Changes in the urine proteome in patients with advanced 1 lung cancer after different drug treatments

Lung cancer is one of the most threatening diseases to human life and health because of its 21 mortality. How to choose more efficient drugs and the appropriate timing of treatment for 22 patients with advanced lung cancer is still a problem. As the urine proteome can sensitively 23 reflect the pathological or physiological changes of the body, it has the potential to reflect the 24 dynamic changes of the body after drug treatment. To investigate the changes in the urine proteome of patients with advanced lung cancer after different drug treatments, urine samples were collected and analyzed at different time points. The changes in the urine proteome from the 27 pretreatment state were different in each patient, although some of them were treated with the 28 same drugs. The changes in the biological processes reflected by the differential urinary proteins were consistent with the changes in the clinical manifestations of the patients. This study 30 demonstrates that the pathophysiological changes of patients with advanced lung cancer can be 31 reflected by changes in urinary protein after different drug treatments. In addition, the changes in 32 urinary proteins can reflect the different biological processes in patients after the same drug 33 treatment, and the patients’ clinical condition assessment results are consistent with these 34 changes. These findings may provide additional information for clinical treatment. activation alternative extracellular peptide process, sulfate

patients with advanced lung cancer is still a problem. As the urine proteome can sensitively 23 reflect the pathological or physiological changes of the body, it has the potential to reflect the 24 dynamic changes of the body after drug treatment. To investigate the changes in the urine 25 proteome of patients with advanced lung cancer after different drug treatments, urine samples 26 were collected and analyzed at different time points. The changes in the urine proteome from the 27 pretreatment state were different in each patient, although some of them were treated with the 28 same drugs. The changes in the biological processes reflected by the differential urinary proteins 29 Introduction 38 In recent years, lung cancer has become a serious threat to human lives and health because of its 39 rapidly increasing incidence and mortality. It is reported to be the most commonly diagnosed 40 cancer (11.6%) and the leading cause of cancer deaths (18.4% of total cancer deaths) 1 . In 41 addition, lung cancer is the second most frequent cancer in both sexes combined worldwide, and 42 it is also the most frequent cancer in the Chinese population 2, 3 . Among lung cancers, non-small- 43 cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) are the two main histologic classes. 44 NSCLC is the most common subtype, accounting for approximately 83% of all lung cancers 4 . 45 Treatment options for lung cancer include surgery, chemotherapy, radiation therapy, and 46 immunotherapy. The choice of therapeutic modality depends on multiple factors, including the 47 type and stage of the cancer 5 . Platinum-based regimens are the standard of treatment for 48 advanced lung cancer in current clinical treatment, and they had better advantages in the survival 49 rate and symptom control when combined with chemotherapy. However, their clinical 50 effectiveness is affected by cumulative hematotoxicities and neurotoxicities, highlighting the 51 requirement for alternative treatments 5 . In terms of adverse events, carboplatin mostly causes 52 thrombocytopenia, and cisplatin mostly causes nausea and vomiting. Therefore, the choice of 53 platinum compounds should take into account the expected toxicity profile, patient comorbidities, 54 and patient preferences 6 . Angiogenesis inhibitors are promising and effective ways to treat lung 55 cancer. For example, bevacizumab, as a commonly used therapeutic drug for lung cancer, can 56 promote tumor cell apoptosis. However, recent studies have found that it has certain toxic side 57 effects in clinical applications, such as hypertension 7 , bleeding 8 , and proteinuria 9 . Among 58 immunotherapies, pembrolizumab is an immune checkpoint inhibitor that is being rapidly 59 developed and is approved as a first-line treatment for advanced NSCLC 10 . However, immune-60 related adverse effects caused by immunotherapy can affect various systems, such as abnormal 61 thyroid function, pneumonia, and severe skin reactions 11 . Some patients will have different 62 degrees of resistance after clinical targeted therapy, which further impacts the clinical prognosis 63 and quality of life. Based on these problems, determining the most effective treatment for each 64 patient, the appropriate treatment method and combined application of different methods, and the 65 ideal timing of the treatment as well as providing the best treatment effect and reducing adverse 66 effects are still challenges that cannot be ignored in the treatment of lung cancer. Clinicians 67 should be mindful of these factors when considering therapeutic options for patients. 68 Urine proteome can sensitively reflect physiological changes in the body. With no 69 regulation of homeostatic mechanisms, urine can accumulate changes throughout the body at a 70 very early stage 12 . Many studies have shown that changes in the urine proteome can provide 71 clues for the early diagnosis of many diseases, such as myocarditis model 13 , Alzheimer model 14 , 72 liver fibrosis model 15 , glioma model 16 , lung fibrosis model 17 , and chronic pancreatitis model 18 . In 73 addition, the urine proteome can sensitively distinguish more subtle differences. It has been 74 reported that urinary proteins have the potential to differentiate the same tumor cells grown in 75 different organs 19 . Urinary proteins can also sensitively reflect the changes caused by very few 76 cells growing in the body 20 . We speculate that the potential of the urine proteome likely remains 77 to be unleashed completely. Theoretically, if urine samples taken at different periods from one 78 person are compared, and that the person had no significant lifestyle changes during that period, 79 the changes in the urine should reflect the person's physiological or pathological changes during 80 this period. The careful analysis of changes in urinary proteins caused by many different drugs 81 with different side effects may provide clues about disease-related pathways and biological 82 processes in the patient, which can aid in choosing the appropriate drugs to avoid adverse side 83 effects 21 . 84 In this study, we collected urine samples from eight patients with advanced lung cancer 85 after different drug treatments and analyzed them by liquid chromatography-mass spectrometry 86 (LC-MS/MS). Each patient chose their own controls to compare differences in their urine 87 proteome at different stages. This study aims to discover dynamic changes in urinary proteins in 88 patients with advanced lung cancer after different drug treatments and whether the urine 89 proteome has the potential to reflect the pathophysiological changes that are consistent with 90 clinical manifestations. It also further investigates the ability of the urine proteome to 91 differentiate the changes in biological process induced by different drugs.  Changes in the urine proteome and functional analysis in patient P1. The urine samples of 105 patient P1 at four time points before and after drug treatment were collected and analyzed. More 106 details about the urine samples and the clinical symptoms of the patient are shown in Table 2. 107 Compared to those seen before drug treatment, a total of 1,448 urinary proteins were identified 108 after treatment. After screening, 472 differential urinary proteins were identified, of which 262, 109 223, and 241 were identified at the three time points, T1, T2 and T3 (Table 1). A Venn diagram 110 showed overlapping differential urinary proteins at the three time points (Fig. 1A). Seventy-four 111 proteins changed continuously at three time points, and 130, 66, and 96 unique differential 112 urinary proteins changed at the T1, T2 and T3 time points, respectively. 113 This study was evaluated by a single-blind study in which the medication and clinical 114 manifestations of all patients were unknown when the functional analysis of urinary proteins was 115 performed. The differential proteins identified at the T1, T2, and T3 time points were analyzed 116 by the DAVID database and classified based on biological processes. At the three time points, 117 some biological processes related to immunity and tumor growth changed significantly (Table 2,   118   Fig. 1B). At the T1 time point, complement activation, cellular response to growth factor   119   stimulus, complement activation and classical pathway, cell response to tumor necrosis factor,   120 regulation of immune, innate immune response, antigen processing and presentation, Fc-epsilon 121 receptor signaling pathway, Fc-gamma receptor signaling pathway involved in phagocytosis, and 122 chronic inflammatory response were altered. Compared with that before administration, the   T3: proteolysis, receptor-mediated endocytosis, cell adhesion, platelet degranulation, cellular protein metabolic process, Fc-gamma receptor signaling pathway involved in phagocytosis, regulation of immune system process, complement activation and classical pathway, Fcepsilon receptor signaling pathway, proteolysis involved in cellular protein catabolic process, complement activation, Wnt signaling pathway, planar cell polarity pathway, acute inflammatory response, tumor necrosis factor-mediated signaling pathway, leukocyte migration, immune response, L-methionine salvage, carbohydrate metabolic process, negative regulation of angiogenesis  Table 1. 145 Compared to those seen before drug treatment, a total of 1,534 urinary proteins were identified. 146 After screening, 491 differential urinary proteins were identified, of which 161, 302 and 232 147 were identified at the three time points, T1, T2, and T3, respectively (Table 1). A Venn diagram 148 showed overlapping differential urinary proteins at the three time points (Figure 2A). Twenty-149 seven proteins changed continuously at three time points, and 79, 146, and 89 unique differential 150 urinary proteins changed at the T1, T2 and T3 time points, respectively.  Table 3 and Figure 2B. At the T1 time point, processes related to proteolysis, 159 cellular protein metabolic process, methionine biosynthetic process, fructose metabolic process, 160 gluconeogenesis, serine family amino acid biosynthetic process, neutrophil aggregation and 161 positive regulation of inflammatory response changed significantly, and proteolysis continuously 162 changed significantly at each of the three time points. Compared with those before 163 administration, few immune responses in patient P2 changed, and the biological processes 164 associated with tumor growth changed more significantly. At this point, the evaluation of the 165 patient was stable disease. Some of these changed biological processes have been reported in 166 cancer, such as neutrophil aggregation, which is involved in many processes, including acute 167 injury and repair, chronic inflammatory processes, cancer, and autoimmunity 23 . It has been 168 reported that methionine biosynthetic process is related to cancer, as many kinds of cancer cells 169 require exogenous methionine to survive 24 Table 4. 203 Compared to those seen before drug treatment, a total of 1,049 urinary proteins were identified. 204 After the screening, 71 differential urinary proteins were identified (Table 1). 205 Functional enrichment analysis was performed on the identified differential urinary proteins by 206 using the DAVID database. Changes in biological processes are shown in Table 4 Table 5. 234 Compared to before drug treatment, a total of 1,107 urinary proteins were identified. After the 235 screening, 315 differential urinary proteins were identified (Table 1). 236 The differential proteins were analyzed by the DAVID database and classified based on 237 biological processes. Changes in biological processes are shown in Table 5 Table 6. 273 Compared to those seen before drug treatment, a total of 1,178 urinary proteins were identified. 274 After the screening, 233 differential urinary proteins were identified (Table 1). 275 Functional enrichment analysis was performed on the identified differential urinary proteins 276 by using the DAVID database. Changes in biological processes are shown in Table 6 and Fig. 5. 277 Patient P5 also had many differential proteins that were the same as those in patient P4. In terms 278 of biological processes with significant changes, a large number of catabolic processes that may 279 be related to tumors changed significantly, including glycosphingolipid metabolic process, 280 chondroitin sulfate catabolic process, carbohydrate metabolic process, proteolysis, 281 glycosphingolipid metabolic process, glycosaminoglycan catabolic process, retinoid metabolic 282 process, cellular protein metabolic process, oligosaccharide catabolic process, and fibrinolysis. 283 Although there are many changed biological processes, there are fewer biological processes 284 related to immune response than to other signaling processes. The significance of changes in 285 immune-related processes, such as acute phase response, complement activation and alternative 286 pathway, regulation of complement activation, and innate immune response was lower. One 287 possible reason is related to medication use.    Table 7. 299 Compared to those seen before drug treatment, a total of 1,838 urinary proteins were identified. 300 After screening, 850 differential urinary proteins were identified, of which 276, 330 and 628 301 were identified at the three time points, T1, T2 and T3 (Table 1). A Venn diagram showed 302 overlapping differential urinary proteins at the three time points (Fig. 6A). Eight-six proteins 303 changed continuously at each of the three time points, and 84, 109, and 359 unique differential 304 urinary proteins changed at the T1, T2 and T3 time points, respectively. 305 The differential proteins identified at the T1, T2, and T3 time points were analyzed by the 306 DAVID database and classified based on biological processes. Changes in biological processes 307 are shown in Table 7  The immune response-related processes continued to be identified at the same time.  T2:cell-cell adhesion, proteolysis, proteolysis involved in cellular protein catabolic process, platelet degranulation,, glycosaminoglycan catabolic process, regulation of complement activation, blood coagulation, metabolic process, leukocyte migration, cell adhesion, negative regulation of endopeptidase activity, oxidation-reduction process, canonical glycolysis, gluconeogenesis, complement activation and alternative pathway, carbohydrate metabolic process, tumor necrosis factor-mediated signaling pathway, ATP metabolic process, glutathione biosynthetic process, glycolytic process, complement activation, acute-phase response, Wnt signaling pathway, planar cell polarity pathway, cellular amino acid metabolic process, regulation of immune response, negative regulation of cell adhesion, NIK/NF-kappaB signaling, glucose metabolic process, complement activation and classical pathway T3: cell-cell adhesion, oxidation-reduction process, cellular amino acid metabolic process, platelet degranulation, carbohydrate metabolic process, proteolysis, NIK/NF-kappaB signaling, Wnt signaling pathway and planar cell polarity pathway, canonical glycolysis, tumor necrosis factor-mediated signaling pathway, gluconeogenesis, negative regulation of endopeptidase activity, Fcepsilon receptor signaling pathway, glucose metabolic process, proteolysis involved in cellular protein catabolic process, glycolytic process, T cell receptor signaling pathway, metabolic process, blood coagulation, cell adhesion, Fcgamma receptor signaling pathway involved in phagocytosis, innate immune response, regulation of complement activation, complement activation and alternative pathway  Table 7. 346 Compared to those seen before drug treatment, a total of 1,215 urinary proteins were identified. 347 After the screening, 212 differential urinary proteins were identified (Table 1). 348 The differential proteins were analyzed by the DAVID database and classified based on 349 biological processes. Changes in biological processes are shown in Table 8   T1: platelet degranulation, gluconeogenesis, leukocyte migration, retinoid metabolic process, cell adhesion, movement of cell or subcellular component, negative regulation of endopeptidase activity, extracellular matrix organization, cellular response to tumor necrosis factor, glycosaminoglycan catabolic process, cellular aldehyde metabolic process, response to nutrient, oxaloacetate metabolic process, receptor-mediated endocytosis, negative, regulation of apoptotic process, glutathione biosynthetic process, oxidation-reduction process, retina homeostasis, negative regulation of coagulation, receptormediated virion attachment to host cell, fructose catabolic process to hydroxyacetone phosphate and glyceraldehyde-3-phosphate, carnitine biosynthetic process, aspartate metabolic process, lung development, extracellular matrix disassembly, organ regeneration, phagocytosis, cellular protein metabolic process, proteolysis, canonical glycolysis 367 The patient experienced severe adverse reactions after a period of treatment with sintilimab, 368 pemetrexed, and carboplatin, but significant changes in a large number of immune and cytokine-369 related biological processes were not observed in the urine samples collected after symptoms 370 appeared. It is speculated that the reason may be that the occurrence of irAEs was very sudden 371 and rapid. Prior to this, the biological processes in the body were still in a relatively normal state, 372 and the urine proteome in patient P7 was not affected.  Table 9. 377 Compared to those seen before drug treatment, a total of 1,248 urinary proteins were identified. 378 After the screening, 408 differential urinary proteins were identified, of which 194 and 329 were 379 identified at the time points T1 and T2, respectively (Table 2). A Venn diagram showed 380 overlapping differential urinary proteins at two time points (Fig. 8A). One hundred and fifteen 381 proteins changed at both time points, and 79 and 214 unique differential urinary proteins 382 changed at the T1 and T2 time points, respectively. 383 The differential proteins identified at the T1 and T2 time points were analyzed by the 384 DAVID database and classified based on biological processes. Changes in biological processes 385 are shown in Table 9   T1: negative regulation of endopeptidase activity, platelet degranulation, glycosaminoglycan catabolic process, cellular protein metabolic process, fibrinolysis, carbohydrate metabolic process, positive regulation of nitric oxide biosynthetic process, gluconeogenesis, canonical glycolysis, glycolytic process, proteolysis, retinoid metabolic process, cell-cell adhesion, tricarboxylic acid cycle, glycosaminoglycan metabolic process, citrulline metabolic process, glutathione metabolic process, acutephase response, ATP biosynthetic process, antigen processing and presentation of peptide antigen via MHC class I, complement activation, classical pathway, negative regulation of tumor necrosis factor production, complement activation and alternative pathway, complement activation, negative regulation of complement activation, lectin pathway, leukocyte cell-cell adhesion T2:negative regulation of endopeptidase activity, gluconeogenesis, platelet degranulation, canonical glycolysis, cellular protein metabolic process, glycolytic process, glucose metabolic process, cell adhesion, proteolysis, carbohydrate metabolic process, cell-cell adhesion, oxidation-reduction process, glycosaminoglycan catabolic process, retinoid metabolic process, complement activation, alternative pathway, glycosaminoglycan metabolic process, regulation of complement activation, inflammatory response, acutephase response, complement activation, fibrinolysis, innate immune response, positive regulation of nitric oxide biosynthetic process, ATP biosynthetic process, glutathione metabolic process, adaptive  Diego, CA) software. The differential proteins were analyzed by DAVID 6.8 483 (https://david.ncifcrf.gov/). The proteins were described according to biological processes.  Table 1 The number of urinary proteins identified in 8 patients.
651 Table 2 The clinical information of patient P1 and the changed biological processes at different 652 time points.
653 Table 3 The clinical information of patient P2 and the changed biological processes at different 654 time points.
655 Table 4 The clinical information of patient P3 and the changed biological processes.
656 Table 5 The clinical information of patient P4 and the changed biological processes.
657 Table 6 The clinical information of patient P5 and the changed biological processes.
658 Table 7 The clinical information of patient P6 and the changed biological processes at different 659 time points.
660 Table 8 The clinical information of patient P7 and the changed biological processes.
661 Table 9 The clinical information of patient P8 and the changed biological processes at different 662 time points. 663   Table 10 Clinical profiles of patients with advanced lung cancer. 664