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.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

scholarly journals Practical classification of triple-negative breast cancer: intratumoral heterogeneity, mechanisms of drug resistance, and novel therapies

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Antonio Marra ◽  
Dario Trapani ◽  
Giulia Viale ◽  
Carmen Criscitiello ◽  
Giuseppe Curigliano
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Residual Disease ◽  
Therapeutic Approaches ◽  
Minimal Residual Disease Monitoring ◽  
Anticancer Treatment ◽  
Immune Contexture

Abstract Triple-negative breast cancer (TNBC) is not a unique disease, encompassing multiple entities with marked histopathological, transcriptomic and genomic heterogeneity. Despite several efforts, transcriptomic and genomic classifications have remained merely theoretic and most of the patients are being treated with chemotherapy. Driver alterations in potentially targetable genes, including PIK3CA and AKT, have been identified across TNBC subtypes, prompting the implementation of biomarker-driven therapeutic approaches. However, biomarker-based treatments as well as immune checkpoint inhibitor-based immunotherapy have provided contrasting and limited results so far. Accordingly, a better characterization of the genomic and immune contexture underpinning TNBC, as well as the translation of the lessons learnt in the metastatic disease to the early setting would improve patients’ outcomes. The application of multi-omics technologies, biocomputational algorithms, assays for minimal residual disease monitoring and novel clinical trial designs are strongly warranted to pave the way toward personalized anticancer treatment for patients with TNBC.

Brominated phenols: characterization of estrogen-like activity in the human breast cancer cell-line MCF-7

2002 ◽  
Vol 129 (1-2) ◽  
pp. 55-63 ◽  
Author(s):  
Christel M Olsen ◽  
Elise T.M Meussen-Elholm ◽  
Jørn A Holme ◽  
Jan K Hongslo
Keyword(s):  
Breast Cancer ◽  
Cell Line ◽  
Breast Cancer Cell ◽  
Breast Cancer Cell Line ◽  
Human Breast Cancer ◽  
Cancer Cell Line ◽  
Human Breast Cancer Cell ◽  
Human Breast ◽  
Mcf 7

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 Electroencephalographic Findings at an Electrodiagnostic Unit of a Tertiary Hospital, North-central Nigeria

2021 ◽  
Vol 2 (1) ◽  
pp. 49-55
Author(s):  
E U Iwuozo ◽  
J O Enyikwola ◽  
I O Obekpa ◽  
O O Ijachi ◽  
A A Godwin ◽  
...  
Keyword(s):  
Tertiary Hospital ◽  
Seizure Disorder ◽  
Cross Sectional ◽  
North Central ◽  
Younger Age ◽  
High Prevalence ◽  
Diagnosis And Classification ◽  
Age Range

Electroencephalography (EEG) remains an important investigative tool in supporting the diagnosis and classification of various seizure types. We sought to examine and characterize the EEG findings from all patients referred for the procedure. This cross-sectional retrospective study was carried out at an EEG unit in Federal Medical Centre, Makurdi, Benue State, North Central Nigeria from May 2016 to December 2020. Relevant patients' information were extracted and analysed using SPSS version 21. A total of 484 patients were seen over the study period with age range of 1-87 years and median age of 23 years. They comprised of 254 (52.5%) male and 230 (47.5%) female. The psychiatrist and the Physicians/Neurologist referred most of them for EEG, 201 (41.5%) and 124 (25.6%) respectively. The most reported indication for EEG was clinical suspicion of seizure disorder 291 (60.1%), whilst some did not have a clear indication 111 (22.9%). About 417 (86.2%) of our patients had abnormal EEG finding out of which 414 (99.3%) were diagnostic of seizure disorder made up of generalized seizure in 255 (61.6%) and focal seizure in 159 (38.4%). About 237 (48.9%) of them were already on antiepileptic drugs (AEDs) at referral of which 190 (80.2%0 were taking carbamazepine. This study showed a high prevalence of abnormal EEG with most of them diagnostic of seizure disorder especially generalized seizure. They were mostly of younger age group with about half of them already on AEDs at referral, majority of who were sent by the Psychiatrist.

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