alberto bardelli institute for cancer research and treatment

Alberto Bardelli

Institute for Cancer Research and Treatment University of Torino - Medical School

Molecular mechanisms of resistance to anti EGFR based therapies in colorectal cancer

DISCLOSURES

Founder: Horizon Discovery (Cambridge, UK)

Consultant: Merck-Serono, Amgen

Mutations and the cancer genome

Mutations and resistance to therapies in CRCs

Parallel clinical trials in cells, mice and patients

“Cancer is, in essence, a genetic disease. Although cancer is complex, and environmental and other nongenetic factors clearly play a role in many stages of the neoplastic process, the tremendous progress made in understanding tumorigenesis in large part is owing to the discovery of the genes, that when mutated, lead to cancer.”

Bert Vogelstein (1988)

NEJM 1988; 319:525-532.

Cancer: a genetic disease

Tumour

Normal

Mutation

DNA IS DIGITAL

Bardelli et. Al., Science: 300;949 (2003)

Tyrosine kinome mutations

Residue is evolutionarily conservedMutations of equivalent residues in other kinases are pathogenic

PIK3CATP53

TP53

APC

KRAS

PIK3CA

Wood et al., Science : 318 (2007)

Mutational lansdscapes of cancer genomes

N o r m a lE p i t h e l i u m D y s p l a s t i c A C F E a r l yA d e n o m a L a t eA d e n o m a C a r c i n o m a M e t a s t a s i sI n t e r m e d i a t e A d e n o m a

A P C / - c a t e n i n K - R A S 1 8 q p 5 3 O t h e rC h a n g e s ?

NormalEpithelium

Dysplastic ACF

EarlyAdenoma

LateAdenoma

Carcinoma MetastasisIntermediate Adenoma

APC/ -catenin K-RAS 18q p53Other

Changes?

PIK3CABRAF

The genetic bases of response and resistance to EGFR therapies


Drug Y

Mutation X

EGFR-targeted therapies in CRCs

Noonberg SB, Benz CC. Drugs 2000;59:753–67

Anti-HER1/EGFR-blocking antibodies

1

Anti-ligand-blocking

Antibodies2

TKInhibitors

3

Ligand–toxin

Conjugates4

Antibody–toxin

Conjugates5

Responders (15-20%) Non-Responders

Who will benefit from treatment with antibodies targeting EGFR in mCRCs ?

Bardelli and Siena, J Clin Oncol 2010

EGFR

CetuximabPanitumumab

DUSPs

SOS

Ras

Raf

MEK

MAPK

ShcGrb2

S6K

AKT

PDK

PTEN

PI3K

p85

GSK

Ras

Raf

MEK

Ras

Raf

MAPK

MEK

Ras

Raf

PI3K

MAPK

MEK

Ras

Raf

p85

PI3K

MAPK

MEK

Ras

Raf

p85

PI3K

MAPK

MEK

Ras

Raf

PDK

p85

PI3K

MAPK

MEK

Ras

Raf

AKT

PDK

p85

PI3K

MAPK

MEK

Ras

Raf

S6K

AKT

PDK

p85

PI3K

MAPK

MEK

Ras

Raf

GSK

S6K

AKT

PDK

p85

PI3K

MAPK

MEK

Ras

Raf

GSK

S6K

AKT

PDK

p85

PI3K

MAPK

MEK

Ras

Raf

Moroni et al Lancet Oncology 2005

EGFR Mutations

EGFR Gene Copy Number

EGFR Protein expression (IHC)

mCRC patients treated with panitumumab or cetuximab, N=114

BRAF mutational status on Wild-Type KRAS tumors (N=79)

Di Nicolantonio et al., J Clin Oncol. 2008

*P<0.05 (P=.011)*P<0.05 (P=.011)Mutated KRAS

34/113 (30%)

Wild-Type KRAS

79/113 (70%)

Responders 2/34 (6%)* 22/79 (28%)*

Non Responders 32/34 (94%)* 57/79 (72%)*

**P<0.05 (P=.029)**P<0.05 (P=.029)Mutated BRAF

11/79 (14%)

Wild-Type BRAF

68/79 (86%)

Responders 0/11 (0%)** 22/68 (32%)**

Non Responders 11/11 (100%)** 46/68 (68%)**

Benvenuti et al., Cancer Research. 2007

Responder (15%)

PIK3CA mutated and/or PTEN loss (15-20%)

BRAF mutated (8%)

KRAS/PIK3CA mutated

BRAF/PIK3CA mutatedKRAS-NRAS mutated (35-45%)

20-25% ???

Bardelli and Siena, J Clin Oncol 2010

Sartore-Bianchi A et al., PLOS One 21010

Siena; Di Nicolantonio and Bardelli JNCI 2009

Janakiraman M et al., Cancer Res; 70(14) July 15, 2010

KRAS, NRAS, or BRAF mutations are non overlapping, while PIK3CA mutations may occur concomitantly with any of the above

• Example 1: PIK3CA mutations

• Example 2: KRAS mutations

From gene targeted therapies to mutant targeted therapies

• Sartore-Bianchi A et al., Cancer Res 2009 YES• Prenen et al., Clin Cancer Res 2009 NO

PIK3CA mutations and resistance to anti EGFR MoAbs ?

Zhao and Vogt PNAS 2008

Different role for individual PIK3CA mutations on the response to EGFR MoAbs in mCRCs

658/717 (91.8%)Cetuximab + chemotherapy

43/717 (6%)Cetuximab monotherapy

16/717 (2.2%)Panitumumab monotherapy

Treatment type in chemotherapy-refractory tumors

717/969 (74%)Total number of chemotherapy-refractory tumors

61/969 (6.3%) Missing

118/969 (12.2%) Metastasis

790/969 (81.5%) Primary tumor

Type of tissue sample

969/1000 (97%)Total number of samples successfully assessed

Sample characteristics

De Roock et al., EU Consortium Lancet Oncology, 2010

Effects of KRAS, BRAF, NRAS and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in

chemotherapy-refractory metastatic colorectal

0.0421.96(1.08-3.55)

0.0691.81(1.00-3.26)

NRAS (mutant vs. wild-type)

<0.00012.97 (1.88-4.70)

0.000162.68(1.70-4.22)

BRAF (mutant vs. wild-type)

0.00553.69(1.69-8.02)

0.141.57 (0.90-2.76)

PIK3CA exon 20(mutant vs. wild-type)

0.391.27(0.75-2.14)

0.671.08(0.77-1.51)

PIK3CA exon 9(mutant vs. wild-type)

NCNC<0.00011.87(1.51-2.31)

KRAS (mutant vs. wild-type)

LRTp-value

Adjusted hazard ratio

OS(95% CI)

LRT p-value

Adjusted hazard ratio

OS(95% CI)

Genotype

KRAS wild-type population

Unselected population

De Roock et al., EU ConsortiumLancet Oncology, 2010

Multivariate Cox regression analysis of overall survival in the unselected and KRAS wild-type population

• Example 1: PIK3CA mutations

• Example 2: KRAS mutations

From gene targeted therapies to mutant targeted therapies

*P<0.05 (P=.011)*P<0.05 (P=.011)Mutated KRAS

34/113 (30%)

Wild-Type KRAS

79/113 (70%)

Responders 2/34 (6%)* 22/79 (28%)*

Non Responders 32/34 (94%)* 57/79 (72%)*

Cancer Res 2007;67(6):2643–8 & J Clin Oncol. 2008; 26:5705-5712.

mCRC patients N=114

KRAS mutations: clinical results from cetuximab treated mCRC

Moroni Lancet Oncol 2005 n=31

Lièvre Clin Cancer Res 2006 n=30

Di Fiore Br J Cancer 2007 n=59

Frattini Br J Cancer 2007 n=27

Benvenuti Cancer Res 2007 n=48

Khambata-Ford J Clin Oncol 2007 n=80

De Roock ASCO Proc 2007 n=37

Finocchiaro ASCO Proc 2007 n=81

Response rate:analysis of 8 studies available in PubMed or from ASCO

Responders (n=82)

wt (93.0%)

RAS mutated (7.0%)

wt (56.1%)

RAS mutated (43.9%)

Non-Responders (n=312)

Smith G, et al., British Journal of Cancer (2010), 1 –11

RAS(inactive)

GDP

GAPPi

GEP GDI

GTP

GDP

RAS(active)

GTP

Effectors:RAF/MAPK/ERK

PI3K/AKT

Farnesyl Geranylgeranyl

KRAS mutations

Meta-analysis of 3 Chemotherapy Meta-analysis of 3 Chemotherapy Refractory DatasetsRefractory Datasets

• NCIC CTG dataset– from CO.17 trial

• Leuven dataset– from clinical trials: EVEREST, BOND, SALVAGE, BABEL

• Italian dataset: – from clinical trials mentioned above– from non-trial patients with advanced, irinotecan-

refractory CRC considered suitable to receive an EGFR MAb

KRAS Mutation Status and Therapy by Dataset

Number of patients (%)Number of patients (%)

Dataset NCIC CTG Leuven Italian

Kras results and treatment information available

394 282 125

Kras mutation status

G13D 20 (5) 20 (7) 8 (6)

Other mutation 144 (37) 102 (36) 24 (19)

Wild-type 230 (58) 160 (57) 93 (74)

Treatment

Cetuximab monotherapy 199 (50.5%) 33 (11.7%) 15 (12%)

Panitumumab monotherapy 0 (0%) 0 (%) 23 (18.4%)

Cetuximab + chemotherapy 0 (0%) 249 (88.3%) 87 (63.6%)

No cetuximab or panitumumab 195 (49.5%) 0 (0%) 0 (0%)

Baseline Patient Characteristics by Tumour KRAS statusG13D Mutation

(N = 48) Other mutations

(N = 270)Wild type KRAS

(N = 483)p-value*

Age – median (range) in year 65.5 (39.4-80.0) 62.0 (34.0- 89.0) 62.0 (26.0- 85.9) .79 <65 23 ( 47.9) 157 ( 58.1) 287 ( 59.4) ≥65 25 ( 52.1) 113 ( 41.9) 192 ( 39.8)

Missing 0 ( 0.0) 0 ( 0.0) 4 ( 0.8)Gender Female 22 ( 45.8) 109 ( 40.4) 161 ( 33.3) .06 Male 26 ( 54.2) 161 ( 59.6) 322 ( 66.7) ECOG performance status 0 12 ( 25.0) 54 ( 20.0) 118 ( 24.4) .45 1 26 ( 54.2) 166 ( 61.5) 264 ( 54.7) 2 7 ( 14.6) 30 ( 11.1) 48 ( 9.9) Missing 3 ( 6.3) 20 ( 7.4) 53 ( 11.0) Site of primary Rectum only 10 ( 20.8) 57 ( 21.1) 116 ( 24.0) .61 Colon 38 ( 79.2) 213 ( 78.9) 366 ( 75.8) Missing 0 ( 0.0) 0 ( 0.0) 1 ( 0.2)Number of prior chemotherapy regimens 0 0 ( 0.0) 3 ( 1.1) 8 ( 1.7) .80 1 5 ( 10.4) 17 ( 6.3) 25 ( 5.2) 2 13 ( 27.1) 74 ( 27.4) 156 ( 32.3)

3 16 ( 33.3) 93 ( 34.4) 151 ( 31.3) 4 10 ( 20.8) 56 ( 20.7) 87 ( 18.0)

≥5 4 ( 8.3) 25 ( 9.3) 47 ( 9.7) Missing 0 ( 0.0) 2 ( 0.7) 9 ( 1.9)Treatment Mono Cetuximab 10 ( 20.8) 91 ( 33.7) 146 ( 30.2) .34

Mono panitumumab 3 ( 6.3) 5 ( 1.9) 15 ( 3.1) Cetuximab + chemotherapy 22 ( 45.8) 105 ( 38.9) 209 ( 43.3) No cetuximab or panitumumab 13 ( 27.1) 69 ( 25.6) 113 ( 23.4)

* between biomarker positive and negative groups from chi-square test for categorical variables and t-test for continuous variables.

KRAS G13D Mutation status as a prognostic factor for OS in KRAS G13D Mutation status as a prognostic factor for OS in patients patients not treatednot treated with Cetuximab or Panitumumab? with Cetuximab or Panitumumab?

KRAS subsetMedian OS (months)

Wild-type 4.5

G13D mutation 3.6

Other Mutation 4.7

Pro

port

ion

aliv

e

0

20

40

60

80

100

0.0 5.0 10.0 15.0Time from randomization (months)

De Roock et al JAMA 2010

OS Predictive Analysis by KRAS status: OS Predictive Analysis by KRAS status: EGFR Mab Monotherapy EGFR Mab Monotherapy vsvs no EGFR Mab no EGFR Mab

Pro

po

rtio

n a

live

Time from randomization (months)

Monotherapy with cetuximab or panitumumab

No Treatment with cetuximab or panitumumab

KRAS G13D Mutation Other KRAS Mutation KRAS Wild-type

0

20

40

60

80

100

0.0 5.0 10.0 15.0

Pro

po

rtio

n a

live


0

20

40

60

80

100

0.0 5.0 10.0 15.0 20.00

20

40

60

80

100

0.0 5.0 10.0 15.0 20.0


Pro

po

rtio

n a

live HR 0.56 (0.42 to 0.73)

p<0.0001

HR 0.23 (0.09 to 0.61)p=0.002

HR 0.98 (0.70 to 1.38)p=0.91


Molecular bases of G12V versus G13D mediated resistance to cetuximab in

cellular and animal models


Drug Y

Mutation X

Knock-out of cancer genes Knock-in of oncogenic mutations

EGFRRas / Raf PI3Kp53 -/-wt

Homologous recombination

Isogenic cells carrying cancer mutations

A B

A

B

Isogenic models of tumour progression

Parental cell line Knock-in cell line

Incubate cells with drugs

Mutated genotype selective drug

Drug with no selectivity

Wild genotype selective drug

Mutation-specific pharmacogenomic profiles

Di Nicolantonio; Arena et al., PNAS 2008

Drug screening

Di Nicolantonio et al., J Clin Invest, 2010

Experimental design

Measure drug response

Biochemical validation (pathway activation)

Cellular model

Gene targeting (Knock-in approach)

KRAS: G12D, G12V, G12C, G12A, G12S, G12R, G13D

BRAF: V600E, PIK3CA: E545K (exon 9), H1047R (exon 20)

NotI

ITR ITRNeoPLoxP

LoxP

NotI

AAV-KRas-12VNotI

ITR ITRNeoPLoxP

LoxP

AAV-KRas-13D

NotI

LoxP

G12V (G35>G/T)

G13D (G38>G/A)

SW48 KRAS WT

Homologous recombination

Knock-in G12V(or G12D / G12C)

A

B

KRAS WT CRC cells

Knock-in G13D

C

SW48 KRAS G12V

SW48 KRAS G13D

SW48

0

10

20

30

40

50

60

70

80

90

100

0,01 0,1 1 10 100

Irinotecan µM

% C

on

tro

l

WTG13DG12V

SW48

0

10

20

30

40

50

60

70

80

90

100

0,01 0,1 1 10 100

Oxaliplatin µM

% C

on

tro

l

WTG13DG12V

KRAS G12V or G13D and

chemotherapy in cellular models


SW48

40

50

60

70

80

90

100

0.01 0.1 1 10 100

Cetuximab µg/ml

% C

on

tro

l WT

KRAS G13D

KRAS G12V

KRAS G12D

KRAS G12C

KRAS G12V and G13D and

ceruximab in cellular models


0

500

1000

1500

2000

2500

0 5 10 15 20 25 30 35Days

Tu

mo

r vo

lum

e (

mm3

)

Cetuximab delays growth of SW48 tumor xenografts


0

500

1000

1500

2000

2500

0 10 20 30 40 50Days

Tu

mo

r vo

lum

e (

mm3

)0

500

1000

1500

2000

2500

0 10 20 30 40 50

Days

Tum

or v

olum

e (m

m3 )

Start of treatment Start of treatment

SW48 KRAS G13DSW48 KRAS G12V

Cetuximab does not affect growth of G12V tumors, but inhibits the growth of G13D tumor xenografts



Secondary resistance to targeted therapies

2007


Secondary resistance to targeted therapies

2010


Drug Y

Mutation X

Patient undergoing liver metastasectomy of CRC

Expansion

Liver Met implanted s.c. in NOD SCID mice Marker A Drug X

Marker B Drug Y

DNA, RNA and protein extraction,FFPE blocks stored by the pathologist

DNA, RNA and protein extraction,FFPE blocks stored by the pathologist

Using this approach 112 samples were succesfully engrafted since Oct 2008

A. Bertotti & L. Trusolino, Molecular Oncology, IRCC

SURGERY DMSORNA later

DMSORNA later

DMSORNA laterSnap Frozen

p0engraftment(2 mice)

DMSORNA laterSnap FrozenFFPE blocks

p1expansion(6 mice)

p2treatment(24 mice)

NUMBER OF SAMPLES

148

>90%

44

Xenopatients

Archive

RNA extraction

Genomic DNA extraction

FFPE blocksfrom the pathologist

A. Bertotti & L. Trusolino, Molecular Oncology, IRCC

In vivo – M016

control

Chronic treatment with cetuximab (0.5 mg/injection/2x/week)

secondary resistance

Time x: Molecular analysis using multiple omics’ technologies (WP3)

Time 0: Molecular analysis using multiple omics’ technologies (WP3)

cetuximab

Understanding secondary resistance to cetuximab

Xenopatient M026: development of resistance

RESISTANCE- RESISTANCE




COLTHERES

“Modelling and predicting resistance to molecular therapies in colorectal cancers”

Executive Summary:

COLTHERES is a consortium of EU-clinical centres and translational researchers who have received 6M Euros of core funding from the EU Framework-7 program to define and perform biomarker driven clinical trials to improve cancer therapy outcomes. This is a 4-year programme that will use comprehensively molecularly-annotated colon cancers as a ‘test-bed’ to define specific biomarkers of response or resistance to signalling pathway agents. This consortium is open to any Institution who wishes to determine which patients are most likely to respond to novel CRC therapies and perform rapid proof-of-concept clinical trials.

Alberto Bardelli (University of Torino-IRCC): Cancer mutations and targeted therapies. Drug resistance mechanisms

Sabine Tejpar (University Hospital Leuven): Clinical trials with molecularly targeted therapies

Josep Tabernero (Hospital Vall d’Hebron): Clinical trials with molecularly targeted therapies

Salvatore Siena (Ospedale Niguardia): Targeted clinical trials and patient drug resistance mechanisms

Horizon Discovery: (Cambridge UK) Novel gene-targeting platform to create genetically-defined human cancer models + drug screening

Agendia : (Amsterdam) Microarrays on clinical samples and diagnosis based on molecular profiles

Rene Bernards: (NKI Amsterdam) Functional genomics, screens for drug-response modifying genes

Manel Esteller: (Barcelona) Epigenomic profiling of clinical samples

Michael Clague: (University of Liverpool) Global proteomic profiling in cancer models

Mauro Delorenzi (Swiss Institute of Bioinformatics) Bioinformatics, statistical analysis

Paul Crompton (ARTTIC Brussels) Administration and management

Consortium Members

Royal Mail Stamp Issue 25 February 2003

The doctor’s perspective

"Here's my

sequence..."

The patient’s perspective

Molecular Genetics Lab:

Federica Di Nicolantonio

Sabrina Arena

Miriam Martini

Emily Crowley

Elisa Scala

Carlotta Cancelliere

Sebastijan Hobor

Davide Zecchin

Simona Lamba

Michela Buscarino

Livio Trusolino

Andrea Bertotti

Milo Frattini Salvatore SienaAndrea Sartore BianchiMarcello GambacortaJosep

Tabernero

alberto bardelli institute for cancer research and treatment

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