Immunohistochemical characterization of molecular subtypes of invasive breast cancer: a study from North India

Apmis ◽  
2012 ◽  
Vol 120 (12) ◽  
pp. 1008-1019 ◽  
Author(s):  
Sangeeta Verma ◽  
Amanjit Bal ◽  
Kusum Joshi ◽  
Sunil Arora ◽  
Gurpreet Singh
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Molecular Subtypes ◽  
North India

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.

Characterization of Metastatic Sternal Lesions on Dynamic Contrast-Enhanced Breast MRI in Women with Invasive Breast Cancer

2019 ◽  
Vol 26 (10) ◽  
pp. 1358-1362
Author(s):  
Amie Y. Lee ◽  
Ryan Navarro ◽  
Lindsay P. Busby ◽  
Heather I. Greenwood ◽  
Matthew D. Bucknor ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Breast Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Radiomics- Quantitative Biomarker Analysis for Breast Cancer Diagnosis and Prediction: A Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Priscilla Dinkar Moyya ◽  
Mythili Asaithambi
Keyword(s):  
Breast Cancer ◽  
Molecular Subtypes ◽  
Breast Cancer Diagnosis ◽  
Benign Tumors ◽  
Genomic Information ◽  
Tumor Characterization ◽  
Breast Malignancy ◽  
The Past ◽  
Biomarker Analysis

Background: Cancer of the breast has become a global problem for women's health. Though concerns regarding early detection and accurate diagnosis were raised, an effort is required for precision medicine as well as personalized treatment. In the past years, the area of medicinal imaging has seen an unprecedented growth that leads to an advancement of radiomics, which provides countless quantitative biomarkers extracted from modern diagnostic images, including a detailed tumor characterization of breast malignancy. Discussion: In this research, we presented the methodology and implementation of radiomics, together with its future trends and challenges by the basis of published papers. Radiomics could distinguish between malignant from benign tumors, predict prognostic factors, molecular subtypes of breast carcinoma, treatment response to neoadjuvant chemotherapy (NAC), and recurrence survival. The incorporation of quantitative knowledge with clinical, histopathological and genomic information will enable physicians to afford customized care of treatment for patients with breast cancer. Conclusion: Our research was intended to help physicians and radiologists learn fundamental knowledge about radiomics and also to work collaboratively with researchers to explore evidence for further usage in clinical practice.

scholarly journals Proteomic Characterization of Serum Small Extracellular Vesicles in Human Breast Cancer

2021 ◽  
Author(s):  
Ganfei Xu ◽  
Weiyi Huang ◽  
Shaoqian Du ◽  
Minjing Huang ◽  
Jiacheng Lyu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Reference Value ◽  
Human Breast ◽  
Serum Samples ◽  
Healthy Donors ◽  
Diagnosis And Classification

There is a lack of comprehensive understanding of breast cancer (BC) specific sEVs characteristics and composition on BC unique proteomic information from human samples. Here, we interrogated the proteomic landscape of sEVs in 167 serum samples from patients with BC, benign mammary disease (BD) and from healthy donors (HD). The analysis provides a comprehensive landscape of serum sEVs with totally 9,589 proteins identified, considerably expanding the panel of sEVs markers. Of note, serum BC-sEVs protein signatures were distinct from those of BD and HD, representing stage- and molecular subtype-specific patterns. We constructed specific sEVs protein identifiers that could serve as a liquid biopsy tool for diagnosis and classification of BC from benign mammary disease, molecular subtypes, as well as assessment of lymph node metastasis. We also identified 11 potential survival biomarkers for distant metastasis. This work may provide reference value for the accurate diagnosis and monitoring of BC progression using serum sEVs.

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

26 BIOLOGICAL AND PATHOLOGICAL CHARACTERIZATION OF INVASIVE BREAST CANCER WITH CHROMOSOME 17 (Ch17) ANEUSOMY

2010 ◽  
Vol 36 ◽  
pp. S102
Author(s):  
S. Petroni ◽  
T. Addati ◽  
F. Giotta ◽  
C. Quero ◽  
M.A. Caponio ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Chromosome 17

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.

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