Integrative analysis of multi-platform reverse-phase protein array data for the pharmacodynamic assessment of response to targeted therapies

被引:8
作者
Byron, Adam [1 ]
Bernhardt, Stephan [2 ,5 ]
Ouine, Berengere [3 ]
Cartier, Aurelie [3 ,6 ]
Macleod, Kenneth G. [1 ]
Carragher, Neil O. [1 ]
Sibut, Vonick [4 ,7 ]
Korf, Ulrike [2 ]
Serrels, Bryan [1 ,8 ]
de Koning, Leanne [3 ]
机构
[1] Univ Edinburgh, Inst Genet & Mol Med, Canc Res UK Edinburgh Ctr, Crewe Rd South, Edinburgh EH4 2XR, Midlothian, Scotland
[2] German Canc Res Ctr, Div Mol Genome Anal, Heidelberg, Germany
[3] PSL Res Univ, Inst Curie, Dept Translat Res, 26 Rue Ulm, F-75005 Paris, France
[4] PSL Res Univ, Inst Curie, INSERM, U900, Paris, France
[5] Pfizer Pharma GmbH, Berlin, Germany
[6] Sederma, Le Perray En Yvelines, France
[7] Univ Rennes 1, Fac Med, U1236, INSERM, Rennes, France
[8] NanoString Technol Inc, Seattle, WA USA
基金
英国医学研究理事会;
关键词
HUMAN BREAST-CANCER; SIGNALING PATHWAYS; SURVIVAL PATHWAYS; LAPATINIB; VALIDATION; ACTIVATION; BIOMARKERS; INHIBITOR; REPRODUCIBILITY; NORMALIZATION;
D O I
10.1038/s41598-020-77335-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy.
引用
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页数:12
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