Medical Policy: 02.04.60
Original Effective Date: August 2016
Reviewed: August 2018
Revised: August 2018
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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.
Plasma based proteomic screening and gene expression profiling 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.
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 is plasma-based proteomic screening test that measures the relative abundance of 11 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.
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.
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.
The test being considered is plasma-based proteomic screening. Of particular focus is the Xpresys Lung test, which is the only commercially available test identified.
The following practice is currently being used: standard clinical management using clinical and radiographic risk factors.
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.
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).
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.
Clinical validation studies were identified for two proteomic classifiers, both of which appear to be related to the development and validation of closely related versions of the Xpresys test. In general, the 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.
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 the Xpresys Lung was identified.
Vachani et. al. (2015) reported on a multicenter prospective-retrospective study of patients with indeterminate pulmonary nodules. A plasma protein classifier 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.
No evidence directly demonstrating improved outcomes in patients managed with the Xpresys Lung was identified. Indirect evidence suggests that proteomic classifier with high NPV (negative predictive value) has the potential to reduce the number of unnecessary procedures to definitively diagnose benign disease versus malignancy. However, stronger clinical validity data would be needed to rely on indirect evidence for clinical utility.
Gene expression profiling is the measurement of activity of genes with 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. An important role of gene expression profiling in molecular diagnostics is to detect cancer associated gene expression of clinical samples to assess for the risk of malignancy.
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.
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.
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.
The test being considered is gene expression profiling of bronchial brushings to include Percepta Bronchial Genomic Classifier (Varacyte).
The following practice is currently being used: standard clinical management without gene expression profiling. 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).
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.
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.
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.
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 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.
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.
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.
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. The commercially available tests have been designed to have a high negative predictive value (NPV) of 90%, although studies have reported on slightly different versions. A single multicenter prospective-retrospective study revealed that 32% of surgeries and 31.8% of invasive procedures could have been avoided; however, 24.0% of patients with malignancy would have been triaged to computed tomography (CT) surveillance (false-negative). Studies have not reported how this proteomic screening reclassifies patients relative to clinical classifiers regarding risk. Indirect evidence has suggested that a proteomic classifier with high negative predictive value (NPV) has the potential to reduce the number of unnecessary invasive procedures to definitively diagnose benign disease versus malignancy. The evidence is insufficient to determine the effects of the technology on net health outcomes.
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 and an impact survey study. 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.
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 (Indi) and Percepta Bronchial Genomic Classifer (Varacyte) are available under the auspices of CLIA. Laboratories that offer 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.
No practice guidelines or position statements were identified that indicate the use of molecular testing (plasma based proteomic screening or gene expression profiling) in the management of indeterminate or undiagnosed pulmonary nodules.
Not applicable
See Related Medical Policy
Plasma-based proteomic screening testing, including but not limited to Xpresys Lung in patients with undiagnosed pulmonary nodule(s) detected by imaging is considered investigational.
Based on review of the peer reviewed medical literature for individuals with pulmonary nodules discovered by imaging who receive plasma-based proteomic screening, the evidence includes an analytic validity study, prospective cohort and prospective-retrospective studies. Direct evidence of clinical utility is limited. Additional randomized controlled studies are necessary to establish the impact of this testing in decreasing unnecessary invasive procedures or surgeries and concurrently decreasing cancer related deaths and other positive outcomes. Also, no professional society guidelines indicate the utilization of plasma-based proteomic screening (Xpresys Lung) 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 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.
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 and an impact survey study. 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. Also, no professional society guidelines indicate the utilization of gene expression profiling on bronchial brushings (Percepta Bronchial Genomic Classifier) 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.
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