2010 Spring, Bioinformatics II Presentation

  • 317 views
Uploaded on

2010 Spring, Bioinformatics II, Prof. Yu Zhang

2010 Spring, Bioinformatics II, Prof. Yu Zhang

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
317
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
6
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • 2 nd most common cancer, after lung cancer (and melanomas) (the WHO) Strongly related to age, less than 5% of cases are women under 40 yrs 1.3 million women diagnosed annually worldwide
  • Cancer that forms in tissues of the breast, usually the ducts (tubes that carry milk to the nipple) and lobules (glands that make milk). It occurs in both men and women, although male breast cancer is rare. Risk factors
  • After the mammogram… Or other disease such as mastitis
  • Round, ellipse, moon, rough
  • Primary tumor is a tumor growing at the anatomical site where tumor progression began and proceeded to yield a cancerous mass. Metastasis(Pleural effusion) is excess fluid that accumulates in the pleural cavity, the fluid-filled space that surrounds the lungs. Cancer cells can break away, leak, or spill from a primary tumor, enter lymphatic and blood vessels, circulate through the bloodstream. Metastasis is one of three hallmarks of malignancy.
  • Fig. 2. Morphologies of breast cancer cell lines cultured in 2D and 3D. (Top) Images of 4 representative breast cancer cell lines cultured as 2D monolayer, (middle) and in 3D lrBM grouped by 3D morphological classification: Round, Mass, Grape-like and Stellate. (Bottom) 3D cultures were stained for F-actin and nuclei were counterstained with DAPI. Scale bars: top panel, 100$m; middle panel, 50$m; bottom panel, 20$m. Adapted from ref. [77].

Transcript

  • 1. Presented by Dannise Jangyoung Marcus Bongsoo Breast Cancer Diagnostics
  • 2. Outline
    • Introduction
    • SVM
    • Logistic Regression
    • Conclusion & Discussion
  • 3. Introduction
    • Epidemiology
      • World wide the second most common cancer
        • 1.3 million cases
      • Most common type of cancer in women
        • US (2009) approximately 40,170 women were expected to die from breast cancer
      • Most common in well developed countries
      • Strongly related to age
    World Health Organization, American Cancer Society
  • 4. Introduction
    • Cancer that forms in tissues of the breast, usually ducts and lobules
    • Diagnosis: mammogram, FNA or surgical biopsy to identify the nature of the mass
    Normal breast Breast cancer Fine Needle Aspiration Surgical biopsy
  • 5. Introduction
    • Benign and malignant tumors
      • Benign: cyst or other disease
      • Malignant: cancer
    • Goal: To reduce the number of predictors classifying tumors to simplify diagnosis
  • 6. Data characteristics
      • radius
      • texture
      • perimeter
      • area
      • smoothness
      • compactness
      • concavity
      • Concave points
      • symmetry
      • Fractal dimension
    Mangasarian, et al (1994) Wolberg, et al. (1994)
  • 7. SVM (Support vector machine)
    • Breast cancer Wisconsin data set (569*32)
    • Linearly separable (Benign & Malignant )
  • 8. SVM
    • only means model ( 3-12)
    • Benign 99.43 %
    • Malignant 97.63%
  • 9. SVM
    • cross-validation of the model - fit a model with 80% of the rows, check if it can predict the type of the other 20% of rows
    • Benign 94.80%
    • Malignant 91.66%
  • 10. Logistic Regression
    • Reduce the number of predictors
    • Simplify the diagnosis
    • Less measurements, less time, less cost
  • 11. Logistic Regression Estimate Std. Error z value Pr(>|z|) (Intercept) 7.35952 12.85259 0.573 0.5669 radius 2.04930 3.71588 0.551 0.5813 texture -0.38473 0.06454 -5.961 2.5e-09 *** perimeter 0.07151 0.50516 0.142 0.8874 area -0.03980 0.01674 -2.377 0.0174 * smoothness -76.43227 31.95492 -2.392 0.0168 * compactness 1.46242 20.34249 0.072 0.9427 concavity -8.46870 8.12003 -1.043 0.2970 concave_points -66.82176 28.52910 -2.342 0.0192 * symmetry -16.27824 10.63059 -1.531 0.1257 fractal_dimension 68.33703 85.55666 0.799 0.4244 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  • 12. SVM
    • Perform SVM again
    • Used predictors: texture, area, smoothness, and concave points
    • To assure the validity of the model, we fit it to 80% of the data and make predictions about the remaining 20%
  • 13. SVM Results
    • Full dataset
    • Bootstrap
    Type Benign Malign Correct (%) 96.92 90.57 Type Benign Malign Correct (%) 96.63 89.85
  • 14. Conclusion & Discussion
    • Summary table
    Type Benign Malign Mean model 99.43 97.63 Cross Validation (80%) 94.80 91.66 The reduced model (Full Dataset) 96.62 90.57 The reduced model (Bootstrap) 96.63 89.85
  • 15. Conclusion & Discussion
    • The characteristics of cells are key to diagnose malign of breast cancer
    • SVM was good to validate diagnostic model
    • The reduced model is quiet accurate, and it will help doctors to save the cost and efforts of diagnostics
  • 16. Conclusion & Discussion
    • Treatment is based on the diagnostics of cell lines (Examples of invasive ductal carcinoma)
    Lasfargues, EY et al. 1958. Cultivation of human breast carcinomas. Borras, M et al. 1997. Estrogen receptor negative/progesterone receptor-positive evsa-T mammary tumor cells: a model for assessing the biological property of this peculiar phenotype of breast cancers. Cell line Origin of cell Estrogen receptors Progesterone receptors ERBB2 Amplification BT-20 Primary No No No BT-474 Primary Yes Yes Yes MCF-7 Metastasis Yes Yes No SK-BR-3 Metastasis No No Yes
  • 17. Conclusion & Discussion
    • Current breast cancer researches and diagnostics by 3D pictures
    • (Dr. Mina J. Bissell, Lawrence Berkeley National Laboratory)
    Britta Weigelt, Mina J. Bissell. 2008 Unraveling the microenvironmental influences on the normal mammary gland and breast cancer. Seminar in Cancer Biology. (18) 311-321
  • 18. Thank you very much ! Any questions ?