ABCR  Vol.10 No.4 , October 2021
A Predictive Model for Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy Using Machine Learning
Abstract: Background: In patients with breast cancer after Neoadjuvant Chemotherapy (NAC), pathological Complete Response (pCR) was associated with better long-term outcomes. We here attempted to predict pCR using machine learning. Patients and Methods: From 2008 to 2017, 1308 breast cancer patients underwent NAC before surgery, of whom 377 patients underwent Cancer SCANTM for gene data. Of 377, 238 were analyzed here, with 139 excluded due to incomplete medical data. Results: The pCR (-) vs. (+) group had 200 vs. 38 patients. In our predictive model with gene data, the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve was 0.909 and accuracy was 0.875. In another model without gene data, the AUC of ROC curve was 0.743 and accuracy was 0.800. We also conducted internal validation with 72 patients undergoing NAC and Cancer SCANTM during July 2017 and April 2018. When we applied a 0.4 threshold value, accuracy was 0.806 and 0.778 in the predictive model with vs. without gene profiles, respectively. Conclusion: The present predictive model may be a useful and easy-to-access tool for pCR-prediction in breast cancer patients treated with NAC.
Cite this paper: Kim, I. , Lee, K. , Lee, S. , Park, Y. and Kim, S. (2021) A Predictive Model for Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy Using Machine Learning. Advances in Breast Cancer Research, 10, 141-155. doi: 10.4236/abcr.2021.104012.

[1]   Loibl, S., Denkert, C. and von Minckwitz, G. (2015) Neoadjuvant Treatment of Breast Cancer—Clinical and Research Perspective. Breast, 24, S73-S77.

[2]   Kummel, S., Holtschmidt, J. and Loibl, S. (2014) Surgical Treatment of Primary Breast Cancer in the Neoadjuvant Setting. British Journal of Surgery, 101, 912-924.

[3]   Cameron, D.A., Anderson, E.D., Levack, P., Hawkins, R.A., Anderson, T.J., Leonard, R.C., et al. (1997) Primary Systemic Therapy for Operable Breast Cancer—10-Year Survival Data after Chemotherapy and Hormone Therapy. British Journal of Cancer, 76, 1099-1105.

[4]   Liedtke, C., Mazouni, C., Hess, K.R., Andre, F., Tordai, A., Mejia, J.A., et al. (2008) Response to Neoadjuvant Therapy and Long-Term Survival in Patients with Triple-Negative Breast Cancer. Journal of Clinical Oncology, 26, 1275-1281.

[5]   Cortazar, P., Zhang, L., Untch, M., Mehta, K., Costantino, J.P., Wolmark, N., et al. (2014) Pathological Complete Response and Long-Term Clinical Benefit in Breast Cancer: The CTNeoBC Pooled Analysis. Lancet, 384, 164-172.

[6]   Lee, J., Kim, S.H. and Kang, B.J. (2018) Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: Perfusion Metrics of Dynamic Contrast Enhanced MRI. Scientific Reports, 8, Article No. 9490.

[7]   Weber, J.J., Jochelson, M.S., Eaton, A., Zabor, E.C., Barrio, A.V., Gemignani, M.L., et al. (2017) MRI and Prediction of Pathologic Complete Response in the Breast and Axilla after Neoadjuvant Chemotherapy for Breast Cancer. Journal of the American College of Surgeons, 225, 740-746.

[8]   Asano, Y., Kashiwagi, S., Goto, W., Takada, K., Takahashi, K., Hatano, T., et al. (2018) Prediction of Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer by Subtype Using Tumor-Infiltrating Lymphocytes. Anticancer Research, 38, 2311-2321.

[9]   Cabrera-Galeana, P., Munoz-Montano, W., Lara-Medina, F., Alvarado-Miranda, A., Perez-Sanchez, V., Villarreal-Garza, C., et al. (2018) Ki67 Changes Identify Worse Outcomes in Residual Breast Cancer Tumors after Neoadjuvant Chemotherapy. Oncologist, 23, 670-678.

[10]   Wang, G., Chen, X., Liang, Y., Wang, W. and Shen, K. (2017) A Long Noncoding RNA Signature That Predicts Pathological Complete Remission Rate Sensitively in Neoadjuvant Treatment of Breast Cancer. Translational Oncology, 10, 988-997.

[11]   Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V. and Fotiadis, D.I. (2015) Machine Learning Applications in Cancer Prognosis and Prediction. Computational and Structural Biotechnology Journal, 13, 8-17.

[12]   Shin, H.T., Choi, Y.L., Yun, J.W., Kim, N.K.D., Kim, S.Y., Jeon, H.J., et al. (2017) Prevalence and Detection of Low-Allele-Fraction Variants in Clinical Cancer Samples. Nature Communications, 8, Article No. 1377.

[13]   Miller, S., Curran, K. and Lunney, T. (2016) Cloud-Based Machine Learning for the Detection of Anonymous Web Proxies. 2016 27th Irish Signals and Systems Conference, Londonderry, 21-22 June 2016, 1-6.

[14]   Chang, E., Goh, K., Sychay, G. and Wu, G. (2003) CBSA: Content-Based Soft Annotation for Multimodal Image Retrieval Using Bayes Point Machines. IEEE Transactions on Circuits and Systems for Video Technology, 13, 26-38.

[15]   Yerushalmi, R., Woods, R., Ravdin, P.M., Hayes, M.M. and Gelmon, K.A. (2010) Ki67 in Breast Cancer: Prognostic and Predictive Potential. The Lancet Oncology, 11, 174-183.

[16]   Brown, J.R., DiGiovanna, M.P., Killelea, B., Lannin, D.R. and Rimm, D.L. (2014) Quantitative Assessment Ki-67 Score for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer. Laboratory Investigation, 94, 98-106.

[17]   Denkert, C., Loibl, S., Noske, A., Roller, M., Muller, B., Komor, M., et al. (2010) Tumor-Associated Lymphocytes as an Independent Predictor of Response to Neoadjuvant Chemotherapy in Breast Cancer. Journal of Clinical Oncology, 28, 105-113.

[18]   Yamaguchi, R., Tanaka, M., Yano, A., Tse, G.M., Yamaguchi, M., Koura, K., et al. (2012) Tumor-Infiltrating Lymphocytes Are Important Pathologic Predictors for Neoadjuvant Chemotherapy in Patients with Breast Cancer. Human Pathology, 43, 1688-1694.

[19]   Von Minckwitz, G., Hahnen, E., Fasching, P.A., Hauke, J., Schneeweiss, A., Salat, C., et al. (2014) Pathological Complete Response (pCR) Rates after Carboplatin-Containing Neoadjuvant Chemotherapy in Patients with Germline BRCA (g BRCA) Mutation and Triple-Negative Breast Cancer (TNBC): Results from GeparSixto. Journal of Clinical Oncology, 32, 1005.

[20]   Arun, B., Bayraktar, S., Liu, D.D., Gutierrez Barrera, A.M., Atchley, D., Pusztai, L., et al. (2011) Response to Neoadjuvant Systemic Therapy for Breast Cancer in BRCA Mutation Carriers and Noncarriers: A Single-Institution Experience. Journal of Clinical Oncology, 29, 3739-3746.

[21]   Mazouni, C., Peintinger, F., Wan-Kau, S., Andre, F., Gonzalez-Angulo, A.M., Symmans, W.F., et al. (2007) Residual Ductal Carcinoma in Situ in Patients with Complete Eradication of Invasive Breast Cancer after Neoadjuvant Chemotherapy Does Not Adversely Affect Patient Outcome. Journal of Clinical Oncology, 25, 2650-2655.

[22]   Fumagalli, D., Bedard, P.L., Nahleh, Z., Michiels, S., Sotiriou, C., Loi, S., et al. (2012) A Common Language in Neoadjuvant Breast Cancer Clinical Trials: Proposals for Standard Definitions and Endpoints. The Lancet Oncology, 13, e240-e248.

[23]   von Minckwitz, G., Rezai, M., Loibl, S., Fasching, P.A., Huober, J., Tesch, H., et al. (2010) Capecitabine in Addition to Anthracycline- and Taxane-Based Neoadjuvant Treatment in Patients with Primary Breast Cancer: Phase III GeparQuattro Study. Journal of Clinical Oncology, 28, 2015-2023.

[24]   Bear, H.D., Anderson, S., Brown, A., Smith, R., Mamounas, E.P., Fisher, B., et al. (2003) The Effect on Tumor Response of Adding Sequential Preoperative Docetaxel to Preoperative Doxorubicin and Cyclophosphamide: Preliminary Results from National Surgical Adjuvant Breast and Bowel Project Protocol B-27. Journal of Clinical Oncology, 21, 4165-4174.

[25]   Hortobagyi, G., Hayes, D. and Pusztai, L. (2002) Integrating Newer Science into Breast Cancer Prognosis and Treatment: A Review of Current Molecular Predictors and Profiles. American Society of Clinical Oncology 2002 Annual Meeting Summaries, Cicago, 192-201.

[26]   Ayers, M., Symmans, W.F., Stec, J., Damokosh, A.I., Clark, E., Hess, K., et al. (2004) Gene Expression Profiles Predict Complete Pathologic Response to Neoadjuvant Paclitaxel and Fluorouracil, Doxorubicin, and Cyclophosphamide Chemotherapy in Breast Cancer. Journal of Clinical Oncology, 22, 2284-2293.