DIA-Based Proteomics Identifies IDH2 As a Targetable Regulator

1 DIA-based Proteomics Identifies IDH2 as a Targetable

2 Regulator of Acquired Drug Resistance in Chronic

3 Myeloid Leukemia

4 Wei Liu1,2,3,4, Yaoting Sun2,3,4, Weigang Ge5, Fangfei Zhang2,3,4, Lin Gan2,3,4, Yi Zhu2,3,4, 5 Tiannan Guo2,3,4#, Kexin Liu1#

6 1Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian, Liaoning,

7 China

8 2Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University,

9 Hangzhou, Zhejiang, China;

10 3Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine,

11 Hangzhou, Zhejiang, China;

12 4Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China;

13 5Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou 310024, China

15 #Correspondence: [email protected] (T.G.); [email protected] (K.L.) bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

16 Abstract 17 Drug resistance is a critical obstacle to effective treatment in patients with chronic 18 myeloid leukemia (CML). To understand the underlying resistance mechanisms in response 19 to imatinib (IMA) and adriamycin (ADR), the parental K562 cells were treated with low doses 20 of IMA or ADR for two months to generate derivative cells with mild, intermediate and severe 21 resistance to the drugs as defined by their increasing resistance index (RI). PulseDIA-based 22 quantitative proteomics was then employed to reveal the proteome changes in these 23 resistant cells. In total, 7,082 proteotypic proteins from 98,232 peptides were identified and 24 quantified from the dataset using four DIA software tools including OpenSWATH, 25 Spectronaut, DIA-NN, and EncyclopeDIA. Sirtuin Signaling Pathway was found to be 26 significantly enriched in both ADR- and IMA-resistant K562 cells. In particular, IDH2 was 27 identified as a potential drug target correlated with the drug resistance phenotype, and its 28 inhibition by the antagonist AGI-6780 reversed the acquired resistance in K562 cells to 29 either ADR or IMA. Together, our study has implicated IDH2 as a potential target that can be 30 therapeutically leveraged to alleviate the drug resistance in K562 cells when treated with 31 IMA and ADR. 32 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

33 Introduction 34 The treatment of chronic myeloid leukemia (CML) patients includes targeted therapy 35 (tyrosine kinase inhibitors, TKIs), chemotherapy, biological therapy, hematopoietic cell

36 transplant (HCT), and donor lymphocyte infusion (DLI) [1-3]. Chemotherapy inhibits the 37 rapidly proliferating tumor cells by interfering with cell replication. However, drug resistance 38 leads to failed chemotherapy treatment in 90% patients [4]. Adriamycin (ADR) is a traditional 39 chemotherapeutic drug that disturbs the DNA replication process, which can be 40 therapeutically leveraged against certain hematologic tumors. Using a K562 cell model, 41 various mechanisms have been uncovered to explain ADR-induced drug resistance, such as 42 transporter-mediated drug efflux [5, 6], altered mitochondrial function [7, 8], and changes in

43 survival-related signaling pathway including EGFR/ERK/ NF-κB /PTEN/AKT [9-11]. 44 As one of the most effective clinical regimens in the chronic phase, TKIs have 45 dramatically improved survival rate in CML patients [12]. Indeed, imatinib (IMA) is among the 46 first generation of TKIs and the first-line drug for CML treatment, which targets BCR-ABL1 47 and inhibits tumor growth [13]. Notably, the five-year survival rate of CML patients with IMA 48 treatment was increased to 89% [14]. However, about 20-25% of CML patients showed a 49 suboptimal response to IMA, who have likely developed drug resistance [15]. Several 50 mechanisms have been proposed to explain the failed IMA treatment in CML patients, 51 including for example altered conformation of the BCR-ABL1 kinase domain by mutations 52 that reduces its binding affinity to IMA [12]. Other resistance mechanisms independent of 53 BCR-ABL1 have also been reported, including P-glycoprotein upregulation, activation of 54 alternative PI3K/AKT, JAK-2, or MAPK-signaling [16, 17], changes in the intracellular 55 environment such as endoplasmic reticulum stress induced autophagy [18]. 56 Drug resistance remains a clinical hurdle to traditional chemotherapy and targeted 57 therapy [19]. Given its complex nature, both genetic mutations and non-genetic changes 58 (such as epigenetics) may contribute to a drug-resistance phenotype [20]. To establish a cell 59 model system and study drug resistance mechanism, we treated K562 cells with ADR or IMA 60 and obtained a series of K562 cells with different drug sensitivities, which were used to 61 recapitulate the process of acquired resistance. 62 The highly dynamic proteome, including changes in protein expression levels, bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

63 protein-protein interactions and post-translational modifications, contributes to the 64 phenotype in a cell [21]. Monitoring protein abundance through drug treatment may help 65 reveal the underlying resistance mechanism [22, 23]. Mass spectrometry (MS)-based 66 proteomics enables identification and quantification of thousands of proteins and thus offers

67 unique insights into the dysregulated pathways [24]. In particularly, data-independent 68 acquisition (DIA) is an effective proteomic method with rigorous quantitative accuracy and 69 reproducibility [25, 26]. Based on DIA-MS, our group has recently developed PulseDIA-MS 70 as an improved approach that utilizes gas phase fractionation to achieve a greater proteome 71 depth [27]. 72 In this work, we employed the pressure cycling technology (PCT)-based peptide 73 preparation [28, 29], and the PulseDIA-MS strategy to quantify the dynamic proteome 74 changes upon drug treatment, using time-series K562 drug-resistant cell line models treated 75 by ADR and IMA, respectively. Due to the lack of correspondence information between 76 precursor ions and fragment ions in DIA data, DIA data analysis has become a huge 77 challenge. The different DIA tools have different scoring methods and core algorithm for 78 peptides prediction, which may lead to certain technical deviations in the analytical results of 79 the same DIA data [30]. To reduce the technical deviations of the quantitative proteome by 80 DIA software tools, we quantified the proteome with four commonly used independent tools, 81 Spectronaut [31], DIA-NN [32], EncyclopeDIA [33] and OpenSWATH [34]. With this cell 82 model and quantitative approaches, we were able to pinpoint and characterize pathways 83 that were significantly altered upon ADR or IMA treatment. Notably, we identified IDH2 a 84 previously unknown potential target that can be therapeutically leveraged to reverse drug 85 resistance in K562 cells. 86 Results 87 Establishment of drug-resistance cell models. Drug-resistant K562 cell models were 88 developed as shown in the workflow (Fig. 1A). The parental K562 cells were sensitive to 89 ADR and IMA treatment, and then treated with increasing concentrations of each drug for 90 one, two, and four weeks to obtain derivative cell lines with differential drug sensitivities. 91 Each model contains three time points during the drug resistance acquisition, with each time 92 point containing cells showing a different degree of drug sensitivity (Fig. 1A, method). bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

93 At this point, we have generated derivative K562 cells with different degrees of IMA 94 resistance: mild, intermediate and severe, which were defined by a resistant index (RI) at 95 2.76, 6.41 and 7.06 (Fig. 1B), respectively, as shown in Table 1. Similarly, we have mild, 96 intermediate, and severe ADR-resistant K562 cells with a RI at 2.00, 3.49 and 11.59, 97 respectively (Fig. 1C; Table 1). These K562 cells with different degrees of IMA and ADR 98 resistance were all collected for PCT processing and further PulseDIA-MS analysis (Fig. 99 2A). 100 Peptide and protein identification. PCT-assisted peptide preparation and 101 PulseDIA-MS were then carried out to analyze the parental K562 cells and the derivative 102 resistant cells in biological triplicates (Fig. 2A). In total, 98,232 peptides, 8630 protein 103 groups,7082 proteotypic proteins were quantified from 26 independent PulseDIA-MS runs 104 (Fig. 2B, D; Table. S1). 30,289 peptides and 4,493 proteotypic proteins were consistently 105 quantified by four DIA software (Fig. 2C, E). We used these commonly identified proteins for 106 the subsequent quantitative analysis of proteome changes during the development of drug 107 resistance in K562 cells. 108 Quality control (QC) of PulseDIA proteome dataset. We examined the reproducibility 109 of PulseDIA data by calculating the Pearson correlation coefficient between technical 110 duplicates and the coefficient of variation (CV) of the protein intensity among the biological 111 triplicates. Using four different DIA analysis tools as described above, the five pairs of 112 technical duplicates showed a strong correlation (r>0.9) (Fig. 3A), and the median CV of 113 protein intensity among biological triplicates was around 20% (Fig. 3B). These results

114 confirmed the high reproducibility of MS data acquired by the PulseDIA method. To compare 115 the quantified result of the same peptides and proteins in the four DIA software, we 116 calculated the Spearman correlation of the overlapped 30,289 peptides and 4,493 proteins 117 in four DIA software quantified. At the peptide and protein levels, DIA-NN and Spectronaut 118 exhibited the strongest correlation (r >0.9), with the correlation of any two DIA software as no 119 less than 0.85 (Fig. 3C, D). To check whether the quantified proteome thus acquired classify 120 different drug resistance model, we performed also principal component analysis (PCA) 121 analysis between the cells in ADR- and IMA-resistant cells, and parental K562 cells. The 122 result showed that the two resistant cell lines and parental K562 cells were separated into bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

123 three clusters as analyzed by the four software tools (Fig. 3E-H). 124 Dynamic proteomic changes during acquisition of drug resistance. To minimize 125 the statistical variation for each analytic step, these commonly identified 4,493 proteins were 126 selected and those with less than 25% missing ratio in each derivative resistant cell line 127 were subject to the analysis of variance (ANOVA). Proteins with a p value less than 0.05 128 were selected for further downstream analysis. 129 By fuzzy c-means clustering [35, 36], we identified four clusters related to the 130 resistance to ADR and IMA (Fig. S1; Fig. 4), amongst which only those that were 131 continuously upregulated and downregulated were further selected and studied (Fig. 4). In 132 addition, we selected 1035 (by Spectronaut), 1273 (by EncyclopeDIA), 1088 (by 133 OpenSWATH), and 2161 (by DIA-NN) proteins related to resistance to ADR, (Table S2; Fig. 134 4A), and 1662 (EncyclopeDIA), 747 (OpenSWATH), 950 (Spectronaut) and 1027 (DIA-NN) 135 proteins related to resistance to IMA (Table S3; Fig. 4B). 136 Activated sirtuin signaling pathway and abnormal mitochondrial function in drug 137 resistant K562 cell. The selected proteins from the four DIA analytic tools as described 138 above in resistance to ADR and IMA were further analyzed by IPA (Table S4, S5), with the 139 top three pathways (sorted by p value) as listed in Figure 5A and 5B. Notably, EIF2 signaling 140 and sirtuin signaling pathway were enriched from as least three DIA tools related to ADR 141 resistance. In contrast, sirtuin signaling pathway and oxidative phosphorylation were 142 frequently enriched related to IMA resitance (Fig. 5A, B). These data indicate that the sirtuin 143 signaling pathway was significantly enriched in both types of drug resistance with a positive 144 activation mode (Z-score>0, Table 2). 145 We then focused on the proteins involved in the sirtuin signaling pathway, whose 146 expression levels were shown in a heatmap (Fig. 5C). As a result, 16 proteins, which were 147 involeved in sirtuin signaling pathway, were quantified between all four DIA analytic tools in 148 model ADR (Fig. 5D), and 5 proteins in model IMA (Fig. 5E). The overlapped proteins 149 between all four DIA analytic tools showed the same expression pattern in the analysis 150 results of four DIA software (Fig. 5C). Among the overlapped proteins, 13 out 16 related to 151 ADR resistance were involved in maintaining mitochondrial function, and three out of five 152 related to IMA resistance participated in the formation of the mitochondrial respiratory chain bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

153 (Fig. 6A). 154 NDUFB1, NDUFB6, NDUFA4, and NDUFA6 are the subunits of NADH-ubiquinone

155 oxidoreductase (Complex I) of the mitochondrial respiratory chain (Fig. 6A) [37]. UQCRFS1 156 is a subunit of ubiquinol-cytochrome c oxidoreductase (complex III) (Fig. 6A). The main 157 physiological function of Complex I and Complex III is to oxidize NADH, transfer electrons to 158 ubiquinone and from ubiquinone to cytochrome C, and carry out the next electron transfer in 159 the mitochondrial respiratory chain (Fig. 5A) [38]. In addition, Complex 1and Complex III also 160 regulate mitochondria production of reactive oxygen species (ROS) [39]. Our results showed 161 decreased expression levels of these Complex I and III components (Fig. 6A, B), suggesting

162 low oxidative stress levels in drug-resistant cells. 163 We also identified upregulation of succinate dehydrogenase complex iron sulfur subunit 164 B (SDHB), which participates in the electron transfer process of succinate dehydrogenase 165 (Complex II) from succinate to ubiquinone (Fig. 6A, B), and when mutated, is closely related 166 to pheochromocytoma [40]. 167 Voltage dependent anion channel proteins (VDACs) are the most abundant protein [41] 168 on the outer mitochondrial membrane (Fig. 6A), and function to maintain free diffusion of 169 small molecules inside and outside the mitochondrial membrane [42]. In tumor cells, the 170 interaction between VDAC and hexokinase inhibits apoptosis [43], and therefore targeting 171 both molecules could potentially offer improved anti-tumor benefits. Our results showed that 172 the three VDAC isoforms, namely VDAC1, VDAC2, and VDAC3, were significantly 173 downregulated upon acquision of ADR resistance (Fig. 6C), suggesting that targeting VDAC 174 may not be an effective strategy in drug-resistant tumor cells. 175 The translocase of the outer mitochondrial membrane complex proteins (TOMMs) 176 regulates entry of mitochondrial protein precursors into the mitochondria cytoplasm (Fig. 6A)

177 [44]. Our data showed that the subunits of TOMMs, TOMM40, TOMM5, TOMM6, TOMM22, 178 and TOMM20, were significantly downregulated in cells resistant to ADR or IMA (Fig. 5C; Fig. 179 6A, D). In contrast, we found increased expression of TOMM34 upon acquision of drug 180 resistance (Fig. 5D). Located in the cytoplasm, TOMM34 is known to interact with HSP70

181 and HSP90, and regulate the activity of ATPase [45]. High expression of TOMM34 has been

182 found in colon cancer[46], breast cancer[47], ovarian cancer [48]. bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

183 Glutaminase (GLS) can convert glutamine into glutamic acid, which constitutes the major

184 source for α-ketoglutarate (α-KG) production(Fig. 6A) [49, 50], which promotes cell 185 differentiation through dioxygenases [51]. Our results showed significantly down-regulated 186 expression of GLS when K562 cells became resistant to ADR (Fig. 6E). 187 Isocitrate Dehydrogenase (NADP(+)) 2 (IDH2) catalyzes the oxidative 188 decarboxylation of isocitrate into α-ketoglutarate (α-KG ) in tricarboxylic acid cycle (TCA

189 cycle), and NADPH was synchronously produced at the biochemical process [52] (Fig. 6A). 190 NADPH is essential in protecting cells from oxidative damage[53]. We found the IDH2 191 abundance increased upon resistance to both ADR and IMA (Fig. 6E), indicating that IDH2 192 overexpression may promote cellular resistance against high doses of both therapeutic 193 drugs. 194 Taken together, our results showed significantly changed abundance of proteins 195 involved in mitochondria function that likely mediate survival of drug-resistant cells, which 196 may be therapeutically targeted to enhance drug sensitivity and response in tumor cells. 197 IDH2 is a potential target for reversing drug resistance in K562 cells. As discussed 198 above, among the dysregulated proteins, IDH2 was upregulated in K562 cells resistant to 199 both ADR and IMA. To validate its biological function, we utilized a selective inhibitor of IDH2 200 [54], AGI-6780, and treated sensitive or resistant K562 cells with ADR and IMA alone or 201 combined with AGI-6780, followed by monitoring cell survival with a cytotoxicity assay. Our 202 results showed that, compared with ADR and IMA treatment alone, combination with 203 AGI-6780 did not affect the survival in the parental sensitive K562 cells (Fig. 7A, B; Table 3). 204 However, in resistant K562 cells, the sensitivity to ADR and IMA was significantly increased

205 (IMA+AGI-6780, IC50=0.53μM; ADR+AGI-6780, IC50=0.29μM), when combined with 206 AGI-6780. We further show in IMA-resistant cells the reversal index of 2 μM and 4 μM 207 AGI-6780 was 1.92 and 2.94, respectively (Fig. 7C; Table 3), whereas in ADR-resistant cells, 208 the reversal index of 2 μM and 4 μM AGI-6780 was 1.60 and 2.74, respectively (Fig. 7D; 209 Table 3). Taken together, the cytotoxicity assay confirmed the ability of AGI-6780 to reverse 210 drug resistance in vitro, implicating IDH2 as a potential target to improve drug sensitibity and 211 clinical responses in patients. 212 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

213 Discussion 214 Two major paradigms have been widely proposed to elucidate the underlying 215 mechanism of drug resistance. One is based on the principle of Darwinian selection, which 216 posits that a fraction of tumor cells is inherently more tolerant and thus become enriched 217 after drug treatment, thereby gradually developing and exhibiting a drug-resistant phenotype. 218 The other mechanism, which is called Lamarckian induction, proposes that drug treatment 219 induces a resistance phenotype in tumor cells [55]. In our resistant cell line model, our 220 focused was on the proteins that showed continuous and consistent changes upon 221 acquisition drug resistance. The underlying cause for these changes is not immediately clear 222 from our work, but our results demonstrated that altered expression levels of these proteins 223 were responsible for the drug resistance phenotype in K562 cells, which could uncover 224 potentially important targets for managing and reversing drug resistance in clinics. 225 While establishing the resistance cell lines, we also constantly monitored drug 226 sensitivity in the parental sensitive K562 cells (Fig. 1B, C). Different from other similar 227 studies [56], we did not consider morphology changes as a parameter, primarily because 228 K562 cells are suspension cells in the medium in a spherical shape while the drug resistant 229 phenotype was established. After K562 cells with mild, intermediate and severe resistance 230 to ADR and IMA were developed and collected, we performed PCT-based peptides 231 extraction and PulseDIA-based mass spectrometry raw data acquisition. The efficient and 232 prompt sample preparation under high pressure (lysis in 45K psi; digest in 20K psi) by PCT 233 ensures that the process of peptides extraction is reproducible and stable [29]. 234 Spectronaut[31], DIA-NN[32], EncyclopeDIA[33], and OpenSWATH[34] utilize different 235 algorithms to analyze the DIA data, and the results from these distinct analytic tools are 236 therefore expected to be different. To avoid the algorithm bias from an individual software 237 analysis, we used all four tools to analyze our PulseDIA data. Our results showed that the 238 coefficient of variation of protein intensity in three biological replicates was around 20% (Fig. 239 3B). For PulseDIA based mass spectrometry raw data acquisition, we identified and 240 quantified 83.1% (7082/8524) proteotypic proteins of the DDA library from 26 samples by the 241 four DIA datasets, and 63.4% (4493/7082) proteotypic proteins were detected by all four 242 tools (Table. S1; Fig. 2D, E). The PulseDIA data stability was assessed by the Pearson bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

243 correlation of two technical replicates, which was no less than 0.9 (Fig. 3A). The PCA result 244 demonstrated that the resistant K562 cells can be discriminated from the parental cells at 245 the whole proteome level (Fig. 2E-H). These high-quality mass spectrometry data provides a 246 key basis for our subsequent data mining analysis. 247 We independently analyzed 4493 proteins identified by all four tools. Based on cluster 248 analysis and ANOVA (p≤0.05), we selected proteins that exhibited continuous changes 249 during development of IMA and ADR resistance (Table S2, S3; Fig. 4), followed by IPA 250 analysis. Pathway analysis revealed common characteristics associated with drug 251 resistance. Significant changed stress signaling pathways including Oxidative 252 Phosphorylation and the Sirtuin Signaling Pathway (Fig. 5A, B) are known to enhance the 253 resistance and plasticity of tumor cells[57-59]. We then decided to focus on the identified 254 proteins involved in the Sirtuin Signaling Pathway as identified by all four DIA tools, including 255 IDH2, NDUFB1, NDUFB6, NDUFA4, NDUFA6, SHDB, and GLS, which are involved in 256 mitochondrial ATP generation (Fig. 5D, E; Fig. 6A)[60, 61]. These results suggest that 257 inhibiting ATP production and blocking P-gp energy supply could potentially enable reversal 258 of drug resistance [5, 62]. 259 IDH2 mutation catalyzes D-2-hydroxyglutarate (2-HG) production, which leads to 260 competitive inhibition of α-KG-dependent DNA demethylases and consequently promotes 261 tumorigenesis [51]. Preclinical experiments have shown that IDH2 inhibitors promote 262 leukemic cell differentiation in 40% of the patients with relapsed/refractory AML[54]. Here, 263 we used the selective IDH2 inhibitor AGI-6780, and demonstrated IDH2 as a potential target 264 for reversing drug resistance in tumor cells. Recent research has shown that 5 μM AGI-6780 265 selectively impaired wild type IDH2 enzymatic activity in multiple myeloma cells in vitro[61]. 266 In contrast, our data showed no effects on cell proliferation even 48 h after 4 μM AGI-6780 267 treatment in either sensitive or resistant K562 cells (Fig. S2). As a potential therapeutic 268 avenue for reversing drug resistance, IDH2 inhibitors are specifically efficacious in resistant 269 tumors cells.

270 Derivative cell lines that have developed drug resistance could provide an important 271 and informative model to help elucidate the underlying mechanism that can be strategically 272 leveraged to manipulate and improve the sensitivity of tumor cells to therapeutic treatment. bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

273 Single-cell proteomics studies of melanoma-derived cells with different levels of drug 274 sensitivity revealed changes in intracellular signals before drug resistance has developed 275 [22]. By constructing a cisplatin-resistant neuroblastoma cell line, Olga et al. characterized 276 the epithelial to mesenchymal transition during the development of drug resistance [56]. 277 Herein, we have developed multiple K562 cell lines with different degrees of resistance to 278 ADR and IMA treatment, which could be used for preclinical investigation of the molecular 279 and cellular mechanisms that underlie the therapeutic resistance in CML patients. We further 280 identified and characterized IDH2 as a potentially useful target that can be utilized to 281 enhance tumor cell sensitivity to targeted treatment. 282 283 Acknowledgments 284 This work is supported by grants from National Key R&D Program of China (No. 285 2020YFE0202200), the National Natural Science Foundation of China (81874324, 286 81972492, 21904107), Zhejiang Provincial Natural Science Foundation for Distinguished 287 Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program 288 (20190101A04), and Westlake Education Foundation. We thank Westlake University 289 Supercomputer Center for assistance in data storage and computation. 290 Data availability 291 All data are available in the manuscript or the supplementary material. The proteomics data 292 are deposited in ProteomeXchange Consortium. All the data will be publicly released upon

293 publication. The project data analysis codes are deposited in GitHub. 294 Declaration of interests 295 NA bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

296 Materials and Methods 297 Establishment of drug-resistance K562 cell models 298 The parental sensitive K562 cells were purchased from Nanjing KeyGen Biotech Co., Ltd. 299 (Nanjing, China) and authenticated via ShortTandem Repeat (STR) profiling by Shanghai 300 Biowing Applied Biotechnology Co., Ltd (Shanghai, China) on Feb. 28, 2019. The parental 301 K562 cells were cultured in RPMI medium (Cromwell, USA) with 10% fetal bovine serum 302 (Waltham, USA) and 1% penicillin-streptomycin (HyClone, USA) at 37 °C with 5% CO2 and 303 95% humidity. 304 The building of drug-resistance K562 cell models includes three phases (Fig. 1A). In the 305 first phase, 1X105 K562 cells were treated with 0.1 μM ADR (A1) or IMA (I1). After one week, 306 K562 cells were cultured to about 10 million cells. Then drugs were removed by 307 centrifugation and cells were divided into four aliquots for cell cytotoxicity determination, 308 subsequent drug-resistance induction, cell collection, and cell freezing. In the second phase, 309 the concentration of ADR and IMA was increased to 0.4 μM (A2) and 0.8 μM (I2), 310 respectively. The treatment lasted for two weeks. In the third phase, the concentration of 311 ADR and IMA increased to 0.8 μM (A3) and 1.6 μM (I3), respectively. The treatment lasted 312 for four weeks. In this way, we established two drug-resistance K562 models for ADR and 313 IMA, separately. 314 Cytotoxicity assay 315 The cytotoxicity of Adriamycin (CAS: 25316-40-9, meilunbio, Dalian, China), imatinib 316 mesylate (CAS: 220127-57-1, meilunbio, Dalian, China) or AGI-6780 (CAS: 1432660-47-3, 317 MedChemExpress, New Jersey, USA) to native K562 cells and the model cells were 318 detected by cell counting kit-8 (CCK-8) (Bimake, Houston, USA). Cells were planted into 319 96-well plates in a density of 5000 cells/100uL medium/well. After 24h, cells were treated 320 with drugs for 48h. After 48h, 10μL CCK-8 was added to each well and incubated about 2h in 321 dark. The absorbance value of each well was determined by a Synergy™ H1 (BioTek, State 322 of Vermont, USA) at 450nm. The IC50 value was calculated by SPSS 22.0. 323 PCT based peptide extraction 324 The workflow of PCT based peptide extraction is described as Shao et al [29]. For each 325 sample, 500,000 cells were harvested and cleaned by PBS three times to remove all traces bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

326 of fetal bovine serum. 327 Cells were transferred into PCT-Microube (Pressure Biosciences Inc.) with 30uL lysis 328 buffer, 5 uL 1ug/uL DNAase (STEMCELL) and 15uL 0.1M ammonium bicarbonate (ABB) 329 (GENERAL-REAGENT®). The lysis buffer includes 6M urea (SIGMA-ALDRICH®) and 2 M 330 thiourea (SIGMA-ALDRICH®. Then the cells were lysis by Barocycler NEP2320-45k 331 (PressureBioSciences Inc.) with 90 cycles containing 25 s of 45,000 p.s.i. high pressure plus 332 10 s at ambient pressure, at 30 °C The lysate solution was added 2.5 µL 800 mM 333 tris(2-carboxyethyl)phosphine (ALDRICH®) and 5 µL 100 mM iodoacetamide (SIGMA®) to in 334 PCT-Micro tube to dilute into a final concentration of 10 mM and 40 mM, followed by a 30 335 min incubation in the dark with gentle vortexing (800 rpm) at room temperature in shaker. 336 After reduction and alkylation, the proteins solution were added with 57.5µL 0.1 M 337 ABB and 25 µL 0.1 mg/mL Lys-C (Hualishi) and digested using Barocycler NEP2320-45k 338 (Pressure Biosciences Inc., MA, USA) with 45 cycles containing 50 s of 20,000 p.s.i. high 339 pressure plus 10 s at ambient pressure, at 30 °C. 340 After Lys-C digestion, the solution was added with 10 µL 0.2 mg/mL trypsin (Hualishi) 341 and were tryptic digested by Barocycler NEP2320-45k with 90 cycles containing 50 s of 342 20,000 p.s.i. high pressure plus 10 s at ambient pressure, at 30. Then, 15 µL 10% 343 trifluoroacetic acid (Fisher Scientific®) was added to the lysate solution at a final 344 concentration of 1% to stop digestion. 345 Then the digested peptides were cleaned in micro spin columns (The Nest Group Inc.) 346 and dried in CentriVap DNA Vacuum Concentrators (LABCONCO). Peptides were 347 redissolved in ms buffer (0.1% formic acid and 2% acetonitrile in HPLC water). The peptide 348 concentration was measured using ScanDrop2 (Analytik Jena). 349 PulseDIA mass spectrometry 350 The PulseDIA mass spectrometry method was performed as previously described [63]. 351 The redissolved peptides of each sample were acquired and analyzed by EASY-nLC™ 1200 352 System (Thermo Fisher Scientific™) coupled to a QE HF-X mass spectrometer (Thermo 353 Fisher Scientific™). The MS1 was acquired in an m/z range of 390 to 1210 with the 354 resolution at 60,000, AGC target of 3e6, and the maximum ion injection time of 80 ms. The 355 MS2 was performed with the resolution at 30,000, AGC target of 1e6, and the maximum ion bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

356 injection time of 50 ms. Different from the conventional DIA method (the 400-1200 m/z mass 357 range is divided into 24 windows) [64], the PulseDIA symmetrically divided each window into 358 four parts and acquired the data of each part independently. For one sample, we acquired 359 four injections with different MS2 range. For each pulse acquisition, 0.2 μg of peptides was 360 injected and separated across a linear 45 min LC gradient (from 8% to 40% buffer B) at a 361 flowrate of 300 nL/min (precolumn, 3μm, 100 Å, 20mm*75μm i.d.; analytical column, 1.9um, 362 120 Å,150mm*75um i.d.). Buffer A was HPLC-grade water containing 0.1% FA, and buffer B 363 was 80%ACN, 20%H2O containing 0.1%FA. 364 Quality control samples 365 Cells with mild, intermediate and severe resistance to ADR and IMA were analyzed in 3 366 duplicates for peptide extraction and acquisition as biological replicates. Five samples were 367 repeatedly injected by PulseDIA as the technical replicates to evaluate the data quality. 368 Generating Spectral library for DIA-MS 369 To build the spectral library, we acquired 37 data-dependent acquisition (DDA) files 370 including 10 in-solution digestion files, 7 PCT-assisted digestion files and 20 high-PH 371 fraction files on a QE-HFX mass spectrometer in DDA mode. Library was built by 372 Spectronaut (version: 13.5.190902.43655) for Spectronaut and DIA-NN analysis. Library 373 was built by OpenSWATH (version 2.0) for OpenSWATH and EncyclopeDIA analysis. 374 For Spectronaut library building, Carbamidomethyl (C) was set as the fixed modification, 375 and Acetyl (Protein N-term) and Oxidation (M) were set as the variable modification. Data 376 were searched against the SwissProt Human database in February 2018. Q value (FDR) 377 cutoff on precursor and protein was 0.01. Finally, a K562 library including identified 191,008 378 precursors, 133,025 peptides, 8226 protein groups and 8313 proteins was built. 379 For OpenSWATH library building, the pFind[65] (version 2.8) was used as a search 380 engine with the parameters including carbamidomethyl (C) as a fixed modification and 381 methionine (M) and oxidation (O) as a variable modification. Other parameters were 382 performed as default. Data were searched against the SwissProt Human database in 383 February 2018 and further filtered the data with FDR≤1%. DDA library was built 384 according to the workflow [66] (http://openswath.org/en/latest/ docs/pqp.html#id3). Finally, a 385 K562 library including identified 110,583 transition groups, 84,548 target peptides, 84,910 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

386 decoy peptides, 9511 target protein groups, 9575 decoy protein groups and 7935 target 387 proteotypic proteins was built. 388 Base on the analysis of K562 Spectral library, we finally identified a total of 8524 389 proteotypic proteins and up to 10,732 protein groups. 390 PulseDIA data analysis 391 Library-based PulseDIA data analysis was performed by Spectronaut, OpenSWATH, 392 DIA-NN, and EncyclopeDIA. 393 For Spectronaut (version: 13.5.190902.43655) analysis, the default setting of 394 library-based DIA analysis was used for PulseDIA analysis. PulseDIA analysis was 395 performed according to the standard workflow in Spectronaut (Manual for Spectronaut, 396 available on the Biognosys website). The FDR was set to 1% at the peptide precursor level. 397 For DIA-NN analysis, we used the same library with Spectronaut analysis. Library 398 search was performed according to the DIA-NN manual 399 (https://github.com/vdemichev/DiaNN/blob/ master/ DIA-NN%20GUI%20manual.pdf). The 400 precursor FDR was set as 0.01. 401 For OpenSWATH (version: 2.4) analysis, the retention time extraction window was 300 402 seconds. Peptide precursors that were identified by OpenSWATH and pyprophet with 403 qvalue=0.01. 404 For EncyclopeDIA (versions: 0.9.0) analysis, the library was converted from 405 OpenSWATH library. The precursor, fragment, and library mass tolerance were set as 10 406 ppm for the PulseDIA data. 407 The peptide matrixes from four DIA software tools were converted to protein matrixed by 408 R code

409 （https://github.com/Allen188/PulseDIA/blob/master/Pulsedia_DIANNresult_combine.R）

410 Bioinformatics analysis 411 Soft cluster analysis was performed by R/Bioconductor package Mfuzz [36]. The 412 average protein intensity of the parental K562 cells and each type of resistant cells as the 413 input data for clustering. The time-series were separated according to the cell sensitivity to 414 ADR or IMA, with the initial being the parental K562 cells. Ingenuity Pathway Analysis (IPA, bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

415 QIAGEN) was performed to outline the significant canonical pathways [67].

416 Calculation of IC50 values

417 The IC50 values were calculated by a non-linear least squares regression model to fit the 418 data log (inhibitor) vs. normalized response in GraphPad Prism 6. The statistical significance 419 in different types of resistant cells was determined by one-way analysis of variance (ANOVA), 420 and the p value of <0.05 was regarded as statistically significant. In IPA, the p-value was 421 calculated using the right-tailed Fisher's exact test and a P value less than 0.05 is 422 considered significant[68]. 423 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

424 Tables 425 Table 1. IC50 values and resistance index of the derivative K562 cells.

IC50 (μM, 95%CI)a Resistance Model IMA ADR Indexb Native K562 cells 0.37(0.26 to 0.54) 0.29 (0.24 to 0.35) 1 Model IMA phase 1 0.74 (0.61 to 0.90) / 2 Model IMA phase 2 1.29 (1.06 to 1.58) / 3.49 Model IMA phase 3 4.29 (3.03 to 6.36) / 11.59 Model ADR phase 1 / 0.80 (0.51 to 1.24) 2.76 Model ADR phase 2 / 1.86 (1.5 to 2.30) 6.41 Model ADR phase 3 / 2.05 (1.32 to 3.32) 7.06 426 aIC50 values represent the best-fit values and 95% CI of three independent experiments 427 performed. bResistance Index was calculated by dividing the best-fit IC50 values of native 428 K562 cells in response to ADR and IMA to the best-fit IC50 values of model cells. 429 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

430 Table 2. The enrichment statistics of Sirtuin Signaling Pathway in four DIA software. Software P valuea Ratiob Z-scorec Model OpenSWATH 4.48 0.0825 1.155 Model IMA Spectronaut 13.7 0.155 2.414 Model IMA DIA-NN 8.97 0.134 2.449 Model IMA EncyclopeDIA 20.6 0.251 3.202 Model IMA OpenSWATH 9.87 0.144 2.4 Model ADR Spectronaut 6.8 0.12 1 Model ADR DIA-NN 21.8 0.296 1.336 Model ADR EncyclopeDIA 9.31 0.155 3.138 Model ADR 431 aP-value is the result of Fisher’s exact test. bRatio is the number of proteins from different 432 DIA software that maps to the pathway divided by the total number of proteins that map to 433 the same pathway. cZ-score is calculated by the IPA z-score algorithm to predict the direction 434 of change for the function. An absolute z-score of ≥ 2 is considered significant. bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

435 Table 3 IC50 values and reverse index of the derivative resistant K562 cells

a IC50 (μM, 95%CI) Resistance Reversal Drugs Native K562 Model IMA Model ADR Indexb Indexc 0.26 (0.19 to 1.55 (1.12 to 1 IMA / 5.96 0.36) 2.19) 0.35 (0.26 to 1.09 (0.80 to 1.92 IMA+AGI-6780(2 μM) / 3.11 0.48) 1.49) 0.26 (0.20 to 0.53 (0.27 to 2.94 IMA+AGI-6780(4 μM) / 2.03 0.33) 1.16) 0.37 (0.31 to 0.67 (0.49 1 adramycin / 1.81 0.45) to 0.95) adramycin+AGI-6780(2 0.38 (0.31 to 0.43 (0.27 1.60 / 1.13 μM) 0.46) to 0.68) adramycin+AGI-6780(4 0.44 (0.31 to 0.29 (0.18 / 0.66 2.74 μM) 0.61) to 0.47)

a 436 IC50 values represent the best-fit values and 95% CI of three independent experiments

b 437 were performed. Resistance Index was calculated by dividing the best-fit IC50 values of

c 438 native K562 cells to ADR or IMA by the best-fit IC50 values of model cells. Reversal Index 439 was calculated by dividing the values of Resistance Index in the absence ACI-6780 by the 440 Resistance Index in the presence of AGI-6780. 441 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

442 Figure legends 443 Figure 1. Establish derivitative K562 cells with mild, intermediate and severe 444 resistance to ADR and IMA. (A) Overview of the drug resistance model. (B) Native K562 445 cells and IMA-resistant K562 cells from each of the three phases were treated with a series 446 of cytotoxicity concentrations (4, 2, 1, 0.5……0 μM) IMA for 48 h. (C) Native K562 cells and 447 ADR-resistant K562 cells from each of the three phases were treated with a series of 448 concentrations (4, 2, 1, 0.5……0 μM) ADR for 48 h. The cell survival rate was calculated and 449 plotted in each group. The downward shift of the survival curves by increasing treating 450 concentration (I1, I2, I3 and A1, A2, A3) indicated suppressed proliferation. 451 Figure 2. PulseDIA raw data acquisition and analysis. (A) The workflow of peptide 452 extraction from samples and analysis of mass spectrometry raw data. (B) The numbers of 453 identified peptides by four DIA software tools, and their Venn diagram (C). (D)The numbers 454 of identified proteins by four DIA software, and their Venn diagram (E). 455 Figure3. PulseDIA proteome data quality control (QC) analysis. (A)The box plot showed 456 the Pearson correlation coefficient of 5 technical replicates in four DIA software

457 quantification. (B) The violin plot showed the distribution of the CV of each protein's 458 quantitative values in three biological replicates. Three lines of the black box inside the violin 459 represented lower quartile, median, higher quartile. (C) Spearman correlation coefficient of 460 overlapped 30,289 peptides quantitative values in four DIA software. (D) Spearman 461 correlation coefficient of overlapped 4493 proteotypic proteins quantitative values in four DIA 462 software. (E, F, G, H) PCA plot shows the distribution of the sample in the first two principal 463 component levels. The red, blue, green dots mean samples from model ADR, model IMA, 464 native K562 cells, respectively. 465 Figure 4. Protein cluster analysis. The cluster for proteins that were continuously 466 upregulated and downregulated from K562 cells resistant to ADR (A) and IMA (B).The

467 horizontal axis represented the progress of the model (K-native K562 cells, A1/I1‐first phase

468 of model ADR/IMA, A2/I2‐second phase of model ADR/IMA, A3/I3‐third phase of model

469 ADR/IMA). The vertical axis represents proteins expression changes in each cluster. 470 Figure 5. Enriched proteins that exhibit continuous changes as identified by IPA in bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

471 ADR- and IMA-resistant cells. (A) In ADR-resistant cells, the most significantly changed 3 472 pathways were enriched by IPA from four DIA software. (B) In IMA-resistant cells, the most 473 significantly changed 3 pathways were enriched by IPA from four DIA software. (C) The 474 heatmap showed the proteins (p value<0.05 calculated by ANOVA) involved in Sirtuin 475 signaling pathway from four DIA software expression in IMA- and ADR-resistant cells, with 476 the overlapped quantified proteins being highlighted in red. Each row indicated a protein, 477 and each column indicated a sample. The protein intensity matrix was normalized by 478 Z-score and colored in the heatmap. (D) Venn diagram showed the 16 overlapped proteins 479 involved in Sirtuin Signaling Pathway in ADR-resistant cells. (E) Venn diagram showed the 5 480 overlapped proteins involved in Sirtuin Signaling Pathway in IMA-resistant cells. 481 Figure 6. Proteins involevd in mitochondrial function. (A) Schemathic diagram of TCA 482 cycle and electron respiratory chain in mitochondria. (B) Normalized protein expression in 483 ADR- and IMA-resistant cells from four DIA software. 484 Figure 7. IDH2 enhanced the sensitivity to ADR and IMA in K562 cells. (A) and (C) The 485 parental sensitive and IMA-resisant K562 cells were treated with various concentrations (4, 486 2, 1, 0.5……0 μM) IMA alone or combined with 4 μM or 2 μM AGI-6780 for 48h. (B) and (D) 487 The parental sensitive and ADR-resisant K562 cells were treated with various 488 concentrations (4, 2, 1, 0.5……0 μM) ADR alone or combined with 4 μM or 2 μM AGI-6780 489 for 48h. The cell survival rate was calculated and plotted in each group. The downward shift 490 of the survival curves indicated suppressed proliferation. Columns are expressed as mean ± 491 SD. 492 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

493 Legends of Supplementary Files 494 Supplementary Figure 1. Protein cluster analysis. The protein cluster from model ADR (A) 495 and IMA (B).The horizontal axis represented the progress of the model (K-native K562 cells,

496 A1/I1‐first phase of model ADR/IMA, A2/I2‐second phase of model ADR/IMA, A3/I3‐third

497 phase of model ADR/IMA). The vertical axis represented changes in proteins expression in 498 each cluster. 499 Supplementary Figure 2. Changes in cell survival after different concentrations AGI-6780 500 (0, 0.5, 1, 2 4 μM) treated IMA-resistant (A), ADR-resistant (B) and parental K562 cell (C) for 501 48h. 502 Supplementary Table 1. Protein matrix of quantified proteins in 26 samples by DIA-NN, 503 Spectronaut, EncyclopeDIA and OpenSWATH, the quantitative protein abundance was log2 504 converted. 505 Supplementary Table 2. In ADR-resistant cells, the list of continuously changed proteins (p

506 ＜0.05) with associated p values.

507 Supplementary Table 3. In IMA-resistant cells, the list of continuously changed proteins (p

508 ＜0.05) with associated p values.

509 Supplementary Table. 4 IPA analysis of continuously changed proteins from ADR-resistant 510 cells. 511 Supplementary Table. 5 IPA analysis of continuously changed proteins from IMA-resistant 512 cells. 513 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

514 References 515 1. Radujkovic, A., et al., Donor Lymphocyte Infusions for Chronic Myeloid Leukemia Relapsing after 516 Allogeneic Stem Cell Transplantation: May We Predict Graft-versus-Leukemia Without 517 Graft-versus-Host Disease? Biol Blood Marrow Transplant, 2015. 21(7): p. 1230-6. 518 2. Radich, J.P., et al., Chronic Myeloid Leukemia, Version 1.2019, NCCN Clinical Practice Guidelines in 519 Oncology. J Natl Compr Canc Netw, 2018. 16(9): p. 1108-1135. 520 3. Pal, S.K., et al., Clinical Cancer Advances 2019: Annual Report on Progress Against Cancer From the 521 American Society of Clinical Oncology. Journal of Clinical Oncology, 2019. 37(10): p. 834-849. 522 4. Mansoori, B., et al., The Different Mechanisms of Cancer Drug Resistance: A Brief Review. Advanced 523 pharmaceutical bulletin, 2017. 7(3): p. 339-348. 524 5. Liu, W., et al., Targeting P-Glycoprotein: Nelfinavir Reverses Adriamycin Resistance in K562/ADR 525 Cells. Cell Physiol Biochem, 2018. 51(4): p. 1616-1631. 526 6. Sun, Y., et al., Targeting P-glycoprotein and SORCIN: Dihydromyricetin strengthens anti-proliferative 527 efficiency of adriamycin via MAPK/ERK and Ca(2+) -mediated apoptosis pathways in MCF-7/ADR and 528 K562/ADR. J Cell Physiol, 2018. 233(4): p. 3066-3079. 529 7. De Oliveira, F., et al., Effects of permeability transition inhibition and decrease in cytochrome c content 530 on doxorubicin toxicity in K562 cells. Oncogene, 2006. 25(18): p. 2646-55. 531 8. Li, R.J., et al., Down-regulation of mitochondrial ATPase by hypermethylation mechanism in chronic 532 myeloid leukemia is associated with multidrug resistance. Ann Oncol, 2010. 21(7): p. 1506-14. 533 9. Zhao, L., et al., Functional screen analysis reveals miR-3142 as central regulator in chemoresistance 534 and proliferation through activation of the PTEN-AKT pathway in CML. Cell Death Dis, 2017. 8(5): p. 535 e2830. 536 10. Jiang, L., et al., Ivermectin reverses the drug resistance in cancer cells through EGFR/ERK/Akt/NF-κB 537 pathway. J Exp Clin Cancer Res, 2019. 38(1): p. 265. 538 11. Dong, J., et al., Medicinal chemistry strategies to discover P-glycoprotein inhibitors: An update. Drug 539 Resist Updat, 2020. 49: p. 100681. 540 12. Holyoake, T.L. and G.V. Helgason, Do we need more drugs for chronic myeloid leukemia? Immunol 541 Rev, 2015. 263(1): p. 106-23. 542 13. Buchdunger, E., T. O'Reilley, and J. Wood, Pharmacology of imatinib (STI571). European journal of 543 cancer, 2002. 38: p. S28-S36. 544 14. Druker, B.J., et al., Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. New 545 England Journal of Medicine, 2006. 355(23): p. 2408-2417. 546 15. Milojkovic, D. and J. Apperley, Mechanisms of Resistance to Imatinib and Second-Generation Tyrosine 547 Inhibitors in Chronic Myeloid Leukemia. Clin Cancer Res, 2009. 15(24): p. 7519-7527. 548 16. Burchert, A., Roots of imatinib resistance: a question of self-renewal? Drug Resist Updat, 2007. 549 10(4-5): p. 152-61. 550 17. Weisberg, E. and J.D. Griffin, Resistance to imatinib (Glivec): update on clinical mechanisms. Drug 551 Resist Updat, 2003. 6(5): p. 231-8. 552 18. Bellodi, C., et al., Targeting autophagy potentiates tyrosine kinase inhibitor-induced cell death in 553 Philadelphia chromosome-positive cells, including primary CML stem cells. J Clin Invest, 2009. 119(5): 554 p. 1109-23. 555 19. Cree, I.A. and P. Charlton, Molecular chess? Hallmarks of anti-cancer drug resistance. BMC Cancer, 556 2017. 17(1): p. 10. 557 20. Aleksakhina, S.N., A. Kashyap, and E.N. Imyanitov, Mechanisms of acquired tumor drug resistance. bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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602 41. Camara, A.K.S., et al., Mitochondrial VDAC1: A Key Gatekeeper as Potential Therapeutic Target. 603 Frontiers in Physiology, 2017. 8(460). 604 42. Mathupala, S.P. and P.L. Pedersen, Voltage dependent anion channel-1 (VDAC-1) as an anti-cancer 605 target. Cancer biology & therapy, 2010. 9(12): p. 1053-1056. 606 43. Pastorino, J.G. and J.B. Hoek, Regulation of hexokinase binding to VDAC. Journal of bioenergetics 607 and biomembranes, 2008. 40(3): p. 171-182. 608 44. Sokol, A.M., et al., Mitochondrial protein translocases for survival and wellbeing. FEBS Letters, 2014. 609 588(15): p. 2484-2495. 610 45. Durech, M., et al., Novel Entropically Driven Conformation-specific Interactions with Tomm34 Protein 611 Modulate Hsp70 Protein Folding and ATPase Activities. Molecular & Cellular Proteomics, 2016. 612 15(5): p. 1710-1727. 613 46. Shimokawa, T., et al., Identification of TOMM34, which shows elevated expression in the majority of 614 human colon cancers, as a novel drug target. Int J Oncol, 2006. 29(2): p. 381-6. 615 47. Muller, P., et al., Tomm34 is commonly expressed in epithelial ovarian cancer and associates with 616 tumour type and high FIGO stage. J Ovarian Res, 2019. 12(1): p. 30. 617 48. Aleskandarany, M.A., et al., TOMM34 expression in early invasive breast cancer: a biomarker 618 associated with poor outcome. Breast Cancer Research and Treatment, 2012. 136(2): p. 419-427. 619 49. Matés, J.M., et al., Glutaminase isoenzymes as key regulators in metabolic and oxidative stress 620 against cancer. Curr Mol Med, 2013. 13(4): p. 514-34. 621 50. Wu, N., et al., Alpha-Ketoglutarate: Physiological Functions and Applications. Biomol Ther (Seoul), 622 2016. 24(1): p. 1-8. 623 51. Dang, L., K. Yen, and E.C. Attar, IDH mutations in cancer and progress toward development of 624 targeted therapeutics. Ann Oncol, 2016. 27(4): p. 599-608. 625 52. Reitman, Z.J. and H. Yan, Isocitrate dehydrogenase 1 and 2 mutations in cancer: alterations at a 626 crossroads of cellular metabolism. J Natl Cancer Inst, 2010. 102(13): p. 932-41. 627 53. Biaglow, J.E. and R.A. Miller, The thioredoxin reductase/thioredoxin system: novel redox targets for 628 cancer therapy. Cancer Biol Ther, 2005. 4(1): p. 6-13. 629 54. Quek, L., et al., Clonal heterogeneity of acute myeloid leukemia treated with the IDH2 inhibitor 630 enasidenib. Nature medicine, 2018. 24(8): p. 1167. 631 55. Pisco, A.O. and S. Huang, Non-genetic cancer cell plasticity and therapy-induced stemness in tumour 632 relapse: 'What does not kill me strengthens me'. Br J Cancer, 2015. 112(11): p. 1725-32. 633 56. Piskareva, O., et al., The development of cisplatin resistance in neuroblastoma is accompanied by 634 epithelial to mesenchymal transition in vitro. Cancer Lett, 2015. 364(2): p. 142-55. 635 57. Vellinga, T.T., et al., SIRT1/PGC1α-Dependent Increase in Oxidative Phosphorylation Supports 636 Chemotherapy Resistance of Colon Cancer. Clinical Cancer Research, 2015. 21(12): p. 2870-2879. 637 58. Verdin, E., et al., Sirtuin regulation of mitochondria: energy production, apoptosis, and signaling. 638 Trends in biochemical sciences, 2010. 35(12): p. 669-675. 639 59. Alexa-Stratulat, T., et al., What sustains the multidrug resistance phenotype beyond ABC efflux 640 transporters? Looking beyond the tip of the iceberg. Drug Resist Updat, 2019. 46: p. 100643. 641 60. Brandt, U., Energy converting NADH:quinone oxidoreductase (complex I). Annu Rev Biochem, 2006. 642 75: p. 69-92. 643 61. Parker, S.J. and C.M. Metallo, Metabolic consequences of oncogenic IDH mutations. Pharmacology & 644 therapeutics, 2015. 152: p. 54-62. 645 62. Hedley, D., et al., A novel energy dependent mechanism reducing daunorubicin accumulation in acute bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

646 myeloid leukemia. Leukemia, 1997. 11(1): p. 48-53. 647 63. Cai, X., et al., 2019. 648 64. Gillet, L.C., et al., Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent 649 Acquisition: A New Concept for Consistent and Accurate Proteome Analysis. Molecular & Cellular 650 Proteomics, 2012. 11(6): p. O111.016717. 651 65. Chi, H., et al., Comprehensive identification of peptides in tandem mass spectra using an efficient open 652 search engine. Nature Biotechnology, 2018. 36(11): p. 1059-1061. 653 66. Schubert, O.T., et al., Building high-quality assay libraries for targeted analysis of SWATH MS data. 654 Nat Protoc, 2015. 10(3): p. 426-41. 655 67. Jimenez-Marin, A., et al., Biological pathway analysis by ArrayUnlock and Ingenuity Pathway Analysis. 656 BMC Proc, 2009. 3 Suppl 4: p. S6. 657 68. Kramer, A., et al., Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics, 2014. 658 30(4): p. 523-30. 659 bioRxiv preprint B A

Cell survival (%control) 100 150 50 0

0.015625 was notcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. doi:

0.03125 https://doi.org/10.1101/2021.04.08.438976 Concentration ofIMA(μM) 0.0625

0.125

0.25

0.5

1 2 2 weeks 4 4 weeks 1 1 weeks 2 2R .9I3 RI=11.59 I2 RI=3.49 I1 RI=2 4 K RI=1

8 ; this versionpostedApril9,2021. C Cell survival (%control) 100 150 50 0 0.015625

0.03125

Concentration ofADR(μM) 0.0625

0.125

0.25

0.5 Cell cytotoxicity detection Cell collection Adriamycin Cell freezing Imatinib K562 cell

1 The copyrightholderforthispreprint(which

2 A3 RI=7.06 A2 RI=6.41 A1 RI=2.76 K RI=1 4

8 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Reduction and Lysis A +Lysis alkylation Lys-C digestion +TCEP buffer PCT +IAA PCT

K562 cells Protein solution Redissolving Tryptic digestion C18 desalting Drying peptides peptides

PCT

Peptide solution Detecting peptides MS raw data concentration PulseDIA analysing Protein matrix

B C 100000 98232 90000 EncyclopeDIA OpenSWATH 80000 76407 73938 70000 DIA-NN Spectronaut 56350 4862 4135 60000

45098 2063 1342 50000 1212 40000 725 2547 6219 10748

Pep�de number 30000 20000 30289

10000 549 657 0 4780 2743 25361 Total A-NN eDIA DI tronaut pec yclop S Enc OpenSWATH

D protein group proteotypic protein E 9000 8630

8000 7082 7040 6935 7031 EncyclopeDIA OpenSWATH 7000 6509 DIA-NN Spectronaut 5603 88 48 6000 5354 5030 4926 37 3 5000 365 9 9 512 4000 1011

3000 4493 Protein number 1 2000 143 98 109 1000 204 0

Total A-NN eDIA DI tronaut pec yclop S Enc OpenSWATH bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which A was not certified by peer review) isB the author/funder. All rights reserved. No reuse allowed without permission. 1.000 ● ●● 1.5 OpenSWATH ● ●●●

● ●● CV ● ● ● ● ● ● 00.5 1 ● ● 0.975 ● ● ● ● EncyclopeDIA ●● CV 0.950 ● ● ● ● ● 00.5 1 ● ●

Pearson correlation Pearson ● DIA-NN CV 0.925 0.51 ● ● ●

0 ● ● ● ● Spectronaut ● CV ● ● ● ● ● 0 0.51 ● ● k A1 A2 A3 I1 I2 I3 DIA-NN

EncyclopeDIASpectronautOpenSWATH C D E EncyclopeDIA ●ADR ● K● IMA ● Spectronaut EncyclopeDIA OpenSWATH Spectronaut EncyclopeDIAOpenSWATH ● ● ●● ● ● DIA-NN DIA-NN ●● ●● ● 0.91 0.85 0.88 0.9 0.87 0.85 0 ●●● ● ● ● Spectronaut 0.86 0.89 0.86 0.85 Spectronaut −50 PC2(18.65%)

● 0.87 0.87 EncyclopeDIA EncyclopeDIA −100 ● −40 −20 0 20 40 60 PC1(24.68%) F G H DIA-NN Spectronaut OpenSWATH ●ADR ● K● IMA ●ADR ● K● IMA ●ADR ● K● IMA 40 ● 75 ● ● ● 40 ●● ● ● ● ● ● ● ● ● ● ● 50 ● ●● ● ● ● 20 ● 0 ● ● ● ● ● 25 ● ● ● ● ● ● ●● ● ● 0 ● ● ● PC2(15.93%)

−40 PC2(17.92%) ● 0 ●● PC2(13.45%) ● ●● ●●● ● ●● ● ● ● ●● ● ● ● −20 ● ● ● −25 ● ● ●● ● −80 ● ● ● ● ● −40 −20 0 20 40 −50 −25 0 25 50 −40 −20 0 20 40 PC1(21.35%) PC1(22.96%) PC1(22.43%) A 550 proteins 485 proteins

bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Expression changes Expression changes −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 K A1 A2 A3 K A1 A2 A3 Spectronaut Spectronaut 563 proteins 525 proteins 663 proteins 610 proteins Expression changes −1.5 −0.5 0.5 1.5 Expression changes Expression changes Expression changes −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 K A1 A2 A3 K A1 A2 A3 K A1 A2 A3 K A1 A2 A3 OpenSWATH OpenSWATH EncyclopeDIA EncyclopeDIA

549 proteins 635 proteins 345 proteins 612 proteins Expression changes Expression changes Expression changes Expression changes −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 K A1 A2 A3 K A1 A2 A3 K A1 A2 A3 K A1 A2 A3 DIA-NN DIA-NN DIA-NN DIA-NN B 591 proteins 359 proteins 480 proteins 267 proteins Expression changes Expression changes −1.5 −0.5 0.5 1.5 Expression changes Expression changes −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 K I1 I2 I3 K I1 I2 I3 K I2 I2 I3 K I1 I2 I3 Spectronaut Spectronaut OpenSWATH OpenSWATH 611 proteins 334 proteins 717 proteins Expression changes Expression changes Expression changes −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 K I1 I2 I3 K I1 I2 I3 K I1 I2 I3 EncyclopeDIA EncyclopeDIA EncyclopeDIA

504 proteins 523 proteins Expression changes Expression changes −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 K I1 I2 I3 K I1 I2 I3 DIA-NN DIA-NN Sirtuin Signaling Pathway C A K A1 A2 A3 K I1 I2 I3 EncyclopeDIA DIA-NN Spectronaut OpenSWATH RELA NDUFA6 SIRT2 NDUFA10 SIRT5 SDHA IDH2 TOMM5 bioRxivOxidati preprintve doi: https://doi.org/10.1101/2021.04.08.438976; this version posted AprilSOD1 9, 2021. The copyright holder for this preprint (which EIF2 Signaling TOMM34 SDHC was not certified by peer review) is the author/funder. All rights reserved.SUV39H1 No reuse allowed without permission. TUBA4A Phosphorylation SLC25A5 POLR1E Sirtuin Signaling Pathway POLR1B NDUFB1 PPIF TIMM17A UQCRC2 UQCRFS1 GLS NDUFA6 SF3A1 TIMM50 NDUFV2

NDUFB1 TOMM40 Spectronaut TIMM9 CYC1 RRP9 NDUFA8 2.5 TOMM20 tRNA Charging ATP5F1E TIMM9 NQO1 TOMM20 VDAC3 RRP9 VDAC2 TIMM13 TIMM17A NDUFS4 GLS TIMM13 UQCRC2 Spliceosomal Count NDUFB6 TP53BP1 Spectronaut TOMM40 NDUFB10 TOMM5 SLC2A1 Cycle TIMM17B ATP5F1D H1−5 CPS1 20 POLR1E NDUFA4 ATG12 CYC1 NDUFV1 0.0 SMARCA5 ATG4B

−log ( p− value) 40 VDAC1 TIMM17B NDUFB8 ATG12 NDUFS3 RELA BPGM NDUFA2 60 AGTRAP ATP5F1B LDHA NDUFS2 Necroptosis GLUD2 UQCRFS1 SIRT5 NDUFB4 Signaling SOD1 80 TOMM34 SLC25A4 BAX SDHB Pathway IDH2 IDH2 −2.5 PDK1 SIRT5 PGAM1 POLR1D CRTC2 SP1 ATG9A −2.5 0.0 2.5 NAMPT TUBA1C ACSS2 NDUFS7 ATG7 Z-score SIRT2 NDUFB1 SLC2A1 PPID Model ADR STK11 NDUFS8 PDHA1 ARG2 XPC NDUFA13 ARG2 TUBA4A XRCC5 H1−10 CPT1A B NDUFA12 VDAC3 NDUFA7 TIMM10 EncyclopeDIA DIA-NN Spectronaut OpenSWATH ACLY ATP5PB MTOR H1−5 RPTOR NDUFB8 TOMM40 TP53BP1 SLC25A5 PCK2 TIMM50 20 SDHB TIMM17A Oxidative Phosphorylation NDUFA9 POLR1E ATP5F1A NDUFA6 MAPK1 NDUFB4 SLC2A1 Remodeling of Epithelial NDUFB10 POLR1B Sirtuin Signaling Pathway DIA-NN NQO1 NDUFA2 DIA-NN Adherens Junctions ATP5F1B ATG7 NDUFV2 TOMM34 POLR1A SOD1 15 NRF2−mediated UQCRFS1 Colanic Acid TIMM22 GABARAPL2 TOMM6 PGAM1 Oxidative TOMM22 ACLY Building Blocks VDAC2 SDHB PAM16 ATG9A NER Pathway Stress Response TIMM8A PFKM Biosynthesis CYC1 GOT2 BAX VDAC3 PGK1 10 Cholesterol TOMM40 NDRG1 TIMM17A IDH2 Biosynthesis NQO1 TP53BP1 Count GLS NDUFA4 ACSS2 NDUFB6 NDUFA5 TIMM10 XPC −log ( p− value) 20 ATP5F1E SIRT5 SMARCA5 IDH2 5 VDAC1 NDUFS2 NBN 40 GABARAP TP53BP1 SDHA PAM16 Glutaryl−CoA NDUFS4 NQO1 NDUFAF2 H1−4 SDHC OpenSWATH Degradation 60 SLC25A5 TIMM50 TIMM50 SLC25A6 SUV39H1 VDAC1 0 NDUFB1 SOD2 H1−5 SDHB NDUFAB1 NDUFA4 ATP5PB TIMM17B ATP5F1A POLR1B VDAC3 −5.0 −2.5 0.0 2.5 ATP5PF NDUFB6 PPIF NDUFA6 TIMM9 NDUFS7 Z-score UQCRC2 POLR1E RRP9 NDUFA6 TOMM22 Model IMA SLC25A5 MTOR NDUFA13 PARP1 ATP5F1C PCK2 CPT1A GLUD2 D EncyclopeDIA ATG7 BPGM OpenSWATH NAMPT PGK1 PGK1 PFKM TUBA4A BAX BAX 8 3 Spectronaut PGAM1 TOMM34 IDH2 SOD1 DIA-NN TOMM34 ATG5 VDAC1 GABARAPL2 5 UQCRFS1 H1−10 NDUFB1 TOMM34 NQO1 SDHB 4 0 TIMM50 POLR1E GABPA NDUFB1 ATG7 GLS VDAC2 RRP9 SIRT5 39 4 2 1 TIMM17A TP53BP1 NDUFB6 G6PD SLC25A4 IDH2 EncyclopeDIA VDAC3 UQCRFS1 ATP5F1A PPIF APEX1 NDUFA8 TUBA4A

16 OpenSWATH TIMM44 PCK2 VDAC1 NDUFA6 GLS SDHA TIMM13 NDUFV1 7 1 NDUFA4 SMARCA5 GLS 5 5 POLR1B SMARCA5 H1−5 TRIM28 H1−4 NDRG1 VDAC2 H1−4 SLC25A6 TOMM20 H1-5 TOMM40 TOMM40 H1−5 5 SLC25A5 POLR1B RPTOR NDUFA6 TOMM40 NDUFA4 SLC25A5 TOMM22 TOMM5 VDAC3 TIMM50 POLR1A NDUFA12 NDUFB6 IDH2 GOT2 NDUFA6 Model ADR SOD1 RRP9 BAX POLR1E TOMM34 ATP5F1B SIRT5 NDUFA2 PGK1 TIMM8A ATG7 NDUFV2 TUBA4A E PFKM ATP5F1D ATG2A NDUFB8 TP53BP1 NDUFB10 PCK2 MAPK1 EncyclopeDIA OpenSWATH GABPA TIMM17B IDH2 TRIM28 SLC25A5 ACSS2 NDUFA13 NDUFA5 SLC25A4 17 Spectronaut VDAC1 ATP5F1A DIA-NN 8 NDUFV1 POLR1D SIRT1 TIMM44 UQCRFS1 TIMM10 ATP5PF 3 NDUFB1 ATG12 10 0 POLR1E NDUFA10 NDUFA6 NDUFB4 POLR1E TOMM20 NDUFS2 EncyclopeDIA NDUFS4 NDUFS3 NFKB1 11 6 0 6 POLR1B POLR1B SLC25A6 CYC1 IDH2 VDAC3 VDAC2 GLS UQCRFS1 CYC1 TUBA1C NDUFA6 H1−5 SLC2A1 5 TIMM17B TIMM13 SDHC 1 27 NDUFA4 NDUFA8 TP53BP1 SMARCA5 HSF1 0 4 SLC25A5 TIMM9 H1−4 TIMM13 VDAC2 NDUFS7 SDHB GOT2 NDUFB6 GOT2 TOMM40 CPT1A 2 TIMM44 SLC25A6 UQCRC2 VDAC3

Model IMA −3 −2 −1 0 1 2 3 A TOMM34 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. TOMM40 TOMM20 TOMM22 TOMM6 VADC1 TOMM5 VADC2 Acetyl-CoA VADC3 H2O AGI-6780 HS-CoA Glutamine Oxalacetate Citrate NADH/H+ GLS NDUFA4 Isocitrate NAD+ Glutamate NDUFA6 IDH2 NDUFB1 TCA Cycle α-KG Malate NDUFB6 NAD+ HS-CoA Ⅰ E - UQCRFS1 Succinate H2O Fumarate H+/NADH Ubiquinone E - FADH2FAD Ⅱ Ⅲ E - E - SHDB Cytochrome C H + H2O Ⅴ E - Ⅳ- ATP H + O2

Up-regulated

Down-regulated B

NDUFA4 NDUFA6 NDUFB6 NDUFB1 p=8.7e−6 p=0.02 p=6.2e−5 p=1.5e−3 p=7.6e−6 1.00 1.00 p=1.4e−4 p=0.047 p=7.3e−3 p=1.6e−5 p=0.03 p=4.3e−7 p=2e−4 1.00 p=7e−3 1.00 p=1.1e−5 p=0.013 p=3e−4 p=6.6e−6 p=6.4e−3 p=1.4e−4 p=0.0032 p=0.024 p=7.6e−3 p=7.8e−3 p=7.1e−4 0.75 p=2e−4 p=0.015 0.75 p=1.2e−5 p=0.0018 0.75 p=1.1e−3 0.75

0.50 0.50 0.50 0.50

0.25 0.25 0.25 0.25 Normalized intensity Normalized intensity 0.00 0.00 Normalized intensity Normalized intensity 0.00 0.00 K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 C UQCRFS1 SDHB VDAC1 VDAC2 p=0.048 p=8.3e−4 1.00 p=0.024 1.00 p=0.032 1.00 p=0.039 1.00 p=1.3e−5 p=9.1e−06 p=0.036 p=3.5e−4 p=0.027 p=7.7e−5 p=5.3e−3 0.75 p=0.032 0.75 0.75 0.75

0.50 0.50 0.50 0.50

0.25 p=6.7e−6 0.25 0.25 0.25 p=8.6e−7 p=2.2e−3 Normalized intensity Normalized intensity Normalized intensity 0.00 0.00 0.00 Normalized intensity 0.00 K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 D VDAC3 TOMM20 TOMM40 TOMM34 p=9.4e−3 p=2.7e−3 p=6.6e−5 p=6.4e−5 p=3.1e−3 1.00 p=0.046 p=1.2e−3 1.00 1.00 1.00 p=1.6e−4 p=6.4e−4 p=9.3e−4 p=2.2e−3 p=8.1e−4 p=0.016 p=0.044 p=2.8e−3 p=8.9e−3 p=1.5e−5 0.75 0.75 0.75 p=2.9e−3 p=0.015 0.75 p=2.3e−3

0.50 0.50 0.50 0.50

p=6.4e−3 0.25 p=0.033 0.25 p=7.3e−5 p=0.034 0.25 0.25 p=0.13 p=7.5e−4 p=1.7e−4 Normalized intensity Normalized intensity

0.00 Normalized intensity 0.00 Normalized intensity 0.00 0.00 K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 E IDH2 GLS p=5e−8 p=8.1e−4 1.00 p=7.5e−9 p=2.8e−5 1.00 p=5.5e−4 p=0.03 p=1.2e−7 p=4e−3 p=5.7e−3 p=8.5e−3 0.75 p=1.1e−5 p=0.011 0.75 p=9.1e−3 DIA-NN 0.50 0.50 EncyclopeDIA 0.25 0.25 OpenSWATH

Normalized intensity 0.00

Normalized intensity 0.00 Spectronaut K A1 A2 A3K I1 I2 I3 K A1 A2 A3K I1 I2 I3 A B bioRxiv preprint

Cell survival (%control)100 150

50 Cell survival (%control) 100 150 0 50 0.015625 0 0.015625 0.03125 Concentration of 0.03125 was notcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. doi: Concentration of 0.0625 0.0625 https://doi.org/10.1101/2021.04.08.438976 0.125 0.125 0.25 0.25 IMA(μM) K+AGI-6780(2μM) K+AGI-6780(4μM) K K+AGI-6780(2μM) 0.5 K+AGI-6780(4μM) K ADR(μM) 0.5 1 1 2 2 4 4 8 8 ; this versionpostedApril9,2021. C Cell survival (%control) D

Cell survival (%control)100 150 100 150 50 50 0 0 0.015625 0.015625

0.03125 0.03125 Concentration of Concentration of 0.0625 0.0625

0.125 0.125 The copyrightholderforthispreprint(which 0.25 0.25 Model ADR+AGI-6780(2μM) Model ADR+AGI-6780(4μM) 0.5 Model ADR 0.5 IMA(μM) ADR(μM) Model IMA+AGI-6780(2μM) Model IMA+AGI6-6780(4μM) Model IMA 1 1

2 2

4 4

8 8 B Expression changes Expression changes Expression changes Expression changes A

−1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 K K K K bioRxiv preprint Spectronaut EncyclopeDIA Spectronaut I1 EncyclopeDIA A1 A1 I1 2I3 I2 2A3 A2 2A K A3 A2 2I3 I2 was notcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. doi: https://doi.org/10.1101/2021.04.08.438976

Expression changes Expression changes Expression changes Expression changes −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 K K K Spectronaut Spectronaut EncyclopeDIA I1 A1 I1 A1 DIA-NN 2I K I3 I2 2A3 A2 2I3 I2 A3 A2 ; this versionpostedApril9,2021.

Expression changes Expression changes Expression changes

−1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5

2I 3K I3 I2 I2 K −1.5 −0.5 0.5 1.5 K I1 OpenSWATH DIA-NN OpenSWATH A1 2I3 I2 2A K A3 A2 The copyrightholderforthispreprint(which

Expression changes Expression changes −1.5 −0.5 0.5 1.5 −1.5 −0.5 0.5 1.5 OpenSWATH OpenSWATH A1 I1 2A3 A2 2I3 I2 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.08.438976; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

A B C