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DECIPHERING THE SIGNALING NETWORK LANDSCAPE OF BREAST CANCER IMPROVES DRUG SENSITIVITY PREDICTION

ABSTRACT: Although genetic and epigenetic abnormalities in breast cancer have been extensively studied, it remains difficult to identify those patients who will respond to particular therapies. This is due in part to our lack of understanding of how the variability of cellular signaling affects drug sensitivity. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data – on more than 80 million single cells from 4,000 conditions – were used to fit mechanistic signaling network models that provide unprecedented insights into the biological principles of how cancer cells process information. Our dynamic single-cell-based models more accurately predicted drug sensitivity than static bulk measurements for drugs targeting the PI3K-MTOR signaling pathway. Finally, we identified genomic features associated with drug sensitivity by using signaling phenotypes as proxies, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. This provides proof of principle that single-cell measurements and modeling could inform matching of patients with appropriate treatments in the future.

INSTRUMENT(S): Q Exactive Plus

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Suspension Culture, Epithelial Cell, Cell Culture

DISEASE(S): Breast Cancer

SUBMITTER: Marco Tognetti

LAB HEAD: Paola Picotti

PROVIDER: PXD017199 | Pride | 2021-04-20

REPOSITORIES: Pride

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Project description:Objective: To assess the role of aldoketoreductases and other doxorubicin pharmacokinetic or pharmacogenomic genes in doxorubicin cytotoxicity, resistance, DNA binding activity, and subcellular localization, Methods: We conducted a whole genome microarray study to identify differences in between doxorubicin-sensitive MCF-7cc cells and doxorubicin-resistant MCF-7Dox2-12 cells in terms of their expression of genes related to doxorubicin pharmacokinetics or pharmacodynamics. Targets were then validated by pharmacologic inhibition in conjunction with drug metabolite profiling, drug localization, drug cytotoxicity, and drug DNA binding studies. Results: 2063 differentially expressed transcripts were identified, including 17% and 43% of genes or gene families associated with doxorubicin pharmacokinetics or pharmacodynamics (p values of significance of 0.05 and <0.0001, respectively). The largest changes in the expression of genes associated with doxorubicin pharmacokinetics and pharmacodynamics were chiefly among the aldo-keto reductases (AKRs) Akr1c2, Akr1c3 and Akr1b10 which convert doxorubicin to doxorubicinol. We observed that doxorubicinol exhibits dramatically reduced drug toxicity, reduced drug DNA-binding activity, and altered drug subcellular localization to lysosomes. Pharmacologic inhibition of these AKRs in MCF-7Dox2-12 cells restored drug cytotoxicity, and drug localization to the nucleus. Conclusion: These findings demonstrate the utility of using curated pharmacokinetic and pharmacodynamic knowledgebases to identify highly relevant genes associated with doxorubicin resistance. The products of one or more of these genes could effectively be shown to alter the drug’s properties, while inhibiting them restored drug DNA binding, cytotoxicity, and subcellular localization. Doxorubicin resistant cell lines of breast MCF-7 cells were generated for gene expression profilling. Two colour microarray of Agilent whole human genome nucleotide arrays was conducted with four labelling replicates of both forward and reverse labellings plus another set of 8 arrays with forward labelling. Sixteen arrays were used for this experiments. The co-cultured control cells MCF-7cc12 was generated by parallel selection process in the absence of drug.

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DECIPHERING THE SIGNALING NETWORK LANDSCAPE OF BREAST CANCER IMPROVES DRUG SENSITIVITY PREDICTION

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