Medical Policy: 02.04.60 

Original Effective Date: August 2016 

Reviewed: August 2019 

Revised: August 2019 

 

Notice:

This policy contains information which is clinical in nature. The policy is not medical advice. The information in this policy is used by Wellmark to make determinations whether medical treatment is covered under the terms of a Wellmark member's health benefit plan. Physicians and other health care providers are responsible for medical advice and treatment. If you have specific health care needs, you should consult an appropriate health care professional. If you would like to request an accessible version of this document, please contact customer service at 800-524-9242.

 

Benefit Application:

Benefit determinations are based on the applicable contract language in effect at the time the services were rendered. Exclusions, limitations or exceptions may apply. Benefits may vary based on contract, and individual member benefits must be verified. Wellmark determines medical necessity only if the benefit exists and no contract exclusions are applicable. This medical policy may not apply to FEP. Benefits are determined by the Federal Employee Program.

 

This Medical Policy document describes the status of medical technology at the time the document was developed. Since that time, new technology may have emerged or new medical literature may have been published. This Medical Policy will be reviewed regularly and be updated as scientific and medical literature becomes available.

 

Description:

Plasma based proteomic screening and gene expression profiling (GEP) of bronchial brushing are molecular tests available in the diagnostic workup of pulmonary nodules. To rule out malignancy, invasive diagnostic procedures such as computed tomography (CT)-guided biopsies, bronchoscopies or video assisted thoracoscopy are often required but each carry procedure related complications ranging from post procedure pain to pneumothorax. Molecular diagnostic tests have been proposed to aid in risk-stratifying patients to eliminate or necessitate the need for subsequent invasive diagnostic procedures.

 

Pulmonary nodules are common clinical problem that may be found as an incidental finding on a chest x-ray or computed tomography (CT) scan during lung cancer screening studies of smokers. The primary question following detection of pulmonary nodules is the probability of malignancy, with subsequent management based on various factors such as the radiographic characteristics of the nodule(s) (e.g. size, shape, density) and patient factors (e.g. age, smoking history, previous cancer history, family history, environmental/occupational exposures). The challenge in the diagnostic workup for pulmonary nodules is appropriately ruling-in patients for invasive diagnostic procedures and ruling-out patients who should forego invasive diagnostic procedures. However, due to the low positive predictive value of pulmon ary nodules detected radiographically, many unnecessary invasive diagnostic procedures and/or surgeries are performed to confirm or eliminate the diagnosis of lung cancer.

 

Plasma-Based Proteomic Screening for Pulmonary Nodules

Proteomics is the study of the structure and function of proteins. The study of the concentration, structure and other characteristics of proteins in various bodily tissues, fluids and other materials has been proposed as a method of identifying and managing various diseases, including cancer. In proteomics, multiple test methods are used to study proteins. Immunoassays use antibodies to detect the concentration and/or structure of proteins. Mass spectrometry is an analytic technique that ionizes proteins into smaller fragments and determine mass and composition to identify and characterize them.

 

Plasma-based proteomic screening has been investigated to risk-stratify pulmonary nodules as likely benign to increase the number of patients who undergo serial computed tomography (CT) scans of their nodules (active surveillance), instead of invasive procedures such as surgery or CT guided biopsy. Additionally, proteomic testing may also determine a likely malignancy in clinically low risk or intermediate risk pulmonary nodules, thereby permitting earlier detection in a subset of patients.

 

Xpresys Lung and BDX-XL2 (Xpresys Lung 2) are plasma-based proteomic screening tests that measures the relative abundance of proteins (Xpresys Lung 13 proteins, Xpresys Lung 2 [BDX-XL2] 3 proteins) across multiple disease pathways associated with lung cancer using an analytic technique called multiple reaction monitoring mass spectroscopy (MRM-mass spec). The role of this test is to aid physicians in differentiating likely benign, from likely malignant nodules. If the test yields a “likely benign” result, patients may choose active surveillance via serial CT scans to monitor the pulmonary nodule(s). If the test yields a “likely malignant” result, invasive diagnostic procedure would be indicated. The test is therefore only used in the management of pulmonary nodules to rule-in or rule-out invasive diagnostic procedures and does not provide a diagnosis of lung cancer.

 

REVEAL Lung Nodule Characterization (MagArray) is a plasma protein biomarker test that may aid clinicians in characterizing indeterminate pulmonary nodules (4-30 mm) in current smokers aged 25 years and older. The test uses immunoassay, microarray, and magnetic nanoparticle detection techniques. The REVEAL Lung Nodule Characterization score is presented on a scale from 0 to 100 with a single cut point at 50, and the score is calculated using an algorithm based on the measurement of 3 clinical factors (smoking history, patient age, nodule size) and 3 blood proteins (epidermal growth factor receptor [EGFR], prosurfactant protein B (ProSB), tissue inhibitor of metalloproteinases 1 (TIMP1) associated with the presence of lung cancer. This result may aid in the decision to perform a biopsy, or to consider routine monitoring.

 

Clinical Context and Test Purpose

The purpose of plasma-based proteomic screening in individuals with undiagnosed pulmonary nodule(s) is to stratify clinical risk for malignancy and eliminate or necessitate the need for invasive diagnostic procedures.

 

Patients

The relevant population of interest is individuals with undiagnosed pulmonary nodules. In particular, as outlined in the evidence based American College of Chest Physicians guidelines (2013) on the evaluation of individuals with pulmonary nodules, diagnosis and management of lung cancer, decision making about a single indeterminate lung nodule 8 to 30 mm in diameter on a computed tomography (CT) scan is complicated, requiring input about the patient’s pretest probability of lung cancer, the characteristics of the lung nodule on CT, and shared decision making between the patient and physician about follow-up. Therefore, additional information in the segment of patients with an indeterminate lung nodule, 8 to 30 mm in diameter would be particularly useful.

 

Interventions

The tests being considered is plasma-based proteomic screening Xpresys Lung, Xpresys Lung test 2 (BDX-XL2) and REVEAL Lung Nodule Characterization.

 

Comparators

The following practice is currently being used: standard clinical management using clinical and radiographic risk factors.

 

Outcomes

The potential beneficial outcomes of primary interest are avoiding an unneeded invasive biopsy of a nodule that would be negative for lung cancer or initiating biopsy for a nodule that would otherwise have been followed with serial CTs.

 

Potential harmful outcomes are those result from false-positive or false-negative results. False-positive test results can lead to unnecessary invasive diagnostic procedures and procedure related complications. False-negative test result can lead to lack of pulmonary nodule surveillance or lack of appropriate invasive diagnostic procedures to diagnosis malignancy.

 

Clinically Valid

A test must detect the presence or absence of a condition, the risk of developing a condition in the future, or treatment response (beneficial or adverse).

 

Several studies were identified that reported on the development and validation of plasma-based classifier test to predict malignancy (Xpresys Lung and Xpresys Lung 2[BDX-XL2]).

 

Pecot et. al. (2012) validated a 7-peak matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) proteomic signature in 2 prospective cohorts of patients with one or more pulmonary nodules on chest computed tomography (CT) (N=379). The first, cohort A, combined patients from Vanderbilt University and the Veterans Affairs Medical Center, Nashville, TN and the second cohort B, included patients from Mayo Clinic, Rochester, MN. The average computed tomography (CT) measured nodule size in cohorts A and B was 37.83 versus 23.15 mm among patients with lung cancer and 15.82 versus 17.18 mm among those without, respectively. In cohort A, the area under the curve (AUC) increased from 0.68 to 0.86 after adding chest CT imaging variables to the clinical results, but the proteomic signature did not provide meaningful added value. In contrast, in cohort B, the AUC improved from 0.46 with clinical data alone to 0.61 when combined with chest CT imaging data and to 0.69 after adding the proteomic signature (IDI of 20% P = 0.0003). In addition, in a subgroup of 100 nodules between 5 and 20 mm in diameter, the proteomic signature added value with an IDI of 15% (P ≤ 0.0001). The authors concluded the results show that this serum proteomic biomarker signature may add value to the clinical and chest CT evaluation of indeterminate lung nodules. Further prospective validation among a larger cohort of patients presenting with indeterminate pulmonary nodules in the context of a screening strategy is needed.

 

Li et. al. (2013) reported on the development and validation of the 13 protein plasma test, or classifier, proposed to differentiate benign from malignant pulmonary lung nodules. They used multiple reaction monitoring (MRM) mass spectrometry (MS) to measure the concentrations of candidate proteins in plasma. The test identifies classifier proteins likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) associated with lung cancer and inflammation. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and Stage IA cancer matched on nodule size, age, gender and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a high negative predictive value (NPV) of 90%. Validation performance on samples from a non-discovery clinical site showed NPV of 94%, The main limitation of this study is that both discovery and validation studies were conducted using retrospective samples. A prospective study on intended use samples is required to further validate the utility of the classifier for clinical use.

 

Vachani et. al. (2015) reported on a retrospective, multicenter, case-control study on the validation for classifier comprising five diagnostic and six normalization proteins designed to identify likely benign lung nodules in a sample of 141 plasma samples associated with indeterminate pulmonary nodules 80 to 30 mm in diameter. The classifier achieved validation on 141 lung nodule-associated plasma samples based on predefined statistical goals to optimize sensitivity. Using a population based
non small-cell lung cancer prevalence estimate of 23% for 8 to 30 mm indeterminate pulmonary nodules (IPNs), the classifier identified likely benign lung nodules with 90% negative predictive value and 26% positive predictive value, at 92% sensitivity and 20% specificity, with the lower bound of the classifier's performance at 70% sensitivity and 48% specificity. Classifier scores for the overall cohort were statistically independent of patient age, tobacco use, nodule size, and chronic obstructive pulmonary disease diagnosis. The classifier also demonstrated incremental diagnostic performance in combination with a four-parameter clinical model. Limitations of this study derive from specifics of the experimental design relating to the classifier performance priorities and molecular biomarkers and the use of archival samples from academic centers, though representative of the target population, may raise questions about the classifier’s prospective performance in the general population. Future evaluations of the proteomic expression classifier in prospective lung nodule studies may clarify some of the performance issues.

 

Kearney et. al. (2017), conducted a prospective, multicenter, observational trial with retrospective evaluation of the performance of molecular and clinical makers (Xpresys Lung). Patients with indeterminate pulmonary nodule were enrolled at 12 geographically diverse sites in the U.S. Of these sites seven were academic sites and five were community sites. Eligible patients were those with a lung nodule of size 8-20 mm, minimum 40 years of age, who had recently completed a CT guided needle aspiration or bronchoscopic biopsy with an established diagnosis or scheduled for a surgical biopsy. The assay Xpresys Lung is based on mass spectroscopy (MRM-MS), clinical factors collected included age, smoking status, nodule diameter, nodule speculation and nodule location. All samples were analyzed using the Xpresys Lung assay. A total of 475 subjects were enrolled prospectively from April 2012 to December 2014 in the registered study NCT01752101. Of these, 50 subjects violated the inclusion/exclusion criteria: 43 additional subjects violated the blood sample collection protocol. Of the remaining 353 patients, 222 had nodule size 8-20 mm. In this population of subjects, the cancer prevalence was 81% (180 out of 222 subjects). The authors concluded, the integration of molecular markers with clinical risk factors can increase classification performance over molecular markers (clinical factors) on their own. However, this requires further validation and investigation on lung nodule population.

 

Silvestri et. al. (2018) reported on the validation of the Xpresys Lung test 2 (BDX-XL2) in a prospective, multicenter observational study (Pulmonary Nodule Plasma Proteomic Classifier Study [PANOPTIC]) with retrospective evaluation of 685 patients with 8 to 30 mm lung nodules and a low pretest probability of malignancy < 50%. Out of the 685 patients a total of 293 were excluded (failed include/exclusion criteria, no blood sample, no baseline CT scan, incomplete clinical data, sample inadequate for protein analysis, protocol deviations), yielding 392 eligible for analysis. This study integrated classifier’s performance focused on the subgroup of 178 patients having a lung nodule with pCA < 50%. Of these, 66 were classified as likely benign, 65 of which had a benign nodule, while 1 of 29 malignant nodules (3%) was misclassified as likely benign. Of the 149 benign nodules in the study, 44% were correctly classified as likely benign. For the 71 patients who had invasive procedures, 42 had benign nodules. The authors concluded, this study is the first we are aware of to assess the accuracy of an integrated plasma proteomics classifier in patients with pulmonary nodules in a geographically diverse population with varying risk of cancer. In those with low to moderate risk nodules (pCA < 50%), a “likely benign” test result could safely allow patients to be followed up by using serial imaging. Further research is needed to assess the effect of incorporating this test into diagnostics algorithm for nodule management in the hope of reducing unnecessary procedures in patients without cancer.

 

Clinical validation studies were identified for two versions of a proteomic classifier (Xpresys Lung and Xpresys Lung test 2 [BDX-XL2]. This classifier has undergone substantial evolution, from a 13 protein assay to a 2 protein assay integrated with clinical factors. Because of this evolution, the most relevant studies are with the most recent version two Xpresys Lung test 2 [BDX-XL2]. One validation study on the version two has been identified. The classifier has been designed to have high pretest probability (< 50%) of a malignant pulmonary nodule. The primary limitation of this study is that a high number of patients were excluded from the study due to incomplete clinical data or because they were subsequently determined to be outside of the intended use population. It is unclear if the intended use population was determined as priori. Validation in an independent sample in the intended use population is needed. In general, the Xpresys Lung classifier has been designed to have a high NPV (negative predictive value). However, its clinical validity is uncertain given that studies have reported on slightly different versions of the test. Also, studies have not reported how it reclassifies patients relative to clinical classifiers regarding risk.

 

The following validation study was identified for the REVEAL Lung Nodule Characterization.

 

Trivedi et. al. (2018) validated a plasma-based multiplexed assay for classifying indeterminate pulmonary nodules (IPN) by discriminating between those with a lung cancer diagnosis established pathologically and those found to be clinically and radiographically stable for at least one year. Using a novel technology (REVEAL Lung Nodule Characterization) they developed assays for plasma proteins associated with lung cancer into a panel for characterizing the risk that an IPN found on chest imaging is malignant. The assay panel was evaluated with a cohort of 277 samples, all from current smokers with an IPN 4-30 mm. Subjects were divided into training and test sets to identify a Support Vector Machine (SVM) model for risk classification containing those proteins and clinical factors that added discriminatory information to the Veteran’s Affairs (VA) Clinical Factors Model. The algorithm was then evaluated in an independent validation cohort. Among the 97 validation study subjects, 68 were grouped as having intermediate risk by the VA model of which the SVM model correctly identified 44 (65%) of these intermediate-risk samples as low (n=16) or high risk (n=28). The SVM model negative predictive value (NPV) was 94% and its sensitivity was 94%. The authors concluded, risk stratification for benign nodules is improved with the SVM model compared to current clinical practice methods. We hypothesize that patients with benign disease may benefit the most from this rule-out assay by avoiding unnecessary lung biopsy and subsequent overtreatment, while improving the quality of care and reducing the risk of harm from these procedures. This study had several limitations, including the need to fully assess the test in other races, and how other conditions (such as obesity and its pro-inflammatory state, or steroid use) may affect the assay performance. This algorithm is also dependent on a compliant patient; those who do not adhere to follow-up appointments may have their cancer diagnosis missed. A clinical utility study to assess the impact of the algorithm on clinical decision making is also needed as outlined in the American Thoracic Society policy statement. Ideally, long-term follow-up including the rate of lung cancer deaths prevented using this test is desired to verify this as an effective marker of aggressive lung cancer.

 

Arfoosh et. al. (2019) assessed the results of a novel, plasma-based multiplexed protein assay (REVEAL Lung Nodule Characterization) in a clinical experience program. Fifty-four consecutive plasma samples were evaluated and all samples were from patients who are current smokers, aged 25 years and older, and have an indeterminate pulmonary nodule 0.4 to 3 cm in diameter. The mean patient age was 65.5 years and the mean nodule size was 1.0 cm. Twenty six patients were male (52% female). Of the 54 tests, the assay results for 23 individuals were determined to be in the lower risk of malignancy range (score < 49). Forty two patients had a pre-test probability in the intermediate risk range as calculated by the VA Clinical Model. Of those patients, the assay characterized 22 as having a lower risk of malignancy (52%). The authors concluded, the novel, multiplexed, plasma protein assay can be used as a non-invasive risk assessment tool by clinicians in characterizing indeterminate pulmonary nodules. When the results of this assay are combined with the traditional clinical risk factors (i.e. patient history), risk stratification for indeterminate pulmonary nodules may be improved compared to current methods in clinical practice. We hypothesize the assay will significantly reduce costs to the healthcare system while further improving a patient’s quality of care. Providers and their patients may consider using this novel assay prior to proceeding with an invasive evaluation of their patient’s indeterminate nodule. Although the blood-based biomarker assay has shown promising results in differentiating malignant from benign lesions, further research is needed to more broadly assess the impact of the test on clinical decision making. Ideally, long-term follow-up including the rate of lung cancer deaths prevented using this test is desired to further verify this as an effective risk assessment of lung cancer.

 

While the REVEAL biomarker assay has shown promising results in differentiating malignant from benign lesions, further research is needed to more broadly assess the impact of the test on clinical decision making. Ideally long-term follow-up including the rate of lung cancer deaths prevented using this test is desired to further verify this an effective risk assessment of lung cancer. This plasma-protein signature should also be more directly assessed in all races, as well as specific conditions such as obesity and its pro-inflammatory state, steroid use, etc., that may affect the test performance. Further clinical studies are warranted to further define the value of the test in accurately identifying patients who are most likely to benefit from serial surveillance or early treatment, while reducing the rate of false-positive results, unnecessary interventions, and their associated morbidity and healthcare costs.

 

Clinically Useful

A test is clinically useful if the use of the results informs management decisions that improve the net health outcome of care. The net health outcome can be improved if patients receive correct therapy, or more effective therapy, or avoid unnecessary therapy, or avoid unnecessary therapy.

 

Direct evidence of clinical utility is provided by studies that have compared health outcomes for patients managed with or without the test. The preferred evidence would be from randomized controlled trials. No evidence directly demonstrating improved outcomes in patients managed with Xpresys Lung test 2 [BDX-XL2] was identified.

 

Vachani et. al. (2015) reported on a multicenter prospective-retrospective study of patients with indeterminate pulmonary nodules. A plasma protein classifier (Xpresys Lung) was used on 475 patients with nodules 8 to 30 mm in diameter who had an invasive procedure to confirm the diagnosis. Using the classifier, 32.0% (95% CI, 19.5% to 46.7%) of surgeries and 31.8% (95% CI, 20.9% to 44.4%) of invasive procedures (biopsy and/or surgery) on benign nodules could have been avoided, while 24.0% (95% CI, 19.2% to 29.4%) of patients with malignancy would have been triaged to CT surveillance. By comparison, 24.5% (95% CI, 16.2% to 34.4%) of patients with malignancy were routed to CT surveillance using clinical parameters alone.

 

Indirect evidence on clinical utility rests on clinical validity. If the evidence is insufficient to demonstrate test performance, no inferences can be made about clinical utility.

  • Changes in Management
    • The clinical setting in which a proteomic classifier with high NPV is used, are individuals with undiagnosed pulmonary nodules detected by CT.
    • Indirect evidence regarding Xpresys Lung test 2 [BDX-XL2] suggests that 36% of invasive procedures (biopsy and/or surgery) on benign nodules could have been avoided, if the test is used in patients with a low to moderate (< 50%) pretest probability of malignancy. Three percent of malignant lesions may be missed, although these patients would be followed by CT to verify lack of progression.
  • Improved Outcomes
    • Indirect evidence suggests that use of proteomic classifer with a high NPV hs the potential to reduce the number of unnecessary invasive procedures to definitively diagnose benign disease versus malignancy. Compared with the standard of care plan, some patients without cancer will have avoided an unnecessary invasive procedure, which is weighed against the increase in missed cancers in patients who had lung cancer but tested as negative on the proteomic classifier with a high NPV test.
    • Whether the tradeoff between avoiding unneeded surgeries and the potential for missed cancer is worthwhile depends, in part, on patient and physician preferences. Missed malignancies would likely be continued to be followed by active surveillance using low-dose CT imaging. In the context of lung cancers, overall survival (OS) depends on the detection of lung cancer at early, more treatable stages.
    • Avoiding invasive procedures in situations where patients are at very low likelihood fo having lung cancer is likely beneficial, given the known complications of invasive procedures (e.g. pneumothorax). However, reduction in unnecessary invasive procedures must be weighed against outcomes and harms associated with a missed diagnosis of lung cancer at earlier, more treatable stages.

 

Smester et. al. (2019) in a case report, reported on the clinical work-up of a high-risk patient with an indeterminate pulmonary nodule. In this clinical case, the assay (REVEAL Lung Nodule Characterization) provided important, additional information that modified the patient’s management. Although the patient was an anxious current smoker, and had a 7 mm nodule, the multiplexed plasma protein assay test score of 34 (out of 100) indicated lower risk that the nodule was malignant. This finding helped the patient and physician make a better-informed decision to adopt a serial surveillance approach. The authors concluded, we reported on a patient case illustrating the benefit of novel, lung cancer-specific biomarker assay. The assay can be used as a non-invasive risk assessment toold for clinicians in characterizing indeterminate pulmonary nodules. When the results of the assay are combined with the traditional risk factors, risk stratification for indeterminate pulmonary nodules is improved compared to current methods in clinical practice. We hypothesize the assay will significantly reduce costs to the healthcare system while further improving a patient’s quality of care. Providers and their patients may consider using this novel assay prior to proceeding with an invasive evaluation of their patient’s indeterminate pulmonary nodule. Although the REVEAL biomarker assay has shown promising results in differentiating malignant from benign lesions, further research is needed to more broadly assess the impact of the test on clinical decision making. Ideally long-term follow-up including the rate of lung cancer deaths prevented using this test is desired to further verify this an effective risk assessment of lung cancer.

 

Summary

Indirect evidence suggest that a proteomic classifier with high NPV has the potential to reduce the number of invasive procedures to definitively diagnose benign d.isease versus malignancy. While these results are promising further clinical studies are warranted to more broadly assess the impact of these tests on clinical decision making, by accurately identifying patients who are most likely to benefit from serial surveillance or early treatment, while reducing the rate of false-positive results, and unnecessary interventions. Also, long-term follow-up is needed to include the rate of lung cancer deaths prevented using this test to further verify this an effective risk assessment of lung cancer.

 

Gene Expression Profiling

Gene expression profiling (GEP) is the measurement of the activity of genes within cells. Messenger RNA serves at the bridge between DNA and functional proteins. Multiple molecular techniques such as Northern blots, ribonuclease protection assay, in situ hybridization, spotted complementary DNA arrays, oligonucleotide arrays, reverse transcriptase polymerase chain reaction, and transcriptome sequencing are used in gene expression profiling (GEP). An important role of gene expression profiling (GEP) in molecular diagnostics is to detect cancer associated gene expression of clinical samples to assess for the risk of malignancy.

 

Gene Expression Profiling for an Indeterminate Bronchoscopy Result

The Percepta Bronchial Genomic Classifier (Veracyte) is a 23 gene, gene expression profiling test that analyzes genomic changes in the airways of current of former smokers to assess a patient’s risk of having lung cancer, without the direct testing of a pulmonary nodule. The test is indicated for current and former smokers following an indeterminate bronchoscopy result to determine the subsequent management of pulmonary nodules (e.g. active surveillance or invasive diagnostic procedures), and does not diagnose lung cancer.

 

Clinical Context and Test Purpose

The purpose of gene expression profiling of bronchial brushings in individuals who undergo bronchoscopy for the diagnosis of suspected lung cancer but who have an indeterminate cytology result is to stratify the clinical risk for malignancy and eliminate the need for invasive diagnostic procedures.

 

Patients

The relevant population of interest, according to the manufacturer, is individuals with physician-assessed low or intermediate pretest risk of malignancy who are current or former smokers with inconclusive bronchoscopy for suspected lung cancer.

 

Interventions

The test being considered is gene expression profiling (GEP) of bronchial brushings to include Percepta Bronchial Genomic Classifier (Varacyte).

 

Comparators

The following practice is currently being used: standard clinical management without gene expression profiling (GEP). The management of patients with suspected lung cancer who have an indeterminate bronchoscopy result is not entirely standardized. However, it is likely that in standard practice many patients would have a surgical biopsy, transthoracic needle aspiration, or another test, depending on the location of the nodule. According to the guidelines from the American College of Chest Physicians (2013) for establishing the diagnosis of lung cancer, in patients suspected of having lung cancer, who have a central lesion, bronchoscopy is recommended to confirm the diagnosis. If the bronchoscopy results are nondiagnostic and suspicion of lung cancer remains, additional testing is recommended (Grade 1B recommendation).

 

Outcomes

The potential beneficial outcomes of primary interest are avoiding an unneeded invasive biopsy of a nodule that would be negative for lung cancer.

 

Potential harmful outcomes are those result from false-positive or false-negative results. False-positive test results can lead to unnecessary invasive diagnostic procedures and procedure related complications. False-negative test result can lead to lack of pulmonary nodule surveillance or lack of appropriate invasive diagnostic procedures to diagnosis malignancy.

 

Clinically Valid

A test must detect the presence or absence of a condition, the risk of developing a condition in the future, or treatment response (beneficial or adverse).

 

Whitney et. al. (2015) reported on the development and initial validation of an RNA based gene expression classifier from airway epithelial cells designed to be predictive of cancer in current and former smokers undergoing bronchoscopy for suspected lung cancer. Samples were from patients in the Airway Epithelium Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials, which were 2 prospective observational, cohort studies (AEGIS-1, AEGIS-2), for current or former smokers undergoing bronchoscopy for suspected lung cancer. Cohort details are described in Silvestri et. al. (2015) below. A total of 299 samples from AEGIS-1 (223 cancer positive and 76 cancer free subjects) were used to derive the classifier. Data from 123 patients in a prior study with a non-diagnostic bronchoscopy were used as an independent test set. In the final model, the classifier included 17 genes, patient age, and gene expression correlates and was reported as a dichotomous score (> 0.65 as cancer positive, < 0.65 as cancer negative). This classifier had a receiver operating characteristic (ROC) curve AUC (area under the curve) of 0.78 (95% CI, 0.70-0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n = 134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI, 0.73-0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, cancer histologies and stages. The negative predictive value was 94% (95% CI, 83-99%) in subjects with a non-diagnostic bronchoscopy.

 

Silvestri et. al. (2015) reported on the diagnostic performance of the gene expression classifier developed in Whitney et. al. (2015), in a sample size of 639 patients enrolled in 2 multicenter prospective studies (AEGIS-1, n=298 patients; AEGIS-2 n=341 patients). The study enrolled patients who were undergoing clinically indicated bronchoscopy for a diagnosis of possible lung cancer and had a history of smoking. Before the bronchoscopy, the treating physician assessed each patients’ probability of having cancer with a 5 level scale (<10%, 10.-39%, 40.60%, 61.85%, >85%). Patients were followed until a diagnosis was established (either at the time of bronchoscopy or subsequently by another biopsy means) or until 12 months after bronchoscopy. Of the 639 patients in the validation study who underwent bronchoscopy. A total of 43% of bronchoscopic examinations were nondiagnostic for lung cancer, and invasive procedures were performed after bronchoscopy in 35% of patients with benign lesions. In AEGIS-1, the classifier had an area under the receiver-operating-characteristic curve (AUC) of 0.78 (95% confidence interval [CI], 0.73 to 0.83), a sensitivity of 88% (95% CI, 83 to 92), and a specificity of 47% (95% CI, 37 to 58). In AEGIS-2, the classifier had an AUC of 0.74 (95% CI, 0.68 to 0.80), a sensitivity of 89% (95% CI, 84 to 92), and a specificity of 47% (95% CI, 36 to 59). The combination of the classifier plus bronchoscopy had a sensitivity of 96% (95% CI, 93 to 98) in AEGIS-1 and 98% (95% CI, 96 to 99) in AEGIS-2, independent of lesion size and location. In 101 patients with an intermediate pretest probability of cancer, the negative predictive value of the classifier was 91% (95% CI, 75 to 98) among patients with a nondiagnostic bronchoscopic examination. The classifier improved prediction of cancer compared with bronchoscopy alone, but comparisons with a clinical predictor were not reported. For the subset of patients with a nondiagnostic bronchoscopy, the classifier performance was presented by the pretest physician-predicted risk if cancer. For most subpopulations, there was a very high NPV. However, there were 13 false negatives, 10 of which were considered at high (>60%) risk of cancer pre-bronchoscopy.

 

Summary

Two multicenter prospective studies have provided evidence of the clinical validity of a bronchial genomic classifier in current or former smokers undergoing bronchoscopy for suspicion of lung cancer. For patients with intermediate risk of lung cancer with nondiagnostic bronchoscopic examination, the NPV was 91%. However, there has been limited replication outside of a single trial group.

 

Clinically Useful

A test is clinically useful if the use of the results informs management decisions that improve the net health outcome of care. The net health outcome can be improved if patients receive correct therapy, or more effective therapy, or avoid unnecessary therapy, or avoid unnecessary testing.

 

Direct Evidence

Direct evidence of clinical utility is provided by studies that have compared health outcomes for patients managed with and without the test. The preferred evidence would be from randomized controlled trials.

 

Chain of Evidence

Indirect evidence on clinical utility rests on clinical validity. If the evidence is insufficient to demonstrate test performance, no inferences can be made about clinical utility.

 

Vachani et. al. (2016) reported on rates of invasive procedures from AEGIS 1 and 2. Of 222 patients, 188 (85%) had an inconclusive bronchoscopy and follow-up procedure data available for analysis. Seventy-seven (41%) patients underwent an additional 99 invasive procedures, which included surgical lung biopsy in 40 (52%) patients. Benign and malignant diseases were ultimately diagnosed in 62 (81%) and 15 (19%) patients, respectively. Among those undergoing surgical biopsy, 20 (50%) were performed in patients with benign disease. If the classifier had been used to guide decision making, procedures could have been avoided in 21 (50%) of 42 patients who had additional invasive testing. Further, among 35 patients with an inconclusive index bronchoscopy who were diagnosed with lung cancer, the sensitivity of the classifier was 89%, with 4 (11%) patients having a false-negative classifier result. Invasive procedures after an inconclusive bronchoscopy occur frequently, and most are performed in patients ultimately diagnosed with benign disease.

 

Ferguson et. al. (2016) conducted a randomized, prospective, decision impact survey study assessing pulmonologist recommendations in patients undergoing workup for lung cancer who had an inconclusive bronchoscopy. Cases with an intermediate pretest risk for lung cancer were selected from the AEGIS trials and presented in a randomized fashion to the pulmonologists either with or without the patient’s bronchial genomic classifier result to determine how the classifier results impacted physician decisions. Two hundred two physicians provided 1523 case evaluations on 36 patients. Invasive procedure recommendations were reduced from 57% without the classifier result to 18% with a negative (low risk) classifier result (p < 0.001). Invasive procedure recommendations increased from 50 to 65% with a positive (intermediate risk) classifier result (p < 0.001). When stratifying by ultimate disease diagnosis, there was an overall reduction in invasive procedure recommendations in patients with benign disease when classifier results were reported (54 to 41 %, p < 0.001). For patients ultimately diagnosed with malignant disease, there was an overall increase in invasive procedure recommendations when the classifier results were reported (50 to 64 %, p = 0.003). Limitations of this study include: the physicians were provided a summary of the patient’s clinical presentation (age, gender, comorbidities), smoking history (pack years, years since quitting) and exposure history, physical exam findings, lesion details from CT and PET reports (nodule diameter, location, presence of adenopathy), but did not have direct access to the imaging which can impact decision making in this setting; physicians who chose PET as the management option were notified that the PET results were indeterminate, prompting them to make another management choice; this was a clinical decision impact study presented in survey form and not a clinical trial or registry, as such it can only approximate clinical utility using responses from a population who may have some form of selection bias for entering this type of study and decisions made in the survey may not accurately reflect those made at point of care; and clinical decision making is ultimately modulated by patient preferences which was not captured by this survey. The authors concluded that the findings suggest that a negative (low risk) bronchial genomic classifier result reduces invasive procedure recommendations following an inconclusive bronchoscopy and that the classifier overall reduces invasive procedure recommendations among patients ultimately diagnosed with benign disease. These results support the potential clinical utility of the classifier to improve management of patients undergoing bronchoscopy for suspect lung cancer by reducing additional invasive procedures in the setting of benign disease.

 

Summary

Direct evidence of the clinical utility for gene expression profiling of bronchial brushings is lacking. Indirect evidence suggest that Percepta Bronchial Genomic Classifier has the potential to reduce the number of unnecessary invasive procedures to definitively diagnose benign disease versus malignancy. However, long-term follow-up data would be required to determine the survival outcomes in patients with a missed diagnosis of lung cancer at earlier, more treatable stages.

 

Summary of Evidence

For individuals with undiagnosed pulmonary nodules detected by computed tomography (CT) who receive plasma-based proteomic screening, the evidence includes a prospective cohort and prospective-retrospective studies. Clinical validation studies were identified for two versions of a proteomic classifier Xpresys Lung and Xpresys Lung test 2 [BDX-XL2]. This classifier has undergone substantial evolution, from a 13 protein assay to a 2 protein assay integrated with clinical factors. Because of this evolution, the most relevant studies are with the most recent version two (Xpresys Lung test 2 [BDX-XL2]). One validation study on the version two has been identified. The classifier has been designed to have high specificity for malignant pulmonary nodules and the validation study showed a specificity of 97% for patients with low to moderate pretest probability (< 50%) of a malignant pulmonary nodule. The primary limitation of this study is that a high number of patients were excluded from the study due to incomplete clinical data or because they were subsequently determined to be outside of the intended use population. It is unclear if the intended use population was determined a priori. Validation in an independent sample in the intended use population is needed. The evidence is insufficient to determine the effects of the technology on net health outcomes.

 

For individuals with indeterminate pulmonary nodules (4-30 mm) by computed tomography (CT), who receive plasma-based proteomic screening using the REVEAL Lung Nodule Characterization test, the evidence includes retrospective validation studies and case reports. While the REVEAL biomarker assay has shown promising results in differentiating malignant from benign lesions, further research is needed to more broadly assess the impact of the test on clinical decision making. Ideally long-term follow-up including the rate of lung cancer deaths prevented using this test is desired to further verify this an effective risk assessment of lung cancer. This plasma-protein signature should also be more directly assessed in all races, as well as specific conditions such as obesity and its pro-inflammatory state, steroid use, etc., that may affect the test performance. Further clinical studies are warranted to further define the value of the test in accurately identifying patients who are most likely to benefit from serial surveillance or early treatment, while reducing the rate of false-positive results, unnecessary interventions, and their associated morbidity and healthcare costs.

 

For individuals with undiagnosed pulmonary nodules following indeterminate bronchoscopy results for suspected lung cancer who receive gene expression profiling of bronchial brushings, the evidence includes multicenter prospective studies. Direct evidence of the clinical utility for gene expression profiling of bronchial brushings is lacking. Indirect evidence suggest that Percepta Bronchial Genomic Classifier has the potential to reduce the number of unnecessary invasive procedures to definitively diagnose benign disease versus malignancy. However, long-term follow-up data would be required to determine the survival outcomes in patients with a missed diagnosis of lung cancer at earlier, more treatable stages. The evidence is insufficient to determine the effects of the technology on net health outcomes.

 

Regulatory Status

Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratory developed tests (LDTs) must meet general regulatory standards of the Clinical Laboratory Improvement Amendments (CLIA). Xpresys Lung test and Xpresys Lung 2 (BDX-XL2) (Integrated Diagnostics [Indi.] purchased by Biodesix), Percepta Bronchial Genomic Classifer (Varacyte) and REVEAL Lung Nodule Characterization (MagArray) are available under the auspices of CLIA. Laboratories that offer laboratory developed tests (LDTs) must be licensed by CLIA for high complexity testing. To date, the U.S. Food and Drug Administration has chosen not to require any regulatory review of these tests.

 

Practice Guidelines and Position Statements

American Thoracic Society

In 2017, the American Thoracic Society published a position statement on the evaluation of molecular biomarkers for the early detection of lung cancer. The goal was to develop a policy statement that provides guidance about the level of evidence required to determine that a molecular biomarker, used to support early lung cancer detection, is appropriate for clinical use. Key points made in the statement include the following:

  • A clinically useful molecular biomarker applied to the evaluation of lung nodules may lead to expedited therapy for early lung cancer and/or fewer aggressive interventions in patients with benign lung nodules.
  • To be considered clinically useful a molecular biomarker used to assist with lung nodule management must lead to:
    • Earlier diagnosis of malignant nodules without substantially increasing the number of procedures performed on patients with benign nodules; or
    • Fewer procedures for patients with benign nodules without substantially delaying the diagnosis of cancer in patients with malignant nodules.

 

The society concluded the application of molecular biomarkers to assist with the early detection of lung cancer has the potential to substantially improve our ability to select patients for lung cancer screening, and to assist with the characterization of indeterminate lung nodules. To support the application of molecular biomarkers in these clinical settings there must be evidence that the molecular biomarker leads to clinical decisions whose benefits outweigh their harms. Although it is tempting to apply novel testing based on promising discovery or validation level studies, the lung cancer community should insist on additional evidence of clinical utility before changing practice.

 

Prior Approval:

Not applicable

 

Policy:

See Related Medical Policy

  • 02.04.55 Epidermal Growth Factor Receptor (EGFR) Testing
  • 02.04.16 Circulating Tumor DNA and Circulating Tumor Cells for Cancer Management (Liquid Biopsies)

 

Plasma-Based Proteomic Screening

Plasma-based proteomic screening testing, including but not limited to Xpresys Lung, Xpresys Lung 2 (BDX-XL2) and REVEAL Lung Nodule Characterization in patients with undiagnosed pulmonary nodule(s) detected by imaging is considered investigational.

 

Based on review of the peer reviewed medical literature the indirect evidence of the clinical utility suggest that a proteomic classifier with high negative predictive value (NPV) has the potential to reduce the number of invasive procedures to definitively diagnose benign disease versus malignancy. While these results are promising further clinical studies are warranted to more broadly assess the impact of these tests on clinical decision making, by accurately identifying patients who are most likely to benefit from serial surveillance or early treatment, while reducing the rate of false-positive results, and unnecessary interventions. Also, long-term follow-up is needed to include the rate of lung cancer deaths prevented using this test to further verify this an effective risk assessment of lung cancer. Also, no professional society guidelines indicate the utilization of plasma-based proteomic screening in the management of patients with undiagnosed pulmonary nodule(s). The evidence is insufficient to determine the effects of this technology on net health outcomes.

 

Gene Expression Profiling

Gene expression profiling on bronchial brushings, including but not limited to Percepta Bronchial Genomic Classifier, in patients with indeterminate bronchoscopy results for undiagnosed pulmonary nodule(s) is considered investigational.

 

Direct evidence of the clinical utility for gene expression profiling (GEP) of bronchial brushings is lacking. Indirect evidence of the clinical utility suggest that Percepta Bronchial Genomic Classifier has the potential to reduce the number of unnecessary invasive procedures to definitively diagnose benign disease versus malignancy. However, long-term follow-up data would be required to determine the survival outcomes in patients with a missed diagnosis of lung cancer at earlier, more treatable stages. Also, no professional society guidelines indicate the utilization of gene expression profiling (GEP) on bronchial brushings in the management of patients with indeterminate bronchoscopy results for undiagnosed pulmonary nodule(s). The evidence is insufficient to determine the effects of the technology on net health outcomes.

 

Procedure Codes and Billing Guidelines:

To report provider services, use appropriate CPT* codes, Alpha Numeric (HCPCS level 2) codes, Revenue codes and / or diagnosis codes.

  • 84999 Unlisted chemistry procedure (when specified as Xpresys Lung or Percepta Bronchial Genomic Classifier)
  • 0080U Oncology (lung), mass spectrometric analysis of galectin-3 binding protein and scavenger receptor cysteine-rich type 1 protein M130, with five clinical risk factors (age, smoking status, nodule diameter, nodule speculation status and nodule location) utilizing plasma, algorithm reported as a categorical probability of malignancy (Xpresys Lung 2 [BDX-XL2])
  • 0092U Oncology (lung), three protein biomarkers, immunoassay using magnetic nanosensor technology, plasma, algorithm reported as risk score for likelihood of malignancy (REVEAL Lung Nodule Characterization)

 

Selected References:

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  • Bach P, Mirkin J, Oliver T, Azzoli C, et. al. The Role of CT Screening for Lung Cancer in Clinical Practice. JAMA Vol 307, No. 22 June 13, 2012
  • Leighl N, Rekhtman N, Biermann W, et. al. Molecular Testing for Selection of Patients with Lung Cancer for Epidermal Growth Factor Response and Anaplastic Lymphoma Kinase Tyrosine Kinase Inhibitors: American Society of Clinical Oncology Endorsement of the College of American Pathologists/International Association for the Study of Lung Cancer/Association for Molecular Pathology Guideline. Journal of Clinical Oncology Volume 32, Number 32, 3673-3679 November 10, 2014
  • National Comprehensive Cancer Network (NCCN) Lung Cancer Screening Version 1.2020.
  • National Comprehensive Cancer Network (NCCN) Non-Small Cell Lung Cancer Version 4.2016.
  • National Comprehensive Cancer Network (NCCN) Small Cell Lung Cancer Version 1.2017.
  • U.S. Preventative Services Task Force (USPSTF) Lung Cancer Screening December 2013.
  • UpToDate. Screening for Lung Cancer. Mark E Deffebach M.D., Linda Humphrey M.D., Topic last upated June 10, 2016.
  • UpToDate. Overview of the Initial Evaluation, Diagnosis, and Staging of Patients with Suspected Lung Cancer. Karl W. Thomas M.D., Michael K. Gould M.D., M.S.. Topic last updated April 3, 2019.
  • UpToDate. Overview of the Initial Evaluation, Treatment and Prognosis of Lung Cancer. David E. Midthun M.D., Topic last updated March 9, 2016.
  • Innovative Diagnostic Laboratory. EarlyCDT Lung
  • Integrated Diagnostics, Inc. Xpresys Lung.
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  • Tanner NT, Aggarwal J, Gould MK, et. al. Management of pulmonary nodules by community pulmonologists: a multicenter observational study. Chest 2015 Dec;148(6):1405-14
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  • Murray A, Chapman CJ, Healey G. et.al. Technical validation of an autoantibody test for lung cancer. Ann Oncol 2010 Aug;21(8):1687-93
  • Chapman CJ, Healey GF, Murray A, et. al. EarlyCDT Lung Test: improved clinical utility through additional autoantibodyd assays. Tumour Biol 2012 Oct;33(5):1319-26
  • Lam S, Boyle P, Healey GF, et. al. EarlyCDT-Lung: an immunobiomarker test as an aid to early detection of lung cancer. Cancer Prev Res 2011 Jul;4(7):1126-34
  • Boyle P, Chapman CJ, Holdenrieder S, et. al. Clinical validation of an autoantibody test for lung cancer. Annals of Oncology May 2010
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  • UpToDate. Diagnostic Evaluation of the Incidental Pulmonary Nodule. Steven E. Weinberger M.D., Shaunagh McDermott M.D., Topic last updated June 21, 2019.
  • Pecot CV, Li M, Zhang XJ, et. al. Added value of serum proteomic classifier for the molecular characterization of pulmonary nodules. Sci Transl Med Oct 16 2013;5(207):207ra142. PMID 24132637
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  • Pecot CV, Li M, Zhang XJ, et. al. Added value of a serum proteomic signature in the diagnostic evaluation of lung nodules. Cancer Epidemiol Biomarkers Prev. May 2012;21(5):786-792. PMID 22374995
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  • Whitney DH, Elashoff MR, Porta-Smith K,e t. al. Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy. BMC Med Genomics. May 6 2015;8:18. PMID 25944280
  • Silvestri GA, Vachani A, Whitney D, et. al. A bronchial genomic classifier for the diagnostic evaluation of lung cancer. N Engl J Med. Jul 16 2015;373(3):243-251. PMID 25981554
  • Vachani A, Whitney DH, Parsons EC, et. al. Clinical utility of a bronchial genomic classifier in patients with suspected lung cancer. Chest. Jul 2016;150(1):2010-218. PMID 26896702
  • Ferguson JS, Van Wert R, Choi Y, et. al. Impact of a bronchial genomic classifier on clinical decision making in patients undergoing diagnostic evaluation for lung cancer. BMC Pulm Med. May 17 2016;16(1):66. PMID 27184093
  • Detterbeck FC, Lewis SZ, Diekemper R, et. al. Executive Summary: Diagnosis and management of lung cancer, 3rd ed. American College of Chest Physicians evidence based clinical practice guidelines. Chest. May 2013;143(5 Suppl):7S-37S. PMID 23649434
  • Silvestri GA, Tanner NT, Kearney P, et. al. Assessment of plasma proteomics biomarker’s ability to distinguish benign from malignant lung nodules: results of the PANOTPIC (Pulmonary Nodule Plasma Proteomic Classifier) trial. Chest 2018 Sep;154(3):491-500. PMID 29496499
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  • Mazzone PP, Sears CC, Arenberg DD, et. al. Evaluating molecular biomarkers for the early detection of lung cancer: when is a biomarker ready for clinical use? American Thoracic Society Policy Statement. Am J Respir Crit Care Med 2017 Sep 30;196(7). PMID 28960111
  • REVEAL Lung Nodule Characterization.
  • Nasrallah N, Sears C. Biomarkers in pulmonary nodule diagnosis. Chest 2018 .04.032
  • Trivedi N, Brown J, Rubenstein T, et. al. Analytical validation of a novel multi-analyte plasma test for lung nodule characterization. Biomedical Research and Reviews 2018 Volume 293):1-10
  • Smester G, Median JG, Brown C, et. al. Overcoming the pitfalls of current lung cancer risk assessment: improved lung nodule characterization by a novel plasma protein biomarker test. Biomedical Research and Reviews 2019 Volume 3: 1-4
  • Trivedi N, Arjomandi M, Brown J, et.al Risk assessment for indeterminate pulmonary nodules using a novel, plasma protein based biomarker assay. Biomedical Research and Clinical Practice 2018 Volume 3(4):1-8
  • BDX-XL2 Test (Xpresys Lung 2)
  • Fish A, Vachani A, Massion P et. al. Novel multiplexed plasma biomarkers and clinical factors augment risk assessment for indeterminate pulmonary nodules in former smokers. American Journal of Respiratory and Critical Care Medicine 2019;199:A7452        

 

Policy History:

  • August 2019 - Annual Review, Policy Revised
  • August 2018 - Annual Review, Policy Revised
  • August 2017 - Annual Review, Policy Revised
  • August 2016 - New Policy

Wellmark medical policies address the complex issue of technology assessment of new and emerging treatments, devices, drugs, etc.   They are developed to assist in administering plan benefits and constitute neither offers of coverage nor medical advice. Wellmark medical policies contain only a partial, general description of plan or program benefits and do not constitute a contract. Wellmark does not provide health care services and, therefore, cannot guarantee any results or outcomes. Participating providers are independent contractors in private practice and are neither employees nor agents of Wellmark or its affiliates. Treating providers are solely responsible for medical advice and treatment of members. Our medical policies may be updated and therefore are subject to change without notice.

 

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