Comparison of targeted proteomics approaches for detecting and quantifying proteins derived from human cancer tissues

Faktor, Jakub; Sucha, Rita; Páralová, Vendula; Liu, Yansheng; Bouchal, Pavel

doi:10.1002/pmic.201600323

Cited by 27 publications

(27 citation statements)

References 20 publications

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“…In Figure 1 row (1), the heatmaps (a) and (b) contrast Affymetrix gene expression data of fresh frozen material of 40 non-Hodgkin lymphomas from ( Klapper et al , 2012 ) to matching nCounter data of FFPE material ( Masqué-Soler et al , 2013 ). The plots in row (2) show proteomics data for 23 of the lymphomas using (a) sequential window acquisition of all theoretical fragment ion spectra (SWATH) ( Gillet et al , 2012 ) and (b) targeted selected reaction monitoring (SRM) ( Faktor et al , 2016 ). For measurement details we refer to the methods section.…”

Section: Resultsmentioning

confidence: 99%

Molecular signatures that can be transferred across different omics platforms

et al. 2017

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MotivationMolecular signatures for treatment recommendations are well researched. Still it is challenging to apply them to data generated by different protocols or technical platforms.ResultsWe analyzed paired data for the same tumors (Burkitt lymphoma, diffuse large B-cell lymphoma) and features that had been generated by different experimental protocols and analytical platforms including the nanoString nCounter and Affymetrix Gene Chip transcriptomics as well as the SWATH and SRM proteomics platforms. A statistical model that assumes independent sample and feature effects accounted for 69–94% of technical variability. We analyzed how variability is propagated through linear signatures possibly affecting predictions and treatment recommendations. Linear signatures with feature weights adding to zero were substantially more robust than unbalanced signatures. They yielded consistent predictions across data from different platforms, both for transcriptomics and proteomics data. Similarly stable were their predictions across data from fresh frozen and matching formalin-fixed paraffin-embedded human tumor tissue.Availability and ImplementationThe R-package ‘zeroSum’ can be downloaded at https://github.com/rehbergT/zeroSum. Complete data and R codes necessary to reproduce all our results can be received from the authors upon request.

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Section: Resultsmentioning

confidence: 99%

Molecular signatures that can be transferred across different omics platforms

et al. 2017

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“…A strong correlation between SWATH-MS and S/MRM was demonstrated already in the first SWATH-MS publication (Gillet et al, 2012) and confirmed in other independent studies (Kockmann et al, 2016;Liu et al, 2013;Nakamura et al, 2016;Schmidlin et al, 2016). These studies include our recent comparison of S/MRM, pseudo-SRM/MRM HR and SWATH-MS analytical parameters in selected samples from the same breast cancer tissue collection (Faktor et al, 2017). Based on the above data, it has been well demonstrated both experimentally in our breast tumour sample set and in the literature that SWATH-MS provides data highly correlated with S/MRM.…”

Section: Advantages Of Swath-ms To Classify Breast Cancer Tumoursmentioning

confidence: 72%

Breast cancer classification based on proteotypes obtained by SWATH mass spectrometry

Bouchal

Schubert

Faktor

et al. 2019

Preprint

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Accurate breast cancer classification is vital for patient management decisions, and better tumour classification is expected to enable more precise and eventually personalized treatment to improve patient outcomes. Here, we present a novel quantitative proteotyping approach based on SWATH mass spectrometry and establish key proteins for breast tumour classification derived from proteotype data. The study was based on 96 tissue samples representing five breast cancer subtypes according to conventional classification. Correlation of SWATH proteotype patterns indicated groups that largely recapitulate these subtypes. However, 2 the proteotype-based classification also revealed varying degrees of heterogeneity within the conventional subtypes, with triple negative tumours being the most heterogeneous. Proteins that contributed most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2, which are associated with oestrogen receptor status, tumour grade, and HER2 status, respectively. While these three key proteins exhibited high levels of correlation between protein and transcript levels (R>0.67), general correlation did not exceed R=0.29, indicating the value of protein-level measurements of biomarkers and disease-regulated genes. Overall, our data shows how large-scale protein-level measurements by next-generation proteomics can lead to improved patient stratification for precision medicine.

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“…We expect that in the future, more sensitive assays will reveal the now imperceptible quantities of ORF2p. If shotgun proteomic approaches are not sufficient to address ORF2p detection, targeted methods can be developed to maximally leverage the sensitivity of MS instruments (potentially 100s of attomoles [61]). Similarly, approaches such as proximity ligation assays (PLA) or other technologies, may amplify detection by antibodies.…”

Section: Discussionmentioning

confidence: 99%

LINE-1 ORF2p Expression is Nearly Imperceptible in Human Cancers

Ardeljan

Wang

Oghbaie

et al. 2019

Preprint

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Background: Long interspersed element-1 (LINE-1, L1) is the major driver of mobile DNA activity in modern humans. When expressed, LINE-1 loci produce bicistronic transcripts encoding two proteins essential for retrotransposition, ORF1p and ORF2p. Many types of human cancers are characterized by L1 promoter hypomethylation, L1 transcription, L1 ORF1p protein expression, and somatic L1 retrotransposition. ORF2p encodes the endonuclease and reverse transcriptase activities required for L1 retrotransposition. Its expression is poorly characterized in human tissues and cell lines. Results:We report mass spectrometry based tumor proteome profiling studies wherein ORF2p eludes detection. To test whether ORF2p could be detected with specific reagents, we developed and validated five rabbit monoclonal antibodies with immunoreactivity for specific epitopes on the protein. These reagents readily detect ectopic ORF2p expressed from bicistronic L1 constructs. However, endogenous ORF2p is not detected in human tumor samples or cell lines by western blot, immunoprecipitation, or immunohistochemistry despite high levels of ORF1p expression. Moreover, we report endogenous ORF1p-associated interactomes, affinity isolated from colorectal cancers, wherein we similarly fail to detect ORF2p. These samples include primary tumors harboring hundreds of somatically-acquired L1 insertions. The new data are available via ProteomeXchange with identifier PXD013743. Conclusions:Although somatic retrotransposition provides unequivocal genetic evidence for the expression of ORF2p in human cancers, we are unable to directly measure its presence using several standard methods. Experimental systems have previously indicated an unequal stoichiometry between ORF1p and ORF2p, but in vivo, the expression of these two proteins may be more strikingly uncoupled. These findings are consistent with observations that ORF2p is not tolerable for cell growth. References

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Comparison of targeted proteomics approaches for detecting and quantifying proteins derived from human cancer tissues

Cited by 27 publications

References 20 publications

Molecular signatures that can be transferred across different omics platforms

Molecular signatures that can be transferred across different omics platforms

Breast cancer classification based on proteotypes obtained by SWATH mass spectrometry

LINE-1 ORF2p Expression is Nearly Imperceptible in Human Cancers

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