1. Gene-expression signatures for breast cancer prognosis, site of metastasis, and therapy resistance John Foekens Josephine Nefkens Institute Dept. Medical Oncology Mediterranean School of Oncology: Highlights in the Management of Breast Bancer Rome, November 16, 2006

2. Breast cancer incidence Worldwide ~1,000,000 new cases / year 1 out of 9 women will get breast cancer during life ~40% of the patients will die of breast cancer Reason: Development of resistance to therapy in metastatic disease

3. What do we need? Prognostic factors that accurately can predict which patient will develop a metastasis and who does not.  High-risk patients should receive adjuvant therapy, while the low-risk patients could be spared the burden of the often toxic therapy or could be offered a less aggressive treatment.

4. Metastasis-Free Survival (%) Time (months) 0 306090120 MFS as a function of the number of involved lymph nodes 100 80 60 40 20 0 0 1 2-4 5-9 1010 ~35%

5. Metastasis-Free Survival (%) 0 306090120 MFS as a function of the number of involved lymph nodes 100 80 60 40 20 0 Adjuvant hormonal or chemotherapy } } } Absolute survival benefit: 5 - 15% Time (months)

6. Metastasis-Free Survival (%) 0 306090120 100 80 60 40 20 0 MFS in lymph-node negative patients ~35% ~65% cured by local treatment: surgery ± radiotherapy Adjuvant therapy necessary ?? Time (months)

7. Consensus criteria for node-negative breast cancer Age and menopausal status Histological tumor grade Tumor size Steroid hormone-receptor and HER2 status  85 – 90% of node-negative patients should receive adjuvant therapy  Over-treatment since only 5 – 10% of the node-negative patients will benefit by cure

8. Predictive factors that accurately can predict which patient will respond favorably to a certain type of treatment and who does not. Final goal: Individualized targeted treatment which is based on prognostic and predictive factors, and new targets for treatment. What do we need more?

9. Steps in tumor progression ? ?

10. High-throughput methodologies mRNA Proteomics Epigenomics SNP arrays CGH of BAC arrays DNA-methylation profiling TK profiling Multiplex ELISA Mass-spectrometry Genomics Genetics Gene-expression profiling Multiplex RT-PCR

11. High-throughput methodologies mRNA Proteomics Gene-expression profiling Epigenomics SNP arrays CGH of BAC arrays DNA-methylation profiling TK profiling Multiplex ELISA Mass-spectrometry Genomics Genetics Multiplex RT-PCR

12. Gene expression analysis <1995: Northern Blotting, RNAse protection etc 1 Week: Analyse several genes on 10s of samples >1995: DNA Microarrays 1 Week: Analyse whole genome on 10s of samples

13. Chip design Microarray Add Sample Silicon wafer Glass microscope slide Nitrocellulose DNA Probes: 20 – 70 bases Fluorescently labeled sample Hybridization between sample and probe

14. Chip workflow Sample prep

15. Perou & Sorlie et al. Nature 2000; PNAS 2001 78 breast carcinomas 3 fibroadenoma’s 4 normal breast tissues Patients from Norway: Very heterogeneous with respect to nodal status, adjuvant and neo- adjuvant therapy Subtypes of breast cancer “Molecular portraits of human breast tumors” 496 “intrinsic” genes described by Perou et al. (Nature 2000); array with 8102 human genes 65 breast samples / 42 patients

16. Rotterdam data set: Affymetrix U133A chip 344 untreated lymph node-negative patients Subtypes of breast cancer

17. The Amsterdam prognostic profile van ‘t Veer et al, Nature 2002 Training set: 78 patients 70-gene signature  Validation

18. MFS in 151 LNN patients van de Vijver et al, NEJM 2002 Testing set: 295 patients, including 151 lymph-node negative patients

19. The Rotterdam – Veridex study Aim: To develop a prognostic profile that can be used for all lymph-node negative breast cancer patients, irrespective of age, tumor size, and steroid hormone-receptor status. Lancet 365:671-679 (2005)

20. Patients & Methods Total: 286 primary breast cancer patients No (neo-)adjuvant systemic therapy ( pure prognosis) Median follow-up 101 months Clinical endpoint: metastasis-free survival (MFS) Patients Quality check of RNA by Agilent BioAnalyzer Affymetrix oligonucleotide microarray U133A GeneChip (22,000 transcripts) Methods

21. RNA isolation frozen primary breast cancer tissue >70% tumor areacheck 30  sections RNA isolation combine RNA quality check Clear distinct 18S and 28S peaks No minor peaks present Area under 18S and 28S peaks >15% of total RNA area 28S/18S ratio should be between 1.2 and 2.0 Agilent BioAnalyzer

22. Analysis of metastasis-free survival primary tumor surgery metastasis time Affymetrix oligonucleotide microarray metastasis-free survival NO adjuvant systemic therapy

23. Gene-expression profiling Training set to generate profile Independent testing set for validation of the profile Multi-center (retrospective) study Prospective clinical trial Steps to follow in the clinical development of expression profiles

24. Gene-expression profiling Training set to generate profile Independent testing set for validation of the profile Multi-center (retrospective) study Prospective clinical trial Steps to follow in the clinical development of expression profiles

25. Unsupervised clustering analysis ER-ER+ Genes Tumors

26. Determining the signature for ER+ and ER- patients 286 LNN patients 209 patients77 patients supervised classification gene selection (Cox model, bootstrapping) 76 gene set ER status ER-positiveER-negative validation 80 patients (training) 35 patients (training) 171 patients (testing)

27. Determining the 76-gene signature Wang et al, Lancet 2005 AUCs of ROC 0 0.80 0.85 0.90 0.95 1.00 ER positive ER negative 16 genes ~

28. Gene-expression profiling Training set to generate profile Independent testing set for validation of the profile Multi-center (retrospective) study Prospective clinical trial Steps to follow in the clinical development of expression profiles

29. Comparison of the 76-gene signature and the current conventional consensus on treatment of LNN breast cancer Patients guided to receive adjuvant therapy Metastatic disease at 5 years Metastatic disease free at 5 years St. Gallen 2003 NIH 2000 76-gene signature 52/55 (95%) 52/65 (93%) 104/115 (90%) 101/114 (89%) 60/115 (52%)

30. MFS in patients with T1 tumors Metastasis-Free Survival 0.0 0.2 0.4 0.6 0.8 1.0 Months 0 80 HR: 14.1 (95% CI: 3.34–59.2), P = 1.6x10 -4 good signature (n = 32) poor signature (n = 47) Sensitivity 96% (24/25) Specificity 57% (31/54) 40 6020

31. Gene-expression profiling Training set to generate profile Independent testing set for validation of the profile Multi-center (retrospective) study Prospective clinical trial Steps to follow in the clinical development of expression profiles

32. Participating institutions: - University Medical Center Nijmegen, The Netherlands - Technische Universität München, Germany - National Cancer Institue, Bari, Italy - Institute of Oncology, Ljubljana, Slovenia 2 nd validation: EORTC - RBG

33. Total: 180 node-negative primary breast cancer patients No (neo-)adjuvant systemic therapy Median follow-up: 100 months Clinical endpoint: metastasis-free survival (MFS) Patients Tissues sent to Rotterdam for RNA isolation Affymetrix dedicated VDX2 oligonucleotide microarray (76 genes + 221 control genes) analysis at Veridex Methods Quality check of RNA by Agilent BioAnalyzer Methods EORTC – PBG validation study 43% of the tumors have a ‘good’ signature

34. 2 nd validation: MFS in 180 patients Metastasis-Free Survival 0.0 0.2 0.4 0.6 0.8 1.0 Years 0 510 HR: 7.41 (95% CI: 2.63–20.9), P = 8.5x10 -6 good signature (n = 78) poor signature (n = 102) Foekens et al, JCO 2006

35. Multivariate analysis in multi-center validation Age (per 10 yr increment)0.70(0.44-1.11)0.13 Menopausal status (post vs. pre)1.26(0.43-3.70)0.67 Tumor size (>20 mm vs. ≤20 mm)1.71(0.84-3.49)0.14 Grade (moderate/good vs. poor)1.24(0.61-2.52)0.56 ER (per 100 increment)1.00(0.99-1.01)0.13 76-gene signature(poor vs. good) 11.36(2.67-48.4)0.001 HR(95% CI)P-value Metastasis-Free Survival

36. MFS in post-menopausal patients Metastasis-Free Survival 0.0 0.2 0.4 0.6 0.8 1.0 Years 0 510 HR: 9.84 (95% CI: 2.31–42.0), P = 0.0001 good signature (n = 57) poor signature (n = 69)

37. MFS in St. Gallen average risk group Metastasis-Free Survival 0.0 0.2 0.4 0.6 0.8 1.0 Years 0 510 HR: 6.08 (95% CI: 2.15–17.2), P = 0.0001 good signature (n = 64) poor signature (n = 97)

38. Site of metastasis AIM: Identify genes associated with a relapse to the bone since biological features (e.g. homing) may be present in the primary breast tumor.

39. Bone metastasis The bone is the most abundant site of distant relapse in breast, prostate, thyroid, kidney and lung cancer patients. Bone micro-environment may facilitate circulating cancer cells to home and proliferate. Bisphosphonate therapy available.

40. Profile for bone metastasis 286 patients, 107 relapses (Lancet, 2005) 72 patients: - 46 x bone - 26 x non-bone Training SAM and PAM analysis Validation 31 - gene set 35 patients: - 23 x bone - 12 x non-bone

41. Performance of the 31-gene predictor Sensitivity:100% (23/23) Specificity: 50% (6/12) Validation set of 35 patients Smid et al, JCO 2006

42. All gene signatures for separating patients into different risk groups, so far, were derived based on the performance of individual genes, regardless of its biological processes or functions. It might be more appropriate to study biological themes, rather than individual genes. Pathway analysis There is criticism and non-understanding about the minimal overlap of individual genes between various multigene prognostic signatures.

43. Diagnosis / SurgeryRelapse Systemic therapy Predictive signatures Response No response ? Predictive profile

44. Analysis of type of response primary tumor surgery metastasis tamoxifen time PD CR / PR Microarray metastasis-free survival

45. Tamoxifen profile in ER+ tumors 112 patients (60 progressive disease, PD, 52 objective response, OR) 46 patients (25 PD, 21 OR) Training BRB, duplicate arrays P<0.05, QC spots 66 patients (35 PD, 31 OR) Validation QC arrays 44 - gene set 81 - gene set Discriminatory genes Predictive signature cDNA array analysis

46. Molecular classification: 1 st line tamoxifen Jansen et al, JCO 2005 112 ER + primary breast tumors from patients with recurrent disease and treated with first-line tamoxifen Training set: 21 OR v 25 PD 81 genes differentially expressed 44-gene predictive signature Validation: 31 OR v 35 PD Response :OR = 3.16 (P=0.03) PFS: HR = 0.48 (P=0.03)

47. What do we need more? Predictive factors that accurately can predict which patient will respond favorably to a certain type of treatment and who does not. Approach: Microarray analysis of primary tumor RNA to assess the type of response (objective measure) in the metastatic setting; - 1 st line tamoxifen therapy - 1 st line chemotherapy

48. Analysis of type of response primary tumor surgery metastasis chemotherapy time PD CR / PR Affymetrix U133plus2 array: 54,000 probe IDs metastasis-free survival

49. - 76-gene prognostic signature Summary gene expression signatures - Bone metastasis signature - Chemotherapy resistance signature - Tamoxifen resistance signature - Liver metastasis signature (in progress) - Pathway-derived signatures - Others …… + a growing number of published signatures for various clinical questions

50. Contributors gene-expression profiling Yixin Wang, Yi Zhang, Dimitri Talantov, Jack Yu, Tim Jatkoe & David Atkins Veridex LLC (Johnson & Johnson), La Jolla, USA -Nijmegen: P. Span, V. Tjan-Heijnen, L.V.A.M. Beex, C.G.J. Sweep -Munich: N. Harbeck, K. Specht, H. Höfler, M. Schmitt -Bari:A. Paradiso, A. Mangia, A.F. Zito, F. Schittulli -Ljubljana:R. Golouh, T. Cufer Third multi-center validation, institutions above + +BaselS. Eppenberger et al. +DresdenM. Kotzsch et al. +InnsbruckG. Daxenbichler et al. EORTC – RBG members (1 st multi-center validation) Anieta Sieuwerts, Mieke Timmermans, Marion Meijer-van Gelder, Maxime Look, Anita Trapman, Miranda Arnold, Anneke Goedheer, Roberto Rodriguez-Garcia, Els Berns, Marcel Smid, John Martens, Jan Klijn & John Foekens Erasmus MC TransBig group: second multicenter validation study