Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer

Breast Cancer ◽  
2014 ◽  
Vol 22 (5) ◽  
pp. 496-502 ◽  
Author(s):  
Ken Yamaguchi ◽  
Hiroyuki Abe ◽  
Gillian M. Newstead ◽  
Ryoko Egashira ◽  
Takahiko Nakazono ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Kinetic Parameters ◽  
Invasive Breast Cancer ◽  
Molecular Subtypes ◽  
Intratumoral Heterogeneity

Heterogeneity Within Molecular Subtypes of Breast Cancer

2021 ◽  
Author(s):  
Kevin M. Turner ◽  
Syn Kok Yeo ◽  
Tammy M Holm ◽  
Elizabeth Shaughnessy ◽  
Jun-Lin Guan
Keyword(s):  
Breast Cancer ◽  
Molecular Subtypes ◽  
Tumor Biology ◽  
Treatment Resistance ◽  
Intratumoral Heterogeneity ◽  
Molecular Heterogeneity ◽  
Improve Treatment ◽  
Therapeutic Decisions ◽  
Single Tumor

Breast cancer is the quintessential example of how molecular characterization of tumor biology guides therapeutic decisions. From the discovery of the estrogen receptor to current clinical molecular profiles to evolving single cell analytics, the characterization and compartmentalization of breast cancer into divergent subtypes is clear. However, competing with this divergent model of breast cancer is the recognition of intratumoral heterogeneity, which acknowledges the possibility that multiple different subtypes exist within a single tumor. Intratumoral heterogeneity is driven by both intrinsic effects of the tumor cells themselves as well as extrinsic effects from the surrounding microenvironment. There is emerging evidence that these intratumoral molecular subtypes are not static; rather, plasticity between divergent subtypes is possible. Inter-conversion between seemingly different subtypes within a tumor drives tumor progression, metastases, and treatment resistance. Therapeutic strategies must therefore contend with changing phenotypes in an individual patient's tumor. Identifying targetable drivers of molecular heterogeneity may improve treatment durability and disease progression.

scholarly journals The prognostic significance of BMI1 expression in invasive breast cancer is dependent on its molecular subtypes

2020 ◽  
Vol 182 (3) ◽  
pp. 581-589
Author(s):  
Maryam Althobiti ◽  
Abir A. Muftah ◽  
Mohammed A. Aleskandarany ◽  
Chitra Joseph ◽  
Michael S. Toss ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Molecular Subtypes ◽  
Prognostic Significance ◽  
Bmi1 Expression

scholarly journals Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Eun Kyung Park ◽  
Kwang-sig Lee ◽  
Bo Kyoung Seo ◽  
Kyu Ran Cho ◽  
Ok Hee Woo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Invasive Breast Cancer ◽  
Low Dose ◽  
Molecular Subtypes ◽  
Random Forest Model ◽  
Prognostic Biomarkers ◽  
Learning Approaches ◽  
Forest Model

AbstractRadiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of machine learning approaches to radiogenomics using low-dose perfusion computed tomography (CT) to predict prognostic biomarkers and molecular subtypes of invasive breast cancer. This prospective study enrolled a total of 723 cases involving 241 patients with invasive breast cancer. The 18 CT parameters of cancers were analyzed using 5 machine learning models to predict lymph node status, tumor grade, tumor size, hormone receptors, HER2, Ki67, and the molecular subtypes. The random forest model was the best model in terms of accuracy and the area under the receiver-operating characteristic curve (AUC). On average, the random forest model had 13% higher accuracy and 0.17 higher AUC than the logistic regression. The most important CT parameters in the random forest model for prediction were peak enhancement intensity (Hounsfield units), time to peak (seconds), blood volume permeability (mL/100 g), and perfusion of tumor (mL/min per 100 mL). Machine learning approaches to radiogenomics using low-dose perfusion breast CT is a useful noninvasive tool for predicting prognostic biomarkers and molecular subtypes of invasive breast cancer.

Sign in / Sign up

Export Citation Format

Share Document