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metabolomics 2011 CCE project update LingYan Liu and Dan Raftery

metabolomics 2011 CCE project update LingYan Liu and Dan Raftery. NMR Analysis from original dataset. CRC (n=23), adenomatous polyps (n=14), non- adenomatous polyps (n=7) and healthy controls (n=31). Table 1 . Summary of demographic and clinical parameters for recruited subjects.

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metabolomics 2011 CCE project update LingYan Liu and Dan Raftery

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  1. metabolomics 2011 CCE project update LingYan Liu and Dan Raftery

  2. NMR Analysis from original dataset CRC (n=23), adenomatous polyps (n=14), non-adenomatous polyps (n=7) and healthy controls (n=31). Table 1. Summary of demographic and clinical parameters for recruited subjects.

  3. OSC-PLS score plot from 1H NMR data.

  4. Sample grouping • Samples were divided into • training set • - samples obtained in the first batch • - to identify distinguishing metabolites and build a statistical mode • validation set. • samples obtained in the second batch • used for validation of the model

  5. Six marker metabolites Six metabolites were found to differentiate CRC from Healthy Controls in the training set. Table 2. Quantitative comparison of 1HNMR marker metabolites in CRC and healthy serum. a The percentage changes of CRC from healthy controls were calculated by 100x(CRC-healthy)/healthy. b p-values (CRC vs healthy) were calculated using unpaired t test with Welch’s correction on log2 transformed total sum normalized data. C area under ROC curve from cross-validation of individual marker model.

  6. Six marker metabolites Marker 1 Marker 2 Marker 3 Healthy CRC Healthy CRC CRC Healthy Marker 6 Marker 4 Marker 5 Relative integrals (log2 scaled) Figure 3. Box-and-whisker plot of six metabolites selected from training set. Pathways involved include TCA and urea cycles, pyruvate and proprionate metabolism. Healthy CRC Healthy CRC CRC Healthy

  7. L1-regularization path selected marker metabolites constructed model for CRC discrimination B. C. A. Relative integrals (log2 scaled) True positive rate True positive rate cutoff value AUC=1 AUC=0.99 False positive rate Healthy CRC False positive rate Figure 4. Results of logistic regression analysis using the five metabolite markers. A) ROC curves obtained from the cross-validation of training set with CRC (n=12) and healthy controls (n=17) samples; the arrow indicates the cutoff selected for calculating sensitivity and specificity; B) ROC curve obtained from the prediction of validation sample set; and C) box- and-whisker plot for the prediction of validation set with CRC (n=5) and healthy controls (n=14).

  8. Difference between adenomatous polyps and healthy controls Marker 3 Marker 1 Marker 7 Relative integrals (log2 scaled) Healthy Healthy AP AP Healthy AP Marker 4 Marker 2 Healthy AP Healthy AP Figure 7. Box-and-whisker plot for the five metabolites that show some difference between adenomatous polyps (n=14), and healthy controls (n=31). The differences were statistically significant for two of the 5 marker candidates (p <0.05 for both).

  9. Sample information from CCE • 120 serum samples were received between Nov. 2009 and Sept. 2010. • In total 230 serum samples were received, including • 35 Colon cancers (7 w/o any treatment; 28 w. treatment.) • 14 Rectal cancers (8 w/o any treatment; 6 w. treatment.) • 93 Healthy controls • 86 Polyps • 2 unknown (CCE-015-1 and CCE-015-2)

  10. CCE NMR Analysis • 230 serum samples from CCE have been analyzed using 500 MHz 1H NMR • All spectra were preprocessed, baseline corrected, aligned, and uploaded to CCEhub.org. • 26 metabolite features were identified and quantified by relative integrals.

  11. GCxGC -TOFMS Analysis • Experimental conditions and parameters such as GC column combination, gas flow rate, gradient were evaluated and optimized. • 230 serum samples have been analyzed using Leco GC x GC –TOFMS. • All spectra were processed. Net cdf. File and peak table of each spectrum were uploaded to CCEhub.org. (With Ann Caitlin’s support.)

  12. GC x GC – TOFMS Analysis on original data • 24 metabolites were identified as putative markers from training set and quantified. • Appear in 90% samples; Similarity > 700(in scale of 1000) • Metabolites can be found in hmdb • OPLS on training set which grouped as same as NMR data (12 CRC and 17 Healthy Controls)

  13. GC-MS Analysis: 10 metabolites were found significantly differentiate in CRC from Healthy controls in training set. Marker 1 Marker 3 Marker 2 p=0.03 P=0.005 Marker 6 Marker 4 P=0.01 P=0.04 P=0.02

  14. Marker 6 Marker 7 Marker 8 P=0.02 P=0.003 P=0.003 Marker 10 Marker 9 P=0.02 P=0.002

  15. Findings Thus Far • NMR and GCxGC-MS data obtained on original 44 samples and over 230 CCE samples thus far. • NMR analysis indicates that several markers are holding up for distinguishing polyps from healthy controls as well as colon cancer. • These markers generally show a progression from health to polyps to cancer • Some indications that treatment can be followed • May work best using ratio of metabolites over time • GC-MS data on original samples looks promising • GC-MS data is expected to improve metabolite profiles

  16. Supplementary slides

  17. Prediction on validation set 5 cancer , 12 healthy, 14 Adenomatous polyps, 7 non-adenomatous polyps. Healthy Cancer Adenomatous polyps Non-adenomatous polyps AUC=0.8

  18. L1-regularization path selected marker metabolites constructed model for CRC discrimination Model built on 7 GC-MS detected markers. Cross-validation ROC AUC=0.84375

  19. 1.0 0.8 0.87 0.6 0.4 0.47 0.2 0.0 0.06 0.0 0.2 0.4 0.6 0.8 1.0 Differentiating CRC from Adenomatous Polyps (AP). Relative integrals (log2 scaled) A. B. . C. NMR Marker 5 NMR Markr 3 True positive rate P - AUC=0.83 P-value= 0.037 P-value= 0.0054 AP CRC CRC AP False positive rate Figure 5. Box-and-whisker plot for two metabolites, A) Marker 3(p-value=0.037) and B) NMR Marker 5 (p-value=0.0054) which classify CRC and APin the training set samples. C. ROC for the prediction of the validation set of samples using the model built from the two metabolites.

  20. 1HNMR spectra formate histidine 1,2-propanediol creatinine lactate glucose valine Figure 2. Overlap of the mean 1H NMR spectra for CRC (red dashed) and healthy controls (blue) (bottom); difference of the mean spectra (top). The inset shows vertical expansion for the marker region.

  21. Metabolic profiling Metabolic profiling was explored based on markers find from the data. Figure 8. Pathway diagram showing altered metabolite for patients with CRC and those with AP. Upward arrow indicates significantly higher in CRC and downward arrow indicates significantly lower in CRC compared to healthy controls. The downward arrow with dashed line indicates significantly lower in AP compared to healthy controls.

  22. Formic acid in Cancer patients for disease monitoring

  23. Formic acid in Cancer patients for disease monitoring Healthy Controls

  24. CCE NMR Analysis on polyps. Lactic acid p-value=0.018 Adenomatous Polyps (n=76) vs. Healthy Controls (n=93) Formic acid p-value=0.048 Relative Integrals Acetoacetic acid, p-value=0.039 Healthy Controls Adenomatous Polyps Healthy Controls Adenomatous Polyps Healthy Controls Adenomatous Polyps

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