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Journal of Thoracic Oncology:
doi: 10.1097/JTO.0b013e3182307f17
Original Articles: Biology/Basic Science

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

Lazzari, Chiara MD*; Spreafico, Anna MD*; Bachi, Angela PhD†; Roder, Heinrich PhD‡; Floriani, Irene PhD§; Garavaglia, Daniela PhD§; Cattaneo, Angela PhD†; Grigorieva, Julia PhD‡; Viganò, Maria Grazia MD*; Sorlini, Cristina MD*; Ghio, Domenico MD∥; Tsypin, Maxim PhD‡; Bulotta, Alessandra MD*; Bergamaschi, Luca MD*; Gregorc, Vanesa MD*

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Author Information

*Department of Oncology, San Raffaele Scientific Institute; †Biomolecular Mass Spectrometry Unit, Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy; ‡Biodesix, Steamboat Springs, Colorado; §Istituto di Ricerche Farmacologiche “Mario Negri”; and ∥Department of Radiology, San Raffaele Scientific Institute, Milan, Italy.

Address for correspondence: Vanesa Gregorc, San Raffaele Scientific Institute, Via Olgettina, 60. 20132 Milan, Italy. E-mail:

The first two authors equally contributed to the work.

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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.

The epidermal growth factor receptor (EGFR) pathway plays a key role in the development and progression of non-small cell lung cancer (NSCLC) and many other malignant epithelial tumors.1 Major achievements in the treatment of lung, colorectal, and head and neck cancer were associated with the development of targeted drugs inhibiting the EGFR pathway.2

Gefitinib and erlotinib are oral EGFR tyrosine kinase inhibitors (EGFR TKIs) shown to be active in NSCLC patients.3–7 Nevertheless, despite a high expression level of EGFR receptor in most lung cancers, only a certain fraction of NSCLC patients benefits from EGFR TKIs.8 Identification of biomarkers predictive of response to treatment became a focus of the clinical research in the recent decade. Multiple studies have shown a positive correlation between activating EGFR gene mutations (exon 19 deletion, exon 18 G719X, and exon 21 L858R mutations), response rate, and progression-free survival (PFS) for EGFR-TKI therapies.9–14 Results from recent phase III clinical trials, comparing gefitinib and chemotherapy arms in front line (IPASS, First SIGNAL, NEJ002, and WJTOG 3405)15–18 and in second line (INTEREST),19,20 have led to the regulatory approval of gefitinib in Europe in all lines of therapy for patients carrying EGFR mutations. At the same time, the INTEREST study showed that in unselected and EGFR wild-type patients, gefitinib and docetaxel provide similar PFS and overall survival (OS).19,20 The BR.21 trial7 (phase III trial of erlotinib versus placebo in previously treated advanced NSCLC patients) showed that erlotinib significantly prolonged OS, compared with placebo, leading to acceptance of erlotinib as a treatment in advanced NSCLC patients after the failure of first line chemotherapy. Nevertheless, correlative studies performed in a subset of patients with available tissue enrolled in the BR.21 trial showed that EGFR mutations were prognostic for OS but were not predictive, whereas increased EGFR copy number was both prognostic and predictive for OS benefit.21 None of measured biomarkers was predictive of OS in the INTEREST trial.20

Considering that prolonged OS and stabilization of the disease are important criteria of benefit from treatment, especially in advanced lines of treatment, and taking into account the low frequency of EGFR mutations in the Caucasian population (approximately 10%),22 and possible discordance in the mutation status between primary NSCLC and corresponding metastatic tumors,23,24 and absence of consensus on the role of molecular markers in second-line treatment selection in EGFR wild-type patients, it is of clinical importance to find additional independent biomarkers predictive of benefit from treatment. The insufficient availability of tumor tissue for molecular analysis, which ranges between 20 and 30% even in large well-designed clinical trials, makes the discovery and validation of a serum or plasma-based predictive test especially desirable. In addition, such a noninvasive test, if it were correlated with a switch from drug sensitivity to drug resistance, would be extremely valuable in monitoring the onset of the acquired resistance that eventually develops in the majority of patients, even those with good initial clinical response.25

In our previous multiinstitutional study, Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI ToF MS) was used to build, develop, and independently validate a serum/plasma test (VeriStrat), able to identify, before treatment, a subset of NSCLC patients with better outcome from EGFR TKI therapy in terms of time to progression and OS.26 The algorithm in the core of the test uses the integrated intensities of eight mass spectral peaks and assigns a classification label “good” or “poor” or “indeterminate.” The identity of the peaks constituting the test and the underlying biological mechanism related to the VeriStrat signature are subjects of ongoing investigations. The test was developed using a training set of samples from three different cohorts of patients treated with gefitinib and validated in blinded fashion in two other independent cohorts treated with gefitinib and erlotinib and in three control cohorts of patients treated with chemotherapy and surgery. The blinded validation showed statistically significant separation in terms of both OS and in terms of time to progression in NSCLC patients treated with EGFR TKIs, whereas no statistically significant separation was observed in cohorts treated with chemotherapy or surgery. The original study was followed by multiple clinical validation studies in NSCLC and in other epithelial tumors. Application of VeriStrat to a subset of samples from the BR.21 trial showed that the test has a significant prognostic component, i.e., demonstrates a separation by VeriStrat for OS and PFS both in placebo and in treatment arms. However, VeriStrat “good” patients received statistically significant benefit from erlotinib therapy over placebo, whereas in VeriStrat “poor” patients, the separation was not statistically significant. In addition, VeriStrat classification was predictive of objective response to erlotinib and significantly correlated with disease control rate in the treatment arm.27 VeriStrat predicted OS of patients with head and neck squamous cell carcinoma (HNSCC) treated with gefitinib, erlotinib/bevacizumab, and cetuximab and PFS in colorectal cancer patients treated with cetuximab; a chemotherapy cohort again showed no statistically significant survival difference.28 In the case of combination therapy targeting EGFR and vascular endothelial growth factor (VEGF), the magnitude of separation, in comparison with monotherapies, increased dramatically.29 The body of results obtained demonstrated the applicability of VeriStrat in various types of epithelial cancers and in a variety of targeted therapies, including EGFR-TKIs, anti-EGFR, and anti-VEGF antibodies and their combinations. The dependence of the magnitude of the separation of survival curves between VeriStrat “good” and “poor” patients on the particular treatment suggests the relation of the test not just to the natural history of the disease of a patient, but to the therapy itself, and provides additional evidence of VeriStrat predictive properties.

The results obtained in the analyses of pretreatment samples inspired the current study of the stability of VeriStrat classification in the course of gefitinib treatment of NSCLC patients, as well of the possible correlation of its changes with disease progression, and of the potential utility of the test for treatment monitoring.

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Patient and Sample Characteristics

This is a retrospective study of samples from NSCLC patients treated with gefitinib in advanced lines of treatment at the Scientific Institute San Raffaele University Hospital of Milan, Italy. Patients provided written informed consent for the study; analyses were performed under the protocol approved by the local institutional review boards. Eligibility criteria included patients aged 18 years and older with cytological or histological diagnosis of advanced or inoperable NSCLC; Eastern Cooperative Oncology Group performance status (ECOG PS) ≤2; adequate hepatic (total bilirubin ≤2.5× the upper limit of normal and AST ≤2.5× normal) function, treatment with gefitinib at the recommended dose (250 mg/daily). Plasma samples were collected at baseline, after 1 month and concomitantly with CT scan evaluation performed every other month until withdrawal from treatment with EGFR TKIs for either toxicity or progression. Progression was defined according to RECIST criteria, version 1.0. CT scan evaluation was performed by a designated radiologist. The collected blood was centrifuged at 2000g for 10 minutes at 4°C, and plasma was separated, aliquoted, and properly stored at −80°C at Scientific Institute San Raffaele University Hospital of Milan, Italy, until analysis.

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MALDI ToF Mass Spectrometry

Proteomic spectra were collected in the Biological Spectra Unit at the Scientific Institute San Raffaele University Hospital, Milan, Italy. Mass spectra were generated on a Voyager DE-STR MALDI ToF mass spectrometer (Applied Biosystems, Framingham, MA). Plasma was thawed on ice and diluted 1:10 in deionized water. One microliter of each diluted sample was spotted on the MALDI target and 1 μl of matrix solution (35 mg/ml sinapinic acid [Sigma, St. Louis, MO], 50% acetonitrile [Burdick & Jackson, Muskegon, MI], and 0.1% trifluoroacetic acid [Sigma]) was added. The solution was mixed by drawing the mixture up and down into the pipette tip 10 times and then expelling it. The MALDI plates were then allowed to dry at room temperature in a dark place. Samples were spotted in triplicate on the MALDI target and at least three spectra were collected for each sample. Positive ion mass spectra were acquired in linear mode in an automated manner. One hundred fifty shots were collected from four unique locations within the perimeter of each MALDI spot to generate an average spectrum from 600 independent spectra for each plasma specimen. Raw spectra were sent electronically to Biodesix (Steamboat Springs, CO) and analyzed using the previously validated VeriStrat algorithm (Biodesix, Inc.) that assigns either “good” or “poor” or “indeterminate” classification to a sample.

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EGFR Mutational Status and EGFR Gene Amplification

All specimens were obtained from the original biopsy, before any treatment. GenomicDNA was derived from tumor tissue after laser capture microdissection. Deletions in exon 19 (del 19) were determined by length analysis after polymerase chain reaction amplification with the use of a FAM-labeled primer in an ABI Prism 3130 DNA Analyzer (Applied Biosystems). Exon 21 point mutations in codon 858 were detected with a 5′ nuclease PCR assay (TaqMan assay) using FAM and VIC MGB-labeled probes for the wild type and the mutant sequence, respectively. All mutants were confirmed by DNA sequencing.

Gene copy number per cell was investigated by fluorescence in situ hybridization (FISH) using the LSI EGFR SpectrumOrange/CEP 7 SpectrumGreen probe (Vysis; Abbott Laboratories, IL), according to a published protocol.30 High EGFR gene copy number was defined as high polysomy (≥4 copies in ≥40% of cells) or gene amplification (presence of tight gene clusters; a gene: chromosome ratio per cell of ≥2; or ≥15 copies of EGFR per cell in ≥10% of cells analyzed).

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Statistical Analysis

PFS was defined as the time from the beginning of gefitinib treatment to first appearance of progressive disease or death from any cause. Patients known to be alive, and who had not progressed at the time of analysis were censored at their last available follow-up assessment. OS was defined as the time from the beginning of gefitinib treatment to the date of death from any cause. Patients not reported as having died at the time of the analysis were censored at the date they were last known to be alive.

Survival curves were estimated using the Kaplan-Meier method. Cox proportional hazards models were used for univariate and multivariate analyses to test demographic characteristics, clinical features, and VeriStrat profile (included as time-dependent variable) for their associations with PFS and OS. Variables found to be associated (p < 0.10) with PFS and OS in the univariate model were included in the multivariate analysis. A logistic regression model was used to assess the association of demographic characteristics and clinical features with VeriStrat status at baseline. Correlation between EGFR amplification by FISH and VeriStrat classification was assessed with χ2 test. Results were expressed as hazard ratios (HRs) or odds ratios (ORs) and relative 95% confidence intervals (CIs). The statistical significance was set at p = 0.05 for a bilateral test. Analyses were carried out with SAS Software, version 9.1 (SAS Institute, Cary, NC).

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Patients' Characteristics

Consecutive plasma samples from NSCLC patients, admitted to Scientific Institute San Raffaele Hospital from October 2001 until December 2004 and included in the previous study,26 were collected in the course of treatment and evaluated in this study. One hundred eleven patients having received gefitinib for a median duration of 3.5 (range, 0.7–47.0) months were analyzed. Patients' characteristics are shown in Table 1. Patients were predominantly males (77%), ever smokers (84%), with ECOG-PS of 0–1 (82%). The majority of them (96%) had advanced disease and 50% had adenocarcinoma histology. Median age was 68 years, ranging from 36 to 91 years. Most of the patients (72%) were treated with gefitinib in a second/third-line setting; 21% of patients received gefitinib as a first-line therapy because of their clinical conditions and comorbidities.

Table 1
Table 1
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In the course of treatment and follow-up, 110 patients progressed and 109 deceased. Median PFS and OS were 3.4 (interquartile range, 2.0–8.5) and 8.3 (interquartile range, 4.0–22.4) months, respectively, as expected in locally advanced and metastatic NSCLC patients (Supplementary digital content 1,

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VeriStrat Classification at Baseline

VeriStrat classification was performed at baseline, after 1 month of gefitinib therapy, and every 2 months concomitantly to CT scan evaluation until withdrawal in a total of 476 plasma samples.

At baseline, 69% of patients were classified as VeriStrat “good” and 28% as VeriStrat “poor.” Concordantly with previously published results,26 only 3% of patients had an “indeterminate” classification; they were excluded from the statistical analyses. Patients classified as VeriStrat “good” had longer PFS (HR, 0.54; 95% CI, 0.35–0.83; p = 0.005) and OS (HR, 0.40; 95% CI, 0.26–0.61; p < 0.0001) than VeriStrat “poor” patients (Figures 1 and 2). VeriStrat “good” classification was associated with adenocarcinoma histology and ECOG PS both in univariate and multivariate analyses. For all other variables, no statistically significant correlation was detected (Table 2).

Figure 1
Figure 1
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Figure 2
Figure 2
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Table 2
Table 2
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VeriStrat Classification in the Course of Treatment

In the course of treatment (before progression or withdrawal for other reasons), 98 of 111 (88%) patients maintained their baseline VeriStrat classification and only 13 (11%) presented one or more intraindividual changes of label (from “good” to “poor” or vice versa). VeriStrat classification of individual patients in the course of gefitinib treatment, along with PFS and OS, is presented in Figure 3.

Figure 3
Figure 3
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At treatment withdrawal, the number of VeriStrat “good” profile patients decreased from 69 to 51%, whereas the number of VeriStrat “poor” profile patients increased from 28 to 43%; 6 patients (6%) were “indeterminate.” The data are summarized in Table 3. Twenty of 71 (28%) of “good” classified patients shifted to a “poor” profile at withdrawal, and in 90% of these cases they either stopped treatment because of the evidence of new lesions or died early. Within this subgroup, 61% of patients presented new lesions. Patients who shifted from “good” to “poor” classification had a higher risk of developing new lesions in comparison with other patients (OR, 2.9; 95% CI, 1.02–8.37; p = 0.049). Nevertheless, new lesions were also observed in 22% of patients who remained “good” at progression. Interestingly, in 12% of the cases that remained good at progression with new lesions, the brain was the only site of new metastases. Of 32 baseline VeriStrat “poor” classified patients, 27 (84%) maintained a “poor” profile at treatment withdrawal, 96% of whom remained steadily “poor”: 7 of them died within 1 month from the beginning of treatment and 20 patients progressed early during gefitinib therapy. Of the five patients who changed profile, one (3%) became “indeterminate” and four (12%) shifted to “good.” Two of these four patients had a pronounced clinical benefit and remained on treatment for 3 years.

Table 3
Table 3
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VeriStrat classification, considered as a time-dependent variable, had a statistically significant effect both on PFS and OS. “Good” classification was associated with longer PFS both in univariate (HR, 0.54; 95% CI, 0.35–0.82; p = 0.004) and multivariate (HR, 0.52; 95% CI, 0.30–0.92; p = 0.025) analysis. “Good” VeriStrat classification was associated with longer OS both in univariate and multivariate analysis (HR, 0.35; 95% CI, 0.23–0.55; p < 0.0001 and HR, 0.44; 95% CI, 0.26–0.72; p = 0.001, respectively). Smoking history, ECOG-PS, platinum-based first line chemotherapy (only in univariate analysis), and presence of metastases seemed to significantly affect PFS and OS in both models. Results are reported in Table 4.

Table 4
Table 4
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EGFR Gene Amplification and Mutational Analysis

Of 111 patients, 17 (15%) had an adequate tissue sample to perform both EGFR gene amplification and EGFR mutation analysis; for 34 patients (30%), only EGFR gene amplification was assessable. Mutation analysis identified one EGFR mutation (exon 21 L858R). No correlation between known EGFR status and baseline VeriStrat classification was found (Supplementary digital content 2,

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In our previous work, VeriStrat was developed as a test for patient selection for EGFRTKIs treatment in NSCLC. Further studies showed that it was also applicable to other epithelial cancers, such as colorectal and HNSCC, and to other targeted therapies, including anti-EGFR and anti-VEGF, actually demonstrating larger effects in cases of combination treatments. In the studied chemotherapy-treated cohorts of NSCLC and HNSCC patients, the test did not show significant separation of survival curves between “good” and “poor” subgroups.26,28 Retrospective analysis of the available samples from the BR.21 trial showed that the VeriStrat test has a significant prognostic component, i.e., OS and PFS were significantly different between VeriStrat “good” and “poor” groups not only in the erlotinib arm but also in the placebo arm. Nevertheless, VeriStrat “good” patients received statistically significant benefit from the targeted therapy when compared with placebo, whereas in the VeriStrat “poor” group, this difference was not statistically significant. Response to treatment was also significantly correlated with the VeriStrat “good' classification.27 Apparently, differences in PFS and OS of VeriStrat “good” and “poor” patients are caused by the combination of their prognostic characteristics and differences in their reaction to specific treatments. The relative impact of both components depends on the particular treatment and requires further investigation. The clinical relevance of VeriStrat in the advanced NSCLC population is defined by its ability to assist in the choice of the optimal treatment between chemotherapy and targeted therapy in the second-line setting. This is especially important in EGFR wild-type patients and in patients with unknown EGFR status.

Our preliminary data presented at 13th World Conference on Lung Cancer give an indication that VeriStrat “poor” patients have longer OS when treated with chemotherapy, rather than with gefitinib, suggesting that chemotherapy might be preferred in this subgroup. In the VeriStrat “good” group, there was no statistically significant difference in survival between the chemotherapy- and gefitinib-treated patients.32 Taken together, these data suggest that VeriStrat “poor” classified patients have worse clinical outcome than VeriStrat “good” patients, especially when treated with targeted agents, whereas chemotherapy might improve survival in this subgroup. This observation needs further validation and the ongoing prospective phase III trial “Randomized Proteomic Stratified Phase III Study of Second Line Erlotinib versus Chemotherapy in Patients with Inoperable Non-Small Cell Lung Cancer (PROSE)” was designed to provide further clarification of this question.

The aim of this study was to investigate possible changes in VeriStrat classification of plasma samples from NSCLC patients collected in the course of treatment with gefitinib, concomitantly with CT evaluations. Statistical reanalysis of baseline samples, now using the mature survival data, confirmed a significant separation in OS and PFS curves between VeriStrat “good” and “poor” patients. Seventy per cent of patients retained their baseline VeriStrat classification at treatment withdrawal, whereas 30% experienced a change. The majority of the observed changes were from “good” to “poor” at progression of the disease. About one-third of baseline “good” classifications had changed to “poor,” and in 90% of these cases, progression was associated with the development of new lesions, detected by radiological assessment, or patients died within 1 month from the start of treatment. The risk of new lesions in patients with the shift in classification from “good” to “poor” was significantly higher than in the rest of the population (OR, 2.9; p = 0.049).

There were three distinct types of progression in patients who remained “good” in the course of observation. Twenty-two per cent of cases had progression with new lesions. Other patients did not develop new lesions, and disease progression was defined as an increase of the target lesion diameter by RECIST criteria. This observation supports criticism of the clinical impact of RECIST criteria, in particular, of the relation between critical dimensional increase of target lesions and OS. Finally, in a small group of stably “good” patients, progression was diagnosed as occurrence of new lesions in the brain only. Apparently, this type of progression is not associated with changes of VeriStrat molecular profile, probably because of the specific nature of brain metastases.

The majority of baseline “poor” patients maintained their classification in the course of treatment and at withdrawal; four patients had shifted to “good” and, interestingly, two of them had pronounced clinical benefits and long survival times. Of note, the change in the VeriStrat classification was observed in these patients at the time of the second blood draw (after 1 month of therapy), whereas the timing of the baseline sample coincided with patient conditions associated with acute inflammation, i.e., surgery (femoral prosthesis) in one case and pulmonary thrombosis in the other case.

In summary, the data obtained in this study indicate that the molecular species responsible for the VeriStrat mass spectral signature are time-dependent dynamic markers, probably related to some specific unknown primary (in the case of baseline VeriStrat “poor” patients) or acquired (in the case of change of classification from “good” to “poor”) EGFR TKI resistance mechanisms. Recent results presented at 2011 AACR conference showed that VeriStrat “poor” and “good” serum can have different biological effects on NSCLC cell lines: incubation of HCC4006 EGFR-TKI sensitive cell lines in a medium containing 10% serum from VeriStrat “poor” patients increased the resistance of these cells to gefitinib, whereas addition of VeriStrat “good” serum did not cause changes in the drug sensitivity. These data support the hypothesis that the VeriStrat “poor” signature may be associated with some mechanisms that influence cell sensitivity to targeted agents.33 The ongoing study aimed at the identification of the proteins constituting the VeriStrat mass-spectral signature and understanding the biological mechanism associated with it is supported by Associazione Italiana sulla Ricerca del Cancro.

Although sensitivity to EGFR-TKIs is considered to be associated with activating mutations in tyrosine-kinase domain, the data on primary resistance in NSCLC are conflicting, with some studies showing the involvement of KRAS, BRAF, and PI3K mutations. A secondary T790M mutation in the kinase domain of the EGFR34 and activation of alternative pathways allowing the bypassing of inhibition of EGFR signaling, such as overexpression of hepatocyte growth factor35 and MET amplification,36 activation of AKT/mTOR, or changes in signaling via insulin-like growth factor-1 receptor,37–39 are known resistance mechanism to EGFR TKI therapy.

Available data on the VeriStrat test in relation to studied genetic markers obtained in the retrospective analysis of the BR.21 study27 as well on several smaller studies28,40 did not show any significant correlations between VeriStrat classification and EGFR mutational status, EGFR gene amplification, or KRAS mutations. In this study, we also did not find a correlation between EGFR gene amplification by FISH and VeriStrat. Possible associations with other genetic characteristics, especially with T790M, need further investigation. Nevertheless, it has been shown that the T790M increases ATP affinity only for the EGFR mutant L858R and not for the wild-type receptor, which is a predominant population in our study.41

In addition, not fully understood mechanisms, probably involving concomitant activation of multiple, often overlapping signaling pathways, may also be involved in resistance to targeted agents.42,43 It was shown that lung cancer develops in a host environment in which the deregulated inflammatory response promotes tumor progression, and inflammatory mediators are derived from neoplastic cells and from stromal and inflammatory cells surrounding the tumor.44

A substantial role of inflammatory processes in the resistance mechanisms is supported by a growing body of experimental evidence. For example, it has been shown that activation of the proinflammatory cytokine IL-6 reduced the sensitivity to erlotinib in NSCLC cells harboring EGFR mutations.45 Activation of the NFkB, a major regulator of immune response, has been recently proposed as another resistance mechanism to EGFR TKIs. Low IkB expression (“high-NFkB” activation state) was predictive of worse PFS and decreased OS in 52 NSCLC patients harboring EGFR activating mutations and treated with erlotinib.46 Although the biological mechanism associated with the VeriStrat test is as yet unknown, the available body of evidence allows us to hypothesize that they may be related to host-response processes.

The results of this study, and previously published data, suggest that proteins involved with the “poor” signature may be associated both with the intrinsic resistance to anti-EGFR agents and with the switch to a certain type of acquired resistance, which is associated with metastases and the formation of new lesions. This noninvasive test may be especially useful for the selection of second-line therapy in the absence of available tumor tissue and in patients who do not harbor EGFR-activating mutations. The ongoing phase III PROSE study is prospectively validating this approach. Possibly the test can be used as an additional tool for treatment monitoring and investigation of mechanisms of primary and acquired resistance.

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Supported by Associazione Italiana per la Ricerca sul Cancro (AIRC).

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Back to Top | Article Outline

Non-small cell lung cancer; Epidermal growth factor receptor tyrosine kinase inhibitors; Proteomics; Resistance to epidermal growth factor tyrosine kinase inhibitor

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© 2012International Association for the Study of Lung Cancer


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