bioRxivCancer Biology
Imprint of parity and age at first birth on the genomic landscape of subsequent breast cancer
B. Nguyen, D. Venet, M. Lambertini, C. Desmedt, R. Salgado, H. Horlings, F. Rothe, C. Sotiriou
Abstract
BackgroundAlthough parity and age at first birth are among the most known extrinsic factors that modulates breast cancer risk, their impact on the biology of subsequent breast cancer has never been explored in depth. In this study, we investigate the imprint of parity and age at first birth on the pattern of somatic mutations, somatic copy number alterations (SCNAs), transcriptomic profiles, and tumor infiltrating lymphocytes (TILs) levels of subsequent breast cancer.\n\nMethodsA total of 313 patients with primary breast cancer with available whole genome and RNA sequencing data were included in this study. We used a multivariate analysis adjusted for age at diagnosis, pathological stage, molecular subtypes and histological subtypes. We compared nulliparous vs. parous, late parous vs. early parous, and nulliparous vs. pregnancy associated breast cancer (PABC) patients. Late and early parous patients were grouped by using the median age at first birth as a cut-off value. PABC was defined as patients diagnosed up to 10 years postpartum.\n\nResultsGenomic alterations of breast cancer are associated with age at first birth but not parity status alone. Independently of clinicopathological features, early parous patients developed tumors characterized by a higher number of Indels (Padj = 0.002), a lower frequency of CDH1 mutations (1.2% vs. 12.7% Padj = 0.013), a higher frequency of TP53 mutations (50% vs. 22.5%; Padj = 0.010) and MYC amplification (28% vs. 7% Padj = 0.008), and a lower prevalence of mutational signature 2. PABC were associated with increased TILs infiltration (Padj = 0.0495).\n\nConclusionsThese findings highlight an unprecedented link between reproductive history and the genomic landscape of subsequent breast cancer. With the rapid development of precision oncology, this work advocates that reproductive history should not be underestimated in future clinical studies of breast cancer.
- organism: human
- assay: whole-genome sequencing
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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