Targeting RAS-driven human cancer cells with antibodies to upregulated and essential cell-surface proteins

Martinko, Alexander J.; Truillet, Charles; Julien, Olivier; Diaz, Juan E.; Horlbeck, Max A.; Whiteley, Gordon; Blonder, Josip; Weissman, Jonathan S.; Bandyopadhyay, Sourav; Evans, Michael J.; Wells, James A.

doi:10.7554/elife.31098

Cited by 80 publications

(97 citation statements)

References 48 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These oncogenes can drive multiple branched pathways yet it was previously shown that inhibition of the MAPK pathway with the potent and selective MEK inhibitor (PD032590, MEKi) significantly reverses the surfaceome changes of MCF10A cells transformed with KRAS G12V (Martinko et al, 2018). Indeed, MEKi substantially hampered growth for all cell lines either in the absence or presence of growth factors (Figure 1D, Figure S1).…”

Section: Results: Phenotypic Analysis Of Oncogene Transformed Mcf10amentioning

confidence: 88%

“…Further inspection revealed that nearly all the upregulated glycopeptides with a complex/hybrid glycan from the cell lines harboring HER2 (12 of 12) and KRAS G12V (13 of 18) mapped to ANPEP. This protein was also upregulated on the KRAS G12V surfaceome (Martinko et al, 2018), displayed the highest degree of glycan heterogeneity within the glycoproteomic data, and has previously been implicated in tumorigenesis (Dong et al, 2000;Pasqualini et al, 2000).…”

Section: Oncogenes Induce Large Changes To the Glycoproteomementioning

confidence: 82%

“…Many of the differentially expressed proteins overlapped, but each cell line had a substantial number of uniquely differentially regulated proteins, presumably resulting from slight differences in signaling between each oncogene. Although it is well known that correlations between protein and RNA levels are not often strongly correlated (Lundberg et al, 2010;Martinko et al, 2018;Schwanhäusser et al, 2011), we were prompted to determine whether the most common changes we observed. BCAM and NRCAM downregulation, in particular, were also observed in patient samples harboring the same oncogenic signatures in a number of cancers types of epithelial origin (Figure 2B-C).…”

Section: Differential Expression Of Oncogene-induced Surfaceomes In Mmentioning

confidence: 99%

“…Recent surfaceome studies in an isogenic MCF10A breast epithelial cell line transformed with oncogenic KRAS have identified more than two dozen up-regulated surface proteins that function in a cell autologous fashion to promote increased cell proliferation, metastasis, metabolic activity and immunologic suppression (Martinko et al, 2018;Ying et al, 2012). Many of the most powerful oncogenes are linked to KRAS and the MAPK pathway including overactivation of receptor tyrosine kinases, such as EGFR and HER2, or mutations in BRAF or RAS (Fig 1A).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Broad and thematic remodeling of the surface glycoproteome on isogenic cells transformed with driving proliferative oncogenes

Leung¹,

Wilson

Kirkemo³

et al. 2019

Preprint

View full text Add to dashboard Cite

The cell surface proteome, the surfaceome, is the interface for engaging the extracellular space in normal and cancer cells. Here we apply quantitative proteomics of N-linked glycoproteins to reveal how a collection of some 700 surface proteins is dramatically remodeled in an isogenic breast epithelial cell line stably expressing any of six of the most prominent proliferative oncogenes, including the receptor tyrosine kinases, EGFR and HER2, and downstream signaling partners such as KRAS, BRAF, MEK and AKT. We find that each oncogene has somewhat different surfaceomes but the functions of these proteins are harmonized by common biological themes including up-regulation of nutrient transporters, down-regulation of adhesion molecules and tumor suppressing phosphatases, and alteration in immune modulators. Addition of a potent MEK inhibitor that blocks MAPK signaling brings each oncogene-induced surfaceome back to a common state reflecting their strong dependence on the MAPK pathway to propagate signaling. Using a recently developed glyco-proteomics method of activated ion electron transfer dissociation (AI-ETD) we found massive oncogene-induced changes in 142 N-linked glycans and differential increases in complex hybrid glycans especially for KRAS and HER2 oncogenes. Overall, these studies provide a broad systems level view of how specific driver oncogenes remodel the surface glycoproteome in a cell autologous fashion, and suggest possible surface targets, and combinations thereof, for drug and biomarker discovery.

show abstract

Section: Results: Phenotypic Analysis Of Oncogene Transformed Mcf10amentioning

confidence: 88%

Section: Oncogenes Induce Large Changes To the Glycoproteomementioning

confidence: 82%

Section: Differential Expression Of Oncogene-induced Surfaceomes In Mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Broad and thematic remodeling of the surface glycoproteome on isogenic cells transformed with driving proliferative oncogenes

Leung¹,

Wilson

Kirkemo³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Hence, we sought to benchmark the performance of GenieScore ranking against two published studies that performed flow cytometry analyses to orthogonally validate putative markers for cell types of interest which were originally identified from proteomics and/or transcriptomic data. In the first study, Martinko et al performed CSC and RNA-Seq on MCF10A KRAS G12V cells (comparing the results to empty vector control MCF10A cells) to identify surface proteins indicative of RAS-driven cancer phenotype (24). Antibodies were subsequently developed against seven candidate markers, all of which demonstrated positive signal on the MCF10A KRAS G12V cells.…”

Section: Benchmarking Geniescore Against Two Published Surface Proteimentioning

confidence: 99%

SurfaceGenie: a web-based application for prioritizing cell-type specific marker candidates

Waas

Snarrenberg

Littrell

et al. 2019

Preprint

View full text Add to dashboard Cite

MotivationCell-type specific surface proteins can be exploited as valuable markers for a range of applications including immunophenotyping live cells, targeted drug delivery, and in vivo imaging. Despite their utility and relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. A significant challenge in analyzing ‘omic’ discovery datasets is the selection of candidate markers that are most applicable for downstream applications.ResultsHere, we developed GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type specific surface markers. In this report, we demonstrate the utility of GenieScore for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell, and islet biology. We also demonstrate that permutations of GenieScore, termed IsoGenieScore and OmniGenieScore, can efficiently prioritize co-expressed and intracellular cell-type specific markers, respectively.Availability and ImplementationCalculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web-application: www.cellsurfer.net/surfacegenie.

show abstract

Efficient Generation of Paired Single‐Cell Multiomics Profiles by Deep Learning

Zhang

Gao

2023

Advanced Science

View full text Add to dashboard Cite

Recent advances in single-cell sequencing technology have made it possible to measure multiple paired omics simultaneously in a single cell such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq). However, the widespread application of these single-cell multiomics profiling technologies has been limited by their experimental complexity, noise in nature, and high cost. In addition, single-omics sequencing technologies have generated tremendous and high-quality single-cell datasets but have yet to be fully utilized. Here, single-cell multiomics generation (scMOG), a deep learning-based framework to generate single-cell assay for transposase-accessible chromatin (ATAC) data in silico is developed from experimentally available single-cell RNA-seq measurements and vice versa. The results demonstrate that scMOG can accurately perform cross-omics generation between RNA and ATAC, and generate paired multiomics data with biological meanings when one omics is experimentally unavailable and out of training datasets. The generated ATAC, either alone or in combination with measured RNA, exhibits equivalent or superior performance to that of the experimentally measured counterparts throughout multiple downstream analyses. scMOG is also applied to human lymphoma data, which proves to be more effective in identifying tumor samples than the experimentally measured ATAC data. Finally, the performance of scMOG is investigated in other omics such as proteomics and it still shows robust performance on surface protein generation.

show abstract

Targeting RAS-driven human cancer cells with antibodies to upregulated and essential cell-surface proteins

Cited by 80 publications

References 48 publications

Broad and thematic remodeling of the surface glycoproteome on isogenic cells transformed with driving proliferative oncogenes

Broad and thematic remodeling of the surface glycoproteome on isogenic cells transformed with driving proliferative oncogenes

SurfaceGenie: a web-based application for prioritizing cell-type specific marker candidates

Efficient Generation of Paired Single‐Cell Multiomics Profiles by Deep Learning

Contact Info

Product

Resources

About