Background: The aetiology of breast cancers diagnosed ≤ 50 years of age remains unclear. We aimed to compare reproductive risk factors between molecular subtypes of breast cancer, thereby suggesting possible aetiologic clues, using routinely collected cancer registry and maternity data in Scotland.

Methods: We conducted a cross-sectional study of 4108 women aged ≤ 50 years with primary breast cancer diagnosed between 2009 and 2016 linked to maternity data. Molecular subtypes of breast cancer were defined using immunohistochemistry (IHC) tumour markers, oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), and tumour grade. Age-adjusted polytomous logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association of number of births, age at first birth and time since last birth with IHC-defined breast cancer subtypes. Luminal A-like was the reference compared to luminal B-like (HER2-), luminal B-like (HER2+), HER2-overexpressed and triple-negative breast cancer (TNBC).

Results: Mean (SD) for number of births, age at first birth and time since last birth was 1.4 (1.2) births, 27.2 (6.1) years and 11.0 (6.8) years, respectively. Luminal A-like was the most common subtype (40%), while HER2-overexpressed and TNBC represented 5% and 15% of cases, respectively. Larger numbers of births were recorded among women with HER2-overexpressed and TNBC compared with luminal A-like tumours (> 3 vs 0 births, OR 1.87, 95%CI 1.18-2.96; OR 1.44, 95%CI 1.07-1.94, respectively). Women with their most recent birth > 10 years compared to < 2 years were less likely to have TNBC tumours compared to luminal A-like (OR 0.63, 95%CI 0.41-0.97). We found limited evidence for differences by subtype with age at first birth.

Conclusion: Number of births and time since last birth differed by molecular subtypes of breast cancer among women aged ≤ 50 years. Analyses using linked routine electronic medical records by molecularly defined tumour pathology data can be used to investigate the aetiology and prognosis of cancer.