Serum proteomic test in advanced non-squamous non-small cell lung cancer treated in first line with standard chemotherapy

BACKGROUND

VeriStrat is a blood-based proteomic test with predictive and prognostic significance in second-line treatments for non-small cell lung cancer (NSCLC). This trial was designed to investigate the role of VeriStrat in first-line treatment of advanced NSCLC with standard chemotherapy. Here we present the results for 76 non-squamous patients treated with a combination of carboplatin or cisplatin with pemetrexed.

METHODS

The test-assigned classifications of VeriStrat Good or VeriStrat Poor to samples collected at baseline. The primary end point was progression-free survival (PFS); secondary end points included overall survival (OS) and objective response. Exploratory analyses of end points separately in carboplatin/pemetrexed and cisplatin/pemetrexed subgroups were also conducted.

RESULTS

Patients classified as VeriStrat Good had longer PFS and OS than VeriStrat Poor: 6.5 vs 1.6 months and 10.8 vs 3.4 months, respectively; the corresponding hazard ratios (HRs) were 0.36 (P<0.0001) and 0.26 (P<0.0001); they were also more likely to achieve objective response. Prognostic significance of VeriStrat was confirmed in multivariate analysis. Significant differences in OS and PFS between Veristrat classifications were also found when treatment subgroups were analysed separately.

CONCLUSIONS

The trial demonstrated clinical utility of VeriStrat as a prognostic test for standard first-line chemotherapy of non-squamous advanced NSCLC.

VeriStrat(®) has prognostic value in advanced stage NSCLC patients treated with erlotinib and sorafenib

Background:

The serum proteomic test VeriStrat has been shown to be able to classify advanced non-small cell lung cancer (NSCLC) patients for overall survival (OS) after treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). In this study, VeriStrat was evaluated as a pre-treatment stratification tool in patients with advanced stage NSCLC for treatment with the combination of erlotinib and sorafenib, considering both OS and progression-free survival (PFS) as end points.

Methods:

Serum samples from 50 patients treated within the context of a phase II trial of first-line erlotinib and sorafenib were analysed with VeriStrat, a fully locked mass spectrometry-based test that identifies patients likely to have good or poor outcome on EGFR therapy based on eight distinct features in mass spectra. Analysis was performed fully blinded to all clinical data, and then the outcome data were analysed with respect to the obtained serum classifications.

Results:

VeriStrat classified pre-treatment samples into two groups, VeriStrat Good and VeriStrat Poor, which were significantly different in OS (hazard ratio (HR) 0.30, log-rank P=0.009) and in PFS (HR 0.40, log-rank P=0.035).

Conclusion:

VeriStrat has shown its potential for stratification of unselected, advanced stage NSCLC patients treated in first line with a combination of erlotinib and sorafenib.

Mass spectrometry profile as a predictor of overall survival benefit after treatment with epidermal growth factor receptor inhibitors in head and neck squamous cell carcinoma

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Background: Our previous study has shown that a matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) profile in serum or plasma can predict lung cancer patient survival after treatment with epidermal growth factor receptor inhibitors (EGFRI). We examined the ability of this same MALDI-MS profile in plasma or sera to predict for survival benefit of EGFRIs in patients with head and neck squamous cell carcinoma (HNSCC). Methods: Spectra were obtained in triplicate using MALDI-MS from 314 samples obtained from five HNSCC cohorts treated with 1) gefitinib (G, n = 100); 2) erlotinib and bevacizumab (E/B, n = 81); 3) cetuximab (C, n = 21); 4) surgery (S, n = 78); and 5) palliative chemotherapy (PC, n = 34). Each sample was classified into “good” or “poor” outcome groups and overall survival was examined using this MALDI-MS classifier (VeriStrat, Biodesix, Steamboat Springs, CO). Results: Successful classification could be achieved in 311/314 (98%) of the samples. In all EGFRI-treated cohorts, the classifier predicted survival benefit while the cohorts without EGFRI-treatment showed no survival difference by log-rank testing (G: p = 0.007, HR 0.41, 95%-CI 0.22–0.79; E/B: p = 0.02, HR 0.20, 95%-CI 0.05–0.78; C: p=0.06, HR 0.26, 95%-CI 0.06–1.06; and PC: p = 0.76, HR 0.88, 95%-CI 0.4–1.97), independent of performance status, age, gender and smoking history. Conclusions: This study suggests that the same predictive algorithm for MALDI-MS generated from patients with lung cancer treated with EGFRIs is also predictive of survival outcome in HNSCC patients treated with both TKIs and cetuximab, and may allow rational selection of patients most likely to benefit from an EGFRI monotherapy.

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