Abstract P4-21-21: Her2 is not a cancer subtype but rather a driver found in all intrinsic subtypes and highly enriched in molecular apocrine tumors

2017 ◽  
Author(s):  
A Daemen ◽  
G Manning
Keyword(s):  
Intrinsic Subtypes ◽  
Molecular Apocrine ◽  
Cancer Subtype

scholarly journals An androgen receptor mutation in the MDA-MB-453 cell line model of molecular apocrine breast cancer compromises receptor activity

2012 ◽  
Vol 19 (4) ◽  
pp. 599-613 ◽  
Author(s):  
Nicole L Moore ◽  
Grant Buchanan ◽  
Jonathan M Harris ◽  
Luke A Selth ◽  
Tina Bianco-Miotto ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Androgen Receptor ◽  
Ligand Binding ◽  
Cell Line ◽  
Breast Cancer Subtype ◽  
Wnt Signaling Pathway ◽  
Intramolecular Interactions ◽  
Gene Set Enrichment Analysis ◽  
Molecular Apocrine ◽  
Cancer Subtype

Recent evidence indicates that the estrogen receptor-α-negative, androgen receptor (AR)-positive molecular apocrine subtype of breast cancer is driven by AR signaling. The MDA-MB-453 cell line is the prototypical model of this breast cancer subtype; its proliferation is stimulated by androgens such as 5α-dihydrotestosterone (DHT) but inhibited by the progestin medroxyprogesterone acetate (MPA) via AR-mediated mechanisms. We report here that theARgene in MDA-MB-453 cells contains a G-T transversion in exon 7, resulting in a receptor variant with a glutamine to histidine substitution at amino acid 865 (Q865H) in the ligand binding domain. Compared with wild-type AR, the Q865H variant exhibited reduced sensitivity to DHT and MPA in transactivation assays in MDA-MB-453 and PC-3 cells but did not respond to non-androgenic ligands or receptor antagonists. Ligand binding, molecular modeling, mammalian two-hybrid and immunoblot assays revealed effects of the Q865H mutation on ligand dissociation, AR intramolecular interactions, and receptor stability. Microarray expression profiling demonstrated that DHT and MPA regulate distinct transcriptional programs in MDA-MB-453 cells. Gene Set Enrichment Analysis revealed that DHT- but not MPA-regulated genes were associated with estrogen-responsive transcriptomes from MCF-7 cells and the Wnt signaling pathway. These findings suggest that the divergent proliferative responses of MDA-MB-453 cells to DHT and MPA result from the different genetic programs elicited by these two ligands through the AR-Q865H variant. This work highlights the necessity to characterize additional models of molecular apocrine breast cancer to determine the precise role of AR signaling in this breast cancer subtype.

scholarly journals Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2013
Author(s):  
Edian F. Franco ◽  
Pratip Rana ◽  
Aline Cruz ◽  
Víctor V. Calderón ◽  
Vasco Azevedo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Similarity Measures ◽  
Research Problem ◽  
Optimal Number ◽  
Performance Comparison ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Omics Data ◽  
Cancer Subtype

A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score and found that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we compared the effect of feature selection and similarity measures for subtype detection. For further evaluation, we used the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes. The results obtained are consistent with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

scholarly journals Detailed head localization and incidence of skin cancers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marta Fijałkowska ◽  
Mateusz Koziej ◽  
Bogusław Antoszewski
Keyword(s):  
Skin Cancer ◽  
Cell Carcinoma ◽  
Vertical Dimension ◽  
Surgical Department ◽  
Surface Areas ◽  
Common Cancer ◽  
Skin Cancers ◽  
Sex Type ◽  
Cancer Subtype ◽  
The Face

AbstractSkin cancers are the most common neoplasms; frequently, they localize on the face. The aim of paper is to present the incidence of skin tumors in a single center from 2017 to 2019, describe trends in its frequency and find relations between neoplasms and sex, type of cancer, and its size. An analysis of histopathological files from the surgical department between 2017 and 2019 was calculated. These items were selected: sex, age, type of skin cancer, subtype of basal cell carcinoma (BCC), grading of squamous cell carcinoma (SCC), localization and dimensions of the tumor. The study sample consisted of 387 cases. BCC was the most common cancer and its nodular type was the most frequent. In older patients, the vertical dimension of excised carcinoma was significantly larger. Moreover, this connection was detected only in women compared to men. There were statistically significant differences between dimensions of the skin cancer and sex. In men group, skin cancers had statistically higher vertical dimensions and larger surface areas. On the face and head, BCC more often localizes in the nasal area, while SCC on the auricle. It has been demonstrated that the older the patient, the larger the vertical dimension of the tumor. As such, tumor size is larger in men than in women, as women usually see their physicians sooner than men: cosmetic concerns are more important to them.

scholarly journals Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 638
Author(s):  
Linjing Liu ◽  
Xingjian Chen ◽  
Olutomilayo Olayemi Petinrin ◽  
Weitong Zhang ◽  
Saifur Rahaman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Detection ◽  
Liquid Biopsy ◽  
Cancer Diagnostics ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Cancer Subtype ◽  
Tumor Origin ◽  
Complex Relationships ◽  
Learning Protocols

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

Targeting Prostate Cancer Subtype 1 by Forkhead Box M1 Pathway Inhibition

2017 ◽  
Vol 23 (22) ◽  
pp. 6923-6933 ◽  
Author(s):  
Kirsi Ketola ◽  
Ravi S.N. Munuganti ◽  
Alastair Davies ◽  
Ka Mun Nip ◽  
Jennifer L. Bishop ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Forkhead Box ◽  
Forkhead Box M1 ◽  
Cancer Subtype ◽  
Pathway Inhibition
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