A survey on the current status of clinical resources for diagnosis and treatment of breast cancer in rural hospitals of the Tohoku region in Japan

: Immunotherapy emerges as a treatment strategy for breast cancer marker, diagnosis and treatment. In this review, monoclonal antibodies (mAbs)-based passive and peptide vaccines as active immunotherapy approaches like activation of B-cells and T-cells are studied. Passive immunotherapy is mAbs-based therapy effective against tumor cells, which acts by targeting HER2, IGF 1R, VEGF, BCSC and immune checkpoints. Neuropeptide Y (NPY) and GPCR are the areas of interest to target BC metastases for on-targeting therapeutic action. Neuropeptide S (NPS) or NPS receptor 1, acts as a biomarker for Neuroendocrine tumors (NET), mostly characterized by synaptophysin and chromogranin-A expression or Ki-67 proliferation index. The protein fusion technologies arise as a promising avenue in plant expression systems for increased recombinant Ab accumulation and cost-efficient purification. Recently, mAbs-based immunotherapy effectiveness is appreciated as a novel therapeutic combination of chemotherapy and immunotherapy to reduce the side effects and improve therapeutic responsiveness. Synthetic drug resistance will be overcome by mAbs-based therapy through several clinical trials and detection methods need to be optimized for accuracy and precision. Pharmacokinetic attributes need to be accessed for preferred receptor-agonist activity without ligand accumulation.

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Phytochemicals for Breast Cancer Therapy: Current Status and Future Implications

Current Cancer Drug Targets ◽

10.2174/1568009615666141229152256 ◽

2015 ◽

Vol 15 (2) ◽

pp. 116-135 ◽

Cited By ~ 27

Author(s):

Jawed Siddiqui ◽

Aru Singh ◽

Megha Chagtoo ◽

Nidhi Singh ◽

Madan Godbole ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Therapy ◽

Current Status ◽

Breast Cancer Therapy

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Clinical applications of circulating tumor DNA in monitoring breast cancer drug resistance

Future Oncology ◽

10.2217/fon-2019-0760 ◽

2020 ◽

Vol 16 (34) ◽

pp. 2863-2878

Author(s):

Yang Liu ◽

Qian Du ◽

Dan Sun ◽

Ruiying Han ◽

Mengmeng Teng ◽

...

Keyword(s):

Breast Cancer ◽

Drug Resistance ◽

Digital Pcr ◽

Circulating Tumor Dna ◽

Resistance Mechanisms ◽

Clinical Applications ◽

Current Status ◽

Cancer Drug ◽

Genomic Alteration ◽

Tumor Dna

Breast cancer is one of the leading causes of cancer-related deaths in women worldwide. Unfortunately, treatments often fail because of the development of drug resistance, the underlying mechanisms of which remain unclear. Circulating tumor DNA (ctDNA) is free DNA released into the blood by necrosis, apoptosis or direct secretion by tumor cells. In contrast to repeated, highly invasive tumor biopsies, ctDNA reflects all molecular alterations of tumors dynamically and captures both spatial and temporal tumor heterogeneity. Highly sensitive technologies, including personalized digital PCR and deep sequencing, make it possible to monitor response to therapies, predict drug resistance and tailor treatment regimens by identifying the genomic alteration profile of ctDNA, thereby achieving precision medicine. This review focuses on the current status of ctDNA biology, the technologies used to detect ctDNA and the potential clinical applications of identifying drug resistance mechanisms by detecting tumor-specific genomic alterations in breast cancer.

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An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images

Contrast Media & Molecular Imaging ◽

10.1155/2019/5982834 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Andrea Duggento ◽

Marco Aiello ◽

Carlo Cavaliere ◽

Giuseppe L. Cascella ◽

Davide Cascella ◽

...

Keyword(s):

Breast Cancer ◽

Network Architecture ◽

Ad Hoc ◽

False Negative ◽

Selection Criterion ◽

Current Status ◽

Clinical Image ◽

Imaging Data ◽

Breast Cancer Death ◽

Random Initialization

Breast cancer is one of the most common cancers in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. In this context, recent studies showed that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long term. X-ray mammography is still the instrument of choice in breast cancer screening. In this context, the false-positive and false-negative rates commonly achieved by radiologists are extremely arduous to estimate and control although some authors have estimated figures of up to 20% of total diagnoses or more. The introduction of novel artificial intelligence (AI) technologies applied to the diagnosis and, possibly, prognosis of breast cancer could revolutionize the current status of the management of the breast cancer patient by assisting the radiologist in clinical image interpretation. Lately, a breakthrough in the AI field has been brought about by the introduction of deep learning techniques in general and of convolutional neural networks in particular. Such techniques require no a priori feature space definition from the operator and are able to achieve classification performances which can even surpass human experts. In this paper, we design and validate an ad hoc CNN architecture specialized in breast lesion classification from imaging data only. We explore a total of 260 model architectures in a train-validation-test split in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve an area under the receiver operatic characteristics curve of 0.785 (accuracy 71.19%) on the test set, demonstrating how an ad hoc random initialization architecture can and should be fine tuned to a specific problem, especially in biomedical applications.

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