1. Benign Breast Disease – Focus on Atypia Lynn C. Hartmann, MD Women’s Cancer Program Mayo Clinic Cancer Center

2. 1967-2001: 13,647 women 1,173 cancers Iowa SEER Registry for comparison Mayo Benign Breast Disease Cohort

3. Breast Carcinogenesis (presumed) Benign Breast Disease Breast Cancer

4. BBD: Risk Prediction Relative risks by histology Non- proliferative Proliferative no atypia Atypical hyperplasia 1.271.88 4.24

5. Cancer Prev Res 2011 Dec;4(12):1947-52.

6. On premalignant lesions “If becoming a BC results from randomly acquiring multiple mutations enabling malignant behavior (growth, motility, invasion, etc), why is there an apparent order to the development and progression of premalignant lesions?” D. Craig Allred Ca Prev Res, 2011

7. On premalignant lesions Incidence of premalignant lesions in non-cancerous breasts declines significantly from PDWA to atypia to CISIncidence of premalignant lesions in non-cancerous breasts declines significantly from PDWA to atypia to CIS Regions of gradual histologic continuity between these existRegions of gradual histologic continuity between these exist Premalignant lesions much more common in cancer-containing breasts (coexistent atypia ~ 50%).Premalignant lesions much more common in cancer-containing breasts (coexistent atypia ~ 50%).

8. On premalignant lesions Atypias coexisting with DCIS and invasive BC share genetic alterationsAtypias coexisting with DCIS and invasive BC share genetic alterations ~ 50% of atypias contain monoclonal populations of cells~ 50% of atypias contain monoclonal populations of cells

9. BBD: Risk Prediction Relative risks by histology Non- proliferative Proliferative no atypia Atypical hyperplasia 1.271.88 4.24

10. Atypia (ADH and ALH) How well do current models work for women with atypical hyperplasia?How well do current models work for women with atypical hyperplasia? Does a positive family history further increase risk?Does a positive family history further increase risk? Can we better stratify risk within atypia?Can we better stratify risk within atypia? Can biomarkers stratify risk?Can biomarkers stratify risk?

11. Atypical Hyperplasia ~ 50,000 US women diagnosed with AH each year~ 50,000 US women diagnosed with AH each year ~ 54,000 US women diagnosed with DCIS each year~ 54,000 US women diagnosed with DCIS each year

13. Atypical Ductal Hyperplasia (ADH) Within ducts, proliferation of monotonous cells in architecturally complex patterns including secondary lumens and micropapillary formations.

14. Atypical Lobular Hyperplasia (ALH) Expanded acini filled with monotonous polygonal cells with loss of acini lumens.

15. Major Studies of Atypia and Associated Breast Cancer StudyYearPopulationMethod of Diagnosis Pathology Review Median f/u (yrs) # (%) with atypia Breast Cancer Relative Risk (95% CI) Comments Nashville 1 198510542 Tennessee patients with BBD 1950-1968 Surgical breast biopsy Central review 17.5377 (3.6%) 268 women with FU (283 biopsies) ALH 4.2 (2.6-6.9) ADH 4.3 (2.7-6.9) 39 women with AH & FH with RR=8.9 (4.8-17) Nurses Health Study 2 1992Nurses Health Study part’s with cancer or BBD, 121 cases/488 controls Surgical breast biopsy Central review 9 3.7 (2.1-6.8) Risk higher in premenopausal than postmenopausal. FH does not increase risk. Mayo 3 20059087 women with biopsied BBD 1967-1991 Surgical breast biopsy Central review 12.2336 (3.7%) 4.4 (3.4-5.6) REF Iowa SEER 1 Dupont WD, Page DL: Risk factors for breast cancer in women with proliferative breast disease. N Engl J Med 1985; 312:146-151. 2 London SJ, Connolly JL, Schnitt SJ, Colditz GA: A prospective study of benign breast disease and the risk of breast cancer. JAMA 1992; 267:941-944 3 Hartmann LC, Sellers TA, Frost MH, et al.: Benign breast disease and the risk of breast cancer. N Engl J Med 2005; 353:229-237.

20. Gail model Best-known model; incorporates multiple factors in risk calculationBest-known model; incorporates multiple factors in risk calculation Gail model on NCI website is viewed ~ 500,000 times a yearGail model on NCI website is viewed ~ 500,000 times a year

21. Atypia and Gail model 331 women with atypia in Mayo BBD cohort331 women with atypia in Mayo BBD cohort Median follow-up 13.7 yearsMedian follow-up 13.7 years 58 invasive breast cancers; 8 in the first five years58 invasive breast cancers; 8 in the first five years

22. c-statistic 0.50 (0.44-0.55) Pankratz et al JCO 11/08

23. Gail model features and association with breast cancer risk CharacteristicsP-value Age at biopsy 0.903 Age at menarche 0.398 Age at first live birth 0.269 First degree relatives with breast cancer 0.263 Number of biopsies 0.491 331 women with atypia—Mayo Pankratz, Hartmann et al. JCO, 2008

24. If tissue is the basis of the model... In displaying the phenotype of atypia, the tissue has already integrated various risk exposures.

25. Boughey J C et al. JCO 2010;28:3591-3596 ©2010 by American Society of Clinical Oncology Cumulative risk of BC after atypia

26. Cumulative breast cancer incidence among women with atypical hyperplasia Breast Cancer Incidence 3+ 38% 23% 12% 5%

29. Involution (Regression)

32. “In summary, recent work by the Mayo group emphasizes the potential for using visual assessments of tissue architecture as integrated measures of risk. JNCI, Nov 2010 J Natl Cancer Inst (2010) 102 (22): 1685-1687.

33. JNCI editorial, Nov 2010 Improving breast cancer risk prediction is critically important given the limitations of currently available models and the desirability of tailoring screening and prevention to levels of risk.“Improving breast cancer risk prediction is critically important given the limitations of currently available models and the desirability of tailoring screening and prevention to levels of risk.“

34. No breast cancer Breast cancer Matched on age, era, histology Can molecular analyses differentiate between cases and controls? Biomarker Studies

36. Association of cyclooxygenase-2 staining intensity in atypia with risk of breast cancer * COX-2 staining intensity No. of women RR (95% CI)** All women 235 3.31 (2.38 to 4.49) Staining category 0-1+ 0-1+130 2.63 (1.56 to 4.15) 2+ 2+71 3.56 (1.94 to 5.97) 3+ 3+34 5.66 (2.59 to 10.75) *COX-2 = cyclooxygenase-2; RR = relative risk; CI = confidence interval **Standarized incidence ratio and 95% confidence intervals, comparing observed number of breast cancers with those expected. All results account for age and calendar period. Visscher DW et al, J Natl Cancer Inst 2008

38. Santisteban M et al. Breast Cancer Res Treat 2009

39. ER Expression in Atypia 246 women with AH who had open surgical biopsy, 49 of whom developed BC246 women with AH who had open surgical biopsy, 49 of whom developed BC 87 with ADH87 with ADH 141 with ALH141 with ALH Median F/U = 14.4 yrsMedian F/U = 14.4 yrs Used computerized digital image analysis to quantitate ER expressionUsed computerized digital image analysis to quantitate ER expression

40. Cancer Prev Res 2011 Mar;4(3):435-44.

41. Relationship between ER expression and atypia type. Cancer Prev Res 2011 Mar;4(3):435-44.

43. ADH showing strong, homogeneous ER staining

44. ALH showing moderate ER staining heterogeneously

45. Relationship between ER expression and age in women with atypical hyperplasia. Cancer Prev Res 2011 Mar;4(3):435-44.

46. ER Expression in Atypia ER expression did not vary by –ER expression did not vary by – -- # of foci of atypia -- # of foci of atypia -- Involution extent (controlling for age) -- Involution extent (controlling for age) -- Extent of family history -- Extent of family history

47. Atypia: Summary Atypia = High risk finding (RR ~ 4)Atypia = High risk finding (RR ~ 4) Features that do NOT further stratify risk:  Family historyFeatures that do NOT further stratify risk:  Family history  Type of atypia  Type of atypia

48. Atypia: Summary Features that DO further stratify risk:Features that DO further stratify risk:  # of foci of atypia  # of foci of atypia  extent of involution  extent of involution  younger age  younger age  biomarkers, eg COX-2 and Ki67  biomarkers, eg COX-2 and Ki67 require validation require validation

49. Missing ADH ALH ADH and ALH

50. Better risk prediction model Clinical Risk Factors Benign Histology Biomarkers Mammographic Density

51. Mayo BBD Cohort Breast Clinicians Lynn Hartmann, MD Amy Degnim, MD Karthik Ghosh, MD Judy Boughey, MD Basic Science Derek Radisky, PhD Database Management Marlene Frost, PhDTeresa Allers Jo Johnson Mary Campion Melanie Kasner Biostatisticians Shane Pankratz, PhD Rob Vierkant, MAS Lorelle Benetti, BA Ryan Frank, BA Pathologists Dan Visscher, MD Aziza Nassar, MD Carol Reynolds, MD Epidemiology Celine Vachon, PhD