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

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

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 tissuesNL
Relative Intensity LC * * * * *
3000
5500
8000
10500
13000
(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
Survival
0.6
0.4
P <
0.2 50
Time in Months 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. Bone marrow Bone marrow Assembly Incorporation Neo-angiogenic Niche
Mobilization
Recruitment
Differentiation
Incorporation
Assembly
Niche Migration
(endosteal  vascular)
Neo-angiogenic Niche
Inflammation
Tumor, Ischemia
Regenerating Tissue
Hypoxia
Wound Healing
Chemokine
(SDF-1)
Bone marrow
Bone marrow
Pro
Pro - -
angiogeic
angiogeic
Endothelial
Endothelial
hematopoietic
hematopoietic
progenitors
progenitors
stem/progenitor cells
stem/progenitor cells
CXCR4+VEGFR1+
CD133+VEGFR2+

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. HUVEC-Based Functional Angiogenic Scale5 4 3
Angiogenic Activity 2 1
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