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Jiang M, Wu P, Zhang Y, Wang M, Zhang M, Ye Z, Zhang X, Zhang C. Artificial Intelligence-Driven Platform: Unveiling Critical Hepatic Molecular Alterations in Hepatocellular Carcinoma Development. Adv Healthc Mater 2024:e2400291. [PMID: 38657582 DOI: 10.1002/adhm.202400291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 04/19/2024] [Indexed: 04/26/2024]
Abstract
Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damage, the hepatic molecular characteristics are closely related to the occurrence of HCC. Gaining comprehensive information about the location, morphology, and hepatic molecular alterations related to HCC is essential for accurate diagnosis. However, there is a dearth of technological advancements capable of concurrently providing precise HCC diagnosis and discerning the accompanying hepatic molecular alterations. In this study, an integrated information system is developed for the pathological-level diagnosis of HCC and the revelation of critical molecular alterations in the liver. This system utilizes computed tomography/Surface-enhanced Raman scattering combined with an artificial intelligence strategy to establish connections between the occurrence of HCC and alterations in hepatic biomolecules. Employing artificial intelligence techniques, the SERS spectra from both healthy and HCC groups are successfully classified into two distinct categories with a remarkable accuracy rate of 91.38%. Based on molecular profiling, it is identified that the nucleotide-to-lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance.
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Affiliation(s)
- Miao Jiang
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300203, China
| | - Pengyun Wu
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, 154 Anshan Ave, Heping, 300052, China
| | - Yuwei Zhang
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Mengling Wang
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Mingjie Zhang
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Zhaoxiang Ye
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Xuejun Zhang
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300203, China
| | - Cai Zhang
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
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Prasad M, Postma G, Franceschi P, Buydens LMC, Jansen JJ. Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI). Sci Rep 2022; 12:15687. [PMID: 36127378 PMCID: PMC9489880 DOI: 10.1038/s41598-022-19365-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/29/2022] [Indexed: 11/18/2022] Open
Abstract
For the extraction of spatially important regions from mass spectrometry imaging (MSI) data, different clustering methods have been proposed. These clustering methods are based on certain assumptions and use different criteria to assign pixels into different classes. For high-dimensional MSI data, the curse of dimensionality also limits the performance of clustering methods which are usually overcome by pre-processing the data using dimension reduction techniques. In summary, the extraction of spatial patterns from MSI data can be done using different unsupervised methods, but the robust evaluation of clustering results is what is still missing. In this study, we have performed multiple simulations on synthetic and real MSI data to validate the performance of unsupervised methods. The synthetic data were simulated mimicking important spatial and statistical properties of real MSI data. Our simulation results confirmed that K-means clustering with correlation distance and Gaussian Mixture Modeling clustering methods give optimal performance in most of the scenarios. The clustering methods give efficient results together with dimension reduction techniques. From all the dimension techniques considered here, the best results were obtained with the minimum noise fraction (MNF) transform. The results were confirmed on both synthetic and real MSI data. However, for successful implementation of MNF transform the MSI data requires to be of limited dimensions.
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Affiliation(s)
- Mridula Prasad
- IMM/Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ, Nijmegen, The Netherlands.,Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010, San Michele all' Adige, Italy
| | - Geert Postma
- IMM/Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ, Nijmegen, The Netherlands.
| | - Pietro Franceschi
- Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010, San Michele all' Adige, Italy
| | - Lutgarde M C Buydens
- IMM/Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ, Nijmegen, The Netherlands
| | - Jeroen J Jansen
- IMM/Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ, Nijmegen, The Netherlands
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Morosi L, Meroni M, Ubezio P, Fuso Nerini I, Minoli L, Porcu L, Panini N, Colombo M, Blouw B, Kang DW, Davoli E, Zucchetti M, D'Incalci M, Frapolli R. PEGylated recombinant human hyaluronidase (PEGPH20) pre-treatment improves intra-tumour distribution and efficacy of paclitaxel in preclinical models. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2021; 40:286. [PMID: 34507591 PMCID: PMC8434701 DOI: 10.1186/s13046-021-02070-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 08/10/2021] [Indexed: 01/04/2023]
Abstract
BACKGROUND Scarce drug penetration in solid tumours is one of the possible causes of the limited efficacy of chemotherapy and is related to the altered tumour microenvironment. The abnormal tumour extracellular matrix (ECM) together with abnormal blood and lymphatic vessels, reactive stroma and inflammation all affect the uptake, distribution and efficacy of anticancer drugs. METHODS We investigated the effect of PEGylated recombinant human hyaluronidase PH20 (PEGPH20) pre-treatment in degrading hyaluronan (hyaluronic acid; HA), one of the main components of the ECM, to improve the delivery of antitumor drugs and increase their therapeutic efficacy. The antitumor activity of paclitaxel (PTX) in HA synthase 3-overexpressing and wild-type SKOV3 ovarian cancer model and in the BxPC3 pancreas xenograft tumour model, was evaluated by monitoring tumour growth with or without PEGPH20 pre-treatment. Pharmacokinetics and tumour penetration of PTX were assessed by HPLC and mass spectrometry imaging analysis in the same tumour models. Tumour tissue architecture and HA deposition were analysed by histochemistry. RESULTS Pre-treatment with PEGPH20 modified tumour tissue architecture and improved the antitumor activity of paclitaxel in the SKOV3/HAS3 tumour model, favouring its accumulation and more homogeneous intra-tumour distribution, as assessed by quantitative and qualitative analysis. PEGPH20 also reduced HA content influencing, though less markedly, PTX distribution and antitumor activity in the BxPC3 tumour model. CONCLUSION Remodelling the stroma of HA-rich tumours by depletion of HA with PEGPH20 pre-treatment, is a potentially successful strategy to improve the intra-tumour distribution of anticancer drugs, increasing their therapeutic efficacy, without increasing toxicity.
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Affiliation(s)
- Lavinia Morosi
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy.,Present address: IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Marina Meroni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy
| | - Paolo Ubezio
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy
| | - Ilaria Fuso Nerini
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy.,Present address: IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Lucia Minoli
- Department of Veterinary Medicine, University of Milan, Lodi, Italy.,Mouse and Animal Pathology Laboratory (MAPLab), Fondazione UniMi, Milan, Italy
| | - Luca Porcu
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy
| | - Nicolò Panini
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy
| | - Marika Colombo
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy
| | | | - David W Kang
- Halozyme Therapeutics, San Diego, California, USA
| | - Enrico Davoli
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Mass Spectrometry, Milan, Italy
| | - Massimo Zucchetti
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy
| | - Maurizio D'Incalci
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy.,Present address: IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.,Present address: Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Roberta Frapolli
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Oncology, via M. Negri 2, 20156, Milan, Italy.
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He Q, Sun C, Liu J, Pan Y. MALDI-MSI analysis of cancer drugs: Significance, advances, and applications. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116183] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Prasad M, Postma G, Franceschi P, Morosi L, Giordano S, Falcetta F, Giavazzi R, Davoli E, Buydens LMC, Jansen J. A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data. Gigascience 2020; 9:6006351. [PMID: 33241286 PMCID: PMC7688471 DOI: 10.1093/gigascience/giaa131] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/28/2020] [Accepted: 11/01/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection. RESULTS The number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method. CONCLUSIONS In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.
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Affiliation(s)
- Mridula Prasad
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands.,Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010 San Michele all' Adige, Italy
| | - Geert Postma
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
| | - Pietro Franceschi
- Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010 San Michele all' Adige, Italy
| | - Lavinia Morosi
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Silvia Giordano
- Mass Spectrometry Laboratory, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Francesca Falcetta
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Raffaella Giavazzi
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Enrico Davoli
- Mass Spectrometry Laboratory, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Lutgarde M C Buydens
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
| | - Jeroen Jansen
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
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Alexandrov T. Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence. Annu Rev Biomed Data Sci 2020; 3:61-87. [PMID: 34056560 DOI: 10.1146/annurev-biodatasci-011420-031537] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, USA
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7
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Guo D, Bemis K, Rawlins C, Agar J, Vitek O. Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues. Bioinformatics 2019; 35:i208-i217. [PMID: 31510675 PMCID: PMC6612871 DOI: 10.1093/bioinformatics/btz345] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
MOTIVATION Mass spectrometry imaging (MSI) characterizes the spatial distribution of ions in complex biological samples such as tissues. Since many tissues have complex morphology, treatments and conditions often affect the spatial distribution of the ions in morphology-specific ways. Evaluating the selectivity and the specificity of ion localization and regulation across morphology types is biologically important. However, MSI lacks algorithms for segmenting images at both single-ion and spatial resolution. RESULTS This article contributes spatial-Dirichlet Gaussian mixture model (DGMM), an algorithm and a workflow for the analyses of MSI experiments, that detects components of single-ion images with homogeneous spatial composition. The approach extends DGMMs to account for the spatial structure of MSI. Evaluations on simulated and experimental datasets with diverse MSI workflows demonstrated that spatial-DGMM accurately segments ion images, and can distinguish ions with homogeneous and heterogeneous spatial distribution. We also demonstrated that the extracted spatial information is useful for downstream analyses, such as detecting morphology-specific ions, finding groups of ions with similar spatial patterns, and detecting changes in chemical composition of tissues between conditions. AVAILABILITY AND IMPLEMENTATION The data and code are available at https://github.com/Vitek-Lab/IonSpattern. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Kylie Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Catherine Rawlins
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Jeffrey Agar
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
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