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Microarray analysis as a prognostic and predictive tool:

Microarray analysis as a prognostic and predictive tool: are we ready? Enzo Medico Laboratory of Functional Oncogenomics Institute for Cancer Research and Treatment University of Torino enzo.medico@ircc.it. Topics. Platforms for gene expression profiling Breast cancer signatures

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Microarray analysis as a prognostic and predictive tool:

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  1. Microarray analysis as a prognostic and predictive tool: are we ready?Enzo MedicoLaboratory of Functional Oncogenomics Institute for Cancer Research and TreatmentUniversity of Torinoenzo.medico@ircc.it

  2. Topics • Platforms for gene expression profiling • Breast cancer signatures • From cell-based models to cancer classifiers

  3. AFFYMETRIX GeneChip

  4. AFFYMETRIX GeneChip 45,000 gènes !

  5. The Probe Sets

  6. Hybridization on the chip

  7. Signal detection

  8. Genomic raw data

  9. Gene expression profilingby spotted microarrays Oligonucleotides or cDNAs Robotic printing

  10. Gene expression profilingby spotted/dual colour microarrays “Reference” RNA sample (pool)‏ “Test” sample (tumour specimen)‏ RNA extraction, cDNA labelling Hybridization

  11. Different platforms generatedifferent data types Ref – 1 Ref - 2 Sample – 1 Sample - 2 Ref vs Sample – 1 Ref vs Sample - 2 vs Two-colour One-colour Paired samples Independent samples

  12. Topics • Platforms for gene expression profiling • Breast cancer signatures • From cell-based models to cancer classifiers

  13. SHOULD ONE TREAT A SMALL (<1CM) ENDOCRINE UNRESPONSIVE TUMOR ? 50 40 30 20 10 0 AUCUN CMFx6 ACx4 TAM AUTRE % Choices of 40 experts worldwide 48 61 years IDC Postmenopausal N - pT = 0.9 cm Grade 2 ER et PgR - HER2 - 25 8 15 4 FA(E)C x 6

  14. THERAPY DECISION-MAKING FOR EARLY BREAST CANCER WHO CAN BE SPARED THERAPY? WHICH THERAPY WILL WORK BEST? Prognostic factors needed Predictive factors needed

  15. The “Intrinsic” Breast Cancer Signatures ER- ER+ PNAS vol 98, no 19, 10869-10874, 2001 Clinical Outcome

  16. Confirmatory Study grade ER- ER+ PNAS vol 100, no 18, 10393-10398, 2003 Clinical Outcome

  17. Discovery of «poor prognosis signatures» for distant relapses Amsterdam’s Signature 312 patients 70 genes Rotterdam’s Signature 286 patients 76 genes

  18. 70-gene poor prognosis signature 78 tumor samples

  19. 70-gene expression signature outperforms clinicopathological criteria High Risk Low Risk Marc J Van de Vijver et al., NEJM, 347, 25, 2002

  20. 286 tumor samples Lancet, 2005, 365, 671-679

  21. Histologic Grade Genomic Grade G1 GG1 GG2 GG3 G2 G3 Sotiriou et al., JNCI 2006 • Poor inter observer reproducibility • G2: difficult treatment decision making, under- or over-treatment likely • Findings consistent across multiple data sets and microarray platforms • More objective assessment • Easier treatment decision-making • High proportion of genes involved in cell proliferation !

  22. Definition and validation of the Genomic Grade Analyze on validation set (n = 125)‏ Identify genes correlated with grade 1 vs grade 3 Grade 1 Grade 2 Grade 3 Grade 1 Grade 3

  23. Consistent Distribution of GG in Different Populations and Microarrays Platforms Sorlie et al. PNAS 2001 Van de Vijveret al. NEJM 2002 Central Pathology Review! Sotiriou et al. PNAS 2003

  24. GENE EXPRESSION SIGNATURE = POWERFUL PROGNOSTIC TOOL Highest priority = Transfer from bench to bedside HOW ?

  25. TRANSLATING MOLECULAR KNOWLEDGE INTO EARLY BREAST CANCER MANAGEMENT Validation study…

  26. THERAPY DECISION-MAKING FOR EARLY BREAST CANCER WHO CAN BE SPARED THERAPY? WHICH THERAPY WILL WORK BEST? Prognostic factors needed Predictive factors needed

  27. Topics • Platforms for gene expression profiling • Breast cancer signatures • From cell-based models to cancer classifiers

  28. The Invasive Growth biological program Scattering and migration Differentiation, cell polarity, tubulogenesis Proliferation Survival and protection against apoptosis

  29. MLP-29 liver stem/progenitor cells activate the invasive growth program in response to HGF CTRL Day 1 MET SHH SMO PTCH1 GLI AFP CK19 ALB -AT TO Hedgehog pathway HGF 6h Day 2 Liver lineage HGF 16h Day 4 Liver differentiation -3 +3 MLP29 / liver log2 ratio

  30. The Invasive Growth Transcriptional Program HGF/CTRL 1h 6h 24h EGF/CTRL 1h 6h 24h HGF/CTRL 1h 6h 24h EGF/CTRL 1h 6h 24h HGF/CTRL 1h 6h 24h EGF/CTRL 1h 6h 24h 1 Induced at 1h 10 6 2 Suppr. at 1h 7 3 11 8 Induced at 6h Suppressed at 6h 4 9 12 Suppressed at 24h 10 13 Induced at 24h 5 11 14 15 12 Suppressed at 24h 13 14 15

  31. Classifier construction and in silico validation using breast cancer microarray datasets Total NKI Breast cancer Dataset (311 samples - Agilent)‏ Rotterdam Breast cancer Dataset (286 samples - Affymetrix)‏ IG genes ranked by their individual performance (SNR over 1000 bootstraps)‏ Statistical analysis Number of genes in the classifier optimized and definition of the nearest mean classifier (NMC)‏ Kaplan-Meier COX proportional hazard

  32. The Nearest Mean Classifier Sample 1 Sample 2 Sample 3 Class A Sample 4 Sample 5 Sample 6 Class B GeneX X1 X2 X3 AVG X X4 X5 X6 AVG X GeneY Y1 Y2 Y3 AVG Y Y4 Y5 Y6 AVG Y Z4 Z5 Z6 AVG Z Gene Z Z1 Z2 Z3 AVG Z Training Group A Training Group B

  33. The Nearest Mean Classifier Test sample Sample 1 Sample 2 Sample 3 Good Progn Class Sample 4 Sample 5 Sample 6 Poor Prog Class Gene X Xs GeneX X1 X2 X3 AVG X X4 X5 X6 AVG X Gene Y Ys GeneY Y1 Y2 Y3 AVG Y Y4 Y5 Y6 AVG Y Z4 Z5 Z6 AVG Z Gene Z Zs GeneZ Z1 Z2 Z3 AVG Z Group A Group B Pearson correlation -> classification

  34. Invasive growth genes classify breast cancersamples by their metastatic propensity

  35. IG 49 genes NKI 49 genes Cumulative Survival Cumulative Survival Time to relapse or last follow-up (months)‏ Time to relapse or last follow-up (months)‏ Good prognosis Cox’s proportional hazards model Poor prognosis Validation on the Rotterdam dataset(286 breast samples, Wang et all., Lancet, 2005)‏ Legend: 0 = Good prognosis samples 1 = Poor prognosis samples

  36. Breast cancer expression profiling: towards an integrated approach to personalized therapy

  37. Acknowledgments IRCC Laboratory of Functional Oncogenomics Tommaso Renzulli Claudio Isella Daniela Cantarella Barbara Martinoglio Roberta Porporato IRCC Gynaecological Oncology Daniela Cimino Luca Fuso Prof. Michele De Bortoli Prof. Piero Sismondi

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