Interaction Between SNP Genotype and Efficacy of Anastrozole and Exemestane in Early‐Stage Breast Cancer

Cairns, Junmei; Kalari, Krishna R.; Ingle, James N.; Shepherd, Lois E.; Ellis, Matthew J.; Goss, Paul E.; Barman, Poulami; Carlson, Erin E.; Goodnature, Barbara; Goetz, Matthew P.; Weinshilboum, Richard M.; Gao, Huanyao; Wang, Liewei

doi:10.1002/cpt.2311

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…It is an antigen-uptake receptor on dendritic cells [ 32 ]. In other studies, LY75 was found to participate in controlling cellular phenotypes in breast cancer and thus their metastatic potential [ 33 ]. Regarding environmental factors, DEC-205, together with IL-6 and CD86, was found to be predictive biomarker for respiratory and immune effects of particulate matter [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Novel inflammatory biomarkers associated with stroke severity: results from a cross-sectional stroke cohort study

Braadt

Naumann

Freuer

et al. 2023

Neurol. Res. Pract.

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Background Stroke is a leading cause of mortality and disability worldwide and its occurrence is expected to increase in the future. Blood biomarkers have proven their usefulness in identification and monitoring of the disease. Stroke severity is a major factor for estimation of prognosis and risk of recurrent events, but knowledge on respective blood biomarkers is still scarce. Stroke pathophysiology comprises a multitude of ischemia-induced inflammatory and immune mediated responses. Therefore, the assessment of an immune-related panel in correlation with stroke severity seems promising. Methods In the present cross-sectional evaluation, a set of 92 blood biomarkers of a standardized immune panel were gathered (median 4.6 days after admission) and related to stroke severity measures, assessed at hospital admission of acute stroke patients. Multivariable logistic regression models were used to determine associations between biomarkers and modified Rankin Scale (mRS), linear regression models were used for associations with National Institute of Health Stroke Scale. Results 415 patients (mean age 69 years; 41% female) were included for biomarker analysis. C-type lectin domain family 4 member G (CLEC4G; OR = 2.89, 95% CI [1.49; 5.59], padj = 0.026, Cytoskeleton-associated protein 4 (CKAP4; OR = 2.38, 95% CI [1.43; 3.98], padj = 0.019), and Interleukin-6 (IL-6) (IL6; OR = 1.97, 95% CI [1.49; 2.62], padj < 0.001) were positively associated with stroke severity measured by mRS, while Lymphocyte antigen 75 (LY75; OR = 0.37, 95% CI [0.19; 0.73], padj = 0.049) and Integrin alpha-11 (ITGA11 OR = 0.24, 95% CI [0.14, 0.40] padj < 0.001) were inversely associated. When investigating the relationships with the NIHSS, IL-6 (β = 0.23, 95% CI [0.12, 0.33] padj = 0.001) and ITGA11 (β = − 0.60, 95% CI [− 0.83, − 0.37] padj < 0.001) were significantly associated. Conclusions Higher relative concentrations of plasma CLEC4G, CKAP4, and IL-6 were associated with higher stroke severity, whereas LY75 and ITGA11 showed an inverse association. Future research might show a possible use as therapeutic targets and application in individual risk assessments.

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Section: Discussionmentioning

confidence: 99%

Novel inflammatory biomarkers associated with stroke severity: results from a cross-sectional stroke cohort study

Braadt

Naumann

Freuer

et al. 2023

Neurol. Res. Pract.

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“…Specific siRNAs that targeted RPP25 , EMP1 , EXTL3 , EXOC2 , NUP37 , RPL13 , WBP2NL , RPS6 , GBP1 , JAK2 , and negative siRNA controls (Horizon Discovery) were transfected into MCF7 and T47D cells in 96-well plates using Lipofectamine RNAiMAX Transfection Reagent (Catalog No. 13778500, ThermoFisher Scientific, Waltham, MA) according to the vendor’s protocol [67] , [83] . Total RNA was extracted 48 h after transfection for RNA quantification.…”

Section: Methodsmentioning

confidence: 99%

Preclinical-to-Clinical Anti-Cancer Drug Response Prediction and Biomarker Identification Using TINDL

Hostallero

Wei

Wang

et al. 2023

Genomics, Proteomics &Amp; Bioinformatics

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Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

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“…205 Aromatase inhibitors, such as anastrozole and exemestane, inhibit the conversion of androstenedione to estrone in breast cancer patients, thereby significantly reducing plasma estrogen levels to inhibit the proliferation of ER-positive breast tumors. 206 However, there are few studies on the relationship between lncRNAs and the function of aromatase inhibitors. Until now, only lncRNA MIR2052HG has been reported to be associated with breast cancer resistance to aromatase inhibitors.…”

Section: Lncrnas Mediate Breast Cancer Invasion and Metastasismentioning

confidence: 99%

lncRNA and breast cancer: Progress from identifying mechanisms to challenges and opportunities of clinical treatment

Jin

Huang

et al. 2021

Molecular Therapy - Nucleic Acids

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Breast cancer is a malignant tumor that has a high mortality rate and mostly occurs in women. Although significant progress has been made in the implementation of personalized treatment strategies for molecular subtypes in breast cancer, the therapeutic response is often not satisfactory. Studies have reported that long non-coding RNAs (lncRNAs) are abnormally expressed in breast cancer and closely related to the occurrence and development of breast cancer. In addition, the high tissue and cell-type specificity makes lncRNAs particularly attractive as diagnostic biomarkers, prognostic factors, and specific therapeutic targets. Therefore, an in-depth understanding of the regulatory mechanisms of lncRNAs in breast cancer is essential for developing new treatment strategies. In this review, we systematically elucidate the general characteristics, potential mechanisms, and targeted therapy of lncRNAs and discuss the emerging functions of lncRNAs in breast cancer. Additionally, we also highlight the advantages and challenges of using lncRNAs as biomarkers for diagnosis or therapeutic targets for drug resistance in breast cancer and present future perspectives in clinical practice.Long non-coding RNAs (lncRNAs) are originally thought to be the noise of transcripts in the genome and have no biological function. Recently, the function of lncRNAs is beginning to attract widespread attention. 7 Increasing studies have shown that lncRNAs are involved in various aspects of cellular physiological processes, such as proliferation, differentiation, migration, and apoptosis, by regulating gene transcription and post-transcriptional processing. 8,9 In addition, lncRNAs are closely related to the occurrence, development, and prognosis of various cancers, such as breast, liver, colon, and lung cancer and even leukemia. [10][11][12][13] According to lncRNA genomic position, subcellular localization, and function, they can be divided into six types (Figure 1). 14 (1) Enhancer lncRNAs are derived from the promoter enhancer region, such as lncRNA-LEENE. 15 (2) Intron lncRNAs are transcribed from the intron region of the gene. The gene-coding protein completely contained the intron lncRNA, which can stabilize the transcription or regulate the alternative splicing of the coding gene. (3) Antisense lncRNA: its transcription orientation is opposite to the transcription orientation of the adjacent protein-encoding gene, such as lncTALAM1 and PDCD4-AS1. 16,17 (4) The sense lncRNA: its transcription orientation is the same as that of the adjacent protein-encoding gene, such as lncRNAGAS5 and ecCEPBA. 18,19 (5) Intergenic lncRNA, which can be transcribed between two protein-coding genes, is an autonomously transcribed

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Interaction Between SNP Genotype and Efficacy of Anastrozole and Exemestane in Early‐Stage Breast Cancer

Cited by 5 publications

References 43 publications

Novel inflammatory biomarkers associated with stroke severity: results from a cross-sectional stroke cohort study

Novel inflammatory biomarkers associated with stroke severity: results from a cross-sectional stroke cohort study

Preclinical-to-Clinical Anti-Cancer Drug Response Prediction and Biomarker Identification Using TINDL

lncRNA and breast cancer: Progress from identifying mechanisms to challenges and opportunities of clinical treatment

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