Heinrich Roder

Quantitative matrix-assisted laser desorption/ionization mass spectrometry

This review summarizes the essential characteristics of matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (TOF MS), especially as they relate to its applications in quantitative analysis. Approaches to quantification by MALDI-TOF MS are presented and published applications are critically reviewed.

Diagnostic Accuracy of MALDI Mass Spectrometric Analysis of Unfractionated Serum in Lung Cancer

Purpose: There is a critical need for improvements in the noninvasive diagnosis of lung cancer. We hypothesized that matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) analysis of the most abundant peptides in the serum may distinguish lung cancer cases from matched controls. Patients and Methods: We used MALDI MS to analyze unfractionated serum from a total of 288 cases and matched controls split into training (n = 182) and test sets (n = 106). We used a training–testing paradigm with application of the model profile defined in a training set to a blinded test cohort. Results: Reproducibility and lack of analytical bias was confirmed in quality-control studies. A serum proteomic signature of seven features in the training set reached an overall accuracy of 78%, a sensitivity of 67.4%, and a specificity of 88.9%. In the blinded test set, this signature reached an overall accuracy of 72.6 %, a sensitivity of 58%, and a specificity of 85.7%. The serum signature was associated with the diagnosis of lung cancer independently of gender, smoking status, smoking pack-years, and C-reactive protein levels. From this signature, we identified three discriminatory features as members of a cluster of truncated forms of serum amyloid A. Conclusions: We found a serum proteomic profile that discriminates lung cancer from matched controls. Proteomic analysis of unfractionated serum may have a role in the noninvasive diagnosis of lung cancer and will require methodological refinements and prospective validation to achieve clinical utility.

Predictive value of a proteomic signature in patients with non-small-cell lung cancer treated with second-line erlotinib or chemotherapy (PROSE): a biomarker-stratified, randomised phase 3 trial.

BACKGROUND An established multivariate serum protein test can be used to classify patients according to whether they are likely to have a good or poor outcome after treatment with EGFR tyrosine-kinase inhibitors. We assessed the predictive power of this test in the comparison of erlotinib and chemotherapy in patients with non-small-cell lung cancer. METHODS From Feb 26, 2008, to April 11, 2012, patients (aged ≥18 years) with histologically or cytologically confirmed, second-line, stage IIIB or IV non-small-cell lung cancer were enrolled in 14 centres in Italy. Patients were stratified according to a minimisation algorithm by Eastern Cooperative Oncology Group performance status, smoking history, centre, and masked pretreatment serum protein test classification, and randomly assigned centrally in a 1:1 ratio to receive erlotinib (150 mg/day, orally) or chemotherapy (pemetrexed 500 mg/m(2), intravenously, every 21 days, or docetaxel 75 mg/m(2), intravenously, every 21 days). The proteomic test classification was masked for patients and investigators who gave treatments, and treatment allocation was masked for investigators who generated the proteomic classification. The primary endpoint was overall survival and the primary hypothesis was the existence of a significant interaction between the serum protein test classification and treatment. Analyses were done on the per-protocol population. This trial is registered with ClinicalTrials.gov, number NCT00989690. FINDINGS 142 patients were randomly assigned to chemotherapy and 143 to erlotinib, and 129 (91%) and 134 (94%), respectively, were included in the per-protocol analysis. 88 (68%) patients in the chemotherapy group and 96 (72%) in the erlotinib group had a proteomic test classification of good. Median overall survival was 9·0 months (95% CI 6·8-10·9) in the chemotherapy group and 7·7 months (5·9-10·4) in the erlotinib group. We noted a significant interaction between treatment and proteomic classification (pinteraction=0·017 when adjusted for stratification factors; pinteraction=0·031 when unadjusted for stratification factors). Patients with a proteomic test classification of poor had worse survival on erlotinib than on chemotherapy (hazard ratio 1·72 [95% CI 1·08-2·74], p=0·022). There was no significant difference in overall survival between treatments for patients with a proteomic test classification of good (adjusted HR 1·06 [0·77-1·46], p=0·714). In the group of patients who received chemotherapy, the most common grade 3 or 4 toxic effect was neutropenia (19 [15%] vs one [<1%] in the erlotinib group), whereas skin toxicity (one [<1%] vs 22 [16%]) was the most frequent in the erlotinib group. INTERPRETATION Our findings indicate that serum protein test status is predictive of differential benefit in overall survival for erlotinib versus chemotherapy in the second-line setting. Patients classified as likely to have a poor outcome have better outcomes on chemotherapy than on erlotinib. FUNDING Italian Ministry of Health, Italian Association of Cancer Research, and Biodesix.

Genetic and Proteomic Features Associated with Survival after Treatment with Erlotinib in First-Line Therapy of Non-small Cell Lung Cancer in Eastern Cooperative Oncology Group 3503

Introduction: Serum proteomics and mutations in the epidermal growth factor receptor (EGFR) and KRAS have been associated with benefit after therapy with EGFR-targeted therapies in non-small cell lung cancer, but all three have not been evaluated in any one study. Hypothesis: Pretreatment serum proteomics predicts survival in Western advanced non-small cell lung cancer patients with wild-type EGFR and independent of KRAS mutation status. Methods: We analyzed available biospecimens from Eastern Cooperative Oncology Group 3503, a single-arm phase II study of erlotinib in first-line advanced lung cancer, for proteomics signatures in the previously described serum matrix-assisted laser desorption ionization proteomic classifier (VeriStrat) as well as for KRAS and EGFR mutations. Results: Out of 137 enrolled patients, analyzable biologic samples were available on 102. Nine of 41 (22%) demonstrated KRAS mutations and 3 of 41 (7%) harbored EGFR mutations. VeriStrat classification identified 64 of 88 (73%) as predicted to have “good” and 24 of 88 (27%) predicted to have “poor” outcomes. A statistically significant correlation of VeriStrat status (p < 0.001) was found with survival. EGFR mutations, but not KRAS mutations, also correlated with survival. Conclusions: The previously defined matrix-assisted laser desorption ionization predictor remains a potent and highly clinically significant predictor of survival after first-line treatment with erlotinib in patients with wild-type EGFR and independent of mutations in KRAS.

Detection of Tumor Epidermal Growth Factor Receptor Pathway Dependence by Serum Mass Spectrometry in Cancer Patients

Background: We hypothesized that a serum proteomic profile predictive of survival benefit in non–small cell lung cancer patients treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) reflects tumor EGFR dependency regardless of site of origin or class of therapeutic agent. Methods: Pretreatment serum or plasma from 230 patients treated with cetuximab, EGFR-TKIs, or chemotherapy for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) or colorectal cancer (CRC) were analyzed by mass spectrometry. Each sample was classified into “good” or “poor” groups using VeriStrat, and survival analyses of each cohort were done based on this classification. For the CRC cohort, this classification was correlated with the tumor EGFR ligand levels and KRAS mutation status. Results: In the EGFR inhibitor–treated cohorts, the classification predicted survival (HNSCC: gefitinib, P = 0.007 and erlotinib/bevacizumab, P = 0.02; CRC: cetuximab, P = 0.0065) whereas the chemotherapy cohort showed no survival difference. For CRC patients, tumor EGFR ligand RNA levels were significantly associated with the proteomic classification, and combined KRAS and proteomic classification provided improved survival classification. Conclusions: Serum proteomic profiling can detect clinically significant tumor dependence on the EGFR pathway in non–small cell lung cancer, HNSCC, and CRC patients treated with either EGFR-TKIs or cetuximab. This classification is correlated with tumor EGFR ligand levels and provides a clinically practical way to identify patients with diverse cancer types most likely to benefit from EGFR inhibitors. Prospective studies are necessary to confirm these findings. Cancer Epidemiol Biomarkers Prev; 19(2); 358–65

Differentiating Proteomic Biomarkers in Breast Cancer by Laser Capture Microdissection and MALDI MS

We assessed proteomic patterns in breast cancer using MALDI MS and laser capture microdissected cells. Protein and peptide expression in invasive mammary carcinoma versus normal mammary epithelium and estrogen-receptor positive versus estrogen-receptor negative tumors were compared. Biomarker candidates were identified by statistical analysis and classifiers were developed and validated in blinded test sets. Several of the m/ z features used in the classifiers were identified by LC-MS/MS and two were confirmed by immunohistochemistry.

VeriStrat® classifier for survival and time to progression in non-small cell lung cancer (NSCLC) patients treated with erlotinib and bevacizumab

We applied an established and commercially available serum proteomic classifier for survival after treatment with erlotinib (VeriStrat) in a blinded manner to pretreatment sera obtained from recurrent advanced NSCLC patients before treatment with the combination of erlotinib plus bevacizumab. We found that VeriStrat could classify these patients into two groups with significantly better or worse outcomes and may enable rational selection of patients more likely to benefit from this costly and potentially toxic regimen.

Changes in Plasma Mass-Spectral Profile in Course of Treatment of Non-small Cell Lung Cancer Patients with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors

Introduction: Our previous study showed that pretreatment serum or plasma Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry may predict clinical outcome of non-small cell lung cancer (NSCLC) patients treated with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). In this study, plasma proteomic profiles of NSCLC patients were evaluated in the course of EGFR TKIs therapy. Materials and Methods: Plasma samples were collected at baseline, in the course of gefitinib therapy and at treatment withdrawal. Samples were analyzed by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry. Acquired spectra were classified by the VeriStrat test into “good” and “poor” profiles. The association between VeriStrat classification and progression-free survival (PFS) and overall survival (OS), and types of clinical progression, was analyzed. Results: Plasma samples from 111 NSCLC patients treated with gefitinib were processed. VeriStrat “good” classification at baseline correlated with longer PFS (hazard ratio [HR], 0.54; 95% confidence interval, 0.35–0.83; p = 0.005) and OS (HR, 0.40; 95% confidence interval, 0.26–0.61; p < 0.0001), when compared with VeriStrat “poor.” Multivariate analysis confirmed longer PFS (HR, 0.52; p = 0.025) and OS (HR, 0.44; p = 0.001) in patients classified as VeriStrat “good”, when VeriStrat was considered as a time-dependent variable. About one-third of baseline “good” classifications had changed to “poor” at the time of treatment withdrawal; progression in these patients was associated with the development of new lesions. Conclusions: Our findings support the role of VeriStrat in the assistance in treatment selection of NSCLC patients for EGFR TKI therapy and its potential utility in treatment monitoring.

VeriStrat® has a prognostic value for patients with advanced non-small cell lung cancer treated with erlotinib and bevacizumab in the first line: pooled analysis of SAKK19/05 and NTR528.

BACKGROUND VeriStrat(®) is a serum proteomic test used to determine whether patients with advanced non-small cell lung cancer (NSCLC) who have already received chemotherapy are likely to have good or poor outcomes from treatment with gefitinib or erlotinib. The main objective of our retrospective study was to evaluate the role of VS as a marker of overall survival (OS) in patients treated with erlotinib and bevacizumab in the first line. PATIENTS AND METHODS Patients were pooled from two phase II trials (SAKK19/05 and NTR528). For survival analyses, a log-rank test was used to determine if there was a statistically significant difference between groups. The hazard ratio (HR) of any separation was assessed using Cox proportional hazards models. RESULTS 117 patients were analyzed. VeriStrat classified patients into two groups which had a statistically significant difference in duration of OS (p = 0.0027, HR = 0.480, 95% confidence interval: 0.294-0.784). CONCLUSION VeriStrat has a prognostic role in patients with advanced, nonsquamous NSCLC treated with erlotinib and bevacizumab in the first line. Further work is needed to study the predictive role of VeriStrat for erlotinib and bevacizumab in chemotherapy-untreated patients.

In situ mass spectrometry of autoimmune liver diseases

Primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC) and autoimmune hepatitis (AIH) are the major forms of autoimmune liver diseases each characterized by the destruction of a specific liver cell type and the presence of differing auto-antibodies. We took a proteomic approach utilizing in situ matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) to obtain profiles directly from liver samples of patients with PBC, PSC, AIH and controls. The ability to precisely localize the region for acquisition of MALDI MS allowed us to obtain profiles from bile ducts, inflammatory infiltrates and hepatocytes from each biopsy sample. Analysis tools developed to identify peaks and compare peaks across diseases and cell types were used to develop models to classify the samples. Using an initial set of testing samples from PBC patients and controls, we identified unique peaks present in bile ducts, inflammatory infiltrates and hepatocytes that could classify samples in a validation cohort with 88–91% accuracy. Interestingly, profiles of PSC and AIH did not differ significantly from PBC. Identification of proteins in these peaks may represent novel autoantigens or effector molecules. These findings illustrate the potential of a proteomic approach to autoimmune diseases with in situ MALDI MS.