René Bernards

Nieuwe ontwikkelingen en hun vertaling naar

de kliniek

The Netherlands Cancer Institute

Amsterdam

Olympus microscope, late 1900sVan Leeuwenhoek microscope, late 1600s

The microscope, a major tool for cancer

diagnostics for the last 350 years

• Grade 3• Grade 1

Histological grade

Using microscopy to predict

disease outcome in cancer

Low risk High risk

Molecular diagnostics, better than the microscope?

Micro-scope Micro-array

MammaPrint: a 70 Gene Prognosis Profile

van´t Veer et al., Nature 415, p. 530-536, 2002

70 significant prognosis genes

Tum

or

sam

ple

s

High riskLow riskMammaPrint®

The molecular pathology challenge: how to integrate the old and the new?

High riskLow risk

Historical Grade versus MDx

Grade 3Grade 1

Low risk High risk

Grade 2

“Intermediate”

0%10%20%30%40%50%60%70%80%90%100%

Grade1

Grade2

Grade3

70-genehighrisk

70-genelowrisk

Responses to cancer drugs are

also determined by the genes!

Conventional cancer drugs are not very effective

One in four women benefit

from chemotherapy

Young women < 50 year Older women > 50 year

One in thirty women benefit

from chemotherapy

Breast cancer mortality

J Natl Cancer Inst. 2009;10:1044-8Targeted cancer drugs are very expensive

and often fail to yield clinical benefit

NSCLC

Breast

Pancreas

Renal

Strong correlations between cancer genotype

and drug response

• HER2 expression (breast) Trastuzumab

• BCR-ABL translocation (CML) Gleevec

• KRAS mutation (colon) Cetuximab (no response)

• EGFR mutation (lung) Erlotinib

• BRAF mutation (melanoma, colon) PLX4270

• ALK mutation/translocation (neuroblastoma, lung, breast) PF02341066 (crizotinib)

Personalized medicine 101

20 patients will validate your biomarker!

Some genotype - drug response

relationships are readily explained

Grb

RAF

EGFR

KRAS

MEK

ERK

Cetuximab

Wild type

Clinical response

Grb

BRAF

RTK

RAS

MEK

ERK

PLX4270

Clinical response when mutant

Downstream

pathway activation

Oncogene

addiction

Mutant

No clinical response

KRAS

Different types of breast cancer have different

mutations

Triple negative ductal

Carcinoma

Invasive lobular

CarcinomaPIK3CA

Mutation: 8% 40%

Response to

chemotherapy: good poor

Sorlie et al. (2001), PNAS 98, 10869

Hierarchical clustering of breast cancer gene

expression defines distinct molecular subtypes

BluePrint: Molecular subtyping Profile

Three classes were determined by

IHC in concordance with

TargetPrint microarray single gene

read-out:

– Luminal-type = positive ER and/or PR

hormone receptor status (hormone

positive)

– HER2-type = positive HER2 status

– Basal-type = negative ER, PR and HER2

status (triple negative)Dataset used: NEJM-295 Rosetta data

Profile: 80 genes (overlap with Perou ~25 genes, overlap

with PAM50 9 genes, overlap with MP 4 genes of 70-set, 10

genes of 231-set, overlap with TP 3 genes).

Development of BluePrint Molecular Subtyping Profile:

TargetPrint Single

Gene Expression

Concordance

IHC Testing

Basal-TypeHER2-TypeLuminal-Type

Measuring estrogen receptor function by

analysis of 80 downstream targets

•3 classes:–Hormone receptor positive

–HER2 positive

–Triple negative

Luminal reporter genes often have ER binding

sites in their promoters

32 out of 58 luminal signature genes

have an ERE in their promoter

p= 1.2 .10-5

Collaboration:

Wilbert Zwart

Jason Caroll

Cambridge UK

The response to chemotherapy varies in the different molecular subtypes

in-silico analysis of patients (n=133) described in Hess et al. 2006 JCO 24:4236

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

pCR

RD

pCR 50% 56% 9%

RD 50% 44% 91%

Her2+ Triple - Hormone +

Basal-type 50% pCR

HER2-type 56% pCR

Luminal-type 9% pCR

Basal and HER2-type show significant higher pCR rates

Molecular subtypes also exist in colon cancer

and have different mutation

Hierarchical Clustering training set (n= 188, whole genome)

- reveals 3 molecular subgroups with different outcome

High throughput “kinome” sequencing in breast

cancer

• 518 putative kinases

• 12 PI3K domain-containing genes

• 6 PI3K regulatory components

• 13 diglyceride kinases

• 18 genes frequently mutated in human cancer

• 19 genes specifically known to be mutated in breast cancer

586 genes total analyzed

SureSelect Exon capture technology: allows isolation of specific sequences from genome

Capture

HMEC

BT474

PB CF001

PB CF003

PB CF004

XG BC60P

XG MT201

XG HBCX17

Deep sequencing identifies regions of copy number

gain and loss

All regions shown on chromosome 17

ERBB2

Overview copy number alterations in breast tumor

samples

Erbb2 Erbb2

PTENCDKN2A/B (no coverage)

Each point represents the average of all regions for a particular gene

Gene level assessment can miss small structural changes

TAOK1, NEK8, NLK, KSR1

EXAMPLES OF SOME SNV found in COSMIC

HMEC BT474 XG BC60P XG MT201 XG HBCX17 CRC met TN001 TN002

APC - - - - - L1129S - -

EGFR - - - - - - E872Q -

KRAS - - - - - G13D - -

PIK3CA - K111N - - - - - -

TP53 C176P E285K R175H - - - - R248W

FRAMESHIFT/STOP CODON

TP53 BRCA2 GEN1 TP53

SPEG BRCA2

ACVR2B C9ORF96

AMPLIFIED (>3 fold)

MYC ERBB2 TTBK1 ERBB2 NRBP2

CRKRS PTK7 TAOK1 PTK2

PIP4K2B NEK8 MAPK15

RPS6KB1 NLK ADCK5

PDK2 KSR1 MYC

AURKA DGKE IKBKB

COL1A1

STK4

HOMOZYGOUS DELETIONS

PTEN JAK1 (E6) CDKN2A

BMPR1A CDKN2B

Summary SNV and CNA

Genotype-driven clinical trials

Conventional cancer treatment

Patient’s

tissue sample

pathology

grade, size, IHC

Chemotherapy

Personalized cancer treatment

Patient’s

tissue sample

Targeted (combination)

therapy

PPP

PP PPP

P P P P P P

PP

P

Molecular

diagnostics

which pathways

are active?

Conventional cancer treatment:

Rx

Treatment

Chemotherapy

Dx

Diagnosis

Stage, Grade, IHC

Personalized cancer treatment:

Rx

Treatment:

Pathway targeted therapy

Rapid progress in DNA sequencing

2000: first human genome:

cost $ 2,700,000,000

2010: many cancer genomes

cost: $ 10.000

Extrapolate to 2020: cost $ 0.04 per genome

Conclusion:

Need to change tissue preservation

Frozen

RNA integrityFFPE Protein integrity

The future of personalized medicine?

Acknowledgments

MammaPrint

Laura van ‘t Veer

Marc van de Vijver

Stephen Friend

Kinome sequencing

Ian Majewski

Astrid Bosma

NKI screening center

Wouter Nijkamp

Roderick Beijersbergen

Microarray facility

Ron Kerkhoven

Arno Velds

Our funding agencies:

Annuska Glas

Arno Floore

Iris Simon

Paul Roepman

Femke de Snoo