Microtubule-associated protein tau is associated with the resistance to docetaxel in prostate cancer cell lines

Yang, Jian; Yu, Yang; Liu, Wei; Li, Zhi; Wei, Zuoan; Jiang, Rongjiang

doi:10.2147/rru.s118966

Cited by 27 publications

(30 citation statements)

References 19 publications

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“…The obtained results are in concordance with previous studies. For example, we observed that Microtubule-Associated Proteins (MAPs) were significant for Docetaxel and Paclitaxel in the studied cancers which aligns with previous research on this family of proteins (Yang et al, 2017;Smoter et al, 2011;Bhat and Setaluri, 2007). For Bortezomib, we observed significant associations for different proteasome subunits such as subunit alpha (PSMA) and beta (PSMB).…”

Section: Aitl Predictions For Tcga Patients Have Significant Associatsupporting

confidence: 89%

“…Since the majority of the studied baselines cannot use the continuous IC50 values in the source domain, binarized IC50 labels provided by (Iorio et al, 2016) using the Waterfall approach (Barretina et al, 2012) were used to train them. Finally, for the minimax optimization, a gradient reversal layer was employed by AITL and the adversarial baselines (Ganin et al, 2016) which is a well-established approach in domain adaptation (Zhang et al, 2019;Long et al, 2018;You et al, 2019). We performed 3-fold cross validation in the experiments to tune the hyper-parameters of AITL and the baselines based on the AUROC.…”

Section: Does Aitl Outperform a Baseline For Inductive Transfer Learnmentioning

confidence: 99%

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AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

Noghabi

Peng

Zolotareva

et al. 2020

Preprint

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Motivation: the goal of pharmacogenomics is to predict drug response in patients using their singleor multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: 1) in the input space, the gene expression data due to difference in the basic biology, and 2) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution. Results: We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately. Availability of codes and supplementary material: https://github.com/hosseinshn/AITL Contact: ccollins@prostatecentre.com and ester@cs.sfu.ca © 1

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Section: Aitl Predictions For Tcga Patients Have Significant Associatsupporting

confidence: 89%

Section: Does Aitl Outperform a Baseline For Inductive Transfer Learnmentioning

confidence: 99%

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

Noghabi

Peng

Zolotareva

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…However, many patients show continued resistance to taxane-based chemotherapy. Multiple molecular players are linked to taxane resistance; these include efflux pumps (10), DNA repair mechanisms (11), and microtubule alterations (12). In addition, emerging evidence indicates that both non-coding RNAs and epigenetic effectors may also be implicated in taxane resistance (7).…”

Section: Introductionmentioning

confidence: 99%

Downregulation of GPSM2 is associated with primary resistance to paclitaxel in breast cancer

Zhang

Deng

et al. 2020

Oncol Rep

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Paclitaxel is one of the most effective chemotherapy drugs for breast cancer worldwide but 20-30% patients show primary resistance to the drug. Screening and identification of markers that facilitate effective and rapid prediction of sensitivity to paclitaxel is therefore an urgent medical requirement. In the present study, G protein signaling modulator 2 (GPSM2) mRNA levels were significantly associated with taxane sensitivity in experiments based on the Gene Expression Omnibus (GEO) online database. Immunohistochemical analysis consistently revealed a significant association of GPSM2 protein levels with paclitaxel sensitivity in breast cancer patients. Knockdown of GPSM2 reduced the sensitivity of breast cancer cells to paclitaxel via regulation of the cell cycle. Animal experiments further corroborated our in vitro findings. These results suggest that GPSM2 plays an important role in breast cancer resistance, supporting its utility as a potential target for improving drug susceptibility in patients as well as a marker of paclitaxel sensitivity.

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“…This stabilizes microtubules and influences transportation of cellular secretory proteins. Moreover, MAPT has been reported to accelerate cancer cell growth [ 119 ], while its inactivation through gene knockdown suppressed cell proliferation [ 120 ]. Therefore, it is speculated that the diverse affinity of a natural drug to different functional protein targets may be one of the key factors for different selectivity profiles on VSMCs or VECs.…”

Section: Selective Inhibition Of Vsmcs Versus Vecs Shows Significamentioning

confidence: 99%

Plant‐Derived Products for Treatment of Vascular Intima Hyperplasia Selectively Inhibit Vascular Smooth Muscle Cell Functions

Al-ani

Pan

et al. 2018

Evidence-Based Complementary and Alternative Medicine

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Natural products are used widely for preventing intimal hyperplasia (IH), a common cardiovascular disease. Four different cells initiate and progress IH, namely, vascular smooth muscle, adventitial and endothelial cells, and circulation or bone marrow-derived cells. Vascular smooth muscle cells (VSMCs) play a critical role in initiation and development of intimal thickening and formation of neointimal hyperplasia. In this review, we describe the different originating cells involved in vascular IH and emphasize the effect of different natural products on inhibiting abnormal cellular functions, such as VSMC proliferation and migration. We further present a classification for the different natural products like phenols, flavonoids, terpenes, and alkaloids that suppress VSMC growth. Abnormal VSMC physiology involves disturbance in MAPKs, PI3K/AKT, JAK-STAT, FAK, and NF-κB signal pathways. Most of the natural isolate studies have revealed G1/S phase of cell cycle arrest, decreased ROS production, induced cell apoptosis, restrained migration, and downregulated collagen deposition. It is necessary to screen optimal drugs from natural sources that preferentially inhibit VSMC rather than vascular endothelial cell growth to prevent early IH, restenosis following graft implantation, and atherosclerotic diseases.

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Microtubule-associated protein tau is associated with the resistance to docetaxel in prostate cancer cell lines

Cited by 27 publications

References 19 publications

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

Downregulation of GPSM2 is associated with primary resistance to paclitaxel in breast cancer

Plant‐Derived Products for Treatment of Vascular Intima Hyperplasia Selectively Inhibit Vascular Smooth Muscle Cell Functions

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