Mapping the metabolic responses to oxaliplatin-based chemotherapy with <i>in vivo</i> spatiotemporal metabolomics

Mariola Olkowicz; Khaled Ramadan; Hernando Rosales-Solano; Miao Yu; Aizhou Wang; Marcelo Cypel; Janusz Pawliszyn

doi:10.1016/j.jpha.2023.08.001

Volume 14 Issue 2

Feb. 2024

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Article Navigation > Journal of Pharmaceutical Analysis > 2024 > 14(2): 196-210

Mariola Olkowicz, Khaled Ramadan, Hernando Rosales-Solano, Miao Yu, Aizhou Wang, Marcelo Cypel, Janusz Pawliszyn. Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics[J]. Journal of Pharmaceutical Analysis, 2024, 14(2): 196-210. doi: 10.1016/j.jpha.2023.08.001

Citation:

Mariola Olkowicz, Khaled Ramadan, Hernando Rosales-Solano, Miao Yu, Aizhou Wang, Marcelo Cypel, Janusz Pawliszyn. Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics[J]. Journal of Pharmaceutical Analysis, 2024, 14(2): 196-210. doi: 10.1016/j.jpha.2023.08.001

Citation:

Mariola Olkowicz, Khaled Ramadan, Hernando Rosales-Solano, Miao Yu, Aizhou Wang, Marcelo Cypel, Janusz Pawliszyn. Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics[J]. Journal of Pharmaceutical Analysis, 2024, 14(2): 196-210. doi: 10.1016/j.jpha.2023.08.001

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Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics

doi: 10.1016/j.jpha.2023.08.001

a. Department of Chemistry, University of Waterloo, Waterloo, ON, Canada;
b. Jagiellonian Centre for Experimental Therapeutics (JCET), Jagiellonian University, Krakow, Poland;
c. Latner Thoracic Surgery Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada;
d. The Jackson Laboratory, JAX Genomic Medicine, Farmington, CT, USA;
e. Division of Thoracic Surgery, Department of Surgery, University Health Network, University of Toronto, Toronto Lung Transplant Program, Toronto, ON, Canada

Funds:

This study was supported by the Canadian Institute of Health Research (CIHR)–Natural Sciences and Engineering Research Council (NSERC) of Canada Collaborative Health Research Projects program (Grant No.: 355935), as well as by NSERC through the Industrial Research Chair (IRC) program (Program No.: #IRCPJ 184412e15). The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. We would also like to thank Supelco (Oakville, ON, Canada) for the MM and C₁₈ SPME fibres, as well as the analytical columns that were used in this study.

Received Date: Apr. 15, 2023
Accepted Date: Aug. 07, 2023
Rev Recd Date: Jul. 14, 2023
Publish Date: Feb. 29, 2024

Abstract

Abstract

Adjuvant chemotherapy improves the survival outlook for patients undergoing operations for lung metastases caused by colorectal cancer (CRC). However, a multidisciplinary approach that evaluates several factors related to patient and tumor characteristics is necessary for managing chemotherapy treatment in metastatic CRC patients with lung disease, as such factors dictate the timing and drug regimen, which may affect treatment response and prognosis. In this study, we explore the potential of spatial metabolomics for evaluating metabolic phenotypes and therapy outcomes during the local delivery of the anticancer drug, oxaliplatin, to the lung. 12 male Yorkshire pigs underwent a 3 h left lung in vivo lung perfusion (IVLP) with various doses of oxaliplatin (7.5, 10, 20, 40, and 80 mg/L), which were administered to the perfusion circuit reservoir as a bolus. Biocompatible solid-phase microextraction (SPME) microprobes were combined with global metabolite profiling to obtain spatiotemporal information about the activity of the drug, determine toxic doses that exceed therapeutic efficacy, and conduct a mechanistic exploration of associated lung injury. Mild and subclinical lung injury was observed at 40 mg/L of oxaliplatin, and significant compromise of the hemodynamic lung function was found at 80 mg/L. This result was associated with massive alterations in metabolic patterns of lung tissue and perfusate, resulting in a total of 139 discriminant compounds. Uncontrolled inflammatory response, abnormalities in energy metabolism, and mitochondrial dysfunction next to accelerated kynurenine and aldosterone production were recognized as distinct features of dysregulated metabolipidome. Spatial pharmacometabolomics may be a promising tool for identifying pathological responses to chemotherapy.
- Pulmonary metastases,
- Colorectal cancer,
- Adjuvant chemotherapy,
- In vivo lung chemo-perfusion,
- Solid-phase microextraction (SPME) microprobes,
- Spatial metabolomics

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References(52)

References

[1]	E. Dekker, P.J. Tanis, J.L.A. Vleugels, et al., Colorectal cancer, Lancet 394 (2019) 1467-1480.
[2]	F. Di Nicolantonio, P.P. Vitiello, S. Marsoni, et al., Precision oncology in metastatic colorectal cancer - from biology to medicine, Nat. Rev. Clin. Oncol. 18 (2021) 506-525.
[3]	H. Wang, X. Shan, M. Zhang, et al., Homogeneous and heterogeneous risk and prognostic factors for lung metastasis in colorectal cancer patients, BMC Gastroenterol. 22 (2022), 193.
[4]	H. Vogelsang, S. Haas, C. Hierholzer, et al., Factors influencing survival after resection of pulmonary metastases from colorectal cancer, Br. J. Surg. 91 (2004) 1066-1071.
[5]	C. Carvajal Fierro, H. Facundo-Navia, P. Puerto, et al., Lung metastasectomy from colorectal cancer, 10-year experience in a South American Cancer Center, Front. Surg. 9 (2022), 913678.
[6]	Y. Okazaki, M. Shibutani, E. Wang, et al., Efficacy of adjuvant chemotherapy after complete resection of pulmonary metastasis from colorectal cancer, Mol. Clin. Oncol. 15 (2021), 205.
[7]	C. Rapicetta, F. Lococo, F. Davini, et al., Is adjuvant chemotherapy worthwhile after radical resection for single lung metastasis from colorectal cancer? A multicentric analysis evaluating the risk of recurrence, Front. Oncol. 9 (2019), 00763.
[8]	L. Cui, H. Lu, Y.H. Lee, Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases, Mass Spectrom. Rev. 37 (2018) 772-792.
[9]	R.K. Azad, V. Shulaev, Metabolomics technology and bioinformatics for precision medicine, Brief. Bioinform. 20 (2019) 1957-1971.
[10]	Y. Hu, Z. Wang, L. Liu, et al., Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine, Chem. Sci. 12 (2021) 7993-8009.
[11]	D.S. Wishart, Emerging applications of metabolomics in drug discovery and precision medicine, Nat. Rev. Drug Discov. 15 (2016) 473-484.
[12]	V. Kantae, E.H.J. Krekels, M.J. Van Esdonk, et al., Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: Towards personalized drug therapy, Metabolomics 13 (2017), 9.
[13]	M. Jacob, A.L. Lopata, M. Dasouki, et al., Metabolomics toward personalized medicine, Mass Spectrom. Rev. 38 (2019) 221-238.
[14]	L.M. Nascentes Melo, N.P. Lesner, M. Sabatier, et al., Emerging metabolomic tools to study cancer metastasis, Trends Cancer 8 (2022) 988-1001.
[15]	N. Reyes-Garces, E. Gionfriddo, G.A. Gomez-Rios, et al., Advances in solid phase microextraction and perspective on future directions, Anal. Chem. 90 (2018) 302-360.
[16]	H. Piri-Moghadam, M.N. Alam, J. Pawliszyn, Review of geometries and coating materials in solid phase microextraction: Opportunities, limitations, and future perspectives, Anal. Chim. Acta 984 (2017) 42-65.
[17]	G. Ouyang, D. Vuckovic, J. Pawliszyn, Nondestructive sampling of living systems using in vivo solid-phase microextraction, Chem. Rev. 111 (2011) 2784-2814.
[18]	B. Bojko, K. Gorynski, G.A. Gomez-Rios, et al., Low invasive in vivo tissue sampling for monitoring biomarkers and drugs during surgery, Lab. Investig. 94 (2014) 586-594.
[19]	N. Reyes-Garces, E. Gionfriddo, Recent developments and applications of solid phase microextraction as a sample preparation approach for mass-spectrometry-based metabolomics and lipidomics, Trac Trends Anal. Chem. 113 (2019) 172-181.
[20]	E.G. Armitage, A.D. Southam, Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics, Metabolomics 12 (2016), 146.
[21]	S. Lendor, S.A. Hassani, E. Boyaci, et al., Solid phase microextraction-based miniaturized probe and protocol for extraction of neurotransmitters from brains in vivo, Anal. Chem. 91 (2019) 4896-4905.
[22]	P. Reck dos Santos, J. Sakamoto, M. Chen, et al., Modified in vivo lung perfusion for local chemotherapy: A preclinical study with doxorubicin, Ann. Thorac. Surg. 101 (2016) 2132-2140.
[23]	K. Ramadan, B. Gomes, M. Pipkin, et al., A model to assess acute and delayed lung toxicity of oxaliplatin during in vivo lung perfusion, J. Thorac. Cardiovasc. Surg. 161 (2021) 1626-1635.
[24]	M. Olkowicz, H. Rosales-Solano, K. Ramadan, et al., The metabolic fate of oxaliplatin in the biological milieu investigated during in vivo lung perfusion using a unique miniaturized sampling approach based on solid-phase microextraction coupled with liquid chromatography-mass spectrometry, Front. Cell. Dev. Biol. 10 (2022), 928152.
[25]	B. Bojko, N. Looby, M. Olkowicz, et al., Solid phase microextraction chemical biopsy tool for monitoring of doxorubicin residue during in vivo lung chemo-perfusion, J. Pharm. Anal. 11 (2021) 37-47.
[26]	E. Boyaci, N. Reyes-Garces, Applying green sample preparation techniques to in vivo analysis and metabolomics. Green Approaches for Chemical Analysis. Amsterdam: Elsevier, (2023) 205-239.
[27]	S. Alseekh, A. Aharoni, Y. Brotman, et al., Mass spectrometry-based metabolomics: A guide for annotation, quantification and best reporting practices, Nat. Methods 18 (2021) 747-756.
[28]	J. Kirwan, H. Gika, R. Beger, et al., Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management, Metabolomics. 18 (2022) 70.
[29]	M.C. Chambers, B. MacLean, R. Burke, et al., A cross-platform toolkit for mass spectrometry and proteomics, Nat. Biotechnol. 30 (2012) 918-920.
[30]	G. Libiseller, M. Dvorzak, U. Kleb, et al., IPO: A tool for automated optimization of XCMS parameters, BMC Bioinform. 16 (2015) 1-10.
[31]	M. Yu, M. Olkowicz, J. Pawliszyn, Structure/reaction directed analysis for LC-MS based untargeted analysis, Anal. Chim. Acta 1050 (2019) 16-24.
[32]	Z. Pang, G. Zhou, J. Ewald, et al., Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data, Nat. Protoc. 17 (2022) 1735-1761.
[33]	K. Uppal, D.I. Walker, D.P. Jones, xMSannotator: An R package for network-based annotation of high-resolution metabolomics data, Anal. Chem. 89 (2017) 1063-1067.
[34]	T. Kind, H. Tsugawa, T. Cajka, et al., Identification of small molecules using accurate mass MS/MS search, Mass Spectrom. Rev. 37 (2018) 513-532.
[35]	W. Bittremieux, M. Wang, P.C. Dorrestein, The critical role that spectral libraries play in capturing the metabolomics community knowledge, Metabolomics 18 (2022), 94.
[36]	N.F. de Jonge, K. Mildau, D. Meijer, et al., Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools, Metabolomics 18 (2022), 103.
[37]	P.R. dos Santos, I. Iskender, T. Machuca, et al., Modified in vivo lung perfusion allows for prolonged perfusion without acute lung injury, J. Thorac. Cardiovasc. Surg. 147 (2014) 774-782.
[38]	K.A. Stringer, R.T. McKay, A. Karnovsky, et al., Metabolomics and Its Application to Acute Lung Diseases, Front. Immunol. 7 (2016), 00044.
[39]	C.T. Robb, K.H. Regan, D.A. Dorward, et al., Key mechanisms governing resolution of lung inflammation, Semin. Immunopathol. 38 (2016) 425-448.
[40]	G. Liu, R. Summer, Cellular metabolism in lung health and disease, Annu. Rev. Physiol. 81 (2019) 403-428.
[41]	A.B. Fisher, H. Steinberg, M.D. David Bassett, Energy utilization by the lung, Am. J. Med. 57 (1974) 437-446.
[42]	A.B. Fisher, Intermediary metabolism of the lung, Environ. Health Perspect. 55 (1984) 149-158.
[43]	R.J.A. Wanders, J. Komen, S. Kemp, Fatty acid omega-oxidation as a rescue pathway for fatty acid oxidation disorders in humans, Febs J. 278 (2011) 182-194.
[44]	D. de A.F. Caldeira, D.J. Weiss, P.R.M. Rocco, et al. Mitochondria in focus: From function to therapeutic strategies in chronic lung diseases, Front. Immunol. 12 (2021), 782074.
[45]	P.J. Garlick, I. Grant, Amino acid infusion increases the sensitivity of muscle protein synthesis in vivo to insulin. Effect of branched-chain amino acids, Biochem. J. 254 (1988) 579-584.
[46]	T. Wiggins, S. Kumar, S.R. Markar, et al., Tyrosine, phenylalanine, and tryptophan in gastroesophageal malignancy: A systematic review, Cancer Epidemiol. Biomark. Prev. 24 (2015) 32-38.
[47]	G. Neurauter, A.V. Grahmann, M. Klieber, et al., Serum phenylalanine concentrations in patients with ovarian carcinoma correlate with concentrations of immune activation markers and of isoprostane-8, Cancer Lett. 272 (2008) 141-147.
[48]	M. Gulcev, C. Reilly, T.J. Griffin, et al., Tryptophan catabolism in acute exacerbations of chronic obstructive pulmonary disease, Int. J. Chron. Obstruct. Pulmon. Dis. 11 (2016) 2435-2446.
[49]	F. Rossi, R. Miggiano, D.M. Ferraris, et al., The synthesis of kynurenic acid in mammals: An updated kynurenine aminotransferase structural KATalogue, Front. Mol. Biosci. 6 (2019), 00007.
[50]	J. Wang, L. Chen, B. Chen, et al., Chronic activation of the renin-angiotensin system induces lung fibrosis, Sci. Rep. 5 (2015), 15561.
[51]	D. Gupta, A. Kumar, A. Mandloi, et al., Renin angiotensin aldosterone system in pulmonary fibrosis: Pathogenesis to therapeutic possibilities, Pharmacol. Res. 174 (2021), 105924.
[52]	N. Looby, A. Roszkowska, M. Yu, et al., In vivo solid phase microextraction for therapeutic monitoring and pharmacometabolomic fingerprinting of lung during in vivo lung perfusion of FOLFOX, J. Pharm. Anal. (2023) [in press].