1. IMPROVED BREAST CANCER DIAGNOSIS AND PROGNOSIS BY
COMUTATIONAL MODELING
AND IMAGE ANALYSIS
David E. Axelrod
J.-A. Chapman, W.A. Christens-Barry,
H.L. Lickley, N.A. Miller, J. Qian,
L. Sontag, B. Subramanian, Y. Yuan
NCI, NJCCR, Busch
111704

2. BREAST CANCER
GOAL
Clinical data  Models  Patient prognosis
OUTLINE
Breast cancer stages: in situ and invasive
Clinical data
Models
Prediction
Image analysis
Prognosis

3. NORMAL BREAST ANATOMY
Skarin, A.T. Breast Cancer I Slide Atlas of Diagnostic Oncology,
Bristol-Myers Squibb Oncology

4. BREAST CANCER
Skarin, A.T. Breast Cancer I Slide Atlas of Diagnostic Oncology,
Bristol-Myers Squibb Oncology

5. BREAST TUMOR PROGRESSION Conventional View
Normal  Ductal Carcinoma In Situ  Invasive Ductal Carcinoma  Metastasis
( DCIS) (IDC) (M)

6. BREAST TUMOR PROGRESSION Conventional View
Normal  Ductal Carcinoma In Situ  Invasive Ductal Carcinoma  Metastasis
( DCIS) (IDC) (M)
Normal Atypical Hyperplasia DCIS1 DCIS2 DCIS3 IDC1 IDC2  IDC3  M
(AH)

7. DUCTAL CARCINOMA IN SITU
IMPORTANCE OF GRADING
DUCTAL CARCINOMA IN SITU
220,000 Breast Cancers / year
20% DCIS
32% recurrence free
DCIS outcome
68% recur (DCIS or IDC)
DCIS heterogeneity:
25% intermediate grade
50% mixed grades

8. PROGNOSIS BY PATHOLOGIST
Miller, N.A. et al. The Breast Journal 7: (2001)
Nuclear grade No. Recurrence Recurrence
Worst DCIS Invasive
Grade
Grade
Grade
p = p = 0.73
Conclude: Nuclear grade is not prognostic.

9. BREAST TUMOR PROGRESSION Conventional View
Normal  Ductal Carcinoma In Situ  Invasive Ductal Carcinoma  Metastasis
( DCIS) (IDC) (M)
Normal Atypical Hyperplasia DCIS1 DCIS2 DCIS3 IDC1 IDC2  IDC3  M
(AH)

10. CLINICAL OBSERVATIONS
EXPECTATION
DCIS1 DCIS2 DCIS3 IDC1 IDC2  IDC3
CLINICAL OBSERVATIONS
Van Nuys Classification Holland Classification
IDC DCIS IDC DCIS
Sum of observations of Gupta, Cadman and Leong, normalized to 498.

11. BREAST TUMOR PROGRESSION Conventional View
Normal  Ductal Carcinoma In Situ  Invasive Ductal Carcinoma  Metastasis
( DCIS) (IDC) (M)
Normal Atypical Hyperplasia DCIS1 DCIS2 DCIS3 IDC1 IDC2  IDC3  M
(AH)

12. Mommers et al. J. Pathol. 194: 327-333 (2001)

13. Buerger et al. J. Pathol. 187; 396-402 (1999)

14. " Unless you can express your knowledge with numbers,
your knowledge is meager and unsatisfactory."
William Thompson
Lord Kelvin
Smithsonian Institution of Washington, 1857

15. B. Subramanian and D.E. Axelrod
Progression of Heterogeneous Breast Tumors
J. Theoret. Biol. 210: (2001)
Purpose: Pathways for tumor progression (compartment models)
Transition rates between compartments
Data: Co-occurrence frequencies of DCIS and IDC
Method: Genetic algorithm (GA) search for transition rates
Result: GA can’t reproduce data with models
Conclusion: GA and/or models not adequate

16. PROBLEMS Genetic algorithm limitations, stuck in local minimum
Pathway models
not describe the biological situation
Polluted data
combined data from five labs
different criteria to classify grades

17. PROBLEMS SOLUTIONS Genetic algorithm 1. Directed search
limitations, stuck in local minimum seed Nelder-Mead simplex
Pathway models New pathway
not describe the biological situation relax assumption DCIS -> IDC
Polluted data Combine similar data
combined data from five labs combine data from three labs
different criteria to classify grades same criteria to classify grades

18. CLINICAL OBSERVATIONS
Van Nuys Classification Holland Classification
IDC DCIS IDC DCIS
Sum of observations of Gupta, Cadman and Leong, normalized to 498.

19. PATHWAYS
Linear
Nonlinear
Branched

20. DIFFERENTIAL EQUATIONS

21. BRANCHED PATHWAY

22. PATHWAY SIMULATIONS Linear IDC DCIS 1 2 3 1 94.62 49.80 0
1 2 3
Non-linear
IDC DCIS
1 2 3
Branched
IDC DCIS
1 2 3

23. PATHWAY SIMULATIONS Linear IDC DCIS 1 2 3 1 94.62 49.80 0
1 2 3
Non-linear
IDC DCIS
1 2 3
Observed - Van Nuys Classification
IDC DCIS
1 2 3
Branched
IDC DCIS
1 2 3

24. PATHWAYS
Linear
Nonlinear
Branched
Parallel

25. PARALLEL PATHWAY
Common Progenitor

26. PARALLEL PATHWAY
p (0.642)

27. PARALLEL PATHWAY p (0.642) p (0.326) 11 12 13 21 22 23 31 32 33

28. PARALLEL PATHWAY p (0.032) p (0.642) p (0.326) 11 12 13 21 22 23

29. PATHWAY SIMULATIONS Linear IDC DCIS 1 2 3 1 94.62 49.80 0
1 2 3
Non-linear
IDC DCIS
1 2 3
Branched
IDC DCIS
1 2 3
Parallel
IDC DCIS
1 2 3

30. COMPARISON OF RESULTS Clinical Observation Model Simulation
Van Nuys Classification Parallel Model
IDC DCIS IDC DCIS

32. BREAST TUMOR PROGRESSION
Conventional View - Linear Progression
Normal
Ductal Carcinoma In Situ
Invasive Ductal Carcinoma
Metastasis
New View - Parallel Progression
Ductal Carcinoma In Situ
Common Progenitor
Normal
Metastasis
Invasive Ductal Carcinoma

33. Evaluation of pathways for progression of heterogeneous breast tumors
PARALLEL PATHWAY
Common Progenitor
L. Sontag and D. E. Axelrod
Evaluation of pathways for progression of heterogeneous breast tumors
J. Theoret. Biol. 232: (2005)

34. They include data on diagnosis and prognosis
Slides are excluded.
They include data on diagnosis and prognosis
of breast ductal carcinoma in situ by image analysis
which has been submitted for publication.

35. CONCLUSION
GOAL:
Clinical data  Models  Patient prognosis
OUTCOME:
Clinical data  Models  Improved Patient prognosis