Identification of New Mechanisms of Cellular Response to Chemotherapy by Tracking Changes in Post-Translational Modifications by Ubiquitin and Ubiquitin-Like Proteins

Bonacci, Thomas; Audebert, Stéphane; Camoin, Luc; Baudelet, Emilie; Bidaut, Ghislain; Garcia, Manon; Witzel, Ini Isabée; Perkins, Neil D.; Borg, Jean-Paul; Iovanna, Juan; Soubeyran, Philippe

doi:10.1021/pr401258d

Cited by 26 publications

(29 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The in vivo ubiquitination assay was performed as described previously (Bonacci et al, 2014). Briefly, 293T cells were transfected with the indicated plasmids and harvested in PBS 24 hours following transfection.…”

Section: Methodsmentioning

confidence: 99%

FOXM1 Deubiquitination by USP21 Regulates Cell Cycle Progression and Paclitaxel Sensitivity in Basal-like Breast Cancer

et al. 2019

Self Cite

View full text Add to dashboard Cite

SUMMARYThe transcription factor FOXM1 contributes to cell cycle progression and is significantly upregulated in basal-like breast cancer (BLBC). Despite its importance in normal and cancer cell cycles, we lack a complete understanding of mechanisms that regulate FOXM1. We identified USP21 in an RNAi-based screen for deubiquitinases that control FOXM1 abundance. USP21 increases the stability of FOXM1, and USP21 binds and deubiquitinates FOXM1 in vivo and in vitro, indicating a direct enzyme-substrate relationship. Depleting USP21 downregulates the FOXM1 transcriptional network and causes a signifi-cant delay in cell cycle progression. Significantly, USP21 depletion sensitized BLBC cell lines and mouse xenograft tumors to paclitaxel, an anti-mitotic, frontline therapy in BLBC treatment. USP21 is the most frequently amplified deubiquitinase in BLBC patient tumors, and its amplification co-occurs with the upregulation of FOXM1 protein. Altogether, these data suggest a role for USP21 in the proliferation and potentially treatment of FOXM1-high, USP21-high BLBC.

show abstract

Section: Methodsmentioning

confidence: 99%

FOXM1 Deubiquitination by USP21 Regulates Cell Cycle Progression and Paclitaxel Sensitivity in Basal-like Breast Cancer

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…We also apply our attributed SBM approach to the protein interaction network presented in [32]. This network represents…”

Section: B Protein Interaction Network Resultsmentioning

confidence: 99%

Stochastic block models with multiple continuous attributes

et al. 2019

Self Cite

View full text Add to dashboard Cite

The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in which nodes have an associated vector of continuous attributes that are also used to learn the node-to-community assignments and corresponding SBM parameters. While this assumption is not realistic for every application, our model assumes that the attributes associated with the nodes in a network's community can be described by a common multivariate Gaussian model. In this augmented, attributed SBM, the objective is to simultaneously learn the SBM connectivity probabilities with the multivariate Gaussian parameters describing each community. While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes. This provides the flexibility in biological data to, for example, augment connectivity information with continuous measurements from multiple experimental modalities. Because the lack of labeled network data often makes community detection results difficult to validate, we highlight the usefulness of our model for two network prediction tasks: link prediction and collaborative filtering. As a result of fitting this attributed stochastic block model, one can predict the attribute vector or connectivity patterns for a new node in the event of the complementary source of information (connectivity or attributes, respectively). We also highlight two biological examples where the attributed stochastic block model provides satisfactory performance in the link prediction and collaborative filtering tasks.

show abstract

“…These studies show that phenotyping PDAC can predict the response to the treatment by immunotherapy [2] and the sensitivity to gemcitabine and erlonitib [7]. In addition, overexpression of CYP3A5 protein is responsible of drug resistance [24] and some post-translational modifications are also associated to chemoresistance [5]. It was in this context our hypothesis emerged: if the transcriptome of tumors is informative about the clinical outcome, it could also be informative about the chemosensitivity of tumoral cells to different drugs.…”

Section: The Transcriptome Of the Pdac Correlates With The Clinical Omentioning

confidence: 92%

Speeding towards individualized treatment for pancreatic cancer by taking an alternative road

Iovanna

Dusetti

2017

Cancer Letters

View full text Add to dashboard Cite

Accumulation of genetic mutations drives the development of pancreatic ductal adenocarcinoma (PDAC). Contrary to what it is expected, however, genetic analyses, no matter how precise or detailed, do not allow the identification of patient groups with different clinical outcomes or the selection of specific treatments. In fact, clinical outcome and sensitivity to treatments are associated with a given phenotype and are therefore associated at a transcriptomic level. In practical terms, therefore, the most appropriate readout for phenotypically stratifying PDACs should be transcriptomic and not genetic analysis. Recently data indicate that studying the expression of a selected gene set could inform selection of the most appropriate treatment for patients, moving towards an individualized medicine approach for this dismal disease. We are optimizing this approach by developing a platform based on obtaining organoids directly from surgical as well as endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) biopsies of tumors, which serve as a source of RNA, allowing determination of the transcription level of some informative genes. We are convinced that in the near future, the treatment of cancers will be preceded by an extensive molecular characterization of cancer cells in order to select the most appropriate treatments.

show abstract

Identification of New Mechanisms of Cellular Response to Chemotherapy by Tracking Changes in Post-Translational Modifications by Ubiquitin and Ubiquitin-Like Proteins

Cited by 26 publications

References 47 publications

FOXM1 Deubiquitination by USP21 Regulates Cell Cycle Progression and Paclitaxel Sensitivity in Basal-like Breast Cancer

FOXM1 Deubiquitination by USP21 Regulates Cell Cycle Progression and Paclitaxel Sensitivity in Basal-like Breast Cancer

Stochastic block models with multiple continuous attributes

Speeding towards individualized treatment for pancreatic cancer by taking an alternative road

Contact Info

Product

Resources

About