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Duncan KD, Pětrošová H, Lum JJ, Goodlett DR. Mass spectrometry imaging methods for visualizing tumor heterogeneity. Curr Opin Biotechnol 2024; 86:103068. [PMID: 38310648 DOI: 10.1016/j.copbio.2024.103068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/09/2024] [Accepted: 01/09/2024] [Indexed: 02/06/2024]
Abstract
Profiling spatial distributions of lipids, metabolites, and proteins in tumors can reveal unique cellular microenvironments and provide molecular evidence for cancer cell dysfunction and proliferation. Mass spectrometry imaging (MSI) is a label-free technique that can be used to map biomolecules in tumors in situ. Here, we discuss current progress in applying MSI to uncover molecular heterogeneity in tumors. First, the analytical strategies to profile small molecules and proteins are outlined, and current methods for multimodal imaging to maximize biological information are highlighted. Second, we present and summarize biological insights obtained by MSI of tumor tissue. Finally, we discuss important considerations for designing MSI experiments and several current analytical challenges.
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Affiliation(s)
- Kyle D Duncan
- Department of Chemistry, Vancouver Island University, Nanaimo, British Columbia, Canada; Department of Chemistry, University of Victoria, Victoria, British Columbia, Canada.
| | - Helena Pětrošová
- University of Victoria Genome British Columbia Proteomics Center, University of Victoria, Victoria, British Columbia, Canada; Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada.
| | - Julian J Lum
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada; Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, British Columbia, Canada
| | - David R Goodlett
- University of Victoria Genome British Columbia Proteomics Center, University of Victoria, Victoria, British Columbia, Canada; Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada
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2
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Li CMY, Briggs MT, Lee YR, Tin T, Young C, Pierides J, Kaur G, Drew P, Maddern GJ, Hoffmann P, Klingler-Hoffmann M, Fenix K. Use of tryptic peptide MALDI mass spectrometry imaging to identify the spatial proteomic landscape of colorectal cancer liver metastases. Clin Exp Med 2024; 24:53. [PMID: 38492056 PMCID: PMC10944452 DOI: 10.1007/s10238-024-01311-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 03/18/2024]
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. CRC liver metastases (CRLM) are often resistant to conventional treatments, with high rates of recurrence. Therefore, it is crucial to identify biomarkers for CRLM patients that predict cancer progression. This study utilised matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) in combination with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to spatially map the CRLM tumour proteome. CRLM tissue microarrays (TMAs) of 84 patients were analysed using tryptic peptide MALDI-MSI to spatially monitor peptide abundances across CRLM tissues. Abundance of peptides was compared between tumour vs stroma, male vs female and across three groups of patients based on overall survival (0-3 years, 4-6 years, and 7+ years). Peptides were then characterised and matched using LC-MS/MS. A total of 471 potential peptides were identified by MALDI-MSI. Our results show that two unidentified m/z values (1589.876 and 1092.727) had significantly higher intensities in tumours compared to stroma. Ten m/z values were identified to have correlation with biological sex. Survival analysis identified three peptides (Histone H4, Haemoglobin subunit alpha, and Inosine-5'-monophosphate dehydrogenase 2) and two unidentified m/z values (1305.840 and 1661.060) that were significantly higher in patients with shorter survival (0-3 years relative to 4-6 years and 7+ years). This is the first study using MALDI-MSI, combined with LC-MS/MS, on a large cohort of CRLM patients to identify the spatial proteome in this malignancy. Further, we identify several protein candidates that may be suitable for drug targeting or for future prognostic biomarker development.
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Affiliation(s)
- Celine Man Ying Li
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Matthew T Briggs
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | - Yea-Rin Lee
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | - Teresa Tin
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Clifford Young
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | - John Pierides
- SA Pathology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia
| | - Gurjeet Kaur
- Institute for Research in Molecular Medicine, University Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Paul Drew
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Guy J Maddern
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Peter Hoffmann
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | | | - Kevin Fenix
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia.
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia.
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3
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Vandenbosch M, Mutuku SM, Mantas MJQ, Patterson NH, Hallmark T, Claesen M, Heeren RMA, Hatcher NG, Verbeeck N, Ekroos K, Ellis SR. Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture. Anal Chem 2023; 95:18719-18730. [PMID: 38079536 PMCID: PMC11372745 DOI: 10.1021/acs.analchem.3c02724] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Mass spectrometry imaging (MSI) has accelerated our understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focused on a single lipid class. We overcome this limitation by using a multiclass internal standard (IS) mixture sprayed homogeneously over the tissue surface with concentrations that reflect those of endogenous lipids. This enabled quantitative MSI (Q-MSI) of 13 lipid classes and subclasses representing almost 200 sum-composition lipid species using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalization of each lipid species in a manner analogous to that widely used in shotgun lipidomics. The Q-MSI approach covered 3 orders of magnitude in dynamic range (lipid concentrations reported in pmol/mm2) and revealed subtle changes in distribution compared to data without normalization. The robustness of the method was evaluated by repeating experiments in two laboratories using both timsTOF and Orbitrap mass spectrometers with an ∼4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI results obtained by using the two approaches. Outliers were mostly rationalized by isobaric interferences or the higher sensitivity of one instrument for a particular lipid species. These data provide insight into how the mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and subclasses and revealing how absolute lipid concentrations vary throughout and between biological tissues.
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Affiliation(s)
- Michiel Vandenbosch
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht 6229ER, Netherlands
| | - Shadrack M Mutuku
- Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, NSW 2522, Australia
| | | | | | | | | | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht 6229ER, Netherlands
| | - Nathan G Hatcher
- Merck & Co., Inc., 770 Sumneytown Pk, West Point, Pennsylvania 19486, United States
| | | | - Kim Ekroos
- Lipidomics Consulting Ltd., Esbo 02230, Finland
| | - Shane R Ellis
- Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, NSW 2522, Australia
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Katsaounou K, Nicolaou E, Vogazianos P, Brown C, Stavrou M, Teloni S, Hatzis P, Agapiou A, Fragkou E, Tsiaoussis G, Potamitis G, Zaravinos A, Andreou C, Antoniades A, Shiammas C, Apidianakis Y. Colon Cancer: From Epidemiology to Prevention. Metabolites 2022; 12:metabo12060499. [PMID: 35736432 PMCID: PMC9229931 DOI: 10.3390/metabo12060499] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 02/01/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most prevalent cancers affecting humans, with a complex genetic and environmental aetiology. Unlike cancers with known environmental, heritable, or sex-linked causes, sporadic CRC is hard to foresee and has no molecular biomarkers of risk in clinical use. One in twenty CRC cases presents with an established heritable component. The remaining cases are sporadic and associated with partially obscure genetic, epigenetic, regenerative, microbiological, dietary, and lifestyle factors. To tackle this complexity, we should improve the practice of colonoscopy, which is recommended uniformly beyond a certain age, to include an assessment of biomarkers indicative of individual CRC risk. Ideally, such biomarkers will be causal to the disease and potentially modifiable upon dietary or therapeutic interventions. Multi-omics analysis, including transcriptional, epigenetic as well as metagenomic, and metabolomic profiles, are urgently required to provide data for risk analyses. The aim of this article is to provide a perspective on the multifactorial derailment of homeostasis leading to the initiation of CRC, which may be explored via multi-omics and Gut-on-Chip analysis to identify much-needed predictive biomarkers.
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Affiliation(s)
- Kyriaki Katsaounou
- Department of Biological Sciences, University of Cyprus, Nicosia 2109, Cyprus; (K.K.); (S.T.)
| | | | - Paris Vogazianos
- Stremble Ventures Ltd., Limassol 4042, Cyprus; (P.V.); (C.B.); (A.A.)
| | - Cameron Brown
- Stremble Ventures Ltd., Limassol 4042, Cyprus; (P.V.); (C.B.); (A.A.)
| | - Marios Stavrou
- Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 2109, Cyprus; (M.S.); (C.A.)
| | - Savvas Teloni
- Department of Biological Sciences, University of Cyprus, Nicosia 2109, Cyprus; (K.K.); (S.T.)
| | - Pantelis Hatzis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center Alexander Fleming, Vari 16672, Greece;
| | - Agapios Agapiou
- Department of Chemistry, University of Cyprus, Nicosia 2109, Cyprus;
| | | | | | | | - Apostolos Zaravinos
- Department of Life Sciences, European University Cyprus, Nicosia 1516, Cyprus;
- Basic and Translational Cancer Research Center, Nicosia 1516, Cyprus
| | - Chrysafis Andreou
- Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 2109, Cyprus; (M.S.); (C.A.)
| | - Athos Antoniades
- Stremble Ventures Ltd., Limassol 4042, Cyprus; (P.V.); (C.B.); (A.A.)
| | | | - Yiorgos Apidianakis
- Department of Biological Sciences, University of Cyprus, Nicosia 2109, Cyprus; (K.K.); (S.T.)
- Correspondence:
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A Mass Spectrometry Imaging Based Approach for Prognosis Prediction in UICC Stage I/II Colon Cancer. Cancers (Basel) 2021; 13:cancers13215371. [PMID: 34771536 PMCID: PMC8582467 DOI: 10.3390/cancers13215371] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/15/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Tumor treatment is heavily dictated by the tumor progression status. However, in colon cancer, it is difficult to predict disease progression in the early stages. In this study, we have employed a proteomic analysis using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). MALDI-MSI is a technique that measures the molecular content of (tumor) tissue. We analyzed tumor samples of 276 patients. If the patients developed distant metastasis, they were considered to have a more aggressive tumor type than the patients that did not. In this comparative study, we have developed bioinformatics methods that can predict the tendency of tumor progression and advance a couple of molecules that could be used as prognostic markers of colon cancer. The prediction of tumor progression can help to choose a more adequate treatment for each individual patient. Abstract Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizing mass spectrometry imaging. Two distinct approaches are herein presented for data processing and analysis. In one approach, four different machine learning algorithms were applied to predict the tendency to develop metastasis, which yielded accuracies over 90% for three of the models. In the other approach, 1007 m/z features were evaluated with regards to their prognostic capabilities, yielding two m/z features as promising prognostic markers. One feature was identified as a fragment from collagen (collagen 3A1), hinting that a higher collagen content within the tumor is associated with poorer outcomes. Identification of proteins that reflect changes in the tumor and its microenvironment could give a very much-needed prediction of a patient’s prognosis, and subsequently assist in the choice of a more adequate treatment.
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Patel SK, George B, Rai V. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology. Front Pharmacol 2020; 11:1177. [PMID: 32903628 PMCID: PMC7438594 DOI: 10.3389/fphar.2020.01177] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 07/20/2020] [Indexed: 12/13/2022] Open
Abstract
The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.
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Affiliation(s)
- Sandip Kumar Patel
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
- Buck Institute for Research on Aging, Novato, CA, United States
| | - Bhawana George
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Vineeta Rai
- Department of Entomology & Plant Pathology, North Carolina State University, Raleigh, NC, United States
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7
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Validation of Breast Cancer Margins by Tissue Spray Mass Spectrometry. Int J Mol Sci 2020; 21:ijms21124568. [PMID: 32604966 PMCID: PMC7349349 DOI: 10.3390/ijms21124568] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 02/07/2023] Open
Abstract
Current methods for the intraoperative determination of breast cancer margins commonly suffer from the insufficient accuracy, specificity and/or low speed of analysis, increasing the time and cost of operation as well the risk of cancer recurrence. The purpose of this study is to develop a method for the rapid and accurate determination of breast cancer margins using direct molecular profiling by mass spectrometry (MS). Direct molecular fingerprinting of tiny pieces of breast tissue (approximately 1 × 1 × 1 mm) is performed using a home-built tissue spray ionization source installed on a Maxis Impact quadrupole time-of-flight mass spectrometer (qTOF MS) (Bruker Daltonics, Hamburg, Germany). Statistical analysis of MS data from 50 samples of both normal and cancer tissue (from 25 patients) was performed using orthogonal projections onto latent structures discriminant analysis (OPLS-DA). Additionally, the results of OPLS classification of new 19 pieces of two tissue samples were compared with the results of histological analysis performed on the same tissues samples. The average time of analysis for one sample was about 5 min. Positive and negative ionization modes are used to provide complementary information and to find out the most informative method for a breast tissue classification. The analysis provides information on 11 lipid classes. OPLS-DA models are created for the classification of normal and cancer tissue based on the various datasets: All mass spectrometric peaks over 300 counts; peaks with a statistically significant difference of intensity determined by the Mann–Whitney U-test (p < 0.05); peaks identified as lipids; both identified and significantly different peaks. The highest values of Q2 have models built on all MS peaks and on significantly different peaks. While such models are useful for classification itself, they are of less value for building explanatory mechanisms of pathophysiology and providing a pathway analysis. Models based on identified peaks are preferable from this point of view. Results obtained by OPLS-DA classification of the tissue spray MS data of a new sample set (n = 19) revealed 100% sensitivity and specificity when compared to histological analysis, the “gold” standard for tissue classification. “All peaks” and “significantly different peaks” datasets in the positive ion mode were ideal for breast cancer tissue classification. Our results indicate the potential of tissue spray mass spectrometry for rapid, accurate and intraoperative diagnostics of breast cancer tissue as a means to reduce surgical intervention.
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8
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Swiner DJ, Jackson S, Burris BJ, Badu-Tawiah AK. Applications of Mass Spectrometry for Clinical Diagnostics: The Influence of Turnaround Time. Anal Chem 2020; 92:183-202. [PMID: 31671262 PMCID: PMC7896279 DOI: 10.1021/acs.analchem.9b04901] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
This critical review discusses how the need for reduced clinical turnaround times has influenced chemical instrumentation. We focus on the development of modern mass spectrometry (MS) and its application in clinical diagnosis. With increased functionality that takes advantage of novel front-end modifications and computational capabilities, MS can now be used for non-traditional clinical analyses, including applications in clinical microbiology for bacteria differentiation and in surgical operation rooms. We summarize here recent developments in the field that have enabled such capabilities, which include miniaturization for point-of-care testing, direct complex mixture analysis via ambient ionization, chemical imaging and profiling, and systems integration.
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Affiliation(s)
- Devin J. Swiner
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210
| | - Sierra Jackson
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210
| | - Benjamin J. Burris
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210
| | - Abraham K. Badu-Tawiah
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210
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9
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Judd AM, Gutierrez DB, Moore JL, Patterson NH, Yang J, Romer CE, Norris JL, Caprioli RM. A recommended and verified procedure for in situ tryptic digestion of formalin-fixed paraffin-embedded tissues for analysis by matrix-assisted laser desorption/ionization imaging mass spectrometry. JOURNAL OF MASS SPECTROMETRY : JMS 2019; 54:716-727. [PMID: 31254303 PMCID: PMC6711785 DOI: 10.1002/jms.4384] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/20/2019] [Accepted: 06/20/2019] [Indexed: 05/06/2023]
Abstract
Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a molecular imaging technology uniquely capable of untargeted measurement of proteins, lipids, and metabolites while retaining spatial information about their location in situ. This powerful combination of capabilities has the potential to bring a wealth of knowledge to the field of molecular histology. Translation of this innovative research tool into clinical laboratories requires the development of reliable sample preparation protocols for the analysis of proteins from formalin-fixed paraffin-embedded (FFPE) tissues, the standard preservation process in clinical pathology. Although ideal for stained tissue analysis by microscopy, the FFPE process cross-links, disrupts, or can remove proteins from the tissue, making analysis of the protein content challenging. To date, reported approaches differ widely in process and efficacy. This tutorial presents a strategy derived from systematic testing and optimization of key parameters, for reproducible in situ tryptic digestion of proteins in FFPE tissue and subsequent MALDI IMS analysis. The approach describes a generalized method for FFPE tissues originating from virtually any source.
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Affiliation(s)
- Audra M. Judd
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
- Correspondence: Dr. Richard M. Caprioli, 9160 MRB III, Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA, Phone: (615) 322-4336, Fax: (615) 343-8372,
| | - Danielle B. Gutierrez
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
- Correspondence: Dr. Richard M. Caprioli, 9160 MRB III, Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA, Phone: (615) 322-4336, Fax: (615) 343-8372,
| | - Jessica L. Moore
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
| | - Junhai Yang
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
| | - Carrie E. Romer
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
| | - Jeremy L. Norris
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
- Departments of Chemistry, Vanderbilt University, Nashville TN, 37235
| | - Richard M. Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville TN, 37235
- Departments of Biochemistry, Vanderbilt University, Nashville TN, 37235
- Departments of Chemistry, Vanderbilt University, Nashville TN, 37235
- Departments of Pharmacology, Vanderbilt University, Nashville TN, 37235
- Departments of Medicine, Vanderbilt University, Nashville TN, 37235
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Ucal Y, Coskun A, Ozpinar A. Quality will determine the future of mass spectrometry imaging in clinical laboratories: the need for standardization. Expert Rev Proteomics 2019; 16:521-532. [DOI: 10.1080/14789450.2019.1624165] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Yasemin Ucal
- School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Abdurrahman Coskun
- School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Aysel Ozpinar
- School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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11
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Phillips L, Gill AJ, Baxter RC. Novel Prognostic Markers in Triple-Negative Breast Cancer Discovered by MALDI-Mass Spectrometry Imaging. Front Oncol 2019; 9:379. [PMID: 31139569 PMCID: PMC6527753 DOI: 10.3389/fonc.2019.00379] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 04/23/2019] [Indexed: 11/29/2022] Open
Abstract
There are no widely-accepted prognostic markers currently available to predict outcomes in patients with triple-negative breast cancer (TNBC), and no targeted therapies with confirmed benefit. We have used MALDI mass spectrometry imaging (MSI) of tryptic peptides to compare regions of cancer and benign tissue in 10 formalin-fixed, paraffin-embedded sections of TNBC tumors. Proteins were identified by reference to a peptide library constructed by LC-MALDI-MS/MS analyses of the same tissues. The prognostic significance of proteins that distinguished between cancer and benign regions was estimated by Kaplan-Meier analysis of their gene expression from public databases. Among peptides that distinguished between cancer and benign tissue in at least 3 tissues with a ROC area under the curve >0.7, 14 represented proteins identified from the reference library, including proteins not previously associated with breast cancer. Initial network analysis using the STRING database showed no obvious functional relationships except among collagen subunits COL1A1, COL1A2, and COL63A, but manual curation, including the addition of EGFR to the analysis, revealed a unique network connecting 10 of the 14 proteins. Kaplan-Meier survival analysis to examine the relationship between tumor expression of genes encoding the 14 proteins, and recurrence-free survival (RFS) in patients with basal-like TNBC showed that, compared to low expression, high expression of nine of the genes was associated with significantly worse RFS, most with hazard ratios >2. In contrast, in estrogen receptor-positive tumors, high expression of these genes showed only low, or no, association with worse RFS. These proteins are proposed as putative markers of RFS in TNBC, and some may also be considered as possible targets for future therapies.
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Affiliation(s)
- Leo Phillips
- Hormones and Cancer Group, University of Sydney, Kolling Institute, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Anthony J Gill
- Cancer Diagnosis and Pathology Group, University of Sydney, Kolling Institute, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Robert C Baxter
- Hormones and Cancer Group, University of Sydney, Kolling Institute, Royal North Shore Hospital, St Leonards, NSW, Australia
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12
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Mass Spectrometry Imaging and Integration with Other Imaging Modalities for Greater Molecular Understanding of Biological Tissues. Mol Imaging Biol 2019; 20:888-901. [PMID: 30167993 PMCID: PMC6244545 DOI: 10.1007/s11307-018-1267-y] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Over the last two decades, mass spectrometry imaging (MSI) has been increasingly employed to investigate the spatial distribution of a wide variety of molecules in complex biological samples. MSI has demonstrated its potential in numerous applications from drug discovery, disease state evaluation through proteomic and/or metabolomic studies. Significant technological and methodological advancements have addressed natural limitations of the techniques, i.e., increased spatial resolution, increased detection sensitivity especially for large molecules, higher throughput analysis and data management. One of the next major evolutions of MSI is linked to the introduction of imaging mass cytometry (IMC). IMC is a multiplexed method for tissue phenotyping, imaging signalling pathway or cell marker assessment, at sub-cellular resolution (1 μm). It uses MSI to simultaneously detect and quantify up to 30 different antibodies within a tissue section. The combination of MSI with other molecular imaging techniques can also provide highly relevant complementary information to explore new scientific fields. Traditionally, classical histology (especially haematoxylin and eosin–stained sections) is overlaid with molecular profiles obtained by MSI. Thus, MSI-based molecular histology provides a snapshot of a tissue microenvironment and enables the correlation of drugs, metabolites, lipids, peptides or proteins with histological/pathological features or tissue substructures. Recently, many examples combining MSI with other imaging modalities such as fluorescence, confocal Raman spectroscopy and MRI have emerged. For instance, brain pathophysiology has been studied using both MRI and MSI, establishing correlations between in and ex vivo molecular imaging techniques. Endogenous metabolite and small peptide modulation were evaluated depending on disease state. Here, we review advanced ‘hot topics’ in MSI development and explore the combination of MSI with established molecular imaging techniques to improve our understanding of biological and pathophysiological processes.
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Wang SS, Wang YJ, Zhang J, Sun TQ, Guo YL. Derivatization Strategy for Simultaneous Molecular Imaging of Phospholipids and Low-Abundance Free Fatty Acids in Thyroid Cancer Tissue Sections. Anal Chem 2019; 91:4070-4076. [PMID: 30807109 DOI: 10.1021/acs.analchem.8b05680] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has been applied in many fields for detecting and imaging a variety of metabolites. In cancer research, this fast-growing imaging method also helps to elucidate the connection between the changes of metabolites in the microenvironment and the proliferation and survival of cancer cells. Free fatty acids (FFAs) are a vital building block of phospholipids (PLs) that can serve as a second cellular messenger and provide nutrients in the cancer microenvironment. The metabolism process of FFAs and PLs is highly relevant to the initiation and progression of different cancers. To better understand the metabolism process in cancer tissues, simultaneously detecting and imaging FFAs and PLs is essential. Despite the crucial developments that have been performed in the field of lipids imaging, FFAs and PLs have rarely been detected and imaged simultaneously in positive ion mode with good detection sensitivity. In this work, an on-tissue derivatization method was used to add a permanently quaternary amine onto FFAs; then, the FFAs and PLs were simultaneously imaged in positive ion mode. The derivatized FFAs are suitable for detection in positive ion mode. In comparison with the traditional matrix and the previous derivatization method, our derivatization reagent has a higher sensitivity for imaging FFAs. In addition, for simultaneous imaging analysis of FFAs and PLs, the number of imaged FFAs and PLs is greater than that with the previous on-tissue derivatization method. This high-sensitivity on-tissue derivatization method was applied to detect and image PLs and fatty acids in thyroid cancer tissues. In the MSI experiment, FFA derivatives and PLs were imaged while molecular localization and tissue integrity were maintained. Meanwhile, the correlation between PLs and FFAs was also studied, and the results showed that the correlations between saturated FFAs of C16:0 and C18:0 and PLs are better than the correlations of unsaturated FFAs with PLs.
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Affiliation(s)
- Shan-Shan Wang
- State Key Laboratory of Organometallic Chemistry and National Center for Organic Mass Spectrometry in Shanghai, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry , University of Chinese Academy of Sciences, Chinese Academy of Sciences , 345 Lingling Road , Shanghai 200032 , People's Republic of China
| | - Yun-Jun Wang
- Department of Head and Neck Surgery , Fudan University Shanghai Cancer Center , Shanghai 200032 , People's Republic of China.,Department of Oncology, Shanghai Medical College , Fudan University , Shanghai 200032 , People's Republic of China
| | - Jing Zhang
- State Key Laboratory of Organometallic Chemistry and National Center for Organic Mass Spectrometry in Shanghai, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry , University of Chinese Academy of Sciences, Chinese Academy of Sciences , 345 Lingling Road , Shanghai 200032 , People's Republic of China
| | - Tuan-Qi Sun
- Department of Head and Neck Surgery , Fudan University Shanghai Cancer Center , Shanghai 200032 , People's Republic of China.,Department of Oncology, Shanghai Medical College , Fudan University , Shanghai 200032 , People's Republic of China
| | - Yin-Long Guo
- State Key Laboratory of Organometallic Chemistry and National Center for Organic Mass Spectrometry in Shanghai, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry , University of Chinese Academy of Sciences, Chinese Academy of Sciences , 345 Lingling Road , Shanghai 200032 , People's Republic of China
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He H, Qin L, Zhang Y, Han M, Li J, Liu Y, Qiu K, Dai X, Li Y, Zeng M, Guo H, Zhou Y, Wang X. 3,4-Dimethoxycinnamic Acid as a Novel Matrix for Enhanced In Situ Detection and Imaging of Low-Molecular-Weight Compounds in Biological Tissues by MALDI-MSI. Anal Chem 2019; 91:2634-2643. [DOI: 10.1021/acs.analchem.8b03522] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Huixin He
- Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Liang Qin
- Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Yawen Zhang
- Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Manman Han
- Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Jinming Li
- Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Yaqin Liu
- Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Kaidi Qiu
- Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Xiaoyan Dai
- Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Yanyan Li
- The Hospital of Minzu University of China, Minzu University of China, Beijing 100081, China
| | - Maomao Zeng
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China
| | - Huihong Guo
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing 100083, China
| | - Yijun Zhou
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Xiaodong Wang
- Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
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Mascini NE, Teunissen J, Noorlag R, Willems SM, Heeren RM. Tumor classification with MALDI-MSI data of tissue microarrays: A case study. Methods 2018; 151:21-27. [DOI: 10.1016/j.ymeth.2018.04.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 03/04/2018] [Accepted: 04/09/2018] [Indexed: 11/25/2022] Open
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16
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Longuespée R, Kriegsmann K, Cremer M, Zgorzelski C, Casadonte R, Kazdal D, Kriegsmann J, Weichert W, Schwamborn K, Fresnais M, Schirmacher P, Kriegsmann M. In MALDI-Mass Spectrometry Imaging on Formalin-Fixed Paraffin-Embedded Tissue Specimen Section Thickness Significantly Influences m/z Peak Intensity. Proteomics Clin Appl 2018; 13:e1800074. [PMID: 30216687 DOI: 10.1002/prca.201800074] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 08/03/2018] [Indexed: 12/18/2022]
Abstract
BACKGROUND In matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) standardized sample preparation is important to obtain reliable results. Herein, the impact of section thickness in formalin-fixed paraffin embedded (FFPE) tissue microarrays (TMA) on spectral intensities is investigated. PATIENTS AND METHODS TMAs consisting of ten different tissues represented by duplicates of ten patients (n = 200 cores) are cut at 1, 3, and 5 μm. MSI analysis is performed and mean intensities of all evaluable cores are extracted. Measurements are merged and mean m/z intensities are compared. RESULTS Visual inspection of spectral intensities between 1, 3, and 5 μm reveals generally higher intensities in thinner tissue sections. Specifically, higher intensities are observed in the vast majority of peaks (98.6%, p < 0.01) in 1 μm compared with 5 μm sections. Note that 28.4% and 2.1% of m/z values exhibit a at least two- and threefold intensity difference (p < 0.01) in 1 μm compared to 5 μm sections, respectively. CONCLUSION A section thickness of 1 μm results in higher spectral intensities compared with 5 μm. The results highlight the importance of standardized protocols in light of recent efforts to identify clinically relevant biomarkers using MSI. The use of TMAs for comparative analysis seems advantageous, as section thickness displays less variability.
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Affiliation(s)
- Rémi Longuespée
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
| | - Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
| | - Martin Cremer
- Department of Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
| | | | | | - Daniel Kazdal
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
| | - Jörg Kriegsmann
- Proteopath Trier, Trier, Germany.,Institute of Molecular Pathology Trier, Trier, Germany
| | | | | | - Margaux Fresnais
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany.,German Cancer Consortium (DKTK)German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Schirmacher
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
| | - Mark Kriegsmann
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
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Ryabchykov O, Popp J, Bocklitz T. Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples. Front Chem 2018; 6:257. [PMID: 30062092 PMCID: PMC6055053 DOI: 10.3389/fchem.2018.00257] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Accepted: 06/08/2018] [Indexed: 01/03/2023] Open
Abstract
Despite of a large number of imaging techniques for the characterization of biological samples, no universal one has been reported yet. In this work, a data fusion approach was investigated for combining Raman spectroscopic data with matrix-assisted laser desorption/ionization (MALDI) mass spectrometric data. It betters the image analysis of biological samples because Raman and MALDI information can be complementary to each other. While MALDI spectrometry yields detailed information regarding the lipid content, Raman spectroscopy provides valuable information about the overall chemical composition of the sample. The combination of Raman spectroscopic and MALDI spectrometric imaging data helps distinguishing different regions within the sample with a higher precision than would be possible by using either technique. We demonstrate that a data weighting step within the data fusion is necessary to reveal additional spectral features. The selected weighting approach was evaluated by examining the proportions of variance within the data explained by the first principal components of a principal component analysis (PCA) and visualizing the PCA results for each data type and combined data. In summary, the presented data fusion approach provides a concrete guideline on how to combine Raman spectroscopic and MALDI spectrometric imaging data for biological analysis.
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Affiliation(s)
- Oleg Ryabchykov
- Spectroscopy and Imaging Research Department, Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
| | - Juergen Popp
- Spectroscopy and Imaging Research Department, Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
| | - Thomas Bocklitz
- Spectroscopy and Imaging Research Department, Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
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18
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Machine learning techniques for mass spectrometry imaging data analysis and applications. Bioanalysis 2018; 10:519-522. [DOI: 10.4155/bio-2017-0281] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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Kather JN, Halama N, Jaeger D. Genomics and emerging biomarkers for immunotherapy of colorectal cancer. Semin Cancer Biol 2018; 52:189-197. [PMID: 29501787 DOI: 10.1016/j.semcancer.2018.02.010] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 02/19/2018] [Accepted: 02/28/2018] [Indexed: 02/06/2023]
Abstract
Colorectal cancer (CRC) is a common and lethal disease with a high therapeutic need. For most patients with metastatic CRC, chemotherapy is the only viable option. Currently, immunotherapy is restricted to the particular genetic subgroup of mismatch-repair deficient (MMRd)/microsatellite instable (MSI) CRC. Anti-PD1 therapy was recently FDA-approved as a second-line treatment in this subgroup. However, in a metastatic setting, these MMRd/MSI tumors are vastly outnumbered by mismatch-repair proficient (MMRp)/microsatellite stable (MSS) tumors. These MMRp/MSS tumors do not meaningfully respond to any traditional immunotherapy approach including checkpoint blockade, adoptive cell transfer and vaccination. This resistance to immunotherapy is due to a complex tumor microenvironment that counteracts antitumor immunity through a combination of poorly antigenic tumor cells and an immunosuppressive tumor microenvironment. To find ways of overcoming immunotherapy resistance in the majority of CRC patients, it is necessary to analyze the immunological makeup in an in-depth and personalized way and in the context of their tumor genetic makeup. Flexible, biomarker-guided early-phase immunotherapy trials are needed to optimize this workflow. In this review, we detail key mechanisms for immune evasion and emerging immune biomarkers for personalized immunotherapy in CRC. Also, we present a template for biomarker-guided clinical trials that are needed to move new immunotherapy approaches closer to clinical application.
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Affiliation(s)
- Jakob Nikolas Kather
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany; Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Niels Halama
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany; Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Dirk Jaeger
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany; Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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20
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Lim LC, Lim YM. Proteome Heterogeneity in Colorectal Cancer. Proteomics 2018; 18. [PMID: 29316255 DOI: 10.1002/pmic.201700169] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 12/17/2017] [Indexed: 01/26/2023]
Abstract
Tumor heterogeneity is an important feature of colorectal cancer (CRC) manifested by dynamic changes in gene expression, protein expression, and availability of different tumor subtypes. Recent publications in the past 10 years have revealed proteome heterogeneity between different colorectal tumors and within the same tumor site. This paper reviews recent research works on the proteome heterogeneity in CRC, which includes the heterogeneity within a single tumor (intratumor heterogeneity), between different anatomical sites at the same organ, and between primary and metastatic sites (intertumor heterogeneity). The potential use of proteome heterogeneity in precision medicine and its implications in biomarker discovery and therapeutic outcomes will be discussed. Identification of the unique proteome landscape between and within individual tumors is imperative for understanding cancer biology and the management of CRC patients.
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Affiliation(s)
- Lay Cheng Lim
- Centre for Cancer Research, Faculty of Medicine and Health Sciences, University of Tunku Abdul Rahman, Selangor, Malaysia
| | - Yang Mooi Lim
- Centre for Cancer Research, Faculty of Medicine and Health Sciences, University of Tunku Abdul Rahman, Selangor, Malaysia
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Rae Buchberger A, DeLaney K, Johnson J, Li L. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. Anal Chem 2018; 90:240-265. [PMID: 29155564 PMCID: PMC5959842 DOI: 10.1021/acs.analchem.7b04733] [Citation(s) in RCA: 561] [Impact Index Per Article: 93.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Amanda Rae Buchberger
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Kellen DeLaney
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Jillian Johnson
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705, United States
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