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Predictive and prognostic value of total tumor load in sentinel lymph nodes in breast cancer patients after neoadjuvant treatment using one-step nucleic acid amplification: the NEOVATTL study

Clinical and Translational Oncology, ISSN: 1699-3055, Vol: 23, Issue: 7, Page: 1377-1385
2021
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Article Description

Objective: To evaluate the predictive and prognostic value of total tumor load (TTL) in sentinel lymph nodes (SLNs) in patients with infiltrating breast cancer after neoadjuvant systemic therapy (NST). Methods: This retrospective multicenter study used data from a Spanish Sentinel Lymph Node database. Patients underwent intraoperative SLN biopsy after NST. TTL was determined from whole nodes using a one-step nucleic acid amplification (OSNA) assay and defined as the total sum of CK19 mRNA copies in all positive SLNs. Cox-regression models identified independent predictive variables, which were incorporated into a nomogram to predict axillary non-SLN metastasis, and identified prognostic variables for incorporation into a disease-free survival (DFS) prognostic score. Results: A total of 314 patients were included; most had no lymph node involvement prior to NST (cN0; 75.0% of patients). Most received chemotherapy with or without biologic therapy (91.7%), and 81 patients had a pathologic complete response. TTL was predictive of non-SLN involvement (area under the concentration curve = 0.87), and at a cut-off of 15,000 copies/µL had a negative predictive value of 90.5%. Nomogram parameters included log (TTL + 1), maximum tumor diameter and study-defined NST response. TTL was prognostic of disease recurrence and DFS at a cut-off of 25,000 copies/µL. After a 5-year follow-up, DFS was higher in patients with ≤ 25,000 copies/µL than those with > 25,000 (89.9% vs. 70.0%; p = 0.0017). Conclusions: TTL > 15,000 mRNA copies/µL was predictive of non-SLN involvement and TTL > 25,000 mRNA copies/µL was associated with a higher risk of disease recurrence in breast cancer patients who had received NST.

Bibliographic Details

B. Vieites; M. Á. López-García; M. D. Martín-Salvago; C.L. Ramirez-Tortosa; R. Rezola; M. Sancho; L. López-Vilaró; F. Villardell; O. Burgués; B. Fernández-Rodriguez; L. Alfaro; V. Peg

Springer Science and Business Media LLC

Medicine; Biochemistry, Genetics and Molecular Biology

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