskyGarrett Frampton Sean DowningGary Palmer Selmira BeckstromGeneva Young Siraj AliGeoff Otto Sohail BalasubramanianJared White Tina Brennan Jeff Ross Vera Banning John Curran Vincent MillerJie He Zac Zwirko

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