15 research outputs found
Abstract 5325: Associations between adipose tissue compartments and the plasma metabolome in colorectal cancer patients: Results from the ColoCare Study
Abstract
BACKGROUND Obesity is associated with colorectal cancer (CRC) risk and prognosis. We investigated associations between plasma metabolites and multiple dimensions of body fatness in early- and late-stage CRC patients enrolled in the ColoCare Study, a multicenter international cohort.
METHODS Pre-operatively collected plasma samples of newly diagnosed CRC patients [n=212; (stage I-IV)] from the ColoCare Study were utilized to perform targeted metabolomics by mass spectrometry using the AbsoluteIDQ p180 Kit assay (Biocrates; intra-plate CVs <20%, inter-plate CVs <20%) as part of the MetaboCCC consortium. Abdominal adipose tissue (AT) was assessed by area-based quantification of visceral (VAT), and subcutaneous AT (SAT), as well as their ratio (VAT:SAT) on levels L3/L4 and L4/L5. Body mass index (BMI) was calculated (kg/m2). Demographic and clinical data were abstracted from medical records. Partial correlations and regression analyses were used, adjusting for age, sex, batch, stage overall and in stratified analyses by early- (I/II; n=111) and late-stage (III/IV; n=101) and corrected for multiple hypotheses testing (FDR). We used Cox proportional hazard models to investigate overall survival (OS) after 24 months of follow-up.
RESULTS A total of 127 metabolites from 5 different compound classes (i.e., amino acids, biogenic amines, glycerophospholipids, sphingomyelins, acylcarnitines) were included for statistical analysis. Overall obesity (BMI) and VAT were not associated with any metabolites in early-stage or late-stage tumors. SAT (L3/L4 and L4/L5) was inversely associated with 3 glycerophospholipids in analyses restricted to late-stage, but not early-stage tumors: PC-ae-C34_0 (pFDR=0.02), PC-ae-C36_0 (pFDR=0.03), PC-ae-C36_1 (pFDR=0.03). A doubling of concentration in selected glycerophospholipids was associated with a significant increase in risk for death in late-stage (III and IV) CRC.
CONCLUSIONS We observed a negative association between subcutaneous abdominal adiposity and glycerophospholipids in late-stage CRC. Glycerophospholipids are major components of cellular membranes and are pertinent to cancer cells that undergo progression and metastasis. Our results suggest that a metabolic shift in glycerophospholipid metabolism in late-stage tumors may take place and have potential impact on survival.
Note: This abstract was not presented at the meeting.
Citation Format: Jennifer Ose, Tengda Lin, Nina Habermann, David Achaintre, Pekka Keski-Rahkonen, Augustin Scalbert, Juergen Boehm, Biljana Gigic, Dominique Scherer, Johanna Nattenmueller, Mariam Salou, Lin Zielske, Alexis Ulrich, Jewel Samadder, Hanno Glimm, Stephen Hursting, Hans-Ulrich Kauczor, Cornelia M. Ulrich. Associations between adipose tissue compartments and the plasma metabolome in colorectal cancer patients: Results from the ColoCare Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5325. doi:10.1158/1538-7445.AM2017-5325</jats:p
Ablation of Breast Cancer Stem Cells with Radiation
AbstractTumor radioresistance leads to recurrence after radiation therapy. The radioresistant phenotype has been hypothesized to reside in the cancer stem cell (CSC) component of breast and other tumors and is considered to be an inherent property of CSC. In this study, we assessed the radiation resistance of breast CSCs using early passaged, patient-derived xenografts from two separate patients. We found a patient-derived tumor in which the CSC population was rapidly depleted 2 weeks after treatment with radiation, based on CD44+ CD24- lin- phenotype and aldehyde dehydrogenase 1 immunofluorescence, suggesting sensitivity to radiotherapy. The reduction in CSCs according to phenotypic markers was accompanied by a decrease in functional CSC activity measured by tumor sphere frequency and the ability to form tumors in mice. In contrast, another patient tumor sample displayed enrichment of CSC after irradiation, signifying radioresistance, in agreement with others. CSC response to radiation did not correlate with the level of reactive oxygen species in CSC versus non-CSC. These findings demonstrate that not all breast tumor CSCs are radioresistant and suggest a mechanism for the observed variability in breast cancer local recurrence
Self-reported Physical Activity Is Associated With Angiogenesis- And Inflammation-related Biomarkers In Colorectal Cancer Patients
Associations of branched-chain amino acids with parameters of energy balance and survival in colorectal cancer patients: results from the ColoCare study
A translational protocol optimizes the isolation of plasma-derived extracellular vesicle proteomics
In translational research and clinical routine, liquid biopsy is a promising tool to direct individually targeted treatments. Among the components of liquid biopsy, extracellular vesicles (EVs) carry manyfold molecular cargo and are increasingly being studied for biomarker identification. In order to identify potential confounding factors and determine optimal conditions when studying blood-derived EV proteins, the impact of pre-analytical variables needs to be assessed. Here we establish an EV enrichment for proteomic analysis workflow in a real-world clinical setting in which we evaluate variables from blood collection through protein preparation and storage for mass spectrometry (MS). We assess hemolysis, particle concentration and size, protein quantity, protein markers and comprehensive proteomic analysis using mass spectrometry to assess the influence of different pre-analytical variables like blood collection tubes, transportation of blood samples and delayed processing. Under these conditions, density gradient and size exclusion chromatography using Sepharose CL-4B show good EV enrichment. For MS, lysis with increased protease inhibitors shows high protein yields while TCA protein precipitation results in high numbers of identified proteins. In summary, we develop here an optimized protocol for the analysis of plasma EV-derived proteomics, evaluating pre-analytical variables relevant for implementation in a clinical setting
Multiplatform Urinary Metabolomics Profiling to Discriminate Cachectic from Non-Cachectic Colorectal Cancer Patients: Pilot Results from the ColoCare Study
Cachexia is a multifactorial syndrome that is characterized by loss of skeletal muscle mass in cancer patients. The biological pathways involved remain poorly characterized. Here, we compare urinary metabolic profiles in newly diagnosed colorectal cancer patients (stage I–IV) from the ColoCare Study in Heidelberg, Germany. Patients were classified as cachectic (n = 16), pre-cachectic (n = 13), or non-cachectic (n = 23) based on standard criteria on weight loss over time at two time points. Urine samples were collected pre-surgery, and 6 and 12 months thereafter. Fat and muscle mass area were assessed utilizing computed tomography scans at the time of surgery. N = 152 compounds were detected using untargeted metabolomics with gas chromatography–mass spectrometry and n = 154 features with proton nuclear magnetic resonance spectroscopy. Thirty-four metabolites were overlapping across platforms. We calculated differences across groups and performed discriminant and overrepresentation enrichment analysis. We observed a trend for 32 compounds that were nominally significantly different across groups, although not statistically significant after adjustment for multiple testing. Nineteen compounds could be identified, including acetone, hydroquinone, and glycine. Comparing cachectic to non-cachectic patients, higher levels of metabolites such as acetone (Fold change (FC) = 3.17; p = 0.02) and arginine (FC = 0.33; p = 0.04) were observed. The two top pathways identified were glycerol phosphate shuttle metabolism and glycine and serine metabolism pathways. Larger subsequent studies are needed to replicate and validate these results
Abstract A26: Body fatness and adipose tissue subtypes are associated with circulating biomarkers of inflammation and angiogenesis in colorectal cancer patients: The ColoCare Study
Multiplatform Urinary Metabolomics Profiling to Discriminate Cachectic from Non-Cachectic Colorectal Cancer Patients: Pilot Results from the ColoCare Study
Cachexia is a multifactorial syndrome that is characterized by loss of skeletal muscle mass in cancer patients. The biological pathways involved remain poorly characterized. Here, we compare urinary metabolic profiles in newly diagnosed colorectal cancer patients (stage I–IV) from the ColoCare Study in Heidelberg, Germany. Patients were classified as cachectic (n = 16), pre-cachectic (n = 13), or non-cachectic (n = 23) based on standard criteria on weight loss over time at two time points. Urine samples were collected pre-surgery, and 6 and 12 months thereafter. Fat and muscle mass area were assessed utilizing computed tomography scans at the time of surgery. N = 152 compounds were detected using untargeted metabolomics with gas chromatography–mass spectrometry and n = 154 features with proton nuclear magnetic resonance spectroscopy. Thirty-four metabolites were overlapping across platforms. We calculated differences across groups and performed discriminant and overrepresentation enrichment analysis. We observed a trend for 32 compounds that were nominally significantly different across groups, although not statistically significant after adjustment for multiple testing. Nineteen compounds could be identified, including acetone, hydroquinone, and glycine. Comparing cachectic to non-cachectic patients, higher levels of metabolites such as acetone (Fold change (FC) = 3.17; p = 0.02) and arginine (FC = 0.33; p = 0.04) were observed. The two top pathways identified were glycerol phosphate shuttle metabolism and glycine and serine metabolism pathways. Larger subsequent studies are needed to replicate and validate these results
Smoking is Associated with Hypermethylation of the APC 1A Promoter in Colorectal Cancer: the ColoCare Study
Smoking tobacco is a known risk factor for the development of colorectal cancer, and for mortality associated with the disease. While smoking has been reported to be associated with changes in DNA methylation in blood and in lung tumour tissues, there has been scant investigation of how epigenetic factors may be implicated in the increased risk of developing colorectal cancer. To identify epigenetic changes associated with smoking behaviours, we performed epigenome-wide analysis of DNA methylation in colorectal tumours from 36 never smokers, 47 former smokers and 13 active smokers, and adjacent mucosa from 49 never smokers, 64 former smokers and 18 active smokers. Our analyses identified 15 CpG sites within the APC 1A promoter that were significantly hypermethylated and 14 CpG loci within the NFATC1 gene body that were significantly hypomethylated (pLIS<1x10-5) in tumours of active smokers. The APC 1A promoter was hypermethylated in 7 of 36 tumours from never smokers (19%), 12 of 47 tumours from former smokers (26%), and 8 of 13 tumours from active smokers (62%). Promoter hypermethylation was positively associated with duration of smoking (Spearman rank correlation, =0.26, p=0.03) and was confined to tumours, with hypermethylation never observed in adjacent mucosa. Further analysis of adjacent mucosa revealed significant hypomethylation of four loci associated with the TNXB gene in tissue from active smokers. Our findings provide exploratory evidence for hypermethylation of the key tumour suppressor gene APC being implicated in smoking-associated colorectal carcinogenesis. Further work is required to establish the validity of our observations in independent cohorts
Thermodynamic And Structural Characterization Of Zwitterionic Micelles Of The Membrane Protein Solubilizing Amidosulfobetaine Surfactants Asb-14 And Asb-16
Surface tension and isothermal titration calorimetry (ITC) were used to determine the critical micelle concentration (cmc) of the zwitterionic amidosulfobetaine surfactants ASB-14 and ASB-16 (linear- alkylamidopropyldimethylammoniopropanosulfonates) at 25 °C. The cmc and the heat of micellization were determined from 15 to 75 °C by ITC for both surfactants. The increase in temperature caused significant changes in the enthalpy and in the entropy of micellization, with small changes in the standard Gibbs energy (ΔGmic), which is consistent to an enthalpy-entropy compensation with a compensatory temperature of 311 K (ASB-14) and 314 K (ASB-16). In the studied temperature range, the heat capacity of micellization (ΔCpmic) was essentially constant. The experimental ΔCpmic was lower than that expected if only hydrophobic interactions were considered, suggesting that polar interactions at the head groups are of significant importance in the thermodynamics of micelle formation by these surfactants. Indeed, a NMR NOESY spectrum showed NOEs that are improbable to occur within the same monomer, resulting from interactions at the polar head groups involving more than one monomer. The ITC and NMR results indicate a tilt in the polar headgroup favoring the polar interactions. We have also observed COSY correlations typical of dipolar interactions that could be recovered with the partial alignment of the molecule in solution, which results in an anisotropic tumbling. The anisotropy suggested an ellipsoidal shape of the micelles, which results in a positive magnetic susceptibility, and ultimately in orientation induced by the magnetic field. Such an ellipsoidal shape was confirmed from results obtained by SAXS experiments that revealed aggregation numbers of 108 and 168 for ASB-14 and ASB-16 micelles, respectively. This study characterizes an interesting micelle system that can be used in the study of membrane proteins by solution NMR spectroscopy. © 2011 American Chemical Society.271382488256Laughlin, R.G., (1991) Langmuir, 7, p. 842Correia, V.P., Cuccovia, I.M., Stelmo, M., Chaimovich, H., (1992) J. Am. Chem. Soc., 114, p. 2144Di Profio, P., Germani, R., Savelli, G., Cerichelli, G., Chiarini, M., Mancini, G., Bunton, C.A., Gillitt, N.D., (1998) Langmuir, 14, p. 2662Baptista, M.S., Cuccovia, I., Chaimovich, H., Politi, M.J., Reed, W.F., (1992) J. Phys. Chem., 96, p. 6442Cuccovia, I.M., Romsted, L.S., Chaimovich, H., (1999) J. Colloid Interface Sci., 220, p. 96Tondo, W.T., Priebe, J.M., Souza, B.S., Priebe, J.P., Bunton, C.A., Nome, F., (2007) J. Phys. Chem. B, 111, p. 11867Farrukh, M.A., Beber, R.C., Priebe, J.P., Satnami, M.L., Micke, G.A., Costa, A.C.O., Fiedler, H.D., Nome, F., (2008) Langmuir, 24, p. 12995Priebe, J.P., Souza, B.S., Micke, G.A., Costa, A.C.O., Fiedler, H.D., Bunton, C.A., Nome, F., (2010) Langmuir, 26, p. 1008Blandamer, M.J., Briggs, B., Cullis, P.M., Engberts, J.B.F.N., Kevelam, J., (2000) Phys. Chem. Chem. Phys., 2, p. 4369Blandamer, M.J., Cullis, P.M., Soldi, L.G., Engberts, J.B.F.N., Kacperska, A., Van Os, N.M., Subha, M.C.S., (1995) Adv. Colloid Interface Sci., 58, p. 171Zielinski, R., Ikeda, S., Nomura, H., Kato, S., (1989) J. Colloid Interface Sci., 129, p. 175Matsuno, R., Takami, K., Ishihara, K., (2010) Langmuir, 26, p. 13028Diaz Garcia, M.E., Sanz-Medel, A., (1986) Talanta, 33, p. 255Kunieda, H., Shinoda, K., (1976) J. Phys. Chem., 80, p. 246Mills, C.O., Martin, G.H., Elias, E., (1986) Biochim. Biophys. Acta, 876, p. 677Patist, A., Bhagwat, B.B., Penfield, K.W., Aikens, P., Shah, D.O., (2000) J. Surfactants Deterg., 3, p. 53Dar, A.A., Rather, G.M., Das, A.R., (2007) J. Phys. Chem. B, 111, p. 3122Domingues, C.C., Malheiros, S.V.P., De Paula, E., (2008) Braz. J. Med. Biol. Res., 41, p. 758Bijma, K., Engberts, J.B.F.N., Haandrikman, G., Van Os, N.M., Blandamer, M.J., Butt, M.D., Cullis, P.M., (1994) Langmuir, 10, p. 2578Bhattacharya, S., Haldar, J., (2005) Langmuir, 21, p. 5747Olofsson, G., (1985) J. Phys. Chem., 89, p. 1473Kresheck, G.C., (1998) J. Phys. Chem. B, 102, p. 6596Blandamer, M.J., Cullis, P.M., Engberts, J.B.F.N., (1996) Pure Appl. Chem., 68, p. 1577Heerklotz, H., Epand, R.M., (2001) Biophys. J., 80, p. 271Hildebrand, A., Garidel, P., Neubert, R., Blume, A., (2004) Langmuir, 20, p. 320Kresheck, G.C., (2006) J. Colloid Interface Sci., 298, p. 432Paula, S., Siis, W., Tuchtenhagen, J., Blume, A., (1995) J. Phys. Chem., 99, p. 11742Garidel, P., Hildebrand, A., Neubert, R., Blume, A., (2000) Langmuir, 16, p. 5267Majhi, P.R., Blume, A., (2001) Langmuir, 17, p. 3844Mehrian, T., De Keizer, A., Korteweg, A.J., Lyklema, J., (1993) Colloids Surf. A: Physicochem. Eng. Asp., 71, p. 255Nusselder, J.J.H., Engberts, J.B.F.N., (1992) J. Colloid Interface Sci., 148, p. 353Kresheck, G.C., (2009) J. Phys. Chem. B, 113, p. 6732Lah, J., Bešter-Rogač, M., Perger, T.-N., Vesnaver, G., (2006) J. Phys. Chem. B, 110, p. 23279Chevallet, M., Sabtoni, V., Poinas, A., Rouquié, D., Fuchs, A., Kieffer, S., Rossignol, M., Rabilloud, T., (1998) Electrophoresis, 19, p. 1901Henningsen, R., Gale, B.L., Straub, K.M., De Nagel, D.C., (2002) Proteomics, 2, p. 1479Luche, L., Santoni, V., Rabilloud, T., (2003) Proteomics, 3, p. 249Dias, R.S., Svingen, R., Gustavsson, B., Lindman, B., Miguel, M.G., Åkerman, B., (2005) Electrophoresis, 26, p. 2908Wiseman, T., Williston, S., Brandts, J.F., Lin, L.-N., (1989) Anal. Biochem., 179, p. 131Piotto, M., Saudek, V., Sklenar, V., (1992) J. Biomol. NMR, 2, p. 661Bax, A., Davis, D.G., (1985) J. Magn. Reson., 65, p. 355Bain, A.D., Burton, I.W., (1996) Concepts Magn. Reson., 8, p. 191Marion, D., Wuthrich, K., (1983) Biochem. Biophys. Res. Commun., 113, p. 967Sklenar, V., Piotto, M., Leppik, R., Saudek, V., (1993) J. Magn. Reson., Ser. A, 102, p. 241Sattler, M., Maurer, M., Schleucher, J., Griesinger, C., (1995) J. Biomol. NMR, 5, p. 97Guinier, A., Fournet, G., (1955) Small Angle Scattering of X-Rays, , Wiley: New YorkSvergun, D.I., Feigin, L.A., (1987) Structure Analysis by Small-angle X-ray and Neutron Scattering, , Plenum Press: New YorkGlatter, O., Kratky, O., (1982) Small Angle X-ray Scattering, , Academic Press: San Diego, CAMarignan, J., Basserau, P., Delord, P., (1986) J. Phys. Chem., 90, p. 645Barbosa, L.R.S., Caetano, W., Itri, R., Homem-De-Mello, P., Santiago, P.S., Tabak, M., (2006) J. Phys. Chem. B, 110, p. 13086Sinibaldi, R., Ortore, M.G., Mariani, P., (2007) J. Chem. Phys., 126, p. 235101Sinibaldi, R., Ortore, M.G., Mariani, P., (2008) Eur. Biophys. J., 37, p. 673Barbosa, L.R.S., Ortore, M.G., Spinozzi, F., Mariani, P., Bernstorff, S., Itri, R., (2010) Biophys. J., 98, p. 147Barbosa, L.R.S., Rigos, C.F., Yoneda, J.S., Itri, R., Ciancaglini, P., (2010) J. Phys. Chemistry B, 114, p. 11371Press, W.H., Teukolsky, S.A., Flannery, B.P., (1994) Numerical Recipes. the Art of Scientific Computing, , Cambridge University Press: Cambridge, U.KTanford, C., (1972) J. Phys. Chem., 76, p. 3020Teixeira, C.V., Itri, R., Casallanovo, F., Schreier, S., (2001) Biochim. Biophys. Acta, 93, p. 1510Weers, J.G., Rathman, J.F., Axe, F.U., Crichlow, C.A., Foland, L.D., Scheuing, D.R., Zielske, A.G., (1991) Langmuir, 7, p. 854Graciani, M.M., Rodríguez, A.M., Muñoz, M., Moyá, M.L., (2005) Langmuir, 21, p. 7161Lumry, R., Rajender, S., (1970) Biopolymers, 9, p. 1125Sugihara, G., Hisatomi, M., (1999) J. Colloid Interface Sci., 219, p. 31Gill, S.J., Wadso, I., (1976) Proc. Natl. Acad. Sci. U.S.A., 73, p. 2955Priebe, J.P., Satnami, M.L., Tondo, D.W., Souza, B.S., Priebe, J.M., Micke, G.A., Costa, A.C.O., Nome, F., (2008) J. Phys. Chem. B, 112, p. 14373Ken Dill A, K.A., Stigter, D., (1988) Biochemistry, 27, p. 3446Scherer, P.G., Seelig, J., (1987) EMBO J., 10, p. 2915Sachs, J.N., Nanda, H., Petrache, H.I., Woolfz, T.B., (2004) Biophys. J., 86, p. 187Caetano, W., Barbosa, L.R.S., Itri, R., Tabak, M., (2003) J. Colloid Interface Sci., 260, p. 41
