Literature DB >> 32375854

Quantitative stain-free imaging and digital profiling of collagen structure reveal diverse survival of triple negative breast cancer patients.

Laurent Gole1, Joe Yeong1,2,3, Jeffrey Chun Tatt Lim1,2, Kok Haur Ong1, Hao Han1,4, Aye Aye Thike2, Yong Cheng Poh5, Sidney Yee5, Jabed Iqbal2, Wanjin Hong6, Bernett Lee7, Weimiao Yu8, Puay Hoon Tan9.   

Abstract

BACKGROUND: Stromal and collagen biology has a significant impact on tumorigenesis and metastasis. Collagen is a major structural extracellular matrix component in breast cancer, but its role in cancer progression is the subject of historical debate. Collagen may represent a protective layer that prevents cancer cell migration, while increased stromal collagen has been demonstrated to facilitate breast cancer metastasis.
METHODS: Stromal remodeling is characterized by collagen fiber restructuring and realignment in stromal and tumoral areas. The patients in our study were diagnosed with triple-negative breast cancer in Singapore General Hospital from 2003 to 2015. We designed novel image processing and quantification pipelines to profile collagen structures using numerical imaging parameters. Our solution differentiated the collagen into two distinct modes: aggregated thick collagen (ATC) and dispersed thin collagen (DTC).
RESULTS: Extracted parameters were significantly associated with bigger tumor size and DCIS association. Of numerical parameters, ATC collagen fiber density (CFD) and DTC collagen fiber length (CFL) were of significant prognostic value for disease-free survival and overall survival for the TNBC patient cohort. Using these two parameters, we built a predictive model to stratify the patients into four groups.
CONCLUSIONS: Our study provides a novel insight for the quantitation of collagen in the tumor microenvironment and will help predict clinical outcomes for TNBC patients. The identified collagen parameters, ATC CFD and DTC CFL, represent a new direction for clinical prognosis and precision medicine. We also compared our result with benign samples and DICS samples to get novel insight about the TNBC heterogeneity. The improved understanding of collagen compartment of TNBC may provide insights into novel targets for better patient stratification and treatment.

Entities:  

Keywords:  Collagen profile; Quantitative imaging; Second harmonic generation microscopy; Stroma; Triple-negative breast cancers

Year:  2020        PMID: 32375854     DOI: 10.1186/s13058-020-01282-x

Source DB:  PubMed          Journal:  Breast Cancer Res        ISSN: 1465-5411            Impact factor:   6.466


  4 in total

1.  Biochemical "decoding" of breast ultrasound images with optoacoustic tomography fusion: First-in-human display of lipid and collagen signals on breast ultrasound.

Authors:  Yonggeng Goh; Ghayathri Balasundaram; Hui Min Tan; Thomas Choudary Putti; Siau Wei Tang; Celene Wei Qi Ng; Shaik Ahmad Buhari; Eric Fang; Mohesh Moothanchery; Renzhe Bi; Malini Olivo; Swee Tian Quek
Journal:  Photoacoustics       Date:  2022-06-15

2.  A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients.

Authors:  Wei Jiang; Min Li; Jie Tan; Mingyuan Feng; Jixiang Zheng; Dexin Chen; Zhangyuanzhu Liu; Botao Yan; Guangxing Wang; Shuoyu Xu; Weiwei Xiao; Yuanhong Gao; Shuangmu Zhuo; Jun Yan
Journal:  Ann Surg Oncol       Date:  2021-06-19       Impact factor: 5.344

3.  Canine mammary cancer tumour behaviour and patient survival time are associated with collagen fibre characteristics.

Authors:  Ana P V Garcia; Luana A Reis; Fernanda C Nunes; Francis G J Longford; Jeremy G Frey; Ana M de Paula; Geovanni D Cassali
Journal:  Sci Rep       Date:  2021-03-11       Impact factor: 4.379

Review 4.  The Tumor Microenvironment of Primitive and Metastatic Breast Cancer: Implications for Novel Therapeutic Strategies.

Authors:  Giovanni Zarrilli; Gianluca Businello; Maria Vittoria Dieci; Silvia Paccagnella; Valentina Carraro; Rocco Cappellesso; Federica Miglietta; Gaia Griguolo; Valentina Guarneri; Marcello Lo Mele; Matteo Fassan
Journal:  Int J Mol Sci       Date:  2020-10-30       Impact factor: 5.923

  4 in total

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