The changing face of cancer
diagnosis
George Vassiliou
November 2011
Overview
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•
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Today’s cancer diagnostic lab
The era of cancer genomics
Novel diagnostic applications
Introducing genomics to cancer diagnosis
Overview
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•
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Today’s cancer diagnostic lab
The era of cancer genomics
Novel diagnostic applications
Introducing genomics to cancer diagnosis
The light microscope remains the central cancer
diagnostic tool for 400 years
Zacharias and Hans Jansen
(ca 1595)
Modern microscope
(ca 1995)
Today’s cancer diagnostic lab
Cellular Phenotyping
Microscopy (histology/cytology)
Immunohistochemistry
Flow Cytometry
Genetic tests
Cytogenetics
Molecular Genetics
Genotyping for specific mutations (PCR/RT-PCR)
Minimal Residual Disease monitoring
(CGH and SNP/LOH genotyping)
(Gene Expression Profiling)
Haemato-oncology lab
Sample
Microscopy
Immunophenotyping
Cytology
Histology
Integrated report
Immunohistochemistry
Diagnostic panels
Cytogenetics
Karyotyping
FISH
Molecular Genetics
Mutational screening
RT-PCR
qPCR (MRD)
MICROSCOPY:
>80% undifferentiated blasts
Morphology of acute lymphoblastic leukaemia
Eosinophils, basophils and small megakaryocytes suggest blast phase of chronic myeloid leukaemia
IMMUNOPHENOTYPE:
Blast cells are CD10, CD19, CD79a, CD34, HLA-DR, TdT positive. Weak CD13.
They do not express CD33 or myeloperoxidase. DNA index is 1.0
Phenotype of B lymphoblastic leukaemia or B lymphoblastic transformation of CML
CYTOGENETICS:
Karyotype:
FISH:
MOLECULAR GENETICS:
BCR-ABL fusion transcript type: p210 e13a2 by RT-PCR
OVERALL CONCLUSION:
B lymphoblastic blast crisis of chronic myeloid leukaemia
46,XY,t(9;22)(q34;q11) in 10 of 10 metaphases
BCR/ABL 92% positive
Diagnostic CGH/SNP genotyping
Diagnostic gene expression profiling
n=241
MammaPrint – 70 gene signature (NKI)
Lymph node positive breast cancer
CR-UK stratified medicines initiative
Tumour type
Gene
Mutation
Drug
Colorectal
KRAS
Codons 12, 13, 61, 146
Cetuximab/Panitumumab
BRAF
V600E/D/K/R/M
Sorafenib/Cetuximab
TP53
Exons 2-‐11
PI3KCA
Exons 9 and 20
PI3Kinase inhibitors
UGT1A1
UGT1A1*28
Irinotecan toxicity
PI3KCA
Exons 9 and 20
Breast
Prostate
Lung
Ovary
Melanoma
TP53
Exons 2-‐11
PTEN
LOH/mutation hotspots
mTOR inhibitors
CYP2D6
5 SNPs
Response to tamoxifen
PTEN
LOH/mutation hotspots
mTOR inhibitors
TMPRSS-‐ERG
Junction fragment PCR
TLR4
2 SNPs
EGFR
Exons 18-21
Erlotinib/gefitinib
EML4-‐ALK
Fusion product
PF02341066 ALK/c-Met inhibitor
XRCC2
5 SNPs
Response to platinum agents
ERCC1
mRNA expression
RRMI
mRNA expression
PTEN
LOH/mutation hotspots
PI3KCA
Exons 9 & 20
mTOR inhibitors
BRAF
V600E/D/K/R/M
Sorafenib/Braf inhibitors
BRAF
V600E/D/K/R/M
Sorafenib/Braf inhibitors
CKIT
Exons 11,13,17
Overview
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Today’s cancer diagnostic lab
The era of cancer genomics
Novel diagnostic applications
Introducing genomics to cancer diagnosis
Advances in DNA sequencing technologies
ABI
capillary
Technologies
454
pyroseq
Solexa/
Illumina
ABI
SOLID
ABI
Roche/454
Titanium SOLID 3.0
Ion
Torrent
Illumina
HiSeq
1016
1014
12
Output 10
kbp / run 1010
108
Capillary (Sanger)
Sequencing
Next Generation
Sequencing
(NGS)
106
104
102
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Rapid reduction in sequencing costs
Sanger Institute
Total yield by week (Gigabases)
2008
2009
2010
2011
How Fast is That?
6000 Gb per week (6 Tb) =
10,000,000 bases per second
½ hour per 6Gb (= 1x Human Genome)
Genome Sequencing
Sanger (capillary) sequencing
Next generation sequencing
2000
~10 years
~$ 3.5 billion
2005
~3 years
~$ 20million
2010
2008
~4 months ~1month
~$ 1.5million $9,500
(Illumina)
Mouse AML
CLL
Myeloma
Hepatocellular
Lung (NSS)
Melanoma
Small-cell lung
Breast
AML
Cancer Genomics
2015
? ~1day
?? $100
Analysing cancer genomes
From Ding et al, Hum Mol Gen, 2010
Genomic Circos Plot
Genomic coordinates
Deletions/Insertions
Substitution density (het)
Substitution density (homo)
Coding Substitutions
Silent
Missense
Nonsense
Splice site
Copy number
Regions of LOH
Structural rearrangements
Intrachromosomal
Interchromosomal
Circos Plot from Pleasance et al, Nature 2010
This decade
• 1000s of individual cancer genomes
• 100s of recurrent mutations
• Aetiological links
• Clinico-pathological correlates
• Delineation of effects of many mutations
• Development of new therapies
> Increasing use of genomics in cancer
diagnosis, prognosis & therapy
Overview
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•
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Today’s cancer diagnostic lab
The era of cancer genomics
Novel diagnostic applications
Introducing genomics to cancer diagnosis
The clinical process in oncology
Pre-clinical
phase

Presentation
Relapse


Diagnosis
Follow-up

Treatment

Assessment
of response


New diagnostic applications
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Whole genome sequencing (~6Gb)
Exome sequencing (~60Mb)
Selected gene/exon DNA sequencing
Residual disease monitoring (plasma DNA)
Whole genome sequencing
A
B
C
D
Diagnostic whole genome sequencing
Constitutional
genome
Cancer
genome
Compare
Inherited mutations
& polymorphisms
Somatic mutations
Subclonal heterogeneity
Other diagnostic
data
Clinical Report
Substitutions
Indels
Copy number changes
Translocations
Exome sequencing: target enrichment
Chr1
NRAS
“Baits” or
PCR amplicons
regions
covered
Diagnostic whole exome sequencing
Constitutional
exome
Cancer
exome
Compare
Inherited mutations
& polymorphisms
Somatic mutations
Subclonal heterogeneity
Other diagnostic
data
Clinical Report
Substitutions
Indels
Copy number changes
Translocations
Selective sequencing
example: an AML toolkit
•
~20 genes known to be recurrently mutated
• Prognostic/treatment implications known for some
• Target enrichment
by “pull down”
Can detect:
Sequence changes
Copy number (UPD/LOH)
ASXL1
NF1
BRAF
CBL
NPM1
IKZF1
CEBPA
NRAS
HPRT1
CSF1R
RUNX1
PAX5
DNMT3A
TET2
PIK3CA
FLT3
WT1
UGT1A1
IDH1
EZH2
CYP2D6
IDH2
KIT
TLR4
JAK2
KDM6A
KRAS
TP53
MLL
PTPN11
EGFR
XRCC2
PTEN
Selective sequencing
example: an AML toolkit
AML1
Type A
(Transcription Factor)
Type B
(DNA modification)
Type C
(Signal transduction)
PROGNOSIS
NPM1
DNMT3A R882C
FLT3-TKD
Intermediate
TET2
KRAS K117N
Intermediate
NRAS G12D
Intermediate
FLT3-TKD
Favourable
KRAS G12D
Poor
AML2
AML3
CEBPA
AML4
NPM1
AML5
ASXL1
AML6
IDH1 R132H
IDH2 R172K
Poor
Plasma DNA
Normal
tissues
Cancer
DNA with tumour-specific mutation
Slide courtesy of Dr Peter Campbell
Tumour-specific rearrangements
Chr10
1st round PCR
Nested real-time PCR
Individual Breast Cancer Genome
Chr20
Relapsing breast cancer
Non-rearranged genomic region
Tumour-specific rearrangement
Undiluted patient plasma
1:10
1:100
1:1000
1:10,000
1:100,000
1
0.5
Normal
Water
1
Intensity
Intensity
0.5
0.1
0.1
0.05
0.05
0.01
0.01
0
5
10
15
20
25
30
Cycles of real-time PCR
Slide courtesy of Dr Peter Campbell
35
40
0
5
10
15
20
25
30
Cycles of real-time PCR
35
40
Serial measurements
CT scan: Localised
deposits around T9-10
CT scan: Widespread
soft-tissue metastases
Estimated tumour DNA / mL serum (pg)
Chemotherapy:
150
First-line chemotherapy
Second-line
Paclitaxel
125
100
75
Rearrangement 1
Rearrangement 2
50
25
Detectable at
limit of sensitivity
Undetectable
5
6
7
8
Slide courtesy of Dr Peter Campbell
9
10
11
12
13
Months after diagnosis
14
15
16
17
Other applications of NGS
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Multiple biomarker MRD
Methylomics
Transcriptomics (RNAseq)
Cancer screening / Biomarker assays
Overview
•
•
•
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Today’s cancer diagnostic lab
The era of cancer genomics
Novel diagnostic applications
Introducing genomics to cancer diagnosis
Hurdles to the introduction of diagnostic
cancer genome sequencing
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Sample choice/compatibility
Cost
Sample to sequence delay
Mutation calling
FFPE, other
$10,000/genome
8-10 days
Specificity / Sensitivity
Technology
• Clinical relevance/utility
• Personal genomes/Ethics
Evolving
Being tested
Clinic
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Choice/Cost
Bioinformaticians
Petabytes (1015)
Pathologists, Clinicians
Laboratory
Sequencing equipment
New personnel
Computer storage
Education/training
Data storage & analysis
Genome Campus
Research Support Facility
Data Centre
Sulston Building
Morgan Building
European Bioinformatics
Institute
Training of pathologists
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Core training in genomics
New sub-specialty e.g. Molecular Pathology?
Impact on other aspects of training
How will the training be delivered?
Training/role of laboratory scientists
Keeping control of the agenda
Diagnostic reporting of genomic data
• Communicating the cancer genome to the clinician
–
–
–
–
–
–
–
–
–
Diagnosis
Recurrently mutated genes
Non-recurrent/private mutations / pathways
Prognostic relevance
Therapeutic relevance
Pharmacogenomics
Constitutional genome
Mutational signatures
Summary / Imagery
Implications for cancer classification
Cellular origin
Morphology
Differentiation/grading
Mutations:
Unified
Classification?
Diagnosis
Prognosis
Treatment
Acute Myeloid Leukaemia –
a paradigm of evolving classification
Morphology
Single entity
Various
1950s
Morphology & cytochemistry
M0-M7
FAB
1976
Morphology,
AML with recurrent cytogenetic translocations
WHO
2002
Immunophenotyping &
AML with multilineage dysplasia
Cytogenetics
AML, therapy related
WHO
2008
AML not otherwise categorized
Morphology,
AML with recurrent genetic abberations
Immunophenotyping ,
Provisional entity: AML with mutated NPM1
Cytogenetics &
Provisional entity: AML with mutated CEBPA
Genetics
Otherwise as 2002
Will there be a paradigm shift ?
?
Summary
• The advent of cancer genomics is changing
cancer medicine
• Changes will transform cancer diagnosis and
the role of pathologists
• Pathologists need to understand what is
coming in order to lead and formulate the
future for cancer diagnosis