Transcript Slide 1

Molecular mechanisms of resistance to anti EGFR
based therapies in colorectal cancer
Alberto Bardelli
Institute for Cancer Research and Treatment
University of Torino - Medical School
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; 319:525532.
Cancer: a genetic disease
DNA IS DIGITAL
Tumour
Normal
Mutation
Tyrosine kinome mutations
Residue is evolutionarily conserved
Mutations of equivalent residues in other kinases are pathogenic
Bardelli et. Al., Science: 300;949 (2003)
Mutational lansdscapes of cancer
genomes
PIK3CA
TP53
PIK3CA
TP53
APC
KRAS
Wood et al., Science : 318 (2007)
The genetic bases of response and
resistance to EGFR therapies
APC/-catenin
K-RAS
APC/-catenin
Normal
Epithelium
Normal
Epithelium
Dysplastic
ACF
Early
Adenoma
p53
18q
Dysplastic
ACF
K-RAS
18q
Early Intermediate Late
Adenoma Adenoma Adenoma
Other
Changes?
Other
p53 Changes?
Carcinoma
Metastasis
Intermediate
Adenoma
BRAF
Late
Adenoma
Carcinoma
PIK3CA
Metastasis
Parallel clinical trials in cells, mice and
patients
Mutation X
Drug Y
EGFR-targeted therapies in CRCs
Anti-HER1/EGFRblocking antibodies
1
Anti-ligandblocking
Antibodies
2
TK
Inhibitors
3
Ligand–
toxin
Antibody–
Conjugates
toxin
4
Conjugates
5
Noonberg SB, Benz CC. Drugs 2000;59:753–67
Who will benefit from treatment with
antibodies targeting EGFR in mCRCs ?
Responders (15-20%)
Non-Responders
Bardelli and Siena, J Clin Oncol 2010
Cetuximab
Panitumumab
EGFR Mutations
EGFR
EGFR Protein expression (IHC)
EGFR Gene Copy Number
Ras
SOS
Grb2
Shc
Raf
PI3K
MEK
p85
PTEN
DUSPs
MAPK
PDK
AKT
S6K
Moroni et al Lancet Oncology 2005
GSK
mCRC patients treated with panitumumab or cetuximab, N=114
*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%)*
BRAF mutational status on
Wild-Type KRAS tumors (N=79)
**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
Di Nicolantonio et al., J Clin Oncol. 2008
Responder (15%)
KRAS-NRAS mutated (35-45%)
KRAS/PIK3CA mutated
BRAF/PIK3CA mutated
BRAF mutated (8%)
20-25% ???
PIK3CA mutated and/or
PTEN loss (15-20%)
Bardelli and Siena, J Clin Oncol 2010
Sartore-Bianchi A et al., PLOS One 21010
Siena; Di Nicolantonio
and Bardelli JNCI 2009
KRAS, NRAS, or BRAF mutations are non
overlapping, while PIK3CA mutations may
occur concomitantly with any of the above
Janakiraman M et al., Cancer Res; 70(14) July 15, 2010
From gene targeted therapies to
mutant targeted therapies
• Example 1: PIK3CA mutations
• Example 2: KRAS mutations
PIK3CA mutations and resistance
to anti EGFR MoAbs ?
• Sartore-Bianchi A et al., Cancer Res 2009
• Prenen et al., Clin Cancer Res 2009
YES
NO
Different role for individual PIK3CA mutations
on the response to EGFR MoAbs in mCRCs
Zhao and Vogt PNAS 2008
Effects of KRAS, BRAF, NRAS and PIK3CA mutations
on the efficacy of cetuximab plus chemotherapy in
chemotherapy-refractory metastatic colorectal
Sample characteristics
Total number of samples successfully assessed
969/1000 (97%)
Type of tissue sample
Primary tumor
790/969 (81.5%)
Metastasis
118/969 (12.2%)
Missing
61/969 (6.3%)
Total number of chemotherapy-refractory tumors
717/969 (74%)
Treatment type in chemotherapy-refractory tumors
Panitumumab monotherapy
Cetuximab monotherapy
Cetuximab + chemotherapy
16/717 (2.2%)
43/717 (6%)
658/717 (91.8%)
De Roock et al., EU Consortium Lancet Oncology, 2010
Multivariate Cox regression analysis of overall survival
in the unselected and KRAS wild-type population
Unselected
population
KRAS wild-type
population
Genotype
Adjusted
hazard ratio
OS
(95% CI)
KRAS
(mutant vs. wild-type)
1.87
(1.51-2.31)
<0.0001
NC
NC
PIK3CA exon 9
(mutant vs. wild-type)
1.08
(0.77-1.51)
0.67
1.27
(0.75-2.14)
0.39
PIK3CA exon 20
(mutant vs. wild-type)
BRAF
(mutant vs. wild-type)
1.57
(0.90-2.76)
0.14
3.69
(1.69-8.02)
0.0055
2.68
(1.70-4.22)
0.00016
2.97
(1.88-4.70)
<0.0001
NRAS
(mutant vs. wild-type)
1.81
(1.00-3.26)
0.069
1.96
(1.08-3.55)
0.042
LRT
p-value
Adjusted
hazard ratio
OS
(95% CI)
LRT
p-value
De Roock et al., EU Consortium
Lancet Oncology, 2010
From gene targeted therapies to
mutant targeted therapies
• Example 1: PIK3CA mutations
• Example 2: KRAS mutations
mCRC patients N=114
*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.
KRAS mutations:
clinical results from cetuximab treated mCRC
Moroni Lancet Oncol 2005 n=31
Response rate:
analysis of 8 studies
available in PubMed or
from ASCO
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
RAS mutated (7.0%)
RAS mutated
(43.9%)
1
1
wt (93.0%)
Responders (n=82)
2
wt (56.1%)
Non-Responders (n=312)
2
KRAS mutations
Smith G, et al., British Journal of Cancer (2010), 1 –11
GEP
GDP
GDI
GDP
GTP
GTP
RAS
RAS
(inactive)
(active)
Farnesyl
Geranylgeranyl
Pi
GAP
Effectors:
RAF/MAPK/ERK
PI3K/AKT
Meta-analysis of 3 Chemotherapy
Refractory 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, irinotecanrefractory CRC considered suitable to receive an
EGFR MAb
KRAS Mutation Status and Therapy by Dataset
Number of patients (%)
Dataset
NCIC CTG
Leuven
Italian
394
282
125
20 (5)
20 (7)
8 (6)
Other mutation
144 (37)
102 (36)
24 (19)
Wild-type
230 (58)
160 (57)
93 (74)
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%)
195 (49.5%)
0 (0%)
0 (0%)
Kras results and treatment
information available
Kras mutation status
G13D
Treatment
Cetuximab monotherapy
No cetuximab or panitumumab
Baseline Patient Characteristics by Tumour KRAS status
Age – median (range) in year
<65
≥65
Missing
Gender
Female
Male
ECOG performance status 0
1
2
Missing
Site of primary Rectum only
Colon
Missing
Number of prior chemotherapy regimens 0
1
2
3
4
≥5
Missing
Treatment
Mono Cetuximab
Mono panitumumab
Cetuximab + chemotherapy
No cetuximab or panitumumab
G13D Mutation
(N = 48)
65.5 (39.4-80.0)
23 ( 47.9)
25 ( 52.1)
0 ( 0.0)
22 ( 45.8)
26 ( 54.2)
12 ( 25.0)
26 ( 54.2)
7 ( 14.6)
3 ( 6.3)
10 ( 20.8)
38 ( 79.2)
0 ( 0.0)
0 ( 0.0)
5 ( 10.4)
13 ( 27.1)
16 ( 33.3)
10 ( 20.8)
4 ( 8.3)
0 ( 0.0)
10 ( 20.8)
3 ( 6.3)
22 ( 45.8)
13 ( 27.1)
Other mutations
(N = 270)
62.0 (34.0- 89.0)
157 ( 58.1)
113 ( 41.9)
0 ( 0.0)
109 ( 40.4)
161 ( 59.6)
54 ( 20.0)
166 ( 61.5)
30 ( 11.1)
20 ( 7.4)
57 ( 21.1)
213 ( 78.9)
0 ( 0.0)
3 ( 1.1)
17 ( 6.3)
74 ( 27.4)
93 ( 34.4)
56 ( 20.7)
25 ( 9.3)
2 ( 0.7)
91 ( 33.7)
5 ( 1.9)
105 ( 38.9)
69 ( 25.6)
Wild type KRAS p-value*
(N = 483)
62.0 (26.0- 85.9)
.79
287 ( 59.4)
192 ( 39.8)
4 ( 0.8)
161 ( 33.3)
.06
322 ( 66.7)
118 ( 24.4)
.45
264 ( 54.7)
48 ( 9.9)
53 ( 11.0)
116 ( 24.0)
.61
366 ( 75.8)
1 ( 0.2)
8 ( 1.7)
.80
25 ( 5.2)
156 ( 32.3)
151 ( 31.3)
87 ( 18.0)
47 ( 9.7)
9 ( 1.9)
146 ( 30.2)
.34
15 ( 3.1)
209 ( 43.3)
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
patients not treated with Cetuximab or Panitumumab?
Proportion alive
100
80
60
KRAS subset
Median OS
(months)
Wild-type
4.5
G13D mutation
3.6
Other Mutation
4.7
40
20
0
0.0
5.0
10.0
Time from randomization (months)
De Roock et al JAMA 2010
15.0
OS Predictive Analysis by KRAS status:
EGFR Mab Monotherapy vs no EGFR Mab
KRAS G13D Mutation
Other KRAS Mutation
80
60
40
HR 0.23 (0.09 to 0.61)
p=0.002
20
100
HR 0.98 (0.70 to 1.38)
p=0.91
80
60
40
Proportion alive
Proportion alive
100
Proportion alive
KRAS Wild-type
100
20
0.0
5.0
10.0
15.0
Time from randomization (months)
60
40
20
0
0
HR 0.56 (0.42 to 0.73)
p<0.0001
80
0
0.0
5.0
10.0
15.0
Time from randomization (months)
20.0
0.0
5.0
15.0
Time from randomization (months)
Monotherapy with cetuximab or panitumumab
No Treatment with cetuximab or panitumumab
De Roock et al JAMA 2010
10.0
20.0
Molecular bases of G12V versus G13D
mediated resistance to cetuximab in
cellular and animal models
Parallel clinical trials in cells, mice and
patients
Mutation X
Drug Y
Isogenic models of
tumour progression
Knock-out of cancer genes
wt
A
p53 -/-
Knock-in of oncogenic mutations
Ras / Raf
PI3K
EGFR
Homologous recombination
B
A
B
Isogenic cells carrying
cancer mutations
Mutation-specific pharmacogenomic profiles
Parental cell line
Knock-in cell line
Drug screening
Incubate cells with drugs
Mutated genotype
selective drug
Drug with no
selectivity
Di Nicolantonio; Arena et al., PNAS 2008
Wild genotype
selective drug
Di Nicolantonio et al., J Clin Invest, 2010
Experimental design
Cellular model
Gene targeting (Knock-in approach)
KRAS: G12D, G12V, G12C, G12A, G12S, G12R, G13D
BRAF: V600E, PIK3CA: E545K (exon 9), H1047R (exon 20)
Biochemical validation (pathway activation)
Measure drug response
NotI
A
ITR
G12V (G35>G/T)
LoxP
NotI
P
Neo
LoxP
ITR
AAV-KRas-12V
NotI
ITR
G13D (G38>G/A) NotI
LoxP
P
Neo
LoxP
LoxP
ITR
AAV-KRas-13D
Knock-in G12V
Homologous recombination (or G12D / G12C)
B
KRAS WT CRC cells
C
SW48 KRAS WT
SW48 KRAS G12V
SW48 KRAS G13D
Knock-in G13D
KRAS G12V or G13D and
chemotherapy in cellular models
SW48
SW48
100
100
90
90
80
70
WT
G13D
G12V
60
50
40
% Control
% Control
80
70
50
40
30
30
20
20
10
10
0
0,01
0,1
1
10
Irinotecan µM
De Roock et al JAMA 2010
100
WT
G13D
G12V
60
0
0,01
0,1
1
Oxaliplatin µM
10
100
KRAS G12V and G13D and
ceruximab in cellular models
SW48
100
% Control
90
WT
80
KRAS G13D
KRAS G12V
70
KRAS G12D
60
KRAS G12C
50
40
0.01
0.1
1
Cetuximab µg/ml
De Roock et al JAMA 2010
10
100
Cetuximab delays growth of SW48 tumor xenografts
SALINE
CETUXIMAB
2000
3
Tumor volume (mm)
2500
1500
1000
500
0
0
5
De Roock et al JAMA 2010
10
15
20
Days
25
30
35
Cetuximab does not affect growth of G12V tumors,
but inhibits the growth of G13D tumor xenografts
SW48 KRAS G12V
SW48 KRAS G13D
2500
2000
2000
3
Tumor volume (mm)
3
Tumor volume (mm)
2500
1500
Start of treatment
1000
500
0
1500
Start of treatment
1000
500
0
0
10
20
30
40
50
0
10
Days
SALINE
De Roock et al JAMA 2010
CETUXIMAB
20
30
Days
40
50
Secondary resistance to targeted therapies
Responders (15-20%)
Non-Responders
2007
Secondary resistance to targeted therapies
Responders (15-20%)
Non-Responders
2010
Parallel clinical trials in cells, mice and patients
Mutation X
Drug Y
DNA, RNA and protein extraction,
FFPE blocks stored by the pathologist
DNA, RNA and protein extraction,
FFPE blocks stored by the pathologist
Liver Met implanted
s.c. in NOD SCID mice
Marker A
Drug X
Marker B
Drug Y
Patient undergoing liver
metastasectomy of CRC
Using this approach 112 samples were
succesfully engrafted since Oct 2008
A. Bertotti & L. Trusolino,
Molecular Oncology, IRCC
Expansion
Xenopatients
NUMBER OF SAMPLES
148
SURGERY
>90% p0
engraftment
from the pathologist
FFPE blocks
DMSO
RNA later
DMSO
RNA later
(2 mice)
Archive
RNA extraction
Genomic DNA extraction
p1
expansion
(6 mice)
44
p2
treatment
(24 mice)
DMSO
RNA later
Snap Frozen
DMSO
RNA later
Snap Frozen
FFPE blocks
A. Bertotti & L. Trusolino,
Molecular Oncology, IRCC
In vivo – M016
4000
3500
Tumor Volume (mm3)
3000
Tumor M016
Cet 0.5mg x2/week
Cet 0.25mg x2/week
Cet 0.125mg/week
Cet 0.5 mgx1/week
CONTROL
2500
2000
3/4
1500
1000
500
1/2
0
7 14212835394246495356606367707477818488919598102105
109112116
Time (days)
Understanding secondary
resistance to cetuximab
Time 0: Molecular analysis using
multiple omics’ technologies (WP3)
Time x: Molecular analysis using
multiple omics’ technologies (WP3)
control
cetuximab
Chronic treatment with cetuximab (0.5 mg/injection/2x/week)
secondary
resistance
Xenopatient M026: development of resistance
RESISTANCE- RESISTANCE
RESISTANCE- RESISTANCE
RESISTANCE- 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.
Consortium Members
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
The doctor’s perspective
Royal Mail Stamp Issue 25 February 2003
The patient’s perspective
"Here's my
sequence..."
Molecular Genetics Lab:
Federica Di Nicolantonio
Sabrina Arena
Miriam Martini
Emily Crowley
Elisa Scala
Carlotta Cancelliere
Sebastijan Hobor
Davide Zecchin
Simona Lamba
Michela Buscarino
Milo Frattini
Josep
Tabernero
Salvatore Siena
Andrea Sartore Bianchi
Marcello Gambacorta
Livio Trusolino
Andrea Bertotti