A Pharmacogenetic Prediction Model of Progression‐Free Survival in Breast Cancer using Genome‐Wide Genotyping Data from <scp>CALGB</scp> 40502 (Alliance)

Rashkin, Sara R.; Chua, Katherina C.; Ho, Carol; Mulkey, Flora; Jiang, Chen; Mushiroda, Tasei; Kubo, Michiaki; Friedman, Paula N.; Rugo, Hope S.; McLeod, Howard L.; Ratain, Mark J.; Castillos, Francisco; Naughton, Michael; Overmoyer, Beth; Toppmeyer, Deborah; Witte, John S.; Owzar, Kouros; Kroetz, Deanna L.

doi:10.1002/cpt.1241

Cited by 11 publications

(18 citation statements)

References 31 publications

(57 reference statements)

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“…Patient-specific cell lines allow us to realize personal therapy as well as to find out new characteristic of carcinogenesis and metastasis. This approach can give the direct information of the tumor cell’s sensitivity to defined drugs in contrast to the prediction methods based on genome-wide genotyping data [4].…”

Section: Introductionmentioning

confidence: 99%

Establishment of primary human breast cancer cell lines using “pulsed hypoxia” method and development of metastatic tumor model in immunodeficient mice

et al. 2019

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Background Among breast cancer (BC) patients the outcomes of anticancer therapy vary dramatically due to the highly heterogeneous molecular characteristics of BC. Therefore, an extended panel of BC cell lines are required for in vitro and in vivo studies to find out new characteristic of carcinogenesis and metastasis. The purpose of this study was to develop patient-derived BC cell cultures and metastatic tumor models representing a tool for personal therapy and translational research. Methods Breast cancer cells were prepared by optimizing technique from tumor samples. We used real-time RT-PCR, flow cytometry, western blotting, cytotoxicity assay, karyotyping and fluorescent and electron microscopy analyses to characterize the established cell lines. BC xenografts in scid mice were used for in vivo tumorigenicity studies. Results The technique of preparing primary cells was optimized and this resulted in a high output of viable and active proliferated cells of nine patient-derived breast cancer cell lines and one breast non-malignant cell line. High E-cadherine and EpCAM expression correlated positively with epithelial phenotype while high expression of N-cadherine and Vimentin were shown in cells with mesenchymal phenotype. All mesenchymal-like cell lines were high HER3-positive—up to 90%. More interesting than that, is that two cell lines under specific culturing conditions (pulsed hypoxia and conditioned media) progressively transformed from mesenchymal to epithelial phenotypes displaying the expression of respective molecular markers proving that the mesenchymal-to-epithelial transition occurred. Becoming epithelial, these cells have lost HER3 and decreased HER2 membrane receptors. Three of the established epithelial cancer cell lines were tumorigenic in SCID mice and the generated tumors exhibited lobules-like structures. Ultrastructure analysis revealed low-differentiate phenotype of tumorigenic cell lines. These cells were in near-triploid range with multiple chromosome rearrangements. Tumorigenic BrCCh4e cells, originated from the patient of four-course chemotherapy, initiated metastasis when they were grafted subcutaneous with colonization of mediastinum lymph nodes. Conclusions The developed BC cells metastasizing to mediastinum lymph nodes are a relevant model for downstream applications. Moreover, our findings demonstrate that pulsed hypoxia induces transformation of primary fibroblastoid breast cancer cells to epithelial-like cells and both of these cultures—induced and original—don’t show tumor initiating capacity. Electronic supplementary material The online version of this article (10.1186/s12935-019-0766-5) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Establishment of primary human breast cancer cell lines using “pulsed hypoxia” method and development of metastatic tumor model in immunodeficient mice

et al. 2019

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“…The fit is optimal with a k-mer size of 3. In regards to feature sizes, underfitting occurs with feature sizes [7,8,9] and below. In contrast, overfitting occurs with feature size [10,11,12], and possibly [9,10,11].…”

Section: Resultsmentioning

confidence: 99%

“…Many studies have associated variants such as SNPs with disease progression, e.g., cardiovascular risks [4], obesity [5], hypoxia (a condition characterized by a limited oxygen supply) [6,7], and coronary artery disease [8]. Furthermore, most existing work has focused on developing disease prediction models based on SNPs associated with a single disease only, for example, breast cancer [9], inflammatory bowel disease [10], and obesity [11]. These studies use each SNP as a feature.…”

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confidence: 99%

A novel feature extraction method based on highly expressed SNPs for tissue-specific gene prediction

Dhaliwal

Wagner

2021

J Big Data

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Background Gene expression provides a means for an organism to produce gene products necessary for the organism to live. Variation in the significant gene expression levels can distinguish the gene and the tissue in which the gene is expressed. Tissue-specific gene expression, often determined by single nucleotide polymorphisms (SNPs), provides potential molecular markers or therapeutic targets for disease progression. Therefore, SNPs are good candidates for identifying disease progression. The current bioinformatics literature uses gene network modeling to summarize complex interactions between transcription factors, genes, and gene products. Here, our focus is on the SNPs’ impact on tissue-specific gene expression levels. To the best of our knowledge, we are not aware of any studies that distinguish tissue-specific genes using SNP expression levels. Method We propose a novel feature extraction method based on highly expressed SNPs using k-mers as features. We also propose optimal k-mer and feature sizes used in our approach. Determining the optimal sizes is still an open research question as it depends on the dataset and purpose of the analysis. Therefore, we evaluate our algorithm’s performance on a range of k-mer and feature sizes using a multinomial naive Bayes (MNB) classifier on genes in the 49 human tissues from the Genotype-Tissue Expression (GTEx) portal. Conclusions Our approach achieves practical performance results with k-mers of size 3. Based on the purpose of the analysis and the number of tissue-specific genes under study, feature sizes [7, 8, 9] and [8, 9, 10] are typically optimal for the machine learning model.

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“…increased the area under the receiver operating curve for progression-free survival from 0.64 to 0.81. 162 It has also been shown that the cumulative incidence of venous thromboembolism in patients with breast cancer is independently increased by chemotherapy and a PRS consisting of 9 genetic SNPs. Importantly, the influence of chemotherapy and high PRS (>95 th percentile)…”

Section: Rare Variationmentioning

confidence: 99%

“…Nevertheless, in cardiology, for example, a PRS of 61 common variants was a significant predictor of drug‐induced torsade de pointes 161 . Moreover, in patients with advanced breast cancer in a clinical trial of paclitaxel, nab‐paclitaxel and ixabepilone (microtubule targeting agents), a set of 13 variants increased the area under the receiver operating curve for progression‐free survival from 0.64 to 0.81 162 . It has also been shown that the cumulative incidence of venous thromboembolism in patients with breast cancer is independently increased by chemotherapy and a PRS consisting of 9 genetic SNPs.…”

Section: Moving Beyond Common Variant and Single Gene Pharmacogenomicsmentioning

confidence: 99%

Pharmacogenomics of anticancer drugs: Personalising the choice and dose to manage drug response

Carr

Turner

Pirmohamed

2020

Brit J Clinical Pharma

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The field of pharmacogenomics has made great strides in oncology over the last 20 years and indeed a significant number of pre‐emptive genetic tests are now routinely undertaken prior to anticancer drug administration. Many of these gene–drug interactions are the fruits of candidate gene and genome‐wide association studies, which have largely focused on common genetic variants (allele frequency>1%). Examples where there is clinical utility include genotyping or phenotyping for G6PD to prevent rasburicase‐induced RBC haemolysis, and TPMT to prevent thiopurine‐induced bone marrow suppression. Other associations such as CYP2D6 status in determining the efficacy of tamoxifen are more controversial because of contradictory evidence from different sources, which has led to variability in the implementation of testing. As genomic technology becomes ever cheaper and more accessible, we must look to the additional data our genome can provide to explain interindividual variability in anticancer drug response. Clearly genes do not act on their own and it is therefore important to investigate genetic factors in conjunction with clinical factors, interacting concomitant drug therapies and other factors such as the microbiome, which can all affect drug disposition. Taking account of all of these factors, in conjunction with the somatic genome, is more likely to provide better predictive accuracy in determining anticancer drug response, both efficacy and safety. This review summarises the existing knowledge related to the pharmacogenomics of anticancer drugs and discusses areas of opportunity for further advances in personalisation of therapy in order to improve both drug safety and efficacy.

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A Pharmacogenetic Prediction Model of Progression‐Free Survival in Breast Cancer using Genome‐Wide Genotyping Data from CALGB 40502 (Alliance)

Cited by 11 publications

References 31 publications

Establishment of primary human breast cancer cell lines using “pulsed hypoxia” method and development of metastatic tumor model in immunodeficient mice

Establishment of primary human breast cancer cell lines using “pulsed hypoxia” method and development of metastatic tumor model in immunodeficient mice

A novel feature extraction method based on highly expressed SNPs for tissue-specific gene prediction

Pharmacogenomics of anticancer drugs: Personalising the choice and dose to manage drug response

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