Biology subjects

Mittal, V.

Publications and source records attributed to Mittal, V..

2 recordsLinked to original sources

bioRxiv2020

Unacylated ghrelin and AZP531 suppress the 3D growth of breast cancers

Breast cancer is the most common type of cancer in women and notwithstanding important therapeutic advances, it remains the second leading cause of cancer-related death in the US. Despite extensive research relating to the hormone ghrelin, responsible for the stimulation of growth hormone release and appetite, little is known of the effects of its unacylated form, especially in cancer. One major area of research relates to its reported effects to increase insulin sensitivity in diabetics, this being the basis for the development of unacylated ghrelin analog, AZP531 or livoletide, now in clinical trials for the treatment of Prader-Willi Syndrome. The mechanism of action of unacylated ghrelin is largely uncharacterized, in any system, because it does not bind to or activate the cognate ghrelin receptor, GHSR1a. The present study aimed to characterize effects of unacylated ghrelin on breast cancer cells, define its mechanism of action, and explore the therapeutic potential of unacylated ghrelin or analog AZP531. We report potent effects of unacylated ghrelin, at picomolar doses, on the growth of breast cancer cells, dependent on 3D culture and activation of Gi. Suppression of MAPK signaling by unacylated ghrelin leads to cell cycle arrest and apoptosis. AZP531 also suppresses the growth of breast cancer cells in vitro and in xenografts, and may be a novel approach for the safe and effective treatment of breast cancer.

cancer biology↗

bioRxiv2019

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of invasive ductal carcinoma. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods. We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying breast ductal carcinoma progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

bioinformatics↗