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      Frontiers in mass spectrometry–based clinical proteomics for cancer diagnosis and treatment

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          Abstract

          Numerous omics studies, primarily genomics analyses, have been conducted to fully understand the molecular biological characteristics of cancer. In recent years, the depth of proteomic analysis, which comprehensively analyzes proteins and molecules that function directly in vivo, has increased dramatically. Proteomics using mass spectrometry (MS) is a promising technology to directly examine proteoforms, including post‐translational modifications and variants originating from genomic aberrations. Recent advances in MS‐based proteomics have enabled direct, in depth, and quantitative analysis of the expression levels of various cancer‐related proteins, as well as their cancer‐specific proteoforms, and proteins that fluctuate with cancer initiation and progression in cell lines and tissue samples. Additionally, the integration of proteomic data with genomic, epigenomic, and transcriptomic data has formed the growing field of proteogenomics, which is already yielding new biological and diagnostic knowledge. Deep proteomic profiling provides clinically useful information in various aspects, including understanding the mechanisms of cancer development and progression and discovering targets for diagnosis and drug development. Furthermore, it is expected to make a significant contribution to the promotion of personalized medicine. In this review, recent advances and impacts in MS‐based clinical proteomics are highlighted with a focus on oncology.

          Abstract

          Clinical proteomics has become increasingly important in recent years. In this review, we provide an overview of state‐of‐the‐art mass spectrometry techniques in cancer research and describe our targeted glycoform analysis for the development of specific cancer biomarkers and our immunopeptidomics studies for the identification of tumor‐specific immunopeptides.

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Cancer statistics, 2022

            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes. Incidence data (through 2018) were collected by the Surveillance, Epidemiology, and End Results program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2019) were collected by the National Center for Health Statistics. In 2022, 1,918,030 new cancer cases and 609,360 cancer deaths are projected to occur in the United States, including approximately 350 deaths per day from lung cancer, the leading cause of cancer death. Incidence during 2014 through 2018 continued a slow increase for female breast cancer (by 0.5% annually) and remained stable for prostate cancer, despite a 4% to 6% annual increase for advanced disease since 2011. Consequently, the proportion of prostate cancer diagnosed at a distant stage increased from 3.9% to 8.2% over the past decade. In contrast, lung cancer incidence continued to decline steeply for advanced disease while rates for localized-stage increased suddenly by 4.5% annually, contributing to gains both in the proportion of localized-stage diagnoses (from 17% in 2004 to 28% in 2018) and 3-year relative survival (from 21% to 31%). Mortality patterns reflect incidence trends, with declines accelerating for lung cancer, slowing for breast cancer, and stabilizing for prostate cancer. In summary, progress has stagnated for breast and prostate cancers but strengthened for lung cancer, coinciding with changes in medical practice related to cancer screening and/or treatment. More targeted cancer control interventions and investment in improved early detection and treatment would facilitate reductions in cancer mortality.
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              MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

              Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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                Author and article information

                Contributors
                yoshimi.haga@jfcr.or.jp
                Journal
                Cancer Sci
                Cancer Sci
                10.1111/(ISSN)1349-7006
                CAS
                Cancer Science
                John Wiley and Sons Inc. (Hoboken )
                1347-9032
                1349-7006
                01 February 2023
                May 2023
                : 114
                : 5 ( doiID: 10.1111/cas.v114.5 )
                : 1783-1791
                Affiliations
                [ 1 ] Cancer Proteomics Group, Cancer Precision Medicine Center Japanese Foundation for Cancer Research Tokyo Japan
                Author notes
                [*] [* ] Correspondence

                Yoshimi Haga, Cancer Proteomics Group, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, 3‐8‐31 Ariake, Koto‐ku, Tokyo 135‐8550, Japan.

                Email: yoshimi.haga@ 123456jfcr.or.jp

                Author information
                https://orcid.org/0000-0001-7861-617X
                https://orcid.org/0000-0001-9066-4959
                Article
                CAS15731 CAS-RA-2270-2022.R1
                10.1111/cas.15731
                10154896
                36661476
                04034b8d-2076-4663-93db-35330cdf8c25
                © 2023 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 10 January 2023
                : 17 October 2022
                : 15 January 2023
                Page count
                Figures: 6, Tables: 0, Pages: 9, Words: 5480
                Funding
                Funded by: Japan Agency for Medical Research and Development , doi 10.13039/100009619;
                Award ID: JP22ama221409
                Categories
                Review Article
                Review Articles
                Custom metadata
                2.0
                May 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.8 mode:remove_FC converted:03.05.2023

                Oncology & Radiotherapy
                biomarker,cancer,glycoproteomics,mass spectrometry,proteogenomics
                Oncology & Radiotherapy
                biomarker, cancer, glycoproteomics, mass spectrometry, proteogenomics

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