We performed a retrospective chart review of 1308 breast cancer patients who underwent NAC and surgery between August 2008 and June 2017 at Samsung Medical Center in Seoul, Korea. Among them, 377 patients who underwent Cancer SCAN^TM were included. Cancer SCAN^TM test was conducted only on patients who agreed to provide genetic information. As part of this study, DNA sequencing results and electronic medical records including pathology reports were reviewed. 139 cases were excluded from analysis due to incomplete medical data and 238 cases were included for analysis. We used additional retrospective data from 72 patients who underwent NAC, surgery and Cancer SCAN^TM between July 2017 and April 2018 for internal validation. This study adhered to the tenets of the Declaration of Helsinki and was approved by the institutional review board (IRB) of Samsung Medical Center (IRB No. 2018-05-035).

The available data for the cohorts included age at diagnosis, subtype (e.g., Hormone Receptor [HR] positive/Human Epidermal growth factor 2 [HER2] receptor negative, HR positive/HER2 positive, HR negative/HER2 positive, HR negative/ HER2 negative), histopathology (e.g., Invasive Ductal Carcinoma [IDC], Invasive Lobular Carcinoma [ILC], mixed), menopausal status, family history for breast cancer, regimen for NAC (e.g., AC [adriamycin, cyclophosphamide], AC + D/T [docetaxel/taxol], AC + D/T + Herceptin [H], AC + Paclitaxel + Carboplatin, TCHP [docetaxel, carboplatin, trastuzumab, pertuzumab], others), Multiplicity, pathological T-stage, axillary nodal evaluation (clinical N0, axillary fine needle aspiration [FNA] result), results of supraclavicular and internal mammary lymph node (IMLN) FNA, Ki67 status, tumor marker level (carcinoembryonic antigen [CEA], carcinoma antigen15-3 [CA15-3]) and gene profile. We defined pCR as breast and also axillary pCR simultaneously. Breast pCR was defined as no invasive disease (ypT0 or ypTis) on final pathologic results. Axillary pCR was defined as no metastasis (ypN0) or isolated tumor cell on final pathologic results.

Genomic DNA (250 ng) from each tissue was sheared in a Covaris S220 Ultrasonicator (Covaris, Woburn, MA) and used with CancerSCAN™ probes and a Sure Select XT reagent kit HSQ (AgilentTechnologies) for construction of a library according to the manufacturer’s protocol [12].

This panel is designed to enrich exons of 81 genes, covering 366.2 kb of the human genome. After enriched exome libraries were multiplexed, the libraries were sequenced on a HiSeq 2500 sequencing platform (Illumina). Briefly, a paired-end DNA sequencing library was prepared through gDNA shearing, end-repair, A-tailing, paired-end adaptor ligation, and amplification. After hybridization of the library with bait sequences for 27 hours, the captured library was purified and amplified with an index barcode tag, and library quality and quantity were assessed [12]. We defined mutation as single nucleotide variants or copy number variation or translocation.

Variables were compared between pCR (−) and pCR (+) groups using chi-squared test or Fisher’s exact test, while mean age was compared between the two groups via Mann-Whitney U tests with SAS version 9.4 (SAS Institute, Cary, NC, USA). Receiver Operating Characteristic (ROC) curves and Areas Under the ROC Curve (AUC) were calculated. All tests were two-sided and a p-value of <0.05 was considered statistically significant.

Azure Machine Learning (Azure ML; Microsoft, Redmond, WA, USA) is a cloud service that enables the execution of machine learning processes. The Azure Machine Learning Studio (Microsoft, Redmond, WA, USA) is also available as a workspace to help users build and test predictive models [13]. We built a supervised machine learning classification model using the Azure ML platform. This was accomplished using the steps of: 1) edit the data; 2) split the data; 3) train the model; 4) score the model; and 5) evaluate the model (Figure 1). We split the modeling data (238 cases) into training and testing sets using a randomized 60 - 40 split. We then trained our training set using a Two-class Bayes point machine method [14] for the prediction of pCR.

The clinicopathologic characteristics of included patients are summarized in Table 1. The pCR (−) group had 200 patients and the pCR (+) group had 38 patients. The median age was older in pCR (+) group (p-value = 0.038) and pCR (−) group had more premenopausal patients than pCR (+) group (p-value = 0.045). IDC, AC/AC + Taxane regimen and triple negative breast cancer (HR-/HER2-) subtype were majority in both groups. There was no difference in both groups according to family history, subtype, multiplicity, T stage, axillary nodal status, Ki-67 and tumor marker status. In gene profile results, only BRCA2 mutation was associated with pCR (+) status statistically (p-value = 0.014). Patients with BRCA2 mutation were more in pCR (−) group (36.5%) than pCR (+) group (15.8%). We developed a predictive model with 238 cases using the Azure ML platform (Figure 1) using various classification algorithms, such as Two-class Decision Forest, Two-class Decision Jungle, Two-class Decision Forest, Two-class Support Vector Machine, and Two-class Neural Network. Among them, Two-class Bayes Point Machine was the most suitable method for prediction of pCR. We assessed Area Under the Curve (AUC). The AUC of the Receiver Operating Characteristic (ROC) curve was 0.909 and accuracy was 0.875 (Figure 2(a)). In addition, we developed a predictive model without gene profiles. We used only clinical data but patients pool (n = 238) and process were same with previous model. Through additional model, the AUC of ROC curve was 0.743 and accuracy was 0.800 (Figure 2(b)).

We developed a predictive model with 238 cases using the Azure ML platform (Figure 1) using various classification algorithms, such as Two-class Decision Forest, Two-class Decision Jungle, Two-class Decision Forest, Two-class Support Vector Machine, and Two-class Neural Network. Among them, Two-class Bayes Point Machine was the most suitable method for prediction of pCR. We assessed Area Under the Curve (AUC). The AUC of the receiver operating characteristic (ROC) curve was 0.909 and accuracy was 0.875 (Figure 2(a)). In addition, we developed a predictive model without gene profiles. We used only clinical data but patients pool (n = 238) and process were same with previous model. Through additional model, the AUC of ROC curve was 0.743 and accuracy was 0.800 (Figure 2(b)).

We also conducted internal validation using 72 patients who underwent NAC and Cancer SCAN^TM during July 2017 and April 2018. When we applied a 0.4 threshold value, accuracy was 0.806 in predictive model with gene profiles and 0.778 in model without gene profiles respectively (Table 2). As threshold value was decreased, sensitivity was increased but specificity was decreased.

The Azure ML platform provides a function for the set-up of web services: (http://docs.microsoft.com/en-us/azure/machine-learning/studio/consume-web-services).

Abbreviation: FHx, family history; AC, adriamycin + cyclophosphamide; TCHP, docetaxel + carboplatin + trastuzumab + pertuzumab; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; HR, hormone receptor; HER2, human epidermal growth factor receptor 2; FNA, fine needle aspiration; SCN, supraclavicular lymph node; IMLN, internal mammary lymph node; CEA, carcino embryonic antigen; CA15-3, carcinoma antigen 15-3. ^*HER2− means HER2 1+ or 2+ with SISH negative, ^#HER2+ means HER2 3+ or 2+ with SISH positive.

Sensitivity = true positive/true positive + false negative; specificity = true negative/ false positive + true negative; Precision, positive predictive value = true positive/ true positive + false positive; accuracy = true positive+ true negative/total.

After using the Azure ML predictive model as a web service, we used a Representational State Transfer application programming interface to send data and obtain predictions in real-time. For example, when we input data according to each variable excluding the final value “pCR,” an external application communicated with a machine learning workflow scoring model in real-time, enabling the predicted value to be calculated in only a few seconds (Figure 3).