Biomarker Discovery: Study Design and Execution - Proteomic and Metabolomic Approaches to Biomarker Discovery - page 13

multivariate analysis with SVM for the analysis of
NMR data. Their results indicated that SVMwere
superior to PLS-DA in terms of predictive accu-
racy with the least number of features. Van
et al. 50 used 2D total correlation NMR spectros-
copy and statistical analysis to compare the global
metabolic pro

parameters such as extraction ef ciency, the use

of internal standards is advised.

CONCLUDING REMARKS
AND RECOMMENDATION S

les of urines obtained from wild-
type and ABCC6-knockout mice. Three statistical
methods were used to analyze the NMR spectra;
PCA, PLS-DA, and OPLS-DA. The PLS-DA and
OPLS-DA gave almost identical results, and
PCA gave slightly different results. However, all
three methods could successfully discriminate
between the two groups.
Issaq et al. 51 used PCA and OPLS-DA to
analyze HPLC/MS data obtained from the
urines of 41 bladder patients and 48 healthy
volunteers. The PCA analysis resulted in two
separate groups corresponding to normal and
cancer urines and correctly predicted 40 of 41
bladder cancer and 46 of 48 healthy volunteers.
The OPLS-DA con

Can metabolomic and proteomic studies lead
to a cancer biomarker? In short, yes. The ultimate
diagnostic biomarker for any disease is one that
provides 100% sensitivity and speci city. It

seems that this level of accuracy is more an
ideal than an attainable goal for discovering
biomarkers using metabolomics and proteomics.
That concession does not mean that the search
for biomarkers should be stopped; on the
contrary,

the search should be intensi ed

because of the bene ts of detecting cancer or

any disease at an early stage. The failure in

c metabolic and pro-
teomic biomarkers for cancer may be attributed
to different factors: the small number of samples
that are analyzed; lack of information on the
history of the samples; case and control
specimens are not age and sex matched; limited
metabolomic and proteomic coverage; and the
need to follow clear standard operating proce-
dures for sample selection, collection, storage,
handling, analysis, and data interpretation.
Also, most studies to date used serum, plasma,
urine, or tissue from cancer patients and controls.
A more sound approach is to search for proteins
in the cancer tissue

finding sensitive and speci

rmed the predicted results
of the PCA program in terms of sensitivity and
speci city; however, OPLS-DA correctly pre-

dicted 48 of 48 healthy and 41 of 41 of bladder
cancer urines. 51

RECOMMENDATIONS

fluids and tissues should
be handled carefully using safe procedures.
When taken out of the freezer, sample vials
should be checked for breakage prior to defrost-
ing. Samples should be thawed at room tempera-
ture and not by heating or placed in a hot water
bath. Standard operating procedures should be
followed in the same manner for all samples in
a study. Urine specimens may contain different
amounts of analytes; therefore, peak intensities
should be normalized and aligned. To prevent
loss of sample and information, minimum sample
steps should be used. In the case of global meta-
bolic studies, different solvents should be used
for maximum analyte extraction. To assess

Caution: Biological

first, then look for the
discriminating proteins in the blood or urine as
was suggested by Zhang and Chan. 52 Johann
et al. 53 studied renal cell carcinoma tissue, adja-
cent normal tissue, and preoperative blood taken
from the same patient. The proteomes extracted
from the tissues and preoperative plasma were
analyzed using 2D LC-MS. They identi

ed
proteins that were present in the tumor but not
the normal tissue. Also, discriminating proteins
found in the tumor tissue were found in the
preoperative plasma. In a recent study of kidney
cancer, Ganti et al. 54 performed a simultaneous