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Machine Learning and Bioinformatic Analyses Link the Cell Surface Receptor Transcript Levels to the Drug Response of Breast Cancer Cells and Drug Off-target Effects

Don't release Sinkala, Krupa Naran, Dharanidharan Ramamurthy, Neelakshi Mungra, Kevin Dzobo, Darren Martin, Stefan Barth 


Breast cancer responds variably to anticancer therapies, often leading to significant off-target effects. This study proposes that the variability in tumour responses and drug-induced adverse events is linked to the transcriptional profiles of cell surface receptors (CSRs) in breast tumours and normal tissues. We analysed multiple datasets to compare CSR expression in breast tumours with that in non-cancerous human tissues. Our findings correlate the drug responses of breast cancer cell lines with the expression levels of their targeted CSRs. Notably, we identified distinct differences in CSR expression between primary breast tumour subtypes and corresponding cell lines, which may influence drug response predictions. Additionally, we used clinical trial data to uncover associations between CSR gene expression in healthy tissues and the incidence of adverse drug reactions. This integrative approach facilitates the selection of optimal CSR targets for therapy, leveraging cell line dose-responses, CSR expression in normal tissues, and patient adverse event profiles.


The aberrant overexpression of cell surface receptors (CSRs) distinguishes cancer cells from their normal counterparts and is implicated in oncogenesis [1–4]. CSRs, encompassing receptor tyrosine kinases and G-protein coupled receptors, mediate extracellular and intracellular signalling interactions, often becoming dysregulated in cancer [5–7]. This dysregulation [8], along with their accessibility on the cell surface, renders CSRs prime targets for anticancer therapeutics [7,9–11]. Changes in CSR expression during oncogenesis may involve mutations, gene amplifications, or transcriptional modifications [12–14]. Traditional methods of selecting CSR targets for therapies, particularly in breast cancer, have focused on their overexpression or mutational alterations in tumours relative to adjacent non-cancerous tissues [15–17]. However, the expression profiles of CSRs in non-target organs, which could be affected by anticancer treatments, have been largely overlooked [15–17]. This oversight can lead to unintended effects on other organs when targeting CSRs in tumour-specific tissues.


Analysing CSR expression and drug response in breast cancer
To explore the link between cell surface receptor (CSR) expression and drug response in breast cancer, we accessed healthy tissue transcriptome profiles from the Genotype-Tissue Expression (GTEx) consortium ( Specifically, we extracted transcript abundance data for 54 different healthy tissues [23,24], as detailed in S1 File. A comprehensive list of CSRs was compiled using information from various sources, including academic literature, the UniProt Knowledgebase [25], the Surfaceome database [26], and the Gene Ontology Consortium [27] focusing on the Gene Ontology term “plasma membrane” (S1 File). This list facilitated the extraction of mRNA transcription data for genes identified as CSRs (see Fig 1). We then employed unsupervised hierarchical clustering on this data to analyse the expression patterns of CSRs across healthy tissues, visualising the results in a dendrogram.


The transcriptional landscape of CSRs across breast tumours and healthy tissues
In our quest to discern CSR transcription patterns, we analysed mRNA expression data from 54 organs and tissues sourced from the GTEx project [23,24,48] focusing on 1,140 CSR-encoding genes. Unsupervised hierarchical clustering was employed to examine CSR expression variations across these healthy tissues (Fig 2A).


Cell surface receptors (CSRs) are frequently overexpressed in various cancers, rendering them effective targets for small-molecule inhibitors and antibody therapies [56–59]. Our study reveals that CSR gene expression varies markedly across healthy tissues; a factor critical in predicting adverse reactions to anticancer drugs. Notably, some CSRs targeted in breast cancer treatment and abundantly expressed in tumours also show higher expression in certain healthy tissues, leading to significant drug-induced collateral damage [56–59]. Our study’s approach to predicting breast cancer subtypes through CSR transcription data offers a significant advancement in the understanding of breast cancer heterogeneity. By utilising a machine learning model, we successfully categorised tumours into PAM50 subtypes based solely on CSR expression levels, a method that could refine the current subtyping system predominantly reliant on genetic and phenotypic markers. This finding aligns with recent studies emphasising the role of transcriptomic profiling in improving cancer classification and prognosis [4,60,61], and by extension, how these profiles predicts the chemosensitivity of tumour to anticancer drugs [62].


An earlier version of this manuscript has been deposited as a preprint on bioRxiv. The preprint can be accessed at the following URL: [80].

Citation: Sinkala M, Naran K, Ramamurthy D, Mungra N, Dzobo K, Martin D, et al. (2024) Machine learning and bioinformatic analyses link the cell surface receptor transcript levels to the drug response of breast cancer cells and drug off-target effects. PLoS ONE 19(2): e0296511.

Editor: Pan Li, Institute for Basic Science, REPUBLIC OF KOREA

Received: October 1, 2023; Accepted: December 13, 2023; Published: February 2, 2024

Copyright: © 2024 Sinkala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data that support the findings of this study are available from the following repositories: The GTEx portal (, ARCHS4 (, (, Genomics of Drug Sensitivity in Cancer (

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors declare that they have no competing interests.

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