Literature DB >> 31778145

Organoid model of mammographic density displays a higher frequency of aberrant colony formations with radiation exposure.

Qingsu Cheng1, Bahram Parvin1.   

Abstract

MOTIVATION: Aberrant three-dimensional (3D) colony organization of premalignant human mammary epithelial cells (HMECs) is one of the indices of dysplasia. An experiment has been designed where the stiffness of the microenvironment, in 3D culture, has been set at either low or high level of mammographic density (MD) and the organoid models are exposed to 50 cGy X-ray radiation. This study utilizes published bioinformatics tools to quantify the frequency of aberrant colony formations by the combined stressors of stiffness and X-ray exposure. One of the goals is to develop a quantitative assay for evaluating the risk factors associated with women with high MD exposed to X-ray radiation.
RESULTS: Analysis of 3D colony formations indicate that high stiffness, within the range of high MD, and X-ray radiation have an approximately additive effect on increasing the frequency of aberrant colony formations. Since both stiffness and X-ray radiation are DNA-damaging stressors, the additive effect of these stressors is also independently validated by profiling activin A-secreted protein. Secretion of activin A is known to be higher in tissues with a high MD as well as tumor cells. In addition, we show that increased stiffness of the microenvironment also induces phosphorylation of γH2AX-positive foci. The study uses two HMECs derived from a diseased tissue (e.g. MCF10A) and reduction mammoplasty of normal breast tissue (e.g. 184A1) to further demonstrate similar traits in the frequency of aberrant colony organization. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Year:  2020        PMID: 31778145      PMCID: PMC7141857          DOI: 10.1093/bioinformatics/btz888

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  19 in total

1.  Three-dimensional culture models of normal and malignant breast epithelial cells.

Authors:  Genee Y Lee; Paraic A Kenny; Eva H Lee; Mina J Bissell
Journal:  Nat Methods       Date:  2007-04       Impact factor: 28.547

2.  The morphologies of breast cancer cell lines in three-dimensional assays correlate with their profiles of gene expression.

Authors:  Paraic A Kenny; Genee Y Lee; Connie A Myers; Richard M Neve; Jeremy R Semeiks; Paul T Spellman; Katrin Lorenz; Eva H Lee; Mary Helen Barcellos-Hoff; Ole W Petersen; Joe W Gray; Mina J Bissell
Journal:  Mol Oncol       Date:  2007-06       Impact factor: 6.603

3.  Benign breast disease. Resolved and unresolved issues.

Authors:  J L Connolly; S J Schnitt
Journal:  Cancer       Date:  1993-02-15       Impact factor: 6.860

Review 4.  Why don't we get more cancer? A proposed role of the microenvironment in restraining cancer progression.

Authors:  Mina J Bissell; William C Hines
Journal:  Nat Med       Date:  2011-03       Impact factor: 53.440

5.  Identifying women with dense breasts at high risk for interval cancer: a cohort study.

Authors:  Karla Kerlikowske; Weiwei Zhu; Anna N A Tosteson; Brian L Sprague; Jeffrey A Tice; Constance D Lehman; Diana L Miglioretti
Journal:  Ann Intern Med       Date:  2015-05-19       Impact factor: 25.391

6.  Molecular predictors of 3D morphogenesis by breast cancer cell lines in 3D culture.

Authors:  Ju Han; Hang Chang; Orsi Giricz; Genee Y Lee; Frederick L Baehner; Joe W Gray; Mina J Bissell; Paraic A Kenny; Bahram Parvin
Journal:  PLoS Comput Biol       Date:  2010-02-26       Impact factor: 4.475

7.  Stress signaling from human mammary epithelial cells contributes to phenotypes of mammographic density.

Authors:  Rosa Anna DeFilippis; Colleen Fordyce; Kelley Patten; Hang Chang; Jianxin Zhao; Gerald V Fontenay; Karla Kerlikowske; Bahram Parvin; Thea D Tlsty
Journal:  Cancer Res       Date:  2014-08-29       Impact factor: 12.701

8.  Mammographic features and breast cancer risk: effects with time, age, and menopause status.

Authors:  C Byrne; C Schairer; J Wolfe; N Parekh; M Salane; L A Brinton; R Hoover; R Haile
Journal:  J Natl Cancer Inst       Date:  1995-11-01       Impact factor: 13.506

9.  Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density.

Authors:  Qingsu Cheng; Cemal Cagatay Bilgin; Gerald Fontenay; Hang Chang; Matthew Henderson; Ju Han; Bahram Parvin
Journal:  Sci Rep       Date:  2016-07-07       Impact factor: 4.379

Review 10.  Mammographic density, breast cancer risk and risk prediction.

Authors:  Celine M Vachon; Carla H van Gils; Thomas A Sellers; Karthik Ghosh; Sandhya Pruthi; Kathleen R Brandt; V Shane Pankratz
Journal:  Breast Cancer Res       Date:  2007       Impact factor: 6.466

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  2 in total

1.  Overexpression of CD36 in mammary fibroblasts suppresses colony growth in breast cancer cell lines.

Authors:  Qingsu Cheng; Kosar Jabbari; Garrett Winkelmaier; Cody Andersen; Paul Yaswen; Mina Khoshdeli; Bahram Parvin
Journal:  Biochem Biophys Res Commun       Date:  2020-03-16       Impact factor: 3.575

2.  An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models.

Authors:  Garrett Winkelmaier; Bahram Parvin
Journal:  Bioinformatics       Date:  2021-02-23       Impact factor: 6.937

  2 in total

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