Detection and characterization of colorectal cancer by autofluorescence lifetime imaging on surgical specimens.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
19 Oct 2024
19 Oct 2024
Historique:
received:
14
05
2024
accepted:
24
09
2024
medline:
20
10
2024
pubmed:
20
10
2024
entrez:
19
10
2024
Statut:
epublish
Résumé
Colorectal cancer (CRC) ranks among the most prevalent malignancies worldwide, driving a quest for comprehensive characterization methods. We report a characterization of the ex vivo autofluorescence lifetime fingerprint of colorectal tissues obtained from 73 patients that underwent surgical resection. We specifically target the autofluorescence characteristics of collagens, reduced nicotine adenine (phosphate) dinucleotide (NAD(P)H), and flavins employing a fiber-based dual excitation (375 nm and 445 nm) optical imaging system. Autofluorescence-derived parameters obtained from normal tissues, adenomatous lesions, and adenocarcinomas were analyzed considering the underlying clinicopathological features. Our results indicate that differences between tissues are primarily driven by collagen and flavins autofluorescence parameters. We also report changes in the autofluorescence parameters associated with NAD(P)H that we tentatively attribute to intratumoral heterogeneity, potentially associated to the presence of distinct metabolic subpopulations. Changes in autofluorescence signatures of malignant tumors were also observed with lymphatic and venous invasion, differentiation grade, and microsatellite instability. Finally, we characterized the impact of radiative treatment in the autofluorescence fingerprints of rectal tissues and observed a generalized increase in the mean lifetime of radiated adenocarcinomas, which is suggestive of altered metabolism and structural remodeling. Overall, our preliminary findings indicate that multiparametric autofluorescence lifetime measurements have the potential to significantly enhance clinical decision-making in CRC, spanning from initial diagnosis to ongoing management. We believe that our results will provide a foundational framework for future investigations to further understand and combat CRC exploiting autofluorescence measurements.
Identifiants
pubmed: 39426971
doi: 10.1038/s41598-024-74224-8
pii: 10.1038/s41598-024-74224-8
doi:
Substances chimiques
Collagen
9007-34-5
NADP
53-59-8
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24575Subventions
Organisme : H2020 Marie Skłodowska-Curie Actions
ID : 857894
Organisme : Russian Science Foundation
ID : 23-15-00294
Informations de copyright
© 2024. The Author(s).
Références
Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71(3), 209–249 (2021).
Johnson, G. G. R. J., Hershorn, O., Singh, H., Park, J. & Helewa, R. M. Sampling error in the diagnosis of colorectal cancer is associated with delay to surgery: A retrospective cohort study. Surg. Endosc. 36(7), 4893–4902 (2022).
pubmed: 34724583
doi: 10.1007/s00464-021-08841-z
Bökkerink, G. M. et al. Value of macrobiopsies and transanal endoscopic microsurgery in the histological work-up of rectal neoplasms; A retrospective study. World J. Gastrointest. Oncol. 9(6), 251 (2017).
pubmed: 28656075
pmcid: 5472555
doi: 10.4251/wjgo.v9.i6.251
Gondal, G. et al. Biopsy colorectal polyps is not adequate grading neoplasia. Endoscopy 37(12), 1193–1197 (2005).
pubmed: 16329016
doi: 10.1055/s-2005-921031
Costantini, M. Interobserver agreement in the histologic diagnosis of colorectal polyps the experience of the multicenter adenoma colorectal study (SMAC). J. Clin. Epidemiol. 56(3), 209–214 (2003).
pubmed: 12725874
doi: 10.1016/S0895-4356(02)00587-5
Jensen, P. et al. Observer variability in the assessment of type and dysplasia of colorectal adenomas, analyzed using kappa statistics. Dis. Colon Rectum. 38(2), 195–198 (1995).
pubmed: 7851176
doi: 10.1007/BF02052450
Mollasharifi, T. et al. Interobserver agreement in assessing dysplasia in colorectal adenomatous polyps: A multicentric Iranian study. Iran. J. Pathol. 15(3), 167–174 (2020).
pubmed: 32754211
pmcid: 7354064
doi: 10.30699/ijp.2020.115021.2250
Smits, L. J. H. et al. Diagnostic variability in the histopathological assessment of advanced colorectal adenomas and early colorectal cancer in a screening population. Histopathology 80(5), 790–798 (2022).
pubmed: 34813117
pmcid: 9306715
doi: 10.1111/his.14601
Glynne-Jones, R. et al. Rectal cancer: ESMO Clinical Practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 28, iv22–iv40 (2017).
pubmed: 28881920
doi: 10.1093/annonc/mdx224
Argilés, G. et al. Localised colon cancer: ESMO Clinical Practice guidelines for diagnosis, treatment and follow-up†. Ann. Oncol. 31(10), 1291–1305 (2020).
pubmed: 32702383
doi: 10.1016/j.annonc.2020.06.022
Benson, A. B. et al. NCCN Guidelines Version 3.2023 Rectal Cancer Continue NCCN Guidelines Panel Disclosures (2023).
Benson, A. B. et al. NCCN Guidelines Version 2.2023 Colon Cancer Continue NCCN Guidelines Panel Disclosures (2023).
Deal, J. et al. Identifying molecular contributors to autofluorescence of neoplastic and normal colon sections using excitation-scanning hyperspectral imaging. J. Biomed. Opt. 24(02), 1 (2018).
pubmed: 30592190
doi: 10.1117/1.JBO.24.2.021207
Waterhouse, D. J. et al. First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett’s-related neoplasia. J. Biomed. Opt. 26(10) (2021).
Yoon, J. et al. First experience in clinical application of hyperspectral endoscopy for evaluation of colonic polyps. J. Biophotonics 14(9) (2021).
Gkouzionis, I. et al. Real-time tracking of a diffuse reflectance spectroscopy probe used to aid histological validation of margin assessment in upper gastrointestinal cancer resection surgery. J. Biomed. Opt. 27(02) (2022).
Lukina, M. et al. Interrogation of metabolic and oxygen states of tumors with fiber-based luminescence lifetime spectroscopy. Opt. Lett. 42(4), 731 (2017).
pubmed: 28198851
doi: 10.1364/OL.42.000731
Wood, H. A. C. et al. Tri-mode optical biopsy probe with fluorescence endomicroscopy, Raman spectroscopy, and time-resolved fluorescence spectroscopy. J. Biophotonics 16(2), (2023).
Lakowicz, J. R., Szmacinski, H., Nowaczyk, K. & Johnson, M. L. Fluorescence Lifetime Imaging Free Protein-Bound NADH 89 (1992).
Marcu, L. Fluorescence lifetime techniques in medical applications. Ann. Biomed. Eng. 40(2), 304–331 (2012).
pubmed: 22273730
pmcid: 3368954
doi: 10.1007/s10439-011-0495-y
Shcheslavskiy, V. I., Yuzhakova, D. V., Sachkova, D. A., Shirmanova, M. V. & Becker, W. Macroscopic temporally and spectrally resolved fluorescence imaging enhanced by laser-wavelength multiplexing. Opt. Lett. 48(20), 5309 (2023).
pubmed: 37831854
doi: 10.1364/OL.501923
Kolenc, O. I. & Quinn, K. P. Evaluating cell metabolism through autofluorescence imaging of NAD(P)H and FAD. Antioxid. Redox Signal. 30(6), 875–889 (2019).
pubmed: 29268621
pmcid: 6352511
doi: 10.1089/ars.2017.7451
Georgakoudi, I. & Quinn, K. P. Optical imaging using endogenous contrast to assess metabolic state. Annu. Rev. Biomed. Eng. 14, 351–367 (2012).
pubmed: 22607264
doi: 10.1146/annurev-bioeng-071811-150108
Lagarto, J. L. et al. Characterization of NAD(P)H and FAD autofluorescence signatures in a Langendorff isolated-perfused rat heart model. Biomed. Opt. Express. 9(10), 4961 (2018).
pubmed: 30319914
pmcid: 6179415
doi: 10.1364/BOE.9.004961
Cao, R., Wallrabe, H., Siller, K. & Periasamy, A. Optimization of FLIM imaging, fitting and analysis for auto-fluorescent NAD(P)H and FAD in cells and tissues. Methods Appl. Fluoresc 8(2) (2020).
Cannon, T. M. et al. Characterization of NADH fluorescence properties under one-photon excitation with respect to temperature, pH, and binding to lactate dehydrogenase. OSA Contin. 4(5), 1610 (2021).
pubmed: 34458690
pmcid: 8367293
doi: 10.1364/OSAC.423082
Schaefer, P. M., Kalinina, S., Rueck, A. & von Arnim, C. A. F. Von Einem, NADH Autofluorescence—A marker on its way to Boost Bioenergetic Research. Cytometry Part. A. 95(1), 34–46 (2019).
doi: 10.1002/cyto.a.23597
Heikal, A. A. Intracellular coenzymes as natural biomarkers for metabolic activities and mitochondrial anomalies. Biomark. Med. 4(2), 241–263 (2010).
pubmed: 20406068
doi: 10.2217/bmm.10.1
Marcu, L., Cohena, D., Maarek, J. M. I. & Grundfesta, W. S. Characterization of Type I, II, III, IV, and V Collagens by Time-Resolved Laser-Induced Fluorescence Spectroscopy (n.d.).
Lagarto, J. et al. Application of time-resolved autofluorescence to label-free in vivo optical mapping of changes in tissue matrix and metabolism associated with myocardial infarction and heart failure. Biomed. Opt. Express. 6(2), 324 (2015).
pubmed: 25780727
pmcid: 4354591
doi: 10.1364/BOE.6.000324
Manning, H. B. et al. Detection of cartilage matrix degradation by autofluorescence lifetime. Matrix Biol. 32(1), 32–38 (2013).
pubmed: 23266527
doi: 10.1016/j.matbio.2012.11.012
Shaik, T. A. et al. Monitoring changes in biochemical and Biomechanical Properties of Collagenous Tissues Using Label-Free and nondestructive optical imaging techniques. Anal. Chem. 93(8), 3813–3821 (2021).
pubmed: 33596051
doi: 10.1021/acs.analchem.0c04306
Zheng, J. Energy metabolism of cancer: glycolysis versus oxidative phosphorylation (review). Oncol. Lett. 4(6), 1151–1157 (2012).
pubmed: 23226794
pmcid: 3506713
doi: 10.3892/ol.2012.928
Vander Heiden, M. G. & DeBerardinis, R. J. Underst. Intersections between Metabolism Cancer Biology Cell. 168(4), 657–669 (2017).
Mylonas, C. C. & Lazaris, A. C. Colorectal cancer and basement membranes: Clinicopathological correlations. Gastroenterol. Res. Pract. 2014 (2014).
Nebuloni, M. et al. Insight on colorectal carcinoma infiltration by studying perilesional extracellular matrix. Sci. Rep. 6 (2016).
Liang, Y. et al. Prognostic significance of abnormal matrix collagen remodeling in colorectal cancer based on histologic and bioinformatics analysis. Oncol. Rep. 44(4), 1671–1685 (2020).
pubmed: 32945508
pmcid: 7448414
Pfefer, T. J., Paithankar, D. Y., Poneros, J. M., Schomacker, K. T. & Nishioka, N. S. Temporally and spectrally resolved fluorescence spectroscopy for the detection of high grade dysplasia in Barrett’s esophagus. Lasers Surg. Med. 32(1), 10–16 (2003).
pubmed: 12516065
doi: 10.1002/lsm.10136
Butte, P. V. et al. Diagnosis of meningioma by time-resolved fluorescence spectroscopy. J. Biomed. Opt. 10(6), 064026 (2005).
pubmed: 16409091
doi: 10.1117/1.2141624
Cicchi, R. et al. Non-linear imaging and characterization of atherosclerotic arterial tissue using combined SHG and FLIM microscopy. J. Biophotonics. 8(4), 347–356 (2015).
pubmed: 25760563
doi: 10.1002/jbio.201400142
Masters, B. R., So, P. T. C. & Gratton, E. Optical biopsy of in vivo human Skin: Multi-photon Excitation Microscopy. Lasers Med. Sci. 13(3), 196–203 (1998).
doi: 10.1007/s101030050074
Xiang, F. et al. Quantitative multiphoton imaging of cell metabolism, stromal fibers, and keratinization enables label-free discrimination of esophageal squamous cell carcinoma. Biomed. Opt. Express. 14(8), 4137 (2023).
pubmed: 37799684
pmcid: 10549756
doi: 10.1364/BOE.492109
Rück, A., Hauser, C., Mosch, S. & Kalinina, S. Spectrally resolved fluorescence lifetime imaging to investigate cell metabolism in malignant and nonmalignant oral mucosa cells. J. Biomed. Opt. 19(9), 096005 (2014).
doi: 10.1117/1.JBO.19.9.096005
Heaster, T. M., Landman, B. A. & Skala, M. C. Quantitative spatial analysis of metabolic heterogeneity across in vivo and in vitro Tumor models. Front. Oncol. 9 (2019).
Skala, M. C. et al. In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia. Proc. Natl. Acad. Sci. USA 104(49), 19494–19499 (2007).
pubmed: 18042710
pmcid: 2148317
doi: 10.1073/pnas.0708425104
Chekulayev, V. et al. Metabolic remodeling in human colorectal cancer and surrounding tissues: alterations in regulation of mitochondrial respiration and metabolic fluxes. Biochem. Biophys. Rep. 4, 111–125 (2015).
pubmed: 29124194
pmcid: 5668899
Reinsalu, L. et al. Energy Metabolic plasticity of Colorectal Cancer cells as a determinant of Tumor Growth and Metastasis. Front. Oncol. 11 (2021).
Robertson-Tessi, M., Gillies, R. J., Gatenby, R. A. & Anderson, A. R. A. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res. 75(8), 1567–1579 (2015).
pubmed: 25878146
pmcid: 4421891
doi: 10.1158/0008-5472.CAN-14-1428
Kim, J. & DeBerardinis, R. J. Mechanisms and implications of metabolic heterogeneity in Cancer. Cell. Metab. 30(3), 434–446 (2019).
pubmed: 31484055
pmcid: 6730674
doi: 10.1016/j.cmet.2019.08.013
Sengupta, D. & Pratx, G. Imaging metabolic heterogeneity in cancer. Mol. Cancer 15(1) (2016).
J. ZHENG, Energy metabolism of cancer: glycolysis versus oxidative phosphorylation (review). Oncol. Lett. 4(6), 1151–1157 (2012).
De Wever, O. & Mareel, M. Role of tissue stroma in cancer cell invasion. J. Pathol. 200(4), 429–447 (2003).
pubmed: 12845611
doi: 10.1002/path.1398
Pouli, D. et al. Two-photon images reveal unique texture features for label-free identification of ovarian cancer peritoneal metastases. Biomed. Opt. Express. 10(9), 4479 (2019).
pubmed: 31565503
pmcid: 6757455
doi: 10.1364/BOE.10.004479
Shcheslavskiy, V. I. et al. Fluorescence time-resolved macroimaging. Opt. Lett. 43(13), 3152 (2018).
pubmed: 29957804
doi: 10.1364/OL.43.003152
Lukina, M. et al. Label-free macroscopic fluorescence lifetime imaging brain tumors. Front. Oncol. 11 (2021).
Mycek, M. A., Schomacker, K. T. & Nishioka, N. S. Colonic polyp differentiation using time-resolved autofluorescence spectroscopy. Gastrointest. Endosc. 48(4), 390–394 (1998).
pubmed: 9786112
doi: 10.1016/S0016-5107(98)70009-4
Coda, S. et al. Fluorescence lifetime spectroscopy of tissue autofluorescence in normal and diseased colon measured ex vivo using a fiber-optic probe. Biomed. Opt. Express. 5(2), 515 (2014).
pubmed: 24575345
pmcid: 3920881
doi: 10.1364/BOE.5.000515
Herrando, A. et al. Dual excitation spectral autofluorescence lifetime and reflectance imaging for fast macroscopic characterization of tissues. Biomed. Opt. Express (2023).
Köllner, M. & Wolfrum, J. How many photons are necessary for fluorescence-lifetime measurements? Chem. Phys. Lett. 200(1–2), 199–204 (1992).
doi: 10.1016/0009-2614(92)87068-Z
Hinsdale, T. et al. Optically sectioned wide-field fluorescence lifetime imaging microscopy enabled by structured illumination. Biomed. Opt. Express. 8(3), 1455 (2017).
pubmed: 28663841
pmcid: 5480556
doi: 10.1364/BOE.8.001455
Lagarto, J., Hares, J. D., Dunsby, C. & French, P. M. W. Development of low-cost instrumentation for single point autofluorescence lifetime measurements. J. Fluoresc (2017).
Walsh, A. J. & Skala, M. C. Optical metabolic imaging quantifies heterogeneous cell populations. Biomed. Opt. Express. 6(2), 559 (2015).
pubmed: 25780745
pmcid: 4354590
doi: 10.1364/BOE.6.000559
Walsh, A. J. et al. Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer. Cancer Res. 73(20), 6164–6174 (2013).
pubmed: 24130112
pmcid: 3801432
doi: 10.1158/0008-5472.CAN-13-0527
Ruppert, D., Wand, M. P. & Carroll, R. J. Semiparametric Regression (Cambridge University Press, 2003).
Kenward, M. G. & Roger, J. H. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53(3), 983 (1997).
pubmed: 9333350
doi: 10.2307/2533558
Alfonso-Garcia, A. et al. Assessment of murine colon inflammation using intraluminal fluorescence lifetime imaging. Molecules 27(4), (2022).
McGinty, J. et al. Wide-field fluorescence lifetime imaging of cancer. Biomed. Opt. Express 1(2), 627 (2010).
pubmed: 21258496
pmcid: 3017991
doi: 10.1364/BOE.1.000627
Lakowicz, J. R. Principles of Fluorescence Spectroscopy (Springer, 2006).
Nebuloni, M. et al. Insight on colorectal carcinoma infiltration by studying perilesional extracellular matrix. Sci. Rep. 6(1), 22522 (2016).
pubmed: 26940881
pmcid: 4778019
doi: 10.1038/srep22522
Liang, Y. et al. Prognostic significance of abnormal matrix collagen remodeling in colorectal cancer based on histologic and bioinformatics analysis. Oncol. Rep. (2020).
Provenzano, P. P., Eliceiri, K. W. & Keely, P. J. Multiphoton microscopy and fluorescence lifetime imaging microscopy (FLIM) to monitor metastasis and the tumor microenvironment. Clin. Exp. Metastasis. 26 (4), 357–370 (2009).
pubmed: 18766302
doi: 10.1007/s10585-008-9204-0
Izuishi, K. et al. The histological basis of detection of adenoma and cancer in the colon by autofluorescence endoscopic imaging. Endoscopy 31(7), 511–516 (1999).
pubmed: 10533733
doi: 10.1055/s-1999-57
Costa, S., Fang, Q., Farrell, T., Dao, E. & Farquharson, M. Time-resolved fluorescence and diffuse reflectance for lung squamous carcinoma margin detection. Lasers Surg. Med. 56(3), 279–287 (2024).
pubmed: 38357847
doi: 10.1002/lsm.23761
Zhang, L. et al. Predictive value of intratumoral-metabolic heterogeneity derived from 18F-FDG PET/CT in distinguishing microsatellite instability status of colorectal carcinoma. Front. Oncol. 13 (2023).
Oh, B. Y. et al. Intratumor heterogeneity inferred from targeted deep sequencing as a prognostic indicator. Sci. Rep. 9(1), 4542 (2019).
pubmed: 30872730
pmcid: 6418103
doi: 10.1038/s41598-019-41098-0
Liu, X. et al. Prognostic value of intratumor metabolic heterogeneity parameters on 18F-FDG PET/CT for patients with colorectal cancer. Contrast Media Mol. Imaging 2022, 1–11 (2022).
Han, Y. H., Jeong, H. J., Sohn, M. H. & Lim, S. T. Clinical value of intratumoral metabolic heterogeneity in [18F]FDG PET/CT for prediction of recurrence in patients with locally advanced colorectal cancer. Q. J. Nuclear Med. Mol. Imaging 62(4) (2018).
Zhang, M. et al. Metabolism-associated molecular classification of colorectal cancer. Front. Oncol. 10 (2020).
Rajput, A., Bocklage, T., Greenbaum, A., Lee, J. H. & Ness, S. A. Mutant-allele Tumor Heterogeneity scores Correlate with risk of metastases in Colon cancer. Clin. Colorectal Cancer. 16(3), e165–e170 (2017).
pubmed: 28073683
doi: 10.1016/j.clcc.2016.11.004
Joung, J. G. et al. Tumor heterogeneity predicts metastatic potential in Colorectal Cancer. Clin. Cancer Res. 23(23), 7209–7216 (2017).
pubmed: 28939741
doi: 10.1158/1078-0432.CCR-17-0306
Read, G. H., Bailleul, J., Vlashi, E. & Kesarwala, A. H. Metabolic response to radiation therapy in cancer. Mol. Carcinog. 61 (2), 200–224 (2022).
pubmed: 34961986
doi: 10.1002/mc.23379
van der Stel, S. D. et al. Size and depth of residual tumor after neoadjuvant chemoradiotherapy in rectal cancer—Implications for the development of new imaging modalities for response assessment. Front. Oncol. 13 (2023).
Loree, J. M. et al. Classifying colorectal cancer by tumor location rather than sidedness highlights a continuum in mutation profiles and consensus molecular subtypes. Clin. Cancer Res. 24(5), 1062–1072 (2018).
pubmed: 29180604
doi: 10.1158/1078-0432.CCR-17-2484
Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21(11), 1350–1356 (2015).
pubmed: 26457759
pmcid: 4636487
doi: 10.1038/nm.3967
Skala, M. C., Fontanella, A., Lan, L., Izatt, J. A. & Dewhirst, M. W. Longitudinal optical imaging of tumor metabolism and hemodynamics. J. Biomed. Opt. 15(1), 011112 (2010).
pubmed: 20210438
pmcid: 2816992
doi: 10.1117/1.3285584
Snyder, C. M. & Chandel, N. S. Mitochondrial regulation of cell survival and death during low-oxygen conditions. Antioxid. Redox Signal. 11(11), 2673–2683 (2009).
pubmed: 19580395
pmcid: 2821141
doi: 10.1089/ars.2009.2730
Lukina, M. M. et al. Interrogation of tumor metabolism in tissue samples ex vivo using fluorescence lifetime imaging of NAD(P)H. Methods Appl. Fluoresc. 8(1), 014002 (2019).
pubmed: 31622964
doi: 10.1088/2050-6120/ab4ed8