Wee1 kinase as a target for cancer therapy Do, Khanh; Doroshow, James H.; Kummar, Shivaani
Cell cycle (Georgetown, Tex.),
10/2013, Letnik:
12, Številka:
19
Journal Article
Recenzirano
Odprti dostop
Wee1, a protein kinase, regulates the G
2
checkpoint in response to DNA damage. Preclinical studies have elucidated the role of wee1 in DNA damage repair and the stabilization of replication forks, ...supporting the validity of wee1 inhibition as a viable therapeutic target in cancer. MK-1775, a selective and potent small-molecule inhibitor of wee1, is under clinical development as a potentiator of DNA damage caused by cytotoxic chemotherapies. We present a review of the role of wee1 in the cell cycle and DNA replication and summarize the clinical development to date of this novel class of anticancer agents.
When it comes to precision oncology, proteogenomics may provide better prospects to the clinical characterization of tumors, help make a more accurate diagnosis of cancer, and improve treatment for ...patients with cancer. This perspective describes the significant contributions of The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium to precision oncology and makes the case that proteogenomics needs to be fully integrated into clinical trials and patient care in order for precision oncology to deliver the right cancer treatment to the right patient at the right dose and at the right time.
Multiomics approaches that collectively integrate proteomics and genomics provide new layers into our understanding of the mechanisms of tumorigenesis across diverse cancer types and patients and provide the path towards treatment through rationalized precision oncology.
Combination therapy programs are the hallmark of the successful treatment of all forms of human malignancies. In this issue of Cell, Palmer and Sorger present data suggesting that cell culture ...results indicative of synergistic anticancer drug interactions rarely translate clinically and that the results of combination therapies in mouse models or human clinical trials, even if successful, are best explained by the independent activities of the individually administered drugs.
Combination therapy programs are the hallmark of the successful treatment of all forms of human malignancies. In this issue of Cell, Palmer and Sorger present data suggesting that cell culture results indicative of synergistic anticancer drug interactions rarely translate clinically and that the results of combination therapies in mouse models or human clinical trials, even if successful, are best explained by the independent activities of the individually administered drugs.
NADPH oxidases and cancer Roy, Krishnendu; Wu, Yongzhong; Meitzler, Jennifer L ...
Clinical science (1979),
06/2015, Letnik:
128, Številka:
12
Journal Article
Recenzirano
The mechanism by which reactive oxygen species (ROS) are produced by tumour cells remained incompletely understood until the discovery over the last 15 years of the family of NADPH oxidases (NOXs 1-5 ...and dual oxidases DUOX1/2) which are structural homologues of gp91phox, the major membrane-bound component of the respiratory burst oxidase of leucocytes. Knowledge of the roles of the NOX isoforms in cancer is rapidly expanding. Recent evidence suggests that both NOX1 and DUOX2 species produce ROS in the gastrointestinal tract as a result of chronic inflammatory stress; cytokine induction (by interferon-γ, tumour necrosis factor α, and interleukins IL-4 and IL-13) of NOX1 and DUOX2 may contribute to the development of colorectal and pancreatic carcinomas in patients with inflammatory bowel disease and chronic pancreatitis, respectively. NOX4 expression is increased in pre-malignant fibrotic states which may lead to carcinomas of the lung and liver. NOX5 is highly expressed in malignant melanomas, prostate cancer and Barrett's oesophagus-associated adenocarcinomas, and in the last it is related to chronic gastro-oesophageal reflux and inflammation. Over-expression of functional NOX proteins in many tissues helps to explain tissue injury and DNA damage from ROS that accompany pre-malignant conditions, as well as elucidating the potential mechanisms of NOX-related damage that contribute to both the initiation and the progression of a wide range of solid and haematopoietic malignancies.
Small-molecule inhibitors of PARP are thought to mediate their antitumor effects as catalytic inhibitors that block repair of DNA single-strand breaks (SSB). However, the mechanism of action of PARP ...inhibitors with regard to their effects in cancer cells is not fully understood. In this study, we show that PARP inhibitors trap the PARP1 and PARP2 enzymes at damaged DNA. Trapped PARP-DNA complexes were more cytotoxic than unrepaired SSBs caused by PARP inactivation, arguing that PARP inhibitors act in part as poisons that trap PARP enzyme on DNA. Moreover, the potency in trapping PARP differed markedly among inhibitors with niraparib (MK-4827) > olaparib (AZD-2281) >> veliparib (ABT-888), a pattern not correlated with the catalytic inhibitory properties for each drug. We also analyzed repair pathways for PARP-DNA complexes using 30 genetically altered avian DT40 cell lines with preestablished deletions in specific DNA repair genes. This analysis revealed that, in addition to homologous recombination, postreplication repair, the Fanconi anemia pathway, polymerase β, and FEN1 are critical for repairing trapped PARP-DNA complexes. In summary, our study provides a new mechanistic foundation for the rational application of PARP inhibitors in cancer therapy.
Highlights • Redox-related transcription factor upregulation characterizes inflammation-related cancer. • NADPH oxidase-dependent ROS are associated with inflammation and tumor promotion. • ...Interdicting ROS-mediated inflammation is a novel cancer prevention strategy.
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, ...most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.
Abstract
Background
In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample ...comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis.
Methods
In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis.
Results
Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data.
Conclusion
We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.
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