Julia Grigorieva

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.

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.

A Retrospective Analysis of VeriStrat Status on Outcome of a Randomized Phase II Trial of First-Line Therapy with Gemcitabine, Erlotinib, or the Combination in Elderly Patients (Age 70 Years or Older) with Stage IIIB/IV Non–Small-Cell Lung Cancer

Purpose: In a multicenter randomized phase II trial of gemcitabine (arm A), erlotinib (arm B), and gemcitabine and erlotinib (arm C), similar progression-free survival (PFS) and overall survival (OS) were observed in all arms. We performed an exploratory, blinded, retrospective analysis of plasma or serum samples collected as part of the trial to investigate the ability of VeriStrat (VS) to predict treatment outcomes. Methods: Ninety-eight patients were assessable, and the majority had stage IV disease (81%), adenocarcinoma histology (63%), reported current or previous tobacco use (84%), and 26% had a performance status (PS) of 2. Results: In arm A, patients with VS Good (n = 20) compared with VS Poor status (n = 8) had similar PFS (hazard ratio [HR]: 1.21; p = 0.67) and OS (HR: 0.82; p = 0.64). In arm B, patients with VS Good (n = 26) compared with VS Poor (n = 12) had a statistically significantly superior PFS (HR: 0.33; p = 0.002) and OS (HR: 0.40; p = 0.014). In arm C, patients with VS Good (n = 17) compared with Poor (n = 1 5) had a superior PFS (HR: 0.42; p = 0.027) and a trend toward superior OS (HR: 0.48; p = 0.051). In the multivariate analysis for PFS, VS status was statistically significant (p = 0.011); for OS, VS status (p = 0.017) and PS (p = 0.005) were statistically significant. A statistically significant VS and treatment interaction (gemcitabine versus erlotinib) was observed for PFS and OS. Conclusions: Gemcitabine is the superior treatment for elderly patients with VS Poor status. First-line erlotinib for elderly patients with VS Good status may warrant further investigation.

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.

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.

A Serum Protein Signature Associated with Outcome after Anti–PD-1 Therapy in Metastatic Melanoma

A proteomic signature in serum identifies melanoma patients who will experience long or short survival after checkpoint inhibition therapy. The signature, determined by mass spectrometry and machine learning, may predict patients who would benefit most from immunotherapy. A mass spectrometry analysis was performed using serum from patients receiving checkpoint inhibitors to define baseline protein signatures associated with outcome in metastatic melanoma. Pretreatment serum was obtained from a development set of 119 melanoma patients on a trial of nivolumab with or without a multipeptide vaccine and from patients receiving pembrolizumab, nivolumab, ipilimumab, or both nivolumab and ipilimumab. Spectra were obtained using matrix-assisted laser desorption/ionization time of flight mass spectrometry. These data combined with clinical data identified patients with better or worse outcomes. The test was applied to five independent patient cohorts treated with checkpoint inhibitors and its biology investigated using enrichment analyses. A signature consisting of 209 proteins or peptides was associated with progression-free and overall survival in a multivariate analysis. The test performance across validation cohorts was consistent with the development set results. A pooled analysis, stratified by set, demonstrated a significantly better overall survival for “sensitive” relative to “resistant” patients, HR = 0.15 (95% confidence interval: 0.06–0.40, P < 0.001). The test was also associated with survival in a cohort of ipilimumab-treated patients. Test classification was found to be associated with acute phase reactant, complement, and wound healing pathways. We conclude that a pretreatment signature of proteins, defined by mass spectrometry analysis and machine learning, predicted survival in patients receiving PD-1 blocking antibodies. This signature of proteins was associated with acute phase reactants and elements of wound healing and the complement cascade. This signature merits further study to determine if it identifies patients who would benefit from PD-1 blockade. Cancer Immunol Res; 6(1); 79–86. ©2017 AACR.

Pre-treatment patient selection for nivolumab benefit based on serum mass spectra

The durability of anti-tumor responses observed in patients treated with antibodies blocking PD-1 has provided a central role for these drugs in melanoma therapeutics. Identifying predictive biomarkers to aid therapeutic decision making is critical for realizing the full potential of these immunotherapies. We report on the development of a pre-treatment serum test to separate melanoma patients into two groups with significantly different outcomes following nivolumab therapy.