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.