NMR Profiling for Liver Cancer

7/31/2019 NMR Profiling for Liver Cancer

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Metabonomics

Metabonomics is formally defined as,

The quantitative measurement of dynamicmulti-parametric metabolic response ofliving systems to physiological stimuli orgenetic modifications.

It summarizes the entire pool of metabolites

in a bio-fluid, thereby promising a powerfuldiagnostic tool in future.

Ref: Nicholson et al., 1999

Metabolome or Metabolic profile


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Diagnosis and classification of diseases(tumor types)

Time course disease progression

Learn pathological mechanisms

identify new biomarkers

Responses to treatment (efficiency, toxicity)

Drug design (decrease development time)

Generating databases (HMDB, tumor

metabolome database)

Metabonomics in health so far


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Post-genomic Era of Biology

Genome

Gene

expression

Proteins

Metabolism

Genomics

Proteomics

Transcriptomics

Metabonomics

Environmental

stressors

Evolution of Metabonomics ?


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Technologies commonly used inMetabolic profiling

NMR: Nuclear magnetic resonance spectroscopy

MS: Mass spectroscopy(coupled with GC or LC)

FTIR: Fourier transformed infra red spectra

CE: capillary electrophoresis

GC- gas chromatography, LC- liquid chromatography


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Tissue or biofluid sample

Measure the metabolite profile

Overview NMR-based metabonomicapproach

Treat profile as statistical

object for classification

purposes

Explore profile to gain

mechanistic insight into

the stress response

Multivariate analysis

Minimal samplepreparation

Rapid analysis

Unbiased detector

Molecular structure

1H NMR

spectroscopy


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1.41.61.822.22.42.62.83

Chemical shift (ppm)

Intensity

dimethylglycine ?

alanine

acetate

zoom

2.462.502.542.582.62

Chemical shift (ppm)

Intens

ity

1-D 1H NMR spectra

extremely congestedspectra (raw data) with

hundreds

of overlapping peaks

carnitine arginine

hypotaurine

Sample spectrum with metabolic profiling


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We hypothesize that NMR-based metabonomics

can provide evidence for the diagnosis ofdiseases, via the identification of metabolic profile.

Hypothesis-driven research

Generate ahypothesis

Record limiteddataset specific to

that hypothesis

Determine ifhypothesis istrue or false(next phase)


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CANCER OF THE LIVER

Chronic liver cell injuries and regeneration stimulatesa pathway of increased liver cell activation resulting in

malignant transformation of hepatocytes, calledhepatocellular carcinoma

Ref:Blum,H.E.(2007)

hepatocellular carcinoma

90% of all primary liver cancer is HCC20 to 50% of patients presenting with hepatocellular

carcinoma had previously undiagnosed cirrhosis.


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Global burden of HCC

Fifth most common malignancy in men & eighth inwomen worldwide

Age and male sex have a positive correlation

A rise in incidence due to cohort effect of hep B and Cinfections during 1980s.

In the year 2000, it was projected that there will be430,000 deaths from HCC all over the world.

The prevalence of HCC in an autopsy study amongIndians were low, and varies from 0.2% to 1.9%.

Ref: Mohandas,K (2004)


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A d di d i id f HCC i diff


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Age standardised incidence rate of HCC in differentcities of India compared with rest of the world

The mean age adjusted incidence of HCC in 6 Indian populations

is 2.77 in males and 1.28 in females per 100,000 people.

Place Men Women

Japan (Osaka)Hong Kong

China (Shanghai)

Singapore Chinese

Singapore Indians

US SEER (Blacks)

US SEER (Whites)

46.736.2

28.2

22.1

9.4

6.5

3.0

11.59.5

9.8

5.8

4.6

2.0

1.2

Mumbai

Trivandrum

Bangalore

Chennai

DelhiBhopal

Karunagapally

Barshi

3.9

3.1

2.7

2.5

2.22.1

2.7

1.8

1.9

1.1

1.3

0.5

1.11.1

1.7

0.7


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Etiology of Liver cancer

Hepatitis B (HBV) and hepatitis C (HCV)

Cirrhosis due to alcohol, hepatitis, or too

much iron in the liver (hemochromatosis)

Aflatoxins (from fungus that can

contaminate peanuts, wheat, soybeans,

groundnuts, corn, and rice)

Tobacco use


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Diagnosis of hepatocellular carcinoma ?recommendations from EASL (European association for studies on liver)

Serology liver function tests.

AFP (alpha fetoprotein) blood test.

Blood tests for Hepatitis B and C.Imaging

Ultrasound of the liver.

CT scan or MRI scan of liver. Biopsy.

Angiogram of the liver.

Laparoscopy.


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According to EASL recommendations

The Gold standard in the diagnosis of liver cancerdepends on the size of nodules.

nodule size 2 cm - biopsy is recommended.

(imaging techniques do not have sufficient accuracy to distinguish HCC

from other conditions & AFP levels usually remain within normal orslightly elevated)

nodules > 2 cm, imaging techniques + appropriateserology can confidently establish the diagnosiswithout confirmation from a positive biopsy.

Ref: Bruix J et al, 2001.


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Rationale for Metabolic profiling in HCC


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Objective of the study

To identify & characterise the distribution ofsignificant metabolites in serum of patientswith HCC, patients with cirrhotic liver (CLD)

and apparently normal people from a basketof metabolome library.

The secondary objective was to find optimal cut-offs, which differentiates between these conditions


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Methodology

This study is a venture to adopt epidemiologicalprinciples into basic science research and is a soleattempt to highlight the use of Nuclear magneticresonance (NMR) spectroscopy and Metabonomicsprinciple in the diagnosis of hepatocellularcarcinoma.


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Research question

Is there a different pattern of metabolitelevels distributed in human serum

samples analysed using1

H NMR basedmetabolome, which can distinguishpatients with hepatocellular carcinomafrom cirrhotic liver or people withapparently normal liver admitted to atertiary care centre.


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Study design:Descriptive study

Sample size:

Keeping sensitivity of new test as 95 %,precision of population parameter as 10 %,and (1- ) at 95%, the sample size was 18

positive cases.


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Study setting

Gastroenterology department, MedicalCollege,Trivandrum. (tertiary care centre)

NIST (National institute of science andtechnology) Pappanamcode, Trivandrum.

Study subjectsCases consecutively admitted to Gastroenterologydepartment in MedicalCollege, with results of USG andappropriate serology tests (Inclusion criteria)

Exclusion criteria

Patients not giving consent.

Patients without or not willing for further tests, for the

confirmation of diagnosis as HCC or no HCC.


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approval from human ethical committee wereobtained.

Data collection using Performa after informed

consent

Performa was completed by patients attendingphysician according to medical files.

Reference Test

USG along with appropriate serologyand expert

opinions.

(relatively imperfect gold standard when compared to histopathology)


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Stepwise protocol in sample collection

Blood samples were collected and routine

biochemical parameters were measured.

Tests for AFP and USG were measured on allpatients. CT and MRI were used wherever required.

CHILD Pugh score were recorded (as a summaryindex in progression of liver cirrhosis).

The patients were grouped as Hepatocellularcarcinoma, chronic liver disease and normal liver.

The serum samples were sent for NMRspectroscopy.


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Methodology for in vitro NMR spectroscopy

Sample collection, serum separation, storage,dilution with D2O, loading into NMR tubes.

Spectrum was obtained from Bruker 400x NMR

spectroscopy. Water suppression experiment.

Preprocessing of spectrum in CHENOMX NMR

SUITE.

Metabolic profiles generated . (250 metabolitesconsidered, 137 metabolites were selected)


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137 Metabolites,(considered as 137independent tests)

AUC was computed,

(P-value and C.I (95%)Z test)

ROC curve plotted& optimal cut offto discriminateHCC and no HCC

2x2 tables foreach metabolite atdifferent levels

Sn & 1-Sp wascalculated atdifferent levels

Data mining using ROC analysis

Area under the ROC curves - test's ability

to discriminate between HCC and no HCC.


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Metabolites with significant

AUC & their Optimal cutoffsMeasures of accuracy

(Sn, Sp, LR+ & LR-)

Combinations

Parallel combinations

of metabolites in metabolome

(i.e., if any one test is positive is considered as test positive)

Combinations in the metabolome which can improve

accuracy of diagnosis of HCC will be

summarized

Metabolome

Res lts


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Results

Baseline characters of total study subjects


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Baseline characters of hepatocellular carcinoma group


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Experimental spectrum after processing


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Biochemical parameters of the total samples

Characteristics HCC group

n=20(Mean, 95% C.I)

CLD group

n=28(Mean, 95% C.I)

Normal liver

n= 20(Mean, 95% C.I)

AST (IU/L) 180

(134.02-225.98)

104.07

(80.71-127.43)

22.35

(19.34-25.36)

ALT (IU/L)80.68

(70.18-91.19)

58.21

(45.49-70.93)

23.35

(20.72-25.98)

ALP (IU/L) 284.26

(183.43-385.10)

165.04

(136.04-194.03)

65.25

(55.94-74.56)

Bilirubin (mg/dl) 4.068

(2.067-

6.069)

2.636

(1.950-

3.322)

0.900

(0.717-

1.083)

CHILD-Pugh 11

(10.26-11.74)

8.75

(7.90-9.60)

5.60

(5.28-5.92)

AST (aspartate transferase), ALT (alkaline transferase), ALP(alkaline phospatase)

Metabolome with significant Area under the ROC curve


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p

value

1. 3-dihydroxyacetone 0.835 0.000 0.741 0.930

2. 3-dimethylurate 0.755 0.001 0.639 0.871

3. 6-anhydro--d-

glucose

0.796 0.000 0.661 0.931

4. 2-aminoadipate 0.809 0.000 0.695 0.924

5. 2-ethylacrylate 0.729 0.003 0.593 0.865

6. 2-hydroxyglutarate 0.835 0.000 0.734 0.936

7. 2-methylglutarate 0.775 0.000 0.660 0.890

8. 2-oxobutyrate 0.756 0.001 0.634 0.878

9. 2-oxocaproate 0.776 0.000 0.659 0.893

10.2-oxoisocaproate 0.724 0.004 0.583 0.86511.2-oxovalerate 0.805 0.000 0.699 0.911

12.2-phosphoglycerate 0.763 0.001 0.644 0.881

13.3-hydroxy-3-

methylglutarate

0.828 0.000 0.731 0.925

14.3-methylglutarate 0.754 0.001 0.628 0.880

15.4-hydroxybutyrate 0.720 0.005 0.589 0.851

16.5-aminolevulinate 0.765 0.001 0.650 0.879

17.acetoacetate 0.850 0.000 0.760 0.940

18.acetone 0.803 0.000 0.700 0.906

19.alanine 0.779 0.000 0.664 0.894

20.ascorbate 0.750 0.001 0.621 0.879

21.aspartate 0.747 0.001 0.622 0.872

22.butanone 0.827 0.000 0.728 0.927

23.citrate 0.821 0.000 0.720 0.922

24.cystathionine 0.781 0.000 0.668 0.89425.dimethylamine 0.807 0.000 0.704 0.911

Metabolome with significant Area under the ROC curve


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26.ethylene glycol 0.748 0.001 0.623 0.873

27.fructose 0.753 0.001 0.621 0.885

28.galactarate 0.857 0.000 0.765 0.949

29.galactitol 0.719 0.005 0.589 0.848

30.galactonate 0.743 0.002 0.615 0.870

31.glucarate 0.802 0.000 0.691 0.914

32.glucose 0.722 0.004 0.595 0.849

33.glutamate 0.818 0.000 0.705 0.930

34.glutarate 0.820 0.000 0.717 0.923

35.glutaric acidmonomethyl ester

0.756 0.001 0.634 0.878

36.glycine 0.808 0.000 0.705 0.912

37.glycolate 0.792 0.000 0.678 0.905

38.glycylproline 0.767 0.001 0.648 0.886

39.guanidoacetate 0.714 0.006 0.573 0.854

40.homoserine 0.700 0.010 0.557 0.843

41.isobutyrate 0.704 0.008 0.573 0.836

42.isocitrate 0.732 0.003 0.604 0.860

43.levulinate 0.818 0.000 0.714 0.92144.malate 0.811 0.000 0.707 0.916

45.mannitol 0.710 0.007 0.567 0.853

46.methionine 0.768 0.001 0.643 0.892

47.methylamine 0.846 0.000 0.755 0.936

48.methylguanidine 0.701 0.009 0.561 0.842

49.methylsuccinate 0.739 0.002 0.611 0.866

50.n-dimethylglycine 0.703 0.009 0.559 0.847


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51 n-carbamoylaspartate 0.731 0.003 0.590 0.873

52 n-carbamoyl--alanine 0.720 0.004 0.578 0.862

53 o-phosphoserine 0.798 0.000 0.683 0.913

54 pimelate 0.783 0.000 0.656 0.911

55 proline 0.733 0.003 0.612 0.85556 propionate 0.804 0.000 0.698 0.911

57 propylene glycol 0.794 0.000 0.688 0.899

58 pyruvate 0.804 0.000 0.699 0.910

59 sarcosine 0.785 0.000 0.670 0.901

60 serine 0.776 0.000 0.654 0.898

61 -alanine 0.786 0.000 0.675 0.898

62 s-sulfocysteine 0.830 0.000 0.731 0.930

63 suberate 0.810 0.000 0.702 0.919

64 succinate 0.816 0.000 0.713 0.918

65 succinylacetone 0.777 0.000 0.661 0.893

66 sucrose 0.724 0.004 0.573 0.875

67 tartrate 0.821 0.000 0.721 0.921

68 threonate 0.785 0.000 0.671 0.900

69 trans-4-hydroxy-l-proline 0.784 0.000 0.671 0.898

70 trimethylamine 0.710 0.007 0.571 0.850

71 trimethylamine n-oxide 0.777 0.000 0.659 0.895

72 valine 0.720 0.005 0.580 0.859

73 xylose 0.736 0.002 0.606 0.867

Metabolome and their measures of accuracy


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1. acetoacetate 30.0808 950.00 700.83 30.26 00.071

2. methylamine 10.6246 950.00 640.58 20.68 00.077

3. 2-oxovalerate 20.8257 950.00 580.33 20.28 00.086

4. dimethylamine 00.6427 950.00 580.33 20.28 00.0865. 2-oxocaproate 30.1926 950.00 540.17 20.07 00.092

6. galactarate 40.8223 900.00 790.17 40.32 00.13

7. 2-hydroxyglutarate 240.7171 900.00 750.00 30.60 00.13

8. acetone 20.3513 900.00 680.75 20.88 00.15

9. glucarate 120.3481 900.00 680.75 20.88 00.15

10. threonate 130.219 900.00 640.58 20.54 00.15

11. O-phosphoserine 200.170 900.00 600.42 20.27 00.17

12. sarcosine 10.0864 900.00 580.33 20.16 00.17

13. propionate 100.7368 900.00 560.25 20.06 00.18

14. butanone 50.5898 850.00 770.08 30.71 00.19

15. citrate 60.9835 850.00 750.00 30.40 00.20

16. tartrate 50.8376 850.00 720.92 30.14 00.21

17. trimethylamine n-oxide

50.8376 850.00 720.92 30.14 00.21

18. 3-hydroxy-3-methylglutarate 40.8658 850.00 700.83 20.91 00.21

19. glutamate 120.4457

850.00 680.75 20.72 00.22

20. glutarate 60.7143 850.00 680.75 20.72 00.22

21. s-sulfocysteine 0.3998 850.00 680.75 20.72 00.22

22. ascorbate 60.127 850.00 560.25 10.94 00.27

23. 6-anhydro--d-

glucose

40.5652 800.00 850.42 50.49 00.23

24. succinate 10.7027 800.00 750.00 30.20 00.27

25. alanine 120.7186 800.00 720.92 20.95 00.27

Metabolome and their measuresof accuracy

26. 2-methylglutarate 100.3909 800.00 700.83 20.74 00.28


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27. glycine 30.6442 800.00 700.83 20.74 00.28

28. malate 140.3447 800.00 680.75 20.56 00.29

29. 2-phosphoglycerate 330.9003 800.00 660.67 20.40 00.30

30. isobutyrate 50.2818 800.00 540.17 10.75 00.37

31. pyruvate 20.939 750.00 750.00 30.00 00.33

32. pimelate 60.1551 750.00 720.92 20.77 00.34

33. trans-4-hydroxy-l-proline

120.8817

750.00 720.92 20.77 00.34

34. valine 00.9707 750.00 720.92 20.77 00.34

35. 2-aminoadipate 90.8691 750.00 700.83 20.57 00.35

36. levulinate 20.9556 750.00 700.83 20.57 00.35

37. propylene glycol 80.2389 750.00 700.83 20.57 00.35

38. -alanine 50.3824 750.00 700.83 20.57 00.35

39. suberate 90.5508 750.00 700.83 20.57 00.35

40. succinylacetone 40.3623 750.00 700.83 20.57 00.35

41. glycolate 40.4765 750.00 680.75 20.40 00.36

42. xylose 110.6971 750.00 660.67 20.25 00.37

43. glycylproline 110.6589 750.00 600.42 10.89 00.41

44. 3-dihydroxyacetone 10.4628 700.00 830.33 40.20 00.36

45. cystathionine 80.0304 700.00 790.17 30.36 00.38

46. proline 200.4469 700.00 750.00 20.80 00.40

47. 3-dimethylurate 20.1598 700.00 720.92 20.58 00.41

48. 3-methylglutarate 120.188 700.00 720.92 20.58 00.41

49. n-carbamoylaspartate

150.342 700.00 700.83 20.40 00.42

50. 4-hydroxybutyrate 60.3615 700.00 680.75 20.24 00.44


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51. ethylene glycol 10.9397 700.00 680.75 20.24 00.44

52. glutaric acidmonomethyl ester

20.2698 700.00 680.75 20.24 00.44

53. serine 160.8712 700.00 680.75 20.24 00.44

54. 2-ethylacrylate 10.3727 700.00 660.67 20.10 00.45

55. aspartate 100.4794 700.00 660.67 20.10 00.45

56. 5-aminolevulinate 30.7126 700.00 640.58 10.98 00.46

57. galactonate 60.0561 700.00 600.42 10.77 00.50

58. homoserine 100.8915 700.00 600.42 10.77 00.50

59. mannitol 60.0926 700.00 600.42 10.77 00.50

60. trimethylamine 00.5 700.00 580.33 10.68 00.51

61. methylguanidine 10.4669 650.00 750.00 20.60 00.47

62. guanidoacetate 40.1396 650.00 720.92 20.40 00.48

63. methionine 30.9777 650.00 700.83 20.23 00.49

64. n-dimethylglycine 00.8293 650.00 700.83 20.23 00.49

65. isocitrate 190.23 650.00 660.67 10.95 00.53

66. methylsuccinate 110.6588 650.00 620.50 10.73 00.56

67. galactitol 20.4011 650.00 600.42 10.64 00.58

68. 2-oxoisocaproate 30.505 600.00 830.33 30.60 00.48

69. n-carbamoyl--

alanine

70.1585 600.00 770.08 20.62 00.52

Combinations of metabolite to improve the diagnostic measures


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Combinations of metabolite to improve the diagnostic measures

1 Galactorate and Cystathionine 90 75 30.60 00.13

2 Succinate and Cystathionine 85 75 30.40 00.20

3 Pyruvate and Cystathionine 80 75 30.20 00.27

4 2-Hydroxyglutarate and Galactarate 95 700.83 30.26 00.07

5 Butanone and pyruvate 85 700.83 20.91 00.21

6 3 hydroxy-3-methylglutarate and

Succinate

85 700.83 20.91 00.21

8 Cystathionine and Succinylacetone 75 700.83 20.57 00.35

9 Serine and 2-Ethylacrylate 100 680.75 30.20 0

10 Guanidoacetate and 1,6-Anhydro--D-

glucose

95 680.75 30.05 00.07

11 2-Methylglutarate and 1,6-Anhydro--

D-glucose

95 660.67 20.85 00.08

12 2 hydroxyglutarate and pimelate 90 660.67 20.70 00.15

13 Pimelate and Cystathionine 85 660.67 20.55 00.23

14 Glycylproline and Methylamine 100 640.58 20.82 0

15 Methylamine and Galactarate 100 640.58 20.82 0

16 Galactarate and Trimethylamine N-

oxide

90 640.58 20.54 00.11

17 Alanine and Pimelate 85 640.58 20.40 00.23

18 Acetone and cystathionine 95 620.50 20.53 00.06

19 1,6-Anhydro--D-glucose and Ethylene

glycol

95 600.42 20.40 00.08

20 2-methylglutarate and guanidoacetate 85 600.42 20.15 00.17

21 Methylamine and Propionate 100 50 20.0 0

22 2-Oxocaproate and Methylamine 100 50 20.0 023 Acetone and 2 ethylacrylate 95 50 10.90 00.10

Discussion of Results compared with AFP


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An approximate comparison of sensitivity and specificity ofmetabolites with AFP, as well as ultrasound can be made.

(Arrieta.O et.al, 2007)

Improved sensitivity of many metabolites than thetechniques in current practise. (Miller.J.C.et.al,2005)

Overall sensitivity and specificity of NMR basedmetabolome in the diagnosis of HCC have to be established.

Discussion of Results compared with AFP

Parallel combinations of metabolites can improve sensitivity.

To improve the specificity, Different cut off

Serial combinations (Consider the test as positive, ifboth metabolites are positive)


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Accuracy of AFP at different cut offs (Literature search)

Sn Sp Sn Sp Sn Sp Sn Sp

Oka et al 1994 39 76 13 97

Pateron et al 1994 21 93

Peng et al 1999 65 87 45 100

Tong et al 2001 41 94

Trevisani et al

2001

60 900.6 310.2 980.8 22 99 170.1 990.4

Gebo et al 2002* 60 064 100

Nguyen et al 2002 63 80 410.4 970.3 32 100

Gupta et al 2003* 41-65 80-94

Farinati et al 2006 54 18

Arrieta et al 2007 600.6 950.9 470.2 99 360.3 100 200.2 100

R d ti


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Recommendations

The Metabolome requires further validation with perfect goldstandard.

The cut offs suggested in this study is arbitrary (screening tool)and have to be changed, if using as a confirmatory test.

Many metabolites like 2 butanone, is unique in hepatocellularcarcinoma which is also reported in another recent study.

Known concentration of internal standards to improve the

reliability.

More precise estimates can be obtained if large molecular sized

proteins are removed. Higher frequency NMR spectroscopy (>500 MHz), can give

greater separation.

NMR based metabonomics are cheaper (400Rs/sample) and

faster (~ 5 min).


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Conclusion

A Metabolome was identified for hepatocellularcarcinoma.

Optimal cut offs to discriminate betweenhepatocellular carcinoma were determined.

Many metabolites in the serum with high sensitivityand reasonable specificity which after validation can

be used in routine clinical practise.


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Acknowledgement

Express my gratitude towards one andevery body, who directly & indirectly gaveme their strength and wisdom to

complete this thesis.

I am deeply indebted to my supervisors,teachers and fellow trainees, for their

helps, stimulating suggestions andencouragements


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THANKING YOU


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Diagnostic measures for AFP, USG, & other imaging

C i f S t l i


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Comparison of Spectral images


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CHILD Pugh Score

MELD Score(Model for end stage liver disease)


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Data mining using ROC analysis

ROC analysis were done for each metabolites (137 metabolites),

(considering each one as independent test)

Sensitivity & 1- Specificity was computed at different levels.

Area under the curve for each metabolite was calculated.

Most significant AUC, above 0.70 was picked up for further analysis.

A suitable cut off for each metabolite, which differentiates into HCCand no HCC were identified.

Sensitivity, specificity and likelihood ratios were computed for allmetabolites individually at this cut off levels

Metabolites were combined parallel, (i.e., if any one test is positive isconsidered as test positive) improves the Sensitivity at minimal cost ofSpecificity.

Documents

NMR Profiling for Liver Cancer