Financial Disclosure. Learning Objectives. None. To understand the clinical applicability of the NCDB Breast Cancer PUF

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1 Preoperative Prediction of Node Negative Disease After Neoadjuvant Chemotherapy in Patients Presenting with Node Negative or Node Positive Breast Cancer Brittany L Murphy MD, Tanya Hoskin MS, Courtney Day, Habermann PhD, Judy C Boughey MD Mayo Clinic Rochester, Minnesota Elizabeth B None Financial Disclosure Learning Objectives To understand the clinical applicability of the NCDB Breast Cancer PUF To identify factors associated with node negative breast cancer after neoadjuvant chemotherapy

2 Background Neoadjuvant chemotherapy (NAC) use for the treatment of breast cancer has been increasing NAC can be considered in any individual in which adjuvant chemotherapy will be given Haddad TC et al. Ann Surg Oncol May;22(5): Wolmark et al. J Natl Cancer Inst Monogr. 2001;(30): Background NAC use is greatest in Large tumors Axillary nodal involvement Triple negative/her2+ tumors Mougalian SS et al. Cancer Aug 1;121(15): Puig CA et al. Ann Surg Oncol May;24(5): Background

3 Background Background Surgical staging of axillary nodes after NAC Background Surgical staging of axillary nodes after NAC cn0

4 Background Surgical staging of axillary nodes after NAC cn0 SLN surgery Background Surgical staging of axillary nodes after NAC cn0 cn+ SLN surgery Background Surgical staging of axillary nodes after NAC cn0 cn+ SLN surgery ALND

5 Background Surgical staging of axillary nodes after NAC cn0 cn+ SLN surgery ALND SLN surgery Aim To develop a model to predict which breast cancer patients are likely to be pathologically node negative at surgery after NAC Methods: Establishing the Model Identified patients in the NCDB From 1/ /2012 ct1-t4c breast cancer Received NAC Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9):

6 Methods: Establishing the Model Identified patients in the NCDB From 1/ /2012 ct1-t4c breast cancer Received NAC Evaluated patient and tumor factors on ypn0 status Multivariable logistic regression Created two prediction models 1) cn0 2) cn+ Methods: Validating the Model Model performance evaluated Identified Mayo Clinic patients From 1/2013-7/2016 ct1-t4c breast cancer Received NAC Model discrimination was assessed by estimating the area under the curve (AUC) Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9): Results: NCDB Cohort ( ) Invasive Breast Cancer Cases N=368,530 Stage IV Disease N=7,687 Missing Clinical T Stage N=35,737 Missing Clinical N Stage N=8,543 IBC N=2,577 Invasive ct1-4c Breast Cancer Cases N=313,986 Primary Surgery N=274,192 Unknown Chemo Timing N=13,804 Invasive ct1-4c with NAC Breast Cancer Cases N=25,990 Other Neoadjuvant Therapy N=3,848 Missing Prognostic Factors N=5,989 Cases Meeting Inclusion Criteria N=16,153

7 Results: NCDB Cohort ( ) 16,153 patients Average Age: % Female Clinical T Stage T1/T2: 65% T3/T4: 35% ER+/Her2+ 16% ER-/Her2+ 11% ER-/Her2-32% Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9): Results: NCDB Cohort ( ) 16,153 NCDB patients Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9): Results: NCDB Cohort ( ) 16,153 NCDB patients cn0 6,659 41% cn+ 9,494 59% Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9):

8 Results: NCDB Cohort ( ) 16,153 NCDB patients cn0 6,659 41% cn+ 9,494 59% ypn0 5,194 78% ypn+ 1,465 22% Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9): cn0: Factors Predicting ypn0 Status OR (95% CI) p-value Age < (Reference) Histology IDC 1.0 (Reference) ILC IMC Other cn0: Factors Predicting ypn0 Status OR (95% CI) p-value Age < (Reference) Histology IDC 1.0 (Reference) ILC IMC Other

9 cn0: Factors Predicting ypn0 Status OR (95% CI) p-value Age < (Reference) Histology IDC 1.0 (Reference) ILC IMC Other cn0: Factors Predicting ypn0 Status Clinical T stage OR (95% CI) p-value ct1/ct <0.001 Grade ct3/ct4 Well differentiated 1.0 (Reference) 1.0 (Reference) Moderately differentiated Poorly differentiated 1.57 <0.001 Biologic subtype ER+/Her2-1.0 (Reference) ER+/Her <0.001 ER-/Her <0.001 ER-/Her <0.001 cn0: Factors Predicting ypn0 Status Clinical T stage OR (95% CI) p-value ct1/ct <0.001 Grade ct3/ct4 Well differentiated 1.0 (Reference) 1.0 (Reference) Moderately differentiated Poorly differentiated 1.57 <0.001 Biologic subtype ER+/Her2-1.0 (Reference) ER+/Her <0.001 ER-/Her <0.001 ER-/Her <0.001

10 cn0: Factors Predicting ypn0 Status Clinical T stage OR (95% CI) p-value ct1/ct <0.001 Grade ct3/ct4 Well differentiated 1.0 (Reference) 1.0 (Reference) Moderately differentiated Poorly differentiated 1.57 <0.001 Biologic subtype ER+/Her2-1.0 (Reference) ER+/Her <0.001 ER-/Her <0.001 ER-/Her <0.001 cn0: Factors Predicting ypn0 Status Clinical T stage OR (95% CI) p-value ct1/ct <0.001 Grade ct3/ct4 Well differentiated 1.0 (Reference) 1.0 (Reference) Moderately differentiated Poorly differentiated 1.57 <0.001 Biologic subtype ER+/Her2-1.0 (Reference) ER+/Her <0.001 ER-/Her <0.001 ER-/Her <0.001 cn0 Model Performance AUC: 0.73

11 cn0 Model Performance AUC: 0.73 AUC=0.73 Results: NCDB Cohort ( ) 16,153 NCDB patients cn0 6,659 41% cn+ 9,494 59% ypn0 5,194 78% ypn+ 1,465 22% ypn0 3,314 35% ypn+ 6,180 65% cn+: Factors Predicting ypn0 Status OR (95% CI) p-value Age < (Reference) ( ) ( ) < ( ) < ( ) <0.001 Histology IDC 1.0 (Reference) ILC 0.51 ( ) <0.001 IMC 0.71 ( ) 0.02 Other 0.81 ( ) 0.20

12 cn+: Factors Predicting ypn0 Status OR (95% CI) p-value Age < (Reference) ( ) ( ) < ( ) < ( ) <0.001 Histology IDC 1.0 (Reference) ILC 0.51 ( ) <0.001 IMC 0.71 ( ) 0.02 Other 0.81 ( ) 0.20 cn+: Factors Predicting ypn0 Status OR (95% CI) p-value Clinical T stage ct1/ct ( ) 0.06 ct3/ct4 1.0 (Reference) Clinical N stage cn ( ) <0.001 cn2/cn3 1.0 (Reference) Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9): cn+: Factors Predicting ypn0 Status OR (95% CI) p-value Clinical T stage ct1/ct ( ) 0.06 ct3/ct4 1.0 (Reference) Clinical N stage cn ( ) <0.001 cn2/cn3 1.0 (Reference) Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9):

13 cn+: Factors Predicting ypn0 Status OR (95% CI) p-value Grade Well differentiated 1.0 (Reference) Moderately differentiated 1.50 ( ) Poorly differentiated 1.97 ( ) <0.001 Biologic subtype ER+/Her2-1.0 (Reference) ER+/Her ( ) <0.001 ER-/Her ( ) <0.001 ER-/Her ( ) <0.001 Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9): cn+: Factors Predicting ypn0 Status OR (95% CI) p-value Grade Well differentiated 1.0 (Reference) Moderately differentiated 1.50 ( ) Poorly differentiated 1.97 ( ) <0.001 Biologic subtype ER+/Her2-1.0 (Reference) ER+/Her ( ) <0.001 ER-/Her ( ) <0.001 ER-/Her ( ) <0.001 Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9): cn+ Model Performance AUC: 0.71

14 cn+ Model Performance AUC: 0.71 AUC=0.71 Validation: Mayo (1/2013-7/2016) 374 patients cn0: 194 (52%) cn+: 180 (48%) Average Age: % Female Clinical T Stage T1/T2: 74% T3/T4: 26% Murphy BL, L Hoskin T, Heins CDN, Habermann EB, Boughey JC. Ann Surg Oncol Sep;24(9): ER-/Her2+ 13% ER+/Her2+ 20% ER-/Her2-28% Validation: Mayo (1/2013-7/2016) cn0 Validation AUC 0.77 Development AUC 0.73

15 Validation: Mayo (1/2013-7/2016) cn0 Validation AUC 0.77 Development AUC 0.73 cn+ Validation AUC 0.74 Development AUC 0.71 Model Calibration: cn0 Category Mean Predicted Probability Actual % Node Negative <50% 46% 46.5% 50-74% 64% 63.5% 75-79% 78% 81.1% 80-89% 85% 85.4% 90% 92% 91.8% Model Calibration: cn0 Category Mean Predicted Probability Actual % Node Negative <50% 46% 46.5% 50-74% 64% 63.5% 75-79% 78% 81.1% 80-89% 85% 85.4% 90% 92% 91.8%

16 Model Calibration: cn0 Category Mean Predicted Probability Actual % Node Negative <50% 46% 46.5% 50-74% 64% 63.5% 75-79% 78% 81.1% 80-89% 85% 85.4% 90% 92% 91.8% Model Calibration: cn0 Category Mean Predicted Probability Actual % Node Negative <50% 46% 46.5% 50-74% 64% 63.5% 75-79% 78% 81.1% 80-89% 85% 85.4% 90% 92% 91.8% Model Calibration: cn0 Category Mean Predicted Probability Actual % Node Negative <50% 46% 46.5% 50-74% 64% 63.5% 75-79% 78% 81.1% 80-89% 85% 85.4% 90% 92% 91.8%

17 Model Calibration: cn+ Category Mean Predicted Probability Actual % Node Negative <20% 14% 13.0% 20-29% 23% 24.7% 30-39% 36% 35.5% 40-49% 45% 45.4% 50-74% 56% 55.7% 75% - 0% Model Calibration: cn+ Category Mean Predicted Probability Actual % Node Negative <20% 14% 13.0% 20-29% 23% 24.7% 30-39% 36% 35.5% 40-49% 45% 45.4% 50-74% 56% 55.7% 75% - 0% cn0 Patients Patient Age Grade Biologic Subtype Histology ct Stage Summary cn+ Patients Patient Age Grade Biologic Subtype Histology ct Stage cn Stage Both show good discrimination for predicting ypn0 status following NAC in cn0 and cn+ patients

18 Clinical Implications Our model may be used in the clinic to predict preoperatively the likelihood of a patient being pathologically node negative This can help guide: Selection of axillary staging procedure SLN surgery vs ALND Counselling regarding likelihood of radiation Influence reconstructive options Clinical Example 1 49 year old female with ct3n0 poorly differentiated ER-/Her2+ IDC Clinical Example 1 49 year old female with ct3n0 poorly differentiated ER-/Her2+ IDC ~89% chance of ypn0 status

19 49 year old female with ct3n0 poorly differentiated ER-/Her2+ IDC ~89% chance of ypn0 status Recommendation: SLN surgery Clinical Example 1 Clinical Example 2 55 year old female with ct1n1 poorly differentiated ER-/Her2- IDC Clinical Example 2 55 year old female with ct1n1 poorly differentiated ER-/Her2- IDC ~ 50% chance of ypn0 status

20 Clinical Example 2 55 year old female with ct1n1 poorly differentiated ER-/Her2- IDC ~ 50% chance of ypn0 status Recommendation: SLN surgery Clinical Example 3 70 year old female with ct2n2 well differentiated ER+/Her2- IDC Clinical Example 3 70 year old female with ct2n2 well differentiated ER+/Her2- IDC ~ 7% chance of ypn0 status

21 Clinical Example 3 70 year old female with ct2n2 well differentiated ER+/Her2- IDC ~ 7% chance of ypn0 status Recommendation: ALND Likely to recommend radiation Discuss radiation/reconstruction Future Breast Surgery is becoming more individualized NAC allows less extensive surgery Breast Axilla Future Trials currently looking at no breast surgery in patients with imaging response to NAC Kuerer H. MD Anderson Cancer Center Feasibility Trial for Eliminating Breast Cancer Surgery in Exceptional with Neoadjuvant Therapy Responders Model could help guide which patients to consider for no axillary surgery

22 Preoperative Prediction of Node Negative Disease After Neoadjuvant Chemotherapy in Patients Presenting with Node Negative or Node Positive Breast Cancer Brittany L Murphy MD, Tanya Hoskin MS, Courtney Day, Habermann PhD, Judy C Boughey MD Mayo Clinic Rochester, Minnesota Elizabeth B