Prediction of outcome of non-small cell lung cancer patients treated with chemotherapy and bortezomib by time-course MALDI-TOF-MS serum peptide profiling
- Johannes Voortman†1, 2,
- Thang V Pham†1,
- Jaco C Knol1,
- Giuseppe Giaccone1, 2 and
- Connie R Jimenez1Email author
© Voortman et al; licensee BioMed Central Ltd. 2009
Received: 9 May 2009
Accepted: 3 September 2009
Published: 3 September 2009
Only a minority of patients with advanced non-small cell lung cancer (NSCLC) benefit from chemotherapy. Serum peptide profiling of NSCLC patients was performed to investigate patterns associated with treatment outcome.
Using magnetic bead-assisted serum peptide capture coupled to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), serum peptide mass profiles of 27 NSCLC patients treated with cisplatin-gemcitabine chemotherapy and bortezomib were obtained. Support vector machine-based algorithms to predict clinical outcome were established based on differential pre-treatment peptide profiles and dynamic changes in peptide abundance during treatment.
A 6-peptide ion signature distinguished with 82% accuracy, sensitivity and specificity patients with a relatively short vs. long progression-free survival (PFS) upon treatment. Prediction of long PFS was associated with longer overall survival. Inclusion of 7 peptide ions showing differential changes in abundance during treatment led to a 13-peptide ion signature with 86% accuracy at 100% sensitivity and 73% specificity. A 5-peptide ion signature could separate patients with a partial response vs. non-responders with 89% accuracy at 100% sensitivity and 83% specificity. Differential peptide profiles were also found when comparing the NSCLC serum profiles to those from cancer-free control subjects.
This study shows that serum peptidome profiling using MALDI-TOF-MS coupled to pattern diagnostics may aid in prediction of treatment outcome of advanced NSCLC patients treated with chemotherapy.
Lung cancer is the most common cause of cancer-related death with an overall 5-year survival rate of 16% . Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung carcinomas and is frequently diagnosed in an advanced stage . First-line treatment of advanced NSCLC typically consists of platinum-based chemotherapy [3, 4]. Only a minority of patients respond to this treatment, at the cost of substantial toxicity for all treated patients . There is an urgent need for methods enabling outcome prediction in order to select patients likely to benefit from treatment.
The serum peptidome, that comprises peptides and proteins with a molecular weight of less than 10 kDa, represents a dynamic reflection of tissue function in health and disease . Mass spectrometry (MS) is increasingly used to profile the serum peptidome [7–9]. Peaks in the serum peptide spectra correspond to peptide ions, with the amplitude of the peaks indicative of relative abundance . Magnetic bead-assisted serum peptide capture coupled to matrix assisted laser desorption/ionization time-of-flight MS (MALDI-TOF-MS) is a serum peptide profiling strategy gaining in popularity compared to surface-enhanced laser desorption/ionization (SELDI)-based platforms due to superior resolution of MALDI instruments, the possibility to obtain structural (MS/MS) information of signature peptides and superior binding capacity of the magnetic beads compared to a flat SELDI-chip surface .
Here we report on a MALDI-TOF-MS dataset of serum samples from advanced NSCLC patients, who were treated with first-line chemotherapy, consisting of cisplatin and gemcitabine, as well as bortezomib, in a previously reported prospective clinical trial . The efficacy of cisplatin-gemcitabine alone is limited, a partial tumor response being achieved in about one third of NSCLC patients and with a median progression free survival of four to five months . Preclinical as well as initial clinical studies suggested combining cisplatin-gemcitabine with proteasome inhibitor bortezomib might enhance efficacy [14–17].
We hypothesized that specific serum peptidome patterns could predict clinical outcome of patients who underwent chemotherapy-based treatment. For this purpose, data analyses of serum peptide profiles were conducted. Our primary aim was to establish serum peptide signatures that could predict positive or negative clinical outcome upon treatment. Clinical endpoints used to establish these signatures were response to treatment according to the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, as well as progression-free survival duration of treated patients. Furthermore, in a secondary analysis, we compared the serum peptide profiles of treated patients with those obtained from cancer-free control subjects in an initial attempt to establish cancer-specific serum peptide patterns.
Pre-treatment serum peptide patterns of NSCLC patients
(n = 27)
Sex, n (%)
Stage, n (%)
Histology, n (%)
Squamous cell carcinoma
Adenosquamous cell carcinoma
Undifferentiated non-small cell carcinoma
Karnofski performance status, n (%)
Smoking history, n (%)
Current or former
RECIST, n (%)
Median progression-free survival
Median overall survival
Comparison serum profiles time-course
Analysis of the clinical outcome: progression-free survival
The patients were divided into two subsets according to PFS duration. Of the 27 NSCLC patients, 11 patients with the shortest PFS were nominated as "short PFS" group (PFS ≤ 127 days), 11 patients with the longest PFS were nominated as "long PFS" group (PFS ≥ 178 days). Four patients with PFS duration around the median PFS (152 days) were excluded from the analysis. A fifth patient was excluded who had a partial response but died 36 days after start of treatment due to a cause not likely due to tumor progression .
Comparison serum profiles short PFS vs. long PFS
Comparison time-course serum profiles short PFS vs. long PFS
Finally, we carried out classification analysis using support vector machine. Using all 682 peptides, the LOOCV accuracy was very poor, at about 50%. When the six differential pre-treatment peptides were used, the LOOCV accuracy was 82% with both 82% sensitivity and 82% specificity. Selecting six different peptides randomly resulted in an average accuracy of 68% (10 runs). The LOOCV prediction accuracy improved when we used also the seven peptides that changed differently in intensity level over the three time points. Here, using the 13 peptides we could separate the two groups with a LOOCV accuracy of 86% at 100% sensitivity and 73% specificity (the average accuracy over 10 runs with random selection of 13 peptides was 71%).
Analysis of the clinical outcome: tumour response
To identify a signature associated with tumour response, we divided the patients into three groups according to tumour response following treatment: (1) partial response (PR), (2) stable disease (SD), (3) progressive disease (PD), as defined by RECIST . Because there were only three patients with progressive disease, we created two groups: PR and SD/PD combined ("no PR"). The first group had 9 patients, and the second group consisted of 18 patients.
Comparison serum profiles PR vs. no PR
Comparison time-course serum profiles PR vs. no PR
Peptide pattern discriminating NSCLC patients from cancer-free controls
Comparison serum profiles NSCLC vs. cancer-free controls
In this secondary analysis, control subjects were unmatched for age and gender. We therefore also considered peptides that express differently in the two gender groups. For this comparison, four peptides were differential according to our criteria described above (m/z = 1458.495, 3215.194, 2602.305, and 2789.091). Of these four peptides, two are present in the list of 47 differential peptides in the healthy versus NSCLC comparison. Ignoring these two peptides, the signature composed of the remaining 45 peptides yielded the same accuracy, sensitivity and specificity as that of the 47-peptide signature. Literature supports that serum peptidome patterns that distinguished advanced cases of cancer from cancer-free controls were unbiased by gender and age, except for the fact that healthy subjects under 35 years could be distinguished with approximately 70% accuracy . All participating patients in our study were 35 years or older. However, in the cancer-free control group, 4 individuals were younger than 35 years and 9 individuals older than 35 years. Comparing these two groups, two peaks met the criteria for differential (m/z = 1020.5133 and 855.0387). These two peaks did not feature in the classifying signature between the NSCLC patients and the cancer-free controls.
MS/MS identification results
Peptide residue location
Peptide amino acid sequence
Time course (44)
Time course (44); PFS (13);
NSCLC vs. Healthy (47)
Time course (44)
Time course (44)
Time course (44)
Time course (44)
Time course (44)
Time course (44)
Time course (44)
Time course (44)
NSCLC vs. Healthy (47)
NSCLC vs. Healthy (47)
NSCLC vs. Healthy (47)
NSCLC vs. Healthy (47)
Time course (44)
Time course (44);
NSCLC vs. Healthy (47)
Time course (44)
In this study, we investigated the use of serum peptide mass profiling by MALDI-TOF-MS coupled to bioinformatics pattern discovery to predict treatment outcome of advanced NSCLC patients treated with platinum-based therapy. Additionally, peptide patterns found in NSCLC patients were differential from those found in healthy volunteers.
To our knowledge we are the first to report on a serum peptide signature for response and survival prediction in NSCLC patients treated with cisplatin-based chemotherapy. For this study, serum samples were obtained not only pre-treatment, but also during treatment and after completion of treatment, whereas serum proteomics studies typically focus on pre-treatment samples only. In a study by Taguchi et al., a predictive MALDI-TOF-MS-based peptide algorithm for "good" or "poor" clinical outcome upon epidermal growth factor tyrosine kinase inhibitor (EGFR-TKI) therapy was established . However, in this study none of the peptides corresponding to the classifying m/z features were identified.
In our study, for the short PFS versus long PFS classification, we achieved 82% accuracy with a 6-peptide-signature. A longer overall survival of the patients classified as "long PFS" and a shorter overall survival of the patients classified as "short PFS" was observed. Interestingly, performance of the signature was improved upon inclusion of 7 differential time-course peptides, the combined 13 peptide-ion signature achieving 86% accuracy. In this regard, it has been previously reported that histological response of locally advanced rectal cancer to radiochemotherapy could be predicted by SELDI-TOF-MS-based profiling and comparison of serum samples collected pre-treatment and during treatment .
For partial responders versus non-partial responders we achieved 89% accuracy with a 5-peptide signature. Inclusion of differential time-course peptides did not improve performance of this signature. Longer duration of progression-free survival was strongly associated with tumour response as seven out of eleven patients in our study with long PFS also had a partial tumour response upon treatment, compared to one out of eleven patients in the short PFS group. This suggests that the survival signature is predictive of therapy-outcome rather than prognostic. As the Kaplan-Meier analysis showed, prediction of response using the 5- or 10-peptide signatures was significantly associated with a longer median PFS of those patients, but this did not reach significance for overall survival.
In several MALDI serum peptide profiling studies involving other solid tumour types (bladder, breast, prostate, thyroid) by Villanueva et al., methodologically comparable to our study, the hypothesis was put forward that cancer-type specific changes in exopeptidase activities yield surrogate biomarkers, reflected in the differential abundance of cleavage products of common serum substrate proteins [19, 22]. The changes in exopeptidase activity were superimposed on the proteolytic events of the complement degradation and ex vivo coagulation pathways and therefore serum-specific. Regulated peptides at m/z 1690.925, 1777.966 and 1865.002 correspond to those identified in our study at 1690.90, 1777.94 and 1864.95, respectively, previously identified by MALDI-TOF-TOF-based MS/MS analysis as Complement C3f. The peptide at 2209.093 corresponds to the one we detected at 2209.08, previously identified as HMW Kininogen .
We show that in NSCLC, the identified differential serum peptides changing in abundance over time and those distinguishing NSCLC patients from cancer-free control subjects consisted of truncated sequence clusters. The identified peptides were derived from naturally occurring serum peptides and protein precursors, and therefore not likely to be tumor-derived, thus supporting the exoprotease hypothesis. For the predictive algorithms, it is important to realize that these might be specific for the treatment combination of bortezomib, cisplatin and gemcitabine and may not apply to other treatment regimens. For this exploratory analysis, we did not perform a power calculation beforehand as we did not know the biological variation in our patient groups, nor the number of peaks we would measure as well as other variables. We only knew the technical variation. It was therefore our approach to collect as many samples as we could accommodate. It is crucial to validate and adjust the established signatures with an independent cohort in a sufficiently powered follow-up study. Additionally, since only one report excludes an age and gender bias for a cancer-specific serum peptide signature, it is advisable to include matched cancer-free control groups for the establishment of cancer-specific peptide patterns.
Ideally, serum peptide mass profiling can be used to identify the therapeutic agents to which the tumour is sensitive, enabling personalized medicine. The method employed here requires readily accessible, non-invasively obtainable patient samples, is high-throughput and cost-efficient, all together important requirements for a screening platform as well as routine clinical use. The biggest challenge might very well remain lack of reproducibility related to sample collection in the clinic. In particular, maintaining a constant and precise clotting time is often difficult in clinical practice. Potentially, after initial discovery of classifying algorithms, functional proteomics tests will facilitate clinical implementation .
Patients and serum preparation
The training set included 27 patients with NSCLC who were treated with chemotherapy and bortezomib as well as 13 healthy volunteers . All patients were treated with cisplatin 70 mg/m2 day 1 and gemcitabine 1,000 mg/m2 days 1 and 8, every 21 days for up to 6 cycles. Fifteen patients were treated with bortezomib on days 1 and 8 of every cycle (ten patients at 1.0 mg/m2 and five patients at 1.3 mg/m2). Twelve patients were treated with bortezomib on days 1,4,8 and 11 of every cycle (one patient at 0.7 mg/m2 and eleven patients at 1.0 mg/m2). There was no indication of superior clinical activity of any schedule of bortezomib in combination with cisplatin and gemcitabine . Blood samples were obtained in BD Vacutainer glass "red-top" tubes (Becton Dickinson, Franklin Lakes, NJ), allowed to clot for 1 hour, and then centrifuged at 1500 g for 10 minutes. Sera were stored in polypropylene cryovials (Nunc; Roskilde, Denmark) at -80°C. Studies were performed after obtaining patient consent and under protocols approved by the institutional review board.
Serum sample processing and mass spectrometry
Samples were processed in randomized order, along with control samples to check consistency in each experiment. Magnetic Dynabeads® RPC 18 (Invitrogen, San Diego, CA) were used for serum peptide capture using the KingFischer96 platform, as described previously . Briefly, in a 96-well format, magnetic beads were washed and equilibrated twice in 200 μl 200 mM NaCl/0.1% TFA, transferred to a mix of 20 μl serum sample and 2 volumes of 0.2% n-octyl glucoside/0.5% TFA (premixed for 5 min), incubated for 2 min, washed thrice with 0.1% TFA, and eluted for 2 min with 40 μl 50% acetonitrile.
Processed samples (1.5 μl eluate) were mixed with 2 volumes α-cyano-4-hydroxycinnamic acid matrix (6.2 mg/ml in 56% acetonitrile, 36% methanol; Agilent, Santa Clara, CA) and 0.7 μl of this mix was spotted on a MALDI plate. A 4800 MALDI-TOF/TOF mass spectrometer (Applied Biosystems, Foster City, CA) was used to record with 5000 shots per spectrum (reflectron mode) serum peptide profiles in the mass range of m/z 800-4000 . Internal calibration was used using a list of exact masses for fibrinogen α/fibrinopeptide A peptides as major components of serum samples. For MS/MS analysis, stepwise attempts of 5000 shots at a time generated spectra for identification by the Mascot search engine or manual identification. For Mascot searches, the SwissProt database (release 54.7) was used at a mass window of 10 ppm for MS and a 1-Da tolerance for MS/MS. Final scores were obtained by narrowing down the window. For manual identification, spectra were compared with theoretical peptide fragments of reported candidate proteins . Fragments having a predicted mass differing less than 10 ppm from the mass of any of the 87 significantly regulated peaks from our profiling study were identified using FindPept http://www.expasy.ch. Fragmentation patterns were predicted using MS-Product http://prospector.ucsf.edu/prospector/mshome.htm, requiring that at least 3 prominent peaks in the experimental spectrum should match b or y ions from the theoretical table.
Spectra were pre-processed using MarkerView, version 1.2 (Applied Biosystems) with a mass tolerance of 200.0 ppm and minimum intensity at 100.0 units. Total signal intensity of all peptide peaks was used for normalization.
Feature selection was performed using the Mann-Whitney U test on each peptide detected in the pre-processing step. We used a common threshold of 5% for the p-value. As p-values were not adjusted for multiple testing, we took additional measures to guard from false discovery. To reduce differences due to noise, each peptide was subjected to intensity filtering, requiring that the median intensity of at least one group must be greater than 80 units and the fold change of the median intensities of the two groups must be greater than 1.5. For time course analysis of the three time points, we treated the problem as three binary comparisons. For each comparison, a paired, two-sided signed rank test was carried out. Each peptide was again subjected to intensity filtering. The results of the three comparisons were merged where the significance level of each peptide was the minimum of the three p-values. Similarly, we analyzed dynamic peptide profiles (time-course), using the group information, in order to identify peptides of which the intensity level changes differently between different clinical groups. Finally, support vector machine with the Gaussian kernel was used to construct classification models. A two dimensional grid search was carried out to set model parameters using the leave-one-out cross-validation (LOOCV) measure. Analogously to Villanueva et al. , we used a statistical test for feature selection. This procedure is based on class label, thus bias might be introduced. Nevertheless, this strategy was shown to be effective in previous studies [19, 22]. Median duration of progression-free and overall survival was calculated using the Kaplan-Meier method; p-values according to the log-rank test (SPSS Statistics17.0, SPSS Inc., Chicago, IL).
List of abbreviations
end of treatment
- MALDI-TOF MS:
matrix-assisted laser desorption/ionization time-of-flight mass spectrometry
non-small cell lung cancer
principal component analysis
Response Evaluation Criteria in Solid Tumors
surface-enhanced laser desorption/ionization.
Drs Lyle Burton and Ron Bonner (Applied Biosystems/Sciex) are acknowledged for early access to new versions of MarkerView software. The VUmc-Cancer Center Amsterdam is acknowledged for support of CRJ, TVP and JCK and proteomics infrastructure. JV was supported by a Netherlands Organization of Scientific Research ZonMW-AGIKO stipend, grant 920-03-290.
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