Ultrastructural analysis of breast cancer patient-derived organoids

Abstract Background Breast cancer Patient Derived Organoids (PDO) have been demonstrated to be a reliable model to study cancer that promised to replace and reduce the use of animals in pre-clinical research. They displayed concordance with the tissue of origin, resuming its heterogenicity and representing a good platform to develop approaches of personalized medicines. Although obtain PDOs from mammary tumour, was a very challenging process, several ongoing studies evaluated them as a platform to study efficacy, sensitivity and specificity of new drugs and exploited them in personalized medicine. Despite tissue organization represented a crucial point to evaluate in a 3-dimensional model, since it could influence drug penetration, morphology of breast cancer PDOs has not been analysed yet. Here, we proposed a complete ultrastructural analysis of breast PDOs obtained from tumour and healthy tissues to evaluate how typical structures observed in mammary gland were resumed in this model. Methods 81 samples of mammary tissue (healthy or tumour) resulting from surgical resections have been processed to obtain PDO. The resulting PDOs embedded in matrigel drop have been processed for transmission electron microscopy and analysed. A comparison between ones from healthy and ones from cancerous tissue has been performed and PDOs derived from tumour tissue have been stratified according to their histological and molecular subtype. Result The morphological analysis performed on 81 PDO revealed an organized structure rich in Golgi, secretion granules and mitochondria, which was typical of cells with a strong secretory activity and active metabolism. The presence of desmosomes, inter and intracellular lumens and of microvilli and interdigitations signified a precise tissue-organization. Each PDO has been classified based on whether or not it possessed (i) peripheral ridges in mitochondria, (ii) intracellular lumens, (iii) intercellular lumens, (iv) micro-vesicles, (v) open desmosomes, (vi) cell debris, (vii) polylobed nuclei, (viii) lysosomes and (ix) secretion granules, in order to identify features coupled with the cancerous state or with a specific histological or molecular subtype. Conclusion Here we have demonstrated the suitability of breast cancer PDO as 3-dimensional model of mammary tissue. Besides, some structural features characterizing cancerous PDO have been observed, identifying the presence of distinctive traits.

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Establishment and Characterization of a HER2-Positive Cell Line Derived From the Pleural Effusion of a Drug-Resistant Breast Cancer Patient

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.680968 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zhaoqing Li ◽

Wenying Zhuo ◽

Lini Chen ◽

Xun Zhang ◽

Cong Chen ◽

...

Keyword(s):

Breast Cancer ◽

Pleural Effusion ◽

Cancer Patient ◽

Breast Cancer Patient ◽

Cell Line ◽

Cell Lines ◽

Molecular Subtype ◽

Metastatic Breast ◽

Drug Resistant ◽

Her2 Positive

Drug resistance is a daunting challenge in the treatment of breast cancer, making it an urgent problem to solve in studies. Cell lines are important tools in basic and preclinical studies; however, few breast cell lines from drug-resistant patients are available. Herein, we established a novel HER2-positive breast cancer cell line from the pleural effusion of a drug-resistant metastatic breast cancer patient. This cell line has potent proliferative capability and tumorigenicity in nude mice but weak invasive and colony-forming capability. The molecular subtype of the cell line and its sensitivity to chemotherapeutics and HER2-targeting agents are different from those of its origin, suggesting that the phenotype changes between the primary and metastatic forms of breast cancer.

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The influence of stage at diagnosis and molecular subtype on breast cancer patient survival: a hospital-based multi-center study

Chinese Journal of Cancer ◽

10.1186/s40880-017-0250-3 ◽

2017 ◽

Vol 36 (1) ◽

Cited By ~ 7

Author(s):

Tingting Zuo ◽

Hongmei Zeng ◽

Huichao Li ◽

Shuo Liu ◽

Lei Yang ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patient ◽

Breast Cancer Patient ◽

Patient Survival ◽

Molecular Subtype ◽

Stage At Diagnosis ◽

Multi Center Study ◽

Center Study

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The prognostic value of SOCS2 expression in breast cancer patient according to molecular subtype

The Breast ◽

10.1016/s0960-9776(17)30177-7 ◽

2017 ◽

Vol 32 ◽

pp. S49

Author(s):

S.U. Woo ◽

S.H. Lee ◽

W.Y. Kim ◽

J.B. Lee

Keyword(s):

Breast Cancer ◽

Cancer Patient ◽

Breast Cancer Patient ◽

Prognostic Value ◽

Molecular Subtype

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THE RELATION OF COPPING STRATEGY BY LOOKING SOCIAL SUPPORT WITH STRESS LEVEL OF BREAST CANCER PATIENT WHO GONE TROUGH CHEMOTHERAPY

Elevate The International Journal of Nursing Education Practice and Research ◽

10.25077/elevate.1.1.51-54.2018 ◽

2018 ◽

Vol 1 (1) ◽

pp. 51-54

Author(s):

Sri Burhani Putri

Keyword(s):

Breast Cancer ◽

Social Support ◽

Cancer Patient ◽

Breast Cancer Patient ◽

Cross Sectional Study ◽

P Value ◽

Spiritual Support ◽

Cross Sectional ◽

Breast Cancer Chemotherapy ◽

Patient Stress

Breast cancer is one of the most common illness that killed woman. One of the therapy to cure breast cancer is chemotherapy. Chemotherapy has side effect either physical and psychology, that caused people who’s in chemo therapy, prone to stress. Stress effected by many factors, such as characteristic and chopping strategy that patient has been using. The aim of this research is to get a perspective about the relation of characteristic and chopping strategy with breast cancer patient stress, whose in chemo therapy. This research using cross sectional study and taking sample by using accidental sampling method. The data analyzed by using bavariat and multivariat with variable result shows that breast cancer patient stress who has chemo therapy realted to age characteristic (p value = 0.00) the time since they diagnosed with cancer (pvalue = 0.03), how long they have chemo therapy (pvalue = 0.00) and chopping strategyby looking social support (pvalue = 0.00) looking for spiritual (pvalue = 0.00) with dominan variable which related to stress is chopping strategy to looking spiritual support (coeffecients B = -1.139). Key words : Breast cancer, chemotherapy, stress

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Metastatic Breast Cancer: Patient Journey, Patient Needs, and Expectations: Results of the RÉALITÉS National Survey

ONCOLOGIE ◽

10.3166/onco-2018-0013 ◽

2018 ◽

Vol 20 (3-4) ◽

pp. 73-83 ◽

Cited By ~ 3

Author(s):

M. Espié ◽

I. Moley-Massol ◽

N. Zernik ◽

D. Debiais ◽

V. Diéras

Keyword(s):

Breast Cancer ◽

Metastatic Breast Cancer ◽

Cancer Patient ◽

Breast Cancer Patient ◽

National Survey ◽

Metastatic Breast ◽

Metastatic Breast Cancer Patient ◽

Patient Needs ◽

Patient Journey

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Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

Scientific Reports ◽

10.1038/s41598-021-94862-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mindaugas Morkunas ◽

Dovile Zilenaite ◽

Aida Laurinaviciene ◽

Povilas Treigys ◽

Arvydas Laurinavicius

Keyword(s):

Breast Cancer ◽

Breast Carcinoma ◽

Cancer Patient ◽

Breast Cancer Patient ◽

Cox Regression ◽

Regional Lymph Node ◽

Histological Grade ◽

Biological Information ◽

Lymph Node Status ◽

Bright Field

AbstractWithin the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p-value < 0.05) superior to conventional clinical parameters (size of the primary tumor (T), regional lymph node status (N), histological grade (G), and patient age). Additionally, we noted statistically significant differences of collagen features between tumor grade groups, and the factor analysis revealed features resembling the TACS concept. Our proposed method offers collagen framework segmentation from bright-field histology images and provides novel image-based features for better breast cancer patient prognostication.

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