Abstract
Proteomics has emerged as a vital discipline within the realm of molecular sciences, offering valuable insights into protein identities, expression levels, and modifications. Notably, in the domain of cancer research, proteomics has played a pivotal role in unravelling intricate mechanistic details concerning tumour growth and metastasis. These revelations have significantly contributed to the discovery of clinically relevant biomarkers and potential therapeutic targets. The establishment of numerous cancer proteome databases on a global scale underscores the collaborative nature of this field. Furthermore, the integration of proteomics with other ‘omics’ disciplines has yielded a wealth of data pertaining to molecular mechanisms and factors that regulate targets. These data sets are amenable to analysis and interpretation through sophisticated bioinformatic pipelines. The primary objective of this comprehensive review is to furnish an all-encompassing overview of the landscape of cancer proteomics, highlighting recent strides in proteomic methodologies. Particularly, the review seeks to provide a nuanced understanding of ongoing proteomics investigations concerning brain cancer—a domain where proteomic applications are in their nascent stages. The scope of this review encompasses the entire spectrum of proteomics applications, ranging from the initial discovery of biomarkers to the intricate characterization of molecular mechanisms, which has been made feasible by the advancement of cutting-edge technologies. Moreover, the review delves into the prevailing trends in proteomics strategies for translational research, serving as an indispensable cornerstone of this critical research paradigm. It is anticipated that the continued evolution of proteomics will yield insights of unprecedented magnitude, transcending previous boundaries. This dynamic field stands poised to revolutionize our understanding of complex biological processes, thereby furnishing invaluable information for translational applications that extend far beyond current horizons.
Similar content being viewed by others
Data availability
The data are not available.
Change history
23 April 2024
A Correction to this paper has been published: https://doi.org/10.1007/s42485-024-00141-z
References
Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422:198–207. https://doi.org/10.1038/nature01511
Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537(7620):347–355
Altelaar AF, Munoz J, Heck AJ (2013) Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet 14:35–48. https://doi.org/10.1038/nrg3356
Altschuler SJ, Wu LF (2010) Cellular heterogeneity: do differences make a difference? Cell 141:559–563. https://doi.org/10.1016/j.cell.2010.04.033
Angel TE, Aryal UK, Hengel SM, Baker ES, Kelly RT, Robinson EW et al (2012) Mass spectrometry-based proteomics: existing capabilities and future directions. Chem Soc Rev 41:3912–3928. https://doi.org/10.1039/c2cs15331a
Armstrong PB, Armstrong MT (2000) Intercellular invasion and the organizational stability of tissues: a role for fibronectin. Biochim Biophys Acta 1470:O9-20. https://doi.org/10.1016/S0304-419X(00)00003-2
Azuaje F, Kim SY, Perez Hernandez D, Dittmar G (2019) Connecting histopathology imaging and proteomics in kidney cancer through machine learning. J Clin Med. https://doi.org/10.3390/jcm8101535
Bai YH, Zhan YB, Yu B, Wang WW, Wang L, Zhou JQ et al (2018) A novel tumor-suppressor, CDH18, inhibits glioma cell invasiveness via UQCRC2 and correlates with the prognosis of glioma patients. Cell Physiol Biochem 48:1755–1770. https://doi.org/10.1159/000492317
Beal J, Montagud A, Traynard P, Barillot E, Calzone L (2018) Personalization of logical models with multi-omics data allows clinical stratification of patients. Front Physiol 9:1965. https://doi.org/10.3389/fphys.2018.01965
Brabletz T, Jung A, Reu S, Porzner M, Hlubek F, Kunz-Schughart LA et al (2001) Variable beta-catenin expression in colorectal cancers indicates tumor progression driven by the tumor environment. Proc Natl Acad Sci U S A 98:10356–10361. https://doi.org/10.1073/pnas.171610498
Brandi J, Dando I, Pozza ED, Biondani G, Jenkins R, Elliott V et al (2017) Proteomic analysis of pancreatic cancer stem cells: functional role of fatty acid synthesis and mevalonate pathways. J Proteomics 150:310–322. https://doi.org/10.1016/j.jprot.2016.10.002
Budnik B, Levy E, Harmange G, Slavov N (2018) SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol 19:161. https://doi.org/10.1186/s13059-018-1547-5
Callesen AK, Vach W, Jorgensen PE, Cold S, Tan Q, Depont Christensen R et al (2008) Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study. J Proteome Res 7:1419–1426. https://doi.org/10.1021/pr7007576
Chae YK, Kim WB, Davis AA, Park LC, Anker JF, Simon NI et al (2020) Mass spectrometry-based serum proteomic signature as a potential biomarker for survival in patients with non-small cell lung cancer receiving immunotherapy. Transl Lung Cancer Res 9:1015–1028. https://doi.org/10.21037/tlcr-20-148
Chang L, Ni J, Beretov J, Wasinger VC, Hao J, Bucci J et al (2017) Identification of protein biomarkers and signaling pathways associated with prostate cancer radioresistance using label-free LC-MS/MS proteomic approach. Sci Rep 7:41834. https://doi.org/10.1038/srep41834
Chen X, Ishwaran H (2012) Random forests for genomic data analysis. Genomics 99(6):323–329
Chen F, Chandrashekar DS, Varambally S, Creighton CJ (2019) Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers. Nat Commun 10:5679. https://doi.org/10.1038/s41467-019-13528-0
Choi D, Montermini L, Kim DK, Meehan B, Roth FP, Rak J (2018) The impact of oncogenic EGFRvIII on the proteome of extracellular vesicles released from glioblastoma cells. Mol Cell Proteomics 17:1948–1964. https://doi.org/10.1074/mcp.RA118.000644
Chu CS, Miller CA, Gieschen A, Fischer SM (2017) Pathway-informed discovery and targeted proteomic workflows using mass spectrometry. Methods Mol Biol 1550:199–221. https://doi.org/10.1007/978-1-4939-6747-6_15
Cleary AS, Leonard TL, Gestl SA, Gunther EJ (2014) Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature 508:113–117. https://doi.org/10.1038/nature13187
Corso S, Migliore C, Ghiso E, De Rosa G, Comoglio PM, Giordano S (2008) Silencing the MET oncogene leads to regression of experimental tumors and metastases. Oncogene 27:684–693. https://doi.org/10.1038/sj.onc.1210697
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10(4):1794–1805
Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M (2014) Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics 13:2513–2526. https://doi.org/10.1074/mcp.M113.031591
Dayon L, Hainard A, Licker V, Turck N, Kuhn K, Hochstrasser DF, Burkhard PR (2008) Relative quantification of proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal Chem 80(8):2921–2931
Domon B, Aebersold R (2010) Mass spectrometry and protein analysis. Science 312(5771):212–217
Eckert MA, Coscia F, Chryplewicz A, Chang JW, Hernandez KM, Pan S et al (2019) Proteomics reveals NNMT as a master metabolic regulator of cancer-associated fibroblasts. Nature 569:723–728. https://doi.org/10.1038/s41586-019-1173-8
Ellis MJ, Gillette M, Carr SA, Paulovich AG, Smith RD, Rodland KK et al (2013) Connecting genomic alterations to cancer biology with proteomics: the NCI clinical proteomic tumor analysis consortium. Cancer Discov 3:1108–1112. https://doi.org/10.1158/2159-8290.CD-13-0219
Enroth S, Berggrund M, Lycke M, Broberg J, Lundberg M, Assarsson E et al (2019) High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer. Commun Biol 2:221. https://doi.org/10.1038/s42003-019-0464-9
Erhart F, Hackl M, Hahne H, Buchroithner J, Meng C, Klingenbrunner S et al (2020) Combined proteomics/miRNomics of dendritic cell immunotherapy-treated glioblastoma patients as a screening for survival-associated factors. NPJ Vaccines 5:5. https://doi.org/10.1038/s41541-019-0149-x
Evers TMJ, Hochane M, Tans SJ, Heeren RMA, Semrau S, Nemes P et al (2019) Deciphering metabolic heterogeneity by single-cell analysis. Anal Chem 91:13314–13323. https://doi.org/10.1021/acs.analchem.9b02410
Faria SS, Morris CF, Silva AR, Fonseca MP, Forget P, Castro MS et al (2017) A timely shift from shotgun to targeted proteomics and how it can be groundbreaking for cancer research. Front Oncol 7:13. https://doi.org/10.3389/fonc.2017.00013
Gao Q, Zhu H, Dong L, Shi W, Chen R, Song Z et al (2019) Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma. Cell 179:1240. https://doi.org/10.1016/j.cell.2019.10.038
Garza S, Moini M (2006) Analysis of complex protein mixtures with improved sequence coverage using (CE-MS/MS)n. Anal Chem 78:7309–7316. https://doi.org/10.1021/ac0612269
Geyer PE, Holdt LM, Teupser D, Mann M (2017) Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol 13(9):942
Ginestier C, Hur MH, Charafe-Jauffret E, Monville F, Dutcher J, Brown M et al (2007) ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell 1:555–567. https://doi.org/10.1016/j.stem.2007.08.014
Goeminne LJE, Gevaert K, Clement L (2018) Experimental design and data-analysis in label-free quantitative LC/MS proteomics: a tutorial with MSqRob. J Proteomics 171:23–36. https://doi.org/10.1016/j.jprot.2017.04.004
Graves PR, Haystead TA (2002) Molecular biologist’s guide to proteomics. Microbiol Mol Biol Rev 66:39–63. https://doi.org/10.1128/MMBR.66.1.39-63.2002
Gupta GP, Massague J (2006) Cancer metastasis: building a framework. Cell 127:679–695. https://doi.org/10.1016/j.cell.2006.11.001
Gupta MK, Polisetty RV, Sharma R, Ganesh RA, Gowda H, Purohit AK et al (2019) Altered transcriptional regulatory proteins in glioblastoma and YBX1 as a potential regulator of tumor invasion. Sci Rep 9:10986. https://doi.org/10.1038/s41598-019-47360-9
Hahn WC, Weinberg RA (2002) Rules for making human tumor cells. N Engl J Med 347:1593–1603. https://doi.org/10.1056/NEJMra021902
Hallal S, Russell BP, Wei H, Lee MYT, Toon CW, Sy J (2019) Extracellular vesicles from neurosurgical aspirates identifies chaperonin containing TCP1 subunit 6A as a potential glioblastoma biomarker with prognostic significance. Proteomics 19:e1800157. https://doi.org/10.1002/pmic.201800157
Hanash S, Taguchi A (2011) Application of proteomics to cancer early detection. Cancer J 17:423–428. https://doi.org/10.1097/PPO.0b013e3182383cab
Harel M, Ortenberg R, Varanasi SK, Mangalhara KC, Mardamshina M, Markovits E et al (2019) Proteomics of melanoma response to immunotherapy reveals mitochondrial dependence. Cell 179(236–50):e218. https://doi.org/10.1016/j.cell.2019.08.012
Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18:83. https://doi.org/10.1186/s13059-017-1215-1
Hemmati HD, Nakano I, Lazareff JA, Masterman-Smith M, Geschwind DH, Bronner-Fraser M et al (2003) Cancerous stem cells can arise from pediatric brain tumors. Proc Natl Acad Sci USA 100:15178–15183. https://doi.org/10.1073/pnas.2036535100
Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D, Ng S, Shen R (2014) Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158(4):929–944
Huang PJ, Lee CC, Tan BC, Yeh YM, Julie Chu L, Chen TW (2015) CMPD: cancer mutant proteome database. Nucleic Acids Res 43:D849-855. https://doi.org/10.1093/nar/gku1182
Hudson TJ, Anderson W, Artez A, Barker AD, Bell C, International Cancer Genome C (2010) International network of cancer genome projects. Nature 464:993–998. https://doi.org/10.1038/nature08987
Hughes AJ, Spelke DP, Xu Z, Kang CC, Schaffer DV, Herr AE (2014) Single-cell western blotting. Nat Methods 11:749–755. https://doi.org/10.1038/nmeth.2992
Hyung SJ, Ruotolo BT (2012) Integrating mass spectrometry of intact protein complexes into structural proteomics. Proteomics 12:1547–1564. https://doi.org/10.1002/pmic.201100520
Indira Chandran V, Welinder C, Mansson AS, Offer S, Freyhult E, Pernemalm M (2019) Ultrasensitive immunoprofiling of plasma extracellular vesicles identifies syndecan-1 as a potential tool for minimally invasive diagnosis of glioma. Clin Cancer Res 25:3115–3127. https://doi.org/10.1158/1078-0432.CCR-18-2946
Issaq H, Veenstra T (2008) Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE): advances and perspectives. Biotechniques 44:697–700. https://doi.org/10.2144/000112823
Jeon SA, Kim DW, Lee DB, Cho JY (2020) NEDD4 plays roles in the maintenance of breast cancer stem cell characteristics. Front Oncol 10:1680. https://doi.org/10.3389/fonc.2020.01680
Kalluri R (2016) The biology and function of exosomes in cancer. J Clin Invest 126:1208–1215. https://doi.org/10.1172/JCI81135
Karczewski KJ, Snyder MP (2018) Integrative omics for health and disease. Nat Rev Genet 19:299–310. https://doi.org/10.1038/nrg.2018.4
Kay RG, Galvin S, Larraufie P, Reimann F, Gribble FM (2017) Liquid chromatography/mass spectrometry based detection and semi-quantitative analysis of INSL5 in human and murine tissues. Rapid Commun Mass Spectrom 31:1963–1973. https://doi.org/10.1002/rcm.7978
Kellogg RA, Dunn J, Snyder MP (2018) Personal omics for precision health. Circ Res 122:1169–1171. https://doi.org/10.1161/CIRCRESAHA.117.310909
Kelly RT (2020) Single-cell proteomics: progress and prospects. Mol Cell Proteom 19:1739–1748. https://doi.org/10.1074/mcp.R120.002234
Kessner D, Chambers M, Burke R, Agus D, Mallick P (2008) ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24(21):2534–2536
Kim M, Tagkopoulos I (2018) Data integration and predictive modeling methods for multi-omics datasets. Mol Omics 14:8–25. https://doi.org/10.1039/C7MO00051K
Koh EY, You JE, Jung SH, Kim PH (2020) Biological functions and identification of novel biomarker expressed on the surface of breast cancer-derived cancer stem cells via proteomic analysis. Mol Cells 43:384–396. https://doi.org/10.14348/molcells.2020.2230
Koren S, Bentires-Alj M (2015) Breast tumor heterogeneity: source of fitness, hurdle for therapy. Mol Cell 60:537–546. https://doi.org/10.1016/j.molcel.2015.10.031
Kottakis F, Nicolay BN, Roumane A, Karnik R, Gu H, Nagle JM et al (2016) LKB1 loss links serine metabolism to DNA methylation and tumorigenesis. Nature 539:390–395. https://doi.org/10.1038/nature20132
Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17
Krug K, Mertins P, Zhang B, Hornbeck P, Raju R, Ahmad R (2019) A curated resource for phosphosite-specific signature analysis. Mol Cell Proteomics 18:576–593. https://doi.org/10.1074/mcp.TIR118.000943
Kucharzewska P, Christianson HC, Welch JE, Svensson KJ, Fredlund E, Ringner M et al (2013) Exosomes reflect the hypoxic status of glioma cells and mediate hypoxia-dependent activation of vascular cells during tumor development. Proc Natl Acad Sci USA 110:7312–7317. https://doi.org/10.1073/pnas.1220998110
Kuczynski EA, Sargent DJ, Grothey A, Kerbel RS (2013) Drug rechallenge and treatment beyond progression–implications for drug resistance. Nat Rev Clin Oncol 10:571–587. https://doi.org/10.1038/nrclinonc.2013.158
Le Large TYS, El Hassouni B, Funel N, Kok B, Piersma SR, Pham TV et al (2019) Proteomic analysis of gemcitabine-resistant pancreatic cancer cells reveals that microtubule-associated protein 2 upregulation associates with taxane treatment. Ther Adv Med Oncol 11:1758835919841233. https://doi.org/10.1177/1758835919841233
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539
Lee SI, Kim DK, Seo EJ, Choi EJ, Kwon YW, Jang IH et al (2017) Role of Kruppel-like factor 4 in the maintenance of chemoresistance of anaplastic thyroid cancer. Thyroid 27:1424–1432. https://doi.org/10.1089/thy.2016.0414
Lignitto L, LeBoeuf SE, Homer H, Jiang S, Askenazi M, Karakousi TR et al (2019) Nrf2 activation promotes lung cancer metastasis by inhibiting the degradation of Bach1. Cell 178(316–29):e318. https://doi.org/10.1016/j.cell.2019.06.003
Lin YH, Eguez RV, Torralba MG, Singh H, Golusinski P, Golusinski W et al (2019) Self-assembled STrap for global proteomics and salivary biomarker discovery. J Proteome Res 18:1907–1915. https://doi.org/10.1021/acs.jproteome.9b00037
Lo CA, Kays I, Emran F, Lin TJ, Cvetkovska V, Chen BE (2015) Quantification of protein levels in single living cells. Cell Rep 13:2634–2644. https://doi.org/10.1016/j.celrep.2015.11.048
Lv LX, Yan R, Shi HY, Shi D, Fang DQ, Jiang HY et al (2017) Integrated transcriptomic and proteomic analysis of the bile stress response in probiotic Lactobacillus salivarius LI01. J Proteomics 150:216–229. https://doi.org/10.1016/j.jprot.2016.08.021
MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Maccoss MJ (2010) Skyline: an open-source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26(7):966–968
Manzoni C, Kia DA, Vandrovcova J, Hardy J, Wood NW, Lewis PA (2018) Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief Bioinform 19:286–302. https://doi.org/10.1093/bib/bbw114
Meacham CE, Morrison SJ (2013) Tumour heterogeneity and cancer cell plasticity. Nature 501:328–337. https://doi.org/10.1038/nature12624
Mehta N, Lyon JG, Patil K, Mokarram N, Kim C, Bellamkonda RV (2017) Bacterial carriers for glioblastoma therapy. Mol Ther Oncolyt 4:1–17. https://doi.org/10.1016/j.omto.2016.12.003
Mertins P, Mani DR, Ruggles KV, Gillette MA, Clauser KR, Wang P, Carr SA (2016) Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534(7605):55–62
Mertins P, Mani DR, Ruggles KV, Gillette MA, Clauser KR, Wang P (2016b) Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534:55–62. https://doi.org/10.1038/nature18003
Mertins P, Tang LC, Krug K, Clark DJ, Gritsenko MA, Chen L (2018) Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat Protoc 13:1632–1661. https://doi.org/10.1038/s41596-018-0006-9
Mezger STP, Mingels AMA, Bekers O, Cillero-Pastor B, Heeren RMA (2019) Trends in mass spectrometry imaging for cardiovascular diseases. Anal Bioanal Chem 411:3709–3720. https://doi.org/10.1007/s00216-019-01780-8
Milac TI, Randolph TW, Wang P (2012) Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies. Stat Interface 5:75–87. https://doi.org/10.4310/SII.2012.v5.n1.a7
Miyauchi E, Furuta T, Ohtsuki S, Tachikawa M, Uchida Y, Sabit H et al (2018) Identification of blood biomarkers in glioblastoma by SWATH mass spectrometry and quantitative targeted absolute proteomics. PLoS ONE 13:e0193799. https://doi.org/10.1371/journal.pone.0193799
Murase S, Saio M, Andoh H, Takenaka K, Shinoda J, Nishimura Y et al (2000) Diagnostic utility of CSF soluble CD27 for primary central nervous system lymphoma in immunocompetent patients. Neurol Res 22:434–442. https://doi.org/10.1080/01616412.2000.11740697
Murciano-Goroff YR, Warner AB, Wolchok JD (2020) The future of cancer immunotherapy: microenvironment-targeting combinations. Cell Res 30:507–519. https://doi.org/10.1038/s41422-020-0337-2
Myers SA, Rhoads A, Cocco AR, Peckner R, Haber AL, Schweitzer LD (2019) Streamlined protocol for deep proteomic profiling of FAC-sorted cells and its application to freshly isolated murine immune cells. Mol Cell Proteomics 18:995–1009. https://doi.org/10.1074/mcp.RA118.001259
Nanjundan M, Byers LA, Carey MS, Siwak DR, Raso MG, Diao L et al (2010) Proteomic profiling identifies pathways dysregulated in non-small cell lung cancer and an inverse association of AMPK and adhesion pathways with recurrence. J Thorac Oncol 5:1894–1904. https://doi.org/10.1097/JTO.0b013e3181f2a266
Ni Y, Zhang F, An M, Yin W, Gao Y (2018) Early candidate biomarkers found from urine of glioblastoma multiforme rat before changes in MRI. Sci China Life Sci 61:982–987. https://doi.org/10.1007/s11427-017-9201-0
Obradovic MMS, Hamelin B, Manevski N, Couto JP, Sethi A, Coissieux MM et al (2019) Glucocorticoids promote breast cancer metastasis. Nature 567:540–544. https://doi.org/10.1038/s41586-019-1019-4
Okawa S, Gagrica S, Blin C, Ender C, Pollard SM, Krijgsveld J (2017) Proteome and secretome characterization of glioblastoma-derived neural stem cells. Stem Cells 35:967–980. https://doi.org/10.1002/stem.2542
Okuda S, Watanabe Y, Moriya Y, Kawano S, Yamamoto T, Matsumoto M (2017) jPOSTrepo: an international standard data repository for proteomes. Nucleic Acids Res 45:D1107–D1111. https://doi.org/10.1093/nar/gkw1080
Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A, Sevinsky JR et al (2005) Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol Cell Proteomics 4:1487–1502. https://doi.org/10.1074/mcp.M500084-MCP200
Omuro A, DeAngelis LM (2013) Glioblastoma and other malignant gliomas: a clinical review. J Am Med Assoc 310:1842–1850. https://doi.org/10.1001/jama.2013.280319
Ong SE, Kratchmarova I, Mann M (2003) Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). J Proteome Res 2:173–181. https://doi.org/10.1021/pr0255708
Pandey A, Mann M (2000) Proteomics to study genes and genomes. Nature 405:837–846. https://doi.org/10.1038/35015709
Pearson JRD, Regad T (2017) Targeting cellular pathways in glioblastoma multiforme. Signal Transduct Target Ther 2:17040. https://doi.org/10.1038/sigtrans.2017.40
Peng DH, Rodriguez BL, Diao L, Chen L, Wang J, Byers LA et al (2020) Collagen promotes anti-PD-1/PD-L1 resistance in cancer through LAIR1-dependent CD8(+) T cell exhaustion. Nat Commun 11:4520. https://doi.org/10.1038/s41467-020-18298-8
Phi LTH, Sari IN, Yang YG, Lee SH, Jun N, Kim KS et al (2018) Cancer stem cells (CSCs) in drug resistance and their therapeutic implications in cancer treatment. Stem Cells Int 2018:5416923. https://doi.org/10.1155/2018/5416923
Pinu FR, Beale DJ, Paten AM, Kouremenos K, Swarup S, Schirra HJ (2019) Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Metabolites 9:40076. https://doi.org/10.3390/metabo9040076
Pollard SM, Yoshikawa K, Clarke ID, Danovi D, Stricker S, Russell R et al (2009) Glioma stem cell lines expanded in adherent culture have tumor-specific phenotypes and are suitable for chemical and genetic screens. Cell Stem Cell 4:568–580. https://doi.org/10.1016/j.stem.2009.03.014
Posadas EM, Simpkins F, Liotta LA, MacDonald C, Kohn EC (2005) Proteomic analysis for the early detection and rational treatment of cancer–realistic hope? Ann Oncol 16:16–22. https://doi.org/10.1093/annonc/mdi004
Prieto P, Jaen RI, Calle D, Gomez-Serrano M, Nunez E, Fernandez-Velasco M et al (2019) Interplay between post-translational cyclooxygenase-2 modifications and the metabolic and proteomic profile in a colorectal cancer cohort. World J Gastroenterol 25:433–446. https://doi.org/10.3748/wjg.v25.i4.433
Prince ME, Sivanandan R, Kaczorowski A, Wolf GT, Kaplan MJ, Dalerba P et al (2007) Identification of a subpopulation of cells with cancer stem cell properties in head and neck squamous cell carcinoma. Proc Natl Acad Sci U S A 104:973–978. https://doi.org/10.1073/pnas.0610117104
Raffel S, Klimmeck D, Falcone M, Demir A, Pouya A, Zeisberger P et al (2020) Quantitative proteomics reveals specific metabolic features of acute myeloid leukemia stem cells. Blood 136:1507–1519. https://doi.org/10.1182/blood.2019003654
Rajagopal MU, Hathout Y, MacDonald TJ, Kieran MW, Gururangan S, Blaney SM et al (2011) Proteomic profiling of cerebrospinal fluid identifies prostaglandin D2 synthase as a putative biomarker for pediatric medulloblastoma: a pediatric brain tumor consortium study. Proteomics 11:935–943. https://doi.org/10.1002/pmic.201000198
Reifenberger G, Wirsching HG, Knobbe-Thomsen CB, Weller M (2017) Advances in the molecular genetics of gliomas—implications for classification and therapy. Nat Rev Clin Oncol 14:434–452. https://doi.org/10.1038/nrclinonc.2016.204
Riley RS, June CH, Langer R, Mitchell MJ (2019) Delivery technologies for cancer immunotherapy. Nat Rev Drug Discov 18:175–196. https://doi.org/10.1038/s41573-018-0006-z
Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Yi EC (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteom 3(12):1154–1169
Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Kohlbacher O (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13(9):741–748
Roy S, Josephson SA, Fridlyand J, Karch J, Kadoch C, Karrim J et al (2008) Protein biomarker identification in the CSF of patients with CNS lymphoma. J Clin Oncol 26:96–105. https://doi.org/10.1200/JCO.2007.12.1053
Samaras P, Schmidt T, Frejno M, Gessulat S, Reinecke M, Jarzab A (2020) ProteomicsDB: a multi-omics and multi-organism resource for life science research. Nucleic Acids Res 48:D1153–D1163. https://doi.org/10.1093/nar/gkz974
Samuel N, Remke M, Rutka JT, Raught B, Malkin D (2014) Proteomic analyses of CSF aimed at biomarker development for pediatric brain tumors. J Neurooncol 118:225–238. https://doi.org/10.1007/s11060-014-1432-3
Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Selbach M (2011) Global quantification of mammalian gene expression control. Nature 473(7347):337–342
Shao X, Taha IN, Clauser KR, Gao YT, Naba A (2020) MatrisomeDB: the ECM-protein knowledge database. Nucleic Acids Res 48:D1136–D1144. https://doi.org/10.1093/nar/gkz849
Shen J, Qi L, Zou Z, Du J, Kong W, Zhao L et al (2020) Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases. Sci Rep 10:4435. https://doi.org/10.1038/s41598-020-61298-3
Shenoy A, Belugali Nataraj N, Perry G, Loayza Puch F, Nagel R, Marin I et al (2020) Proteomic patterns associated with response to breast cancer neoadjuvant treatment. Mol Syst Biol 16:e9443. https://doi.org/10.15252/msb.20209443
Shnaper S, Desbaillets I, Brown DA, Murat A, Migliavacca E, Schluep M et al (2009) Elevated levels of MIC-1/GDF15 in the cerebrospinal fluid of patients are associated with glioblastoma and worse outcome. Int J Cancer 125:2624–2630. https://doi.org/10.1002/ijc.24639
Shruthi BS, Vinodhkumar P, Selvamani S (2016) Proteomics: a new perspective for cancer. Adv Biomed Res 5:67. https://doi.org/10.4103/2277-9175.180636
Skog J, Wurdinger T, van Rijn S, Meijer DH, Gainche L, Sena-Esteves M et al (2008) Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol 10:1470–1476. https://doi.org/10.1038/ncb1800
Slavov N (2021) Single-cell protein analysis by mass spectrometry. Curr Opin Chem Biol 60:1–9. https://doi.org/10.1016/j.cbpa.2020.04.018
Song P, Kwon Y, Joo JY, Kim DG, Yoon JH (2019) Secretomics to discover regulators in diseases. Int J Mol Sci 20:163893. https://doi.org/10.3390/ijms20163893
Soto AM, Sonnenschein C (2004) The somatic mutation theory of cancer: growing problems with the paradigm? BioEssays 26:1097–1107. https://doi.org/10.1002/bies.20087
Specht H, Emmott E, Petelski AA, Huffman RG, Perlman DH, Serra M et al (2021) Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol 22:50. https://doi.org/10.1186/s13059-021-02267-5
Sun YV, Hu YJ (2016) Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv Genet 93:147–190. https://doi.org/10.1016/bs.adgen.2015.11.004
Thompson A, Schafer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Hamon C (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75(8):1895–1904
Vasaikar SV, Straub P, Wang J, Zhang B (2018) LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res 46:D956–D963. https://doi.org/10.1093/nar/gkx1090
Vasaikar S, Huang C, Wang X, Petyuk VA, Savage SR, Wen B (2019) Proteogenomic analysis of human colon cancer reveals new therapeutic opportunities. Cell 177(1035–49):e1019. https://doi.org/10.1016/j.cell.2019.03.030
Visvader JE (2011) Cells of origin in cancer. Nature 469:314–322. https://doi.org/10.1038/nature09781
Wang D, Bodovitz S (2010) Single cell analysis: the new frontier in “omics.” Trends Biotechnol 28:281–290. https://doi.org/10.1016/j.tibtech.2010.03.002
Wang D, Gu J (2018) VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 19(1):1–7
Wang Y, Arribas-Layton M, Chen Y, Lykke-Andersen J, Sen GL (2015) DDX6 orchestrates mammalian progenitor function through the mRNA degradation and translation pathways. Mol Cell 60:118–130. https://doi.org/10.1016/j.molcel.2015.08.014
Wang X, Zhang H, Chen X (2019) Drug resistance and combating drug resistance in cancer. Cancer Drug Resist 2:141–160. https://doi.org/10.20517/cdr.2019.10
Whiteaker JR, Halusa GN, Hoofnagle AN, Sharma V, MacLean B, Yan P (2014) CPTAC assay portal: a repository of targeted proteomic assays. Nat Methods 11:703–704. https://doi.org/10.1038/nmeth.3002
Wilson JJ, Burgess R, Mao YQ, Luo S, Tang H, Jones VS et al (2015) Antibody arrays in biomarker discovery. Adv Clin Chem 69:255–324. https://doi.org/10.1016/bs.acc.2015.01.002
Wu P, Heins ZJ, Muller JT, Katsnelson L, de Bruijn I, Abeshouse AA (2019a) Integration and analysis of CPTAC proteomics data in the context of cancer genomics in the cBioPortal. Mol Cell Proteomics 18:1893–1898. https://doi.org/10.1074/mcp.TIR119.001673
Wu C, Zhou F, Ren J, Li X, Jiang Y, Ma S (2019b) A selective review of multi-level omics data integration using variable selection. High Throughput 8:10004. https://doi.org/10.3390/ht8010004
Xiao Y, Ma D, Zhao S, Suo C, Shi J, Xue MZ et al (2019) Multi-omics profiling reveals distinct microenvironment characterization and suggests immune escape mechanisms of triple-negative breast cancer. Clin Cancer Res 25:5002–5014. https://doi.org/10.1158/1078-0432.CCR-18-3524
Yadav M, Jhunjhunwala S, Phung QT, Lupardus P, Tanguay J, Bumbaca S et al (2014) Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515:572–576. https://doi.org/10.1038/nature14001
Yang Q, Zhang Y, Cui H, Chen L, Zhao Y, Lin Y (2018) dbDEPC 30: the database of differentially expressed proteins in human cancer with multi-level annotation and drug indication. Database. https://doi.org/10.1093/database/bay015
Yi L, Tsai CF, Dirice E, Swensen AC, Chen J, Shi T (2019) Boosting to Amplify Signal with Isobaric Labeling (BASIL) strategy for comprehensive quantitative phosphoproteomic characterization of small populations of cells. Anal Chem 91:5794–5801. https://doi.org/10.1021/acs.analchem.9b00024
Zaslavsky BY, Uversky VN, Chait A (2016) Solvent interaction analysis as a proteomic approach to structure-based biomarker discovery and clinical diagnostics. Expert Rev Proteomics 13:9–17. https://doi.org/10.1586/14789450.2016.1116945
Zetterberg H, Andreasson U, Blennow K (2009) CSF antithrombin III and disruption of the blood-brain barrier. J Clin Oncol 27:2302–2303. https://doi.org/10.1200/JCO.2008.19.8598
Zhang Y, Zhang Z (2020) The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell Mol Immunol 17:807–821. https://doi.org/10.1038/s41423-020-0488-6
Zhang J, Baran J, Cros A, Guberman JM, Haider S, Hsu J (2011) International Cancer Genome Consortium Data Portal–a one-stop shop for cancer genomics data. Database. https://doi.org/10.1093/database/bar026
Zhang B, Wang J, Wang X, Zhu J, Liu Q, Shi Z, Wang W (2014) Proteogenomic characterization of human colon and rectal cancer. Nature 513(7518):382–387
Zhang B, Wang J, Wang X, Zhu J, Liu Q, Shi Z (2014a) Proteogenomic characterization of human colon and rectal cancer. Nature 513:382–387. https://doi.org/10.1038/nature13438
Zhang H, Liu T, Zhang Z, Payne SH, Zhang B, McDermott JE (2016) Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166:755–765. https://doi.org/10.1016/j.cell.2016.05.069
Zhang M, Wang B, Xu J, Wang X, Xie L, Zhang B (2017) CanProVar 2.0: an updated database of human cancer proteome variation. J Proteome Res 16:421–432. https://doi.org/10.1021/acs.jproteome.6b00505
Zhang BL, Dong FL, Guo TW, Gu XH, Huang LY, Gao DS (2017a) MiRNAs mediate GDNF-induced proliferation and migration of glioma cells. Cell Physiol Biochem 44:1923–1938. https://doi.org/10.1159/000485883
Zhang X, Maity TK, Ross KE, Qi Y, Cultraro CM, Bahta M et al (2021) Alterations in the global proteome and phosphoproteome in third generation EGFR TKI resistance reveal drug targets to circumvent resistance. Cancer Res 81:3051–3066. https://doi.org/10.1158/0008-5472.CAN-20-2435
Zhu W, Smith JW, Huang CM (2010) Mass spectrometry-based label-free quantitative proteomics. J Biomed Biotechnol 2010:840518. https://doi.org/10.1155/2010/840518
Author information
Authors and Affiliations
Contributions
The study’s success is the result of collaborative efforts and diverse contributions from each author. Thaddi BN and Kilari ES led in shaping the study's framework, conceiving and designing the research. Their dedication extended beyond the initial stage, delving into existing literature to ensure robustness. Dabbada VB and Ambati B provided invaluable inputs, generating innovative ideas that injected fresh perspectives. As the study progressed, Thaddi BN and Kilari ES composed and refined the manuscript, weaving insights into a coherent narrative. This collaborative process showcased their partnership and commitment to impactful work. The authors' seamless collaboration is clearly demonstrated by their unanimous approval of the polished manuscript.
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflicts of interest in relation to this review article.
Additional information
The original online version of this article was revised: In the original article affiliation of author Bhavani Ambati was incorrectly given as “Department of Biochemistry and Molecular Biology, GITAM Institute of Sciences, GITAM Deemed to Be University, Visakhapatnam, Andhra Pradesh, India”. The correct affiliation is “Department of Medical Oncology, Lions District Cancer Treatment and Research Centre, Visakhapatnam-530013, Andhra Pradesh, India”.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Thaddi, B.N., Dabbada, V.B., Ambati, B. et al. Decoding cancer insights: recent progress and strategies in proteomics for biomarker discovery. J Proteins Proteom 15, 67–87 (2024). https://doi.org/10.1007/s42485-023-00121-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42485-023-00121-9