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Gene-expression signatures for breast cancer prognosis, site of metastasis, and therapy resistance

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.

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Gene-expression signatures for breast cancer prognosis, site of metastasis, and therapy resistance

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  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. MFS as a function of the number of involved lymph nodes 100 ~35% 80 0 60 1 Metastasis-Free Survival (%) 2-4 40 5-9 20 10 0 0 30 60 90 120 Time (months)

  5. MFS as a function of the number of involved lymph nodes 100 Absolute survival benefit: 5 - 15% 80 } 60 Metastasis-Free Survival (%) } 40 } 20 0 0 30 60 90 120 Time (months) Adjuvant hormonal or chemotherapy

  6. Adjuvant therapy necessary ?? MFS in lymph-node negative patients 100 ~35% 80 60 Metastasis-Free Survival (%) ~65% cured by local treatment: surgery ± radiotherapy 40 20 0 0 30 60 90 120 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. 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. Final goal: Individualized targeted treatment which is based on prognostic and predictive factors, and new targets for treatment.

  9. Steps in tumor progression ? ?

  10. High-throughput methodologies SNP arrays Genetics CGH of BAC arrays DNA-methylation profiling Epigen omics mRNA Gene-expression profiling Gen omics Multiplex RT-PCR TK profiling Prote omics Multiplex ELISA Mass-spectrometry

  11. High-throughput methodologies SNP arrays Genetics CGH of BAC arrays DNA-methylation profiling Epigen omics mRNA Gene-expression profiling Gen omics Multiplex RT-PCR TK profiling Prote omics Multiplex ELISA Mass-spectrometry

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

  14. Chip workflow Sample prep

  15. 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 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 Perou & Sorlie et al. Nature 2000; PNAS 2001

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

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

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

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

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

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

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

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

  25. Unsupervised clustering analysis Genes ER- ER+ Tumors

  26. Determining the signature for ER+ and ER- patients 286 LNN patients ER status ER-positive ER-negative 209 patients 77 patients supervised classification 171 patients (testing) 80 patients (training) 35 patients (training) gene selection (Cox model, bootstrapping) validation 76 gene set

  27. 60 16 60 genes ( ) å å = × + × + × - + - × Relapse Score A I I w x B (1 I) 1 I w x i i j j = = i 1 j 1 where > ì 1 if ER level 10 115 training set patients = I í £ 0 if ER level 10 î A and B are constants 50 100 150 200 w is the standardiz ed Cox regression coefficien t i Number of genes x is the expression value in log2 scale i Determining the 76-gene signature 1.00 ER negative 16 genes 0.95 ER positive AUCs of ROC 0.90 0.85 0.80 ~ 0 Wang et al, Lancet 2005

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

  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 52/55 (95%) 104/115 (90%) NIH 2000 52/55 (95%) 101/114 (89%) 76-gene signature 52/65 (93%) 60/115 (52%)

  30. MFS in patients with T1 tumors good signature (n = 32) 1.0 0.8 0.6 Metastasis-Free Survival 0.4 poor signature (n = 47) 0.2 Sensitivity 96% (24/25) Specificity 57% (31/54) HR: 14.1 (95% CI: 3.34–59.2), P = 1.6x10-4 0.0 0 40 80 20 60 Months

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

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

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

  34. 2nd validation: MFS in 180 patients good signature (n = 78) 1.0 0.8 0.6 poor signature (n = 102) Metastasis-Free Survival 0.4 0.2 HR: 7.41 (95% CI: 2.63–20.9), P = 8.5x10-6 0.0 0 5 10 Years Foekens et al, JCO 2006

  35. Multivariate analysis in multi-center validation Metastasis-Free Survival HR (95% CI) P-value 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

  36. MFS in post-menopausal patients good signature (n = 57) 1.0 0.8 0.6 poor signature (n = 69) Metastasis-Free Survival 0.4 0.2 HR: 9.84 (95% CI: 2.31–42.0), P = 0.0001 0.0 0 5 10 Years

  37. MFS in St. Gallen average risk group good signature (n = 64) 1.0 0.8 0.6 poor signature (n = 97) Metastasis-Free Survival 0.4 0.2 HR: 6.08 (95% CI: 2.15–17.2), P = 0.0001 0.0 0 5 10 Years

  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) Training Validation 72 patients: - 46 x bone - 26 x non-bone 35 patients: - 23 x bone - 12 x non-bone SAM and PAM analysis 31 - gene set

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

  42. Pathway analysis There is criticism and non-understanding about the minimal overlap of individual genes between various multigene prognostic signatures. 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.

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

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

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

  46. Molecular classification: 1st line tamoxifen 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) Jansen et al, JCO 2005

  47. Approach:Microarray analysis of primary tumor RNA to assess the type of response (objective measure) in the metastatic setting;- 1st line tamoxifen therapy- 1st line chemotherapy 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.

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

  49. Summary gene expression signatures • 76-gene prognostic signature - 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 Erasmus MC 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 Veridex LLC (Johnson & Johnson), La Jolla, USA Yixin Wang, Yi Zhang, Dimitri Talantov, Jack Yu, Tim Jatkoe & David Atkins EORTC – RBG members (1st multi-center validation) -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 + +Basel S. Eppenberger et al. +Dresden M. Kotzsch et al. +Innsbruck G. Daxenbichler et al. TransBig group: second multicenter validation study

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