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

Bert Vogelstein (1988) NEJM 1988; 319:525-532. “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

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 BRAF PIK3CA

Parallel clinical trials in cells, mice and patients Drug Y Mutation X

EGFR-targeted therapies in CRCs TK Inhibitors 3 Anti-ligand- blocking Antibodies 2 Ligand– toxin Conjugates 4 Antibody– toxin Conjugates 5 Anti-HER1/EGFR- blocking antibodies 1 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 Gene Copy Number EGFR Protein expression (IHC) EGFR GSK S6K AKT PDK p85 PI3K MAPK MEK Ras Raf Ras Ras Ras Ras Ras Ras Ras Ras Ras Ras Ras SOS Raf Raf Raf Raf Raf Raf Raf Raf Raf Raf Raf Grb2 PI3K PI3K PI3K PI3K PI3K PI3K PI3K PI3K Shc MEK MEK MEK MEK MEK MEK MEK MEK MEK MEK p85 p85 p85 p85 p85 p85 p85 PTEN MAPK MAPK MAPK MAPK MAPK MAPK MAPK MAPK MAPK DUSPs PDK PDK PDK PDK PDK GSK GSK AKT AKT AKT AKT S6K S6K S6K Moroni et al Lancet Oncology 2005

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 14

KRAS-NRAS mutated (35-45%) BRAF/PIK3CA mutated Responder (15%) KRAS/PIK3CA mutated KRAS-NRAS mutated (35-45%) 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 YES Prenen et al., Clin Cancer Res 2009 NO

Different role for individual PIK3CA mutations on the response to EGFR MoAbs in mCRCs Zhao and Vogt PNAS 2008

Sample characteristics 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 16/717 (2.2%) Cetuximab monotherapy 43/717 (6%) Cetuximab + chemotherapy 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) LRT p-value Adjusted hazard ratio OS (95% CI) LRT p-value 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) 1.57 (0.90-2.76) 0.14 3.69 (1.69-8.02) 0.0055 BRAF (mutant vs. wild-type) 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 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. 25

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 RAS mutated (7.0%) RAS mutated (43.9%) wt (93.0%) wt (56.1%) Responders (n=82) Non-Responders (n=312)

KRAS mutations Smith G, et al., British Journal of Cancer (2010), 1 –11 GEP GDI GDP GTP GTP GDP RAS (inactive) RAS (active) Effectors: RAF/MAPK/ERK PI3K/AKT GAP Pi Farnesyl Geranylgeranyl

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, irinotecan-refractory CRC considered suitable to receive an EGFR MAb

KRAS Mutation Status and Therapy by Dataset 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 249 (88.3%) 87 (63.6%) No cetuximab or panitumumab 195 (49.5%)

Baseline Patient Characteristics by Tumour KRAS status G13D 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 1 166 ( 61.5) 264 ( 54.7) 2 7 ( 14.6) 30 ( 11.1) 48 ( 9.9) 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) 1 ( 0.2) Number of prior chemotherapy regimens 0 3 ( 1.1) 8 ( 1.7) .80 5 ( 10.4) 17 ( 6.3) 25 ( 5.2) 13 ( 27.1) 74 ( 27.4) 156 ( 32.3) 3 16 ( 33.3) 93 ( 34.4) 151 ( 31.3) 4 56 ( 20.7) 87 ( 18.0) ≥5 4 ( 8.3) 25 ( 9.3) 47 ( 9.7) 2 ( 0.7) 9 ( 1.9) Treatment Mono Cetuximab 91 ( 33.7) 146 ( 30.2) .34 Mono panitumumab 5 ( 1.9) 15 ( 3.1) Cetuximab + chemotherapy 105 ( 38.9) 209 ( 43.3) No cetuximab or panitumumab 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 patients not treated with Cetuximab or Panitumumab? Proportion alive 20 40 60 80 100 0.0 5.0 10.0 15.0 Time from randomization (months) KRAS subset Median OS (months) Wild-type 4.5 G13D mutation 3.6 Other Mutation 4.7 De Roock et al JAMA 2010

OS Predictive Analysis by KRAS status: EGFR Mab Monotherapy vs no EGFR Mab KRAS G13D Mutation Other KRAS Mutation KRAS Wild-type 20 40 60 80 100 0.0 5.0 10.0 15.0 20 40 60 80 100 0.0 5.0 10.0 15.0 20.0 20 40 60 80 100 0.0 5.0 10.0 15.0 20.0 HR 0.98 (0.70 to 1.38) p=0.91 HR 0.56 (0.42 to 0.73) p<0.0001 Proportion alive Proportion alive Proportion alive HR 0.23 (0.09 to 0.61) p=0.002 Time from randomization (months) Time from randomization (months) Time from randomization (months) Monotherapy with cetuximab or panitumumab No Treatment with cetuximab or panitumumab De Roock et al JAMA 2010

Molecular bases of G12V versus G13D mediated resistance to cetuximab in cellular and animal models

Parallel clinical trials in cells, mice and patients Drug Y Mutation X

Isogenic models of tumour progression Knock-out of cancer genes Knock-in of oncogenic mutations wt p53 -/- Ras / Raf PI3K EGFR Homologous recombination A B The technology has already been validated into a murine mouse model. We have succeeded in fact in obtaining MLP 29 mutated in the codon…. A Isogenic cells carrying cancer mutations B

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 Wild genotype selective drug Di Nicolantonio; Arena et al., PNAS 2008 Di Nicolantonio et al., J Clin Invest, 2010

Experimental design Cellular model Measure drug response 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

ITR P AAV-KRas-12V A ITR P Neo ITR AAV-KRas-13D Knock-in G12V NotI ITR Neo P LoxP AAV-KRas-12V G12V (G35>G/T) A NotI G13D (G38>G/A) NotI ITR LoxP P Neo LoxP LoxP ITR AAV-KRas-13D Knock-in G12V (or G12D / G12C) B Homologous recombination KRAS WT CRC cells Supplementary Figure 1. Targeted knock-in (KI) of KRAS cancer mutations in SW48 colorectal cancer cells. (A) Structure of AAV targeting constructs. AAV vectors carrying oncogenic alleles either in the 5’ (KRAS G12V) or the 3’ arm (KRAS G13D) were used to introduce the relevant genetic alterations in human somatic cells by homologous recombination. P, SV40 promoter; Neo, geneticin-resistance gene; ITR, inverted terminal repeat; triangles, loxP sites. The nucleotide and aminoacid changes corresponding to the oncogenic alleles are indicated. (B) The G12V and G13D alleles were introduced in the genome of SW48 colorectal cancer cells by AAV-mediated targeted homologous recombination. (C) The expression of the introduced genetic alterations in the targeted cells was determined by RT-PCR and sequencing of the KRAS transcript. C SW48 KRAS WT Knock-in G13D SW48 KRAS G12V SW48 KRAS G13D

chemotherapy in cellular models KRAS G12V or G13D and chemotherapy in cellular models De Roock et al JAMA 2010

ceruximab in cellular models KRAS G12V and G13D and ceruximab in cellular models De Roock et al JAMA 2010

Cetuximab delays growth of SW48 tumor xenografts De Roock et al JAMA 2010

Cetuximab does not affect growth of G12V tumors, but inhibits the growth of G13D tumor xenografts SW48 KRAS G12V SW48 KRAS G13D Start of treatment Start of treatment De Roock et al JAMA 2010

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 Drug Y Mutation X

Marker A Drug X Marker B Drug Y Expansion 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 Patient undergoing liver metastasectomy of CRC Using this approach 112 samples were succesfully engrafted since Oct 2008 A. Bertotti & L. Trusolino, Molecular Oncology, IRCC Marker B Drug Y Expansion

Xenopatients 148 >90% 44 NUMBER OF SAMPLES p0 p1 p2 SURGERY from the pathologist NUMBER OF SAMPLES FFPE blocks 148 SURGERY DMSO RNA later >90% p0 engraftment (2 mice) DMSO RNA later Archive RNA extraction Genomic DNA extraction p1 expansion (6 mice) DMSO RNA later Snap Frozen DMSO RNA later Snap Frozen FFPE blocks 44 p2 treatment (24 mice) A. Bertotti & L. Trusolino, Molecular Oncology, IRCC

In vivo – M016

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

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

Royal Mail Stamp Issue 25 February 2003 The doctor’s perspective Royal Mail Stamp Issue 25 February 2003

The patient’s perspective EVOLUTION FROM and Anatomic and DISEASE-BASED TO A MORE PERSONALIZED-BASED MEDICINE

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 Salvatore Siena Andrea Sartore Bianchi Marcello Gambacorta Livio Trusolino Andrea Bertotti Josep Tabernero 57