A Nasal Swab Classifier to Evaluate the Probability of Lung Cancer in Patients With Pulmonary Nodules.

BACKGROUND: Accurate assessment of the probability of lung cancer (pCA) is critical in patients with pulmonary nodules (PNs) to help guide decision-making. We sought to validate a clinical-genomic classifier developed using whole-transcriptome sequencing of nasal epithelial cells from patients with a PN ≤ 30 mm who smoke or have previously smoked. RESEARCH QUESTION: Can the pCA in individuals with a PN and a history of smoking be predicted by a classifier that uses clinical factors and genomic data from nasal epithelial cells obtained by cytologic brushing? STUDY DESIGN AND METHODS: Machine learning was used to train a classifier using genomic and clinical features on 1,120 patients with PNs labeled as benign or malignant established by a final diagnosis or a minimum of 12 months of radiographic surveillance. The classifier was designed to yield low-, intermediate-, and high-risk categories. The classifier was validated in an independent set of 312 patients, including 63 patients with a prior history of cancer (other than lung cancer), comparing the classifier prediction with the known clinical outcome. RESULTS: In the primary validation set, sensitivity and specificity for low-risk classification were 96% and 42%, whereas sensitivity and specificity for high-risk classification was 58% and 90%, respectively. Sensitivity was similar across stages of non-small cell lung cancer, independent of subtype. Performance compared favorably with clinical-only risk models. Analysis of 63 patients with prior cancer showed similar performance as did subanalyses of patients with light vs heavy smoking burden and those eligible for lung cancer screening vs those who were not. INTERPRETATION: The nasal classifier provides an accurate assessment of pCA in individuals with a PN ≤ 30 mm who smoke or have previously smoked. Classifier-guided decision-making could lead to fewer diagnostic procedures in patients without cancer and more timely treatment in patients with lung cancer.

Comparing modalities for risk assessment in patients with pulmonary lesions and nondiagnostic bronchoscopy for suspected lung cancer.

BACKGROUND: Bronchoscopy is commonly utilized for non-surgical sampling of indeterminant pulmonary lesions, but nondiagnostic procedures are common. Accurate assessment of the risk of malignancy is essential for decision making in these patients, yet we lack tools that perform well across this heterogeneous group of patients. We sought to evaluate the accuracy of three previously validated risk models and physician-assessed risk (PAR) in patients with a newly identified lung lesion undergoing bronchoscopy for suspected lung cancer where the result is nondiagnostic. METHODS: We performed an analysis of prospective data collected for the Percepta Bronchial Genomic Classifier Multicenter Registry. PAR and three previously validated risk models (Mayo Clinic, Veteran's Affairs, and Brock) were used to determine the probability of lung cancer (low, intermediate, or high) in 375 patients with pulmonary lesions who underwent bronchoscopy for possible lung cancer with nondiagnostic pathology. Results were compared to the actual adjudicated prevalence of malignancy in each pre-test risk group, determined with a minimum of 12 months follow up after bronchoscopy. RESULTS: PAR and the risk models performed poorly overall in the assessment of risk in this patient population. PAR most closely matched the observed prevalence of malignancy in patients at 12 months after bronchoscopy, but all modalities had a low area under the curve, and in all clinical models more than half of all the lesions labeled as high risk were truly or likely benign. The studied risk model calculators overestimate the risk of malignancy compared to PAR, particularly in the subset in older patients, irregularly bordered nodules, and masses > 3 cm. Overall, the risk models perform only slightly better when confined to lung nodules < 3 cm in this population. CONCLUSION: The currently available tools for the assessment of risk of malignancy perform suboptimally in patients with nondiagnostic findings following a bronchoscopic evaluation for lung cancer. More accurate and objective tools for risk assessment are needed. TRIAL REGISTRATION: not applicable.

Reclassification of risk of malignancy with Percepta Genomic Sequencing Classifier following nondiagnostic bronchoscopy.

INTRODUCTION: Bronchoscopic sampling of pulmonary lesions suspicious for lung cancer is frequently nondiagnostic. A genomic sequencing classifier utilizing bronchial brushings obtained at the time of the bronchoscopy has been shown to provide an accurate reclassification of the risk of malignancy (ROM) based on pre-procedure risk. Our objectives for this study were to determine the frequency with which the classifier up- or down-classifies risk in regular clinical practice and to model the potential clinical utility of that reclassification. METHODS: This observational study retrospectively assessed data from four clinical sites that regularly use the genomic classifier in the bronchoscopic evaluation of indeterminate lesions. Demographics and pre-bronchoscopy ROM were recorded. The frequency of up- and down-classification was calculated. Modeling based on reclassification rates and the performance characteristics of the classifier was performed to demonstrate the potential clinical utility of the result. RESULTS: 86 patients who underwent classifier testing following a nondiagnostic bronchoscopy were included. 45% of patients with high ROM prior to bronchoscopy were reclassified very high-risk. 38% of patients with intermediate ROM were up-or down-classified. 56% of patients with low ROM were reclassified to very low-risk. Overall, 42% of patients had a change in classification. 35% of the study cohort could potentially have avoided additional unnecessary procedures with subsequent guideline-adherent management. CONCLUSIONS: The classifier can guide decision-making following a nondiagnostic bronchoscopy, reclassifying risk in a significant percentage of cases. Use of the classifier should allow more patients with early-stage cancer to proceed directly to curative therapy while helping more patients with benign disease avoid further unnecessary procedures.

Clinical validation and utility of Percepta GSC for the evaluation of lung cancer.

The Percepta Genomic Sequencing Classifier (GSC) was developed to up-classify as well as down-classify the risk of malignancy for lung lesions when bronchoscopy is non-diagnostic. We evaluated the performance of Percepta GSC in risk re-classification of indeterminate lung lesions. This multicenter study included individuals who currently or formerly smoked undergoing bronchoscopy for suspected lung cancer from the AEGIS I/ II cohorts and the Percepta Registry. The classifier was measured in normal-appearing bronchial epithelium from bronchial brushings. The sensitivity, specificity, and predictive values were calculated using predefined thresholds. The ability of the classifier to decrease unnecessary invasive procedures was estimated. A set of 412 patients were included in the validation (prevalence of malignancy was 39.6%). Overall, 29% of intermediate-risk lung lesions were down-classified to low-risk with a 91.0% negative predictive value (NPV) and 12.2% of intermediate-risk lesions were up-classified to high-risk with a 65.4% positive predictive value (PPV). In addition, 54.5% of low-risk lesions were down-classified to very low risk with >99% NPV and 27.3% of high-risk lesions were up-classified to very high risk with a 91.5% PPV. If the classifier results were used in nodule management, 50% of patients with benign lesions and 29% of patients with malignant lesions undergoing additional invasive procedures could have avoided these procedures. The Percepta GSC is highly accurate as both a rule-out and rule-in test. This high accuracy of risk re-classification may lead to improved management of lung lesions.

Percepta Genomic Sequencing Classifier and decision-making in patients with high-risk lung nodules: a decision impact study.

BACKGROUND: Incidental and screening-identified lung nodules are common, and a bronchoscopic evaluation is frequently nondiagnostic. The Percepta Genomic Sequencing Classifier (GSC) is a genomic classifier developed in current and former smokers which can be used for further risk stratification in these patients. Percepta GSC has the capability of up-classifying patients with a pre-bronchoscopy risk that is high (> 60%) to "very high risk" with a positive predictive value of 91.5%. This prospective, randomized decision impact survey was designed to test the hypothesis that an up-classification of risk of malignancy from high to very high will increase the rate of referral for surgical or ablative therapy without additional intervening procedures while increasing physician confidence. METHODS: Data were collected from 37 cases from the Percepta GSC validation cohort in which the pre-bronchoscopy risk of malignancy was high (> 60%), the bronchoscopy was nondiagnostic, and the patient was up-classified to very high risk by Percepta GSC. The cases were randomly presented to U.S pulmonologists in three formats: a pre-post cohort where each case is presented initially without and then with a GSG result, and two independent cohorts where each case is presented either with or without with a GSC result. Physicians were surveyed with respect to subsequent management steps and confidence in that decision. RESULTS: One hundred and one survey takers provided a total of 1341 evaluations of the 37 patient cases across the three different cohorts. The rate of recommendation for surgical resection was significantly higher in the independent cohort with a GSC result compared to the independent cohort without a GSC result (45% vs. 17%, p < 0.001) In the pre-post cross-over cohort, the rate increased from 17 to 56% (p < 0.001) following the review of the GSC result. A GSC up-classification from high to very high risk of malignancy increased Pulmonologists' confidence in decision-making following a nondiagnostic bronchoscopy. CONCLUSIONS: Use of the Percepta GSC classifier will allow more patients with early lung cancer to proceed more rapidly to potentially curative therapy while decreasing unnecessary intervening diagnostic procedures following a nondiagnostic bronchoscopy.

The Impact of the Envisia Genomic Classifier in the Diagnosis and Management of Patients with Idiopathic Pulmonary Fibrosis.

Rationale: The diagnosis of idiopathic pulmonary fibrosis (IPF) remains challenging and can result in delayed or misdiagnosis. IPF diagnosis is based on the presence of either a radiographic or histologic usual interstitial pneumonia (UIP) pattern in the absence of an identifiable etiology. The Envisia Genomic Classifier is a clinically validated molecular diagnostic test that identifies UIP in transbronchial biopsies. Objectives: To determine the impact of the Envisia Genomic Classifier on physicians' clinical decision-making in the diagnosis and management of IPF. Methods: This prospective randomized decision impact survey was designed to test the hypothesis that including an Envisia UIP-positive result will increase IPF diagnoses, diagnostic confidence, and the recommendation for antifibrotic therapy. The survey included patients from the BRAVE (Bronchial Sample Collection for a Novel Genomic Test) study who had a high-resolution computed tomographic scan without a typical UIP pattern, an Envisia UIP-positive result, and a final diagnosis of IPF by multidisciplinary team discussion. Each case was presented in three different formats: a pre-post cohort, where each case is presented initially without and then with Envisia, and two independent cohorts, where each case is presented without and with Envisia, respectively. Results: U.S.-based pulmonologists from community and academic centers in geographically diverse practices were approached for inclusion in this study. 103 (65%) U.S.-based pulmonologists met the inclusion criteria and provided 605 case reviews of 11 patient cases. The number of IPF diagnoses increased with Envisia by an absolute difference of 39% from 47 (30%) before Envisia to 107 (69%) after Envisia in the pre-post cohort and by 13% in the independent cohorts. High confidence (⩾90%) of interstitial lung disease diagnoses was more commonly seen with Envisia in both the pre-post cohort and in the independent cohorts. Recommendation for antifibrotic treatment increased with Envisia by an absolute difference of 36% from 15 (10%) before Envisia to 72 (46.4%) after Envisia in the pre-post cohort and by 11% in the independent cohorts. Conclusions: This decision impact survey suggests the clinical utility of the Envisia Classifier by demonstrating a significant increase in IPF diagnoses, diagnostic confidence, and recommendation for antifibrotic therapies to assist physicians in effectively managing patients to improve outcomes of patients with IPF.

Analytical validation of the Percepta genomic sequencing classifier; an RNA next generation sequencing assay for the assessment of Lung Cancer risk of suspicious pulmonary nodules.

BACKGROUND: Bronchoscopy is a common procedure used for evaluation of suspicious lung nodules, but the low diagnostic sensitivity of bronchoscopy often results in inconclusive results and delays in treatment. Percepta Genomic Sequencing Classifier (GSC) was developed to assist with patient management in cases where bronchoscopy is inconclusive. Studies have shown that exposure to tobacco smoke alters gene expression in airway epithelial cells in a way that indicates an increased risk of developing lung cancer. Percepta GSC leverages this idea of a molecular "field of injury" from smoking and was developed using RNA sequencing data generated from lung bronchial brushings of the upper airway. A Percepta GSC score is calculated from an ensemble of machine learning algorithms utilizing clinical and genomic features and is used to refine a patient's risk stratification. METHODS: The objective of the analysis described and reported here is to validate the analytical performance of Percepta GSC. Analytical performance studies characterized the sensitivity of Percepta GSC test results to input RNA quantity, the potentially interfering agents of blood and genomic DNA, and the reproducibility of test results within and between processing runs and between laboratories. RESULTS: Varying the amount of input RNA into the assay across a nominal range had no significant impact on Percepta GSC classifier results. Bronchial brushing RNA contaminated with up to 10% genomic DNA by nucleic acid mass also showed no significant difference on classifier results. The addition of blood RNA, a potential contaminant in the bronchial brushing sample, caused no change to classifier results at up to 11% contamination by RNA proportion. Percepta GSC scores were reproducible between runs, within runs, and between laboratories, varying within less than 4% of the total score range (standard deviation of 0.169 for scores on 4.57 scale). CONCLUSIONS: The analytical sensitivity, analytical specificity, and reproducibility of Percepta GSC laboratory results were successfully demonstrated under conditions of expected day to day variation in testing. Percepta GSC test results are analytically robust and suitable for routine clinical use.

Impact of the Percepta Genomic Classifier on Clinical Management Decisions in a Multicenter Prospective Study.

BACKGROUND: The Percepta genomic classifier has been clinically validated as a complement to bronchoscopy for lung nodule evaluation. RESEARCH QUESTION: The goal of this study was to examine the impact on clinical management decisions of the Percepta result in patients with low- and intermediate-risk lung nodules. STUDY DESIGN AND METHODS: A prospective "real world" registry was instituted across 35 US centers to observe physician management of pulmonary nodules following a nondiagnostic bronchoscopy. To assess the impact on management decisions of the Percepta genomic classifier, a subset of patients was analyzed who had an inconclusive bronchoscopy for a pulmonary nodule, a Percepta result, and an adjudicated lung diagnosis with at least 1 year of follow-up. In this cohort, change in the decision to pursue additional invasive procedures following Percepta results was assessed. RESULTS: A total of 283 patients met the study eligibility criteria. In patients with a low/intermediate risk of malignancy for whom the clinician had designated a plan for a subsequent invasive procedure, a negative Percepta result down-classified the risk of malignancy in 34.3% of cases. Of these down-classified patients, 73.9% had a change in their management plan from an invasive procedure to surveillance, and the majority avoided a procedure up to 12 months following the initial evaluation. In patients with confirmed lung cancers, the time to diagnosis was not significantly delayed when comparing Percepta down-classified patients vs patients who were not down-classified (P = .58). INTERPRETATION: The down-classification of nodule malignancy risk with the Percepta test decreased additional invasive procedures without a delay in time to diagnosis among those with lung cancer.

Calorie intake is associated with weight gain during transition phase of nutrition in female extremely low birth weight infants.

We sought to determine whether there are sex-based differences in the requirements for calories or protein for optimal growth during the transition phase (TP) when an extremely low birth weight (ELBW) infant, defined as a preterm infant with a birth weight of < 1000 g, is progressing from parenteral to enteral feeds. A retrospective review of ELBW infants born from 2014 to 2016 was performed at a tertiary NICU. Infants with necrotizing enterocolitis, short bowel syndrome, or chromosomal anomalies were excluded. TP was defined as the period when the infant's enteral feeds were increased from 30 up to 120 ml/kg/day while weaning parenteral nutrition (PN). Effects of sex and protein-calorie intake on the change in growth parameters from the beginning to the end of TP were analyzed. Pre-TP growth percentiles and calorie and protein intake were similar in both sexes. There was a significant (r = 0.22, p = 0.026) correlation of total calorie intake with a change in weight percentiles (wt.pc) for the whole group, but on sex-specific analysis, this correlation was more robust and significant only in girls (r = 0.28, p = 0.015). Protein intake did not correlate with the changes in wt.pc in either sex. Despite a similar intake of calories and protein during the TP, we found a significant decrease in wt.pc only in girls. More extensive studies are needed to understand the sex-based differences in caloric needs and metabolic rate in ELBW infants.

Clustering of mRNA-Seq data based on alternative splicing patterns.

Sequencing of messenger RNA (mRNA) can provide estimates of the levels of individual isoforms within the cell. It remains to adapt many standard statistical methods commonly used for analyzing gene expression levels to take advantage of this additional information. One novel question is whether we can find clusters of samples that are distinguished not by their gene expression but by their isoform usage. We propose a novel approach for clustering mRNA-Seq data that identifies such clusters. We show via simulation that our methods are more sensitive to finding clusters based on isoform usage than standard clustering techniques. We demonstrate its performance by finding a technical artifact that resulted in different batches having different isoform usage patterns, and illustrate its usage on several The Cancer Genome Atlas datasets.