Prevalence of molecular subtypes and prognosis of invasive breast cancer in Moroccan patients (Fez region): Experience of Hassan II University Hospital.

Background: Several available data suggest the association between specific molecular subtypes and BRCA1/2 mutational status. Previous investigations showed the association between BRCA1/2 pathogenic variants (PVs) in specific genomic regions and phenotypic variations of cancer relative risk, while the role of PV type and location in determining the breast cancer (BC) phenotypic features remains still unclear. The aim of this research was to describe the germline BRCA1/2 PVs in triple-negative breast cancer (TNBC) versus luminal-like BC and their potential leverage on BC phenotype. Patients & methods: We retrospectively collected and analyzed all clinical information of 531 patients with BC genetically tested for germline BRCA1/2 PVs by Next-Generation Sequencing analysis at University Hospital Policlinico “P. Giaccone” of Palermo (Sicily) from January 2016 to February 2020. Results: Our results corroborate the evidence that BRCA1-related tumors often have a profile which resembles the TNBC subtype, whereas BRCA2-associated tumors have a profile that resembles luminal-like BC, especially the Luminal B subtype. Interestingly, our findings suggest that the PVs identified in TNBC were not largely overlapping with those in luminal-like tumors. Differences in the frequency of two PVs potentially associated with different molecular tumor subtypes were observed. BRCA1-633delC was detected with relatively higher prevalence in patients with TNBC, whereas BRCA2-1466delT was found mainly in Luminal B tumors, but in no TNBC patient. Conclusion: Future studies examining the type and location of BRCA1/2 PVs within different molecular subtypes are required to verify our hypothesis and could provide an interesting insight into the complex topic of genotype–phenotype correlations. Additionally, a more in-depth understanding of the potential correlations between BRCA PVs and clinical and phenotypic features of hereditary BC syndrome patients could be the key to develop better strategies of prevention and surveillance in BRCA-positive carriers without disease.

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

Scientific Reports ◽

10.1038/s41598-019-54371-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

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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Molecular subtypes of invasive breast cancer: correlation between PET/computed tomography and MRI findings

Nuclear Medicine Communications ◽

10.1097/mnm.0000000000001220 ◽

2020 ◽

Vol 41 (8) ◽

pp. 810-816

Author(s):

Meliha Akin ◽

Sebnem Orguc ◽

Feray Aras ◽

Ali Riza Kandiloglu

Keyword(s):

Breast Cancer ◽

Computed Tomography ◽

Invasive Breast Cancer ◽

Molecular Subtypes ◽

Mri Findings

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Associations and indications of Ki67 expression with clinicopathological parameters and molecular subtypes in invasive breast cancer: A population-based study

Oncology Letters ◽

10.3892/ol.2015.3461 ◽

2015 ◽

Vol 10 (3) ◽

pp. 1741-1748 ◽

Cited By ~ 9

Author(s):

JINZHONG SUN ◽

CHUANG CHEN ◽

WEN WEI ◽

HONGMEI ZHENG ◽

JINGPING YUAN ◽

...

Keyword(s):

Breast Cancer ◽

Invasive Breast Cancer ◽

Molecular Subtypes ◽

Population Based ◽

Clinicopathological Parameters ◽

Population Based Study ◽

Ki67 Expression

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Analysis of the molecular subtypes of preoperative core needle biopsy and surgical specimens in invasive breast cancer

Journal of Pathology and Translational Medicine ◽

10.4132/jptm.2019.10.14 ◽

2020 ◽

Vol 54 (1) ◽

pp. 87-94 ◽

Cited By ~ 1

Author(s):

Ye Sul Jeong ◽

Jun Kang ◽

Jieun Lee ◽

Tae-Kyung Yoo ◽

Sung Hun Kim ◽

...

Keyword(s):

Breast Cancer ◽

Invasive Breast Cancer ◽

Core Needle Biopsy ◽

Needle Biopsy ◽

Molecular Subtypes ◽

Core Needle

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CLINICOPATHOLOGICAL FEATURES OF NONSPECIFIC INVASIVE BREAST CANCER ACCORDING TO ITS MOLECULAR SUBTYPES

Experimental Oncology ◽

10.31768/2312-8852.2016.38(2):122-127 ◽

2016 ◽

Vol 38 (2) ◽

pp. 122-127 ◽

Cited By ~ 2

Author(s):

M Zavyalova ◽

S Vtorushin ◽

N Telegina ◽

N Krakhmal ◽

O Savelieva ◽

...

Keyword(s):

Breast Cancer ◽

Invasive Breast Cancer ◽

Triple Negative ◽

Molecular Subtypes ◽

Morphological Structure ◽

Tumor Stroma ◽

Clinicopathological Characteristics ◽

Hematogenous Metastasis ◽

Luminal A ◽

Luminal B

The aim of the present study was to investigate the clinical and morphological features of nonspecific invasive breast cancer according to its molecular subtypes. Materials and Methods: 163 women with nonspecific invasive breast cancer (T1–4N0–3M0) were included in the present study. Luminal A type of breast cancer was detected in 101 women, luminal B type — in 23 women, overexpression of HER2/neu was identified in 14 women and triple-negative cancer — in 25 women. Results: The study revealed that various molecular subtypes of breast cancer differ in the morphological structure, the expression characteristics of the primary tumor and the rate of lymphogenous and hematogenous metastasis. Lymphogenous metastases were more frequently (in 71%) detected in HER2/neu overexpressing breast cancer than in luminal A (41%), luminal B (39%) and triple-negative tumors (40%). Hematogenous metastasis did not depend on the morphological structure of carcinoma infiltrative component, the state of tumor stroma as well as the proliferative activity in all the investigated groups. Conclusion: The revealed clinicopathological characteristics of different molecular subtypes of invasive breast cancer allow to predict the possible outcome of the disease and select personalized treatment strategy for patients more reasonably.

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