Molecular diagnostics of prognostic

and predictive markers in colorectal

cancer

Fred T Bosman

CHUV Pathology, Lausanne

Erasmus Medical Center, Rotterdam

Medicine in the post-genome era

P4 Medicine

•Predictive

•Personalised

•Preventive

•Participatory

SH Friend L Hood

Prognostic - Predictive

Prognostic

• a prediction of the probable course and outcome of a disease

• the likelihood of recovery from a disease

Predictive

• to state, tell about, or make known in advance, especially on the basis of special knowledge

• to foretell something; prophesy

The terms significantly overlap!

By convention prognostic bears on disease outcome; predictive implies the capacity to predict the evolution of a disease notably in terms of whether or not a patient will respond to a therapy.

Negative predictive?

• Positive prediction

• The patient will respond

• The survival will be prolonged

• Negative prediction

• The patient will not respond

• For targeted therapy (anti-EGFR) in colorectal

cancer, the presently available test (KRAS

mut.) predicts non-response to the drug

Which colon cancer patient needs

adjuvant chemotherapy?

0%

25%

50%

75%

100%

1 2

No adjuvant TTT Adjuvant TTT

Relapsing patients:

non-responders

Rescued patients:

responders

« Cured » patients:will

not relapse anyway

Grade as a predictor:

for colorectal cancer of limited value

moderately poorly

Prognostic factors in colorectal cancer

T1 T3/4

N

Is TNM stage good enough?

Stage TNM Group Group Dukes Prognosis (5y OS)

Stage I T1 N0 M0 Dukes A >90%

T2 N0 M0

Stage II T3 N0 M0 Dukes B 70-85%

T4 N0 M0 55-65%

Stage III any T N1 M0 Dukes C 45-55%

any T N2,3 M0 20-30%

Stage IV any T any N M1 (distant) Dukes D < 5%

The PETACC3 study

Irinotecan + 5FU/FA (LV5FU2 or AIO) vs 5FU/FA

3’278 patients (2’333 Pts stage III +945 Pts stage II)

– material of around 1504 patients valid for testing

• 1504 in separate sections

• 650 in a TMA

– markers studied so far: the targeted search– TERT

– Thymidylate synthetase

– Smad4

– p53

– KRAS, BRAF mutations

– LOH 18q and 8p (by SNP)

– MSI (BAT-25, BAT-26, D5S346, D2S123, D17S250, BAT-40, TGF-ß RII, D18S58, D18S69,

D17S787).

– UGT1A1 polymorphisms

Bosman et al, Clinical Cancer Research, 2009 ;15:5528

Paraffin embeddedtissue

Genomic DNAmRNAmiRNA

Proteins

Analysis

sequencing

microdissection

What can we do with FFPE tissue?

expression profilingarray CGH MALDI imaging

KRAS mutations are not prognostic in stage II/III

colon cancer patients (Roth et al. JCO, 2009)

Patterns of SMAD-4 expression in colon cancer: (a) complete loss of expression

in tumor glands as compared with normal crypts (arrow); (b) non-homogeneous

expression: loss of expression in the lower part of the field contrasts with marked

expression in the upper part (R.Fiocca).

SMAD4 and prognosis (stage III)

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 1 2 3 4 5 6 7Years:

Pro

port

ion

dise

ase

free p=0.003

expression presentno expression

//

/

// //

/// //

///// /// / // // ///// / /////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// //// ///////// / // / / /

/

/

/

//

/ / / // /////////////////////// ///////////////////// // / / / / // / / / /

At risk: 819 709 602 535 488 417 42 2145 114 85 74 66 61 7 0

no loss

loss

Colorectal cancer 5y disease free survivalof stage II + III patients by MSI Status

*Is MSI a suppressor of lymph node and distant

metastasis?

Frequency Analysis

Stage II Stage III Stage IV

MSI-H 22%

(86/395)

12%

(104/859)

3.5% *

MS- and SMAD4 status allow subtyping of CC (Roth et al. 2011, submitted)

conditional inference

Molecular

markers

differentiate

T3N1 cases

Delorenzi et al. 2012

JNCI In press

Molecular subtypes of colon cancerBudinska et al. 2012 Submitted

Molecular subgroups do not completely overlap

with classical parametersBudinska et al. 2012 Submitted

Are these clinically relevant? Budinska et al. 2012 Submitted

Anti-EGFR therapies: mechanisms

Anti-EGFR therapies for stage IV colorectal cancer:

who can respond?

Not patients with a tumor containing mutations in the RAS/RAF or the PI3K/AKT pathways

Biomarker combinations are necessary

Copyright © American Society of Clinical Oncology

Amado, R. G. et al. J Clin Oncol; 26:1626-1634 2008

Progression-free survival by treatment within KRAS groups

Multiple gene testing improves prediction of response

to anti-EGFR therapy (De Roock et al. Lancet Oncol. 2010)

Case LoadInstitute of Pathology of the University of Munich

Cases

3000

1500

2007 2008 2009 year

Her2/breast, KRAS/colon, EGFR/lung

Other tests

1 : 2,5

1 : 4,51 : 3,5

Source Th.Kirchner 2010

Work flow in molecular diagnostic pathology

Pre-analytical phase

•Sampling

•Fixation

•Embedding

•Microscopic selection of the

appropriate tissue areas

•Microdissection

Analytical phase

•Sequencing

•Methylation

•qRT-PCR

•Arrays (cDNA, CGH etc.)

Post-analytical phase

•Integration of molecular data in the

final report

•Tumor boardPathology

Pathology

Central platforms

Molecular genetics

Target discovery

• Identifying the right target

• Developing the right modulating

molecule

• Establishing efficacy in the right patient

group

• Using the right biomarker

• Choosing the right dose

BRAFm (blue)

versus BRAFwt

(yellow)

Popovici et al. JCO 2012

BRAFm score as predictor of survival

Overall survival Survival after relapse

Will the BRAF signature be the companion test for BRAF inhibitor tretment?

A

Chromosome

B

13q12.2 (CDX2)

8q24.21 (MYC)

17q12 (ERBB2)

7p11.2 (EGFR)

7q31.2 (MET)

20q13.12

14q32.31

12p13.33

20q11.21

20q13.31

8q24.3

13q12.13

11q13.2 (CCND1)

Tao et al. 2012 Submitted

MSI samples are much quieter

CNV: drivers and bystanders

Tao et al. 2012 Submitted

What is the role of pathology?

• Experimental pathology: models (in vitro, in vivo) of disease in man

• Mechanism oriented identification of new pathwaysand within pathways of new target molecules

• Providing human biospecimens for clinical validation (cell lines, tissue specimens): biobanks

• Providing read-outs for clinical efficacy (e.g. tumorregression scores)

• Developing biomarkers to identify patient subgroupslikely to respond

• Introduction of biomarkers and the ensuing new classifications into daily practice

Do pathology archives make biobanks?

• BBMRI vision

“Pathology archives” represent a special type of tissue repository, that may

support tissue banking, provided that they fulfill required standards with respect to (1) documentation of variations; (2) cataloguing; (3) rules of access; (4) fulfillment of legal requirements for use as research resource. The primary role of these archives is to document diagnosis and to support later/metachronous diagnostic analyses but they should be developed in a way that allows them to fulfill roles in research.

• PALGA: the Dutch pathology network

• Daily reality: pathology institutes as a rule provide research specimens

• most projects ‘in house’

• under specific conditions to outside researchers

PALGA: a Dutch solution

Currently, more than 50 million records on almost 10 million patients are stored in the central databank. Each excerpt contains patient identifiers, including demographic data and the so-called PALGA diagnosis. The latter is structured along five classification axes: topography, morphology, function, procedure, and diseases. All data transfer and communication occurs electronically with encryption of patient and laboratory identifiers. All excerpts are continuously available to all participating pathology laboratories, thus contributing to the quality of daily patient care. In addition, external parties may obtain permission to use data from the PALGA system, either on an ongoing basis or on the basis of a specific permission. Annually, 40 to 60 applications for permission to use PALGA data are submitted.

Is this what awaits tomorrows physician?SHFriend Nat Biotechnol. 2011;29:215-8.

Conclusions

– For stratification of CRC TNM is not enough

– A new layer of subgroups can be obtained with ‘classical’

molecular markers

– New technology will reveal more heterogeneity

(molecular and clinical) in CRC

– Such heterogeneity should go along with the development

of new molecular targets and accompanying predictive

tests

– To do this large series of patients with detailed clinical

data need to be compiled (e.g. EORTC SPECTAcolor)

– FFPE tissues can and will be effectively used for these

efforts

– Pathologists are an essential cornerstone in this

development

The PETACC3 consortium

IPA Lausanne

Pu Yan

Stephanie Bougel

HUG Genève

Arnaud Roth

GI oncology/Genetics Leuven

Sabine Tejpar

Pathology Genova

Roberto Fiocca

SAKK Bern

Dirk Klingbiel

SIB Lausanne

Mauro Delorenzi

Vlad Popovici

Eva Budinska

Pfizer Oncology San Diego

Scott Weinrich

Graeme Hodgson

Mao Mao

Is QC necessary?

2008 (1rst and 2nd RRT) 2009 (3rd and 4th RRT)

Participants (Germany) 60 63

Passed (Germany) 51 (85%) 56 (89%)

Failed (Germany) 9 (15%) 7 (11%)

141

69 72

8 13 1

107

1 2 1 3 1 1 1 1 2

19

8 11

2

16

0

20

40

60

80

100

120

140

160

180

Bestanden Nicht bestanden

All

Aca

d

Non

-A

cad

Passed Failed