scholarly journals Lymph node metastases in breast cancer: investigating associations with tumor characteristics, molecular subtypes, and polygenic risk score using a continuous growth model

2021 ◽  
Author(s):  
Gabriel Isheden ◽  
Felix Grassmann ◽  
Kamila Czene ◽  
Keith Humphreys
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Risk Score ◽  
Lymph Node Metastases ◽  
Molecular Subtypes ◽  
Polygenic Risk Score ◽  
Tumor Characteristics ◽  
Continuous Growth ◽  
Polygenic Risk ◽  
Node Metastases

A Simple Risk Score to Predict the Presence of Non-Sentinel Lymph Node Metastases in Breast Cancer Patients with a Positive Sentinel Node

2013 ◽  
Vol 38 (5) ◽  
pp. 1070-1076 ◽  
Author(s):  
Raquel F. D. van la Parra ◽  
Petronella G. M. Peer ◽  
Wilfred K. de Roos ◽  
Miranda F. Ernst ◽  
Johannes H. W. de Wilt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Sentinel Node ◽  
Cancer Patients ◽  
Risk Score ◽  
Lymph Node Metastases ◽  
Positive Sentinel Node ◽  
Breast Cancer Patients ◽  
Node Metastases

scholarly journals Association of Immunohistochemically Defined Molecular Subtypes With Clinicopathologic Factors in Indonesian Women With Breast Cancer

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 79s-79s
Author(s):  
D. Purnomosari ◽  
Y. Rahmawati ◽  
J. Judistira ◽  
I. Widodo
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastases ◽  
Molecular Subtypes ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B ◽  
Clinicopathologic Factors ◽  
Node Metastases

Background: Breast cancer is a heterogeneous disease. It is classified into various subtypes based on molecular difference, primarily through gene expression profiling (GEP) and immunohistochemistry (IHC) techniques. GEP is able to reveal gene signatures that predict clinical outcomes, and can discern prognostically relevant breast cancer subtypes, but it is not yet used routinely in the clinical practice, especially in the developing countries. On the other hand, IHC analysis is commonly used for breast cancer subtyping and provides critical prognostic and predictive information. Aim: The current study was aimed to established a comprehensive outline of Indonesian breast cancer subtypes distribution and their associations with the clinicopathologic factors based on a standard routinely used biomarker panel, i.e., estrogen-receptor (ER), progesterone-receptor (PR), and human epidermal growth factor receptor 2 (HER2). Methods: A retrospective cross-sectional study was conducted of 390 breast cancer cases in Dr. Sardjito General Hospital Yogyakarta, Indonesia from 2010 to 2015. Breast cancer subtypes were classified based on the expression of ER, PR, HER2 and histologic grade. The association of Indonesian breast cancer subtypes with clinicopathologic factors was evaluated using χ2 tests. Results: The majority of Indonesian breast cancer patients were older than 50 years, have larger tumor size (> 2 cm), high grade and absence of lymph node metastases. Among 390 cases, 32.1% were luminal A, 24.6% were luminal B, 17.9% were HER2+ and the remaining 25.4% were triple negative breast cancer (TNBC). High association was found in breast cancer molecular subtypes with regard to patients age, tumor size, histologic grade and lymph node metastases. Conclusion: Luminal A is the most common Indonesian breast cancer subtypes, followed by TNBC, luminal B and HER2. Immunohistochemistry-based subtyping is essential to classify breast cancer into subtypes that vary in clinicopathologic characteristics, which implies distinct prognosis and therapy response. Further studies are needed to clarify the mechanisms associated with development of each subtype.

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