Methods: Metabolomics Workflow

Introduction

Figure 1a: 1H NMR spectrum of blood serum sample from a breast cancer patient.

Results The emerging area of metabolomics or metabolite profiling focuses on the quantitative detection of

multiple small molecules that provide vast information on biological status of healthy and disease conditions by elucidating relationships that occur through regulation at the metabolic level. Using this approach, we report here on the development of a new test for detecting recurrent breast cancer using metabolite profiling of serum samples. The approach combines powerful bio-analytical and multivariate statistical methods. We applied a combination of two dimensional gas chromatography mass spectrometry (GCxGC-MS) and nuclear magnetic resonance (NMR) spectroscopy methods to investigate metabolite profiles of 257 serial blood serum samples from breast cancer patients. A total of 116 samples were from recurrent breast cancer patients and 141 samples from breast cancer patients with no evidence of disease (NED). Retrospective serial samples were obtained from the MD Anderson Cancer Center. GCxGC-MS and NMR data were analyzed to identify metabolite markers that distinguish patients with cancer recurrence from those without the recurrence. The analysis was focused on a set of putative biomarker candidates from previous work on breast cancer patient and normal serum samples. A set of eleven metabolite biomarkers (7 from NMR and 4 from GCxGC-MS) were shortlisted from an analysis of logistic regression model using 5-fold cross validation. A PLS-DA (partial least squares discriminant analysis) model built using these markers provided a sensitivity of 86% and a specificity of 84% (AUROC =0.88). One of the important outcomes of this study is that 55% of the patients could be correctly predicted to have recurred 13 months or more before clinical diagnosis, which represents an improvement over the current breast cancer monitoring assay CA 27.29. This study clearly demonstrates that the combination of two advanced bio-analytical methods, GCxGC-MS and NMR, provides a powerful approach to develop a blood based diagnostic test for the early detection of recurrent breast cancer. Validation of the results of this study will pave a new way for earlier detection of the recurrent breast cancer and creates the potential for a simple and sensitive test that may well improve patient survival.

Conclusions Figure 2: An overview describing marker selection, model development and validation.

1

We present the development of a blood-based test for the surveillance of breast cancer recurrence patients using metabolic profiling methods. The performance of the model was optimal when metabolites detected by both NMR and MS were combined. These findings represent an improvement over the current breast cancer-monitoring assay CA 27.29. A combination of GCxGC-MS and NMR methods were demonstrated to be a powerful approach in the development of a blood-based diagnostic test for the early detection of recurrent breast cancer. Validation of the results of this study will pave a new way for earlier detection of the recurrent breast cancer and may help advance further development of early detection method for breast cancer.

Figure 4: Box and whisker plots for the two sample classes showing discrimination between Within/Post recurrence patient samples and NED patients using the model predicted scores.

Analytical Methods for Metabolomics

NMR

Con

cent

ratio

n

Hydrophobicity

MS

Nuclear Magnetic Resonance1D and 2D NMRChromatography-resolved NMR Isotope-enhanced NMR

Mass Spectrometry LC-MSGC-MS

GC×GC-MS CE-MS

FT-ICR-MS

0.51.52.53.54.55.56.57.58.5

Lact

ate

Glu

cose

Hist

idin

e

Lipi

dsA

ceto

acet

ate

Valin

e

Glu

tam

ine

Cre

atin

ine

Lact

ate

Figure 3: ROC curves generated from PLS-DA model using post and within recurrence versus NED samples, and showing the performance of CA 27.29 obtained from the same samples.

Figure 5: Percentage of recurrence patients correctly identified using the 11 marker model (red squares) and CA 27.29 (blue triangles) as a function of time for all recurrence patients.

Recurrence NED

020

4060

8010

012

0

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

ROC Curve

False Alarm Rate

Hit

Rat

e

00.1

0.20.3

0.4

0.5

0.6

0.7

0.8

0.91

Sens

itivi

ty

1-Specificity

CA27.29

Figure 1b: Three-dimensional GCxGC-TOF total ion current (TIC) surface plot chromatogram of serum sample from a breast cancer patient.

Sensitivity Specificity

BCR86%68%

84%94%

CA27.29 35% 96%

Table 2: Comparison of the diagnostic performances of the BCR metabolite profile (at cut-off values of 48 and 54) and CA 27.29.

This work was supported in part by NIH (NIGMS R01GM085291-02 and R25CA128770, Cancer Prevention Internship Program); Purdue University Center for Cancer Research; Purdue Research Foundation; and Oncological Sciences Center in Discovery Park at Purdue University. Partial funding for this work from Matrix-Bio, Inc. is also appreciated. Third Nerve, LLC provided access to specimen materials and data through its ongoing survey study of both new and established cancer markers.

Coverage of detectible metabolites by NMR and MS methods.

BCR model

Metabolite Profiling of Blood Serum for the Early Detection of Recurrent Breast Cancer

Acknowledgements

Total Samples: n=257Recurrence: n=116

No Evidence Disease: n=141

Within and Post n=49NED: n=141

Divided into 5 CV groupsPRE Recurrence n= 67

Prediction Scores

Prediction using Leave One Patient OutCross Validation

Validation5th CV group

PLS-DA Model7 NMR Markers, 4 GC Markers

Variable Selection on 22 NMR, 18 MS markers

Using 4 CV groups

1,245.3 1,245.5 1,245.7

NED

RECURRENCE

NED 89%

POST 89%

STANDARD

(a)

(b)

(c)

(e)

(f)

(g)

Figure 6: Validation procedure for MS biomarkers detected by GCxGC-MS:

(a) Three dimensional GCxGC-TOF total ion current (TIC) surface plot chromatogram; (b) a typical one dimension TIC GCxGC-TOF

chromatogram; (c) selected metabolite (e.g, glutamic acid) chromatogram for the peak at m/z 432; (d) mass spectrum for an NED patient; (e) mass spectrum for a patient with recurrent breast cancer; and (f) mass spectrum for the authentic commercial compound

Metabolites Within and Post vs. NEDp-value

Pre-recurrence vs. NEDp-value

1 Formate 0.0022 0.22 Histidine 0.000041 0.183 Proline 0.018 0.94 Choline 0.000022 0.775 Tyrosine 0.25 0.16 3-hydroxybutyrate 0.86 0.967 Lactate 0.96 0.548 Glutamic acid 0.000018 0.749 N-acetyl-glycine 0.01 0.96

10 3-hydroxy-2-methyl-butanoic acid 0.00004 0.3511 Nonanedioic acid 0.4 0.089

Table 1: P-values for 11 markers, 7 NMR (1-7) and 4 GCxGC-MS markers (8-11) for different groups using all samples; Within and Post recurrence vs. NED; Pre recurrence vs. NED as determined from the univariate student’s t-test

Leiddy Z. Alvarado1, Vincent Asiago1 Narasimhamurthy Shanaiah2, G. A. Nagana Gowda1, Kwadwo Owusu-Sarfo1, Robert Ballas3 and Daniel Raftery1,2

1Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN- 47907,USA; 2Matrix-Bio, Inc., Win Hentschel Blvd, West Lafayette, IN 47906;3Biomarkers Associates, Inc, 25 Meteor Ct., Newark , DE, 19711