? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
Differential exoprotease activities confer
tumor-specific serum peptidome patterns
Josep Villanueva, David R. Shaffer, John Philip, Carlos A. Chaparro, Hediye Erdjument-Bromage,
Adam B. Olshen, Martin Fleisher, Hans Lilja, Edi Brogi, Jeff Boyd, Marta Sanchez-Carbayo,
Eric C. Holland, Carlos Cordon-Cardo, Howard I. Scher, and Paul Tempst
Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Recent scientific advances, including sequencing of the genome (1)
and new approaches to modeling complex biological systems (2)
may ultimately lead to improved anticancer therapies. However, at
this time, the best anticancer strategies still rely on early detection
followed by close monitoring for early relapse so that therapies
can be appropriately adjusted (3). There is optimism, however, that
advances in genomics and proteomics may more readily lead to
new and improved approaches in molecular diagnostics, capable
of classifying patients into subgroups based on their predicted
response to individual treatments (4, 5). Appropriate biomarker-
based screens should be minimally invasive and reproducible. A
simple blood or urine test that detects molecules specific to tumor
tissues would be ideal. In addition, screening technology must be
sufficiently sensitive to detect early cancers but specific enough to
classify individuals without cancer as being free of disease (3).
While genes contain hereditary information, including genetic
predisposition to cancer and other diseases, it is their products
that confer the actual phenotypes of living organisms and, in the
case of disease, normal versus pathological states. Since there are
many posttranslational events that can modify biological struc-
ture, function, and degradation of proteins, the knowledge of
genes alone does not even begin to describe the full complexity of
biological systems. From a screening perspective, it is also mostly
the proteins that are secreted or otherwise released from tissues
into the bloodstream (6, 7). Yet, despite an intensive search dur-
ing the past decade(s), only a very small number of identified
cancer biomarkers, all plasma proteins (e.g., prostate-specific
antigen [PSA], carcinoembryonic antigen [CEA], cancer antigen
125 [CA125], and thyroglobulin), have proven clinically useful,
often in combination with other diagnostic tools, for the prog-
nosis of response to therapy, relapse, and survival and for defin-
ing the rate of progression and monitoring of treatment, but they
have been less useful for broad-based population screening (8, 9).
Those proteins are typically present in plasma or serum at sub-
nanomolar concentrations and require individual immunoassays
for detection and quantitation (10, 11). New and improved can-
cer biomarkers and facile detection methods are clearly in order
but have so far eluded discovery and implementation. Even the
most recent approaches, using identity-based proteomics that
involve digesting (e.g., with trypsin) complex protein mixtures
into peptides for mass spectrometric (MS) analysis, have yet to
translate into any practical applications, largely because of insuf-
ficient instrumental dynamic range and because the elaborate
fractionation procedure coupled to multiple MS runs to detect
low-abundant tryptic peptides precludes processing statistically
relevant sample numbers (12).
As cancer involves the transformation and proliferation of
altered cell types that produce high levels of specific proteins and
enzymes such as proteases, e.g., PSA and prostate-specific mem-
brane antigen (PSMA) (13, 14), it not only modifies the array of
existing serum proteins (the serum proteome; ref. 6, 7) but also
their metabolic products, i.e., peptides (the serum peptidome). It
is well established that human serum contains thousands of pro-
teolytically derived peptides (15–17), yet it remains unclear to date
whether this complex peptidome may provide a robust correlate
Nonstandard?abbreviations?used: AP-A, aminopeptidase A; desArg-bradykinin,
bradykinin that has the Arg removed; FPA, fibrinopeptide A; HMW, high molecular
weight; ITIH4, inter-a-trypsin inhibitor heavy chain H4; k-NN, k-nearest neighbor;
LAP, leucine aminopeptidase; MALDI-TOF, matrix-assisted laser desorption/ioniza-
tion–time-of-flight; MS, mass spectrometric, mass spectrometry; PR1, prostate 1
(group); PSA, prostate-specific antigen; PSMA, prostate-specific membrane antigen;
SVM, support vector machine.
Conflict?of?interest:?The authors have declared that no conflict of interest exists.
Citation?for?this?article: J. Clin. Invest. 116:271–284 (2006).
Related Commentary, page 26
272? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
of some biological events occurring in the entire organism. As
advances in MS now permit the display of hundreds of small- to
medium-sized peptides using only microliters of serum (17, 18),?
several recent reports have advocated the use of MS-based serum
peptide profiling to determine qualitative and quantitative pat-
terns, often referred to as signatures or barcodes, that indicate the
presence/absence of diseases such as cancer (19–24). However, this
work has come under intense criticism as growing evidence has
indicated that uncontrolled variables related to both clinical and
analytical chemistry and/or signal processing artifacts may have
tainted the published results (12, 25–29). Skepticism was further
fueled by the use of low-grade MS equipment in these analyses,
which precluded comprehensive, high-resolution read-outs, and
because the identities of only a few putative markers have been
established so far (30–32). The proof of the potential value of this
new approach will be in the ability of several laboratories to inde-
pendently show that the highly discriminatory peptides have the
same amino acid sequences. To date, this has not been done.
Working toward this stated goal, we have previously developed
an automated procedure for the simultaneous measurement of
peptides in serum that utilizes magnetic, reverse-phase beads for
analyte capture and a matrix-assisted laser desorption/ioniza-
tion–time-of-flight (MALDI-TOF) MS read-out (18, 29). This sys-
tem is more sensitive than surface capture on chips (33), as spheri-
cal particles have larger combined surface areas and therefore
higher binding capacity than small-diameter spots. Coupled to
Unsupervised hierarchical clustering and principal component analysis of MS-based serum peptide profiling data derived from 3 groups of
cancer patients and healthy controls. (A) Serum samples from healthy volunteers and patients with advanced prostate, bladder, and breast
cancer were prepared following the standard protocol. The 4 groups were randomized before automated solid-phase peptide extraction and
MALDI-TOF MS. Spectra were processed and aligned using the Qcealign script (see Supplemental Methods). A peak list containing normal-
ized intensities of 651 m/z values for each of the 106 samples was generated. Numbers indicate the number of patients and controls analyzed
in the respective groups. (B) Unsupervised, average-linkage hierarchical clustering using standard correlation as a distance metrics between
each cancer group and the control in binary format. The entire peak list (651 × 106) was used. Columns represent samples; rows are m/z
peaks (i.e., peptides). Dendrogram colors follow the color coding scheme of A. The heat map scale of normalized ion intensities is from 0
(green) to 200 (red) with the midpoint at 100 (yellow). (C) Hierarchical clustering of the 3 cancer groups plus controls (as in B). (D) Principal
component analysis (PCA) of the 3 cancer groups plus controls. Color coding is as in A. The first 3 principal components, which account for
most of the variance in the original data set, are shown.
? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
high-resolution MS and MS/MS, hundreds of peptides have been
detected in a single droplet of serum, many of which can be read-
ily identified without further fractionation. The automation ele-
ment facilitates throughput and ensures reproducibility. To round
out the system, we have also developed a minimal entropy-based
algorithm that simplifies and improves alignment of spectra and
subsequent statistical analysis (29). With these tools in hand, we
now sought to determine if selected patterns of serum peptides
with known sequences can (a) separate cancer from noncancer, (b)
distinguish among different types of solid tumors, and (c) allow
class prediction with an independent validation set.
To this end, we have used visual inspection of spectral overlays,
peptide ion relative intensity comparisons, and statistical analysis
to sort through hundreds of features obtained by rigorous peptide
profiling of 106 serum samples from patients with advanced pros-
tate cancer or bladder or breast cancer and from healthy controls
to identify several that are most predictive of outcome. We show
that reduction in the number of key peptides to only a few (i.e., the
signatures) that were easily recognized between samples did not
adversely affect class predictions. MS/MS-based sequence identifi-
cation of 61 signature peptides indicated that all were breakdown
products, many related, of abundant proteins in the blood. By cor-
relating the proteolytic patterns with disease groups and controls,
we show that exoprotease activities superimposed on the ex vivo
coagulation and complement-degradation pathways contribute to
generation of not only cancer-specific but also cancer type–specific
serum peptides. Our study therefore provides a direct link between
peptide marker profiles of disease and differential protease activ-
ity. The patterns we describe may have clinical utility as surrogate
markers for detection and classification of cancer.
Unsupervised analysis of 651 peptide ion signals from MS-based serum pro-
filing differentiates 3 types of cancer and controls. We analyzed the serum
peptide profiles of 73 patients with advanced prostate (n = 32),
breast (n = 21), and bladder (n = 20) cancer, as well as 33 con-
trol sera from healthy volunteers, all collected at our institution
using a single standard clinical protocol (29). Age distribution,
gender, and clinical characteristics are provided in Supplemental
Table 1 (supplemental material available online with this article;
doi:10.1172/JCI26022DS1). Sample handling after collection
was uniform, involving 2 freeze-thaw cycles to accomplish initial
storage and subsequent aliquoting for peptide extraction and MS
analysis (29). All 106 serum samples were processed fully auto-
matically (i.e., peptides extracted on magnetic beads coated with
C8 phase, washed, eluted, mixed with matrix, and deposited on the
MALDI target plate) as a single batch, using a customized robot
liquid handler followed within 1 hour by automated MALDI-TOF
MS analysis (see Supplemental Methods). System reproducibility
was verified on the same day by analysis, computer alignment, and
visual comparison of 12 reference samples/spectra (see Supple-
mental Methods) as described (18, 29). Samples from patients with
different cancers and from control individuals were then randomly
distributed during processing and analysis. Processed spectra (see
Supplemental Methods) were aligned using the custom entropy-
cal program (29) and a total of 651 distinct mass/charge (m/z)
values resolved in the 700–15,000 Da range. A spreadsheet (peak
list) containing the normalized intensities (i.e., signal intensities,
after baseline subtraction, were divided by the total ion current of
the corresponding spectrum and multiplied by a scaling factor of
107) of all 651 peaks for each of the 106 samples was then taken
for unsupervised, average-linkage hierarchical clustering using
standard correlation. This resulted in clear, distinct patterns that
differentiate disease from control as well as different types of solid
tumor cancers in binary and multiclass comparisons (Figure 1).
Feature selection and comparative analysis of serum peptide profiling
data derived from 3 groups of cancer patients and healthy controls.
(A) The peak list was subjected to a Mann-Whitney U test for each
individual cancer versus the control. Only peaks with adjusted P values
of less than 0.00001 were passed through a second filter (median peak
intensity > 500 units); a peak was selected if it passed the threshold
in 1 cancer or in the control. (B) Venn diagrams show the number of
peptides that passed both feature selection steps. The numbers shown
outside the diagrams indicate the total number of peptides of a specific
cancer group that were either up (Higher intensity) or down (Lower
intensity). (C) Heat maps compare the selected features of the 3 can-
cer groups with controls in multiclass and binary formats. Columns
represent samples (per group); rows are m/z peaks (not in numerical
order). Peptides used in each binary comparison are the sum of those
specifically higher and lower in each cancer group; the multiclass heat
map contains the combined, nonredundant number of peptides. The
multiclass, bladder, and breast heat map scales of normalized intensi-
ties range from 0 (green) to 500 (red) with the midpoint at 250 (yellow);
those of the prostate map are from 0 (green) to 2,000 (red), with the
midpoint at 1,000 (yellow).
274? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
Feature selection yields a 68–peptide ion signature that separates the
3 clinical groups and controls. Anticipating future clinical devel-
opment of this technology, we felt that correlations between
patient samples involving 651 features would be difficult at dif-
ferent times and locations. Thus, a feature selection was per-
formed using discriminant analysis to identify the most distin-
guishing peaks. A Mann-Whitney U test for each of the 3 cancer
groups individually versus the control selected 196 peaks with a
multiple comparison corrected P value of less than 1 × 10–5 for
at least 1 type of cancer (Figure 2A). This number was further
reduced to 68 by applying a threshold to the median ion intensi-
ties of each individual peak within a sample cohort (Figure 2A
and Supplemental Table 2). The threshold was set high enough
to select only robust peaks in the spectra with intensities that
would permit MALDI MS/MS-based tandem MS sequencing and
to exclude closely positioned neighboring peaks or “shoulders.”
An m/z peak was selected if this criterion was met in at least 1
of the cancer groups or the control (see Supplemental Table 2).
When feature selection was repeated using a multiclass Kruskal-
Wallis test (adjusted P < 1 × 10–5) and the same median intensity
threshold as above, 214 and 67 peaks were selected (data not
shown). The majority of selected peaks corresponded to pep-
tides with molecular mass less than 2,000 Da; most peptides
with a mass of greater than 4,000 Da were removed (Figure 2A
and Supplemental Table 2). Spectra from all samples were then
color coded and overlaid to visually inspect the 68 peaks for
correct assignment, degree of separation, and overall difference
between cancer and control. Examples are shown in Figure 3.
Forty-seven m/z peaks had higher ion intensities in 1 (or more)
of the cancer groups, and 23 m/z peaks had lower intensities
(Figure 2B). Interestingly, 2 were up in 1 type of cancer and
down in another. Of the 68 peaks, 14 had biomarker (up or
down) potential for prostate cancer (1 unique; 13 shared), 14 (11
unique) for breast cancer, and 58 (43 unique) for bladder cancer
MALDI-TOF mass spectral overlays of selected peaks derived from
serum peptide profiling of 3 groups of cancer patients and healthy con-
trols. Spectra were obtained, aligned, and normalized as described in
Methods and were displayed using the mass spectra viewer. Peptide
ions have been selected to illustrate group-specific differences in nor-
malized intensities, except for 2021.05, which is provided here as an
example of the vast majority of peptide ions with intensities that were
not statistically different between any 2 groups. The 24 overlays (not
to scale) each show a binary comparison for all spectra from either the
bladder cancer (n = 20; green), prostate cancer (n = 32; blue), or breast
cancer patient group (n = 21; red) versus the control group (n = 33;
yellow). They are arrayed so that an identical mass range window is
shown for each of the 3 binary comparisons in which spectral intensi-
ties have been normalized and scaled to the same size. The monoiso-
topic mass (m/z) is shown for each peptide ion peak.
MALDI-TOF/TOF MS/MS identification of serum peptide 2305.20 as
a fragment of complement C4a. Peptides from a serum sample of a
breast cancer patient were extracted and analyzed by MS and the ion
of choice selected for MS/MS analysis, as described in Supplemen-
tal Methods. The fragment ion spectrum shown here was taken for a
Mascot MS/MS ion search of the human segment of the NR database
retrieved a sequence, GLEEELQFSLGSKINVKVGGNS ([MH]+ = 2305.19;
∆ = 4 ppm), with a Mascot score of 38. b and y fragment ion series are
indicated together with the limited sequences (arrows at top). Note
that y ions originate at the C terminus and that the sequence therefore
reads backwards (see direction of the arrows).
? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
(Figure 2, B and C). The results, when represented in the form of
heat maps in Figure 2C, indicated that data reduction (by ∼90%)
did not adversely affect the separation of the clinical groups.
The results also illustrated that cancer-specific serum peptide
signatures are not likely just indicators of a nonspecific inflam-
matory condition, such as arthritis or infection, in addition to
cancer but are specific enough to distinguish different types of
cancer from each other and from controls without cancer.
Serum peptide signatures consist of a small but discrete set of sequence
clusters. Of the 68 selected peptides, 46 were positively identified
by MALDI-TOF/TOF (Figure 4) and MALDI-Q/TOF MS/MS
analysis and database searches (Figure 5). Note that the m/z
values listed in Figure 5 are monoisotopic and therefore smaller
than the corresponding average isotopic values listed in Supple-
mental Table 1. Interestingly, all but a few peptide sequences
clustered into sets of overlapping fragments lined up within
Serum peptide signatures for
advanced prostate, bladder, and
breast cancer. Selected peptides
identified by MALDI-TOF/TOF
MS/MS are listed in clusters (lad-
ders) of overlapping sequences,
including 46 of the initial signa-
ture group of 68 (Figure 2 and
Supplemental Table 2). m/z val-
ues are monoisotopic. Twenty-
three additional peptides were
positively matched to the existing
clusters by hypothesis-driven, tar-
geted MS/MS analysis. Overall, 61
entries had clear marker potential
(adjusted P < 0.0002; Figure 6)
for at least 1 cancer type and are
color-coded blue (prostate can-
cer), green (bladder cancer), or
red (breast cancer). Resulting sig-
natures for the 3 cancers consist
of 26 (prostate), 50 (bladder), and
25 (breast) peptide ions. Color-
coded peptides have either higher
(no filled circles) or lower (filled
circles) differential ion intensities
in a particular cohort of cancer
samples compared with controls.
C3f (m/z = 2021.05) and 1 mem-
ber of the fibrinogen a cluster
(m/z = 2553.01) gave comparable
ion signals in all patient groups and
control sera (see Figure 3, 2021,
and Figure 6) and therefore rep-
resent effective internal standards
(yellow). Six peptides (pink) were
randomly observed. Residues
in brackets were not experimen-
tally observed but are shown to
either indicate putative full-length
sequences of the founder peptides
and/or the positions of trypsin-like
cleavage sites (Arg/Lys–Xaa).
276? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
each group at either the C or N terminal end and with ladder-like
truncations at the opposite ends. In fact, some sequence assign-
ments had below-threshold scores (see Supplemental Methods)
but could nonetheless be unequivocally assigned as the precursor
ion mass and selected fragment ion masses (b or y) matched a par-
ticular rung in the ladder, taking into account whether the limited
CID patterns were in agreement with established rules (34) of
preferential peptide bond cleavage (e.g., Xaa-Pro or Asp/Glu-Xaa)
Serum peptide signatures for
advanced prostate, bladder,
and breast cancer. This table
contains the same 69 entries
as in Figure 5 plus additional
details on the identified pep-
tides (listed as m/z values),
MS ion intensities, and signa-
tures. The significance levels
of 3 different Mann-Whitney
U tests (columns 6–8) and of
a multiclass Kruskal-Wallis
test (column 9) are given. The
actual signatures (blue, green,
or red) are composed of entries
that showed clear peptide ion
marker potential (adjusted
P < 0.0002) for at least 1 type
of cancer. Adjusted P value is
the overriding criterion, lead-
ing to final signatures of 26
(prostate), 50 (bladder), and
25 (breast) peptide ions (identi-
cal to those shown in Figure 5).
The second column lists medi-
an intensities of each m/z peak
in the control samples. Peak
intensity ratios (columns 3–5)
were calculated by dividing
the median values of each m/z
peak in each cancer group by
the median value of the cor-
responding peak in the control
samples. Ratios (r) for those
peptides that are part of 1 or
more signatures are shaded
dark grey when the median
signal is of higher intensity in a
particular cancer (r ≥ 1.4) and
lighter gray when it is lower
(r ≤ 0.75). Norm., normalized.
? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
and the putative sequence. Furthermore, 23 additional peptides
outside the original group of 78 could also be matched to cer-
tain sequence clusters by hypothesis-driven, targeted MS/MS
analysis. Fifteen of those had significant discriminant analysis
adjusted P values (< 0.0002) for at least 1 cancer type but typically
lower ion intensities (Figure 6). Two others (2553 and 2021; Fig-
ures 5 and 6) displayed very high but similar MS ion intensities
across all cancer groups and the control with adjusted P values
> 0.04 and can therefore be regarded as quasi-internal controls.
Six more peptides (Figures 5 and 6) that fit into the clusters were
randomly observed in samples of the cancer and control groups
and had neither discriminant nor internal control value. The
finding that the majority of peptide sequences obtained here
collapsed into 10 or 11 clusters wasn’t entirely surprising in view
of a recent finding that more than 250 of the most abundant
plasma peptides are derived from some 20 serum proteins, also
in largely overlapping clusters (17). It should be noted that
we used an unbiased approach to identify marker peptides
in which the peptides were selected first on the basis of
discriminant analysis and then sequenced. This approach,
commonly referred to as ion mapping, can be taken using
any type of MS platform (35, 36).
Sequence clusters within the marker signatures derive from abun-
dant serum peptides and protein precursors. Three sequence clus-
ters are derived from naturally occurring serum peptides,
fibrinopeptide A (FPA), complement C3f, and bradykinin,
which are each generated at an earlier stage from various
plasma proteins through endoproteolytic cleavage, either
at the initiation of the ex vivo intrinsic pathway (brady-
kinin, cleaved from high molecular weight–kininogen
[HMW-kininogen] by plasma kallikrein) or during serum
preparation (FPA, N terminally cleaved from fibrinogen
by thrombin to form fibrin; C3f, released by factors I and
H after prior conversion of C3 to C3b) (37, 38). The full-
length founder peptides end with Arg or Lys preceded by a
hydrophobic amino acid (Val, Leu, or Phe). Arg is partially
removed from C3f and bradykinin (to form desArg-brady-
kinin [bradykinin that has the Arg removed]). Similar tryp-
sin-like cleavages (Arg/Lys–Xaa) underlie formation of all
other peptide clusters as well (see below). The C terminal
basic amino acid is preceded by a hydrophobic amino acid
(F, L, V, I, W, A) in 21 and by S, Q, or N in 15 out of the 39
observed cleavage sites (see Supplemental Table 4). Arg/Lys
is typically removed (fully or in part) by a carboxypeptidase,
except when preceded by Pro (3 out of 3 cases) or some-
times when preceded by Val (2 out 4). Further exoprotease
degradation then proceeds at the N terminal or C terminal
ends either to completion or until it stalls; many or all of
the intermediates are typically represented (Figure 5 and
Supplemental Table 3). This will be a recurring theme with
most other clusters (see below).
Diagnostic MALDI-TOF spectral patterns consisting of
N terminal FPA and C3f truncations have previously been
found in sera of myocardial infarction patients (30). In
contrast, we detected almost all these peptides (19 total)
in control sera and showed that their presence is either
consistently lower (all FPA fragments in all cancers; 3 C3f
fragments in breast cancer) and/or higher (several C3f frag-
ments in bladder and prostate cancer; 1 FPA fragment in
breast cancer) in patient sera (Figures 5 and 7). Full-length
C3f was present in all samples at equally high levels; full-length
FPA was virtually absent in sera from bladder cancer patients. No
fibrinopeptide B or fragments thereof were found in any of the
samples. Decreased levels of FPA (fragments) in prostate, blad-
der, and breast cancer patients, as shown here, also contrast with
earlier findings of elevated phospho-FPA levels in sera of ovarian
cancer patients (measured by electrospray ionization–MS; ref. 31)
and of FPA levels in gastrointestinal and breast cancers (measured
immunochemically; ref. 39, 40).
Bradykinin is believed to be a cancer growth factor, and vari-
ous antagonists have therefore been tested as anticancer agents
(41). We now find that bradykinin and desArg-bradykinin levels
are higher in sera of breast cancer patients and lower in bladder
cancer patients (Figure 5). The prohydroxylated forms (42) of each
peptide also followed that trend (data not shown). The bradykinin
and FPA parent proteins, fibrinogen a and HMW-kininogen, each
Median ion intensities of serum peptides of selected sequence clusters rela-
tive to the corresponding values in the control group. Median intensity for
each peptide in each of the 3 cancer groups is plotted as the ratio versus the
median intensity of the counterpart in the control group (r = patient/control).
Ratios are plotted on a log scale ranging from 0.1 to 10. Bars pointing to the
left (r < 1) or right (r > 1) indicate, respectively, lower or higher median intensi-
ties in a cancer group as compared with the control group. Peptides that didn’t
show much difference in median ion intensity between patient and control
groups map closely to or onto the center line (r = 1).
278? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
contributed 1 additional sequence cluster, located in a different
section of the precursor sequence, to the cancer serum peptide sig-
natures (Figures 5 and 7 and Supplemental Tables 3 and 4). Inter-
estingly, the bradykinin and other kininogen-derived peptides have
opposite marker properties. For example, whereas bradykinin and
desArg-bradykinin were of lower ion intensity in bladder cancer
than in control sera, the other peptides (1944 and 2209) showed
higher relative intensities (Figures 5 and 6). This observation pro-
vides a decisive argument against the most straightforward expla-
nation of why some peptide ion intensities are higher or lower as
compared with a control group, namely because the parent protein
is up- or downregulated. As the concentration of HMW-kininogen
can’t be up and down at the same time, this is clearly not the case.
One of the peptides (2724; Figure 5) in a cluster derived from the
inter-a-trypsin inhibitor heavy chain H4 (ITIH4) precursor (43)
covers amino acids 662–687 (Supplemental Tables 3 and 4) and
is bracketed by 2 kallikrein cleavage sites (Phe-Arg–Xaa). Residues
662–688 likely represent a propeptide of unknown function (44).
Like bradykinin, it ends with Pro-Phe-Arg. Several longer ITIH4
precursor fragments span the first kallikrein cleavage site, includ-
ing a peptide (3272; at 658–687) reported to be a biomarker for
early stage ovarian cancer (32). It further appears that variations
in N terminal truncation in the ITIH4 cluster by just a few amino
acids can produce fairly selective ion markers for different cancers.
Median ion intensities of peptides 3971 and 3273, for instance,
were clearly highest in bladder cancer samples, peptides 2358 and
2184 were highest in breast cancer, and 2271 was highest in pros-
tate cancer. Also of note, peptide 2115 matches the sequence of
an ITIH4 splice variant (PRO1851; Supplemental Table 4) and
appears to have biomarker capacity for each cancer type, particu-
larly for bladder and breast (Figure 6).
Another cluster consisting of 2 × 4 peptides located on either site
of a single Ile-Arg—Xaa cleavage site is derived from the comple-
ment C4a precursor (45) (Figure 5 and Supplemental Tables 3 and
4). This C4a cluster has the highest incidence of ion markers for
breast cancer, more than in any other cluster and also more than
C4a-derived bladder cancer markers (Figure 6). Only a single ion
(peptide 1763) of this cluster is a marker for prostate cancer and
is shared in that capacity with the other 2 cancer types. On the
other hand, all but 1 ion marker derived from apoA-I, apoA-IV, and
apoE are bladder cancer specific, all with appreciably higher ion
Study overview. The diagram shows the approach used for develop-
ment and validation of the 68-peptide ion signature and the prostate
cancer signature consisting of 26 serum peptides with known sequence
(blue in Figure 5). Numbers that are circled indicate total number of
selected peptides at that stage of the study.
Independent set of prostate cancer serum samples for validation of
established peptide signature biomarkers. (A) Study design. See
Figure 8 and Results. (B) Hierarchical cluster analysis of all spectra
from PR1, PR2, and control groups. Either the 68 peptide ions with
statistically significant intensity differences for the 3 binary compari-
sons (Figure 2) or 26 of the sequenced peptides that constitute the
prostate cancer signature (blue in Figure 5) were used; the rest of the
approximately 650 peptide ions were ignored. The heat map scale of
normalized ion intensities ranges from 0 (green) to 2,000 (red) with
the midpoint at 1,000 (yellow). (C) Principal component analysis of the
PR1 and PR2 groups plus controls, based on the same peptide ions
as in B. The first 3 principal components, accounting for most of the
variance in the original data set, are shown.
? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
intensities; the exception (apoA-IV, peptide 1971) is actually highly
selective and statistically the most significant (P = 5.5 × 10–13) ion
marker for breast cancer (Figures 5 and 6).
Upregulation of clusterin (i.e., apoJ) has been correlated by
immunohistochemistry with progression of both prostate and
bladder cancer (46–48). The 10–amino acid clusterin fragment
that we detected at elevated concentrations in sera of bladder
and prostate cancer patients is located at the C terminus of the
β chain (Supplemental Tables 2 and 3). A single cut is sufficient
to release this peptide, following separation of the clusterin β
(N-t) and a (C-t) chains by cleavage of a Val-Arg–Xaa bond. A
6–amino acid subfragment thereof has in turn statistically sig-
nificant marker potential for bladder cancer (Figures 5 and 6),
which is in keeping with the trend for most other peptides from
apoA-I, apoA-IV, and apoE.
Finally, 2 ions (peptides 2602 and 2451), each with higher median
intensities in breast cancer samples than in controls, corresponded
to peptides derived from Factor XIIIa and thransthyretin (Figures
5 and 6). Peptide 2602 corresponded to the C terminal 25 amino
acids of the factor XIIIa propeptide (37 residues long) (Supplemen-
tal Tables 3 and 4). Interestingly, Factor XIII itself has been found
downregulated in breast tumors compared with normal mammary
tissues (49). While we don’t know whether this was also the case
in the patients from whom the blood samples in our study were
obtained, it would contrast with our observations, further arguing
against a model that higher ion intensities (i.e., peptide concentra-
tions) are the simple consequence of upregulated precursors.
Cancer type–specific peptide signatures contain selected members from
several different sequence clusters. In all, 69 serum peptides are listed
in Figure 5 (with matching information provided in Figure 6).
Of those, 61 have clear MALDI-TOF MS ion marker potential
(adjusted P < 0.0002) for at least 1 type of cancer and are color
coded in blue (prostate cancer), green (bladder cancer) or red
(breast cancer). The resulting signatures for the 3 cancer types
consist of 26 (prostate), 50 (bladder), and 25 (breast) peptides,
several of which occur in 2 or all 3 cancer groups. Compared with
healthy control samples, median intensities of ion markers can
be higher (Figure 5) or lower in any particular cancer group: 16
higher and 10 lower (16+/10–) in prostate cancer; 31+/19– in
bladder cancer; and 19+/6– in breast cancer. Only 3 peptides
in each of the up or down categories were shared by all cancer
groups. One peptide from the C4a and 2 from the ITIH4 clus-
ter had consistently higher ion intensities in all cancers than in
healthy controls; 3 FPA fragments were lower in all cancers. The
rest of the ion markers were either in common between 2 groups
or, more often, unique to a single patient cohort (Figure 5). Of
note are 9 apo peptides (apoA-I, apoA-IV, apoE, and apoJ) and 3
C3f peptides of selectively higher ion intensities in bladder can-
cer and 4 C4a, 2 bradykinin, and 1 transthyretin peptides higher
in breast cancer. All 3 peptide ions that were of uniquely lower
intensity in breast cancer derived from C3f. Interestingly, some of
the shared marker ions had higher median intensities compared
with controls in 1 type of cancer but lower in another (Figures
5 and 6). For instance, 5 peptide ions had higher than control
median intensities in breast cancer samples, lower than control
intensities in bladder cancer samples, and no appreciable marker
value for prostate cancer. A single ITIH4 peptide (842; HAAYPF)
was relatively higher in prostate cancer patients but virtually
absent in bladder cancer.
It appeared there were no clear rules or trends in what clusters
and in particular what rungs in the peptide sequence ladders may
have ion marker value for one or another type of cancer, if any.
In an attempt to find such trends or to at least better visualize
any global differences that might exist, we plotted the ratios of
the median ion intensities for each of the peptides in 4 major clus-
ters between each cancer group and the healthy controls (i.e., r =
patient/control). The center line in the panels of Figure 7 repre-
sents no difference (r = 1); bars pointing to the left (r < 1) or right
(r > 1) indicate, respectively, lower or higher median. Even in the
Class prediction of a prostate cancer validation set (PR2) using
SVM (linear kernel) and the 651-, 68-, and 26-feature sets
No. m/z bins
Percentages of correct predictions are listed; the binomial confidence
intervals (at 95%) were 87.1–99.9% for 40 correct predictions out of 41
and 91.4–100% for 41 out of 41. Training sets were PR1 versus con-
trol (binary) or the 3 cancer groups plus controls (multiclass). m/z bin,
group of identical m/z values (from multiple mass measurements) (see
Plasma exoproteases degrade synthetic C3f in a manner similar to
proteolysis of the endogenous peptide (derived from C3 precursor)
in serum. A MALDI-TOF MS read-out of fresh plasma (top panel)
indicates very low levels of small peptides except for bradykinin and
desArg-bradykinin. After addition of synthetic C3f (1 pmol/µl plasma),
an aliquot was immediately (i.e., after ∼15–20 seconds) withdrawn,
and another was withdrawn after 15 minutes. The sample was kept at
room temperature at all times. The middle panel indicates removal of
the C terminal Arg by a carboxypeptidase in a matter of seconds. C3f
is then further degraded by the activity of aminopeptidases to result in
a type of sequence ladder as endogenously present in serum. Brad
(–R), bradykinin minus C-terminal Arg; R, Arg; RI, Arg-Ile; H, His; T,
Thr; I, Ile; K, Lys; S, Ser.
280? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
case of the FPA ladder where nearly all peptides in cancer sera pro-
duced ion signals of lower intensities than in controls, the actual
ratios vary for each rung and for each cancer type. Of note is the
seemingly total absence (r = 0) of full-length FPA in sera of blad-
der cancer patients. The 3 other clusters exhibited a pronounced
internal variability with median intensity ratios that were mostly
over but also equal to or under 1. Visual inspection of the 4 color-
coded graphs (33 × 3 data points) in Figure 7 readily distinguishes
the 3 cancer types. There is a trend for peptides in bladder cancer
sera to exhibit relatively high ion intensities in the C3f cluster and
rather variable intensities in the C4a and ITIH4 clusters and for
some peptides in the C3f cluster to be of lower intensity and oth-
ers in the C4a cluster to be of higher intensity in breast cancer sera.
Ion intensities of peptides in prostate cancer sera don’t seem to
follow those trends but are selectively more pronounced in some
of the smaller peptides of the ITIH4 cluster. Interestingly, there is
1 rung in each of the C3f, C4a, and ITIH4 ladders (Figure 7) for
which median ion intensities in the control samples were virtually
zero yet were much higher in all 3 cancer types, resulting in very
high ratios for each.
Taken together, the data in Figure 7, based in equal parts on
statistical analysis (Figure 6), visual inspection of spectra over-
lays (Figure 3), peptide sequencing (Figures 4 and 5), and relative
ion intensity analysis, indicate that the human serum peptidome
holds information in the form of signatures consisting of a few
dozen peptides each that can distinguish 3 different cancers from
controls as well as from each other.
Peptide ion signatures provide accurate class prediction for an external
validation set of prostate cancer samples. To evaluate the robustness
of the identified groups of markers, we tested the peptide signa-
tures on a set of 41 independent serum samples from patients
with advanced prostate cancer (prostate 2 [PR2]) (Figures 8 and
9A). The assignment of the prostate cancer samples into the train-
ing set (prostate 1 [PR1]) or the test set (PR2) was random but pre-
served the same demographic/pathological parameters (e.g., age,
PSA levels, Gleason score, and survival time). None of the samples
in the test set had been previously included in the supervised anal-
ysis, which therefore allowed for the estimation of true predictive
accuracy. The 41-member test set was analyzed following stan-
dard protocol and a new spreadsheet generated that also included
all data from the original 106 training samples. Peptide ions from
feature list 2 (68 peptides; see Figures 2A and 8) and from the
prostate cancer signature (26 sequenced peptides; Figures 5 and
6) were then selectively used for comparison of the control, PR1,
and PR2 groups by hierarchical clustering (Figure 9B) and princi-
pal component analysis (Figure 9C). Samples from PR1 and PR2
were for the most part separated from the controls. Individual
comparisons of each of these 26 peptide ions among the 3 sample
groups indicated that the intensities of 26 out of 26 were statisti-
cally different (adjusted P < 0.0002, i.e., the P value to create the
signature; see Figure 6) between PR1 and control, 23 out of 26
between PR2 and control, and only 1 out of 26 between PR1 and
PR2. Finally, support vector machine–based (SVM-based) class
predictions in either binary or multiclass formats were then car-
ried out using all 651 or the 68 or 26 selected (see above) peptide
ions. We obtained similar sensitivities in 3 instances, namely 100%
(41/41) and 97.5% (40/41) accuracy for, respectively, binary and
multigroup class predictions (Table 1).
Aminoprotease activities in plasma generate a sequence ladder from
synthetic C3f. It appears that the serum peptidome is largely the
product of resident substrates, more specifically their proteolytic
breakdown products (ref. 17; this study), and therefore represents
a read-out of the repertoire of proteases that exist in plasma and/or
become activated during clotting. With the exception of bradyki-
nin, we have consistently observed much higher peptide concen-
trations in serum than in plasma (Figure 10 and data not shown),
which makes sense as ex vivo coagulation and complement acti-
vation underlie generation of the founder peptides of nearly every
cluster. Peptides from plasma prepared in heparin-containing
blood collection tubes are likely the result of low-level clotting and
heparin-induced complement activation (ref. 17; J. Villanueva and
P. Tempst, unpublished results). Apparently, the inducible plasma
and serum peptidome is then amplified by exoprotease activities,
which may also account for many or all of the observed differenc-
es. The data presented in this study suggest that cancer cells may
contribute unique proteases, perhaps exoproteases, which result in
subtle but signature alterations of the complex equation of hun-
dreds of peptides that can be resolved from human serum. In an
effort to begin to understand the presence and roles of exoproteas-
es, synthetic C3f was added to fresh plasma at a concentration close
to that observed in serum. As shown in Figure 10, degradation was
very fast. C terminal Arg was removed within seconds, and the N
terminal truncations occurred in 10–15 minutes. The resulting pat-
tern was similar to the endogenous one observed in serum and also
illustrated the disparate ion intensities for different rungs in the
ladder. However, most of the C3f ladder, except its smallest rung,
disappeared upon prolonged incubation (data not shown). Exopro-
teolytic degradation of synthetic FPA in plasma followed a similar
time course, but fibrinopeptide B (FPB) was completely degraded
in just a few minutes (data not shown), which may explain why the
endogenous form was never observed in our serum profiling analy-
ses. The results suggest that the operative exoprotease concentra-
tions and activities are roughly equivalent in plasma and serum and
therefore not the consequence of coagulation.
Activity of serum proteases. Many serum peptides are generated by a
2-step proteolytic process. When used in the proper combinations, 1
or more selected members of 6–12 different clusters create diagnostic
signatures in the form of ion intensities measured by direct MALDI-
TOF MS that can predict cancer and cancer type. Amino acids are
color coded to represent sequence clusters of C3f (left) or FPA (right),
which are just 2 examples of all the observed clusters.
? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
In the search for clinically relevant biomarkers, the low mass
range of the serum proteome, particularly peptides with a molecu-
lar mass below 3,000 Da, has not received the same attention as
higher molecular weight peptides and proteins. Small, preexisting
peptides are not readily picked up by high-throughput liquid chro-
matography/liquid chromatography–MS/MS (LC/LC-MS/MS)
analyses of whole-proteome tryptic digests and have also been
underrepresented in surface-enhanced laser desorption/ioniza-
tion–TOF (SELDI-TOF) MS-based screens that seem to favor
polypeptides in the 5- to 15-kDa mass range (19–24). The cur-
rent study and a recent analysis by Koomen et al. (17) provide the
first details on the composition of the peptide pool in serum and
plasma. Overall, it appears that a large part of the human serum
peptidome as detected by MALDI-TOF MS is produced ex vivo by
degradation of endogenous substrates by endogenous proteases.
As illustrated in Figure 11, peptides are generated during the pro-
teolytic cascades that occur in the intrinsic pathway of coagula-
tion and complement activation (50). Some of these are known
bioactive molecules, others represent cleaved propeptides, and still
others are seemingly random internal fragments of the precursor
proteins. However, the observed cleavage sites are generally consis-
tent with trypsin- and chymotrypsin-like activities of known serine
proteases (kallikreins, plasmin, thrombin, factor I, etc.). Once gen-
erated, the founder peptides are trimmed down by exoproteases
into ladder-like clusters.
Exoproteases form a heterogeneous group of enzymes that play
a role in the regulation of biologically active peptides (51–53). For
instance, leucine aminopeptidase (LAP), aminopeptidase A (AP-A),
aminopeptidase N (AP-N), carboxypeptidase N (CP-N), and the
kininase I family of carboxypeptidases are involved in the produc-
tion of angiotensin, bradykinin, and vasopressin (53), and TAFI (a
carboxypeptidase B enzyme) in the regulation of fibrinolysis (54).
Several exoproteases are transmembrane proteins, anchored in the
plasma membrane of vascular endothelial cells. Heterogeneous dis-
tribution results in the production of a wide variety of proteolytic
peptides in different tissues and contexts (51). In addition, some
exoproteases like AP-N and placental LAP (P-LAP) are shed from
cells through the action of ADAM family proteases (55) and end
up in the bloodstream in soluble form (55, 56), thereby degrading
resident polypeptides in the blood, plasma, and serum.
Depending on the analytical approach and the objectives of a
diagnostic marker search, there are opposing views on the pres-
ence of a vast peptide pool (degradome) in plasma or serum gener-
ated from blood proteins as described above (Figure 11). It can be
considered background noise in peptide marker discovery efforts,
making it all but impossible to find any naturally occurring, true
biomarkers in the peptidome or to obtain mechanistic insights in
specific activities of tumor-associated proteases. Those who sub-
scribe to this view believe that exoprotease activity, or all protease
activity for that matter, should be blocked at the time of sample
collection. However, it has been correctly pointed out (17) that the
protein degradome is the only segment of the serum peptidome
that can be readily interrogated by direct MALDI-TOF MS. Frag-
ments of bona fide marker proteins (for example, PSA in sera of
prostate cancer patients), if present, are currently undetectable
because of sensitivity, ion suppression, and mass resolution issues
inherent in the technology. It can therefore be argued that pre-
cisely this degradome offers the best opportunity at this point for
biomarker or surrogate biomarker discovery.
Whereas the only comprehensive, high-resolution MS analysis of
the plasma/serum peptides to date aimed at providing an inven-
tory (17), we undertook to find peptides and patterns with marker
potential for specific types of solid tumor cancers. In the discovery
phase of our studies, we sorted through hundreds of features to
identify several that were most predictive of outcome and showed
that reduction in the number of key peptides to a few (i.e., the
signatures) that were easily recognized between samples did not
adversely affect class predictions. We then demonstrated that this
signature could be used to discriminate between cancer and con-
trol in an independent validation set comprised of serum samples
obtained from patients with advanced prostate cancer. Strikingly,
all 46 sequence-identified peptides from the initial set of 68 rigor-
ously selected discriminant peptide signals were part of the serum
degradome. With two-thirds of the initial marker group now char-
acterized, we trust that these findings can be generalized.
The small number of blood proteins that are the source of near-
ly all the peptides in prostate, bladder, and breast cancer signa-
tures are naturally not biomarkers but simply serve as an endoge-
nous substrate pool for the real biomarkers, i.e., proteases. There
is no actual relationship between the substrate concentrations
and the MS-ion intensities of many of the degradation products.
Highly abundant serum proteins such as albumin and immuno-
globulins were not represented, and fragments of proteins with
a more than 10-fold difference in concentration had comparable
ion intensities. On the other hand, whereas full-length C3f pro-
duced nearly identical ion intensities in all cancer groups and
controls, several of its truncated forms did not. In fact, 2 or more
patient sera peptides (say, x and y) that derived from the same
protein had often opposite relative ion intensities (i.e., the ion
intensity divided by that of the corresponding peptide in the con-
trol group); for instance, the signal of peptide x was higher and
that of peptide y lower than that?of their counterparts in control
sera. Finally, several of the protein degradome peptides that we
observed and that had high surrogate marker value were virtu-
ally absent from the controls (e.g., several entries in Figure 6 that
list a median normalized intensity value of 1 for the control).
In fact, 7 such peptides (Figures 5 and 6; m/z = 998, 1278, 2053,
2409, 2565, 2704, and 3971), each unique to 1 or more types of
cancer, were not reported in the high-resolution blanket analy-
ses of plasma peptides, possibly because that blood sample was
obtained from a healthy individual (17).
The 2-step proteolytic process depicted in Figure 11 that gener-
ates the most abundant layer of the serum peptidome is subject to
changes in enzyme panels, cofactors, inhibitors, and various other
controlling elements and conditions, which make for a virtually
unlimited combinatorial variability to produce peptides of differ-
ent sizes and composition. Direct MALDI-TOF MS–based serum
peptide profiling is thus a form of activity-based proteomics, mon-
itoring surrogate biomarkers in the form of proteome metabolo-
mic products. This can be exploited for diagnostic and predictive
purposes as a phenotypic read-out of catalytic and other metabolic
activities in body fluids or tissues, utilizing endogenous (or exog-
enous) substrates and quantitative product analysis. It also makes
this approach particularly well suited for detection of cancer, as
proteases are well-established components of cancer progression
and invasiveness (57–60). We provide evidence here that exoprote-
ase activities superimposed on the ex vivo coagulation and comple-
ment-degradation pathways contribute to generation of not only
cancer-specific but also cancer type–specific serum peptides.
282? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
Exoproteases have been previously implicated in cancer (58). For
instance, AP-N/CD13 is highly expressed in bladder, gastric, thy-
roid, and hepatic carcinomas (61–64), and the concentration of
its soluble form is also increased in cancer patients (56). Similarly,
increased concentration of a lysosomal dipeptidyl-aminopeptidase
(DAP II) has been observed in sera of tumor-bearing animals and
cancer patients (65). LAP, aminopeptidase P (AP-P), and enkepha-
lin-degrading tyrosyl aminopeptidase (EDA) have been associated
with breast cancer (57, 66–68) and AP-A, methionine aminopep-
tidase 2 (Met-AP2), and glycylproline dipeptidyl aminopeptidase
(GPDA) with various other types of cancers (69–71). Increased
activity and expression of AP-N and Met-AP2 have been function-
ally correlated with metastasis of cancer cells by promotion of
angiogenesis (72–75). As for carboxypeptidases, carboxypeptidase
D (CP-D) is selectively more highly expressed in hematopoietic
tumor cells (76), and PSMA is overexpressed in prostate cancer and
has been implicated in tumor invasion (14, 77).
How all the above and other, currently unidentified enzymes
may contribute mechanistically to the observed differences in
serum peptide patterns among the 3 different cancers remains
unexplained and may require a great deal of future study to
understand. Nonetheless, the differences are statistically signifi-
cant. It is also important to note some of the overlaps between
the groups. Despite the sex difference, the breast and bladder
cancer signatures overlapped by 8 peptide ions that deviated
in median intensities from the corresponding control ions in a
similar manner; only 1 peptide ion (1865) showed diametrically
up- or downregulated intensities. Breast and bladder (85% males
in the study cohort; see Supplemental Table 1) cancer shared 7
peptide ions with similarly up- or downregulated intensities; 7
others were either higher in breast cancer but lower in bladder
cancer or vice versa, relative to the control. Finally, 23 out of the
26 prostate cancer marker peptides were also part of the larger
bladder cancer signature. However, 19 of these 23 had markedly
better P values for bladder cancer, and 4 were better for prostate
cancer, relative to the controls. We think it unlikely that the over-
laps or differences are sex related, as a preliminary comparison of
serum peptide profiles from healthy men and women indicated
only statistically insignificant differences (J. Villanueva and P.
Tempst, unpublished observations). Furthermore, most peptide
ion markers for each cancer type were equally well separated from
both male and female subsets of the control group (Supplemen-
tal Figure 1). A more likely explanation for the bladder/prostate
cancer overlap is that the prostate gland and bladder (partially)
are derived embryologically from endodermal tissues in the uro-
genital sinus and likely share biological features not seen in tis-
sues from outside the genitourinary tract. For instance, tissue
recombination studies have shown that urogenital mesenchyme
can actually induce differentiation of bladder epithelium toward
a prostatic epithelial–differentiated phenotype, but this property
is restricted to endodermal epithelia (as in the bladder) with sim-
ilar embryonic origin to the prostate (78). Overall, the prostate
cancer signature was sufficiently robust to predict the class of
members of an independent validation set with 97.5% sensitivity
in multiclass SVM analysis (Table 1).
In conclusion, it is our view that proteolytic degradative pat-
terns in the serum peptidome hold important information that
may have direct clinical utility as a surrogate marker for detection
and classification of cancer. Our findings also suggest that future
work to optimize serum peptidomics for clinical practice should
be carried out with the recognition that endogenous proteolytic
activities contribute important cancer type–specific information.
Use of protease inhibitors and, as we have previously cautioned
(29), even the slightest deviation from standard protocol for speci-
men collection, storage and handling, analytical chemistry, and
MS signal processing are particularly ill advised. We anticipate
that as we scale up these efforts using the same general method-
ology, we will expand and refine our definition of key discrimi-
natory peptides for prediction of each cancer type. The patterns
may also have diagnostic value for identifying cancer subtype and
stage or may mark a given clinical outcome of interest or may reli-
ably distinguish clinically insignificant from significant cancer.
Such a blood test could, for example, identify patients with newly
diagnosed prostate cancer who might safely avoid surgery or radia-
tion. Focused MS quantitation of key peptides derived from either
endogenous or custom synthetic substrate and utilizing isotopi-
cally labeled standards should then facilitate introduction of this
technology into clinical practice.
Serum samples. Blood samples from healthy volunteers (mixed sexes; ages 23
to 56; see Supplemental Table 1) with no known malignancies and from
patients diagnosed with either prostate cancer, bladder cancer, or breast can-
cer were all collected at Memorial Sloan-Kettering Cancer Center (MSKCC)
following a standard clinical protocol (29). Details on patient age, sex, and
pathologic diagnosis are given in Supplemental Table 1. All collections were
approved by the MSKCC Institutional Review and Privacy Board. Informed
consent was obtained from all patients. Blood samples were obtained in
8.5-ml, BD Vacutainer, glass red-top tubes (BD; 366430), allowed to
clot at room temperature for 1 hour, and centrifuged at 1,400–2,000 g
for 10 minutes at room temperature. Sera (upper phase) were transferred
to four 4-ml cryovials (Fischer Scientific International, 0566966) with
approximately 1 ml serum in each and stored frozen at –80°C until fur-
ther use (29). A similar procedure was followed for preparation of plasma
in heparin-containing green-top tubes (BD, 366480), except that centrifu-
gation was done immediately after blood collection. Upon delivery at the
MS lab, the cryovials (source vials) were barcoded. One cryovial of each
sample was thawed on ice and used to generate 9 smaller aliquots (50 µl
each) in barcoded microeppendorf tubes and stored at –80°C in barcod-
ed freezer boxes. In this study, all serum samples were always frozen and
thawed twice, the second thawing step immediately before peptide extrac-
tion and MS analysis. We have made a concerted effort to instruct nurses,
phlebotomists, messenger service staff, and clinical technicians about the
importance of strict adherence to the standard protocol.
Analytical chemistry. Automated, solid-phase peptide extraction, MALDI-
TOF MS profiling, signal processing and spectral alignments, and the use
of custom mass spectral viewing tools were all performed as previously
developed in the authors’ laboratory (18, 29). Additional details and a
description of tandem MS identification of selected serum peptides are
given in Supplemental Methods.
Statistics. The binned spreadsheet containing data from spectra obtained
for all samples of cancer patients or healthy subjects (106 samples total;
651 m/z values, with normalized intensities for each sample; > 70,000 data
points) as well as the test set for prostate cancer (PR2; 41 samples; ∼27,000
data points) were imported into the GeneSpring program (version 7; Agi-
lent Technologies) and analyzed using various statistical algorithms such
as 1-way ANOVA, principal component analysis, hierarchical clustering,
k-nearest neighbor (k-NN), and SVM. Different experiments were created
in GeneSpring to represent the masses. No normalizations were applied
to the experiment since the masses were normalized by the database that
? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
1. Lander, E.S., et al. 2001. Initial sequencing and anal-
ysis of the human genome. Nature. 409:860–921.
2. Hood, L. 2003. Leroy Hood expounds the princi-
ples, practice and future of systems biology. Drug
Discov. Today. 8:436–438.
3. Etzioni, R., et al. 2003. The case for early detection.
Nat. Rev. Cancer. 3:243–252.
4. Chung, C.H., Bernard, P.S., and Perou, C.M. 2002.
Molecular portraits and the family tree of cancer.
Nat. Genet. 32(Suppl.):533–540.
5. Staudt, L.M. 2002. Gene expression profiling of lym-
phoid malignancies. Annu. Rev. Med. 53:303–318.
6. Anderson, N.L., and Anderson, N.G. 2002. The
human plasma proteome: history, character, and
diagnostic prospects. Mol. Cell. Proteomics. 1:845–867.
7. Adkins, J.N., et al. 2002. Toward a human blood
serum proteome: analysis by multidimensional
separation coupled with mass spectrometry. Mol.
Cell. Proteomics. 1:947–955.
8. Sidransky, D. 2002. Emerging molecular markers
of cancer. Nat. Rev. Cancer. 2:210–219.
9. Bidart, J.M., et al. 1999. Kinetics of serum tumor
marker concentrations and usefulness in clinical
monitoring. Clin. Chem. 45:1695–1707.
10. Jortani, S.A., Prabhu, S.D., and Valdes, R., Jr. 2004.
Strategies for developing biomarkers of heart failure.
Clin. Chem. 50:265–278.
11. Watts, N.B. 1999. Clinical utility of biochemi-
cal markers of bone remodeling. Clin. Chem.
12. Gillette, M.A., Mani, D.R., and Carr, S.A. 2005.
Place of pattern in proteomic biomarker discovery.
J. Proteome Res. 4:1143–1154.
13. Hugosson, J., et al. 2003. Prostate specific antigen
based biennial screening is sufficient to detect
almost all prostate cancers while still curable.
J. Urol. 169:1720–1723.
14. Ghosh, A., Wang, X., Klein, E., and Heston, W.D.
2005. Novel role of prostate-specific membrane
antigen in suppressing prostate cancer invasiveness.
Cancer Res. 65:727–731.
15. Richter, R., et al. 1999. Composition of the peptide
fraction in human blood plasma: database of circu-
lating human peptides. J. Chromatogr. B Biomed. Sci.
16. Tirumalai, R.S., et al. 2003. Characterization of the
low molecular weight human serum proteome.
Mol. Cell. Proteomics. 1:1096–1103.
17. Koomen, J.M., et al. 2005. Direct tandem mass
spectrometry reveals limitations in protein profil-
ing experiments for plasma biomarker discovery.
J. Proteome Res. 4:972–981.
18. Villanueva, J., et al. 2004. Serum peptide profiling
by magnetic particle-assisted, automated sample
processing and MALDI-TOF mass spectrometry.
Anal. Chem. 76:1560–1570.
19. Petricoin, E.F., et al. 2002. Use of proteomic pat-
terns in serum to identify ovarian cancer. Lancet.
20. Adam, B.L., et al. 2002. Serum protein fingerprinting
coupled with a pattern-matching algorithm distin-
guishes prostate cancer from benign prostate hyper-
plasia and healthy men. Cancer Res. 62:3609–3614.
21. Li, J., Zhang, Z., Rosenzweig, J., Wang, Y.Y., and
Chan, D.W. 2002. Proteomics and bioinformatics
approaches for identification of serum biomarkers
to detect breast cancer. Clin. Chem. 48:1296–1304.
22. Ebert, M.P., et al. 2004. Identification of gas-
tric cancer patients by serum protein profiling.
J. Proteome Res. 3:1261–1266.
23. Ornstein, D.K., et al. 2004. Serum proteomic profil-
ing can discriminate prostate cancer from benign
prostates in men with total prostate specific anti-
gen levels between 2.5 and 15.0 ng/ml. J. Urol.
24. Conrads, T.P., et al. 2004. High-resolution serum
proteomic features for ovarian cancer detection.
Endocr. Relat. Cancer. 11:163–178.
25. Coombes, K.R., Morris, J.S., Hu, J., Edmonson, S.R.,
and Baggerly, K.A. 2005. Serum proteomics profil-
ing-a young technology begins to mature. Nat.
26. Diamandis, E.P. 2004. Mass spectrometry as a
diagnostic and a cancer biomarker discovery tool:
opportunities and potential limitations. Mol. Cell.
27. Check, E. 2004. Proteomics and cancer: running
before we can walk? Nature. 429:496–497.
28. Ransohoff, D.F. 2005. Opinion: bias as a threat to
the validity of cancer molecular-marker research.
Nat. Rev. Cancer. 5:142–149.
29. Villanueva, J., et al. 2005. Correcting common
errors in identifying cancer-specific serum peptide
signatures. J. Proteome Res. 4:1060–1072.
30. Marshall, J., et al. 2003. Processing of serum pro-
teins underlies the mass spectral fingerprinting of
myocardial infarction. J. Proteome Res. 2:361–372.
31. Bergen, H.R., 3rd, et al. 2003. Discovery of ovarian
cancer biomarkers in serum using NanoLC electro-
spray ionization TOF and FT-ICR mass spectrometry.
Dis. Markers. 19:239–249.
32. Zhang, Z., et al. 2004. Three biomarkers identified
from serum proteomic analysis for the detection of
early stage ovarian cancer. Cancer Res. 64:5882–5890.
33. Weinberger, S.R., Dalmasso, E.A., and Fung, E.T.
2002. Current achievements using ProteinChip
Array technology. Curr. Opin. Chem. Biol. 6:86–91.
34. Kapp, E.A., et al. 2003. Mining a tandem mass
spectrometry database to determine the trends and
global factors influencing peptide fragmentation.
Anal. Chem. 75:6251–6264.
35. Gao, J., Opiteck, G.J., Friedrichs, M.S., Dongre,
A.R., and Hefta, S.A. 2003. Changes in the protein
expression of yeast as a function of carbon source.
J. Proteome Res. 2:643–649.
36. Fach, E.M., et al. 2004. In vitro biomarker discov-
ery for atherosclerosis by proteomics. Mol. Cell.
37. Jandl, J.H. 1996. Blood: textbook of hematology. Little,
Brown and Co. New York, New York, USA. 1510 pp.
38. Sahu, A., and Lambris, J.D. 2001. Structure and biol-
ogy of complement protein C3, a connecting link
between innate and acquired immunity. Immunol.
39. Abbasciano, V., Levato, F., and Zavagli, G. 1987.
Specificity of fibrinopeptide A (FpA) as a marker for
gastrointestinal cancers before and after surgery.
Med. Oncol. Tumor Pharmacother. 4:75–79.
40. Auger, M.J., Galloway, M.J., Leinster, S.J., McVerry,
B.A., and Mackie, M.J. 1987. Elevated fibrinopep-
tide A levels in patients with clinically localised
breast carcinoma. Haemostasis. 17:336–339.
41. Stewart, J.M. 2003. Bradykinin antagonists as anti-
cancer agents. Curr. Pharm. Des. 9:2036–2042.
42. Kato, H., Matsumura, Y., and Maeda, H. 1988. Iso-
lation and identification of hydroxyproline ana-
logues of bradykinin in human urine. FEBS Lett.
binned them. In the parameter section of the experiments, a parameter
called cancertype was created to label samples as prostate cancer, breast
cancer, bladder cancer, or control. In the experiment interpretation section,
the analysis mode was set to ratio (signal/control), and all measurements
were used. No cross-gene error model was used for either.
ANOVA. Once the experiments were created, the m/z values (peaks)
were filtered by using nonparametric tests: the Mann-Whitney U test (for
binary comparisons) and the Kruskal-Wallis test (for multiclass compari-
sons). The Benjamini and Hochberg method was used to adjust P values
for multiple comparisons (79). The threshold for significance was an
expected false discovery rate of less than 1 × 10–5. These tests are meant
to find peaks that show statistically significant differences between the
clinical groups studied.
Hierarchical clustering. The 651 m/z values were subjected to average-
linkage hierarchical clustering, using standard correlation (also known
as Pearson correlation around zero) as a distance metrics (GeneSpring
program). The peaks were organized by creating mock-phylogenetic
trees (dendrograms) termed gene trees and experiment trees in the soft-
ware. The trees were displayed with the samples along the x axis and the
masses along the y axis.
Class prediction. SVM and k-NN analyses were done by using the class
prediction tool in GeneSpring. The training groups were either a binary
comparison (PR1 and control) or a multiclass comparison (PR1, breast
cancer, bladder cancer, and control). The test set was PR2. The parameter
to predict was set to cancertype. The gene selection was set to use different
groups of masses previously selected (e.g., 651, 68, 26). In k-NN the num-
ber of neighbors was set to 5 with a P value decision cutoff of 1. The SVM
was done with the same training sets and parameters and set to predict the
PR2 test set. The kernel used was polynomial dot product (order 1) with a
diagonal scaling of 0.
This work was supported by NIH grants 1-R21-CA1119425,
5-P30-CA08748, and 5-P50-CA92629 and awards from the Pros-
tate Cancer Foundation, the Vakil Research Fund, and Accelerate
Brain Cancer Cure. We thank Larry Norton and Mark Kris for sup-
port; Richard Robbins, Mark Robson, and Chris Sander for helpful
discussions; San San Yi for peptide synthesis; Lynne Lacomis for
help with the artwork; and all volunteers for generous donation
of blood samples.
Received for publication June 21, 2005, and accepted in revised
form October 11, 2005.
Address correspondence to: Paul Tempst, Memorial Sloan-Ket-
tering Cancer Center, 1275 York Avenue, New York, New York
10021, USA. Phone: (212) 639-8923; Fax: (212) 717-3604; E-mail:
research article Download full-text
284? The?Journal?of?Clinical?Investigation http://www.jci.org Volume 116 Number 1 January 2006
43. Salier, J.P., Rouet, P., Raguenez, G., and Daveau, M.
1996. The inter-alpha-inhibitor family: from struc-
ture to regulation. Biochem. J. 315:1–9.
44. Nishimura, H., et al. 1995. cDNA and deduced
amino acid sequence of human PK-120, a plas-
ma kallikrein-sensitive glycoprotein. FEBS Lett.
45. Belt, K.T., Carroll, M.C., and Porter, R.R. 1984. The
structural basis of the multiple forms of human
complement component C4. Cell. 36:907–914.
46. July, L.V., et al. 2002. Clusterin expression is signifi-
cantly enhanced in prostate cancer cells following
androgen withdrawal therapy. Prostate. 50:179–188.
47. Scaltriti, M., et al. 2004. Clusterin (SGP-2, ApoJ)
expression is downregulated in low- and high-grade
human prostate cancer. Int. J. Cancer. 108:23–30.
48. Miyake, H., Gleave, M., Kamidono, S., and Hara, I.
2002. Overexpression of clusterin in transitional
cell carcinoma of the bladder is related to disease
progression and recurrence. Urology. 59:150–154.
49. Jiang, W.G., Ablin, R., Douglas-Jones, A., and Man-
sel, R.E. 2003. Expression of transglutaminases in
human breast cancer and their possible clinical sig-
nificance. Oncol. Rep. 10:2039–2044.
50. Tietz, N.W. 1995. Clinical guide to laboratory tests.
Philadelphia, Pennsylvania, USA. W.B. Saunders
Co. 1096 pp.
51. Sanderink, G.J., Artur, Y., and Siest, G. 1988.
Human aminopeptidases: a review of the literature.
J. Clin. Chem. Clin. Biochem. 26:795–807.
52. Silveira, P.F., Gil, J., Casis, L., and Irazusta, J. 2004.
Peptide metabolism and the control of body fluid
homeostasis. Curr. Med. Chem. Cardiovasc. Hematol.
53. Mitsui, T., Nomura, S., Itakura, A., and Mizutani,
S. 2004. Role of aminopeptidases in the blood pres-
sure regulation. Biol. Pharm. Bull. 27:768–771.
54. Nesheim, M., et al. 1997. Thrombin, thrombo-
modulin and TAFI in the molecular link between
coagulation and fibrinolysis. Thromb. Haemost.
55. Ito, N., et al. 2004. ADAMs, a disintegrin and metal-
loproteinases, mediate shedding of oxytocinase.
Biochem. Biophys. Res. Commun. 314:1008–1013.
56. van Hensbergen, Y., et al. 2002. Soluble aminopep-
tidase N/CD13 in malignant and nonmalignant
effusions and intratumoral fluid. Clin. Cancer Res.
57. Martinez, J.M., et al. 1999. Aminopeptidase activities
in breast cancer tissue. Clin. Chem. 45:1797–1802.
58. Matrisian, L.M., Sledge, G.W., Jr., and Mohla, S.
2003. Extracellular proteolysis and cancer: meet-
ing summary and future directions. Cancer Res.
59. Egeblad, M., and Werb, Z. 2002. New functions for
the matrix metalloproteinases in cancer progression.
Nat. Rev. Cancer. 2:161–174.
60. Rao, J.S. 2003. Molecular mechanisms of glioma
invasiveness: the role of proteases. Nat. Rev. Cancer.
61. Moffatt, S., Wiehle, S., and Cristiano, R.J. 2005.
Tumor-specific gene delivery mediated by a novel
peptide-polyethylenimine-DNA polyplex target-
ing aminopeptidase N/CD13. Hum. Gene Ther.
62. Kehlen, A., Lendeckel, U., Dralle, H., Langner, J.,
and Hoang-Vu, C. 2003. Biological significance of
aminopeptidase N/CD13 in thyroid carcinomas.
Cancer Res. 63:8500–8506.
63. Rocken, C., et al. 2004. Ectopeptidases are differen-
tially expressed in hepatocellular carcinomas. Int. J.
64. Carl-McGrath, S., et al. 2004. The ectopeptidases
CD10, CD13, CD26, and CD143 are upregulated
in gastric cancer. Int. J. Oncol. 25:1223–1232.
65. Kojima, K., et al. 1987. Serum activities of dipep-
tidyl-aminopeptidase II and dipeptidyl-aminopep-
tidase IV in tumor-bearing animals and in cancer
patients. Biochem. Med. Metab. Biol. 37:35–41.
66. Essler, M., and Ruoslahti, E. 2002. Molecular spe-
cialization of breast vasculature: a breast-homing
phage-displayed peptide binds to aminopeptidase
P in breast vasculature. Proc. Natl. Acad. Sci. U. S. A.
67. Carrera, M.P., et al. 2005. Serum enkephalin-
degrading aminopeptidase activity in N-methyl
nitrosourea-induced rat breast cancer. Anticancer
68. Pulido-Cejudo, G., et al. 2004. A monoclonal anti-
body driven biodiagnostic system for the quanti-
tative screening of breast cancer. Biotechnol. Lett.
69. Suganuma, T., et al. 2004. Regulation of aminopep-
tidase A expression in cervical carcinoma: role of
tumor-stromal interaction and vascular endothelial
growth factor. Lab. Invest. 84:639–648.
70. Selvakumar, P., et al. 2004. High expression of
methionine aminopeptidase 2 in human colorectal
adenocarcinomas. Clin. Cancer Res. 10:2771–2775.
71. Ni, R.Z., Huang, J.F., Xiao, M.B., Li, M., and Meng,
X.Y. 2003. Glycylproline dipeptidyl aminopepti-
dase isoenzyme in diagnosis of primary hepatocel-
lular carcinoma. World J. Gastroenterol. 9:710–713.
72. Sheppard, G.S., et al. 2004. 3-Amino-2-hydroxy-
amides and related compounds as inhibitors of
methionine aminopeptidase-2. Bioorg. Med. Chem.
73. Griffith, E.C., et al. 1998. Molecular recognition of
angiogenesis inhibitors fumagillin and ovalicin by
methionine aminopeptidase 2. Proc. Natl. Acad. Sci.
U. S. A. 95:15183–15188.
74. Pasqualini, R., et al. 2000. Aminopeptidase N is a
receptor for tumor-homing peptides and a target for
inhibiting angiogenesis. Cancer Res. 60:722–727.
75. Petrovic, N., Bhagwat, S.V., Ratzan, W.J., Ostrowski,
M.C., and Shapiro, L.H. 2003. CD13/APN tran-
scription is induced by RAS/MAPK-mediated phos-
phorylation of Ets-2 in activated endothelial cells.
J. Biol. Chem. 278:49358–49368.
76. O’Malley, P.G., Sangster, S.M., Abdelmagid, S.A.,
Bearne, S.L., and Too, C.K. 2005. Characterization
of a novel, cytokine-inducible carboxypeptidase-D
isoform in hematopoietic tumor cells. Biochem. J.
77. Fair, W.R., Israeli, R.S., and Heston, W.D. 1997.
Prostate-specific membrane antigen. Prostate.
78. Marker, P.C., Donjacour, A.A., Dahiya, R., and
Cunha, G.R. 2003. Hormonal, cellular, and molec-
ular control of prostatic development. Dev. Biol.
79. Benjamini, Y., and Hochberg, Y. 1995. Controlling
the false discovery rate: a practical and powerful
approach to multiple testing. J. R. Stat. Soc. (Ser. B.)