1. Prognose mittels Genexpression Prof. Martin H. Brutsche Kantonsspital St. Gallen-CH

2. Introduction Targeted therapies show low activity when given to all NSCLC, but are very effective in subsets of patients → Personalization Diagnostic refinements → identification of subgroups Prognostic markers Predictive markers Progress is dependent on todays patients → Role for biobanking & high-throughput technologies Precise histo-pathologic Phenotyping Genetics & Genomics & Proteomics Targeted therapy – Need for personalization

3. Introduction Pros of Gene Expression Analysis Focused view on utilized gene code Is the basis of all downstream products, i.e. peptides & proteins High technical standards allow genome-wide analysis Gives quantitative results (Genetics → Y/N) Allows dynamic analyses → Early response measures Role for Gene Expression Analyses?

4. 2007

5. Only high-risk patients (A&C) profit from adjuvant CT JCO 2010

6. Data processing The power of multivariate statistics Acceptable rules for data preprocessing, normalization, classical statistical testing, correction for multiple testing… Multivariate statistics allows a better analysis of gene expression similarities, i.e. potential “relationships” Here an example from our kitchen…

7. BMC Bioinformatics 2008

8. Issues of Practicability Gene expression from easy accessible source Due to advanced stages most patients are not suitable for curative surgery  no surgical bx available Is tumour cell enrichment necessary? –For Genetics  Y –For Genomics  maybe not –For Proteomics  ? Need for specimen from minimally invasive procedures –Bronchoscopic samples, i.e. cytobrush or biopsy –CT-guided biopsy –Blood samples

9. AJRCCM 2010

10. Heterogeneity of tumor cell content Influence on diagnosis but not on prognosis AJRCCM 2010

11. Little Overlap of Signature Genes Limitations of Gene Expression Signatures Reasons for technical variability –Differences between platforms –Different handling & storage SOPs –Single vs. multi center  shipment… –Fresh-frozen vs. formalin-embedded tissue –Probes with tumour cell enrichment, e.g. LCM? Biological redundancy –Genome-wide analyses capture redundancy of co- regulated gene families Insufficiently controlled co-factors like smoking status

12. Inclusion Bevacizumab 15mg/kg i.v.q3w + Erlotinib 150mg/d p.o. Gemcitabine 1250mg/m 2 + Cisplatin 80mg/m2 or Carboplatin AUC 5 q3w x 6 or until progression Follow-up until progression or toxicity Targeted TherapyChemotherapy Primary Endpoint: DSR @ 12 weeks 55 (44-64) % Secondary Endpoint: TTP 4 (2.9-5.5) months Responses: PD 41, SD 44, PR 10, CR 1 OS 13 (10.5-19.4) months Exonic expression variations of EGFR and KRAS in small bronchoscopic biopsies from patients with advanced non- small cell lung cancer treated by combined bevacizumab- erlotinib therapy followed by platinum-based chemotherapy at disease progression A multicenter phase II trial SAKK 19/05 M.H. Brutsche, M. Frueh, S. Crowe, K.J. Na, C. Droege, D.C. Betticher, R. Cathomas, R. von Moos, F. Zappa, M. Pless, L. Bubendorf, F. Baty 101 treatment-naive non-squamous stage IIIB/IV

15. Conclusion Lung Cancer is an orphan disease Feasibility & validity proven for Batched analyses within clinical trials Advanced statistical methods available Prediction of early relapse after curative resection Small bronchoscopic biopsies in all disease stages Open issues Analyses of individual day-to-day samples Utility of genomics from peripheral blood Tumour cell enrichment – if and when? Which signature gene set to be used? Future Subgenic analysis Prognostic Gene Expression Signatures