Comparative analysis and correlation of cancer hotspot proteins and cell markers in tumor-normal adjacent breast and kidney samples using RPPA and LC-MS

Cancer proteomics is crucial for understanding tumor biology, yet consistent quantification of clinically relevant proteins across analytical platforms remains challenging. Here, we compared Reverse Phase Protein Array (RPPA) and Liquid Chromatography-Mass Spectrometry (LC-MS) using identical lysate aliquots prepared from matched tumor and adjacent normal tissues from breast and kidney cancer patients. We focused on proteins encoded by genes represented in the Ion AmpliSeq Cancer Hotspot Panel v2 and included representative cellular markers. RPPA was applied to the larger sample set, whereas data-dependent, data-independent, and targeted parallel reaction monitoring LC-MS modes were performed on a smaller subset of lysates. RPPA identified tumor-normal differences for several proteins and cell markers, while LC-MS quantified a smaller overlapping subset in the matched samples analyzed by both platforms. Cross-platform comparison showed that agreement was partial and strongly protein dependent: some targets displayed concordant tumor/normal fold changes, whereas others exhibited weak or even opposite-direction changes. Targeted PRM modestly improved agreement for selected discordant proteins, but substantial discrepancies remained. These data indicate that RPPA and LC-MS are not interchangeable for quantifying all targets in this panel, calling for a rather confirmatory approach: concordant findings increase confidence in selected candidate biomarkers, whereas discordant findings identify targets that require cautious interpretation.

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Publication Details

Journal
Scientific Reports
Published
2026-05-18
DOI
https://doi.org/10.1038/s41598-026-48754-2
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Comparative analysis and correlation of cancer hotspot proteins and cell markers in tumor-normal adjacent breast and kidney samples using RPPA and LC-MS

Lilla Turiák, Fanni Bugyi, David Bui, Noémi Karnok et al.
Scientific Reports
Advanced Proteomics Techniques and Applications
article

Comparative analysis and correlation of cancer hotspot proteins and cell markers in tumor-normal adjacent breast and kidney samples using RPPA and LC-MS

Lilla Turiák, Fanni Bugyi, David Bui, Noémi Karnok, Csaba Konràd, Christos Chinopoulos, Krisztina Paal
article en

Abstract

Cancer proteomics is crucial for understanding tumor biology, yet consistent quantification of clinically relevant proteins across analytical platforms remains challenging. Here, we compared Reverse Phase Protein Array (RPPA) and Liquid Chromatography-Mass Spectrometry (LC-MS) using identical lysate aliquots prepared from matched tumor and adjacent normal tissues from breast and kidney cancer patients. We focused on proteins encoded by genes represented in the Ion AmpliSeq Cancer Hotspot Panel v2 and included representative cellular markers. RPPA was applied to the larger sample set, whereas data-dependent, data-independent, and targeted parallel reaction monitoring LC-MS modes were performed on a smaller subset of lysates. RPPA identified tumor-normal differences for several proteins and cell markers, while LC-MS quantified a smaller overlapping subset in the matched samples analyzed by both platforms. Cross-platform comparison showed that agreement was partial and strongly protein dependent: some targets displayed concordant tumor/normal fold changes, whereas others exhibited weak or even opposite-direction changes. Targeted PRM modestly improved agreement for selected discordant proteins, but substantial discrepancies remained. These data indicate that RPPA and LC-MS are not interchangeable for quantifying all targets in this panel, calling for a rather confirmatory approach: concordant findings increase confidence in selected candidate biomarkers, whereas discordant findings identify targets that require cautious interpretation.

Scientific Reports
Semmelweis University (HU), Cornell University (US), MIND Research Institute (US), Centre for Social Sciences (HU), Weill Cornell Medicine (US)
Semmelweis Egyetem
Openalex Percentile: Top 10%
Advanced Proteomics Techniques and Applications
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