Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification

Yanovich, Gali; Agmon, Hadar; Harel, Michal; Sonnenblick, Amir; Peretz, Tamar; Geiger, Tamar

doi:10.1158/0008-5472.can-18-1079

Cited by 67 publications

(63 citation statements)

References 48 publications

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“…1D). In agreement with previous reports (Pozniak et al, 2016;Tyanova et al, 2016;Yanovich et al, 2018), the proteomes of luminal and basal cell lines mostly clustered together within each group and the separation between the two groups was mostly driven by the differential expression of proteins involved in metabolic processes and other known proteins such as FOXA1, Vimentin, CD44, HER2, MET, and EGFR (Sup. Fig.…”

Section: The Proteomes Of Breast Cancer and Normal Breast Cell Linessupporting

confidence: 92%

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

Tognetti

Gábor

Yang

et al. 2020

Preprint

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Although genetic and epigenetic abnormalities in breast cancer have been extensively studied, it remains difficult to identify those patients who will respond to particular therapies. This is due in part to our lack of understanding of how the variability of cellular signaling affects drug sensitivity. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data – on more than 80 million single cells from 4,000 conditions – were used to fit mechanistic signaling network models that provide unprecedented insights into the biological principles of how cancer cells process information. Our dynamic single-cell-based models more accurately predicted drug sensitivity than static bulk measurements for drugs targeting the PI3K-MTOR signaling pathway. Finally, we identified genomic features associated with drug sensitivity by using signaling phenotypes as proxies, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. This provides proof of principle that single-cell measurements and modeling could inform matching of patients with appropriate treatments in the future.One-linerSingle-cell proteomics coupled to perturbations improves accuracy of breast tumor drug sensitivity predictions and reveals mechanisms of sensitivity and resistance.HIGHLIGHTSMass cytometry study of signaling responses of 62 breast cancer cell lines and five lines from healthy tissue to EGF stimulation with or without perturbation with five kinase inhibitors.Single-cell signaling features and mechanistic signaling network models predicted drug sensitivity.Mechanistic signaling network models deepen the understanding of drug resistance and sensitivity mechanisms.We identify drug sensitivity-predictive genomic features via proxy signaling phenotypes.

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Section: The Proteomes Of Breast Cancer and Normal Breast Cell Linessupporting

confidence: 92%

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

Tognetti

Gábor

Yang

et al. 2020

Preprint

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“…We applied the WGCNA co-expression analysis to parts of a cancer proteomics dataset 43 , following the recommended workflow 7 from within Perseus. Biweight midcorrelation, a robust alternative to Pearson correlation, was chosen to calculate correlations between all pairs of proteins.…”

Section: Co-expression Clustering and Clinical Datamentioning

confidence: 99%

“…7). Protein quantification data and clinical annotation were obtained from Yanovic et al 43 . SILAC ratios were first transformed to log(light/heavy).…”

Section: Co-expression Analysis Of a Clinical Proteomics Datasetmentioning

confidence: 99%

“…Phenotype data can be correlated with representative co-expression module profiles and provide a high-level interpretation of the modules. c Parameter selection of the power parameter for the Yanovich et al 43 dataset (Supplementary Table 3 and Online Methods). The lowest power reaching close to a high scale-free fit index of 0.9 (red line) was selected.…”

Section: Implementation Of Ksea In Perseusmentioning

confidence: 99%

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A network module for the Perseus software for computational proteomics facilitates proteome interaction graph analysis

Rudolph

Cox

2018

Preprint

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Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g. with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plug-in architecture in a multi-lingual way, integrating analyses in C#, Python and R and is freely available at http://www.perseus-framework.org.

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“…As it is reported, based on proteomic findings, breast cancer classification is affected by proteomic investigation. 7,8 Besides proteomics, bioinformatics affects clinical research, especially in the field of cancer. 9 Network analysis is a method that provides valuable data which are useful to screen the analyzed and ranked subjects based on interactions between the members of a sample.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the SRC Inhibition Role in the Efficacy of Breast Cancer Radiotherapy

2019

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Introduction: Radiotherapy (RT) is an effective therapeutic method for preventing the recurrence of breast cancer after surgery. The development and improvement of RT are of interest to scientists. Possible improvement of RT via study of dysregulated proteins of breast cancer cell line MDA-MB-231 which is exposed to 10 Gray (Gy) radiation is aim of this study. Methods: Using protein-protein interaction (PPI) network analysis by means of running Cytoscape software via the STRING database, the up-regulated proteins of MDA-MB-231 breast cancer cells irradiated by a single and fractioned 10 Gy 137Cs γ-radiation were analyzed. The network was analyzed by using the Network analyzer to characterize the central genes. The action map was mapped for the queried genes and the added neighbors via CluePedia-STRING ACTIONS-v10.5- 20.11.2017. Results: The 14 differentially expressed proteins (DEPs) plus 10 neighbors interacted to construct a network. Among the 14 queried DEPs, FN1, CSPG4, LRP1, GSN, RTN4, and CTSD were highlighted as a complex set in the analysis. The analysis revealed that SRC as an added neighbor was activated by the critical DEPs. The activation of other oncogenes like AKT1 was also determined. Conclusion: The results indicate that the inhibition of SRC activity or the inhibition of its activators is a useful function of breast cancer RT

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Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification

Cited by 67 publications

References 48 publications

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

A network module for the Perseus software for computational proteomics facilitates proteome interaction graph analysis

Assessment of the SRC Inhibition Role in the Efficacy of Breast Cancer Radiotherapy

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