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

The Role of Proteomics and Phosphoproteomics in the Discovery of Therapeutic Targets and Biomarkers in Acquired EGFR-TKI-Resistant Non-Small Cell Lung Cancer

The discovery of potent EGFR-tyrosine kinase inhibitors (EGFR-TKIs) has revolutionized the treatment of EGFR-mutated lung cancer. Despite the fact that EGFR-TKIs have yielded several significant benefits for lung cancer patients, the emergence of resistance to EGFR-TKIs has been a substantial impediment to improving treatment outcomes. Understanding the molecular mechanisms underlying resistance is crucial for the development of new treatments and biomarkers for disease progression. Together with the advancement in proteome and phosphoproteome analysis, a diverse set of key signaling pathways have been successfully identified that provide insight for the discovery of possible therapeutically targeted proteins. In this review, we highlight the proteome and phosphoproteomic analyses of non-small cell lung cancer (NSCLC) as well as the proteome analysis of biofluid specimens that associate with acquired resistance in response to different generations of EGFR-TKI. Furthermore, we present an overview of the targeted proteins and potential drugs that have been tested in clinical studies and discuss the challenges of implementing this discovery in future NSCLC treatment.

Semi-Quantitative MALDI Measurements of Blood-Based Samples for Molecular Diagnostics

Accurate and precise measurement of the relative protein content of blood-based samples using mass spectrometry is challenging due to the large number of circulating proteins and the dynamic range of their abundances. Traditional spectral processing methods often struggle with accurately detecting overlapping peaks that are observed in these samples. In this work, we develop a novel spectral processing algorithm that effectively detects over 1650 peaks with over 3.5 orders of magnitude in intensity in the 3 to 30 kD m/z range. The algorithm utilizes a convolution of the peak shape to enhance peak detection, and accurate peak fitting to provide highly reproducible relative abundance estimates for both isolated peaks and overlapping peaks. We demonstrate a substantial increase in the reproducibility of the measurements of relative protein abundance when comparing this processing method to a traditional processing method for sample sets run on multiple matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) instruments. By utilizing protein set enrichment analysis, we find a sizable increase in the number of features associated with biological processes compared to previously reported results. The new processing method could be very beneficial when developing high-performance molecular diagnostic tests in disease indications.

Mass spectrometry-based serum proteomic signature as a potential biomarker for survival in patients with non-small cell lung cancer receiving immunotherapy

Background

VeriStrat test is a serum assay which uses a mass spectrometry (MS)-based proteomic signature derived from machine learning. It is currently used as a prognostic marker for patients with non-small cell lung cancer (NSCLC) receiving chemotherapy. However, little is known about its role for NSCLC patients receiving immune checkpoint inhibitors (ICIs).

Methods

This is a retrospective study that includes 47 patients with advanced stage NSCLC without an activating EGFR mutation, who underwent the VeriStrat test from 2016 to 2018. Spectra from blood samples were evaluated to assign patients into the VeriStrat ‘Good’ (VS-G) or VeriStrat ‘Poor’ (VS-P) risk group. The clinical outcomes of 32 patients who received programmed cell death 1 (PD-1) inhibitors nivolumab or pembrolizumab were analyzed by VeriStrat status.

Results

The VS-G group demonstrated significantly higher progression-free survival (PFS) and overall survival (OS) compared to the VS-P group among overall NSCLC patients regardless of treatment (median PFS of 7.1 vs. 4.2 months, P=0.013, and median OS, not reached vs. 17.2 months, P=0.012). Among NSCLC patients treated with ICIs, VS-G classification was associated with significantly increased PFS in comparison to VS-P classification (median PFS of 6.2 vs. 3.0 months, P=0.012), while the differences in OS trended towards significance (median OS, not reached vs. 16.5 months P=0.076). Multivariate analysis showed that the VeriStrat status was significantly correlated with PFS and OS in NSCLC patients treated with ICIs (P=0.017, P=0.034, respectively).

Conclusions

MS-based serum proteomic signature has potential as a biomarker for survival outcome in NSCLC patients receiving immunotherapy.

Prognostic performance of proteomic testing in advanced non-small cell lung cancer: a systematic literature review and meta-analysis

OBJECTIVE

Timely assessment of patient-specific prognosis is critical to oncology care involving a shared decision-making approach, but clinical prognostic factors traditionally used in NSCLC have limitations. We examine a proteomic test to address these limitations.

METHODS

This study examines the prognostic performance of the VeriStrat blood-based proteomic test that measures the inflammatory disease state of patients with advanced NSCLC. A systematic literature review (SLR) was performed, yielding cohorts in which the hazard ratio (HR) was reported for overall survival (OS) of patients with VeriStrat Poor (VSPoor) test results versus VeriStrat Good (VSGood). A study-level meta-analysis of OS HRs was performed in subgroups defined by lines of therapy and treatment regimens.

RESULTS

Twenty-four cohorts met SLR criteria. Meta-analyses in five subgroups (first-line platinum-based chemotherapy, second-line single-agent chemotherapy, first-line EGFR-tyrosine kinase inhibitor (TKI) therapy, and second- and higher-line TKI therapy, and best supportive care) resulted in statistically significant (p ≤ .001) summary effect sizes for OS HRs of 0.42, 0.54, 0.41, 0.52, and 0.50, respectively, indicating increased OS by about two-fold for patients who test VSGood. No significant heterogeneity was seen in any subgroup (p > .05).

CONCLUSIONS

Advanced NSCLC patients classified VSGood have significantly longer OS than those classified VSPoor. The summary effect size for OS HRs around 0.4-0.5 indicates that the expected median survival of those with a VSGood classification is approximately 2-2.5 times as long as those with VSPoor. The robust prognostic performance of the VeriStrat test across various lines of therapy and treatment regimens has clinical implications for treatment shared decision-making and potential for novel treatment strategies.

The clinical role of VeriStrat testing in patients with advanced non-small cell lung cancer considered unfit for first-line platinum-based chemotherapy

Purpose

We previously demonstrated that the median survival of patients with poor prognosis non–small cell lung cancer (NSCLC) considered unfit for first-line platinum chemotherapy was <4 months. We evaluated whether VeriStrat could be used as a prognostic or predictive biomarker in this population.

Experimental design

We conducted a randomised double-blind trial among patients with untreated advanced NSCLC considered unfit for platinum chemotherapy because of poor performance status (PS) or multiple comorbidities. All patients received active supportive care (ASC) and were treated with either oral erlotinib or placebo daily. Five hundred twenty-seven patients had plasma samples for VeriStrat classification: good (VeriStrat Good [VSG]) or poor (VeriStrat Poor [VSP]). Main end-point was overall survival.

Results

Fifty-five percent patients had VSG, and 83% had Eastern Cooperative Oncology Group (ECOG) 2–3 at baseline. VeriStrat was strongly associated with survival. Among patients managed with ASC only, the adjusted hazard ratio (HR) was 0.54 (p < 0.001) for VSG versus VSP. The association was consistent across patient factors: HR = 0.25 (p = 0.004) and HR = 0.56 (p < 0.001) for ECOG 0–1 and 2–3, respectively, HR = 0.49 (0070 < 0.001) for age≥75 years and HR = 0.59 (p = 0.007) for stage IV. Several ECOG 2–3 patients had long survival: 2-year survival was 8% for VSG patients who had ASC, compared with 0% for VSP. VeriStrat status did not predict benefit from erlotinib treatment because the HRs for erlotinib versus placebo were similar between VSG and VSP patients.

Conclusions

VeriStrat was not a predictive marker for survival when considering first-line erlotinib for patients with NSCLC who had poor PS and were not recommended for platinum doublet therapies. However, VeriStrat was an independent prognostic marker of survival. It represents an objective measurement that could be considered alongside other patient factors to provide a more refined assessment of prognosis for this particular patient group. VSG patients could be selected for treatment trials because of better survival, while VSP patients can continue to be treated conservatively or offered trials of less toxic agents.

Trial registration ISRCTN Number

ISRCTN02370070.

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83% advanced NSCLC patients unfit for chemotherapy have poor performance status.

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VeriStrat (proteomic blood test) is an independent prognostic marker for survival.

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Patients classified as VeriStrat Good were less likely to die than those classified as VeriStrat Poor.

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VeriStrat can refine patient prognosis in order to alter treatment management.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Serum peptide expression and treatment responses in patients with advanced non-small-cell lung cancer

Epidermal growth factor receptor (EGFR) mutation is an important predictor for response to personalized treatments of patients with advanced non-small-cell lung cancer (NSCLC). However its usage is limited due to the difficult of obtaining tissue specimens. A novel prediction system using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported to be a perspective tool in European countries to identify patients who are likely to benefit from EGFR tyrosine kinase inhibitor (TKI) treatment. In the present study, MALDI-TOF MS was used on pretreatment serum samples of patients with advanced non-small-cell lung cancer to discriminate the spectra between disease control and disease progression groups in one cohort of Chinese patients. The candidate features for classification were subsequently validated in a blinded fashion in another set of patients. The correlation between plasma EGFR mutation status and the intensities of representative spectra for classification was evaluated. A total of 103 patients that were treated with EGFR-TKIs were included. It was determined that 8 polypeptides peaks were significant different between the disease control and disease progression group. A total of 6 polypeptides were established in the classification algorithm. The sensitivity of the algorithm to predict treatment responses was 76.2% (16/21) and the specificity was 81.8% (18/22). The accuracy rate of the algorithm was 79.1% (34/43). A total of 3 polypeptides were significantly correlated with EGFR mutations (P=0.04, P=0.03 and P=0.04, respectively). The present study confirmed that MALDI-TOF MS analysis can be used to predict responses to EGFR-TKI treatment of the Asian population where the EGFR mutation status differs from the European population. Furthermore, the expression intensities of the three polypeptides in the classification model were associated with EGFR mutation.

Underutilization and disparities in access to EGFR testing among Medicare patients with lung cancer from 2010 - 2013

Background

Tumor testing for mutations in the epidermal growth factor receptor (EGFR) gene is indicated for all newly diagnosed, metastatic lung cancer patients, who may be candidates for first-line treatment with an EGFR tyrosine kinase inhibitor. Few studies have analyzed population-level testing.

Methods

We identified clinical, demographic, and regional predictors of EGFR & KRAS testing among Medicare beneficiaries with a new diagnosis of lung cancer in 2011–2013 claims. The outcome variable was whether the patient underwent molecular, EGFR and KRAS testing. Independent variables included: patient demographics, Medicaid status, clinical characteristics, and region where the patient lived. We performed multivariate logistic regression to identify factors that predicted testing.

Results

From 2011 to 2013, there was a 19.7% increase in the rate of EGFR testing. Patient zip code had the greatest impact on odds to undergo testing; for example, patients who lived in the Boston, Massachusetts hospital referral region were the most likely to be tested (odds ratio (OR) of 4.94, with a 95% confidence interval (CI) of 1.67–14.62). Patient demographics also impacted odds to be tested. Asian/Pacific Islanders were most likely to be tested (OR 1.63, CI 1.53–1.79). Minorities and Medicaid patients were less likely to be tested. Medicaid recipients had an OR of 0.74 (CI 0.72–0.77). Hispanics and Blacks were also less likely to be tested (OR 0.97, CI 0.78–0.99 and 0.95, CI 0.92–0.99), respectively. Clinical procedures were also correlated with testing. Patients who underwent transcatheter biopsies were 2.54 times more likely to be tested (CI 2.49–2.60) than those who did not undergo this type of biopsy.

Conclusions

Despite an overall increase in EGFR testing, there is widespread underutilization of guideline-recommended testing. We observed racial, income, and regional disparities in testing. Precision medicine has increased the complexity of cancer diagnosis and treatment. Targeted interventions and clinical decision support tools are needed to ensure that all patients are benefitting from advances in precision medicine. Without such interventions, precision medicine may exacerbate racial disparities in cancer care and health outcomes.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4190-3) contains supplementary material, which is available to authorized users.

The serum-based VeriStrat® test is associated with proinflammatory reactants and clinical outcome in non-small cell lung cancer patients

Background

The VeriStrat test is a serum proteomic signature originally discovered in non-responders to second line gefitinib treatment and subsequently used to predict differential benefit from erlotinib versus chemotherapy in previously treated advanced non-small cell lung cancer (NSCLC). Multiple studies highlight the clinical utility of the VeriStrat test, however, the mechanistic connection between VeriStrat-poor classification and poor prognosis in untreated and previously treated patients is still an active area of research. The aim of this study was to correlate VeriStrat status with other circulating biomarkers in advanced NSCLC patients – each with respect to clinical outcomes.

Methods

Serum samples were prospectively collected from 57 patients receiving salvage chemotherapy and 70 non-EGFR mutated patients receiving erlotinib. Patients were classified as either VeriStrat good or poor based on the VeriStrat test. Luminex immunoassays were used to measure circulating levels of 102 distinct biomarkers implicated in tumor aggressiveness and treatment resistance. A Cox PH model was used to evaluate associations between biomarker levels and clinical outcome, whereas the association of VeriStrat classifications with biomarker levels was assessed via the Mann-Whitney Rank Sum test.

Results

VeriStrat was prognostic for outcome within the erlotinib treated patients (HR = 0.29, p < 0.0001) and predictive of differential treatment benefit between erlotinib and chemotherapy ((interaction HR = 0.25; interaction p = 0.0035). A total of 27 biomarkers out of 102 unique analytes were found to be significantly associated with OS (Cox PH p ≤ 0.05), whereas 16 biomarkers were found to be associated with PFS. Thrombospondin-2, C-reactive protein, TNF-receptor I, and placental growth factor were the analytes most highly associated with OS, all with Cox PH p-values ≤0.0001. VeriStrat status was found to be significantly associated with 23 circulating biomarkers (Mann-Whitney Rank Sum p ≤ 0.05), 6 of which had p < 0.001, including C-reactive protein, IL-6, serum amyloid A, CYFRA 21.1, IGF-II, osteopontin, and ferritin.

Conclusions

Strong associations were observed between survival and VeriStrat classifications as well as select circulating biomarkers associated with fibrosis, inflammation, and acute phase reactants as part of this study. The associations between these biomarkers and VeriStrat classification might have therapeutic implications for poor prognosis NSCLC patients, particularly with new immunotherapeutic treatment options.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4193-0) contains supplementary material, which is available to authorized users.

Blood-based biomarkers in lung cancer: prognosis and treatment decisions

Despite recent advances, non-small cell lung cancer (NSCLC) remains a devastating disease with overall poor prognosis. Major contributing factors include obstacles to diagnosing the disease early in its course during the asymptomatic stage as well as diversity and complexity of its biology underlying tumorigenesis and tumor progression. Advances in molecularly targeted therapies which drives the development of personalized cancer care require precise and comprehensive understanding of tumor biology, not only at the time of diagnosis but also during treatment course and surveillance. As lung tumor tissue can be difficult to obtain without invasive and potentially risky procedures, it is difficult to monitor treatment response with serial tissue biopsies. Development of non-invasive but reliable blood based tumor markers has become an important research area. In this review, we focus on the following circulating biomarkers that have been identified in recent years: circulating tumor cells (CTCs); circulating cell-free nucleic acids, such as circulating tumor DNA (ctDNA) and microRNA (miR); and other biomarkers such as genomic and proteomic features. These biomarkers not only have prognostic values, but also can help guild treatment decisions by monitoring tumor burden, detecting minimal residual disease and/or recurrent disease, as well as monitoring evolution of genetic alterations throughout the treatment course.

Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma

BACKGROUND

Although pemetrexed plus cis/carboplatin has become the most effective chemotherapy regimen for patients with advanced lung adenocarcinoma, predictive biomarkers are not yet available, and new tools to identify chemosensitive patients who would likely benefit from this treatment are desperately needed. In this study, we constructed and validated predictive peptide models using the serum peptidome profiles of two datasets.

METHODS

One hundred eighty-three patients treated with first-line platinum-based pemetrexed treatment for advanced lung adenocarcinoma were retrospectively enrolled and randomized into the training (n = 92) or validation (n = 91) set, and pre-treatment serum samples were analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Serum peptidome profiles from the training set were used to identify potential predictive peptide biomarkers and construct a predictive peptide model for accurate group discrimination; which was then used to classify validation samples into "good" and "poor" outcome groups. The clinical outcomes of objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were analyzed based on the classification result.

RESULTS

Eight potential peptide biomarkers were identified. A predictive peptide model based on four distinct m/z features (2,142.12, 3,316.19, 4,281.94, and 6,624.02 Da) was developed based on the clinical outcomes of training set patients after first-line pemetrexed plus platinum treatment. In the validation set, the good group had significantly higher ORR (49.1% vs. 8.3%, P <0.001) and DCR (96.4% vs. 47.2%, P <0.001), and longer PFS (7.3 months vs. 2.7 months, P <0.001) vs. the poor group. However, the model did not predict OS (13.6 months vs. 12.7 months, P = 0.0675).

CONCLUSION

Our predictive peptide model could predict pemetrexed plus platinum treatment outcomes in patients with advanced lung adenocarcinoma and might thus facilitate appropriate patient selection. Further studies are needed to confirm these findings.

Impact of a multivariate serum-based proteomic test on physician treatment recommendations for advanced non-small-cell lung cancer

OBJECTIVE

The VeriStrat ¹ (VS) test is intended to help guide treatment decisions for patients with advanced non-small-cell lung cancer (NSCLC) without an EGFR-sensitizing mutation, classifying patients into two categories. Patients classified as VSGood have a favorable prognosis and significant clinical response to EGFR tyrosine kinase inhibitors (TKIs). Patients classified as VSPoor have a less favorable prognosis and exhibit no significant response to EGFR-TKIs. The objective of this paper is to assess the real-world impact of VS test results on physicians' treatment recommendations including referrals for best supportive care (BSC).

METHODS

Between 1 January 2012 and 1 November 2016, physician respondents were asked to complete standardized questionnaires before and after receiving VS results in patients meeting criteria for the intended use of the VS test. This study evaluated three endpoints: whether physicians followed VS test results in making treatment recommendations, the extent to which tests results changed these treatment recommendations, and the patterns of care subsequent to VS testing.

RESULTS

Of the tests ordered by 989 physicians, 2494 VS tests had completed treatment recommendation questionnaires both prior to and after testing. Prior to VS testing, physicians were considering treatment with EGFR-TKIs for 2250 patients (90%). The VS test classified 1950 patients as VSGood and 544 patients as VSPoor. For patients classified as VSPoor, physicians recommended BSC for 25% of patients and standard systemic treatments such as chemotherapies for 65% of patients. Consistent with previous publications, physicians recommended EGFR-TKI therapy for only 10% of VSPoor patients but for 89% of VSGood patients. Overall, physician's treatment recommendations were consistent with test results in 98% of cases. Availability of test results decreased ineffective treatment recommendations by 89% for VSPoor patients.

CONCLUSIONS

Among physicians ordering VS, the test significantly influenced treatment recommendations for patients with NSCLC, reducing ineffective and expensive treatment at the end of life.

Evaluation of the VeriStrat® serum protein test in patients with advanced squamous cell carcinoma of the lung treated with second-line afatinib or erlotinib in the phase III LUX-Lung 8 study

OBJECTIVES

Identification of biomarkers associated with clinical benefit may be crucial in establishing optimal treatment choice for patients with squamous cell carcinoma (SCC) of the lung after first-line chemotherapy. In this study, the ability of the VeriStrat serum protein test to predict differential clinical benefit with afatinib versus erlotinib, and the association of VeriStrat status with clinical outcomes irrespective of EGFR-TKI used, was assessed in a retrospective analysis of the phase III LUX-Lung 8 trial.

MATERIALS AND METHODS

Pretreatment plasma samples were analyzed using matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Spectra were evaluated to assign a VeriStrat 'Good' (VS-G) or VeriStrat 'Poor' (VS-P) classification. Overall survival (OS), progression-free survival, and other endpoints were assessed with respect to pretreatment VeriStrat status; OS was the primary efficacy variable. Outcomes with other efficacy endpoints were similar.

RESULTS

Of 795 patients randomized in LUX-Lung 8, 675 were classified (VS-G: 412; VS-P: 263). In the VS-G group, OS was significantly longer with afatinib versus erlotinib (HR 0.79 [95% CI: 0.63-0.98]). In the VS-P group, there was no significant difference in OS between afatinib and erlotinib (HR 0.90 [0.70-1.16]). However, there was no interaction between VeriStrat classification and treatment group for OS (p_interaction=0.5303). OS was significantly longer in VS-G versus VS-P patients, both in the overall VeriStrat-classified population (HR 0.41 [0.35-0.49]) and afatinib-treated patients (HR 0.40 [0.31-0.51]). Multivariate analysis showed that VeriStrat was an independent predictor of OS in afatinib-treated patients, regardless of ECOG PS or best response to first-line chemotherapy.

CONCLUSION

VS-G classification is strongly associated with favorable survival outcomes with either afatinib or erlotinib compared with VS-P classification. In VS-G patients, survival outcomes with afatinib are superior to those with erlotinib. VeriStrat classification may guide treatment decisions in patients with SCC of the lung. ClinicalTrials.gov registration number: NCT01523587.

Targeting Neovasculature with Multitargeted Antiangiogenesis Tyrosine Kinase Inhibitors in Non-small Cell Lung Cancer

Chemotherapy has reached a plateau in the efforts for survival improvement in non-small cell lung cancer (NSCLC). The growing knowledge of NSCLC molecular pathobiology has led to the development of new treatments that target specific tumor functions. Angiogenesis is a tumor function leading to the formation of new tumor vessels that are crucial for its survival. Although vascular endothelial growth factor (VEGF) plays a primary role in angiogenesis, the inhibition of the VEGF pathway with VEGF-receptor (VEGFR) tyrosine kinase inhibitors (TKIs) is associated with a modest survival benefit due to the development of resistance by the tumor that has been mainly attributed to the up-regulation of other stimulators of angiogenesis. Thus, the use of multitargeted antiangiogenesis TKIs (MATKIs) for simultaneous inhibition of multiple angiogenic pathways has been proposed. This review summarizes data about novel treatment strategies incorporating the inhibition of angiogenesis with MATKIs in NSCLC. The data from all relevant studies shows that MATKIs do not offer additional survival benefit to currently available chemotherapeutic options in unselected NSCLC patients. However, the diversity in disease response to MATKI-containing regimens implies that specific patient subgroups may benefit from or be harmed by these agents. In this context, most studies agree that the VEGFR-targeting MATKIs are harmful in squamous NSCLC while specific MATKIs (i.e., motesanib, vandetanib and nintedanib) are associated with improved progression free survival in non-squamous NSCLC. However, overall survival benefit was found only in adenocarcinoma and Asian non-squamous NSCLC patients with the use of nintedanib and motesanib, respectively.

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.

SWOG S0709: Randomized Phase II Trial of Erlotinib versus Erlotinib Plus Carboplatin/Paclitaxel in Patients with Advanced Non-Small Cell Lung Cancer and Impaired Performance Status as Selected by a Serum Proteomics Assay

INTRODUCTION

Patients with advanced-stage non-small cell lung cancer (NSCLC) and borderline performance status (performance status 2 [PS2]) are often excluded from clinical trials and platinum-based therapy. In light of the potential role for serum proteomics in predicting the benefit of erlotinib beyond that of epidermal growth factor receptor gene (EGFR) mutational status, we conducted a trial in which the Veristrat proteomics assay was used for data enrichment when selecting a cohort of patients with NSCLC and PS2 to receive erlotinib with and without chemotherapy.

METHODS

Patients with metastatic NSCLC, PS2, acceptable end-organ function, and Veristrat-good status were randomly assigned to receive either 150 mg of erlotinib orally daily (arm 1) or 150 mg of erlotinib orally daily on days 2 through16 plus four cycles of carboplatin (area under the curve = 5 on day 1) and paclitaxel (200 mg/m(2) intravenously on day 1) followed by 150 mg of erlotinib orally (arm 2). The arm 2 agents were pharmacodynamically separated to mitigate potential antagonism. The arm with superior observed median progression-free survival (PFS) would be selected for further evaluation, but only if PFS lasted for at least 3 months.

RESULTS

The trial terminated before the planned accrual of 98 patients for regulatory reasons. A total of 156 patients were screened. Of the 83 (59%) who were classified as Veristrat good, 59 met the trial eligibility criteria and were randomly assigned to one of two arms (33 patients in arm 1 and 26 in arm 2). The patients in arm 2 patients had a higher response rate (23% versus 6%, p = 0.06), disease control rate (77% versus 41%, p = 0.0046), median PFS (4.6 versus 1.6 months, p = 0.06), and median overall survival (11 versus 6 months, p = 0.27). Treatment-related grade 4 adverse events were seen in two patients in arm 1 (thrombosis and hypomagnesemia) and in five patients in arm 2 (neutropenia in five, febrile neutropenia in one, and leukopenia in one).

CONCLUSIONS

In a proteomics-enriched cohort of patients with NSCLC and PS2, pharmacodynamically separated erlotinib plus chemotherapy had better efficacy than did erlotinib alone and surpassed the protocol-specified benchmark of PFS of at least 3 months required for further study.

Personalized Medicine in Respiratory Disease: Role of Proteomics

Respiratory diseases affect humanity globally, with chronic lung diseases (e.g., asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, among others) and lung cancer causing extensive morbidity and mortality. These conditions are highly heterogeneous and require an early diagnosis. However, initial symptoms are nonspecific, and the clinical diagnosis is made late frequently. Over the last few years, personalized medicine has emerged as a medical care approach that uses novel technology aiming to personalize treatments according to the particular patient's medical needs. This review highlights the contributions of proteomics toward the understanding of personalized medicine in respiratory disease and its potential applications in the clinic.

CKMT1 and NCOA1 expression as a predictor of clinical outcome in patients with advanced-stage head and neck squamous cell carcinoma

BACKGROUND

We studied the association between the expression of a subset of previously identified genes and clinical outcome in patients with head and neck cancer.

METHODS

We analyzed by reverse transcriptase-polymerase chain reaction (RT-PCR) the expression of 89 genes in tumor biopsies from stage III to IVa/b chemotherapy treated patients (n = 46). Two additional cohorts analyzed by RNAseq (The Cancer Genome Atlas [TCGA] project; n = 371) or immunohistochemistry (IHC; n = 73) were used to validate results.

RESULTS

Thirty genes were associated with local-recurrence or progression-free survival. The best multi-gene decision-tree model to predict local recurrence included nuclear receptor coactivator 1 (NCOA1) and serum-amyloid A2 (SAA2) expression, whereas the best model to predict disease recurrence included creatine kinase mitochondrial 1 (CKMT1) and metal-regulatory transcription factor 1 (MTF1). Both models were associated with cancer-specific survival. Results were confirmed analyzing the RNAseq data included in the TCGA project. CKMT1 and NCOA1 were identified as independent risk factors for survival in an independent cohort analyzed by immunohistochemistry.

CONCLUSION

CKMT1 and NCOA1 expression has prognostic significance in advanced-stage head and neck carcinoma. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1392-E1403, 2016.

A 2015 update on predictive molecular pathology and its role in targeted cancer therapy: a review focussing on clinical relevance

In April 2013 our group published a review on predictive molecular pathology in this journal. Although only 2 years have passed many new facts and stimulating developments have happened in diagnostic molecular pathology rendering it worthwhile to present an up-date on this topic. A major technical improvement is certainly given by the introduction of next-generation sequencing (NGS; amplicon, whole exome, whole genome) and its application to formalin-fixed paraffin-embedded (FFPE) tissue in routine diagnostics. Based on this 'revolution' the analyses of numerous genetic alterations in parallel has become a routine approach opening the chance to characterize patients' malignant tumors much more deeply without increasing turn-around time and costs. In the near future this will open new strategies to apply 'off-label' targeted therapies, e.g. for rare tumors, otherwise resistant tumors etc. The clinically relevant genetic aberrations described in this review include mutation analyses of RAS (KRAS and NRAS), BRAF and PI3K in colorectal cancer, KIT or PDGFR alpha as well as BRAF, NRAS and KIT in malignant melanoma. Moreover, we present several recent advances in the molecular characterization of malignant lymphoma. Beside the well-known mutations in NSCLC (EGFR, ALK) a number of chromosomal aberrations (KRAS, ROS1, MET) have become relevant. Only very recently has the clinical need for analysis of BRCA1/2 come up and proven as a true challenge for routine diagnostics because of the genes' special structure and hot-spot-free mutational distribution. The genetic alterations are discussed in connection with their increasingly important role in companion diagnostics to apply targeted drugs as efficient as possible. As another aspect of the increasing number of druggable mutations, we discuss the challenges personalized therapies pose for the design of clinical studies to prove optimal efficacy particularly with respect to combination therapies of multiple targeted drugs and conventional chemotherapy. Such combinations would lead to an extremely high complexity that would hardly be manageable by applying conventional study designs for approval, e.g. by the FDA or EMA. Up-coming challenges such as the application of methylation assays and proteomic analyses on FFPE tissue will also be discussed briefly to open the door towards the ultimate goal of reading a patients' tissue as 'deeply' as possible. Although it is yet to be shown, which levels of biological information are most informative for predictive pathology, an integrated molecular characterization of tumors will likely offer the most comprehensive view for individualized therapy approaches. To optimize cancer treatment we need to understand tumor biology in much more detail on morphological, genetic, proteomic as well as epigenetic grounds. Finally, the complex challenges on the level of drug design, molecular diagnostics, and clinical trials make necessary a close collaboration among academic institutions, regulatory authorities and pharmaceutical companies.

Non-invasive approaches to monitor EGFR-TKI treatment in non-small-cell lung cancer

Tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR-TKIs) are standard treatments for advanced non-small-cell lung cancer (NSCLC) patients harboring activating epidermal growth factor receptor (EGFR) mutations. Nowadays, tumor tissues acquired by surgery or biopsy are the routine materials for EGFR mutation analysis. However, the accessibility of tumor tissues is not always satisfactory in advanced NSCLC. Moreover, a high proportion of NSCLC patients will eventually develop resistance to EGFR-TKIs. Invasive procedures, such as surgery or biopsy, are impractical to be performed repeatedly to assess the evolution of EGFR-TKI resistance. Thus, exploring some convenient and less invasive techniques to monitor EGFR-TKI treatment is urgently needed. Circulating cell-free tumor DNA (ctDNA) has a high degree of specificity to detect EGFR mutations in NSCLC. Besides, ctDNA is capable of monitoring the disease progression during EGFR-TKI treatment. Certain serum microRNAs that correlate with EGFR signaling pathway, such as miR-21 and miR-10b, have been demonstrated to be helpful in evaluating the efficiency of EGFR-TKI therapeutics. A commercialized serum-based proteomic test, named VeriStrat test, has shown an outstanding ability to predict the clinical outcome of NSCLC patients receiving EGFR-TKIs. Analysis of EGFR mutations in circulating tumor cells (CTCs) is feasible, and CTCs represent a promising material to predict EGFR-TKI-treatment efficacy and resistance. These evidences suggested that non-invasive techniques based on serum or plasma samples had a great potential for monitoring EGFR-TKI treatment in NSCLC. In this review, we summarized these non-invasive approaches and considered their possible applications in EGFR-TKI-treatment monitoring.

Molecular histology of lung cancer: from targets to treatments

Lung cancer is the leading cause of cancer-related death worldwide with a 5-year survival rate of less than 15%, despite significant advances in both diagnostic and therapeutic approaches. Combined genomic and transcriptomic sequencing studies have identified numerous genetic driver mutations that are responsible for the development of lung cancer. In addition, molecular profiling studies identify gene products and their mutations which predict tumour responses to targeted therapies such as protein tyrosine kinase inhibitors and also can offer explanation for drug resistance mechanisms. The profiling of circulating micro-RNAs has also provided an ability to discriminate patients in terms of prognosis/diagnosis and high-throughput DNA sequencing strategies are beginning to elucidate cell signalling pathway mutations associated with oncogenesis, including potential stem cell associated pathways, offering the promise that future therapies may target this sub-population, preventing disease relapse post treatment and improving patient survival. This review provides an assessment of molecular profiling within lung cancer concerning molecular mechanisms, treatment options and disease-progression. Current areas of development within lung cancer profiling are discussed (i.e. profiling of circulating tumour cells) and future challenges for lung cancer treatment addressed such as detection of micro-metastases and cancer stem cells.

The value of proteomics in lung cancer

Many studies have identified the prognostic and predictive value of proteins or peptides in lung cancer but most failed to provide strong evidence for their clinical applicability. The strongest predictive proteins seem to be fatty acid-binding protein heart (H-FABP), and the 8-peak mass spectrography signature of VeriStrat. When focusing on VeriStrat, a 'VeriStrat good' profile did not discriminate between chemotherapy and erlotinib. The 'VeriStrat poor' profile showed a better outcome to chemotherapy than to erlotinib. VeriStrat is a prognostic test and only the "poor profile" discriminates for the type of therapy that should be chosen. Whether it adds useful information in patients with advanced non-small cell lung cancer (NSCLC) and wild type EGFR mutations is still doubtful. The position of the VeriStrat test in clinical practice is still not clear and we are waiting for prospective studies where biomarker test are involved in clinical decision.

The promise of psychiatric pharmacogenomics

Clinicians already face "personalized" medicine every day while experiencing the great variation in toxicities and drug efficacy among individual patients. Pharmacogenetics studies are the platform for discovering the DNA determinants of variability in drug response and tolerability. Research now focuses on the genome after its beginning with analyses of single genes. Therapeutic outcomes from several psychotropic drugs have been weakly linked to specific genetic variants without independent replication. Drug side effects show stronger associations to genetic variants, including human leukocyte antigen loci with carbamazepine-induced dermatologic outcome and MC4R with atypical antipsychotic weight gain. Clinical implementation has proven challenging, with barriers including a lack of replicable prospective evidence for clinical utility required for altering medical care. More recent studies show promising approaches for reducing these barriers to routine incorporation of pharmacogenetics data into clinical care.

Respiratory proteomics: from descriptive studies to personalized medicine

Respiratory diseases are highly prevalent and affect humankind worldwide, causing extensive morbidity and mortality with the environment playing an important role. Given the complex structure of the airways, sophisticated tools are required for early diagnosis; initial symptoms are nonspecific, and the clinical diagnosis is made frequently late. Over the past few years, proteomics has made high technological progress in mass-spectrometry-based protein identification and has allowed us to gain new insights into disease mechanisms and identify potential novel therapeutic targets. This review will highlight the contributions of proteomics toward the understanding of the respiratory proteome listing potential biomarkers and its potential application to the clinic. We also outline the contributions of proteomics to creating a personalized approach in respiratory medicine.

Predictive value of a serum-based proteomic test in non-small-cell lung cancer patients treated with epidermal growth factor receptor tyrosine kinase inhibitors: a meta-analysis

OBJECTIVE

Several studies have demonstrated that a serum-based proteomic test (VeriStrat * ) is able to predict the clinical outcome of non-small-cell lung cancer (NSCLC) patients treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). However, these studies have limited power to draw a precise conclusion because of their small sample sizes and inconsistent results. Therefore, a meta-analysis was carried out in an attempt to provide more persuasive evidence.

RESEARCH DESIGN AND METHODS

Electronic searches for relevant articles in PubMed, Embase, Medline, and Web of Science published up to May 2013 were conducted. Stata Statistical Software version 12.0 was applied for statistical analysis. The combined hazard ratio (HR) and 95% confidence interval (CI) were estimated using fixed-effects models.

RESULTS

Eleven cohorts involving 706 patients collected from seven studies were subjected to final analysis. This serum-based proteomic test's 'good' status predicted a better clinical outcome with a pooled HR of 0.40 (95% CI 0.32 to 0.49; p < 0.001) for overall survival (OS), and 0.49 (95% CI 0.39 to 0.60; p < 0.001) for progression-free survival (PFS). There was no significant heterogeneity, but a slight publication bias in this study.

CONCLUSIONS

Our meta-analysis demonstrated that this serum-based proteomic test has a predictive value for NSCLC patients treated with EGFR-TKIs. Future data are needed to validate and update our results.

Mass spectrometry-based serum and plasma peptidome profiling for prediction of treatment outcome in patients with solid malignancies

INTRODUCTION

Treatment selection tools are needed to enhance the efficacy of targeted treatment in patients with solid malignancies. Providing a readout of aberrant signaling pathways and proteolytic events, mass spectrometry-based (MS-based) peptidomics enables identification of predictive biomarkers, whereas the serum or plasma peptidome may provide easily accessible signatures associated with response to treatment. In this systematic review, we evaluate MS-based peptide profiling in blood for prompt clinical implementation.

METHODS

PubMed and Embase were searched for studies using a syntax based on the following hierarchy: (a) blood-based matrix-assisted or surface-enhanced laser desorption/ionization time-of-flight MS peptide profiling (b) in patients with solid malignancies (c) prior to initiation of any treatment modality, (d) with availability of outcome data.

RESULTS

Thirty-eight studies were eligible for review; the majority were performed in patients with non-small cell lung cancer (NSCLC). Median classification prediction accuracy was 80% (range: 66%-93%) in 11 models from 14 studies reporting an MS-based classification model. A pooled analysis of 9 NSCLC studies revealed clinically significant median progression-free survival in patients classified as "poor outcome" and "good outcome" of 2.0 ± 1.06 months and 4.6 ± 1.60 months, respectively; median overall survival was also clinically significant at 4.01 ± 1.60 months and 10.52 ± 3.49 months, respectively.

CONCLUSION

Pretreatment MS-based serum and plasma peptidomics have shown promising results for prediction of treatment outcome in patients with solid tumors. Limited sample sizes and absence of signature validation in many studies have prohibited clinical implementation thus far. Our pooled analysis and recent results from the PROSE study indicate that this profiling approach enables treatment selection, but additional prospective studies are warranted.

Phase II trial of sorafenib and erlotinib in advanced pancreatic cancer

This trial was designed to assess efficacy and safety of erlotinib with sorafenib in the treatment of patients with advanced pancreatic adenocarcinoma. An exploratory correlative study analyzing pretreatment serum samples using a multivariate protein mass spectrometry-based test (VeriStrat®), previously shown to correlate with outcomes in lung cancer patients treated with erlotinib, was performed. Patients received sorafenib 400 mg daily along with erlotinib 150 mg daily with a primary endpoint of 8-week progression free survival (PFS) rate. Pretreatment serum sample analysis by VeriStrat was done blinded to clinical and outcome data; the endpoints were PFS and overall survival (OS). Difference between groups (by VeriStrat classification) was assessed using log-rank P values; hazard ratios (HR) were obtained from Cox proportional hazards model. Thirty-six patients received study drug and were included in the survival analysis. Eight-week PFS rate of 46% (95% confidence interval (CI): 0.32–0.67) did not meet the primary endpoint of a rate ≥70%. Thirty-two patients were included in the correlative analysis, and VeriStrat “Good” patients had superior PFS (HR = 0.18, 95% CI: 0.06–0.57; P = 0.001) and OS (HR = 0.31 95% CI: 0.13–0.77, P = 0.008) compared to VeriStrat “Poor” patients. Grade 3 toxicities of this regimen included fever, anemia, diarrhea, dehydration, rash, and altered liver function. This study did not meet the primary endpoint, and this combination will not be further pursued. In this small retrospective analysis, the proteomic classification was significantly associated with clinical outcomes and is being further evaluated in ongoing studies.

Sorafenib: targeting multiple tyrosine kinases in cancer

Sorafenib (BAY 43-9006, Nexavar®) is an oral multiple tyrosine kinase inhibitor. Main targets are receptor tyrosine kinase pathways frequently deregulated in cancer such as the Raf-Ras pathway, vascular endothelial growth factor (VEGF) pathway, and FMS-like tyrosine kinase 3 (FLT3). Sorafenib was approved by the FDA in fast track for advanced renal cell cancer and hepatocellular cancer and shows good clinical activity in thyroid cancer. Multiple clinical trials are undertaken to further investigate the role of sorafenib alone or in combination for the treatment of various tumor entities.

Serum proteomic study on EGFR-TKIs target treatment for patients with NSCLC

Background

Although epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are widely used for EGFR mutated non-small-cell lung cancer (NSCLC) patients, tumor sample availability and heterogeneity of the tumor remain challenging for physicians’ selection of these patients. Here, we developed a serum proteomic classifier based on matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) to predict the clinical outcome of patients treated with EGFR-TKIs.

Method

A total of 68 patients were included in this study. All patients received EGFR-TKIs as second or third line treatment and blood samples were collected before treatment. Using magnetic bead assisted serum peptide capture coupled to MALDI-TOF-MS, pretreatment serum from 24 NSCLC patients was analyzed to develop a proteomic classifier (training set). In a blinded test set with 44 patients, each sample was classified into “good” or “poor” groups using this classifier. Survival analysis of each group was done based on this classification.

Result

A 3-peptide proteomic classifier was developed from the training set. In the testing set, the classifier was able to distinguish patients of “good” or “poor” outcomes with 93% accuracy, sensitivity, and specificity. The overall survival and progression free survival of the predicted good group were found to be significantly longer than the poor group, not only in the whole population but also in certain subgroups, such as pathological adenocarcinoma and nonsmokers. With respect to the tumor samples available for EGFR mutation detection, all eight EGFR mutant tumors and three of the 12 wild type EGFR tumors were classified as good while nine of the 12 wild type EGFR tumors were classified as poor.

Conclusion

The current study has shown that a proteomic classifier can predict the outcome of patients treated with EGFR-TKIs and may aid in patient selection in the absence of available tumor tissue. Further studies are necessary to confirm these findings.