Translating NGS Data into a Clinically Actionable Assay

Elaine R. Mardis, Ph.D.Professor of GeneticsCo-director, The Genome Institute

NCI Workshop: NGS in Clinical Decision Making

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)

Why is cancer genome analysis challenging?

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

• Conventional pathology may require the majority of the tumor block, leaving little for genomics (melanoma)

• FFPE preparation from pathology (DNA/RNA degradation)• Genomic aneuploidy and amplification of chromosomal

segments impacts the coverage model• Cellular heterogeneity is a reality (not all cells contain all

mutations)• In blood or “liquid” tumors, a skin biopsy is taken for the

normal but may contain high circulating tumor cell counts at diagnosis

Whole Genome Sequencing Process

Human reference alignment

SNP Typing of Tumor and Normal gDNA

Shotgun library construction

Sequence data generation

Computational detection of somatic changes

• The human genome reference sequence is the keystone for cancer genome sequence analysis.

• Tumor and normal genomes are compared separately to the human reference sequence, then to one another, to identify somatic variation of all types.

• Mis-aligning sequences identify structural alterations.

+Normal sequence alignments

1. Strand bias (via binomial test)2. Distance to effective 3’ end of read (via K-S test)3. Paralog filter (via sum of mismatch base qualities)4. Homopolymer filter (number of consecutive bases

preceding or following the variant)

Somatic Point Mutation Discovery

SNVs indentified in Tumor

Predicted Tumor-unique SNVs

Candidate Patient Tumor-unique SNVs

Tier 1:Coding NS SNVsSplice site SNVsCoding SS SNVsSNVs in RNA genes

Tier 2:SNVs in highly conserved blocksSNVs in regulatory regions

Tier 3:SNVs inNon-repetitiveregions

Tier 4:The rest

Somatic SNiPer

Subtract dbSNP

WUGC Somatic SV/CNV Pipeline

Validation data go through parts of this pipeline

Custom Capture for Validation and Read Depth

gDNA

Illumina library

Custom capture probes(target each variant site)

Hybridization

Bind to Streptavidin Magnetic Beads

Sequence Variant Sites and SV assemblies at ~1000-fold Depth

Cancer Genomics

R.K.Wilson 2011

“AML1”: Cancer Genomics by Whole Genome Sequencing

• Caucasian female, mid-50s at diagnosis

• De novo M1 AML

• Family history of AML and lymphoma

• Informed consent for whole genome sequencing

• Solexa sequencer, 32 bp unpaired reads

• 10 somatic mutations detected

• Ley et al., Nature 2008

Tumor Sequencing is Driving Discovery

Total WGS samples: 1351Pediatric and adult tumors with comprehensive clinical data to address clinically relevant questions

Every cancer is different…

Conclusion:“…whole genome characterization will become a routine part of cancer pathology.”

Cancer Genomics in the ClinicTherapeutic Options via NGS

tAML Case Presentation

• 37 y.o. female presented with T2N1 Breast CA ER/PR/Her2+. BRCA1/2-normal.

• At age 39-Stage III-C ovarian CA diagnosed. • At age 43-locally recurrent ovarian CA. • 2 months after completing chemotherapy,

presented with t-AML/respiratory failure. Expired 9 days after presentation.

• Detailed family history did not suggest inherited cancer susceptibility. Patient has three minor children.

Link et al., JAMA 2011; 305(15): 1568-76

tAML Spectral karyotype

46,XX,der(3)ins(3;4)(q26.2;q13.3q31.1)ins(3;3)(q26.2;q27q12)t(3;4)(q26.2;p12),der(3)ins(3;3)(q26.2;q27q12),der(4)ins(3;4)(q26.2;q13.3q31.1)t(3;4)(q26.2;p12),der(5)del(5)(q13.3q34)t(5;12)(q34;p12.3),r(7)(p11.2q11.2),der(12)t(5;12)(q34;p12.3)[14]/ 45,idem,-r(7)(p11.2q11.2)[9]

tAML: TP53 germline deletion

Whole genome analysis indicates the patient has Lei-Fraumeni syndrome.Previously undetected by clinical assay due to nature of the 3 exon deletion.Genomic data are supported by RNA analysis.

Clinical case: atypical APL

37 y.o. female with de novo AML; M3 morphology

Complex cytogenetics, persistent leukemia

First remission, referred to WU for SCT.rBM: normal morphology, cytogenetics;

negative for PML/RARA.

Allogeneic SCT

Consolidation + ATRA

Chemo + ATRA

Chemo only

???

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

“Genome-Guided Medicine”: An early example

37 y.o. female with de novo

AML, M3 morphology,

CTG, no PML-RARA.

Referred to WUSM for SCT.

Detection of PML-RARA by

WGS,Confirmed by FISH, RT-PCR(CLIA/CAP)

Consolidation:Chemo + ATRA

Sustained remission

Cancer Genomics in the ClinicTherapeutic Options via “Gx,Ex,Tx”

NGS “Diagnostic Trials: An N of 1”

• Cancer patients consented for genomic sequencing and return of information

• Cancer biopsies studied by WGS, exome and transcriptome integrated analysis• WGS drives discovery• Exome contributes read depth for heterogeneity/clonality analysis• Transcriptome monitors aberrant gene expression and validates

fusions

• Interpretive analysis should accurately identify actionable targets and available clinical trials.

• All possibly actionable mutations/alterations are verified in CLIA lab with pathology sign-off.

• A Tumor Board model for education, decision-making, and patient monitoring is critical. Sharing results to the community is desired/critical!

SNVs

Indels

SVs

CNVs

Fusions

DE genes

DE isoforms

Somatic/Germline Cancer

Events (DNA+RNA)

TGI Drug-Gene interaction database

(24 database sources)

Filtered (activating/driver

s)

Candidate genes/pathways

Clinically actionable events

Functional annotation

DrugBank

TTD

clinicaltrials.gov

PharmGKB

STICH2

Kinases

RTKs

Etc …

Clinical prioritization and

reporting

Clinical Genome Analysis Pipeline

Tumor Immunoediting : Somatic mutations as vaccine targets

• Combined exome capture and in silico epitope prediction in a chemically-induced mouse sarcoma model

• We identified a highly immunogenic tumor-specific mutated protein antigen that targets tumor cells for elimination in an immune-capable host.

• First demonstration using genomics to identify a tumor antigen from an unedited tumor, and to demonstrate that T-cell-dependent immunoselection is a mechanism underlying the outgrowth of tumor cells that lack a strong rejection antigen(s).

Examples of Diagnostic Sequencing

Metastatic breast cancer

HG1 Patient History

• Female patient, mid-50’s with history of DCIS and Paget’s disease of the left nipple 2007

• Widespread metastatic breast cancer to bone 2009, biopsy shows ER- HER2+ disease (FISH amplified), highly responsive to paclitaxel + trastuzumab

• Brain metastasis in posterior fossa diagnosed May 2010, treated with surgery (sample for sequencing obtained) radiosurgery and lapatinib

• Progressive disease in March 2011: treated with further surgery and whole brain irradiation

• July 2011: systemic disease still under control with trastuzumab in combination with lapatinib

Somatic mutation frequencies hint at heterogeneityR

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Tumor variant allele frequency

Pro

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Tumor variant allele frequencyTumor variant allele frequency Metastatic breast cancer (to brain)

92 point mutations are identified in genes

Somatic copy number variants – genome wide

Metastatic breast cancer (to brain)

Somatic copy number variants – chromosome 17

Metastatic breast cancer (to brain)

HER2 / ERBB2 is heavily amplified in this tumor

HER2

RNA-seq confirms the HER2, PR, & ER status

Metastatic breast cancer (to brain) vs. four primary HER2 –ve breast cancers

HER2 +ive

PR- ER-

• Gene expression values from RNA-seq

• Used to confirm HER2, PR, & ER status of each patient

• Tumor is• HER2+, PR-, ER-

Somatic copy number variants – chromosome 6

Metastatic breast cancer (to brain)

HDAC2 (histone deacetylase 2) is amplified to almost the same degree as HER2

RNA expression – HDAC2

Metastatic breast cancer (to brain) vs. four primary HER2 –ve breast cancers

HDAC2 genomic amplification is accompanied by high RNA expression

RNA expression pattern confirms HDAC2 over-expression. Patient predicted to respond to the HDAC2 inhibitor Vorinostat [DrugBank].

PNC-2 Tumor: Pancreatic Neuroendocrine Metastatic Disease

• Initial diagnosis: Pancreatic Neuroendocrine tumor• First metastatic tumor (liver) banked in 2005 (FFPE),

no adjuvant chemotherapy • Second metastatic tumor to liver banked in

2011(FFPE), following neoadjuvant chemotherapy, including everolimus + Bevacizumab

• Patient consented for return of results from whole genome sequencing

• We produced WGS and exome capture data from the two metastatic tumors and a blood normal. RNA-seq from both metastatic tumors.

PNC2: Comparing metastatic tumor presentations

Met1 Clonality

Met2 Clonality

Although 33 mutations were identified in the tumor genome,none were considered druggable…

PNC2: RNA-seq analysis

PNC2: Met1

Event type gene_name Effect (FPKM) drug_name

RNAseq Cufflinks CCL2 16.682 Mimosine

RNAseq Cufflinks F2 24.89520601 Suramin

RNAseq Cufflinks FKBP1A 16.1605925 Sirolimus

RNAseq Cufflinks PLA2G2A 230.6282525 Suramin

RNAseq Cufflinks PSMD1 23.42608841 Bortezomib

RNAseq Cufflinks SLC25A6 16.1257 Clodronate

RNAseq Cufflinks TUBA1A 21.61057 Vinblastine

RNAseq Cufflinks VEGFA 32.60708007 Bevacizumab

PNC2: Met2

Event type gene_name Effect (FPKM) drug_name

RNAseq Cufflinks CCND1 19.4453 Arsenic trioxide

RNAseq Cufflinks HDAC1 25.03871272 Vorinostat

RNAseq Cufflinks PLA2G2A 23.54109399 Suramin

RNAseq Cufflinks PSMD1 16.82704613 Bortezomib

RNAseq Cufflinks SLC25A5 20.9629519 Clodronate

RNAseq Cufflinks SLC25A6 16.57470001 Clodronate

RNAseq Cufflinks TUBA1A 93.0175 Vinblastine

RNAseq Cufflinks VEGFA 54.39539362 Bevacizumab

• Based on our RNA-seq analysis, VEGFA is increasing in its expression levels from the initial metastatic lesion sampled in 2005, to the present lesion sampled in 2011.

• The DrugBank prediction for VEGFA overexpression is treatment with Bevacizumab/Avastin.

PNC2: “Post-mortem” Diagnostics

Baseline 3 weeks on Everolimus 6 weeks after adding Bevacizumab• As happens in sequencing advanced metastatic patients, this patient died before being treated based on the

genomic predictions.• However, post-mortem consultation with the patient’s oncologist indicated that perfusion CT during

bevacizumab treatment showed response was evident. However, Bevacizumab had been withdrawn due to side effects.

Example case

Acute lymphocytic leukemia

Case study: 2nd relapse B-ALL

• Age 25: Initial presentation of classic pre B-ALL Standard induction, consolidation, and 2 years of

maintenance therapy Marrow banked

• Age 30: 1st relapse CR obtained with salvage chemo consolidation with a matched sibling allo transplant very mild GvHD

• Age 33: 2nd relapse, CNS involvement (July 2011) During induction chemotherapy, we sequenced T/N

genomes using banked blasts from initial presentation, exomes (T/N) and RNA-seq of blasts

ALL-1: Somatic single nucleotide variations

• 91 somatic coding SNVs• 42 with evidence for expression in RNA-seq

Gene Ref. Var. AA Type WGS Exome

RNA-seq

UBXN4 T C DD silent 51.25% 40.56% 53.40%

OGT G T CF missense 39.22% 38.7% 35.79%

KIAA1033 C T TI missense 38.89% 48.1% 50.75%

C15orf39 C G AA silent 47.37% 37.5% 48.74%

SPTAN1 T C LP missense 42.39% 49.03% 50.17%

DDX6 A G LP missense 21.78% 14.29% 18.14%

CCDC47 C T AT missense 22.34% 21.9% 23.36%

NF1 C T R* nonsense 64.58% 64.04% 47.06%

TNRC6B G A SN missense 13.25% 12.8% 19.46%

KIAA1462 G C PA missense 70% 45.05% 41.14%

Although 91 mutations were identified in the tumor genome,none were considered druggable…

ALL-1: Tumor heterogeneity R

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Tumor variant allele frequency

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Tumor variant allele frequencyTumor variant allele frequency

2,074 tier1-3 somatic variants. 91 are tier1 (coding exons)

NF1

Acute lymphocytic leukemia

ALL1: RNA-seq analysis

Naive B-cells

Pre-B-ALL ALL1

• Patient’s activated FLT3 gene was targeted with sunitinib, complete clinical remission was achieved in 12 days, enabling MUD SCT.

• Identification of discrete chromosomal deletions in tumor cells provides a means for ongoing tumor assessment with interphase FISH (presence of MRD)

• Four months post-SCT, the patient is back at work.

• CD135 (FLT3) added to the flow panel for all B-ALL patients at Barnes-Jewish Hospital.

Summary

• We can produce comprehensive whole genome analysis of cancer patients now, and the data can provide very important input for clinical, therapeutic decision making.

• Not all patients will benefit, however, because of our current knowledge gaps and because targeted therapies are not yet available for many important cancer genes.

• Many regulatory issues must be resolved before these tools can be used widely. Discussions are ongoing at NIST, FDA, CAP etc.

• Each case represents a focused effort involving genomicists, oncologists, pharmacologists and pathologists (at least). Physician education in genomic data interpretation is a tangential benefit.

• Off label use of therapies may become common. Sharing results is a critical exercise at sites implementing this approach.

Name email@wustl.edu

Acknowledgements

The Genome InstituteLi Ding, Ph.D.Malachi Griffith, Ph.D.David Dooling, Ph.D.David Larson, Ph.D.Nathan Dees, Ph.D.Vincent Magrini, Ph.D.Sean McGrathJason WalkerAmy LyDaniel KoboldtLucinda FultonRobert FultonLisa CookRyan DemeterTodd WylieKim DelehauntyMichael McLellanRick Wilson

WUSM/Siteman Cancer CenterTimothy Ley, M.D.Matthew Ellis, M.B., Ph.D.Benjamin Tan, M.D.John DiPersio, M.D., Ph.D.Timothy Graubert, M.D.Matthew Walter, M.D.John Welch, M.D., Ph.D.Jackie Payton, M.D., Ph.D.Peter Westervelt, M.D., Ph.D.Lukas Wartman, M.D.

Our patientsNHGRINCIWUCGI