American Association for Cancer Research, Cancer Epidemiology, Biomarkers & Prevention, 1(30), p. 80-88, 2021
DOI: 10.1158/1055-9965.epi-20-0889
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Abstract Background: There is widespread interest in discriminating indolent from aggressive ductal carcinoma in situ (DCIS). We sought to evaluate collagen organization in the DCIS tumor microenvironment in relation to pathologic characteristics and patient outcomes. Methods: We retrieved fixed tissue specimens for 90 DCIS cases within the population-based Vermont DCIS Cohort. We imaged collagen fibers within 75 μm of the tumor/stromal boundary on hematoxylin and eosin–stained slides using multiphoton microscopy with second-harmonic generation. Automated software quantified collagen fiber length, width, straightness, density, alignment, and angle to the tumor/stroma boundary. Factor analysis identified linear combinations of collagen fiber features representing composite attributes of collagen organization. Results: Multiple collagen features were associated with DCIS grade, necrosis pattern, or periductal fibrosis (P < 0.05). After adjusting for treatments and nuclear grade, risk of recurrence (defined as any second breast cancer diagnosis) was lower among cases with greater collagen fiber width [hazard ratio (HR), 0.57 per one standard deviation increase; 95% confidence interval (CI), 0.39–0.84] and fiber density (HR, 0.60; 95% CI, 0.42–0.85), whereas risk was elevated among DCIS cases with higher fiber straightness (HR, 1.47; 95% CI, 1.05–2.06) and distance to the nearest two fibers (HR, 1.47; 95% CI, 1.06–2.02). Fiber length, alignment, and fiber angle were not associated with recurrence (P > 0.05). Five composite factors were identified, accounting for 72.4% of the total variability among fibers; three were inversely associated with recurrence (HRs ranging from 0.60 to 0.67; P ≤ 0.01). Conclusions: Multiple aspects of collagen organization around DCIS lesions are associated with recurrence risk. Impact: Collagen organization should be considered in the development of prognostic DCIS biomarker signatures.