Abstract P4-02-06: Automated molecular diagnosis of suspicious axillary lymph nodes in breast cancer patients

Abstract

Abstract Background: Axillary lymph node metastasis is associated with a high risk of breast cancer recurrence and is the single most powerful predictor of patient prognosis. Various imaging tests alone or in combination with preoperative fine needle aspiration (FNA) cytology are used to evaluate suspicious lymph nodes (LNs). However, these methods have low sensitivity (70%) and their utility varies depending on the local surgical practice and experience of the cytologists. In previous work, we have demonstrated higher than 90% sensitivity/specificity of a panel of ten hypermethylated biomarkers in FNA to distinguish between cancer and benign breast lesions. Analysis of methylated genes in FNA of suspicious LNs may improve sensitivity of cancer detection and assist in staging disease more accurately. Methods: We used Quantitative Multiplex Methylation-Specific PCR (QM-MSP) to determine the utility of the 10-gene panel for detecting tumor cells in LN FNAs. Two studies were performed to test the accuracy of a newly developed automated, cartridge-based GeneXpert® Breast Cancer Detection Assay (RUO*). First, we conducted a prospective case-control study in China in breast cancer patients with palpable LNs undergoing sentinel lymph node biopsy. FNA specimens were collected intraoperatively from a single palpable LN and assayed. Histopathology of the same LN (88 metastatic and 104 benign), was used as the gold standard for comparison. Next, using the automated assay, a pilot validation study was conducted on archival ultrasound-guided FNAs (69 metastatic and 53 benign by cytopathology) collected preoperatively from enlarged axillary LNs in patients with and without breast cancer in the outpatient centers in China and U.S.A. We calculated sensitivity, specificity and area under the receiver operating characteristic curve (AUC) for the marker panel tested by the GeneXpert® assay compared to histopathology for the case control study, and cytopathology for the pilot validation study. We also compared its performance to the highly sensitive laboratory assay, QM-MSP. Results: In the case-control study in China, compared to histopathology, cytopathology of the palpable enlarged LNs achieved cancer detection sensitivity of 85% and specificity of 95% (ROC; AUC=0.898, 95% CI: 0.843-0.953). Using the GeneXpert® assay on the same samples, compared to histopathology, the methylated biomarker panel showed a sensitivity of 92.0% and a specificity of 98.1% based on receiver operating characteristic statistics (ROC; AUC = 0.960, 95% CI: 0.928-0.993). QM-MSP yielded a similar sensitivity of 91.8% and a specificity of 97.7% (ROC; AUC = 0.966, 95% CI: 0.934-0.998). In the pilot validation set, compared to cytopathology, the automated assay achieved a sensitivity of 94.2% and a specificity of 92.5% (ROC; AUC=0.976 (95%CI: 0.950-1.001). Conclusions: We have developed and piloted a 5-hour, automated GeneXpert® Breast Cancer Detection Assay (RUO*) on FNA to detect cancer in suspicious LNs. This assay performs with an accuracy equivalent to that of the highly sensitive but labor-intensive assay, QM-MSP. This test has the potential to determine axillary involvement with higher sensitivity than cytopathology within a very short time in the outpatient setting. A positive test preoperatively could provide guidance for decision-making regarding eligibility of patients for neoadjuvant treatment, post-mastectomy radiotherapy and breast reconstruction. Further evaluation of this automated assay in larger, blinded, prospective studies is warranted. Citation Format: Juanjuan Li, Bradley Downs, Leslie Cope, Xiuyun Zhang, Chuan-gui Song, Kejing Zhang, Yong Han, Mary Jo Fackler, Edwin Lai, Suzana Tulac, Neesha Venkatesan, Timothy Guzman, Chuang Chen, Jingping Yuan, Saraswati Sukumar. Automated molecular diagnosis of suspicious axillary lymph nodes in breast cancer patients [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-02-06.