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Predictive Biomarkers for Lung Cancer PowerPoint Presentation
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Predictive Biomarkers for Lung Cancer

Predictive Biomarkers for Lung Cancer

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Predictive Biomarkers for Lung Cancer

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  1. Predictive biomarkers will allow the selection of lung cancer patients who may need more aggressive screening and treatment 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

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

  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 • 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 • 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 • 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. Spectra from human normal lung and NSCLC tissues NL Relative Intensity LC * * * * * 8000 10500 13000 3000 5500 (Mass/Charge)

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

  15. Kaplan-Meier survival curves based on 15 MS peaks 1.0 Good Prognosis Group Poor Prognosis Group 0.8 0.6 Survival 0.4 P < 0.0001 0.2 50 0 Time in Months 0 10 20 30 40

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

  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 HUVEC-Based Functional Angiogenic Scale 5 4 3 2 1 0 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

  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