The quest to improve the molecular classification of CRC: myth or reality

Abstracts # 3515, 3516, 3517, 3518, 3519, 3520, 3521

Josep Tabernero, MDVall d’Hebron University Hospital and Vall

d’Hebron Institute of Oncology (VHIO)Barcelona

Disclosure• Advisory role for Amgen, BMS, Genentech,

Imclone, Merck-Serono, Novartis, Roche, Sanofi-Aventis and Symphogen

Amphiregulin, Epiregulin and EGFR gene copy number

A3516 – Poster Board #8: Richard Adams et al.Epiregulin (EREG) and amphiregulin (AREG) gene expression to predict response to cetuximab therapy in combination with oxaliplatin (Ox) and 5FU in first-line treatment of advanced colorectal cancer (aCRC) – analysis of the phase III COIN trial

A3519 – Poster Board 11#: Sebastian Stintzing et al.Ligand expression of the EGFR ligands amphiregulin, epiregulin, and amplification of the EGFR gene to predict for treatment efficacy in KRAS wild-type mCRC patients treated with cetuximab plus CAPIRI and CAPOX: Analysis of the randomized phase II AIO CRC-0104 trial

1Singh, AB et al. Cell Signal 2005; 2Shelly, M et al. J Biol Chem 19983Khambata-Ford, S. et al. J Clin Oncol 2007; 4Tejpar S, et al. J Clin Oncol 2009; 5Tabernero, J. et al. J Clin Oncol 2010

Amphiregulin/Epiregulin• EGFR ligands:

– 1 in C. Elegans– 4 in Drosophila– 7 in mammals: EGF, TGF-α,

HB-EGF, amphiregulin (AREG), betacellulin, epiregulin (EREG) and epigen1

– EREG and AREG bind more weakly to EGFR than EGF but much more potently and prolonged

– EREG preferentially activates heterodimers2

• High gene expression levels of EREG and AREG predict response to cetuximab3-5

Endpoint Performance of 2 tumor based tests: EREG and AREG gene expression

Utility Predictive biomarker for cetuximab treatment

Specimen Tumor specimens (FFPE)

PatientsSample size

1st-line mCRC treated with Ox/FP ± Cetuximab (COIN study)1630 pts (Arms A&B) 729 pts KRAS-wt 965 samples dissected & RNA extracted 525 KRAS-wt (176 mFOLFOX & 349 XELOX)

Assay qPCR RNA analysis

Adams: Patients and methods

Adams: Results (1)Characteristic EREG AREG

n/N Pearson’s r

p-value Pearson’s r p-value

KRAS mutation 427/952 -0.140 P<0.001 -0.083 P=0.010NRAS mutation 41/952 0.034 P=0.30 -0.003 P=0.94BRAF mutation 65/952 -0.152 P<0.001 -0.161 P<0.001PIK3CA mutation 119/930 -0.008 P=0.82 0.019 P=0.56Colon vs rectum

*404/696 -0.086 P=0.024 -0.078 P=0.039

Left vs right colon †

687/948 0.097 P=0.003 0.117 P<0.001

Liver mets 723/952 0.140 P<0.001 0.131 P<0.001Peritoneal mets 139/952 -0.224 P<0.001 -0.194 P<0.001Alk. phos. 951 0.075 P=0.021 0.104 P=0.001WBC count 951 0.047 P=0.15 0.068 P=0.036CEA 750 0.122 P=0.001 0.097 P=0.008Platelet count 948 -0.004 P=0.91 0.028 P=0.39Resected

primary *564/952 -0.082 P=0.012 -0.047 P=0.15

Radical surgery † 62/938 0.129 P<0.001 0.078 P=0.0172nd line EGFR tx 36/952 0.026 P=0.42 0.040 P=0.22MSI present 31/767 -0.081 P=0.025 -0.073 P=0.042

Adams: Results (1)Characteristic EREG AREG

n/N Pearson’s r

p-value Pearson’s r p-value

KRAS mutation 427/952 -0.140 P<0.001 -0.083 P=0.010NRAS mutation 41/952 0.034 P=0.30 -0.003 P=0.94BRAF mutation 65/952 -0.152 P<0.001 -0.161 P<0.001PIK3CA mutation 119/930 -0.008 P=0.82 0.019 P=0.56Colon vs rectum

*404/696 -0.086 P=0.024 -0.078 P=0.039

Left vs right colon †

687/948 0.097 P=0.003 0.117 P<0.001

Liver mets 723/952 0.140 P<0.001 0.131 P<0.001Peritoneal mets 139/952 -0.224 P<0.001 -0.194 P<0.001Alk. phos. 951 0.075 P=0.021 0.104 P=0.001WBC count 951 0.047 P=0.15 0.068 P=0.036CEA 750 0.122 P=0.001 0.097 P=0.008Platelet count 948 -0.004 P=0.91 0.028 P=0.39Resected

primary *564/952 -0.082 P=0.012 -0.047 P=0.15

Radical surgery † 62/938 0.129 P<0.001 0.078 P=0.0172nd line EGFR tx 36/952 0.026 P=0.42 0.040 P=0.22MSI present 31/767 -0.081 P=0.025 -0.073 P=0.042

• The combination of KRAS=wt and high EREG expression selects a good prognostic group.

• This is in the absence of cetuximab use, suggesting previously reported similar findings in a non randomised series of patients treated with cetuximab (Jacob) may be a prognostic effect not a predictive effect.

0.00

0.25

0.50

0.75

1.00

Sur

viva

l

0 6 12 18 24 30 36 42Time from randomisation (months)

EREG, OS

0 6 12 18 24 30 36 42Time from randomisation (months)

KRAS-mutlow expressionKRAS-muthigh expressionKRAS-wtlow expressionKRAS-wthigh expression

EREG, PFS

Global log-rank test: P=0.004

Global log-rank test: P=0.014

Adams: Results (2). EREG & KRAS in control arm

0 6 12 18 24 30 36 42Time from randomisation (months)

KRAS-wt, Xelox

00.

20.

40.

60.

81.

0

Sur

vivo

r fun

ctio

n

0 6 12 18 24 30 36 42Time from randomisation (months)

Arm A

Upper quartile of EREG expressionMedian of EREG expressionLower quartile of EREG expression

Arm B

KRAS-wt, mFOLFOX

In the mFOLFOX subgroup, high EREG expression is predictive of increased cetuximab efficacy.

InteractionP=0.0042

InteractionP=0.14

• Modelled survival plots by chemo regimen within the KRAS-wt subgroup

Adams: Results (3). EREG predictive

Endpoint Performance of 3 tumor based tests: EREG and AREG gene expression & EGFR GCN

Utility Predictive biomarker for cetuximab treatment

Specimen Tumor specimens (FFPE)

PatientsSample size

1st-line mCRC treated with CAPIRI or CAPOX + Cetuximab (AIO CRC-0104 study)1

144 pts (Arms A&B) 89 pts KRAS-wt 59-62 samples dissected & RNA extracted

Assay qPCR RNA analysis & EGFR FISH

Stitzing: Patients and methods

1Moosmann et al. J Clin Oncol 2011

Stintzing: Results

Amphiregulin (AREG) Epiregulin (EREG) EGFR-FISH

low(n=35)

high(n=24) p

low(n=28)

high(n=31) p

low(n=27)

high(n=35) p

ORR 46% 83% 0.006 46% 74% 0.036 33% 71% 0.004

mPFS (m) 4.9 8.4 4.9 7.9 4.6 8.4

PFS HR: 0.35<0.001

HR:0.580.026

HR: 0.490.004

mOS (m) 17.1 39.9 20.2 33.0 15.2 30.5

OS HR: 0.36<0.001

HR: 0.570.041

HR 0.440.001

• More robust population for the COIN than the AIO study• COIN (525 pts, control arm):

– EREG and AREG highly prognostic, even after adjustment for other known factors (including KRAS and BRAF)

– High EREG expression predictive for cetuximab + mFOLFOX in KRAS-wt (p=0.0042), not XELOX

• AIO (59-62 pts, no control arm): – AREG, EREG, and EGFR-amplification predictive for

cetuximab + CAPIRI/CAPOX – In KRAS-wt EGFR-FISH and AREG expression more

predictive

Adams & Stintzing: Conclusions

• The quest to identify other predictive factors for cetuximab:– We are not yet there– Are they prognostic?

• EREG & AREG:– Pros: Define addicted tumors to EGFR and predicts effect to EGFR

inhibition, consistent data1-3, prognostic (?)– Cons: threshold, method, validation– Not ready for the clinic

• EGFR amplification:– Pros: Define addicted tumors to EGFR and may predict effect to

EGFR inhibition, conflicting data– Cons: threshold– Not ready for the clinic

Adams & Stintzing: Implications

1Khambata-Ford, S. et al. J Clin Oncol 2007; 2Tejpar S, et al. J Clin Oncol 2009; 3Tabernero, J. et al. J Clin Oncol 2010

BRAF, PIK3CA and other KRAS mutationsPTEN loss

A3515 – Poster Board #7: Derek Jonker et al.BRAF, PIK3CA, and PTEN status and benefit from cetuximab in the treatment of advanced colorectal cancer. Results from NCIC CTG / AGITG CO.17: A phase III trial of cetuximab vs best supportive care

A3520 – Poster Board #12: David Tougeron et al.Effect of low-frequency KRAS mutations on the response to anti-EGFR therapy in metastatic colorectal cancer

Endpoint Performance of 3 tumor based tests: BRAF and PIK3CA mutations and PTEN loss

Utility Predictive biomarker for cetuximab txSpecimen Tumor specimens (FFPE)PatientsSample size

3rd-line mCRC treated with BSC ± Cetuximab (CO.17)572 pts (Arms A&B) 230 pts KRAS-wt 205 TMAs for PTEN, 207 for mutations

Assay Nested PCR for BRAF (ex 15) and PIK3CA (ex 9 & 20). Sequencing confirmedIHC PTEN (CST 9559): 0-4+ (2 pathologists)

Jonker: Patients and methods

Jonker: Results (1) PFSPatient subset

Progression-free survival Adjusted HR

HR (95% C.I.)

Interaction p value

CO17 ITT (n=572) 0.68 [0.57-0.80]

K-ras MUT 0.99 [0.73-1.35]

P<0.001 K-ras WT 0.40 [0.30-0.54]

+ BRAF WT 0.41 [0.30-0.55]p=0.84

+ BRAF MUT 0.76 [0.19-3.08]

+ PIK3CA WT 0.40 [0.29-0.56]p=0.50

+ PIK3CA MUT 0.27 [0.10-0.69]

+ PTEN intact 0.66 [0.31-1.41]p=0.09

+ PTEN loss 0.34 [0.20-0.57]

Favours Cetuximab Favours BSC

N=572

N=230

N=10

N=198

N=148

N=57

Jonker: Results (2) OS Patient subset

Overall survival Adjusted HR

HR (95% C.I.)

Interaction p value

CO17 ITT (n=572) 0.77 [0.64-0.92]

K-ras MUT 0.98 [0.70-1.37]

p=0.01 K-ras WT 0.55 [0.41-0.74]

+ BRAF WT 0.52 [0.37-0.71]p=0.70

+ BRAF MUT 0.84 [0.20-3.58]

+ PIK3CA WT 0.53 [0.37-0.74]p=0.63

+ PIK3CA MUT 0.43 [0.18-1.06]

+ PTEN intact 0.66 [0.29-1.52]

p=0.61 + PTEN loss 0.63 [0.38-1.03]

Favours Cetuximab Favours BSC

N=572

N=230

N=10

N=198

N=148

N=57

• Neither PIK3CA-mt nor PTEN expression were predictive• BRAF-mt limited number of samples, single agent

• BRAF-mt: – Pros: Define tumors with less (no) benefit to EGFR inhibitors.

Consistent data with irinotecan-based chemotherapy in refractory1, 2nd-line2 and 1st-line3 settings. Validated method

– Cons: BRAF-mt uncommon– Ready for the clinic: not from regulatory but…

• PIK3CA-mt:– Inconsistent data, largest dataset predictive (ex 20 vs 9)1

• PTEN loss:– Inconsistent data (60 vs 40%), different methodology (-/+ vs H-score),

low concordance4-6

Jonker: Conclusions

Jonker: Implications

1De Roock, W et al. Lancet Oncol 2011; 2Seymour S. et al. Proc ASCO 2011; 3Van Cutsem, E. et al. J Clin Oncol 2011;4Loupakis, F et al. J Clin Oncol 2009; 5Frattini, M et al. Br J Cancer 2007;

6Perrone F et al. Ann Oncol 2009; 7Laurent-Puig P et al. J Clin Oncol 2009

Endpoint Performance of 1 tumor based tests: low frequency KRAS mutations

Utility Predictive biomarker for anti-EGFR txSpecimen Tumor specimens (FFPE)PatientsSample size

Retrospective analysis 1st (29%), 2nd (29%), 3rd

(32%) or later (11%); + CT (92%)168 pts, initially WT by direct sequencing

Assay Pyrosequencing by Therascreen KRAS Pyro® Kit (Qiagen®)2 cohorts: - KRAS–wt (0-2% mutant alleles)- low-frequency KRAS-mt (2-10% mutant alleles)

Tougeron: Patients and methods

Tougeron: Results

KRAS WT KRAS LowMTORR (%) 37 7

PD (%) 29 70

P<0.01

PFS

6.0 months

2.7 months

• Tumors with KRAS-lowmt have lower benefit from anti-EGFR MoAbs than those KRAS-wt

• Very provocative data• This data may suggest clonal heterogeneity and selection under

treatment pressure• More data is coming soon• The challenges:

– More sensitive methods to detect KRAS mutations, for enhanced predictions of resistance to anti-EGFR MoAbs in mCRC are required:

• Direct sequencing ≈ 10-20% alleles• Mass-Array techs ≈ 5-10% alleles• RT-PCR ≈ 1-2% alleles

– Change of paradigm in treatment: plasticity, heterogenous disease, treatment for multiple and/or predominant clones?

Tougeron: Conclusions

Tougeron: Implications

Micro RNA signatures

A3521 – Poster Board #13: Federico. Cappuzzo et al.MicroRNA signature predicts sensitivity to anti-EGFR monoclonal antibodies in metastatic colorectal cancer (mCRC).

Endpoint Performance of 1 tumor based test: miRNA signature

Utility Predictive biomarker for anti-EGFR txSpecimen Tumor specimens (FFPE)PatientsSample size

Retrospective analysis 1st (1-6%), 2nd (22-26%), 3rd (42-52%) or later (35-16%); + CT (92%)183 pts in 2 cohorts: training 74 & validation 109KRAS-wt 110, BRAF-wt 152

Assay miRNA analysis: Agilent platform

Cappuzzo: Patients and methods

• MicroRNA (miRNA) are a class of small non-coding RNA that bind to mRNA, silencing their mRNA target

• Several recent studies have uncovered a relationship between EGFR pathway and miRNA

• Available data indicate that miRNA levels could modulate sensitivity to target agents including anti-EGFR compounds1-2

– Let-7 complementary site LCS6 (T>G) polymorphism: T/T worse prognosis

Cappuzzo: Results (1)

1Graziano, F. et al. Pharmacogenomics 2010,; 2Zhang, W, et al. Ann Oncol 2011

Let-7c/miR-99a/miR-125b

Cappuzzo: Results (2). Let-7c/miR-99a/miR-125b cluster levels in both cohorts

  N (%) PD rate (%) PFS (months) OS (months)

Total 183 (100) 47.5 4.9 10.5

High levels 57 (31.1) 40.7 7.7 15.8

Low levels 62 (33.9) 45.2 3.5 10.0P value 0.64 0.0002 0.04OR*/HR 0.83* 0.47 0.67

PFS OS

HighLow+ censored

HighLow+ censored

Time (months) Time (months)

Cappuzzo: Results (3). Let-7c/miR-99a/miR-125b cluster levels in KRAS/BRAF wt

  N (%) PD rate (%) PFS (months) OS (months)

Total 98 (100) 37.5 6.2 12.9High levels 31 (31.6) 31.0 8.2 16.9Low levels 33 (33.7) 33.3 4.4 10.9P value 0.84 0.02 0.1OR*/HR 0.90* 0.54 0.68

PFS OS

HighLow+ censored

HighLow+ censored

Time (months) Time (months)

• MiR-99a/Let-7c/miR-125b signature seems useful for improving selection of KRAS/BRAF wild-type mCRC patients candidate for anti-EGFR strategies

• Provocative data• How these miRNAs were selected?• Validation set needed• Predictive vs prognostic• No evaluation of the previously published LCS6 (T>G) • The dark side of the moon:

– Only effects in PFS and OS, not in RR– We need to better understand the biological effect of these miRNAs

to dissect their future role in CRC

Capuzzo: Conclusions

Capuzzo: Implications

VEGF/VEGFR polymorphisms

A3518 – Poster Board #10: Chiara Cremolini et al.Prospective evaluation of candidate SNPs of VEGF/VEGFR pathway in metastatic colorectal cancer (mCRC) patients (pts) treated with first-line FOLFIRI plus bevacizumab (BV)

Endpoint Performance of 1 germ-line (PBMCs) based test: Polymorphism VEGF rs833061 T/T vs C/- 111 pts FOLFIRI + bevacizumab1

Additional analysis: SNPS in VEGF-A (rs699947 A/C, rs699946 A/G), VEGFR-1 (rs9582036 A/C, rs7993418 A/G), VEGFR-2 (rs11133360 C/T, rs12505758 C/T, rs2305948 C/T) and EPAS-1 (rs4145836 A/G)

Utility Predictive biomarker for anti-VEGF txSpecimen Blood (PBMCs)PatientsSample size

Prospective analysis 1st line treatment with FOLFIRI + Bevacizumab424 pts

Assay Genotyping (not described)

Cremolini: Patients and methods

1Loupakis, F. et al. BMC Cancer 2011

Cremolini: Results (1). VEGF rs833061 C/T variants and PFS

TT (N= 147) median PFS: 10.2 mosC- (N= 276) median PFS: 10 mos

HR: 1.17 (0.91-1.50)Log-rank test p=0.218

No association of VEGF rs833061 C/T variants with PFS was found

Cremolini: Results (2). Other SNPs & PFS At the univariate analysis, no association of other candidate SNPs with

PFS was found, except for VEGFR2 12505758 C/T variants

CC (N= 11) mPFS: 10.7 mCT (N= 107) mPFS: 9.5 m

TT (N= 306) mPFS: 10.9 m

Log-rank test p=0.047

C- (N= 118) mPFS: 9.5 m TT (N= 306) m PFS: 10.9 m

HR: 1.40 (1.07-1.84)Log-rank test p=0.015

At the multivariate analysis, including Köhne score, mucinous histology, ECOG PS, LDH levels and primary tumor site as covariates, the association of VEGFR2 125057581 C- variants with shorter PFS was still significant (HR: 1.402 [1.079-1.822], p=0.012)

Significance was lost when applying multiple testing correction1Lambrechts, D. et al. Ann Oncol 2011

• No confirmation of the predictive value of VEGF rs833061 C/T and other SNPs

• The prospective validation is an essential step on biomarkers’ way toward clinical practice– Initial publication: 111 pts FOLFIRI + bev; 107 pts FOLFIRI

T/T shorter PFS (HR 2.13, p=0.0027)– Current presentation: 424 pts FOLFIRI + bev

• No other VEGF & VEGFR SNPs have been confirmed• No clear advances in the field of personalized medicine with

angiogenesis inhibitors (bevacizumab)

Cremolini: Conclusions

Cremolini: Implications

1Loupakis, F. et al. BMC Cancer 2011

TP53 status and gender in adjuvant CC

A3517 – Poster Board #9: Robert Warren et al.A novel interaction of genotype, gender and adjuvant treatment in survival after resection of stage III colon cancer: results of CALGB 89803

Endpoint To investigate whether domain-specific mutations (Zn-binding and non-zinc binding regions of the DNA binding domain) in TP53 are predictive of OS

Utility Predictive biomarker for OS in stage III CCSpecimen Tumor specimens (FFPE)PatientsSample size

Retrospective analysis from the CALGB 89803 study in stage III CC (IFL vs FL)1264 pts included TP53 analyzed in 607 samples 274 mutations identified

Assay Tumor DNA was analyzed by direct sequencing (233 samples) or sequencing by hybridization (426 samples) with 50 sample overlap and near perfect agreement between these methods

Warren: Patients and methods

TP53 in colon cancer

• TP53 mutations occur in ≈ 50% of CRCs

• 95% occur in the DNA binding domain (exons 5-8)

• This is composed by Zn-binding and non-Zn binding regions

• They may have different functional implications

• 274/607 had TP53 mutations

• 190 cause single aa changes resulting in non-functional p53

Kaplan-Meier OS estimatesWarren: Results (1)

Warren: Results (2)

• No confirmation of the predictive value of VEGF rs833061 C/T and other SNPs

• The interface of patient characteristics and tumor characteristics• Very provocative results suggesting that clustering of CRC may be

closer to us than we expect:– Combination of TP53 status and gender

• Clear opportunity for validation: PETACC-3 study– Please contact A. Roth or S. Tejpar!!!!

Warren: Conclusions

Warren: Implications

Conclusions• Each of these studies constitute and

Academic effort to personalize the treatment in patients with CRC by tuning the target population beyond the standard of care

• In order to completely define the ultimate role of the different prognostic/ predictive factors more international collaboration is needed

Acknowledgements• ASCO Program Committee• Poster presenters for providing their

presentations in a timely fashion• Eduardo Vilar, MD PhD• Audience