1. Predictive Biomarkers for Lung Cancer Current Status / Perspectives: Although curative resection of patients with early-stage lung CA are performed, the risk of relapse remains substantial Indicates that there may be micro- invasion/metastasis have not been detected by general imaging and/or pathological examinations Predictive biomarkers will allow the selection of lung cancer patients who may need more aggressive screening and treatment Predictive biomarkers will allow the selection of lung cancer patients who may need more aggressive screening and treatment

2. Predictive Biomarkers for Lung Cancer Intended Goals:  Defining categories or tumor subsets that may improve the diagnostic classification of lung tumors  Identifying specific genes, proteins, or accessory cells that could serve as targets for improved diagnosis and/or therapy  Associating biomarkers with clinical outcomes

3. Predictive Biomarkers for Lung Cancer Hurdles: There are no biomarkers universally recommended to help in the clinical management of lung cancer today.  Probable valid biomarkers  Candidate biomarkers  General trends  Poor study design / analysis  Assay variability  Lack of standardization protocols

4. Predictive Biomarkers for Lung Cancer Challenges:  Single biomarker approach has not been proven to have strong predictive potential in lung cancer  Use of molecular and nano-IVD technologies bring a key promise for identification of clinically meaningful biomarkers  Clinical validation of candidate biomarkers remains a major challenge

5. Predictive Biomarkers for Lung Cancer Challenges:  Use of biomarkers for early detection of lung cancer is promising but still methodologically challenging  Clinical management of lung cancer will most probably first benefit from use of biomarkers  Development of new therapeutic options for lung cancer will stimulate identification and clinical validation of new biomarkers

6. Predictive or diagnostic modelling Tissue based. Serum or urinary based Cellular based Use of one or more biomarkers to determine prognosis or response to treatment beyond usual clinical criteria

7. Overview of Genomic Approach  DNA / RNA microarray  MicroRNA microarray  Single nucleotide polymorphism (SNPs)  Epigenetic (e.g. methylation) profiling

8. Metagene Analysis in NSCLA Potti et al, NEJM, 2006

9. Metagene Analysis in NSCLA Application of the lung metagene model to refine the assessment of risk and guide the use of adjuvant chemotherapy in Stage 1A NSCLC Potti et al, NEJM, 2006

10. Unique Micro RNA Profile in Lung Cancer Diagnosis and Prognosis miRNAs are small non-coding RNAs which play key roles in regulating the translation and degradation of mRNAs Genetic and epigenetic alteration may affect miRNA expression, thereby leading to aberrant target gene(s) expression in cancers Yanaihara et al, Cancer Cell, 2006: - miRNA profiles of 104 pairs of primary lung cancers and corresponding non- cancerous lung tissues were analyzed by miRNA microarrays - 43 miRNAs showed statistical differences

11. Unique Micro RNA Profile in Lung Cancer Diagnosis and Prognosis A univariate Cox proportional hazard regression model with a global permutation test indicated that expression of the miRNAs has-mir-155 and has-let-7a-2 was related to adenocarcinoma patient outcome Yanaihara et al, Cancer Cell, 2006: - miRNA profiles of 104 pairs of primary lung cancers and corresponding non- cancerous lung tissues were analyzed by miRNA microarrays - 43 miRNAs showed statistical differences Lung adenocarcinoma patients with either high has-mir-155 or reduced has-let-7a-2 expression had poor survival

12. Overview of Proteomic Approach

13. (Mass/Charge ) 30005500 80001050013000 Relative Intensity NL LC Spectra from human normal lung and NSCLC tissues ** * * *

14. Cluster analysis between Tumor and Normal lung (82 signals)

15. 010203040 50 Poor Prognosis Group P < 0.0001 Good Prognosis Group 1.0 0.8 0.6 0.4 0.2 0 Kaplan-Meier survival curves based on 15 MS peaks Time in Months Survival

16. Grand Serology: Pedigreed database

17. Clinical Correlations in NSCLC (interim data) Clinical Correlations in Esophageal Cancer (interim data)

18. Cellular Biomarkers  Circulating cancer cells (EpCAM+ cells)  Endothelial progenitor cells (CD133+VEGFR2+ cells)  Hemangiocytes (CXCR4+VEGFR1+ myelomonocytic precursor cells; pro-angiogenic; pre-metastatic niche)  Stromal cells (pericytes, myofibroblasts)

19. Pro-angiogeic Bone marrow Endothelial progenitors hematopoietic stem/progenitor cells Pro-angiogeic Bone marrow Endothelial progenitors hematopoietic stem/progenitor cells Inflammation Tumor, Ischemia Regenerating Tissue Hypoxia Wound Healing CXCR4 + VEGFR1 + CD133 + VEGFR2 + Neo-angiogenic Niche Chemokine (SDF-1) Mobilization Recruitment Differentiation Incorporation Assembly Niche Migration (endosteal  vascular)

20. Hypothesis “NSCLC is associated with an elevated hemangiogenic profile, therefore, surgical removal of primary tumor may normalize this dysregulation in hemangiogenesis”

21. Assessment of Hemangiogenic Biomarkers in NSCLC Schema: EPCs

22. Angiogenic Activity 0: Well separated HUVECs 1: Cells begin to migrate and align 2: Visible capillary tubes; no sprouting 3: Sprouting of new capillary tubes 4: Polygonal structures begin to form 5: Presence of complex mesh-like structures HUVEC-Based Functional Angiogenic Scale 0 1 2 3 4 5

23. Functional Angiogenic Scale

24. Circulating CD133 + VEGFR2 + Endothelial Progenitor Cells

25. Plasma SDF-1 Levels

26. Predictive Modelling  Permit risk stratification.  Customize treatment Less extensive surgery Rational drug selection Monitoring response to therapy.

27. Circulating Hematopoietic Progenitor Cells

28. Intraplatelet VEGF-A Levels

29. . Cancer-Testis Genes are expressed and are markers of poor outcome in pulmonary adenocarcinoma Ali O. Gure,CCR 2005