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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

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slide1

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

slide2

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
slide3

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.

slide4

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

slide5

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

predictive or diagnostic modelling
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

slide7

Overview of Genomic Approach

  • DNA / RNA microarray
  • MicroRNA microarray
  • Single nucleotide polymorphism (SNPs)
  • Epigenetic (e.g. methylation) profiling
slide8

Metagene Analysis in NSCLA

Potti et al,

NEJM, 2006

slide9

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

slide10

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
slide11

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
slide13

Spectra from human normal lung and NSCLC tissues

NL

Relative Intensity

LC

*

*

*

*

*

8000

10500

13000

3000

5500

(Mass/Charge)

slide15

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

slide17

Clinical Correlations in NSCLC (interim data)

Clinical Correlations in Esophageal Cancer (interim data)

slide18

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)
slide19

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

slide20

Hypothesis

“NSCLC is associated with an elevated

hemangiogenic profile, therefore, surgical

removal of primary tumor may normalize

this dysregulation in hemangiogenesis”

slide22

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

predictive modelling
Predictive Modelling
  • Permit risk stratification.
  • Customize treatment

Less extensive surgery

Rational drug selection

Monitoring response to therapy.

slide29

.

Cancer-Testis Genes are expressed and are markers of poor outcome in pulmonary adenocarcinoma

Ali O. Gure,CCR 2005

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