Abstract
Proteins are ultimately responsible for cellular phenotypes and are targeted by most anticancer drugs. However, beyond immunohistochemistry, proteins are not typically measured in precision oncology, meaning transcriptomics is used as a proxy. To determine how informative mRNA is for guiding personalised treatments, mRNA–protein correlations were analysed in three large pan-cancer datasets and made available in a web portal (https://oncorr.aws.procan.org.au/). OnCorr can be integrated into precision medicine programs to augment transcriptomics.
Code availability
The underlying code for this study is available in GitHub and can be accessed via www.github.com/CMRI-procan/OnCorr.
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Acknowledgements
ProCan is supported by the Australian Cancer Research Foundation, Cancer Institute New South Wales (NSW) (2017/TPG001, REG171150), NSW Ministry of Health (CMP-01), the University of Sydney, Cancer Council NSW (IG 18-01), Ian Potter Foundation, the Medical Research Future Fund (MRFF-PD), National Health and Medical Research Council (NHMRC) of Australia European Union grant (GNT1170739, a companion grant to support the ‘iPC-individualized Paediatric Cure’ [ref. 826121]), and National Breast Cancer Foundation (IIRS-18-164). Work at ProCan is done under the auspices of a Memorandum of Understanding between the Children’s Medical Research Institute and the U.S. National Cancer Institute’s International Cancer Proteogenome Consortium (ICPC) that encourages cooperation among institutions and nations in proteogenomic cancer research, in which datasets are made available to the public. R.C.P. and B.P. are supported by a Sydney Cancer Partners Translational Partners Fellowship with funding from a Cancer Institute NSW Capacity Building Grant (grant ID 2021/CBG0002). L.M.S.L. is funded by a CINSW Program Grant (no. 2021/TPG2112) and NHMRC Synergy Grant (APP2018642). This work was supported by NHMRC (GNT2000855, GNT1138536).
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R.C.P. designed and directed the project. U.N. and R.C.P. analysed the data and wrote the paper. U.N. and O.L. built the OnCorr web tool. N.D. contributed statistical oversight of analyses. C.M., L.M.S.L., R.R.R., B.P., and R.C.P. interpreted the results and the implications for clinical implementation. All authors discussed the results and contributed to the final paper.
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Nawaz, U., Deng, N., Livson, O. et al. OnCorr: A pan-cancer mRNA-protein correlation tool for precision oncology. npj Precis. Onc. (2026). https://doi.org/10.1038/s41698-026-01323-2
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DOI: https://doi.org/10.1038/s41698-026-01323-2
