Correlative imaging reveals physiochemical heterogeneity of microcalcifications in human breast carcinomas

© 2017 Elsevier Inc. Microcalcifications (MCs) are routinely used to detect breast cancer in mammography. Little is known, however, about their materials properties and associated organic matrix, or their correlation to breast cancer prognosis. We combine histopathology, Raman microscopy, and electr...

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Bibliographic Details
Main Authors: Kunitake, Jennie AMR, Choi, Siyoung, Nguyen, Kayla X, Lee, Meredith M, He, Frank, Sudilovsky, Daniel, Morris, Patrick G, Jochelson, Maxine S, Hudis, Clifford A, Muller, David A, Fratzl, Peter, Fischbach, Claudia, Masic, Admir, Estroff, Lara A
Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Format: Article
Language:English
Published: Elsevier BV 2021
Online Access:https://hdl.handle.net/1721.1/135775
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Summary:© 2017 Elsevier Inc. Microcalcifications (MCs) are routinely used to detect breast cancer in mammography. Little is known, however, about their materials properties and associated organic matrix, or their correlation to breast cancer prognosis. We combine histopathology, Raman microscopy, and electron microscopy to image MCs within snap-frozen human breast tissue and generate micron-scale resolution correlative maps of crystalline phase, trace metals, particle morphology, and organic matrix chemical signatures within high grade ductal carcinoma in situ (DCIS) and invasive cancer. We reveal the heterogeneity of mineral-matrix pairings, including punctate apatitic particles (<2 µm) with associated trace elements (e.g., F, Na, and unexpectedly Al) distributed within the necrotic cores of DCIS, and both apatite and spheroidal whitlockite particles in invasive cancer within a matrix containing spectroscopic signatures of collagen, non-collagen proteins, cholesterol, carotenoids, and DNA. Among the three DCIS samples, we identify key similarities in MC morphology and distribution, supporting a dystrophic mineralization pathway. This multimodal methodology lays the groundwork for establishing MC heterogeneity in the context of breast cancer biology, and could dramatically improve current prognostic models.