Prognose mittels Genexpression

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

2. Introduction Targeted therapy – Need for personalization • 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

3. Introduction Role for Gene Expression Analyses? • 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

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. 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 • 101 treatment-naive non-squamous stage IIIB/IV 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 Targeted Therapy Chemotherapy Gemcitabine 1250mg/m2 Bevacizumab 15mg/kg i.v.q3w + Erlotinib 150mg/d p.o. + Cisplatin 80mg/m2 or Carboplatin AUC 5 Inclusion Follow-up until progression or toxicity q3w x 6 or until progression • 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

15. Conclusion Prognostic Gene Expression Signatures • 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