Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer

Li Xumiao; Huang Yiming; Zheng Kuo; Yu Guanyu; Wang Qinqin; Gu Lei; Li Jingquan; Wang Hui; Zhang Wei; Sun Yidi; Li Chen

doi:10.52601/bpr.2022.210048

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Li Xumiao, Huang Yiming, Zheng Kuo, Yu Guanyu, Wang Qinqin, Gu Lei, Li Jingquan, Wang Hui, Zhang Wei, Sun Yidi, Li Chen. Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer[J]. Biophysics Reports. doi: 10.52601/bpr.2022.210048

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Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer

doi: 10.52601/bpr.2022.210048

Li Xumiao^{1, #},
Huang Yiming^{2, #},
Zheng Kuo^{3, #},
Yu Guanyu^{3, #},
Wang Qinqin¹,
Gu Lei¹,
Li Jingquan¹,
Wang Hui^{1
,
,},
Zhang Wei^{3
,
,},
Sun Yidi^{2
,
,},
Li Chen^{1
,
,}

1.
Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
2.
Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
3.
Colorectal Surgery Department, Changhai Hospital, Naval Medical University, Shanghai 200433, China

Funds: This work was supported by the National Natural Science Foundation of China (NSFC) (82030099, 82072750, 30700397), the Shanghai Municipal Science and Technology Commission "Science and Technology Innovation Action Plan" technical standard project (21DZ2201700), the Natural Science Fund of Shanghai (20ZR1457200), Shanghai Sailing Program (21YF1459300), and the innovative research team of high-level local universities in Shanghai.

More Information

Corresponding author: huiwang@shsmu.edu.cn (H. Wang); weizhang2000cn@163.com (W. Zhang); ydsun@ion.ac.cn (Y. Sun); cli@shsmu.edu.cn (C. Li)
Received Date: 31 December 2021
Accepted Date: 18 November 2022

Available Online: 17 February 2023

Abstract

Abstract

Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs in vivo and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, in vivo drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation.
- Proteomics,
- Phosphoproteomics,
- Data-independent acquisition (DIA),
- Mini patient derived xenograft (MiniPDX),
- Kinase-substrate network,
- Personalized drug responses

Xumiao Li, Yiming Huang, Kuo Zheng, Guanyu Yu, Qinqin Wang, Lei Gu, Jingquan Li, Hui Wang, Wei Zhang, Yidi Sun and Chen Li declare that they have no conflict of interest.
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients for being included in the study. All institutional and national guidelines for the care and use of laboratory animals were followed.
# Xumiao Li, Yiming Huang, Kuo Zheng and Guanyu Yu contributed equally to this work.

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References(49)

References

Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537(7620): 347−355 doi: 10.1038/nature19949

Bader JM, Geyer PE, Muller JB, Strauss MT, Koch M, Leypoldt F, Koertvelyessy P, Bittner D, Schipke CG, Incesoy EI, Peters O, Deigendesch N, Simons M, Jensen MK, Zetterberg H, Mann M (2020) Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease. Mol Syst Biol 16(6): e9356. https://doi.org/10.15252/msb.20199356

Barkovits K, Pacharra S, Pfeiffer K, Steinbach S, Eisenacher M, Marcus K, Uszkoreit J (2020) Reproducibility, specificity and accuracy of relative quantification using spectral library-based data-independent acquisition. Mol Cell Proteomics 19(1): 181−197 doi: 10.1074/mcp.RA119.001714

Bertotti A, Migliardi G, Galimi F, Sassi F, Torti D, Isella C, Cora D, Di Nicolantonio F, Buscarino M, Petti C, Ribero D, Russolillo N, Muratore A, Massucco P, Pisacane A, Molinaro L, Valtorta E, Sartore-Bianchi A, Risio M, Capussotti L, Gambacorta M, Siena S, Medico E, Sapino A, Marsoni S, Comoglio PM, Bardelli A, Trusolino L (2011) A molecularly annotated platform of patient-derived xenografts ("xenopatients") identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov 1(6): 508−523 doi: 10.1158/2159-8290.CD-11-0109

Bihani T, Patel HK, Arlt H, Tao N, Jiang H, Brown JL, Purandare DM, Hattersley G, Garner F (2017) Elacestrant (RAD1901), a selective estrogen receptor degrader (SERD), has antitumor activity in multiple ER(+) breast cancer patient-derived xenograft models. Clin Cancer Res 23(16): 4793−4804 doi: 10.1158/1078-0432.CCR-16-2561

Bilbao A, Varesio E, Luban J, Strambio-De-Castillia C, Hopfgartner G, Muller M, Lisacek F (2015) Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics 15(5-6): 964−980 doi: 10.1002/pmic.201400323

Bruderer R, Bernhardt OM, Gandhi T, Miladinovic SM, Cheng LY, Messner S, Ehrenberger T, Zanotelli V, Butscheid Y, Escher C, Vitek O, Rinner O, Reiter L (2015) Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol Cell Proteomics 14(5): 1400−1410 doi: 10.1074/mcp.M114.044305

Chen Y, Choong LY, Lin Q, Philp R, Wong CH, Ang BK, Tan YL, Loh MC, Hew CL, Shah N, Druker BJ, Chong PK, Lim YP (2007) Differential expression of novel tyrosine kinase substrates during breast cancer development. Mol Cell Proteomics 6(12): 2072−2087 doi: 10.1074/mcp.M700395-MCP200

Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M (2020) DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 17(1): 41−44 doi: 10.1038/s41592-019-0638-x

Escher C, Reiter L, MacLean B, Ossola R, Herzog F, Chilton J, MacCoss MJ, Rinner O (2012) Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12(8): 1111−1121 doi: 10.1002/pmic.201100463

Gao E, Li W, Wu C, Shao W, Di Y, Liu Y (2021) Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes. Mol Omics 17(3): 413−425 doi: 10.1039/D0MO00188K

Gao H, Korn JM, Ferretti S, Monahan JE, Wang Y, Singh M, Zhang C, Schnell C, Yang G, Zhang Y, Balbin OA, Barbe S, Cai H, Casey F, Chatterjee S, Chiang DY, Chuai S, Cogan SM, Collins SD, Dammassa E, Ebel N, Embry M, Green J, Kauffmann A, Kowal C, Leary RJ, Lehar J, Liang Y, Loo A, Lorenzana E, Robert McDonald E, 3rd, McLaughlin ME, Merkin J, Meyer R, Naylor TL, Patawaran M, Reddy A, Roelli C, Ruddy DA, Salangsang F, Santacroce F, Singh AP, Tang Y, Tinetto W, Tobler S, Velazquez R, Venkatesan K, Von Arx F, Wang HQ, Wang Z, Wiesmann M, Wyss D, Xu F, Bitter H, Atadja P, Lees E, Hofmann F, Li E, Keen N, Cozens R, Jensen MR, Pryer NK, Williams JA, Sellers WR (2015) High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat Med 21(11): 1318−1325 doi: 10.1038/nm.3954

Gao Q, Zhu H, Dong L, Shi W, Chen R, Song Z, Huang C, Li J, Dong X, Zhou Y, Liu Q, Ma L, Wang X, Zhou J, Liu Y, Boja E, Robles AI, Ma W, Wang P, Li Y, Ding L, Wen B, Zhang B, Rodriguez H, Gao D, Zhou H, Fan J (2019) Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma. Cell 179(2): 561−577

Gilar M, Olivova P, Daly AE, Gebler JC (2005) Two-dimensional separation of peptides using RP-RP-HPLC system with different pH in first and second separation dimensions. J Sep Sci 28(14): 1694−1703 doi: 10.1002/jssc.200500116

Gillet LC, Navarro P, Tate S, Rost H, Selevsek N, Reiter L, Bonner R, Aebersold R (2012) Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11(6): O111 016717. https://doi.org/10.1074/mcp.O111.016717

Gotti C, Roux-Dalvai F, Joly-Beauparlant C, Mangnier L, Leclercq M, Droit A (2021) Extensive and accurate benchmarking of DIA acquisition methods and software tools using a complex proteomic standard. J Proteome Res 20(10): 4801−4814 doi: 10.1021/acs.jproteome.1c00490

Hochgräfe F, Zhang L, O'Toole SA, Browne BC, Pinese M, Porta Cubas A, Lehrbach GM, Croucher DR, Rickwood D, Boulghourjian A, Shearer R, Nair R, Swarbrick A, Faratian D, Mullen P, Harrison DJ, Biankin AV, Sutherland RL, Raftery MJ, Daly RJ (2010) Tyrosine phosphorylation profiling reveals the signaling network characteristics of basal breast cancer cells. Cancer Res 70(22): 9391−9401 doi: 10.1158/0008-5472.CAN-10-0911

Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E (2015) PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res 43(Database issue): D512-520

Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics. J Comput Graph Stat 5(3): 299−314

Kitata RB, Choong WK, Tsai CF, Lin PY, Chen BS, Chang YC, Nesvizhskii AI, Sung TY, Chen YJ (2021) A data-independent acquisition-based global phosphoproteomics system enables deep profiling. Nat Commun 12(1): 2539. https://doi.org/10.1038/s41467-021-22759-z

Krasny L, Huang PH (2021) Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology. Mol Omics 17(1): 29−42 doi: 10.1039/D0MO00072H

Li C, Sun YD, Yu GY, Cui JR, Lou Z, Zhang H, Huang Y, Bai CG, Deng LL, Liu P, Zheng K, Wang YH, Wang QQ, Li QR, Wu QQ, Liu Q, Shyr Y, Li YX, Chen LN, Wu JR, Zhang W, Zeng R (2020) Integrated omics of metastatic colorectal cancer. Cancer Cell 38(5): 734−747 doi: 10.1016/j.ccell.2020.08.002

Lou R, Tang P, Ding K, Li S, Tian C, Li Y, Zhao S, Zhang Y, Shui W (2020) Hybrid spectral library combining DIA-MS data and a targeted virtual library substantially deepens the proteome coverage. iScience 23(3): 100903. https://doi.org/10.1016/j.isci.2020.100903

Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R (2018) Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 14(8): e8126. https://doi.org/10.15252/msb.20178126

Manley PW, Bold G, Bruggen J, Fendrich G, Furet P, Mestan J, Schnell C, Stolz B, Meyer T, Meyhack B, Stark W, Strauss A, Wood J (2004) Advances in the structural biology, design and clinical development of VEGF-R kinase inhibitors for the treatment of angiogenesis. Biochim Biophys Acta 1697(1-2): 17−27 doi: 10.1016/j.bbapap.2003.11.010

Meier-Abt F, Lu J, Cannizzaro E, Pohly MF, Kummer S, Pfammatter S, Kunz L, Collins BC, Nadeu F, Lee KS, Xue P, Gwerder M, Roiss M, Hullein J, Scheinost S, Dietrich S, Campo E, Huber W, Aebersold R, Zenz T (2021) The protein landscape of chronic lymphocytic leukemia (CLL). Blood 138(24): 2514−2525 doi: 10.1182/blood.2020009741

Rappsilber J, Mann M, Ishihama Y (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc 2(8): 1896−1906 doi: 10.1038/nprot.2007.261

Ricci F, Bizzaro F, Cesca M, Guffanti F, Ganzinelli M, Decio A, Ghilardi C, Perego P, Fruscio R, Buda A, Milani R, Ostano P, Chiorino G, Bani MR, Damia G, Giavazzi R (2014) Patient-derived ovarian tumor xenografts recapitulate human clinicopathology and genetic alterations. Cancer Res 74(23): 6980−6990 doi: 10.1158/0008-5472.CAN-14-0274

Rosenberger G, Koh CC, Guo T, Röst HL, Kouvonen P, Collins BC, Heusel M, Liu Y, Caron E, Vichalkovski A, Faini M, Schubert OT, Faridi P, Ebhardt HA, Matondo M, Lam H, Bader SL, Campbell DS, Deutsch EW, Moritz RL, Tate S, Aebersold R (2014) A repository of assays to quantify 10, 000 human proteins by SWATH-MS. Sci Data 1(1): 140031. https://doi.org/10.1038/sdata.2014.31

Sajic T, Liu Y, Aebersold R (2015) Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications. Proteomics Clin Appl 9(3-4): 307−321 doi: 10.1002/prca.201400117

Schubert OT, Gillet LC, Collins BC, Navarro P, Rosenberger G, Wolski WE, Lam H, Amodei D, Mallick P, MacLean B, Aebersold R (2015) Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc 10(3): 426−441 doi: 10.1038/nprot.2015.015

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11): 2498−2504 doi: 10.1101/gr.1239303

Song C, Ye M, Han G, Jiang X, Wang F, Yu Z, Chen R, Zou H (2010) Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides. Anal Chem 82(1): 53−56 doi: 10.1021/ac9023044

Speers C, Tsimelzon A, Sexton K, Herrick AM, Gutierrez C, Culhane A, Quackenbush J, Hilsenbeck S, Chang J, Brown P (2009) Identification of novel kinase targets for the treatment of estrogen receptor-negative breast cancer. Clin Cancer Res 15(20): 6327−6340 doi: 10.1158/1078-0432.CCR-09-1107

Sun Y, Li C, Pang S, Yao Q, Chen L, Li Y, Zeng R (2020) Kinase-substrate edge biomarkers provide a more accurate prognostic prediction in ER-negative breast cancer. Genomics Proteomics Bioinformatics 18(5): 525−538 doi: 10.1016/j.gpb.2019.11.012

Surmen MG, Surmen S, Ali A, Musharraf SG, Emekli N (2020) Phosphoproteomic strategies in cancer research: a minireview. Analyst 145(22): 7125−7149 doi: 10.1039/D0AN00915F

Thakur SS, Geiger T, Chatterjee B, Bandilla P, Frohlich F, Cox J, Mann M (2011) Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol Cell Proteomics 10(8): M110 003699. https://doi.org/10.1074/mcp.M110.003699

Tsou CC, Tsai CF, Teo GC, Chen YJ, Nesvizhskii AI (2016) Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers. Proteomics 16(15-16): 2257−2271 doi: 10.1002/pmic.201500526

Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11(12): 2301−2319 doi: 10.1038/nprot.2016.136

Wang X, Sun Y, Xu Y, Wen D, An N, Leng X, Fu G, Lu S, Chen Z (2021) Mini-patient-derived xenograft assay based on microfluidic technology promises to be an effective tool for screening individualized chemotherapy regimens for advanced non-small cell lung cancer. Cell Biol Int 45(9): 1887−1896 doi: 10.1002/cbin.11622

Weitsman G, Lawler K, Kelleher Muireann T, Barrett JE, Barber Paul R, Shamil E, Festy F, Patel G, Fruhwirth GO, Huang L, Tullis Iain DC, Woodman N, Ofo E, Ameer-Beg Simon M, Irshad S, Condeelis J, Gillett Cheryl E, Ellis Paul A, Vojnovic B, Coolen Anthony CC, Ng T (2014) Imaging tumour heterogeneity of the consequences of a PKCα–substrate interaction in breast cancer patients. Biochem Soc Trans 42(6): 1498−1505 doi: 10.1042/BST20140165

Willems P, Fels U, Staes A, Gevaert K, Van Damme P (2021) Use of hybrid data-dependent and -independent acquisition spectral libraries empowers dual-proteome profiling. J Proteome Res 20(2): 1165−1177 doi: 10.1021/acs.jproteome.0c00350

Wiredja DD, Koyuturk M, Chance MR (2017) The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics. Bioinformatics 33(21): 3489−3491 doi: 10.1093/bioinformatics/btx415

Wisniewski JR, Zougman A, Nagaraj N, Mann M (2009) Universal sample preparation method for proteome analysis. Nat Methods 6(5): 359−362 doi: 10.1038/nmeth.1322

Zhan M, Yang RM, Wang H, He M, Chen W, Xu SW, Yang LH, Liu Q, Long MM, Wang J (2018) Guided chemotherapy based on patient-derived mini-xenograft models improves survival of gallbladder carcinoma patients. Cancer Commun (Lond) 38(1): 48. https://doi.org/10.1186/s40880-018-0318-8

Zhang F, Wang W, Long Y, Liu H, Cheng J, Guo L, Li R, Meng C, Yu S, Zhao Q, Lu S, Wang L, Wang H, Wen D (2018) Characterization of drug responses of mini patient-derived xenografts in mice for predicting cancer patient clinical therapeutic response. Cancer Commun (Lond) 38(1): 60. https://doi.org/10.1186/s40880-018-0329-5

Zhang Y, Bilbao A, Bruderer T, Luban J, Strambio-De-Castillia C, Lisacek F, Hopfgartner G, Varesio E (2015) The use of variable Q1 isolation windows improves selectivity in LC-SWATH-MS acquisition. J Proteome Res 14(10): 4359−4371 doi: 10.1021/acs.jproteome.5b00543

Zhao P, Chen H, Wen D, Mou S, Zhang F, Zheng S (2018) Personalized treatment based on mini patient-derived xenografts and WES/RNA sequencing in a patient with metastatic duodenal adenocarcinoma. Cancer Commun (Lond) 38(1): 54. https://doi.org/10.1186/s40880-018-0323-y

Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Statist Soc B 67(2): 301−320 doi: 10.1111/j.1467-9868.2005.00503.x

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