Expression of immunoproteasome genes is regulated by cell-intrinsic and –extrinsic factors in human cancers

Rouette, Alexandre; Trofimov, Assya; Haberl, David; Boucher, Geneviève; Lavallée, Vincent-Philippe; D’Angelo, Giovanni; Hébert, Josée; Sauvageau, Guy; Lemieux, Sébastien; Perreault, Claude

doi:10.1038/srep34019

Cited by 74 publications

(86 citation statements)

References 54 publications

(65 reference statements)

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“…For Bortezomib, we observed significant associations for different proteasome subunits such as subunit alpha (PSMA) and beta (PSMB). These subunits have been shown to be key players across different cancers (Rouette et al, 2016;Tsvetkov et al, 2017;Li et al, 2017). We also observed significant associations for RELA (also known as Transcription Factor p65) in all of the studied cancers which aligns with its oncogenic role across different cancers (Collignon et al, 2018), and moreover, with its reported associations with Bortezomib in breast cancer (Hideshima et al, 2014), prostate cancer (Manna et al, 2013), and lung cancer (Zhao et al, 2015).…”

Section: Aitl Predictions For Tcga Patients Have Significant Associatsupporting

confidence: 76%

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: 76%

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

Noghabi

Peng

Zolotareva

et al. 2020

Preprint

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“…Carfilzomib 3 is the most potent compound displaying fast onset and sustained inhibition at 13 h pre‐incubation time while the ketoamides show slower onset of inhibition highest for 27 and lower for 9 and 7 . However, in THP‐1 cells the inhibitors show altered potency in inhibiting conversion of 30 , presumably as iCP expression is higher than in MV4‐11 and Jurkat cells . After short incubation times 3 is the most potent compound, while inhibition is decreased with incubation time, leading to lower inhibition after 13 h incubation time for 9 and 27 .…”

Section: Resultsmentioning

confidence: 97%

“…Carfilzomib 3 is the most potent compound tested in the acute monocytic leukemia‐derived cell line MV4‐11, whereas 27 is more potent in Jurkat cells than 3 , a cell line derived from T‐cell leukemia. Interestingly, both inhibitors show much reduced activity in THP‐1 cells, also derived from acute monocytic leukemia expressing presumably iCP correlating with more potent inhibition of β5c over β5i by 3 and 27 . Furthermore, inhibition of cellular conversion of substrate 30 correlates with altered sensitivity in these cell lines.…”

Section: Discussionmentioning

confidence: 99%

Cell‐Based Optimization of Covalent Reversible Ketoamide Inhibitors Bridging the Unprimed to the Primed Site of the Proteasome β5 Subunit

et al. 2019

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The ubiquitin‐proteasome system (UPS) is an established therapeutic target for approved drugs to treat selected hematologic malignancies. While drug discovery targeting the UPS focuses on irreversibly binding epoxyketones and slowly‐reversibly binding boronates, optimization of novel covalent‐reversibly binding warheads remains largely unattended. We previously reported α‐ketoamides to be a promising reversible lead motif, yet the cytotoxic activity required further optimization. This work focuses on the lead optimization of phenoxy‐substituted α‐ketoamides combining the structure‐activity relationships from the primed and the non‐primed site of the proteasome β5 subunit. Our optimization strategy is accompanied by molecular modeling, suggesting occupation of P1′ by a 3‐phenoxy group to increase β5 inhibition and cytotoxic activity in leukemia cell lines. Key compounds were further profiled for time‐dependent inhibition of cellular substrate conversion. Furthermore, the α‐ketoamide lead structure 27 does not affect escape response behavior in Danio rerio embryos, in contrast to bortezomib, which suggests increased target specificity.

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“…Analysis using the MALDI-TOF mass spectrophotometry (MS) based on two-dimensional polyacrylamide gel electrophoresis (2-DE) of breast cancer tissues showed over-expression of these proteasomes subunit proteins, including PSMD2 and PSMA1, when compared to normal adjacent tissue, which enhanced the action of ubiquitin-proteasome pathway in breast cancer tissues [31]. Analysis of thousands of tumor samples from The Cancer Genome Atlas (TCGA) reported an increased expression of proteasomes such as PSMB6, PSMB7 [32], and PSMD2 [30] in most cancer types. Other members of the proteasomal protein of this network such as the PSMB7 was reported to be over-expressed in colorectal cancer [33], whilst PSME2 along with PSME3 were reported to be significantly enriched in several biological processes and pathways including cell adhesion, adherent junction organization, regulation of autophagy, cellular protein localization, the cell cycle, and the apoptosis pathway [34].…”

Section: Discussionmentioning

confidence: 99%

Advancing the Role of Gamma-Tocotrienol as Proteasomes Inhibitor: A Quantitative Proteomic Analysis of MDA-MB-231 Human Breast Cancer Cells

et al. 2019

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Tocotrienol, an analogue of vitamin E has been known for its numerous health benefits and anti-cancer effects. Of the four isoforms of tocotrienols, gamma-tocotrienol (γT3) has been frequently reported for their superior anti-tumorigenic activity in both in vitro and in vivo studies, when compared to its counterparts. In this study, the effect of γT3 treatment in the cytoplasmic and nuclear fraction of MDA-MB-231 human breast cancer cells were assessed using the label-free quantitative proteomics analysis. The cytoplasmic proteome results revealed the ability of γT3 to inhibit a group of proteasome proteins such as PSMA, PSMB, PSMD, and PSME. The inhibition of proteasome proteins is known to induce apoptosis in cancer cells. As such, the findings from this study suggest γT3 as a potential proteasome inhibitor that can overcome deficiencies in growth-inhibitory or pro-apoptotic molecules in breast cancer cells. The nuclear proteome results revealed the involvement of important nuclear protein complexes which hardwire the anti-tumorigenesis mechanism in breast cancer following γT3 treatment. In conclusion, this study uncovered the advancing roles of γT3 as potential proteasomes inhibitor that can be used for the treatment of breast cancer.

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Expression of immunoproteasome genes is regulated by cell-intrinsic and –extrinsic factors in human cancers

Cited by 74 publications

References 54 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

Cell‐Based Optimization of Covalent Reversible Ketoamide Inhibitors Bridging the Unprimed to the Primed Site of the Proteasome β5 Subunit

Advancing the Role of Gamma-Tocotrienol as Proteasomes Inhibitor: A Quantitative Proteomic Analysis of MDA-MB-231 Human Breast Cancer Cells

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