Pan-cancer proteomic map of 949 human cell lines

Gonçalvès, Emanuel; Poulos, Rebecca C.; Cai, Zhaoxiang; Barthorpe, Syd; Manda, Srikanth; Lucas, Natasha; Beck, Alexandra; Bucio-Noble, Daniel; Dausmann, Michael; Hall, Caitlin; Hecker, Michael; Koh, Jennifer; Lightfoot, Howard; Mahboob, Sadia; Mali, Iman; Morris, James A.; Richardson, Laura L.; Seneviratne, Akila J.; Shepherd, Rebecca; Sykes, Erin; Thomas, Frances; Valentini, Sara; Williams, Steven G.; Wu, Ya-Na; Xavier, Dylan; Mackenzie, Karen L.; Hains, Peter G.; Tully, Brett; Robinson, Phillip J.; Zhong, Qing; Garnett, Mathew J.; Reddel, Roger R.

doi:10.1016/j.ccell.2022.06.010

Cited by 85 publications

(107 citation statements)

References 87 publications

(179 reference statements)

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“…This data-driven approach was used to estimate relative cell proliferation on proteomics data sets with no growth rates reported: the proteomes of the CRC65 cancer cell lines [ 9 ]; the Cancer Cell Line Encyclopedia (CCLE) that comprises the CRC65, NCI60 and other cell lines [ 12 , 20 ]; and the recently published Pan-Cancer panel [ 21 ] ( S3 Fig shows the cell lines present in each panel). For each data set, we first calculated the pseudo-proliferation indexes, and next the correlations of each protein to this proxy for cell proliferation.…”

Section: Resultsmentioning

confidence: 99%

“…The data from Gonçalves et al . [ 21 ] were retrieved from the S1 Table provided in the paper (6,692 protein groups with a minimum of 2 quantified peptides) and normalized using median subtraction before correlation calculation. We removed the protein groups quantified in less than 10 cell lines (6,451 protein groups remaining), and the four following cell lines from the analysis because their names were too similar and could create mismatch between the different data sets: “TT”, “T-T”, “KMH-2” and “KM-H2”.…”

Section: Methodsmentioning

confidence: 99%

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Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies

Locard‐Paulet

Palasca²,

Jensen³

2022

PLoS Comput Biol

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Hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Changes in gene expression can also be due to confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. We combined the analysis of 1,076 and 1,040 cell lines in five proteomics and three transcriptomics data sets to identify 157 genes that correlate with cell proliferation rates. These include actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. This signature of cell proliferation is a valuable resource when analyzing high-throughput data showing changes in proliferation across conditions. We show how to use this resource to help in interpretation of in vitro drug screens and tumor samples. It informs on differences of cell proliferation rates between conditions where such information is not directly available. The signature genes also highlight which hits in a screen may be due to proliferation changes; this can either contribute to biological interpretation or help focus on experiment-specific regulation events otherwise buried in the statistical analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies

Locard‐Paulet

Palasca²,

Jensen³

2022

PLoS Comput Biol

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“…Multi-omic and drug response data collection. For drug response prediction, multi-omic data were retrieved from 941 CLP 27 and 696 CCLE cell lines 28 For drug response prediction analysis with proteomic data, the ProCan-DepMapSanger dataset 3 was added to the CLP ( CLP + ProCan-DepMapSanger = CLP + ) and the CCLE's proteomic dataset 29 was also used (CCLE + CCLE proteomic data = CCLE + ). The ProCan-DepMapSanger and CCLE proteomic datasets contain 8,498 and 12,755 protein features, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…To date, advances in precision medicine applications in cancer have been driven primarily by the availability of relevant technologies to measure genomic and transcriptomic data 2 . Recent advances in mass spectrometry have enabled large-scale proteomic data to be acquired in human cancer tissues, organoids and cell lines, contributing to expanded possibilities for multi-omic data analysis [3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

Transformer-based deep learning integrates multi-omic data with cancer pathways

Cai

Poulos

Aref

et al. 2022

Preprint

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Multi-omic data analysis incorporating machine learning has the potential to significantly improve cancer diagnosis and prognosis. Traditional machine learning methods are usually limited to omic measurements, omitting existing domain knowledge such as the biological networks that link molecular entities in various omic data types. We develop a Transformer-based explainable deep learning model, DeePathNet, which integrates cancer-specific pathway information into multi-omic data analysis. Using a variety of big datasets, including ProCan-DepMapSanger, CCLE and TCGA, we show that DeePathNet outperforms traditional methods for the prediction of drug response and classification of cancer type and subtype. Combining biomedical knowledge and state-of-the-art deep learning methods, DeePathNet enables biomarker discovery at the pathway level, maximising the power of data-driven approaches to cancer research. DeePathNet is available on GitHub at https://github.com/CMRI-ProCan/DeePathNet.

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“…Of note, these studies reported relatively low number of identifiable proteins, ranging from 193 to 1991. A proteomic study of 949 cancer cell lines from 28 tissue types, including 57 SCLC cell lines, was published only recently, in which a total of 8498 proteins were quantified 21 …”

Section: Introductionmentioning

confidence: 99%

In‐depth proteomic analysis reveals unique subtype‐specific signatures in human small‐cell lung cancer

Szeitz

Woldmar

Valkó

et al. 2022

Clinical & Translational Med

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Background Small‐cell lung cancer (SCLC) molecular subtypes have been primarily characterized based on the expression pattern of the following key transcription regulators: ASCL1 (SCLC‐A), NEUROD1 (SCLC‐N), POU2F3 (SCLC‐P) and YAP1 (SCLC‐Y). Here, we investigated the proteomic landscape of these molecular subsets with the aim to identify novel subtype‐specific proteins of diagnostic and therapeutic relevance. Methods Pellets and cell media of 26 human SCLC cell lines were subjected to label‐free shotgun proteomics for large‐scale protein identification and quantitation, followed by in‐depth bioinformatic analyses. Proteomic data were correlated with the cell lines’ phenotypic characteristics and with public transcriptomic data of SCLC cell lines and tissues. Results Our quantitative proteomic data highlighted that four molecular subtypes are clearly distinguishable at the protein level. The cell lines exhibited diverse neuroendocrine and epithelial–mesenchymal characteristics that varied by subtype. A total of 367 proteins were identified in the cell pellet and 34 in the culture media that showed significant up‐ or downregulation in one subtype, including known druggable proteins and potential blood‐based markers. Pathway enrichment analysis and parallel investigation of transcriptomics from SCLC cell lines outlined unique signatures for each subtype, such as upregulated oxidative phosphorylation in SCLC‐A, DNA replication in SCLC‐N, neurotrophin signalling in SCLC‐P and epithelial–mesenchymal transition in SCLC‐Y. Importantly, we identified the YAP1‐driven subtype as the most distinct SCLC subgroup. Using sparse partial least squares discriminant analysis, we identified proteins that clearly distinguish four SCLC subtypes based on their expression pattern, including potential diagnostic markers for SCLC‐Y (e.g. GPX8, PKD2 and UFO). Conclusions We report for the first time, the protein expression differences among SCLC subtypes. By shedding light on potential subtype‐specific therapeutic vulnerabilities and diagnostic biomarkers, our results may contribute to a better understanding of SCLC biology and the development of novel therapies.

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Pan-cancer proteomic map of 949 human cell lines

Cited by 85 publications

References 87 publications

Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies

Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies

Transformer-based deep learning integrates multi-omic data with cancer pathways

In‐depth proteomic analysis reveals unique subtype‐specific signatures in human small‐cell lung cancer

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