Cancer Cell International (Jun 2024)

Assessing the impact of extracellular matrix fiber orientation on breast cancer cellular metabolism

  • Madison R. Pickett,
  • Yuan-I Chen,
  • Mohini Kamra,
  • Sachin Kumar,
  • Nikhith Kalkunte,
  • Gabriella P. Sugerman,
  • Kelsey Varodom,
  • Manuel K. Rausch,
  • Janet Zoldan,
  • Hsin-Chin Yeh,
  • Sapun H. Parekh

DOI
https://doi.org/10.1186/s12935-024-03385-3
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 13

Abstract

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Abstract The extracellular matrix (ECM) is a dynamic and complex microenvironment that modulates cell behavior and cell fate. Changes in ECM composition and architecture have been correlated with development, differentiation, and disease progression in various pathologies, including breast cancer [1]. Studies have shown that aligned fibers drive a pro-metastatic microenvironment, promoting the transformation of mammary epithelial cells into invasive ductal carcinoma via the epithelial-to-mesenchymal transition (EMT) [2]. The impact of ECM orientation on breast cancer metabolism, however, is largely unknown. Here, we employ two non-invasive imaging techniques, fluorescence-lifetime imaging microscopy (FLIM) and intensity-based multiphoton microscopy, to assess the metabolic states of cancer cells cultured on ECM-mimicking nanofibers in a random and aligned orientation. By tracking the changes in the intrinsic fluorescence of nicotinamide adenine dinucleotide and flavin adenine dinucleotide, as well as expression levels of metastatic markers, we reveal how ECM fiber orientation alters cancer metabolism and EMT progression. Our study indicates that aligned cellular microenvironments play a key role in promoting metastatic phenotypes of breast cancer as evidenced by a more glycolytic metabolic signature on nanofiber scaffolds of aligned orientation compared to scaffolds of random orientation. This finding is particularly relevant for subsets of breast cancer marked by high levels of collagen remodeling (e.g. pregnancy associated breast cancer), and may serve as a platform for predicting clinical outcomes within these subsets [3–6].

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