Comparative Proteomics of Dying and Surviving Cancer Cells Improves the Identification of Drug Targets and Sheds Light on Cell Life/Death Decisions

Abstract

Chemotherapeutics cause the detachment and death of adherent cancer cells. When studying the proteome changes to determine the protein target and mechanism of action of anticancer drugs, the still-attached cells are normally used, whereas the detached cells are usually ignored. To test the hypothesis that proteomes of detached cells contain valuable information, we separately analyzed the proteomes of detached and attached HCT-116, A375, and RKO cells treated for 48 h with 5-fluorouracil, methotrexate and paclitaxel. Individually, the proteomic data on attached and detached cells had comparable performance in target and drug mechanism deconvolution, whereas the combined data significantly improved the target ranking for paclitaxel. Comparative analysis of attached versus detached proteomes provided further insight into cell life and death decision making. Six proteins consistently up- or downregulated in the detached versus attached cells regardless of the drug and cell type were discovered; their role in cell death/survival was tested by silencing them with siRNA. Knocking down USP11, CTTN, ACAA2, and EIF4H had anti-proliferative effects, affecting UHRF1 additionally sensitized the cells to the anticancer drugs, while knocking down RNF-40 increased cell survival against the treatments. Therefore, adding detached cells to the expression proteomics analysis of drug-treated cells can significantly increase the analytical value of the approach. The data have been deposited to the ProteomeXchange with identifier PXD007686.

Keywords: Cancer therapeutics; Cell death*; Cell survival; Drug resistance; Drug targets*; Label-free quantification; Mass Spectrometry; Mechanism of action; chemotherapeutic.

Fig. 1.

Principal component analysis of the whole proteome data set (Q2 = 0.705). A, The first principal component separated dying (detached) versus living (attached) cells, whereas the second component separated the cell lines. B, The third component seems to separate the different states based on carbohydrate metabolic states such as galactose, fructose, mannose and glutathione metabolism.

Fig. 2.

OPLS projections for A, MTX, B, 5-FU and C, PCTL versus all other compounds and controls in different cell lines. The drug targets and other mechanistically involved proteins are shown in color. (The variations in the x axis correspond to regulation and specificity and variations in the y axis arise from the orthogonal components in OPLS).

Fig. 3.

Significant downregulation of ribosomal proteins upon 5-FU treatment in living cells compared with dying cells. A, The regulation frequency of ribosomal subunits in response to 5-FU in living cells and B, in dying cells. C, The average frequency of ribosomal subunits regulation in all three living cell lines versus dying ones. The dashed line represents a regulation of 1 fold (L = living, D = dying, H = HCT116 cells, A = A375 cells, R = RKO cells, F = 5-FU, M = methotrexate and P = paclitaxel).

Fig. 4.

EIF4H as an example of a protein downregulated in all types of detached treated cells treated with different anticancer agents. Error bars (standard deviations) are not available for DAF and DHF, because EIF4H was not quantified in 1–2 replicates (H = HCT116 cells, A = A375 cells, R = RKO cells, F = 5-FU, M = methotrexate and P = paclitaxel).

Fig. 5.

A, The rationale for selection of 6 proteins with potential effect on cell death or survival. B, Workflow for the functional follow-up siRNA experiments. C, The effect of siRNA knockdown on cell viability in the presence or absence of 5-FU, MTX, and PCTL. Knockdown of CTTN, USP11, ACAA2, and EIF4H had no effect on cell viability in the presence of drugs (error bars represent the standard deviations of three independent experiments in 4 replicates).