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Journal of Thoracic Oncology:
doi: 10.1097/JTO.0b013e31826c1155
Original Articles

Prognostic and Predictive Role of the VeriStrat Plasma Test in Patients with Advanced Non–Small-Cell Lung Cancer Treated with Erlotinib or Placebo in the NCIC Clinical Trials Group BR.21 Trial

Carbone, David P. MD, PhD*; Ding, Keyue PhD; Roder, Heinrich PhD; Grigorieva, Julia PhD; Roder, Joanna PhD; Tsao, Ming-Sound MD, PhD§; Seymour, Lesley MD, PhD; Shepherd, Frances A. MD§

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Author Information

*Department of Medicine, Vanderbilt-Ingram Cancer Center, Nashville, Tennessee; †NCIC Clinical Trials Group, Queens University, Kingston, Ontario, Canada; ‡Biodesix, Inc., Boulder, Colorado; and §University Health Network, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada.

Disclosure: Dr. Carbone has received funding from National Cancer Institute Specialized Program of Research Excellence P50-90949 Strategic Partnering to Evaluate Cancer Signatures and U01 CA 114771, Biodesix, Inc. The other authors declare no conflict of interest.

Address for correspondence: David P. Carbone, MD, PhD, B402 Starling Loving Hall, 320 West 10th Avenue, Columbus, OH 43210. E-mail:

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Introduction: We investigated the predictive and prognostic effects of VeriStrat, a serum or plasma-based assay, on response and survival in a subset of patients enrolled on the NCIC Clinical Trials Group, BR.21 phase III trial of erlotinib versus placebo in previously treated advanced non–small-cell lung cancer patients.

Methods: Pretreatment plasma samples were available for 441 of 731 enrolled patients and were provided as anonymized aliquots to Biodesix. The VeriStrat test was performed in a Clinical Laboratory Improvement Act-accredited laboratory at Biodesix, Inc. (Boulder, CO). The results (Good, Poor) were returned to NCIC Clinical Trials Group, which performed all the statistical analyses.

Results: VeriStrat testing was successful in 436 samples (98.9%), with 61% classified as Good. VeriStrat was prognostic for overall survival in both erlotinib-treated patients and those on placebo, independent of clinical covariates. For VeriStrat Good patients, the median survival was 10.5 months on erlotinib versus 6.6 months for placebo (hazard ratio 0.63, 95% confidence interval 0.47–0.85, p = 0.002). For VeriStrat Poor patients, the median survival was 4 months for patients receiving erlotinib, and 3.1 months for placebo (hazard ratio: 0.77, 95% confidence interval 0.55–1.06, p = 0.11). VeriStrat was predictive for objective response (p = 0.002), but was not able to predict for differential survival benefit from erlotinib (interaction p = 0.48). Similar results were found for progression-free survival.

Conclusion: We were able to confirm that VeriStrat is predictive of objective response to erlotinib. VeriStrat is prognostic for both OS and progression-free survival, independent of clinical features, but is not predictive of differential survival benefit versus placebo.

BR.21 was a randomized placebo-controlled study of erlotinib in previously treated patients with advanced non–small cell lung cancer (NSCLC). The overall response rate was 8.9% in the erlotinib arm compared with less than 1% for placebo, and both progression-free survival (PFS) and overall survival (OS) were prolonged by erlotinib.1 Correlative studies performed in patients with available tissue showed that epidermal growth factor receptor (EGFR) protein expression, the presence of activating EGFR mutations, and high EGFR copy number were predictive of response. EGFR mutations were prognostic for OS, but were not predictive, whereas increased EGFR copy number was both prognostic and predictive for OS benefit.2,3 Tumor tissue was not available in all patients, highlighting the need for less-invasive predictive tests such as serum or plasma biomarkers. A recent exploratory study on plasma samples from BR.21 reported amphiregulin as a prognostic marker and transforming growth factor-α as a biomarker predictive of OS benefit from erlotinib4; these observations remain to be validated in prospective clinical trials.

VeriStrat is a commercially available serum- or plasma-based test using matrix-assisted laser desorption ionization (MALDI) mass spectrometry methods. Veristrat analysis was conducted by Biodesix (Boulder, CO) in their CLIA-accredited laboratory. It was developed using a training set of pretreatment serum samples from patients who experienced long-term stable disease or early progression on gefitinib therapy.5 Mass spectra from serum samples of these patients were used to define eight mass spectra features (i.e., peaks), differentiating these two outcome groups. An algorithm, using these features and based on a k-nearest neighbors classification scheme, was created and its parameters were optimized using additional spectra from the training cohort. The current commercial test uses a fixed set of parameters established during the development phase. VeriStrat assigns each spectrum a binary classification of Good or Poor. Validation studies were performed in a blinded fashion using multiple single-arm cohorts of patients undergoing EGFR tyrosine kinase inhibitor (TKI) therapy. Two independent cohorts of patients who were treated with gefitinib or erlotinib confirmed that patients classified as Good had better outcomes than patients classified as Poor (hazard ratio [HR] of death 0.47, p = 0.009 and HR of death 0.33, p = 0.0007).5 In other control cohorts, VeriStrat status did not correlate significantly with clinical outcome after chemotherapy (HR 0.74, p = 0.42 and HR 0.81, p = 0.54) or in the postsurgery setting (HR 0.90, p = 0.79).5 On the basis of these results, it was postulated that VeriStrat might be a predictive marker specifically for EGFR TKI therapy.

The primary goal of the current study was to test VeriStrat’s ability to predict response and survival benefit (PFS and OS) from erlotinib, using pretreatment plasma samples from BR.21.

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Patients and Plasma Samples

In BR.21, 731 patients were randomized (2:1 ratio) to receive erlotinib or placebo. The clinical trial database resides at NCIC Clinical Trials Group. Full details of the methodology have been published previously.1 Blood samples were collected from consenting patients for pharmacokinetic assays and for banking and stored at the Tumor Tissue Repository of the NCIC CTG, Kingston, Ontario. Patients provided separate written consent for this optional tissue banking. Baseline pretreatment samples from consenting patients were anonymized, using a unique ID, aliquoted, and provided to Dr. David Carbone for analyses. No clinical data were sent. The Research Ethics Board at Vanderbilt University approved this study.

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VeriStrat Analysis

VeriStrat analysis was conducted on 441 available plasma samples by Biodesix (Broomfield, CO). Samples were thawed on wet ice and aliquots diluted 1:10 in HPLC-grade water (Burdick & Jackson, Muskegon, MI), then combined with an equal volume of sinapinic acid (Sigma, St. Louis, MO) solution (25 mg/ml sinapinic acid prepared in 50% acetonitrile[Burdick & Jackson]/0.1% trifluoroacetic acid [Sigma]). Each sample-matrix mixture was spotted in triplicate at randomly assigned positions on polished stainless steel MALDI plates (BrukerDaltonics, Bremen, Germany). Positive ion mass spectra for all samples and replicates were acquired in linear mode, using the BrukerAutoflex III mass spectrometer. Averaged spectra, consisting of 2000 independent spectrum acquisitions, from each sample replicate were used for processing and classification. Spectral processing included background and noise estimation, background subtraction, normalization to partial ion current, and alignment. The classification algorithm, a k-nearest neighbor classifier, based on eight distinct m/z features,5 was applied to the averaged, processed spectra. A VeriStrat label of Good or Poor was produced for each sample when all replicates from a sample gave the same classification. When replicates from a sample gave discordant classifications, an indeterminate label was assigned. Results were sent to NCIC CTG where they were merged with the clinical trial database.

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Statistical Analysis

A statistical analysis plan was agreed before any analyses being conducted. Exploratory analyses were performed to characterize the relationship between VeriStrat status and baseline characteristics and outcomes. Chi-square tests or Fisher’s exact tests were used to assess the association between categorical variables; the Kaplan–Meier product limit method and the log-rank test were used to estimate and compare the distributions of time-to-event outcomes. A Cox regression model with interaction terms included was used to verify VeriStrat’s prognostic and predictive effect on the primary endpoint of OS while adjusting for other baseline factors, including sex, age (≤60 years versus >60 years), Eastern Cooperative Oncology Group performance status (0,1 versus 2,3), pathologic subtype (adenocarcinoma versus squamous versus others), response to prior therapy (complete response/partial response versus progressive disease versus stable disease), number of prior regimens (1 versus 2/3), prior platinum, EGFR expression by immunohistochemistry (IHC) (positive versus negative versus unknown), race (Asian versus other), EGFR gene mutation status (exon 19 or 21 versus not mutated + other mutation versus unknown), time from diagnosis to randomization (<12 months versus ≥ 12 months), weight loss (< 5% versus ≥ 5%), smoking status (nonsmoker versus ever smoked versus unknown) and EGFR fluorescence in-situ hybridization (FISH) status: (high copy/amplified [FISH+] versus low copy [FISH−] versus unknown). Prognostic analyses were performed on patients enrolled to the placebo arm only. Statistical analyses were carried out using SAS Version 9.1 (SAS Institute, Cary, NC). All reported p values are two sided and levels of significance taken to 0.05.

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Of 441 plasma samples available, 436 (98.9%) could be classified as Good or Poor. Table S1 summarizes the baseline factors for patients with evaluable results (Supplemental Digital Content 1, The evaluable cohort had significantly more male patients (p = 0.03) and derived better OS benefit from erlotinib (HR 0.67, 95%CI 0.54–0.83, p=0.0003, Fig. 1) compared with the nonevaluable cohort (HR 0.93, 95%CI 0.73–1.22, p = 0.61). In the Cox regression model the test of interaction was 0.06. The reason for the differential benefit is unclear.

Figure 1
Figure 1
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Baseline characteristics for evaluable patients classified as Good and Poor are summarized in Table S2 (Supplemental Digital Content 1, Patients classified as Good were more likely to have characteristics usually associated with better prognosis: female sex (p = 0.02), Asian race (p = 0.005), good performance status (p < 0.0001), adenocarcinoma (p < 0.0001) and weight loss <5% (p < 0.0001). There was no significant correlation between classification and smoking status or response to prior chemotherapy. Although there was a correlation between classification and EGFR IHC status, as in previous studies6,7 no significant correlations were found with EGFR or KRAS mutation status, or EGFR gene copy number. Although the differences were not significant most patients who had EGFR exon 19 or 21 mutations were in the Good cohort (71%) as were lifetime nonsmokers (65%).

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Prognostic Properties of VeriStrat

OS for the 144 placebo patients is shown in Figure 2. VeriStrat was prognostic with Good patients (median survival 6.6 months, 95% CI 4.4–8.2) surviving significantly longer than Poor patients (3.1 months, 95%CI 2.2–3.7; HR 0.44, 95% CI 0.31–0.63, p < 0.0001). VeriStrat remained prognostic (p = 0.05) in multivariate analysis (Table 1). Similar results were obtained for PFS (data not shown); HR Good versus Poor 0.59 (95% CI 0.42–0.83, p = 0.0016) in both univariate and multivariate (p = 0.001) analyses (Table 1).

Table 1
Table 1
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Figure 2
Figure 2
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Predictive Properties of VeriStrat on Survival

The interaction term comparing relative benefit in the two cohorts was not significant (p = 0.48), indicating that both the Good and Poor cohorts derived similar relative benefit from erlotinib. Median survival was 10.5 months for Good patients treated with erlotinib versus 6.6 months for those on placebo (HR 0.63, 95% CI 0.47–0.85; p = 0.002) (Fig. 3A and B) whereas in the Poor cohort, the median survival for erlotinib was 3.98 months and 3.09 months for placebo (HR 0.77, 95%CI 0.55–1.06, p = 0.11). Similar results were found in multivariate analyses (Table 2) adjusted for potential confounding factors and other predictive markers with a nonsignificant interaction test (p = 0.50). In unplanned exploratory analyses EGFR copy number (FISH +) was predictive of erlotinib benefit (p = 0.05).

Table 2
Table 2
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Figure 3
Figure 3
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Similar results were seen for PFS (Fig. 3C and D). Both Good and Poor patients had significant PFS benefit from treatment (p = 0.0000 and 0.05, respectively, interaction p = 0.36). In multivariate adjusted analyses, the interaction p value again was not significant (Table 2).

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Predictive Properties of VeriStrat for Objective Response

Response data for patients on the erlotinib arm8 are summarized in Table 3. Of 252 erlotinib-treated patients evaluable for response, 157 (62%) were classified as Good and 95 (38%) as Poor. Good patients had a significantly higher response rate than Poor patients did (11.5% versus 1.1%, p = 0.002), with a Good classification remaining independently significantly correlated with response after adjustment for potential confounding factors (Table S3).

Table 3
Table 3
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Exploratory Subgroup Analyses

In analyses of patients without detected activating EGFR mutations (exon 19 deletion or exon 21 L858R) and patients without adenocarcinoma histology, there was no significant interaction, indicating no evidence of differential benefit for either subgroup (p = 0.51 and 0.73, respectively). The median OS for Good patients was 10.5 versus 6.3 months (HR 0.63 in patients without detected activating EGFR mutation, p = 0.004) and 10.5 versus 5.8 months (HR 0.60 for nonadenocarcinoma p = 0.02), and for Poor patients, 4.0 versus 3.1 months (HR 0.78 in patients without detected activating EGFR mutation, p = 0.13) and 4.9 versus 3.1 months (HR 0.71 nonadenocarcinoma, p = 0.11).

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Biomarker-based selection of patients for specific targeted therapies is becoming a standard of care.9,10 One example of this is patients whose tumors carry activating EGFR mutations.11,12 The Iressa Pan-Asia Study trial (gefitinib versus carboplatin/paclitaxel as first-line treatment for pulmonary adenocarcinoma among an Asian population of neversmokers or former light smokers) clearly demonstrated the superiority of gefitinib versus chemotherapy in terms of PFS and response rate (although not OS), for those with EGFR mutation-positive tumors.13,14 The results of trials performed after at least second-line treatment also confirmed the predictive effect of EGFR mutations on tumor response and PFS; however, the predictive effect of mutations on OS remains unclear, as patients with EGFR mutant tumors seem to survive longer regardless of therapy. Although this is usually attributed to the effects of postprogression crossover to TKI treatment, trials of unselected patients have demonstrated survival benefit in patients without such mutations.15 An analysis of BR.21 showed that EGFR mutations and high EGFR copy number are predictive of response to erlotinib, but mutation status was not predictive of OS benefit compared with patients with wild-type EGFR tumors, although the number of patients with mutations probably was too low to demonstrate significant quantitative interaction.3 The Iressa Non-small Cell Lung Cancer Trial Evaluating Response and Survival against Taxotere, a randomized study comparing second-line treatment with docetaxel or gefitinib, also showed the predictive role of EGFR mutations in response and PFS to gefitinib, whereas no measured biomarker was predictive of differential survival benefit.10,16 Interestingly, the Sequential Tarceva in Unresectable NSCLC trial of maintenance erlotinib showed statistically significant OS benefit only in patients without EGFR mutations,17 and in BR.19, where unselected patients were randomized to gefitinib versus placebo as postoperative adjuvant therapy, there was no survival benefit in the overall study population, nor in patients with EGFR mutated tumors.18

Activating EGFR mutations are present in approximately 30% to 40% of Asian patients, but only 5% to 15% in whites.19,20 Although large-scale mutation screening is feasible,21 sample collection and successful mutation analysis in most large multisite clinical trials typically have been low, (20%–30%). This may be because of many factors including scanty tissue from cytology diagnostic slides, tissue-quality requirements for the biomarker assay, and the capacity and infrastructure of the investigational site.22 In recent publications, it has been highlighted that although DNA sequence abnormalities may seem to be very readily and reliably measured, in practice the observed discordance of mutation status assessment is highly dependent on sample quality and the method of analysis, and can reach 30%. This may affect the outcome of treatment.23 Thus, the presence of EGFR mutations, although now widely accepted as the basis for choosing an EGFR-TKI for frontline therapy of advanced NSCLC, does not identify the entire population of NSCLC patients who may benefit from these drugs. Especially useful would be biomarkers that can be measured using samples obtained by noninvasive procedures in every patient.

As a blood-based test, VeriStrat, is reproducible, readily available, and has the potential to overcome the difficulties of obtaining fresh biopsy tissue from patients. In published studies using samples from patients not enrolled in randomized controlled trials, it was suggested that VeriStrat might be predictive of EGFR inhibitor benefit even in a population of smokers and in those with squamous carcinoma.5 Further studies in other tumor types also reported different survival outcomes in VeriStrat Good and Poor subsets that was independent of the specific anti-EGFR agents used.7 In squamous cell carcinoma of the head and neck, VeriStrat Good designation was associated with longer survival in patients treated with gefitinib (HR Good versus Poor 0.41, p = 0.007) and in studies of patients treated with erlotinib/bevacizumab (HR 0.2, p = 0.02), and cetuximab (HR 0.26, p = 0.06). The patients in these analyses were not part of randomized trials with control arms that did not include EGFR therapy; however, two chemotherapy-only cohorts of lung cancer patients,5 a chemotherapy-only cohort of squamous cell carcinoma of the head and neck patients,7 and a surgery-only cohort of lung cancer patients5 showed no statistically significant survival difference when classified by the VeriStrat test. In another cohort study of colorectal cancer patients treated with cetuximab, PFS was significantly longer for Good compared with Poor patients (HR 0.51, p = 0.0065).7 In a study of NSCLC patients treated second-line with a combination of erlotinib and bevacizumab, patients classified as Poor were shown to have extremely poor OS and PFS24 compared with those classified as Good (HR 0.14, p = 0.007 and HR 0.045, p = 0.0003 for OS and PFS, respectively). Similar results were obtained in a first-line study of the same combination of targeted treatments in nonsquamous NSCLC, which reported a median PFS of 16.5 weeks in Good patients and 9.3 weeks in Poor patients and median OS of 79.1 weeks in Good patients and 12.5 weeks in Poor patients.25 A study of NSCLC patients treated with first-line erlotinib and sorafenib found an HR of 0.30 (95% CI 0.12–0.74; p = 0.009) for OS and 0.40 (95% CI 0.17–0.94, p = 0.035) for PFS between Good and Poor patients.26

Because of the absence of randomization to control arms that did not include EGFR therapy in the above studies, it was not possible assess whether the VeriStrat test identified two cohorts of patients with different prognoses or whether it truly was predictive of better outcome from EGFR inhibitor therapy. In all three of the chemotherapy-only and surgical studies, VeriStrat Good patients experienced longer survival than Poor patients. Although the differences in survival were not significant, these studies may have provided the first hint that VeriStrat might be a prognostic test.

Our study evaluated both the prognostic and predictive value of VeriStrat. Of 731 patients enrolled on BR.21, plasma samples from 441 were available for testing and it was possible to classify 99% of patients into Good and Poor cohorts. We were able to confirm that VeriStrat was predictive of response. However, for both OS and PFS, VeriStrat was prognostic, but was not predictive of differential benefit from erlotinib. In exploratory analyses, although significant correlation was detected between VeriStrat and EGFR protein expression determined by IHC, no significant correlations were found with other measured biomarkers, including EGFR or KRAS mutation status, and EGFR FISH status, confirming results from previous studies.6,7

There are several limitations to our study. Although plasma collection was planned prospectively in the original clinical trial, this assay and the analyses were not. Although sample size estimations taken before our analyses suggested sufficient power to test the hypothesis, the sample size of the clinical trial was based on clinical outcomes, and not all patients consented to the storage of plasma, leading to a nonrandom subset of patients in this study. Indeed, there were differences in baseline clinical characteristics and outcomes in patients with and without plasma samples,4 a common problem of retrospective biomarker studies on subsets of the trial population.27

Although our results confirm a predictive effect of VeriStrat for response, they do not confirm a predictive effect for VeriStrat on OS or PFS. VeriStrat did seem to identify a subset of previously treated patients who experienced a survival benefit that may be considered more clinically meaningful than that seen in VeriStrat Poor patients in whom survival was short. Alternatives to erlotinib may be considered for these patients. More information should become available from the Randomized Proteomic Stratified Phase III Study of Second-line erlotinib versus Chemotherapy in Patients with Inoperable Non–Small-Cell Lung Cancer currently accruing, which will compare erlotinib with chemotherapy in patient cohorts stratified according to VeriStrat classification.

The nature, origin, and potential direct biological significance of the detected protein biomarkers is as yet not completely clear. As biomarker studies developing classifiers from large protein or RNA expression data sets essentially are correlative in nature, conclusions about cause and effect between the measured biomarkers and the measured outcomes frequently are impossible. Despite these considerations, efforts have been made to identify the proteins constituting the measured features. Several of the peaks seem to contain isoforms of serum amyloid A, but several remain to be identified. However, patients classified as Poor have not yet been seen in our studies of inflammatory diseases associated with high serum amyloid A levels, such as rheumatoid arthritis or chronic obstructive pulmonary disease. As the VeriStrat Poor classification has now been identified in many epithelial cancer types, including breast, renal, colorectal, melanoma, upper gastrointestinal, and head and neck, but not in healthy patients, and because the mechanism of MALDI mass spectrometry makes it easiest to detect high-to-mid-range abundance proteins, it is possible that VeriStrat is detecting a tumor–host response to the presence of the cancer.

Recently, in exploratory hypothesis generating analyses, the EGFR ligand TGF-α was shown to be predictive of benefit from erlotinib versus placebo in this same patient population.4 High baseline transforming growth factor (TGF)-α (present approximately 10 % of study patients) predicted lack of benefit from erlotinib compared with low TGF-alpha (TGF-alpha low, OS HR 0.66; 95% CI 0.54–0.81; p = 0.0001; high, OS HR 1.32; 95% CI 0.73–2.39; p = 0.36; interaction p = 0.04). Baseline TGF-α was not prognostic or predictive for PFS. In the same study, amphiregulin, another EGFR ligand, was found to be prognostic, but not predictive of a differential survival benefit from erlotinib. However, this study did not have separate training and testing cohorts, and thus the results require independent confirmation.

In summary, VeriStrat is able to predict response to erlotinib and is a prognostic biomarker in previously treated patients with advanced NSCLC. Further studies are required to define the clinical utility of VeriStrat and other blood-based biomarkers in defining the appropriate patient population for therapy with erlotinib and other EGFR-based therapeutics.

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Erlotinib; Proteomics; Metastatic Non–small-cell lung cancer; Biomarkers

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© 2012International Association for the Study of Lung Cancer


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