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Serum proteomic profiling in patients with advanced gastric cancer
receiving first – line epirubicine, cisplatin and capecitabine
chemotherapy and identification of disease specific and predictive
biomarkers
H.H. Helgason1,J.Y. Engwegen2
M. Zapatka3, M. Kuiper1, A. Cats1, H. Boot1
J.H. Beijnen2,4
J.H.M. Schellens1,4
1. Dept. of Medical Oncology, Antoni van Leeuwenhoek Hospital/The Netherlands Cancer Institute, Amsterdam, The Netherlands2. Dept. of Pharmacy, Slotervaart Hospital, Amsterdam, The Netherlands
3. Dept. of Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany4. Faculty of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
Introduction
Gastric cancer is a common cause of cancer deaths Prognosis depends on stage at diagnosis making
early diagnosis mandatory Specific serum biomarkers of gastric cancer,
identified by SELDI-TOF mass spectrometry, could be of great value
Methods - 1
Patients with advanced gastric cancer receiving 1st line chemotherapy containing Epirubicine 50 mg/m2, Cisplatin 60 mg/m2 i.v. on day 1 and Capecitabine 1000 mg/m2, orally bid on day 1 – 14 every 3 weeks
We collected serum prior to each chemotherapy cycle in gastric cancer patients (GC) and randomly in matched healthy controls (HC), according to standardized protocol
Methods - 2
Proteomic profiles were generated with SELDI-TOF mass spectrometry, using CM 10 chips with weak cation exchange moiety and buffer pH 5
We collected serum prior to each chemotherapy cycle in gastric cancer patients (GC) and randomly in matched healthy controls (HC), according to standardized protocol
Serum proteomic profiles of GC were compared to profiles of matched HC allowing identification of serum biomarkers gastric cancer
Mass spectrometry data were processed using the tbimass R-package software (www.r-project.org)
Methods - 3
To assess the classification accuracy a tenfold repetition of tenfold cross-validation with a nested threefold parameter optimization loop was conducted
Proteomic profile survival prediction was analyzed with COXpath, a predictor-corrector method for drawing the L1 regularization path for the Cox proportional hazards model
Proteomic profile changes according to response and survival and changes developing during chemotherapy were analyzed
Results – 1Patients 82
Age 57 years (range 34 – 74)
Male 57 (70 %)
Lymphadenopathy 87%
Liver metastasis 26%
Peritoneal metastasis 37%
Lung metastasis 9%
Overall survival 11 months (95% CI 9.5 – 12)
Complete response 5
Partial response 20
Stable disease 38 (18 > 6 months)
Not assessable 14
Progressive disease 5
Results – 1
To assess the classification accuracy a tenfold repetition of tenfold cross-validation with a nested threefold parameter optimization loop was conducted
We identified proteins that differentiated between gastric cancer patients and matched healthy controls
Proteins with the highest intensity difference between patients and healthy controls (Figure 1a and b, table 2) serve as a candidate biomarkers for gastric cancer
Results – 2
Figure 1a: Mean spectra of GC and HC; m/z 3892 Da
Inte
nsi
ty
3892 Da 3892 Da
HC GC
m/zm/z
Results – 3
Figure 1b: Mean spectra of the two groups combined; m/z 3892 Da
GC HC
m/z
Results – 3
Table 2: Most important peaks
in the classification model
Peak (kDa)
3.892
40.544
6.623
13.736
4.245
24.024
3.316
4.641
15.625
29.686
9.989
6.674
4.438
3341
Results – 4
By selecting the most differentiating proteins we built a classification model that correctly classified 81% of the gastric cancer patients and 90% of the healthy controls (Figure 2)
Fre
qu
ency
Figure 2: Diagram of
frequency of correct
classification by SVM
in cross validation
Results – 5
There was some difference in classification quality according to measurement day
No proteins were found to be significantly predictive of survival in gastric cancer patients
Few proteins were found to correlate to response to ECC chemotherapy
Analysis of changes occurring in proteomic profiles of patients with advanced gastric cancer during chemotherapy is ongoing
Conclusions
We identified a number of proteins that differentiated between gastric cancer and healthy controls and could serve as a candidate biomarker for gastric cancer
We were able to correctly classify 81% of gastric cancer patients and 90% of healthy controls
We identified few proteins that correlate to response to 1st line palliative ECC chemotherapy serving as potential predictive biomarkers
Serial analysis is ongoing but a number of proteins change during chemotherapy according to response
Background:
Gastric cancer is the fourth most commonly diagnosed cancer and is the second leading cause of cancer death worldwide. Prognosis is highly dependent on stage at diagnosis making early diagnosis mandatory. By using SELDI – TOF mass spectrometry we compared serum protein profiles of gastric cancer (GC) patients with healthy controls (HC) and those of gastric cancer patients responding to first-line ECC chemotherapy with those with no response or early progressive disease.
Methods:
Serum from patients with advanced gastric cancer was obtained, according to a predefined schedule, prior to start of first-line epirubicin (50 mg/m2 day 1), cisplatin (60 mg/m2 day 1) and capecitabine (1000 mg/m2 d1-14) chemotherapy (ECC) and serially before each treatment cycle every 3 weeks and analyzed by SELDI-TOF MS/MS. Samples were drawn according to standardized protocol, centrifuged within 1 hour and stored at -30°C. After thawing they were analyzed with SELDI-TOF MS/MS on CM10 chips at pH 5. Healthy control subjects were matched according to age, gender and time of serum collection. All patients had given written consent, had WHO PS ≤ 2 and measurable disease, according to RECIST criteria, and/or evaluable disease by clinical assessment or tumor marker. Serum proteomic mass spectrometry data of GC patients and matched HC were processed using the tbimass R-package (www.r-project.org) and compared. After pre-processing (resampling, baseline correction, normalisation, alignment correction) support vector machines (SVM) and a specialised variable filtering procedure based on the relative intensity variance were applied for classification. To assess the classification accuracy a tenfold repetition of tenfold cross validation with a nested threefold parameter optimisation loop was conducted. The number of variables used for classification was reduced by recursive feature elimination (RFE). Furthermore we analyzed proteomic profile changes association with chemotherapy response, gastric cancer patient survival and for changes developing during chemotherapy (ongoing). For the serial analysis the time points T: 0, 6, 12 and 18 - 24 weeks were used.
Results:
In total 82 patients with adenocarcinoma of the stomach (mean age 57 years, male 70%) were treated between 06/2003 and 11/2006 with mean 5 ECC cycles. Response rate was 37% (5 CR, 20 PR) with additionally 12 patients developing stable disease of more than 6 months duration but 5 patients were primarily progressive. Seven patients were not assessable because of additional surgery and other 7 patients were not evaluable. The median time to progression was 7 months (95% CI: 6 - 8). and median overall survival 11 months (95% CI: 9.5 - 12). All patients were previously untreated except respectively 1 and 8 patients who had received chemo-radiotherapy and radiotherapy for proximal gastric carcinoma (all > 6 months previously). Serum from 77 HC was collected. By comparing GC patients and HC we identified number of proteins that differentiated between the two groups. By selecting the most differentiating proteins we built a classification model that correctly classified 81% of the gastric cancer patients and 90% of the healthy controls. Specific prognostic proteomic profile for gastric cancer patients was not found. Few proteins were found to correlate to response serving as potential predicting biomarkers. Proteomic profiling of serial serum collections during chemotherapy in GC is ongoing but some proteins change according to response during chemotherapy serving as potential biomarker for treatment monitoring.
Conclusion:
We identified number of proteins that differentiate between gastric cancer and healthy controls by proteomic profiling with SELDI-TOF MS/MS. By using the most discriminating proteins we were able to correctly classify 81% of the gastric cancer patients and 90% of the healthy controls serving as candidate diagnostic biomarkers.