Related Experiment Video
Updated: Sep 12, 2026
Comparative Lesions Analysis Through a Targeted Sequencing Approach
Published on: November 5, 2019
Distinguishing true recurrence from second primary breast cancer using molecular clonality analysis in clinical
Tess I M Snellen1, Esther H Lips2, Linda J W Bosch3
1Department of Surgical Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.
Purpose:
A second breast cancer (BC) in the ipsilateral breast or regional lymph nodes may represent a true recurrence (TR) or second primary (SP) tumor. When clinicopathological evaluation is inconclusive, molecular clonality analysis (MCA) can establish a clonal relationship between tumor pairs. This study describes the findings and success rates of MCA techniques used to distinguish second ipsilateral BC as TR or SP in a real-world cohort.
Methods:
This retrospective cohort study included patients who underwent MCA for a second ipsilateral locoregional BC at the Netherlands Cancer Institute (NKI) between 2000 and 2024. The primary objective was to describe the findings and success rates of MCA techniques. The secondary objective was to compare MCA-based with clinicopathological classifications according to the Jobsen Morphology method.
Results:
Ninety-nine MCAs were performed in 85 patients. Targeted next-generation sequencing (NGS) panel analysis was most frequently applied (53.5%), followed by loss of heterozygosity (LOH) (31.3%), and copy number variation (CNV) analysis (15.2%). Success rates were highest for CNV analysis (93.3%), followed by targeted NGS panel (73.6%) and LOH analysis (61.3%). A substantial discordance (36.9%) was observed between the MCA-based classification and the Jobsen Morphology method. Clinicopathological assessment demonstrated limited predictive value for MCA-based TRs (PPV 72.5%, NPV 28.6%).
Conclusion:
MCA provides a conclusive classification of second ipsilateral BCs as TR or SP in most cases. Substantial discordance between MCA-based and Jobsen Morphology classification in this selected population was observed. Integration of MCA into the diagnostic workflow may provide additional diagnostic information when clinicopathological classification is uncertain.
