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

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Circulating Osteopontin Predicts Clinical and Radiological Response in First-Line Treatment of Advanced Non-Small Cell Lung Cancer

PURPOSE

Pembrolizumab-based regimens are conditioned by the expression of PD-L1, but durable response rate is limited by innate and acquired resistance mechanisms. Here, we focus on osteopontin (OPN), an upfront biomarker of senescence, which closely associated with natural history of non-small cell lung cancer (NSCLC).

METHODS

Seventy-nine patients eligible to pembrolizumab regimens-alone or in combination with chemotherapy-as first-line treatment of advanced NSCLC were enrolled. Predictive value of OPN toward iRECIST progression disease (PD) was set as first outcome. Secondary ones included performance status (ECOG) at baseline, early (first and best) responses, and overall survival (OS).

RESULTS

High Serum OPN characterized patients with worse ECOG-PS (p = 0.015) at baseline and subjects experienced PD/death at first (OR 1.17 [1.02 to 1.35]; p = 0.030) and best responses (0.04 [0.00 to 0.81]; p = 0.035). OPN was associated with time-to-progression (B -2.74 [-4.46 to -1.01]) and time-to death (-0.13 [-0.20 to -0.05]). Cox regression models unveil a predictive value for iRECIST-PD (HR 1.01 [1.00 to 1.02]; p = -0.005), RECIST-PD (HR 1.01 [1.00 to 1.02]; p = 0.017), and OS (HR 1.02 [1.01 to 1.03]; p = 0.001). These models were internally validated through bootstrap resampling and characterized by relevant discrimination ability at ROC curve analyses.

CONCLUSION

Baseline levels of serum OPN is closely associated with performance status and short/long term outcomes in patients with advanced NSCLC, which are candidate to pembrolizumab-based regimens. As upfront biomarker of senescence, OPN may pave the way for future studies focusing on senescence patterns in NSCLC.

Prognostic and clinicopathological value of osteopontin expression in non-small cell lung cancer: a meta-analysis and systematic review

BACKGROUND

Although Osteopontin (OPN) has been reported to be associated with many different human cancers, the data on non-small cell lung cancer (NSCLC) are not definitive. This study aimed to explore the prognostic effect of OPN expression and clinicopathological characteristics in patients with NSCLC.

METHODS

This study followed all aspects of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) report. PubMed, Embase and the Cochrane Library were searched to identify the relative studies. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to estimate the prognostic value of the OPN in patients with NSCLC. The odds ratio (OR) was calculated to represent the relationship between OPN expression and clinicopathological parameters.

RESULTS

A total of fifteen studies with 2173 participants were finally included. The results revealed that high expression of OPN was significantly associated with poorer overall survival (OS) (HR = 1.89; 95%CI = 1.68-2.11; p < 0.001). Moreover, a significant correlation was observed between increased OPN expression and poorly differentiated (well and moderately differentiated vs. poorly differentiated; pooled OR = 0.38; 95% CI = 0.23-0.64; p < 0.001), lymph node metastasis (absence vs. presence; pooled OR = 0.49; 95%CI = 0.32-0.74; p < 0.001), and distant metastasis (absence vs. presence; pooled OR = 0.18; 95%CI = 0.11-0.29; p < 0.001).

CONCLUSION

This meta-analysis implies that OPN might be a valuable biomarker for a poor prognosis and poor clinicopathological outcomes for patients with NSCLC.

Molecular and translational biology of the blood-based VeriStrat® proteomic test used in cancer immunotherapy treatment guidance

INTRODUCTION

The VeriStrat® test (VS) is a blood-based assay that predicts a patient's response to therapy by analyzing eight features in a spectrum obtained from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis of human serum and plasma. In a recent analysis of the INSIGHT clinical trial (NCT03289780), it was found that the VS labels, VS Good and VS Poor, can effectively predict the responsiveness of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitor (ICI) therapy. However, while VS measures the intensities of spectral features using MALDI-TOF analysis, the specific proteoforms underlying these features have not been comprehensively identified.

OBJECTIVES

The objective of this study was to identify the proteoforms that are measured by VS.

METHODS

To resolve the features obtained from the low-resolution MALDI-TOF procedure used to acquire mass spectra for VS DeepMALDI® analysis of serum was employed. This technique allowed for the identification of finer peaks within these features. Additionally, a combination of reversed-phase fractionation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then used to identify the proteoforms associated with these peaks.

RESULTS

The analysis revealed that the primary constituents of the spectrum measured by VS are serum amyloid A1, serum amyloid A2, serum amyloid A4, C-reactive protein, and beta-2 microglobulin.

CONCLUSION

Proteoforms involved in host immunity were identified as significant components of these features. This newly acquired information improves our understanding of how VS can accurately predict patient response to therapy. It opens up additional studies that can expand our understanding even further.

The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment

Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.

Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy

Immunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligand 1 (PD-L1) remains unreliable and establishing better potential candidate markers is of great importance in selecting patients who would benefit from immunotherapy. Here, we focus on the role of serum-based proteomic tests in predicting the response and toxicity of immunotherapy. Serum proteomic signatures refer to unique patterns of proteins which are associated with immune response in patients with cancer. These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). The tests effectively stratified patients into groups with good and poor treatment outcomes independent of PD-L1 expression. Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. Thus, mass spectrometry-based serum proteomics signatures play an important role in predicting clinical outcomes and toxicity.

The role of IGF-pathway biomarkers in determining risks, screening, and prognosis in lung cancer

Background: Detection rates of early-stage lung cancer are traditionally low, which contributes to inconsistent treatment responses and high rates of annual cancer deaths. Currently, low-dose computed tomography (LDCT) screening produces a high false discovery rate. This limitation has prompted research to identify biomarkers to more clearly define eligible patients for LDCT screening, differentiate indeterminate pulmonary nodules, and select individualized cancer therapy. Biomarkers within the Insulin-like Growth Factor (IGF) family have come to the forefront of this research.

Main Body: Multiple biomarkers within the IGF family have been investigated, most notably IGF-I and IGF binding protein 3. However, newer studies seek to expand this search to other molecules within the IGF axis. Certain studies have demonstrated these biomarkers are useful when used in combination with lung cancer screening, but other findings were not as conclusive, possibly owing to measurement bias and non-standardized assay techniques. Research also has suggested IGF biomarkers may be beneficial in the prognostication and subsequent treatment via systemic therapy. Despite these advances, additional knowledge of complex regulatory mechanisms inherent to this system are necessary to more fully harness the potential clinical utility for diagnostic and therapeutic purposes.

Conclusions: The IGF system likely plays a role in multiple phases of lung cancer; however, there is a surplus of conflicting data, especially prior to development of the disease and during early stages of detection. IGF biomarkers may be valuable in the screening, prognosis, and treatment of lung cancer, though their exact application requires further study.

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.

Real-world performance of blood-based proteomic profiling in first-line immunotherapy treatment in advanced stage non-small cell lung cancer

Purpose

Immune checkpoint inhibition (ICI) therapy has improved patient outcomes in advanced non-small cell lung cancer (NSCLC), but better biomarkers are needed. A clinically validated, blood-based proteomic test, or host immune classifier (HIC), was assessed for its ability to predict ICI therapy outcomes in this real-world, prospectively designed, observational study.

Materials and methods

The prospectively designed, observational registry study INSIGHT (Clinical Effectiveness Assessment of VeriStrat® Testing and Validation of Immunotherapy Tests in NSCLC Subjects) (NCT03289780) includes 35 US sites having enrolled over 3570 NSCLC patients at any stage and line of therapy. After enrolment and prior to therapy initiation, all patients are tested and designated HIC-Hot (HIC-H) or HIC-Cold (HIC-C). A prespecified interim analysis was performed after 1-year follow-up with the first 2000 enrolled patients. We report the overall survival (OS) of patients with advanced stage (IIIB and IV) NSCLC treated in the first-line (ICI-containing therapies n=284; all first-line therapies n=877), by treatment type and in HIC-defined subgroups.

Results

OS for HIC-H patients was longer than OS for HIC-C patients across treatment regimens, including ICI. For patients treated with all ICI regimens, median OS was not reached (95% CI 15.4 to undefined months) for HIC-H (n=196) vs 5.0 months (95% CI 2.9 to 6.4) for HIC-C patients (n=88); HR=0.38 (95% CI 0.27 to 0.53), p<0.0001. For ICI monotherapy, OS was 16.8 vs 2.8 months (HR=0.36 (95% CI 0.22 to 0.58), p<0.0001) and for ICI with chemotherapy OS was unreached vs 6.4 months (HR=0.41 (95% CI 0.26 to 0.67), p=0.0003). HIC results were independent of programmed death ligand 1 (PD-L1). In a subgroup with PD-L1 ≥50% and performance status 0–1, HIC stratified survival significantly for ICI monotherapy but not ICI with chemotherapy.

Conclusion

Blood-based HIC proteomic testing provides clinically meaningful information for immunotherapy treatment decision in NSCLC independent of PD-L1. The data suggest that HIC-C patients should not be treated with ICI alone regardless of their PD-L1 expression.

Matrix-assisted laser desorption ionisation (MALDI) mass spectrometry (MS): basics and clinical applications

Background Matrix-assisted laser desorption ionisation (MALDI) mass spectrometry (MS) has been used for more than 30 years. Compared with other analytical techniques, it offers ease of use, high throughput, robustness, cost-effectiveness, rapid analysis and sensitivity. As advantages, current clinical techniques (e.g. immunoassays) are unable to directly measure the biomarker; rather, they measure secondary signals. MALDI-MS has been extensively researched for clinical applications, and it is set for a breakthrough as a routine tool for clinical diagnostics. Content This review reports on the principles of MALDI-MS and discusses current clinical applications and the future clinical prospects for MALDI-MS. Furthermore, the review assesses the limitations currently experienced in clinical assays, the advantages and the impact of MALDI-MS to transform clinical laboratories. Summary MALDI-MS is widely used in clinical microbiology for the screening of microbial isolates; however, there is scope to apply MALDI-MS in the diagnosis, prognosis, therapeutic drug monitoring and biopsy imaging in many diseases. Outlook There is considerable potential for MALDI-MS in clinic as a tool for screening, profiling and imaging because of its high sensitivity and specificity over alternative techniques.

Prognostic prospect of soluble programmed cell death ligand-1 in cancer management

Aggressive tissue biopsy is commonly unavoidable in the management of most suspected tumor cases to conclusively verify the presence of cancerous cells through histological assessment. The extracted tissue is also immunostained for detection of antigens (tissue tumor markers) of potential prognostic or therapeutic importance to assist in treatment decision. Although liquid biopsies can be a powerful tool for monitoring treatment response, they are still excluded from standard cancer diagnostics, and their utility is still being debated in the scientific community. With a myriad of soluble tissue tumor markers now being discovered, liquid biopsies could completely change the current paradigms of cancer management. Recently, soluble programmed cell death ligand-1 (sPD-L1), which is found in the peripheral blood, i.e. serum and plasma, has shown potential as a pre-therapeutic predictive marker as well as a prognostic biomarker to monitor treatment efficacy. Thus, this review focuses on the emergence of sPD-L1 and promising technologies for its detection in order to support liquid biopsies for future cancer management.

Explaining multivariate molecular diagnostic tests via Shapley values

Background

Machine learning (ML) can be an effective tool to extract information from attribute-rich molecular datasets for the generation of molecular diagnostic tests. However, the way in which the resulting scores or classifications are produced from the input data may not be transparent. Algorithmic explainability or interpretability has become a focus of ML research. Shapley values, first introduced in game theory, can provide explanations of the result generated from a specific set of input data by a complex ML algorithm.

Methods

For a multivariate molecular diagnostic test in clinical use (the VeriStrat® test), we calculate and discuss the interpretation of exact Shapley values. We also employ some standard approximation techniques for Shapley value computation (local interpretable model-agnostic explanation (LIME) and Shapley Additive Explanations (SHAP) based methods) and compare the results with exact Shapley values.

Results

Exact Shapley values calculated for data collected from a cohort of 256 patients showed that the relative importance of attributes for test classification varied by sample. While all eight features used in the VeriStrat® test contributed equally to classification for some samples, other samples showed more complex patterns of attribute importance for classification generation. Exact Shapley values and Shapley-based interaction metrics were able to provide interpretable classification explanations at the sample or patient level, while patient subgroups could be defined by comparing Shapley value profiles between patients. LIME and SHAP approximation approaches, even those seeking to include correlations between attributes, produced results that were quantitatively and, in some cases qualitatively, different from the exact Shapley values.

Conclusions

Shapley values can be used to determine the relative importance of input attributes to the result generated by a multivariate molecular diagnostic test for an individual sample or patient. Patient subgroups defined by Shapley value profiles may motivate translational research. However, correlations inherent in molecular data and the typically small ML training sets available for molecular diagnostic test development may cause some approximation methods to produce approximate Shapley values that differ both qualitatively and quantitatively from exact Shapley values. Hence, caution is advised when using approximate methods to evaluate Shapley explanations of the results of molecular diagnostic tests.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01569-9.

Detection of Hepatocellular Carcinoma in a High-Risk Population by a Mass Spectrometry-Based Test

Simple Summary

Liver cancer is one of the most common causes of cancer worldwide, but unfortunately, current technology has a limited ability to detect it early in high-risk patients. This study investigates a machine learning algorithm based on protein levels in the blood that can be used to help with diagnosis. The test shows promising results, especially in patients with smaller tumors and compared to current blood detection tests. This research suggests an important role in the future for machine learning algorithm-based blood detection tests.

Abstract

Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI^® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat^® classification algorithm. Machine learning techniques then classified each sample as “Cancer” or “No Cancer”. Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.

Baseline and early changes in circulating Serum Amyloid A (SAA) predict survival outcomes in advanced non-small cell lung cancer patients treated with Anti-PD-1/PD-L1 monotherapy

BACKGROUND

Systemic inflammation plays an important role in carcinogenesis and is associated with overall survival in patients with different cancer types, including those treated with immune checkpoint blockade (ICB). Serum Amyloid A (SAA) is an acute-phase protein and a marker of persistent inflammation. We hypothesized that circulating SAA may predict outcomes in advanced non-small cell lung (aNSCLC) patients treated with PD-1/PD-L1 ICB.

MATERIALS AND METHODS

This retrospective study included 91 aNSCLC patients who received anti-PD-(L)1 monotherapy in Sun Yat-sen University Cancer Center (Guangzhou, China) between August 2016 and June 2018. We examined the impact of circulating SAA at baseline and 8 (±2) weeks later on overall survival (OS). X-tile program was used to determine the cut-off values which optimized the significance of the split between Kaplan-Meier survival curves. Kaplan-Meier methodology and Cox regression analyses were conducted for survival analyses.

RESULTS

The optimal cut-off value of baseline SAA for OS stratification was 137.6 mg/L. In univariate analysis, both high level of baseline SAA (hazard ratio [HR], 2.76; 95% confidence interval [CI], 1.47-5.18; P = 0.002) and lack of early SAA descent (HR, 1.51; 95% CI, 1.11-2.06; P = 0.009) were significantly associated with inferior OS. In multivariate analysis, gender, smoking status, performance status, liver metastasis, neutrophil-to-lymphocyte ratio, baseline SAA and early changes in SAA independently predicted OS (all with P < 0.05). A combined baseline SAA ≥ 137.6 mg/L and without early SAA descent identified a small cohort with remarkably worse OS (median, 3.2 months).

CONCLUSIONS

Both high baseline and lack of early decline in circulating SAA are significantly associated with inferior outcomes in aNSCLC patients treated with PD-1/PD-L1 ICB. Combined these two SAA indexes provided improved risk stratification. The prognostic value of this simple, readily-available, and cost-effective biomarker warrants larger, prospective validation before definitive recommendation can be made.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

Relationship between circulating tumor-associated autoantibodies and clinical outcomes in advanced-stage NSCLC patients receiving PD-1/-L1 directed immune checkpoint inhibition

BACKGROUND

Durable tumor regressions are observed in a subset of advanced-stage non-small cell lung cancer (NSCLC) patients receiving PD-1/-L1 targeted immune checkpoint inhibitors (or 'immunotherapy') alone or in combination with chemotherapy. However, the majority of advanced NSCLC patients receiving these agents do not experience long-term disease control. Existing methods to identify patients most likely to gain clinical benefit from PD-1/-L1 immunotherapy have limitations, creating a need for improved methods to guide treatment selection, particularly for those likely to benefit from single-agent immunotherapy. Here, we describe the development of a series of novel assays for tumor-associated autoantibodies as part of an exploratory study intended to determine if these biomarkers have potential prognostic value in this setting.

METHOD

A selection of recombinant tumor autoantigens previously characterized for their diagnostic utility were developed and preliminarily evaluated by this study. These include: Fumarate Dehydrogenase (FH), Hydroxysteroid 17-Beta Dehydrogenase 10 (HSD17B10), Inosine Monophosphate Dehydrogenase 2 (IMPDH2), New York Esophageal Squamous Cell Carcinoma-1 (NY ESO-1), Phosphoglycerate Mutase 1 (PGAM1), and Vimentin. Custom Luminex immunobead assays were developed for these targets to quantitatively assess autoantibody levels in individual patient sera. Assays were erected as indirect immunoassays on MagPlex® Microspheres using standard carbodiimide/NHS-based chemistries, utilizing a biotin-conjugated secondary (i.e. anti-human IgG) antibody and R-phycoerythrin-conjugated streptavidin reporter system. Standard curves were created for quantitative purposes using commercially-available anti-antigen antibodies and permitted analytical performance characteristics to be calculated. These assays were used to preliminarily evaluate a series of pretreatment serum samples from stage IV NSCLC patients receiving anti PD-1/-L1 therapy after failure of at least one prior line of therapy (n = 40) and their classification efficiency calculated based on 12 months overall survival (OS) threshold.

RESULTS

Six assays were developed that each showed dynamic ranges of four orders of magnitude and provided more than 90% classification accuracy based on the observed clinical outcome data. Inter- and intra-assay precision was assessed within these standards and overall %CVs of ≤7% and ≤ 10%, respectively, were calculated. Generally, the baseline level of autoantibodies were significantly (p < 0.05) lower in the ≥12 months survival group relative to the <12 months survival groups. Serum titers of FH, HSD170B, NY-ESO-1, and vimentin were significantly correlated with ≥12 month survival (p-value 0.0038, 0.0061, 0.0073, and 0.022, respectively). IMPDH2 and PGAM1 were found to have marginal significance (p-value 0.08 and 0.076, respectively).

CONCLUSION

This study demonstrates an efficient and promising means for assessing circulating autoantibody titers that could be useful in selecting advanced NSCLC patients for PD-1/-L1 directed immunotherapy. Further exploration and validation of this paradigm is warranted to further refine current treatment selection methods for this therapeutic strategy.

Prediction of the VeriStrat test in first-line therapy of pemetrexed-based regimens for advanced lung adenocarcinoma patients

BACKGROUND

Although advanced non-squamous non-small cell lung cancer (NSCLC) patients have significantly better survival outcomes after pemetrexed based treatment, a subset of patients still show intrinsic resistance and progress rapidly. Therefore we aimed to use a blood-based protein signature (VeriStrat, VS) to analyze whether VS could identify the subset of patients who had poor efficacy on pemetrexed therapy.

METHODS

This study retrospectively analysed 72 advanced lung adenocarcinoma patients who received first-line pemetrexed/platinum or combined with bevacizumab treatment.

RESULTS

Plasma samples from these patients were analysed using VS and classified into the Good (VS-G) or Poor (VS-P) group. The relationship between efficacy and VS status was further investigated. Of the 72 patients included in this study, 35 (48.6%) were treated with pemetrexed plus platinum and 37 (51.4%) were treated with pemetrexed/platinum combined with bevacizumab. Among all patients, 60 (83.3%) and 12 (16.7%) patients were classified as VS-G and VS-P, respectively. VS-G patients had significantly better median progression-free survival (PFS) (Unreached vs. 4.2 months; P < 0.001) than VS-P patients. In addition, the partial response (PR) rate was higher in the VS-G group than that in the VS-P group (46.7% vs. 25.0%, P = 0.212). Subgroup analysis showed that PFS was also significantly longer in the VS-G group than that in the VS-P group regardless of whether patients received chemotherapy alone or chemotherapy plus bevacizumab.

CONCLUSIONS

Our study indicated that VS might be considered as a novel and valid method to predict the efficacy of pemetrexed-based therapy and identify a subset of advanced lung adenocarcinoma patients who had intrinsic resistance to pemetrexed based regimens. However, larger sample studies are still needed to further confirm this result.

Design and Characterization of a Novel Blood Collection and Transportation Device for Proteomic Applications

A major hurdle for blood-based proteomic diagnostics is efficient transport of specimens from the collection site to the testing laboratory. Dried blood spots have shown utility for diagnostic applications, specifically those where red blood cell hemolysis and contamination of specimens with hemoglobin is not confounding. Conversely, applications that are sensitive to the presence of the hemoglobin subunits require blood separation, which relies on centrifugation to collect plasma/serum, and then cold-chain custody during shipping. All these factors introduce complexities and potentially increased costs. Here we report on a novel whole blood-collection device (BCD) that efficiently separates the liquid from cellular components, minimizes hemolysis in the plasma fraction, and maintains protein integrity during ambient transport. The simplicity of the design makes the device ideal for field use. Whole blood is acquired through venipuncture and applied to the device with an exact volume pipette. The BCD design was based on lateral-flow principles in which whole blood was applied to a defined area, allowing two minutes for blood absorption into the separation membrane, then closed for shipment. The diagnostic utility of the device was further demonstrated with shipments from multiple sites (n = 33) across the U.S. sent to two different centralized laboratories for analyses using liquid chromatography/mass spectrometry (LC/MS/MS) and matrix assisted laser desorption/ionization-time of flight (MALDI-ToF) commercial assays. Specimens showed high levels of result label concordance for the LC/MS/MS assay (Negative Predictive Value = 98%) and MALDI-ToF assay (100% result concordance). The overall goal of the device is to simplify specimen transport to the laboratory and produce clinical test results equivalent to established collection methods.

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.

A proposal for score assignment to characterize biological processes from mass spectral analysis of serum

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Biological process-associated scores generated from mass spectrometry of serum.

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Scores demonstrated acceptable levels of reproducibility.

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Scores associated with biological processes and clinical outcome in cancer patients.

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Possible application to biomarker studies for treatment or monitoring of disease.

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Multiple biological processes assessed from 3 µL of serum.

Introduction

Most diseases involve a complex interplay between multiple biological processes at the cellular, tissue, organ, and systemic levels. Clinical tests and biomarkers based on the measurement of a single or few analytes may not be able to capture the complexity of a patient’s disease. Novel approaches for comprehensively assessing biological processes from easily obtained samples could help in the monitoring, treatment, and understanding of many conditions.

Objectives

We propose a method of creating scores associated with specific biological processes from mass spectral analysis of serum samples.

Methods

A score for a process of interest is created by: (i) identifying mass spectral features associated with the process using set enrichment analysis methods, and (ii) combining these features into a score using a principal component analysis-based approach. We investigate the creation of scores using cohorts of patients with non-small cell lung cancer, melanoma, and ovarian cancer. Since the circulating proteome is amenable to the study of immune responses, which play a critical role in cancer development and progression, we focus on functions related to the host response to disease.

Results

We demonstrate the feasibility of generating scores, their reproducibility, and their associations with clinical outcomes. Once the scores are constructed, only 3 µL of serum is required for the assessment of multiple biological functions from the circulating proteome.

Conclusion

These mass spectrometry-based scores could be useful for future multivariate biomarker or test development studies for informing treatment, disease monitoring and improving understanding of the roles of various biological functions in multiple disease settings.

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer

Simple Summary

The heterogeneity of epithelial ovarian cancer and its associated molecular biological characteristics are continuously integrated in the development of therapy guidelines. In a next step, future therapy recommendations might also be able to focus on the patient’s systemic status, not only the tumor’s molecular pattern. Therefore, new methods to identify and validate host-related biomarkers need to be established. Using mass spectrometry, we developed and independently validated a blood-based proteomic classifier, stratifying epithelial ovarian cancer patients into good and poor survival groups. We also determined an age dependence of the prognostic performance of this classifier and its association with important biological processes. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response and could therefore be integrated into future processes of therapy planning.

Abstract

Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.

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.

Mass Spectrometry-Based Multivariate Proteomic Tests for Prediction of Outcomes on Immune Checkpoint Blockade Therapy: The Modern Analytical Approach

The remarkable success of immune checkpoint inhibitors (ICIs) has given hope of cure for some patients with advanced cancer; however, the fraction of responding patients is 15-35%, depending on tumor type, and the proportion of durable responses is even smaller. Identification of biomarkers with strong predictive potential remains a priority. Until now most of the efforts were focused on biomarkers associated with the assumed mechanism of action of ICIs, such as levels of expression of programmed death-ligand 1 (PD-L1) and mutation load in tumor tissue, as a proxy of immunogenicity; however, their performance is unsatisfactory. Several assays designed to capture the complexity of the disease by measuring the immune response in tumor microenvironment show promise but still need validation in independent studies. The circulating proteome contains an additional layer of information characterizing tumor-host interactions that can be integrated into multivariate tests using modern machine learning techniques. Here we describe several validated serum-based proteomic tests and their utility in the context of ICIs. We discuss test performances, demonstrate their independence from currently used biomarkers, and discuss various aspects of associated biological mechanisms. We propose that serum-based multivariate proteomic tests add a missing piece to the puzzle of predicting benefit from ICIs.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

The Prediction Of Epidermal Growth Factor Receptor Mutation And Prognosis Of EGFR Tyrosine Kinase Inhibitor By Serum Ferritin In Advanced NSCLC

Purpose

To investigate the association between level of serum ferritin (SF) and epidermal growth factor receptor (EGFR) mutations and to analyse the impact of SF level on survival times in advanced non-small-cell lung cancer (NSCLC) patients taking EGFR tyrosine kinase inhibitors (EGFR-TKIs).

Methods

A total of 301 patients who were admitted to the Chinese PLA general hospital from August 2015 to August 2017 were enrolled. The association between tumour markers, including SF, CEA, and EGFR mutation, and their impact on the prognosis of patients taking EGFR-TKIs was investigated.

Results

In all patients, the percentage of patients with EGFR mutations was 52.5% (158/301). EGFR mutations were more likely to be detected in younger (<60 years old), adenocarcinoma patients, non-smokers, women, CEA≥5 µg/L and serum ferritin ≥129 µg/L for females or ≥329 µg/L for males (p<0.05). Increased serum ferritin was an independent factor for predicting EGFR mutations (odds ratio (OR)=4.593, 95% CI (2.673–7.890); P <0.001), and an area under curve (AUC) of 0.711 was shown to predict EGFR mutations with a sensitivity of 81.7% and a specificity of 65.2% in women. Sensitivity increased to 91.1% when combining SF and CEA in all patients. SF was also an independent factor (HR=3.531, 95% CI (2.288–5.448); P<0.001) for predicting progression-free survival (PFS) of patients on EGFR-TKIs, analysed by a Cox proportional hazard model, as PFS was shorter in patients with higher SF (15.0 mo. (SF < 129 µg/L for females or <329 for males) vs 10.0 mo. (129–258 µg/L for females or 329–658 µg/L for males) vs 7.3 mo. (>258 µg/L (>258 µg/L for females or >658 µg/L for males) p<0.001).

Conclusion

SF was a significant predictor of EGFR mutation with moderate diagnostic accuracy, and combining SF and CEA increased the diagnostic sensitivity and specificity for EGFR mutations. SF was also useful for predicting survival in EGFR-TKIs.

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.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

Baseline serum levels of osteopontin predict clinical response to treatment with nivolumab in patients with non-small cell lung cancer

Treatment with nivolumab improves survival and response rate in non-small cell lung cancer (NSCLC). Nevertheless, due to its high financial cost, identifying predictors of response to treatment has become an urgent need. Here, we focused on serum osteopontin (OPN), a pleiotropic protein overexpressed in lung cancer and involved in the immune response. A cohort of NSCLC patients (n = 72) treated with nivolumab was enrolled. Blood samples were collected at the time of first five nivolumab administrations. OPN and high-sensitivity C-reactive protein (hs-CRP) were assayed at each time point. The primary endpoint was to assess the predictive value of baseline serum levels of OPN towards overall survival (OS). Secondary endpoints included the potential association between OPN, hs-CRP and response to nivolumab. OPN and hs-CRP correlate with each other, with neutrophil count and biochemical markers of metastatic disease. At baseline, serum OPN increased with increasing Eastern Cooperative Oncology Group scale of Performance Status (ECOG PS). When Eastern Cooperative Oncology Group scale of Performance Status) (RECIST) criteria were considered, high baseline OPN levels were associated with a worse response to nivolumab. Cox hazard regression further confirmed baseline serum OPN as a predictor of mortality with the best predictive accuracy for serum levels above 37.7 ng/mL. Patients above the cut-off value had a higher mortality rate as compared to low serum OPN levels during follow up. Serum OPN may have a predictive role in NSCLC patients treated with nivolumab. Although larger confirmatory studies are needed, measuring serum OPN levels at baseline can be a clinically useful tool in a near future.

L-Lactate dehydrogenase B may be a predictive marker for sensitivity to anti-EGFR monoclonal antibodies in colorectal cancer cell lines

Recently, proteins derived from cancer cells have been widely investigated as biomarkers for predicting the efficacy of chemotherapy. In this study, to identify a sensitive biomarker for the efficacy of anti-epidermal growth factor receptor monoclonal antibodies (anti-EGFR mAbs), proteins derived from 6 colorectal cancer (CRC) cell lines with different sensitivities to cetuximab, an anti-EGFR mAb, were analyzed. Cytoplasmic and membrane proteins extracted from each CRC cell line were digested using trypsin and analyzed comprehensively using mass spectrometry. As a result, 148 and 146 peaks from cytoplasmic proteins and 363 and 267 peaks from membrane proteins were extracted as specific peaks for cetuximab-resistant and -sensitive CRC cell lines, respectively. By analyzing the proteins identified from the peptide peaks, cytoplasmic L-lactate dehydrogenase B (LDHB) was detected as a marker of cetuximab sensitivity, and it was confirmed that LDHB expression was increased in cetuximab-resistant CRC cell lines. Furthermore, LDHB expression levels were significantly upregulated with the acquisition of resistance to cetuximab in cetuximab-sensitive CRC cell lines. In conclusion, LDHB was identified as an important factor affecting cetuximab sensitivity using comprehensive proteome analysis for the first time.

Retrospective Assessment of a Serum Proteomic Test in a Phase III Study Comparing Erlotinib plus Placebo with Erlotinib plus Tivantinib (MARQUEE) in Previously Treated Patients with Advanced Non-Small Cell Lung Cancer

BACKGROUND

The VeriStrat test provides accurate predictions of outcomes in all lines of therapy for patients with non-small cell lung cancer (NSCLC). We investigated the predictive and prognostic role of VeriStrat in patients enrolled on the MARQUEE phase III trial of tivantinib plus erlotinib (T+E) versus placebo plus erlotinib (P+E) in previously treated patients with advanced NSCLC.

METHODS

Pretreatment plasma samples were available for 996 patients and were analyzed by matrix-assisted laser desorption/ionization-time of flight mass spectrometry to generate VeriStrat labels (good, VS-G, or poor, VS-P).

RESULTS

Overall, no significant benefit in overall survival (OS) and progression-free survival (PFS) were observed for the addition of tivantinib to erlotinib. Regardless of treatment arm, patients who were classified as VS-G had significantly longer PFS (3.8 mo for T+E arm, 2.0 mo for P+E arm) and OS (11.6 mo for T+E, 10.2 mo for P+E arm) than patients classified as VS-P (PFS: 1.9 mo for both arms, hazard ratio [HR], 0.584; 95% confidence interval [CI], 0.468-0.733; p < .0001 for T+E, HR, 0.686; 95% CI, 0.546-0.870; p = .0015 for P+E; OS: 4.0 mo for both arms, HR, 0.333; 95% CI, 0.264-0.422; p < .0001 for T+E; HR, 0.449; 95% CI, 0.353-0.576; p < .0001 for P+E). The VS-G population had higher OS than the VS-P population within Eastern Cooperative Oncology Group (ECOG) performance score (PS) categories. VS-G patients on the T+E arm had longer PFS, but not OS, than VS-G patients on the P+E arm (p = .0108). Among EGFR mutation-positive patients, those with VS-G status had a median OS more than twice that of any other group (OS: 31.6 mo for T+E and 22.8 mo for P+E), whereas VS-P patients had similar survival rates as VS-G, EGFR-wild type patients (OS: 13.7 mo for T+E and 6.5 mo for P+E).

CONCLUSION

In these analyses, VeriStrat showed a prognostic role within EGOC PS categories and regardless of treatment arm and EGFR status, suggesting that VeriStrat could be used to identify EGFR mutation-positive patients who will have a poor response to EGFR tyrosine kinase inhibitors.

IMPLICATIONS FOR PRACTICE

This study suggests that VeriStrat testing could enhance the prognostic role of performance status and smoking status and replicates findings from other trials that showed that the VeriStrat test identifies EGFR mutation-positive patients likely to have a poor response to EGFR tyrosine kinase inhibitors (TKIs). Although these findings should be confirmed in other populations, VeriStrat use could be considered in EGFR mutation-positive patients as an additional prognostic tool, and these results suggest that EGFR mutation-positive patients with VeriStrat "poor" classification could benefit from other therapeutic agents given in conjunction with TKI monotherapy.