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

• Published in: Scientific reports Vol. 10; no. 1; p. 21985
• Main Authors: Byron, Adam, Bernhardt, Stephan, Ouine, Bérèngere, Cartier, Aurélie, Macleod, Kenneth G., Carragher, Neil O., Sibut, Vonick, Korf, Ulrike, Serrels, Bryan, de Koning, Leanne
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.12.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2401; 631/1647; 631/1647/2067; 631/67; 631/67/1857; 692/53; Antibodies; Antigens; Antineoplastic Agents - pharmacology; Antineoplastic Agents - therapeutic use; Breast cancer; Breast Neoplasms - drug therapy; Breast Neoplasms - pathology; Cancer; Cell Line, Tumor; Drug development; Female; Humanities and Social Sciences; Humans; Molecular Targeted Therapy; multidisciplinary; Pharmacodynamics; Phosphorylation; Post-translation; Principal Component Analysis; Protein Array Analysis - methods; Protein arrays; Proteins; Science; Science (multidisciplinary); Tumor cell lines
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-77335-0

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.