MALDI-MS–NIST library approach for colorectal cancer diagnosis

RAPID COMMUNICATIONS IN MASS SPECTROMETRY

Rapid Commun. Mass Spectrom. 2009; 23: 2839–2845

ublished online in Wiley InterScience (www.interscience.wiley.com) DO
P
RCM

Letter to the Editor

Dear Editor,

MALDI-MS–NIST library approach for colorectal

cancer diagnosis

Development of preventive therapeutic strategies has

become an important focus in the diagnosis of early stages

of cancer.1,2 To date, many biomarker screening systems

have been investigated using genomic and proteomic

approaches.3–7 Despite considerable effort devoted by the

scientific community, only a few effective cancer risk

biomarkers have been discovered.3 A limited number of

these molecules have been recognized by the Food and Drug

Administration, and even fewer are used in routine

diagnostic tests.4,8 Researchers hypothesize that proteomic

and genomic studies still require an enormous effort to

resolve the complexity linked to the large number of gene

transcripts and their products.9–11

Some biomarker discovery studies have focused on the

metabolomic pathway (�2400 metabolites),12 which is less

complex than the proteome or genome (�1 000 000 proteins

and 30 000 genes, respectively).13 When combined with

genomic- and proteomic-based approaches, metabolomic

studies represent valuable data sets for the early diagnosis of

cancer.3–7 For example, Sreekumar et al.14 showed that the

levels of the analyte sarcosine (an N-methyl derivative of

glycine) varied depending on the progression of prostate

cancer. Metabolite analysis of prostate cancer samples

(n¼ 262; tissues, urine and plasma) by liquid chromatog-

raphy (LC) and gas chromatography/mass spectrometry

(GC/MS) showed a significant increase in sarcosine in the

latter stages of cancer progression. However, there exist only

a small number of analytes that can be correlated directly

with cancer. For instance, among more than 1126 metabolites

profiled by Sreekumar et al.,14 only sarcosine, uracil,

kynurenine, glycerol-3-phosphate, leucine and proline were

significantly increased during disease progression. A more

comprehensive approach to the discovery of cancer bio-

markers would encompass genomic, proteomic, peptidomic

and metabolomic data to profile the abundance of potential

candidates in tissue and biological fluids obtained from

patients and control subjects.

Matrix-assisted laser desorption/ionization (MALDI)-MS

can be effectively used to detect and characterize candidate

protein, peptide and metabolite biomarkers. For instance,

this technology has been used by Vaidyanathan and

Goodacre15 to quantify several metabolites. In the proteome

and metabolome fields, the first step toward the identifi-

cation of potential biomarkers is based on the comparison of

two data sets from a statistically meaningful number of

control and disease specimens. This comparison allows the

researcher to evaluate the power of the analytical strategy

and potentially differentiates the control subjects from

patients. Analysis of MS data is usually performed by

multivariate analysis, mainly by clustering16 and principal

component analysis (PCA).17 These approaches reduce the

dimensionality of data by creating groups on the basis of

similarity, thus allowing an independent classification of

cases. In particular, cluster analysis is a multivariate

procedure of exploratory data analysis, which detects natural

groupings in a data set and examines similarities and

dissimilarities between data sets. Data classification consists

of placing samples into more or less homogeneous groups to

reveal relationships among them. Data classification has

been applied to analyses of gene expression18 as well

as metabolite patterns under pathological conditions.19 PCA

is a mathematical procedure that transforms a large number

of possibly correlated variables into a smaller number of

uncorrelated variables, called ‘principal components’, which

are linear combinations of the original variables. The data are

represented in a dimensional space of ‘n’ variables, which

are reduced into a few principal components, descriptive

dimensions indicating the maximum variation within data.

After obtaining the principal components, they can be

analyzed graphically to reveal groups within the data set.

Thus, PCA is a method of data visualization that is useful

to reduce the overall data dimensionality. PCA can help to

identify new meaningful underlying variables and also to

detect the presence of clusters within multivariate data.

In some cases, these methods have led to promising

results. For instance, multivariate analysis was successfully

employed by Ragazzi et al.20 to identify different patterns in

‘low molecular weight’ (LMW) plasma protein profiles

between control subjects and colorectal cancer patients.

Multivariate analysis also suggested differences in the LMW

protein profiles in urine of control subjects versus diabetic,

nephropathic and diabetic-nephropathic patients (Lapolla

et al.21). However, there was only a partial overlap in these

data, possibly as a consequence of: (i) inability of MALDI to

accurately detect differences in protein profiles from control

versus unhealthy populations, and/or (ii) failure of cluster

analysis/PCA to precisely determine differences between

the two groups.

The evaluation of different tools for data comparison is of

significant interest, and here the results obtained by an approach

different from multivariate analysis are reported and discussed.

Mass spectrometrists and other analytical biochemists

have vast experience in electron ionization spectra compari-

son by means of spectral libraries, including specific libraries

created by collecting spectra related to specific interests of

research groups22–24 or general libraries that are publicly

available.25 The National Institute of Standard and Technol-

ogies database search software (NIST) has been widely used

to characterize LMW (<600 Da) organic compounds24 and

peptides25 based on similarities with known standards.

Briefly, the spectra library consists of relative intensity

versus m/z values, which are converted into a vector model

that considers both of these values.23 For each search, the

I: 10.1002/rcm.4180

Copyright # 2009 John Wiley & Sons, Ltd.

2840 Letter to the Editor

NIST results compare three values: (i) the ‘match factor’

(direct search), (ii) the ‘reverse match factor’ (reverse search),

and (iii) the probability value. The first two numbers are

derived from a modified cosine between the vector

calculated from library spectra and unknown spectra

(normalized dot product). A perfect match yields a value

of 999, whereas spectra having no peaks in common yield a

value of 0. As a general guide, a value of 900 or greater is an

excellent match, 800–900 is a good match, 600–800 is a fair

match, and a value <600 is considered a very poor match.

The ‘probability’ value and the value for the NIST search

require a more detailed explanation provided elsewhere.23

By using the spectra library approach, we developed a

MALDI spectral library using plasma samples from control

subjects and colorectal cancer patients to identify changes in

metabolomic peptides during cancer progression. In parti-

cular, we evaluated the effectiveness of NIST-based classi-

fication in conjunction with MALDI-MS analysis of peptides

and metabolite profiles as a potential diagnostic tool to

differentiate control subjects from cancer patients. It is

important to note that the NIST software analyzes each

sample individually, thus limiting the loss of potentially

relevant information.

MALDI-MS analysis was performed from 30 ‘control’

subjects (CTR) and 30 ‘first-stage colorectal cancer’ (CRCI)

patients. Plasma samples, diluted 1:3 with saline solution,

were centrifuged for 40 min at 4000 g (88C) in Centricon tubes

(30 000 Da molecular weight cut-off). Before MALDI-MS

analyses, the filtrates were then desalted and purified by

ZipTip C18 pipette tips (Millipore) as per the manufacturer’s

instructions. MALDI-MS measurements were performed

using an Ultraflex II MALDI-time of flight (TOF) instrument

(Bruker Daltonics, Bremen, Germany) operating in the

reflectron positive ion mode. Ions were formed by a pulsed

UV laser (l¼ 337 nm) beam. The instrumental conditions

were: IS1¼ 25 kV; IS2¼ 21.65 kV; reflectron potential¼26.3 kV; delay time¼ 0 ns. The matrix was a-cyano-4-

hydroxycinnamic acid (saturated solution in H2O/aceto-

nitrile (50:50; v/v) containing 0.1% trifluoroacetic acid).

Purified plasma samples (5mL) and matrix solution (5mL)

were mixed, and an aliquot (1mL) was deposited on the

stainless steel sample holder and allowed to dry before

introduction into the mass spectrometer. External mass

calibration was performed using the peptide calibration

standard provided by Bruker Daltonics based on the

monoisotopic values of [MþH]þ of angiotensin II, angio-

tensin I, substance P, bombesin, ACTH clip (1-17), ACTH clip

(18-39), somatostatin 28 at m/z 1046.5420, 1296.6853,

1347.7361, 1619.8230, 2093.0868, 2465.1990 and 3147.4714,

respectively. Fifty spectra were averaged for each sample.

Thirty CTR and 30 CRCI samples were used to build the

NIST database. Patient consent forms were obtained from

patients. This study complied with the ethics board of the

local hospital or institution where samples were collected.

The CTR group consisted of 15 males and 15 females and the

same rule was chosen for the CRCI group. Both the CTR and

CRCI age was between 38–78 years old. The 19 ‘unknown’

samples that were not included in the NIST library were

8 CTR and 11 CRCI. The CTR group was composed of

4 males and 4 females while the CRCI group comprised


5 males and 6 females. Every volunteer in this study were

from the same geographic region (Italy). NIST23 software was

used to build the CTR and CRCI spectra database to be used

for spectra classification. Each NIST sub-library (L20, L30,

L40, L50 and L60) contains the same number of randomly

chosen CRCI and CTR subjects. Note that NIST spectra are

compared on the basis of relative abundance normalization

and not on the basis of the absolute intensity of the signals.

This fact makes it possible to overcome any potential

variability associated with the instrument response. Hier-

archical clustering analysis and PCA were obtained by using

R software.26 The spectral data were converted into MSP

format in order to be integrated into the NIST software,

while we employed a PCA elaboration txt format for the

R software.

Following an initial global inspection, MALDI spectra of

plasma samples belonging to the same class (CTR or CRCI)

showed a high degree of variability. For example, the

MALDI spectra of three different CTR subjects (Fig. 1(a))

showed several common peaks (e.g., ions at m/z 904, 1060,

1211, 1500, 2209, and 2659), although the relative abundance

of some varied notably (e.g., ions at m/z 904, 1500, 2209, and

2659). Others peaks of interest were sample-specific (e.g.,

ions at m/z 942 and 1450 in spectrum ‘i’, m/z 1053, 1537, and

2167 in spectrum ‘ii’, and m/z 1077 and 1466 in spectrum

‘iii’). Spectrum variability within the same class may be

related to specific biochemical pathway(s). Similarly, varia-

bility was also observed in the LMW peptide and metabolite

profiles in other CTR plasma samples (data not shown).

Samples from CRCI patients showed both sample-specific

and common ion peaks (Fig. 1(b)). Moreover, evaluation of

CRCI versus CTR spectra revealed some peaks present in

CRCI but not in CTR. It is noteworthy that some of the

common peaks were highly over-expressed in CRCI as

compared to CTR samples (e.g., the ion at m/z 1211,

Figs. 1(a) and 1(b)).

By using a global MS approach (cluster analysis and PCA),

our preliminary results suggested a high degree of

heterogeneity in the data, which provided consistent

differences between the metabolic profiles from patients

and control subjects. These results are consistent with other

cancer-related MS studies.27,28 It is difficult to propose a

limited number of candidate molecules as specific bio-

markers for the diagnosis of first-stage colorectal cancer due

to the large number of differentially expressed metabolites.

In fact some peaks appear clearly over-expressed in some,

but not in all, CRCI subjects (m/z 666, 1212, and 2357). This

hurdle may be overcome by: (i) considering the total number

of peaks from the MALDI spectra at a fixed m/z range (i.e.,

the entire m/z set of the molecular species ionized by

MALDI) for each sample, and (ii) comparing their profiles

with known spectra of a specific library to search for the most

similar ones.

For the above reasons, we tested NIST as an alternative

approach to discriminate the MALDI-generated spectra of

plasma samples from CTR subjects and CRCI. We generated

an internal NIST library containing MALDI spectra from

CTR and CRCI plasma samples. We then classified some

‘unknown’ samples (the classification is known, but they

were not included in the NIST database) by comparison with


DOI: 10.1002/rcm

Figure 1. (a) MALDI mass spectra of three different plasma samples belonging to the control (CTR) subjects. (b) MALDI mass

spectra of three different plasma samples belonging to the first-stage colorectal cancer (CRCI) patients.

Copyright # 2009 John Wiley & Sons, Ltd. Rapid Commun. Mass Spectrom. 2009; 23: 2839–2845

DOI: 10.1002/rcm

Letter to the Editor 2841


library spectra (see working flow in Fig. 2). To generate our

‘internal NIST library’, we analyzed a total of 60 samples

(30 CTRþ 30 CRCI) by MALDI-MS in the m/z range of 500–

5000. The MALDI spectra profiles of 19 ‘unknown’ samples

(8 CTR, 11 CRCI) that were not included in the internal NIST

library were then evaluated by the NIST software. While

the total number of unknown samples remained constant,

the ‘dimension of the library’ varied from 20, 30, 40, 50 and 60

samples, thus creating five different sub-libraries (L20, L30,

L40, L50, and L60). Each of the sub-libraries contained the

same number of randomly chosen CTR and CRCI spectra,

which allowed us to verify a possible correlation between

the sub-library dimension (number of total samples) and the

percentage of NIST-erroneous identifications. For each

analysis, designation of the sample class (CTR or CRCI)

Figure 2. Diagram showing the workflow followed

library containing MALDI spectra from CTR (colum

was generated. Some ‘unknown’ samples (the clas

in the NIST database; column (a)) were classified


was always performed as a consideration of the matched

class with the highest ‘match factor’ value.

The NIST identification results for the CTR and CRCI

classes are summarized in Table 1 in terms of correct

identification (%). Interestingly, independently of the sub-

library dimension, our identification by the NIST-based

interrogation always successfully identified CTR samples.

Table 2 shows the match factor obtained for each sample

separately. The probability thresholds cannot be reported

because NIST is used in similarity database search mode,

due to the high individual variability, and the probability

thresholds are not calculated by the software when using this

mode. Note that all the match factors were higher than 700

and only one sample exhibits a match factor of 700, indicating

a high degree of similarity between the unknowns (control

during this study. Basically, an internal NIST

n (b)) and CRCI plasma (column (c)) samples

sification is known, but theywere not included

by comparison with library spectra.


DOI: 10.1002/rcm

Table 1. Application of NIST software to identify control

(CTR) and first-stage colorectal (CRCI) samples based on

their MALDI plasma profiles. Five different NIST library dimen-

sions were tested

Sub-librarya Class

NIST –identification(%)b

CTR CRCI

L20 CTR 100 0CRCI 18 82

L30 CTR 100 0CRCI 18 82

L40 CTR 100 0CRCI 9 91

L50 CTR 100 0CRCI 9 91

L60 CTR 100 0CRCI 9 91

a Five sub-libraries ranging in sample dimensions from 20 to 60.b For each class, the value is expressed as the percent ratio betweenthe number of correct NIST-identified individuals and the totalnumber of unknown ones.


and cancer) and those from the database. It must be

emphasized that the only one cancer sample (CRCI4795)

was erroneously assigned as control, that led to a decrease in

the correct score identification to 91% (see Table 1), when the

L60 database is used, this exhibits the lower match factor

(700). This seems to suggest that its profile is the most

different with respect to the classified profiled inserted in the

database, probably due to its high individual component. We

thus hypothesize that this misassignment could be explained

by a higher individual variability of this particular subject. In

fact, the presence of a different level of spectra variability

present in both control and unhealthy subjects is shown in

Figs. 1(a) and 1(b).

Table 2. NIST match factors for each of the 19 classified

samples. The sample classification and NIST attribution are

also reported

Unknown samples Match

Samplename

Correctattribution

NISTattribution

Matchfactor

1 CRCI_9 CRCI CRCI 8202 CRCI_7 CRCI CRCI 8783 CRCI_4 CRCI CRCI 8604 CRCI_19 CRCI CRCI 8445 CRCI_1 CRCI CRCI 7116 CRCI_11 CRCI CRCI 8117 CRCI_4795 CRCI CTR 7008 CRCI_10 CRCI CRCI 7659 CRCI_17 CRCI CRCI 878

10 CRCI_13 CRCI CRCI 84811 CRCI_15 CRCI CRCI 78212 CTR_08 CTR CTR 83913 CTR_05 CTR CTR 83714 CTR_06 CTR CTR 80915 CTR_12 CTR CTR 86816 CTR_02 CTR CTR 83917 CTR_20 CTR CTR 80018 CTR_14 CTR CTR 87619 CTR_18 CTR CTR 839


Regarding the classification scores of the 11 ‘unknown’

CRCI spectra, we observed library dimension dependence.

In particular, 82% of the cases were correctly allocated for

L20 and L30, whereas the score increased to 91% for L40, L50

and L60. These preliminary results suggest that our approach

successfully identified the CTR class. In addition, the CRCI

patients were identified with higher accuracy by increasing

the library dimension. These results suggest that the library

dimension influences the method specificity of NIST

identification; however, this hypothesis will need to be

confirmed. Indeed, our current research efforts are devoted

to addressing this issue and the results could potentially

reduce the observed error percentages for CRCI samples

using larger libraries. Note that this method can in principle

‘learn’ much in the same way as neural networks. For

instance, the 9% of error is given by a cancer volunteer

that exhibits a pattern more similar to the control group

than to the CRCI group. Inclusion of this mass spectrum

into the CRCI library would virtually lead to a correct

assignment of other individuals exhibiting a similar

behavior. In other words, the ability of NIST to monitor

individual information (i.e. individual variability) makes

it possible to instruct the database progressively in order

to reduce wrong attributions. Another interesting feature

of this approach is the possibility to take into account

the kinetic profile of mass spectra over time for a given

subject. Actually, earlier reports described that metabolic

and peptidomic profiles are time variable but that a

cyclic and repetitive trend is usually observed.29,30 On

going validation studies aim at testing this important point.

Thus, the constant and progressive update of database

information can in principle be a solution to increase correct

attribution scores.

Finally, the data were also analyzed using hierarchical

clustering and PCA unsupervised approaches in order to

evaluate the variability associated to the data. This is in

contrast with the NIST approach, which does not provide

information on the variability distribution. Hierarchical

clustering and PCA can therefore be seen as complementary

approaches. Note that in this investigation the whole

database spectra (60) and the unknowns were checked. The

hierarchical cluster analysis plot is shown in Fig. 3(a). As can

be seen, four classification groups were obtained, although

some subjects were erroneously classified. For instance,

CRC7236, 7725 and 4795 were erroneously assigned as CTR.

PCA results (Fig. 3(b)) clearly show CTR and CRCI group

separation, but a clear wide sample undifferentiated region is

also present.

In summary, the NIST library-based discrimination of

‘control’ versus ‘cancer’ subjects seems to be a promising

approach considering the robust quantitative and qualitative

variability observed between samples confirmed by hier-

archical clustering and PCA analyses. The availability of

diagnostic bioanalytical tools could not only enable early

disease detection but also contribute to a better under-

standing of colorectal cancer physiology/kinetics. Future

studies will focus on the validation of these results using a

larger number of experimental samples in order to identify

differentially expressed biomarkers and perform ROC curve

studies.


DOI: 10.1002/rcm

Figure 3. (a) Hierarchic cluster analysis and (b) PCA plots obtained by analyz-

ing the total number of CTR (*) and CRC


AcknowledgementsThe authors thank Dr. Remo Cristoni for his advice. The work wassupported by Italian Government Proteome Net (grant numberRBRN07BMCT) and a Marie Curie Intra European Fellowship withinthe 7th European Community Framework Programme (PIEF-GA-2008-220511).

Simone Cristoni1.6,*, Laura Molin1, Antonella Lai1, Luigi RossiBernardi2, Salvatore Pucciarelli3, Marco Agostini3, Chiara

Bedin3, Donato Nitti3, Roberta Seraglia4, Ombretta Repetto1,Vincenza Flora Dibari1, Rosaria Orlandi5, Pablo Martı́nez-

Lozano Sinues6, and Pietro Traldi41

ISB – Ion Source & Biotechnologies,Via Fantoli 16/15, 20138 Milano, Italy

2Multimedica, Via Fantoli 16/15, 20138 Milano, Italy3Sezione Clinica Chirurgica II, Dipartimento di Scienze

Oncologiche e Chirurgiche, Via Giustiniani 2,25128 Padova, Italy

4CNR-ISTM, C.so Stati Uniti 4, 35127 Padova, Italy5Fondazione IRCCS, Istituto Nazionale dei Tumori,

Via Venezian 5, 20133 Milano, Italy6National Research Council, Institute for Biomedical

Technologies Via Fratelli Cervi 93, 20090 Milano, Italy

*Correspondence to: S. Cristoni, ISB – Ion Source&BiotechnologiesSrl, Via Fantoli 16/15, 20138 Milano, Italy.E-mail: [email protected]/grant sponsor: Italian Government Proteome Net;contract/grant number: RBRN07BMCT.Contract/grant sponsor: Marie Curie Intra European Fellowshipwithin the 7th European Community Framework Programme;contract/grant number: PIEF-GA-2008-220511.


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Received 18 March 2009Revised 24 June 2009

Accepted 27 June 2009


DOI: 10.1002/rcm

MALDI-MS–NIST library approach for colorectal cancer diagnosis

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Transcript of MALDI-MS–NIST library approach for colorectal cancer diagnosis