The effects of sample handling on proteomics assessed by reverse phase protein arrays (RPPA): Functional proteomic profiling in leukemia

Horton, Terzah M.; Hoff, Fieke W.; Dijk, Anneke van; Jenkins, Gaye; Morrison, Debra J.; Bhatla, Teena; Hogan, Laura; Romanos‐Sirakis, Eleny; Meyer, Julia; Carroll, William L.; Qiu, Yihua; Wang, Tao; Mo, Qianxing; Kornblau, Steven M.

doi:10.1016/j.jprot.2020.104046

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…1d). The data also complement ongoing efforts to assess the effects of sample handling on RPPA quantification in the setting of multisite clinical trials 54 . Finally, the integrated dataset can act as a framework for future interplatform comparisons, and this study opens the door for crossplatform validation of RPPA data to identify robust markers of disease and response to therapy.…”

Section: Usage Notesmentioning

confidence: 67%

“…Robust, high-quality data are crucial for successful RPPA workflows, especially as RPPA pipelines are continuing to be developed for use in clinical settings [54][55][56][57][58] . Thus, there is a growing need for approaches to record and evaluate the reproducibility of different RPPA platform outputs 59 .…”

Section: Experimental Designmentioning

confidence: 99%

“…To control biological and technical variability in this multicenter study, each cell line was cultured at one center and then lysates were distributed to all centers, so that all platforms analyzed exactly the same batch of protein extract. Sample shipping and transit time have been shown previously to have limited effects on RPPA quantification accuracy 54 . Importantly, the same protein extraction buffer was used for all experiments reported here to avoid differences in efficiency of extraction of nuclear, DNAbound or membrane-bound proteins.…”

Section: Experimental Designmentioning

confidence: 99%

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Multicenter reverse-phase protein array data integration

Koning

Bernhardt

Macleod

et al. 2021

Preprint

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Among the technologies available for protein biomarker discovery and validation, reverse-phase protein array (RPPA) benefits from unequalled sample throughput. Panels of high-quality antibodies enable the quantification by RPPA of protein abundance and posttranslational modifications in biological specimens with high precision and sensitivity. Incorporation of RPPA technology into clinical and drug development pipelines requires robust assays that generate reproducible results across multiple laboratories. We implemented the first international multicenter pilot study to investigate RPPA workflow variability. We characterized the proteomic responses of a series of breast cancer cells to two cancer drugs. This analysis quantified 86,832 sample spots, representing 108 biological samples, arrayed at three independent RPPA platforms. This unique integrated set of data is publicly available as a resource to the proteomic and cancer research communities to catalyse further analysis and investigation. We anticipate that this dataset will form a reference for the comparison of RPPA workflows and reagents, which can be expanded in the future, and will aid the identification of platform-robust treatment-marker antigens in breast cancer cells.

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Section: Usage Notesmentioning

confidence: 67%

Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

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Multicenter reverse-phase protein array data integration

Koning

Bernhardt

Macleod

et al. 2021

Preprint

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“…At both 10 h and 24 h, one dose of each chemotherapeutic agent had been administered. 10 Written informed consent was obtained in accordance with the Declaration of Helsinki and local institutional review boards.…”

Section: Methodsmentioning

confidence: 99%

Clinical relevance of proteomic profiling in <i>de novo</i> pediatric acute myeloid leukemia: a Children’s Oncology Group study

et al. 2022

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Pediatric acute myeloid leukemia (AML) remains a fatal disease for at least 30% of patients, stressing the need for improved therapies and better risk stratification. As proteins are the unifying feature of (epi)genetic and environmental alterations, and are often targeted by novel chemotherapeutic agents, we studied the proteomic landscape of pediatric AML. Protein expression and activation levels were measured in 500 bulk leukemic patient samples and 30 control CD34+ samples, using the reverse phase protein arrays with 296 strictly validated antibodies. The multi-step “MetaGalaxy” analysis methodology was applied and identified nine protein expression signatures (PrSIG), based on strong recurrent protein expression patterns. PrSIGs were associated with cytogenetics and mutational state, and with both favorable or unfavorable prognosis. Analysis based on treatment (i.e., ADE vs. ADE plus bortezomib (ADEB)) identified three PrSIGs that did better with ADEB vs. ADE. When PrSIGs were studied in the context of genetic subgroups, PrSIGs were independently prognostic after multivariate analysis, suggesting a potential value for proteomics in combination with current classification systems. Proteins with universally increased (n=7) or decreased (n=17) expression were observed across PrSIGs. Expression of certain proteins significantly differentially expressed from normal could be identified, forming a hypothetical platform for personalized medicine.

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“…The pediatric AML research community will gain valuable insights from these results and data for decades to come. This article reports the results of profiling 296 candidate proteins by reverse phase protein arrays (RRPA); a technology this team has previously shown to obtain reliable data that is robust against technical preanalytical factors such as shipping, transit time, and temperature [6]. Using their own MetaGalaxy [https://www.leukemiaatlas.org/code] and progenyClust methods [7] the authors compressed these 296 individual protein variables into 31 biologically annotated protein function group variables and then 12 protein constellation variables to eventually assign each patient to one of 9 protein signature classes.…”

mentioning

confidence: 99%