
==== Front
Anticancer Drugs
Anticancer Drugs
ACD
Anti-Cancer Drugs
0959-4973
1473-5741
Lippincott Williams & Wilkins

39011652
ACD-2024-0618
00002
10.1097/CAD.0000000000001633
3
Pre-Clinical Reports
Investigation of the synergistic effect mechanism underlying sequential use of palbociclib and cisplatin through integral proteomic and glycoproteomic analysis
Yang Lulu a
Meng Bo b
Gong Xiaoyun b
Jiang You b
Shentu Xuping a*
Xue Zhichao b*
a Faculty of Life Sciences, China Jiliang University, Hangzhou
b Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China
Correspondence to Xuping Shentu, China Jiliang University, No.258, Xueyuan Street, Higher Education Zone of Xiasha, Hangzhou, Zhejiang 310018, China Tel: +86 13588050115; e-mail: stxp@cjlu.edu.cn.
10 2024
08 7 2024
35 9 806816
13 6 2024
13 6 2024
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Chemoresistance largely hampers the clinical use of chemodrugs for cancer patients, combination or sequential drug treatment regimens have been designed to minimize chemotoxicity and resensitize chemoresistance. In this work, the cytotoxic effect of cisplatin was found to be enhanced by palbociclib pretreatment in HeLa cells. With the integration of liquid chromatography–mass spectrometry-based proteomic and N-glycoproteomic workflow, we found that palbociclib alone mainly enhanced the N-glycosylation alterations in HeLa cells, while cisplatin majorly increased the different expression proteins related to apoptosis pathways. As a result, the sequential use of two drugs induced a higher expression level of apoptosis proteins BAX and BAK. Those altered N-glycoproteins induced by palbociclib were implicated in pathways that were closely associated with cell membrane modification and drug sensitivity. Specifically, the top four frequently glycosylated proteins FOLR1, L1CAM, CD63, and LAMP1 were all associated with drug resistance or drug sensitivity. It is suspected that palbociclib-induced N-glycosylation on the membrane protein allowed the HeLa cell to become more vulnerable to cisplatin treatment. Our study provides new insights into the mechanisms underlying the sequential use of target drugs and chemotherapy drugs, meanwhile suggesting a high-efficiency approach that involves proteomic and N-glycoproteomic to facilitate drug discovery.

cisplatin
palbociclib
proteome
synergistic effect
National Key R&D Program of China2022YFF0705001 Not ApplicableNational Key R&D Program of China2022YFF0705200 Not ApplicableResearch Project of the National Institute of MetrologyAKYZZ2224 Not ApplicableOPEN-ACCESSTRUE
SDCT
==== Body
pmcIntroduction

Cisplatin is one of the most effective chemotherapeutic drugs used for cervical cancer patients, however, resistance to cisplatin is commonly observed [1]. Combination/sequential drug use become common strategies used to maximize the treatment efficacy [2], and overcome drug resistance [3]. Researchers are dedicated to finding the combination/sequential approaches for chemotherapy use. For instance, palbociclib is a CDK4/6 inhibitor that has been approved to be effective for cervical cancer patients [4], and also be able to resensitize cisplatin-resistance cell lines in vitro [5]. Scientists tried to use palbociclib to combine with cisplatin as a substitute regime. However, strong antagonist effects were obtained by directly combining CDK4/6 inhibitor and chemotherapy [5–7]. In contrast, sequential use of these two drugs could have a strong synergistic effect in vitro [8,9]. Multiple studies have been conducted, but this regime is still hard to translate into clinical application since the underlying mechanisms are hardly deciphered by conventional preclinical studies and molecular biology techniques [10].

Profound investigation of the potential mechanism underlying sequential regimen required new technology and new design with obtainable comprehensive high-throughput data. With the recently advanced liquid chromatography–mass spectrometry (LC–MS) technology, proteomic and glycoproteomic studies could offer detailed whole-cell protein and glycoprotein composition data quantitatively for complex mechanism analysis [11,12]. Proteins constitute the vast majority of drug targets, and the comprehensive investigation of these drug target proteins could play essential roles in drug development. Using proteomics to study the interrelationships of protein expressions in drug treatments can provide important insights into drug mechanism and facilitate drug clinical use [13,14].

Glycosylation is a key cellular mechanism that could regulate physiological and pathological changes involved in cancer development [15,16]. Glycosylation provides a set of targets for cancer diagnoses and cancer treatments [16–18]. As one of the most common post-transcriptome modifications in proteins, glycosylation accounts for more than 50% of known eukaryotic proteins. High-throughput glycoproteomic based on the LC-MS technique provides a way to evaluate thousands of glycoproteins in a single experiment [19]. Besides disease biomarker identification and pathological progress illustration, glycoproteomic was also hired to interpret drug mechanisms, including drug resistance, drug efflux, and drug sensitivity [19–21].

A comprehensive understanding of combination/sequential mechanisms is vitally important for the clinical use of drugs. In this study, we generated a study model by sequentially treating the HeLa cells with palbociclib and cisplatin which yielded a synergistic effect. Afterward, an in-depth analysis approach was designed to investigate the mechanisms behind this synergistic effect. Through the application of LC-MS-based techniques, we conducted the proteomic and N-glycoproteomic integration analysis. As a result, we found that the palbociclib-induced glycan modification changes on the membrane proteins could enhance the cytotoxic effect of cisplatin. This study provides a novel strategy to apply the LC-MS technique in biochemical experiments, it builds up a conjoint analysis of protein expressions and post-transcriptome glycosylation modifications in the drug-treated HeLa cells. It offers a deeper insight into the mechanism behind the sequential use of anticancer drugs and paves the way for a new approach to illustrating the complex interaction of two drugs to enhance clinical translation.

Methods

Cell culture and reagents

HeLa cells obtained from BNCC (Beijing New Century Chemical Co., Ltd., Beijing, China) (#338703) were cultured at a 37 °C incubator with a 5% CO2 supply. The culture medium consisted of 90% RPMI-I640 (Solarblo, Beijing, China; #31800) and 10% fetal bovine serum (Every Green, Beijing, China; #11011-8611). Palbociclib (Selleck Chemicals, Houston State, Texas, USA; S1116) was dissolved in distilled H2O at the stock concentration of 10 mmol/L and was further diluted to a working concentration. Cisplatin (Sigma-Aldrich, St. Louis, Missouri, USA; RAB7778) was diluted with distilled H2O to a stock concentration of 6.67 mmol/L.

MTT assay

HeLa cells were seeded in 96-well plates at a density of 4000 cells/well. After palbociclib and cisplatin were applied, cell viability was assessed using an MTT assay (Aladdin, Beijing, China; #J21061611). MTT solution of 5 mg/ml was added into the culture media 1 : 10 v/v. The plates were then incubated for 4 h at 37 °C, the optical density was measured at 490 nm. Growth inhibition in each well was calculated as: (ODcontrol − ODdrug)/OD control × 100%.

Combination index

Open-source software ‘CompuSyn’ based on the Chou–Talalay method was used to calculate the combination index [22,23]. Combination index values of >1, 1, and <1 imply antagonistic effect, additive, and synergistic effects, respectively.

Liquid chromatography–mass spectrometry/mass spectrometry analysis

A proteomics study was conducted as previously described [24]. In glycoproteomic analysis, 0.5 μg of N-glycopeptides were reconstituted in 0.1% formic acid and separated over a gradient of 78 min at a flow rate of 300 nl/min (0 − 8 min, 5 − 8% B; 8 − 58 min, 8 − 22% B; 58 − 70 min, 22 − 32% B; 70 − 71 min, 32 − 90% B; 71 − 78 min, 90% B). For a full mass spectrometry (MS) scan, the Orbitrap resolution was set to 120 000, with an AGC target value of 4 × 105 for a scan range of 800 − 2000 m/z and a maximum injection time of 100 ms. For the MS2 scan, the high energy collision dissociation (HCD) fragmentation was performed at the isolation width of 2 m/z and a segmented HCD collision energy of 20, 30, and 40%. The data analysis process was the same as the previous description [11]. The outcomes of this analysis are presented in Supplementary Material 1, Supplemental digital content 15, http://links.lww.com/ACD/A556 and Supplementary Material 2, Supplemental digital content 16, http://links.lww.com/ACD/A557.

Bioinformatics analysis

Bioinformatics analysis was performed by using the ‘Wu Kong’ platform (https://wkomics.omicsolution.com/wkomics/main/) [25]. For the proteomics study, the LFQ intensities of proteins were extracted from the MaxQuant result file for analysis. The intensities were normalized to the median first and the differential expression analysis-limma was applied to the normalized data when comparing the protein intensities between every two treatment groups. Volcano plots were generated with the P-value and fold changes, and the significant threshold was set as curved with >0.585 and < −0.585, and the confidential interval was set to 0.95. The significantly different expression proteins (DEPs) extracted from volcano plots were subjected to further Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.

For glycoproteomics, the intensities of intact N-glycopeptides (IGP) were extracted from the pGlyco files for analysis. After normalized to medium value, the intensity values were subjected to principal component analysis or analysis of variance hypothesis test. IGP with a Benjamini–Hochberg-adjusted P-value less than 0.01 from the analysis of variance hypothesis test were selected for further analysis. IGP were clustered by Euclidean, and five groups were selected. Different expression glycopeptides were analyzed as the process for proteomics, every two treatment groups were compared one by one.

Results

Sequential use of palbociclib and cisplatin has a synergistic effect

Palbociclib is a specific CDK4/6 inhibitor, which can induce a cytostatic effect rather than a cytotoxic effect on proliferation cells. As a result, the IC50 for 24, 72, and 120 h were 105.6, 6.482, and 6.074 μmol/L, respectively (Fig. 2a). As for cisplatin, the IC50 for three time points were tested to be 2.914, 0.657, and 0.251 μmol/L (Fig. 2b). In this study, low-dose palbociclib (200 nmol/L) and cisplatin (0.02–0.67 μmol/L) were sequentially used. The detailed sequential workflow was illustrated in Figure 1a and six study groups were attributed as control, palbociclib, cisplatin, 72 h cisplatin, 72 h palbociclib as well as sequential. In the sequential treatment group (Fig. 2c), HeLa cells were treated with 200 nmol/L palbociclib for 24 h and then the fresh medium was supplied, after 24 h of palbociclib withdrawal, cisplatin was added to cell culture for another 24 h. As a result, the sequential group did have the highest growth inhibition rate than the others.

Fig. 1 Workflow of drug study and integrated proteomic and N-glycoproteomic methods. (a) Workflow of palbociclib and cisplatin sequential use for MTT growth inhibition assay and LC-MS study. HeLa cells were attributed to six study groups. (b) Schematic illustration of the workflow for the integrated proteomic and N-glycoproteomic analysis. HILIC, hydrophilic interaction liquid chromatography; LC-MS, liquid chromatography–mass spectrometry; MTT, 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide; MS, mass spectrometry.

Fig. 2 Palbociclib enhances the cytotoxic effect of cisplatin in HeLa Cells. (a) Dose–response curve of palbociclib in HeLa cell lines was determined on days 1 and 2 using the MTT assay. Data were expressed as mean ± SD of three replicates. (b) Dose–response curve of cisplatin in HeLa cells. (c) Growth inhibition effect of six study groups as Figure 1a. (d) Pretreated HeLa cells of 200 nmol/l palbociclib increased the growth inhibition effect of cisplatin with six different doses from 0.02 to 0.67 μmol/L. Combination index values were calculated using the Chou–Talalay method. MTT, [3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide].

To further determine if the sequential treatment could be synergistic, multiple doses of cisplatin were designed from 0.02 to 0.67 μmol/L. For all six doses of cisplatin treatments, higher growth inhibition rates were observed in the 200 nmol/L palbociclib pretreated cells than only cisplatin-treated alone (Fig. 2d). Furthermore, Chou–Talalay method-based software ‘CompuSyn’ was used to calculate the combination index [22,23]. As in Figure 2d, strong synergistic effects indicated by combination index value <1 were calculated in all different cisplatin doses (Supplementary Material 6, Supplemental digital content 1, http://links.lww.com/ACD/A542).

To deeply discover the underlying mechanism of this synergistic effect, the LC-MS technique was used to offer us the whole-cell proteomic and glycoproteomic data after drug treatment. The LC-MS study groups were set as Figure 1a and the workflow was illustrated in Figure 1b.

Quantitative proteomic analysis of HeLa cells after drug treatment

A total of 4664 proteins were identified across four study groups. To identify the DEPs associated with drug treatment, we compared palbociclib versus control, cisplatin versus control, and sequential treatment versus control, respectively (Supplementary Figures S2−S4, Supplemental digital content 2, http://links.lww.com/ACD/A543, Supplementary Material 3, Supplemental digital content 3, http://links.lww.com/ACD/A544) and corresponding upregulated and downregulated proteins from each pair were identified (Fig. 3a). All three treatment groups induced significant numbers of DEPs in HeLa cells. The changes in these DEP intensities across four biological replicates could be detected by LC-MS and provided comparatively reliable proteomic data (Supplementary Figure S1, Supplemental digital content 4, http://links.lww.com/ACD/A545). Palbociclib alone induced less growth inhibition effect (Fig. 2c), as expected, the DEPs induced by palbociclib alone were observed to be the lowest among all study groups.

Fig. 3 Quantitative proteomic analysis of HeLa cells. (a) Number of different expression proteins induced by three treatment groups. (b) Gene Ontology circle plot of the top 10 enriched biological processes of the different expression proteins between palbociclib and control. The upregulated (red dots) and downregulated (blue dots) proteins in each process are distributed in the outer circle of the plot. The inner circle displays the Z-score, calculated as the number of upregulated proteins minus the number of downregulated proteins divided by the square root of the total count. The larger Z-score represents more upregulated proteins enriched in the process. (c) Gene Ontology circle plot of different expression proteins between cisplatin and control. (d) Gene Ontology circle plot of different expression proteins between sequential and control. CIS, cisplatin; COM, combination/sequential use; FC, fold change; PD, palbociclib.

To further understand the functions these DEPs played in the cellular biological process, the Gene Ontology enrichment analysis was performed. Gene Ontology circle plots of the top 10 enriched biological processes of DEPs for palbociclib, cisplatin, and sequential treatment were generated based on fold changes and the adjusted P-values (Supplementary Material 4, Supplemental digital content 5, http://links.lww.com/ACD/A546). The major enriched biological processes and the composition of upregulated and downregulated proteins in each biological process were present in Figure 3b−d. The apoptosis process was observed in cisplatin and sequential treatment but not in the palbociclib treatment group.

Sequential treatment could enhance the apoptosis process

Sequential treatment did induce a higher growth inhibition rate than cisplatin alone, therefore, we specifically compare the protein expression changes in sequential treatment cells versus cisplatin treatment cells. Ninety-eight downregulated and 99 upregulated DEPs between cisplatin and sequential treatment were identified (Supplementary Figure S5, Supplemental digital content 6, http://links.lww.com/ACD/A547, Supplementary Material 3, Supplemental digital content 3, http://links.lww.com/ACD/A544). Subject to Gene Ontology analysis, the top 15 biological process terms of these DEPs were listed in Figure 4a. Pathways including cell cycle, response to drug, apoptosis, etc. suggested that sequential treatment did induce a more stressful environment for HeLa cells. Typical biological processes were selected to be presented as Gene Ontology circle plots (Fig. 4b), within which the apoptosis process was observed to be increasing. We further plotted the proteins with their fold change values into the KEGG pathway of apoptosis [26,27] (Fig. 4c). To verify the increased apoptosis induced by sequential treatment, the protein intensities of BAX and BAK were extracted and presented in Figure 4d. The expression of BAX was significantly increased in sequential treatment, and BAK did have an increasing trend observed. Therefore, the increased cell growth inhibition induced by sequential treatment compared with cisplatin alone could have resulted from the enhancement of the apoptotic effect.

Fig. 4 Different expression proteins between sequential treatment versus cisplatin treatment. (a) Top 15 biology processes Gene Ontology (GO) terms of different expression proteins. (b) GO circle plot of the top 10 enriched biological processes (BP) of the different expression proteins. (c) Kyoto Encyclopedia of Genes and Genomes pathway of apoptosis (HSA04210). Red nodes: proteins upregulated in sequential treatment compared with cisplatin alone; gray nodes: the proteins without significant change in expressing level, or not identified in this study; green nodes: proteins downregulated in sequential treatment compared with cisplatin alone. (d) LFQ intensities of BAX and BAK from four repeats across four study groups. LFQ, label-free quantification. *P < 0.05.

N-glycoproteomic profiling suggested a unique pattern of N-glycosylation alterations induced by palbociclib

We next explored if there were glycosylation changes induced by palbociclib that would enhance the cisplatin-induced apoptosis effect. N-glycosylation alterations from multiple aspects were comprehensively studied by N-glycoproteomic analysis. The average numbers of identified IGP, N-glycosites, and N-glycans of each treatment group were summarized in Figure 5a. To our surprise, these numbers identified in the palbociclib treatment group were all significantly higher than those in cisplatin and sequential groups (Fig. 5a, Supplementary Table S1, Supplemental digital content 7, http://links.lww.com/ACD/A548), indicating an increase of global glycosylation in the palbociclib treatment cells. Increasing numbers in all types of glycans were identified in the palbociclib-treated group (Fig. 5b). With the least growth inhibition effect, 200 nmol/L of palbociclib induced the most significant changes in N-glycosylation modification.

Fig. 5 N-glycoproteomic analysis of HeLa cells. (a) The number of identified N-glycoproteins, intact N-glycopeptides, N-glycosites, and N-glycans in four study groups. (b) Number of N-glycans of each type in four treatment groups. (c) Palbociclib-induced differently expressed glycoproteins was enrichment through the Metascape platform, and the selected representative terms were converted into a network layout. Each term is represented by a circle node, where its size is proportional to the number of input genes that fall under that term, and its color represents its cluster identity (i.e. nodes of the same color belong to the same cluster). Terms with a similarity score >0.3 are linked by an edge (the thickness of the edge represents the similarity score). The network is visualized with Cytoscape with a ‘force-directed’ layout and with edge bundled for clarity. One term from each cluster is selected to have its term description shown as a label. *P < 0.0001.

An unsupervised principal component analysis of IGP was performed (Supplementary Figure S6, Supplemental digital content 8, http://links.lww.com/ACD/A549). Compared with the control group, all treatment groups could be separated. To investigate whether these increased global glycosylation induced by palbociclib would enhance the cisplatin drug effect, we further compared the IGP of palbociclib treatment cells versus control. The enriched terms were clustered and converted into a network layout (Fig. 5c, Supplementary Material 5, Supplemental digital content 9, http://links.lww.com/ACD/A550). Extracellular matrix (ECM) organization, lysosome, hemostasis, viral entry into the host cell, and urokinase-type plasminogen activator and urokinase-type plasminogen activator receptor–mediated signaling (PID UPA UPAR) pathway were the first five altered terms induced by palbociclib treatment.

Glycoproteomics-based clustering of HeLa cells from four study groups

To investigate the heterogeneity of HeLa cells under different treatments, we leveraged the expression data of IGPs to cluster the samples. Based on the expression intensities of IGPs, the hierarchical cluster analysis method using Euclidean distance was used to generate five clusters (Fig. 6a). The concordant expressed IGPs that have been attributed in the same cluster suggested an intrinsic biological connection between them. Next, we analyzed the composition of the N-glycan types in these five groups. The modifications of N-glycan types were diversely distributed in five IGP clusters, and the proportion patterns of four glycan types suggested the uniqueness of cluster 2. For cluster 2, the proportion of high-mannose glycan type was the lowest, and the proportion of only fucosylated glycan type was the highest (Fig. 6b). In cluster 2, the N-glycosylation degrees were most dramatically observed in palbociclib and sequential treatment groups.

Fig. 6 Clustering analysis of HeLa cells based on glycoproteomics data. (a) Hierarchical cluster analysis of intact N-glycopeptides. Each sample was displayed in columns. Intact glycopeptides used for the classification are displayed as rows. The five clusters are indicated by five different colors on the left side of the heatmap. The color of each cell indicates the Z-score (log2 of relative abundance scaled by intact glycopeptide SDs) of the intact glycopeptide in that sample. (b) Composition of N-glycan types for each cluster. (c) Most high frequently N-glycosylated proteins and their corresponding N-glycopeptides were distributed in each cluster. The Arabic numerals presented in each column represent the number of intact N-glycopeptides in each cluster, each cluster was represented by a different color of the cells. (d) Right panel: the protein LFQ intensity values of FOLR1, L1CAM, CD63, and LAMP1. Left panel: the heatmap of intact N-glycopeptides in cluster 2 derived from FOLR1, L1CAM, CD63, and LAMP1, respectively. LFQ, label-free quantification.

Through the Venn diagram (Supplementary Figure S7, Supplemental digital content 10, http://links.lww.com/ACD/A551), we identified 54 glycopeptides that were exclusively detected in cluster 1, 46 in cluster 2, seven in cluster 3, three in cluster 4, and 15 in cluster 5. Unique expression N-glycopeptides in clusters 1, 2, and 5 were further analyzed through function enrichment (Supplementary Figure S7, Supplemental digital content 10, http://links.lww.com/ACD/A551). Interestingly, drug sensitivity-related term membrane trafficking was only observed in unique N-glycopeptide in cluster 2.

Membrane proteins FOLR1, L1CAM, CD63, and LAMP1 were the top four frequently glycosylated glycoproteins, and their corresponding IGPs were mainly attributed to cluster 2 (Fig. 6c). Based on the expression level, seven IGPs of FOLR1 were attributed to cluster 2 among all 17 IGPs, eight IGPs of L1CAM were attributed to cluster 2 among all 16, for CD63 the proportion was nine out of 13, and LAMP1 was six out of 13. The heat map of corresponding IGPs in cluster 2 of these four glycoproteins was placed on the right panel of Figure 6d. The expression levels of these four proteins were presented on the left side as a contrast, there was no significant difference obtained through statistical analysis. The detailed altered N-glycosylations mapped to these four proteins were presented in Supplementary Table S2, Supplemental digital content 11, Supplementary Table S3, http://links.lww.com/ACD/A552, Supplementary Table S4, Supplemental digital content 12, http://links.lww.com/ACD/A553, and Supplementary Table S5, Supplemental digital content 13, http://links.lww.com/ACD/A554, Supplemental digital content 14, http://links.lww.com/ACD/A555.

Discussion

In this study, we aimed to generate proteomic and glycoproteomic profiles to disclose the mechanism behind the sequential drug regime. Through N-glycosylation, palbociclib could enhance the cytotoxic effect of cisplatin on HeLa cells. In our study, we designed a sequential treatment of palbociclib and cisplatin to HeLa cells, and a strong synergistic was observed in Figure 2d [22,23]. Notably, antagonistic effects were reported previously when palbociclib was directly combined with chemotherapeutic drugs [7,28]. Palbociclib could arrest cells at the G1 phase, and stop the continuous cell progression, therefore it could protect cells from the cytotoxic effect that depended on the M or S phase. In our study, in contrast to the direct combination, we used sequential treatment of palbociclib and cisplatin in HeLa cells instead. As Huang et al. [8] reported in 2020, pretreatment with palbociclib could stop cells at the G1 phase, after 24 h of drug withdrawal, cells were synchronized to pass through the G1 phase and enter into the S phase through the CDK4/6-cyclin D1-RB-E2F pathway, which increased the cytotoxic effect of cisplatin. A similar effect was also observed when palbociclib was sequentially used with paclitaxel [9]. In our study, palbociclib could improve the cell growth inhibition effect induced cisplatin (Fig. 2d).

In the palbociclib treatment group, there were six Gene Ontology terms related to DNA or RNA process, indicating the cell cycle was interrupted by palbociclib treatment. Different from Wang et al.’s [29] report, 10 μmol/L of palbociclib could induce DNA damage and accelerate cellular apoptosis, a dose of 50 times less of palbociclib in our study exhibited a major effect on cell cycle interruption. Apoptosis was enriched in cisplatin and sequential treatment cells. Since sequential treatment induces more growth inhibitory effects than cisplatin treatment, we suspected that sequential treatment induces more apoptotic effects than cisplatin alone. To further verify this hypothesis, we compared the DEPs between these two groups. According to Gene Ontology circle plots, the apoptosis effect was increased in sequential treatment groups when compared with cisplatin alone. After plotting apoptosis proteins with their fold change into the KEGG pathway, we identified a dramatic decrease in prosurvival protein MEK1/2 and significant increases in apoptotic protein JNK and BAK. Furthermore, we found that the LFQ intensity values of BAK and BAX that represented for intrinsic apoptosis pathway [30] were increased (Fig. 4d). Therefore, we concluded that sequential treatment could induce more cell death through the apoptosis effect compared with cisplatin treatment.

There were no obvious changes induced by palbociclib at the protein level that could enhance the cytotoxic effect of cisplatin, so we turned to a glycoproteomic study to offer us more detailed information. Palbociclib induced the least protein changes, but the global N-glycosylation modification changes were significantly increased. In the palbociclib treatment group, the number of identified IGPs was the highest, the number of N-glycosites was the most, and the types of N-glycans were the most diverse. Compared with vehicle control, the mapped N-glycosylation proteins induced by palbociclib were found to be involved in cell–ECM organization, lysosome, and PID UPA UPAR pathway [31–34], which were all reported to be closely related to the drug sensitivity in cancer cells. Therefore, we hypothesized that palbociclib-induced N-glycosylation might enhance the sensitivity of cisplatin.

The significant heterogeneity was investigated across four study groups by hierarchical cluster analysis, and five major clusters defined by IGP patterns were identified. We found that IGPs in cluster 2 might be the key reason for the synergistic effect. First, the glycopeptides attributed to cluster 2 were generally highly expressed in palbociclib and sequential groups, these might be the proteins that glycosylated by palbociclib first and then functioned as enhancers for cisplatin’s cytotoxic effect. Second, we found that the fucosylated glycan in cluster 2 was the highest among all clusters (Fig. 6b). Fucosylated glycoproteins are often expressed on cell surfaces, The highest fucosylated composition in cluster 2 suggested that the glycan modification changes on the cell surface proteins from cluster 2 were more closely associated with drug permeability and drug sensitivity than in other clusters [35,36]. Third, the unique N-glycoproteins expressed in cluster 2 were found to be related to membrane trafficking, which was a term related to drug sensitivity [37].

Glycans are important participants in many processes involved in cancer progression, ranging from cell–cell and ECM adhesion to intracellular and extracellular communication [38]. In our study, membrane proteins including FOLR1, L1CAM, CD63, and LAMP1 were identified to be the top four frequently glycosylated proteins by palbociclib, and their corresponding IGP were mainly distributed in cluster 2. Folate receptor α (FOLR1), is a glycosylphosphatidylinositol-anchored membrane protein wildly expressed in malignant tumors [39]. Huang et al. [40] have reported that FOLR1 could increase the sensitivity of cisplatin in vitro, and overexpressed FOLR1 in cancer cells was associated with higher cisplatin-induced cell death, however, related evidence in N-glycosylation modification was not observed. Like FOLR1, CD63 at the protein level was also reported to be associated with chemoresistance or chemosensitivity [41]. For L1CAM, Ganesh [42] reported that L1CAM high cells were more stem-like and more chemoresistant, these observations in the protein expression level suggested the function of L1CAM in the chemoresistant. A study regarding L1CAM glycosylation in cancer reported that aberrant glycosylation of L1CAM was associated with tumor progress mechanism, which implied the role it played might allow it to be a novel treatment target. In our study, we found that the protein expression changes of FOLR1, L1CAM, CD63, and LAMP1 were not as obvious as their N-glycosylation changes in the palbociclib treatment group. The N-glycosylation probably played a key role in the enhancement of cisplatin-induced cell death. Therefore, the post-translation modification by glycosylation could play an essential role in drug sensitivity and drug resistance, and the recruitment of N-glycoproteomic analysis could be a potential approach to decipher the complex mechanism behind drug combination or drug sequential use.

In conclusion, the LC-MS-based label-free proteomic and glycoproteomic study could track the mechanism behind the drug sequential use regime. With the advantage of the high throughput of LC-MS detection, thousands of proteins and N-glycosylation proteins with their intensities were detected in a single run. Not limited to palbociclib and cisplatin, the integration analysis of proteomic and glycoproteomic could be applied to promote our understanding of all potential drug combinations or sequential treatments. Second, we found that the palbociclib pretreatment could induce significant N-glycosylation alterations in major membrane proteins that are closely related to drug sensitivity and drug resistance. The N-glycosylation modifications of the key membrane proteins rather than the expression levels of these proteins finally contributed to the synergistic effect. It is suggested that inhibiting N-glycosylation can be a novel way to modulate the sensitivity of chemotherapy.

Acknowledgements

L.Y.: conceptualization, methodology, formal analysis, and writing – original draft. B.M., X.G, and Y.J.: formal analysis and writing – review and editing. X.S. and Z.X.: conceptualization, methodology, formal analysis, and writing – review and editing.

This work was supported by the National Key R&D Program of China (2022YFF0705001 and 2022YFF0705200) and the Research Project of the National Institute of Metrology (AKYZZ2224).

The mass spectrometry proteomics and glycoproteomics data have been deposited to a public repository iProX, which can be accessed by searching project number: IPX0004682000 at https://www.iprox.cn/.

Conflicts of interest

There are no conflicts of interest.

Supplementary Material

* Zhichao Xue and Xuping Shentu contributed equally to the writing of this article.

Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website, www.anti-cancerdrugs.com.
==== Refs
References

1 Mitra T Elangovan S . Cervical cancer development, chemoresistance, and therapy: a snapshot of involvement of microRNA. Mol Cell Biochem 2021; 476 :4363–4385.34453645
2 Bayat Mokhtari R Homayouni TS Baluch N Morgatskaya E Kumar S Das B . Combination therapy in combating cancer. Oncotarget 2017; 8 :38022–38043.28410237
3 Wang L Wang H Song D Xu M Liebmen M . New strategies for targeting drug combinations to overcome mutation-driven drug resistance. Semin Cancer Biol 2017; 42 :44–51.27840276
4 Ruiz FJ Sundaresan A Zhang J Pedamallu CS Halle MK Srinivasasainagendra V . Genomic characterization and therapeutic targeting of HPV undetected cervical carcinomas. Cancers (Basel) 2021; 13 :4551.34572780
5 Xue Z Lui VWY Li Y Jia L You C Li X . Therapeutic evaluation of palbociclib and its compatibility with other chemotherapies for primary and recurrent nasopharyngeal carcinoma. J Exp Clin Cancer Res 2020; 39 :262.33243298
6 McClendon AK Dean JL Rivadeneira DB Yu JE Reed CA Gao E . CDK4/6 inhibition antagonizes the cytotoxic response to anthracycline therapy. Cell Cycle 2012; 11 :2747–2755.22751436
7 Franco J Witkiewicz AK Knudsen ES . CDK4/6 inhibitors have potent activity in combination with pathway selective therapeutic agents in models of pancreatic cancer. Oncotarget 2014; 5 :6512–6525.25156567
8 Huang Y Wu H Li X . Novel sequential treatment with palbociclib enhances the effect of cisplatin in RB-proficient triple-negative breast cancer. Cancer Cell Int 2020; 20 :501.33061853
9 Cretella D Fumarola C Bonelli M Alfieri R La Monica S Digiacomo G . Pre-treatment with the CDK4/6 inhibitor palbociclib improves the efficacy of paclitaxel in TNBC cells. Sci Rep 2019; 9 :13014.31506466
10 Jia J Zhu F Ma X Cao Z Cao ZW Li Y . Mechanisms of drug combinations: interaction and network perspectives. Nat Rev Drug Discov 2009; 8 :111–128.19180105
11 Zhao Y Wang M Meng B Gao Y Xue Z He M . Identification of dysregulated complement activation pathways driven by n-glycosylation alterations in T2D patients. Front Chem 2021; 9 :677621.34178943
12 Zeng W Zheng S Mao Y Wang S Zhong Y Cao W . Elevated N-glycosylation contributes to the cisplatin resistance of non-small cell lung cancer cells revealed by membrane proteomic and glycoproteomic analysis. Front Pharmacol 2021; 12 :805499.35002739
13 Sun X Chiu JF He QY . Application of immobilized metal affinity chromatography in proteomics. Expert Rev Proteomics 2005; 2 :649–657.16209645
14 He QY Chiu JF . Proteomics in biomarker discovery and drug development. J Cell Biochem 2003; 89 :868–886.12874822
15 Pinho SS Reis CA . Glycosylation in cancer: mechanisms and clinical implications. Nat Rev Cancer 2015; 15 :540–555.26289314
16 Haga Y Ueda K . Glycosylation in cancer: its application as a biomarker and recent advances of analytical techniques. Glycoconj J 2022; 39 :303–313.35156159
17 Taniguchi N Hancock W Lubman DM Rudd PM . The second golden age of glycomics: from functional glycomics to clinical applications. J Proteome Res 2009; 8 :425–426.19133724
18 Silsirivanit A . Glycosylation markers in cancer. Adv Clin Chem 2019; 89 :189–213.30797469
19 Waniwan JT Chen Y-J Capangpangan R Weng S-H Chen Y-J . Glycoproteomic alterations in drug-resistant nonsmall cell lung cancer cells revealed by lectin magnetic nanoprobe-based mass spectrometry. J Proteome Res 2018; 17 :3761–3773.30261726
20 Ji Y Wei S Hou J Zhang C Xue P Wang J . Integrated proteomic and N-glycoproteomic analyses of doxorubicin sensitive and resistant ovarian cancer cells reveal glycoprotein alteration in protein abundance and glycosylation. Oncotarget 2017; 8 :13413–13427.28077793
21 Clarke JD Novak P Lake AD Hardwick RN Cherrington NJ . Impaired N-linked glycosylation of uptake and efflux transporters in human non-alcoholic fatty liver disease. Liver Int 2017; 37 :1074–1081.28097795
22 Chou TC Talalay P . Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv Enzyme Regul 1984; 22 :27–55.6382953
23 Chou TC . Drug combination studies and their synergy quantification using the Chou–Talalay method. Cancer Res 2010; 70 :440–446.20068163
24 Xue Z Zeng J Li Y Meng B Gong X Zhao Y . Proteomics reveals that cell density could affect the efficacy of drug treatment. Biochem Biophys Rep 2023; 33 :101403.36561432
25 Yang Y Cheng J Wang S Yang H . StatsPro: systematic integration and evaluation of statistical approaches for detecting differential expression in label-free quantitative proteomics. J Proteomics 2022; 250 :104386.34600153
26 Kanehisa M Sato Y Kawashima M Furumichi M Tanabe M . KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 2016; 44 :D457–D462.26476454
27 Kanehisa M Goto S . KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 2000; 28 :27–30.10592173
28 Fassl A Sicinski P . Chemotherapy and CDK4/6 inhibition in cancer treatment: timing is everything. Cancer Cell 2020; 37 :265–267.32183946
29 Wang TH Chen C-C Leu Y-L Lee Y-S Lian J-H Hsieh H-L . Palbociclib induces DNA damage and inhibits DNA repair to induce cellular senescence and apoptosis in oral squamous cell carcinoma. J Formos Med Assoc 2021; 120 :1695–1705.33342707
30 Pena-Blanco A Garcia-Saez AJ . Bax, Bak and beyond – mitochondrial performance in apoptosis. FEBS J 2018; 285 :416–431.28755482
31 Blehm BH Jiang N Kotobuki Y Tanner K . Deconstructing the role of the ECM microenvironment on drug efficacy targeting MAPK signaling in a pre-clinical platform for cutaneous melanoma. Biomaterials 2015; 56 :129–139.25934286
32 Zhitomirsky B Assaraf YG . Lysosomes as mediators of drug resistance in cancer. Drug Resist Updat 2016; 24 :23–33.26830313
33 Smith HW Marshall CJ . Regulation of cell signalling by uPAR. Nat Rev Mol Cell Biol 2010; 11 :23–36.20027185
34 Mazar AP . The urokinase plasminogen activator receptor (uPAR) as a target for the diagnosis and therapy of cancer. Anticancer Drugs 2001; 12 :387–400.11395568
35 Staudacher E Altmann F Wilson IB März L . Fucose in N-glycans: from plant to man. Biochim Biophys Acta 1999; 1473 :216–236.10580141
36 Listinsky JJ Siegal GP Listinsky CM . Alpha-L-fucose: a potentially critical molecule in pathologic processes including neoplasia. Am J Clin Pathol 1998; 110 :425–440.9763028
37 Kumar A Ahmad A Vyawahare A Khan R . Membrane trafficking and subcellular drug targeting pathways. Front Pharmacol 2020; 11 :629.32536862
38 Magalhaes A Duarte HO Reis CA . Aberrant glycosylation in cancer: a novel molecular mechanism controlling metastasis. Cancer Cell 2017; 31 :733–735.28609653
39 Matsunaga Y Yamaoka T Ohba M Miura S Masuda H Sangai T . Novel anti-FOLR1 antibody-drug conjugate MORAb-202 in breast cancer and non-small cell lung cancer cells. Antibodies (Basel) 2021; 10 :6.33535554
40 Huang MJ Zhang W Wang Q Yang Z-J Liao S-B Li Li . FOLR1 increases sensitivity to cisplatin treatment in ovarian cancer cells. J Ovarian Res 2018; 11 :15.29433550
41 Khushman M Patel GK Laurini JA Bhardwaj A Roveda K Donnell R . Exosomal markers (CD63 and CD9) expression and their prognostic significance using immunohistochemistry in patients with pancreatic ductal adenocarcinoma. J Gastrointest Oncol 2019; 10 :695–702.31392050
42 Ganesh K Basnet H Kaygusuz Y Laughney AM He L Sharma R . L1CAM defines the regenerative origin of metastasis-initiating cells in colorectal cancer. Nat Cancer 2020; 1 :28–45.32656539
