Table of Contents: Oncotarget’s Volume 12, Issue #12

Read short summaries of the latest oncology-focused research published in this week’s issue of Oncotarget, Volume 12, Issue 12.

Oncotarget's Table of Contents

Oncotarget’s Volume 12, Issue #12

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New Publications

Cover (Priority Research Paper): Frame-shift mediated reduction of gain-of-function p53 R273H and deletion of the R273H C-terminus in breast cancer cells result in replication-stress sensitivity

Origin: New York, United States

Institutions: City University of New York, Columbia University, Weill Cornell Medical College

Quote: “We recently documented that gain-of-function (GOF) mutant p53 (mtp53) R273H in triple negative breast cancer (TNBC) cells interacts with replicating DNA and PARP1.”

News: LY6 gene family presents a novel class of potential biomarkers associated with overall survival outcome of pancreatic ductal adenocarcinoma

Origin: New York, United States

Institution: Albert Einstein College of Medicine

Quote: “In this issue of Oncotarget, Russ et al. presents bioinformatic analysis of LY6 gene family in clinical samples of pancreatic ductal adenocarcinoma from TCGA collection.”

Editorial Paper: Neoantigen evolution in head and neck cancer progression: Where do we go from here?

Origin: Illinois, United States

Institution: University of Chicago Medicine

Quote: “Locoregional head and neck squamous cell carcinoma (HNSCC) can be treated with multimodality therapy, however approximately 50% of patients will develop recurrent disease either locally or distantly [1].”

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Editorial Paper: Unexpected zinc dependency of ferroptosis – what is in a name?

Origin: North Carolina, United States

Institution: Duke University

Quote: “The importance of metal homeostasis for our health is illustrated by extensive disease phenotypes associated with their abnormal accumulation or deficiency.”

Research Paper: Role for Fgr and Numb in retinoic acid-induced differentiation and G0 arrest of non-APL AML cells

Origin: New York, United States

Institution: Cornell University

Quote: “Retinoic acid (RA) is a fundamental regulator of cell cycle and cell differentiation. Using a leukemic patient-derived in vitro model of a non-APL AML, we previously found that RA evokes activation of a macromolecular signaling complex, a signalosome, built of numerous MAPK-pathway-related signaling molecules; and this signaling enabled Retinoic-Acid-Response-Elements (RAREs) to regulate gene expression that results in cell differentiation/cell cycle arrest.”

Research Paper: Dynamic cellular biomechanics in responses to chemotherapeutic drug in hypoxia probed by atomic force spectroscopy

Origin: North Dakota, United States

Institution: North Dakota State University

Quote: “The changes in cellular structure play an important role in cancer cell development, progression, and metastasis. By exploiting single-cell, force spectroscopy methods, we probed biophysical and biomechanical kinetics (stiffness, morphology, roughness, adhesion) of brain, breast, prostate, and pancreatic cancer cells with standard chemotherapeutic drugs in normoxia and hypoxia over 12–24 hours.”

Research Paper: Genomic clustering analysis identifies molecular subtypes of thymic epithelial tumors independent of World Health Organization histologic type

Origin: California, Indiana, United States

Institutions: Stanford University School of Medicine, Stanford Cancer Institute, Indiana University School of Medicine

Quote: “Further characterization of thymic epithelial tumors (TETs) is needed. Genomic information from 102 evaluable TETs from The Cancer Genome Atlas (TCGA) dataset and from the IU-TAB-1 cell line (type AB thymoma) underwent clustering analysis to identify molecular subtypes of TETs. Six novel molecular subtypes (TH1-TH6) of TETs from the TCGA were identified, and there was no association with WHO histologic subtype.”

Research Paper: Deep learning with deep convolutional neural network using FDG-PET/CT for malignant pleural mesothelioma diagnosis

Origin: Hyogo, Japan

Institutions: Hyogo College of Medicine, Kobe University Graduate School of Medicine

Quote: “This study analyzed an artificial intelligence (AI) deep learning method with a three-dimensional deep convolutional neural network (3D DCNN) in regard to diagnostic accuracy to differentiate malignant pleural mesothelioma (MPM) from benign pleural disease using FDG-PET/CT results.”

Click here to read Oncotarget’s Volume 12, Issue #12.


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