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Abstract
Rationale: Usual interstitial pneumonia (UIP) is the defining morphology of idiopathic pulmonary fibrosis (IPF). Guidelines for IPF diagnosis conditionally recommend surgical lung biopsy for histopathology diagnosis of UIP when radiology and clinical context are not definitive. A “molecular diagnosis of UIP” in transbronchial lung biopsy, the Envisia Genomic Classifier, accurately predicted histopathologic UIP.
Objectives: We evaluated the combined accuracy of the Envisia Genomic Classifier and local radiology in the detection of UIP pattern.
Methods: Ninety-six patients who had diagnostic lung pathology as well as a transbronchial lung biopsy for molecular testing with Envisia Genomic Classifier were included in this analysis. The classifier results were scored against reference pathology. UIP identified on high-resolution computed tomography (HRCT) as documented by features in local radiologists’ reports was compared with histopathology.
Measurements and Main Results: In 96 patients, the Envisia Classifier achieved a specificity of 92.1% (confidence interval [CI],78.6–98.3%) and a sensitivity of 60.3% (CI, 46.6–73.0%) for histology-proven UIP pattern. Local radiologists identified UIP in 18 of 53 patients with UIP histopathology, with a sensitivity of 34.0% (CI, 21.5–48.3%) and a specificity of 96.9% (CI, 83.8–100%). In conjunction with HRCT patterns of UIP, the Envisia Classifier results identified 24 additional patients with UIP (sensitivity 79.2%; specificity 90.6%).
Conclusions: In 96 patients with suspected interstitial lung disease, the Envisia Genomic Classifier identified UIP regardless of HRCT pattern. These results suggest that recognition of a UIP pattern by the Envisia Genomic Classifier combined with HRCT and clinical factors in a multidisciplinary discussion may assist clinicians in making an interstitial lung disease (especially IPF) diagnosis without the need for a surgical lung biopsy.
An early, accurate diagnosis of interstitial lung disease (ILD) is important to identify appropriate therapeutic options, predict the risk of progression, and assess overall prognosis. Clinical guidelines for the diagnosis of idiopathic pulmonary fibrosis (IPF) conditionally recommend surgical lung biopsy (SLB) for histopathologic diagnosis in patients whose clinical and radiological context is not definitive for IPF. However, SLB is associated with increased morbidity and mortality. Previously, a biomarker that identified a molecular usual interstitial pneumonia (UIP) signature in transbronchial biopsy samples with high specificity and adequate sensitivity was shown to supplement the clinical evaluation for ILD diagnosis without the need for SLB.
This study redemonstrates that the molecular UIP pattern determined from transbronchial biopsies using the Envisia Genomic Classifier has high reproducibility and sustained accuracy for the detection of histopathology features of UIP. Overall, these results suggest that the recognition of a UIP pattern by the Envisia Classifier may be useful as a surrogate histopathology in combination with high-resolution computed tomography image patterns and clinical factors in a multidisciplinary team discussion to assist clinicians in making a diagnosis of ILD, especially IPF.
Interstitial lung diseases (ILDs) are clinically complex, with various causes, treatment responses, and clinical outcomes (1). Chronic ILDs are often associated with progressive scarring, loss of lung architecture and function, and poor prognosis. An early accurate ILD diagnosis is important to identify appropriate therapeutic options, predict risk of progression, and assess overall prognosis (2).
The hallmark of idiopathic pulmonary fibrosis (IPF) is the usual interstitial pneumonia (UIP) pattern of lung fibrosis identified by radiology and/or histopathology (3). IPF is a chronic and progressive ILD of poor outcome and few effective treatment options (2). Clinical guidelines for the diagnosis and treatment of IPF conditionally recommend surgical lung biopsy (SLB) for histopathologic diagnosis in patients whose clinical and radiological context is not definitive for IPF (4). However, SLBs are associated with substantial morbidity and mortality (5), with significant interreader variability of histopathology for UIP pattern potentially impacting the ability to confidently identify UIP (6).
As a result, accurately identifying ILD diagnosis remains challenging in the current era of diagnostic testing. In an effort to address this issue, an international working group proposed a framework for the diagnostic classification of ILD based on clinical and radiological features. This concept of a working diagnosis of IPF (7–9) is emerging as a potential option for informing treatment decisions. In a recent case-cohort study, a working diagnosis of IPF was found to lead to an antifibrotic treatment decision in patients with high-confidence diagnoses of IPF. However, in patients with a low confidence (<70% likelihood) of IPF, physicians were more likely to recommend an SLB, especially in those patients at low risk for SLB (10). This study highlighted the importance of the diagnostic likelihood of IPF on physicians’ decisions for SLB. Similarly, in patients with progressive fibrosing ILD regardless of initial ILD diagnosis and treatment, nintedanib, an antifibrotic treatment decreased the rate of FVC decline in patients with progressive fibrosis defined by a high-resolution computed tomography (HRCT) image or evidence of progressive decline in lung function. Although approximately 28.5% of these patients had an SLB and 15% had transbronchial lung biopsy (TBBx) to make their initial diagnosis, subsequent behavior of their ILD deprioritized the need for high diagnostic confidence with a subsequent SLB (9).
Recently, a biomarker that distinguishes UIP from non-UIP, the Envisia Genomic Classifier, showed benefit in assisting an accurate, confident diagnosis of IPF in subjects suspected to have IPF when compared with diagnoses for the same subjects achieved with the benefit of pathology results (11–13). The Envisia Classifier detects molecular UIP in TBBx samples using a 190-gene machine-learning classifier. In prospective validation against reference pathology (UIP vs. non-UIP) in 49 patients, the classifier demonstrated 88% specificity and 70% sensitivity. Training and previous validation of the classifier (Veracyte) have been previously described (11–13).
We undertook an independent prospective study in a new cohort of patients to further validate the genomic classifier. We demonstrate increased diagnostic yield, improved sensitivity, and negative predictive value for UIP when the classifier is used to complement current HRCT criteria for UIP pattern (4, 8). Some of the results of these studies have been previously reported in the form of abstracts (14, 15).
A total of 447 patients were enrolled in the BRAVE (Bronchial Sample Collection for a Novel Genomic Test) study, which is a comprehensive prospective sample collection protocol to develop a biorepository of biological samples with highly annotated clinical information. Unique BRAVE patients (n = 220) were allocated to this second independent clinical validation cohort of this locked classifier (Figure 1). These 220 patients were recruited from 30 BRAVE clinical sites between 2013 and 2019. Of these 220 patients, 81 patients were excluded because of prespecified criteria for proper sample collection, shipment, and storage, as shown in Figure 1. Of the remaining 139 patients who underwent molecular testing with the classifier, 43 did not have diagnostic pathology (i.e., they lacked a reference truth label). Ten did not have slides available for review, and the remaining 33 were returned a nondiagnostic label, most commonly because of insufficient tissue or no pathologic abnormalities identified. Comparative descriptions between the excluded nondiagnostic cohort and this independent validation cohort are presented in the Results section. Ninety-six patients who had diagnostic pathology (reference truth labels) were included in this new validation cohort (Figure 1). The BRAVE study was reviewed and approved by the Western Institutional Review Board (20130158, 20141155, and 20130705) and site-specific institutional review boards. Written informed consent was obtained from all BRAVE study participants.
Figure 1. CONSORT (Consolidated Standards of Reporting Trials) diagram illustrating the derivation of the patients who were allocated to the independent clinical validation (220 patients) of the Envisia Genomic Classifier from 447 BRAVE (Bronchial Sample Collection for a Novel Genomic Test) patients. All technical exclusions were prespecified and applied prospectively before scoring in validation. *These 220 patients are independent of those used in the classifier development and the prior validation. TBB = transbronchial biopsy.
[More] [Minimize]Training and previous validation of the classifier (Veracyte) have been previously described (11–13). Briefly, the Envisia Classifier is a diagnostic test trained to accurately identify molecular UIP in TBBxs, predicting conventional diagnostic histopathology UIP features interpreted by experienced pathologists (11–13). The test provides a binary result of UIP or non-UIP (11).
The test was developed using biopsies obtained for histopathology as truth labels and TBBx for molecular testing (more specifically, the RNA sequencing derived from TBBx samples as an input for the classifier) from patients enrolled in the prospective noninterventional BRAVE study (11).
Pathology slides were prepared from lung biopsies by participating study-site personnel and centrally reviewed by a team of expert pathologists without access to clinical information other than their knowledge that the lung biopsies were obtained for histopathology diagnosis in patients manifesting ILD. Histopathology diagnoses of UIP as a reference truth label for this study were fulfilled when pathology met either UIP or probable UIP criteria in accordance with the 2018 American Thoracic Society guidelines for IPF diagnosis (4). The terms “favor UIP” and “difficult UIP,” as described in the initial validation of the Envisia Classifier performance (11) and which were used in this study, were determined by a consensus of the expert review pathologists to be in the category of probable UIP. All other diagnostic histopathology was categorized as non-UIP. These histopathological labels were used to derive UIP or non-UIP reference standard truth labels for each subject, as previously described (11). All laboratory and data analysis personnel remained blinded to the identity and reference standard results for each patient until after the scoring of the validation by a third party was complete.
For patients with HRCT scans available for review, the HRCT scans were reviewed both by the local radiologist and by central radiologists consisting of two expert radiologists. Central radiology readings were reviewed according to Fleischner Society criteria for UIP (typical UIP, probable UIP, and indeterminate for UIP) (8) and specific non-IPF ILD diagnoses. Primary, secondary, and tertiary differential diagnoses, with associated confidences on a 1 to 10 scale, were determined. The extent of lung fibrosis was estimated as >10% of lung volume in the absence of consolidation or progressive massive fibrosis. Local radiology readings were performed by the local radiologist review according to their own practice style without prespecified study guidelines.
HRCT scan reports by site-specific local radiologists were available for review for 85 of the 96 patients. To maintain the integrity of the interpretation of the initial HRCT report, a prospectively designed protocol for HRCT review was implemented. A synopsis of this protocol is included in the online supplement (Synopsis of Veracyte Design History File [DHF]: 007-067P: BRAVE Local Radiology Report Review [CV-3]). These original HRCT reports were systematically reviewed by two blinded reviewers (S.M.B., a United States–based board-certified pulmonary/critical care physician, and L.R.L., a master’s level clinical research professional) to ensure the radiologic features described could be expressed within the context of the diagnostic HRCT criteria defined by the Fleischner Society Guidelines (typical UIP, probable UIP, indeterminate for UIP, and features most consistent with non-IPF diagnosis) (8).
Both reviewers were blinded to the clinical information of the patient, the results of the Envisia Classifier, and the histopathological diagnosis associated with each HRCT scan report. Each reviewer interpreted the findings in the body of the local report and the final interpretation according to the Fleischner Society criteria (8). These two interpretations for each HRCT report were independently documented and subsequently compared. If there was discordance between the two reviews, the two reviewers were allowed to confer until an agreement was reached. If there was persistent disagreement between the two reviewers regarding the interpretation of the HRCT report, a third reviewer would have served as a tie breaker. There were no cases that required interpretation by a third reviewer.
For molecular testing, total RNA was extracted from three to five TBBx per patient and pooled by subject, and a single whole-transcriptome library was generated and sequenced as previously described (11). The locked Envisia Classifier, whose training and validation has been previously described, was used to make a molecular UIP designation. The results are presented as a binary result of UIP or non-UIP, as previously described (11).
Descriptive statistics are reported for clinical demographic data of the final validation cohort. The primary study endpoints of test sensitivity and specificity were prespecified in a clinical validation study protocol. Negative and positive predictive values are computed at the UIP prevalence of the validation set. All confidence intervals (CIs) are two-sided, 95% intervals unless otherwise noted. Classifier accuracy is reported as the area under the receiver operating characteristic curve. The significance of difference for subgroup proportions and performance was tested with the χ2 test. Statistical analyses were performed in R (version 3.2.3; www.r-project.org).
Clinical demographic data of this Envisia Classifier validation cohort are shown in Table 1. Overall, there was an even representation of smokers and nonsmokers as well as clinical and academic study sites (Table 1). In addition to a bronchoscopy to obtain TBBx for molecular testing, 64% of the patients underwent SLB, and 35% underwent cryobiopsy to obtain a final histopathologic diagnosis. Only one patient whose histopathology diagnosis from transbronchial biopsy was considered diagnostic was not subjected to the need for obtaining additional lung tissue by cryobiopsy or SLB. The overall UIP prevalence (definite UIP and probable UIP) based on histopathology was 60.4% in this cohort. The Envisia Classifier validation group contains a representative spectrum of ILD, including different UIP patterns on histopathology as well as a variety of non-UIP ILD diagnoses (Table 2).
| Demographics | Clinical Validation (N = 96) |
|---|---|
| Sex, n (%) | |
| F | 41 (43) |
| M | 55 (57) |
| Age, yr, mean (SD) | 62.8 (12.1) |
| Smoker, n (%) | |
| Yes | 48 (50) |
| No | 48 (50) |
| Study-site type, n (%) | |
| U.S. academic | 41 (43) |
| U.S. community | 48 (50) |
| European academic | 7 (7) |
| Biopsy type, n (%) | |
| Surgical | 61 (64) |
| TBBx | 1 (1) |
| Cryobiopsy | 34 (35) |
| UIP frequency in study, n (%) | |
| By pathology | 58 (60) |
| By radiology | 10/65 (15) |
| Clinical Validation (N = 96) | |
|---|---|
| UIP, n (%) | |
| UIP | 32 (33) |
| Probable UIP | 26 (27) |
| Other patterns, n (%) | |
| RB, SRIF | 3 (3) |
| HP, favor HP | 9 (9) |
| Sarcoidosis | 4 (4) |
| NSIP, cellular NSIP, favor NSIP | 9 (9) |
| Bronchiolitis | 8 (8) |
| Organizing pneumonia | 1 (1) |
| Other | 4 (4) |
Thirty-three patients who underwent lung biopsy had a nondiagnostic pathology label and were excluded from this study. Of these patients, 16 had insufficient tissue, 14 had no pathologic diagnosis, and 3 had chronic inflammation that was not specified. Several of these patients with insufficient tissue had nonspecific interstitial fibrosis identified. Patients who had nondiagnostic pathology labels were compared with patients who were included in the Envisia validation cohort. We found that the nondiagnostic pathology cohort had a higher number of women (73% vs. 43%; P = 0.003) and were more likely to undergo either cryobiopsy (79% vs. 35%) or TBBx (12% vs. 1%) and less likely to undergo SLB (9% vs. 64%; P < 0.001) compared with the Envisia validation cohort.
In these 96 patients (Envisia validation cohort), the classifier achieved a sensitivity of 60.3% (CI, 46.6–73.0%) and a specificity of 92.1% (CI, 78.6–98.3%) for histology-proven UIP pattern (Figure 2). With the study histopathologic UIP prevalence of 60.4%, the positive predictive value of an Envisia Classifier UIP result is 92.1% (CI, 78.6–98.3%), and the negative predictive value of an Envisia Classifier non-UIP result is 60.3% (CI, 46.6–73.0%). In a subset of patients with SLB biopsies alone, the classifier performance was similar (see Table E1 in the online supplement).
Figure 2. Clinical validation performance of the Envisia Genomic Classifier. (A) Receiver operating characteristic curve for the Envisia Genomic Classifier on the 96 patients scored in validation. *Indicates the prospectively defined decision boundary. (B) Prevalence-adjusted negative predictive value (NPV) and positive predictive value (PPV) curves for the Envisia Genomic Classifier. The NPV and PPV at the study UIP prevalence of 60%, defined by reference standard histopathology truth, is shown with a dashed line. The curves represent the NPV and PPV values expected across different patient population prevalences. (C) Envisia Genomic Classifier performance against reference standard histopathology for UIP pattern. AUC = area under the curve; ROC = receiver operating characteristic; UIP = usual interstitial pneumonia.
[More] [Minimize]Among the different UIP patterns reported by the central pathologists, a definite UIP pattern was detected in 55% of all UIP cases. Of note, probable UIP pathology was more common when the Envisia Classifier result showed non-UIP (false negatives [FNs]) compared with definite UIP pathology (15 of 26 FNs among probable UIP vs. 8 of 32 FNs among definite UIP; P = 0.02) (Figure 3). When the classifier missed UIP (FN), there were more patients with discordant UIP among different biopsied lobes compared with patients with UIP pathology in all biopsied lung lobes (Table 3). In contrast, there is no enrichment of classifier errors in cases with pathology diagnoses requiring adjudication by a third pathologist compared with those with agreement determined by two reviewing pathologists (Table E2). The Envisia Classifier sensitivity and specificity in the validation group is statistically equivalent between patients who were greater than 65 years old and those who were less than 65 years, between smokers and nonsmokers, and between men and women (Table E3).
Figure 3. The distribution of Envisia Genomic Classifier scores for the 96 patients in the clinical validation. Patients are sorted from left to right by increasing score on the x-axis, and the reference standard pathology diagnosis and central radiology diagnosis for each patient are listed on the y-axis. Patients with discordant usual interstitial pneumonia (UIP) (UIP pattern pathology in one lobe and non-UIP pattern pathology in a different lobe) are indicated with a hatched circle. Patients with scores <0.87 are reported as non-UIP, and patients with scores of ≥0.87 are reported as UIP. Scores are not considered indicative of UIP pattern severity. DIP = desquamative interstitial pneumonia; HP = hypersensitivity pneumonitis; NSIP = nonspecific interstitial pneumonia; OP = organizing pneumonia; RB = respiratory bronchiolitis; SRIF = smoking-related interstitial fibrosis.
[More] [Minimize]| Lung Lobe–Level Histopathology Discordance | FN (n = 23) | TP (n = 35) | FP (n = 3) | TN (n = 35) | P Value | |
|---|---|---|---|---|---|---|
| UIP | UIP in all lung lobes | 12 | 29 | — | — | 0.01 |
| UIP/non-UIP, nondiagnostic | 11 | 6 | — | — | ||
| Non-UIP | Non-UIP in all lung lobes | — | — | 2 | 28 | 0.59 |
| Non-UIP/nondiagnostic | — | — | 1 | 7 |
Sixty-five of the HRCT scans provided by study sites were centrally reviewed by two expert radiologists according to the Fleischner Society criteria for UIP (typical UIP, probable UIP, and indeterminate for UIP) (8) and specific non-IPF ILD diagnoses (nonspecific interstitial pneumonia, hypersensitivity pneumonitis [HP], organizing pneumonia, sarcoidosis, respiratory bronchiolitis, desquamative interstitial pneumonia, emphysema, inflammatory bronchiolitis, obliterative bronchiolitis, lymphangioleiomyomatosis, Langerhans cell histiocytosis, eosinophilic pneumonia, and other). These were included for descriptive analyses in the score plot (Figure 3).
Local HRCT scans for 85 of the 96 patients were available for review. The mean timeframe between the 85 local HRCT scans with reports available and the transbronchial biopsy/diagnostic procedure was 80 days, with a median of 57 days. Among the 53 cases with histopathologically identified UIP by central review, 18 were identified as definite or probable UIP by local radiology reports, resulting in a sensitivity of 34.0% (CI, 21.5–48.3%); for the remaining 32 histopathologically non-UIP diagnoses, local radiology reports identified 31 accordingly as non-UIP, resulting in a specificity of 96.9% (CI, 83.8–100%) (Table 4). When the Envisia Classifier results are used in conjunction with local radiology, 24 additional patients with biopsy-proven UIP are identified (sensitivity of 79.2% [CI, 65.9–89.2%]; specificity of 90.6% [CI, 75.0–98.0%]) (Table 5). The negative predictive value for UIP improves from 47% with HRCT alone to 72.5% with the combination of HRCT and the Envisia Classifier, and the positive predictive value remains above 90% when Envisia Genomic Classifier results are used in complement with local radiology.
| Local Radiology Result | Pathology Reference Standard | |
|---|---|---|
| UIP (n = 53) | Non-UIP (n = 32) | |
| Definite/probable UIP, n | 18 | 1 |
| Indeterminate for UIP/consistent with non-IPF, n | 35 | 31 |
| Sensitivity, % (95% CI) | 34.0 (21.5–48.3) | |
| Specificity, % (95% CI) | 96.9 (83.8–100) | |
| NPV, % (95% CI) | 47.0 (34.6–59.7) | |
| PPV, % (95% CI) | 94.7 (74.0–99.9) | |
| UIP prevalence, % | 62.4 | |
| Local Radiology + Envisia Classifier | Pathology Reference Standard | |
|---|---|---|
| UIP (n = 53) | Non-UIP (n = 32) | |
| Definite/probable UIP or Envisia Classifier UIP, n | 42 | 3 |
| Indeterminate for UIP/consistent with non-IPF and Envisia Classifier non-UIP, n | 11 | 29 |
| Sensitivity, % (95% CI) | 79.2 (65.9–89.2) | |
| Specificity, % (95% CI) | 90.6 (75.0–98.0) | |
| NPV, % (95% CI) | 72.5 (56.1–85.4) | |
| PPV, % (95% CI) | 93.3 (81.7–98.6) | |
| UIP prevalence, % | 62.4 | |
In this second independent validation cohort of patients enrolled in the BRAVE study, we demonstrated that the molecular UIP pattern determined from TBBx using the Envisia Genomic Classifier has high reproducibility and sustained accuracy for the detection of histopathology features of UIP. The results of this second validation cohort confirm and expand on the initial validation performance of the classifier by identifying a UIP pattern with a sensitivity of 60.3% and a specificity of 92.1% (Figure E4). The classifier performance was not impacted by age, sex, or smoking status in this study. In addition, the Envisia Classifier enhanced the diagnostic yield and increased the sensitivity from 34.0% to 79.2% in detecting UIP when the classifier was used in complement with current HRCT criteria for UIP pattern. Overall, these results suggest that the recognition of a UIP pattern by the Envisia Genomic Classifier may be useful as a surrogate histopathology in combination with HRCT image patterns and clinical factors in a multidisciplinary team discussion to assist clinicians in making a diagnosis of ILD, especially IPF.
An early and accurate diagnosis of ILD is essential to initiate appropriate initial therapies as well as to guide further timely interventions, such as antifibrotic treatment, lung transplantation, and palliative care. Importantly, the early diagnosis of ILD may not only be more effective in decreasing the progression of disease but may also lead to the reversal of HP. For example, the early identification and removal of an environmental antigen may enable the reversal of interstitial abnormalities before the development of fibrotic HP/pulmonary fibrosis, leading to a significantly improved outcome.
Unfortunately, delays in ILD diagnosis are common and are particularly common in IPF, as noted in the recent INTENSITY (Interstitial Lung Disease Patient Diagnostic Journey) survey of patients with ILD that showed the median time to diagnosis was 7 months, with 43% of patients experiencing a delay in their diagnosis of greater than 1 year (16). Diagnostic uncertainty of HRCT ILD patterns may contribute to the delay of an accurate ILD diagnosis, with the consequence of inadequate or potentially harmful therapies, leading to worse outcomes in this population (17–22). Our study showed that the Envisia Genomic Classifier complements radiology interpretation of the HRCT scan by local radiologists, significantly increasing the diagnostic yield of UIP by over 130% in 24 additional patients (18 of 53 to 42 of 53), and increases the sensitivity and negative predictive value while maintaining a high specificity and positive predictive value in the detection of UIP. These results highlight the potential utility of the Envisia Genomic Classifier as a complement to HRCT images when the image pattern is inconclusive in making a UIP diagnosis.
Progressive fibrosing ILD (PF-ILD) has been recently proposed as a clinical ILD phenotype on the basis of the clinical behavior of ILD (i.e., progression of clinical symptoms, radiographic findings, and/or pulmonary function testing) regardless of the primary clinical diagnosis (23). Recently, the INBUILD trial showed a decreased rate of decline in FVC with nintedanib compared with placebo only when the heterogeneous group of PF-ILD was grouped as a single entity in patients with PF-ILD, specifically, in patients with a UIP-like fibrotic pattern (9). Although this study suggests a therapeutic response to nintedanib in patients who have PF-ILD, individuals and individual subgroups of the heterogeneous group of ILD may not respond to nintedanib. There were no prespecified diagnostic criteria for the specific PF-ILD diagnoses nor were these adjudicated, and, hence, the results of the INBUILD trial must be interpreted with caution, as several questions are raised (24). Regardless, a subset of grouped patients with PF-ILD did not respond or had adverse events that led to the discontinuation of nintedanib, suggesting that obtaining an initial ILD diagnosis to determine early management strategies and an assessment of the clinical behavior of the lung disease remains necessary to optimally manage patients with ILD. In addition, patients with a non-UIP fibrotic pattern had a nonsignificant change in their lung function with nintedanib, suggesting that distinguishing patients with a UIP pattern early may allow better selection of patients to receive antifibrotic medications, given the potentially debilitating side effects as well as consideration for early referral for lung transplantation in appropriately selected patients. Lastly, because each individual patient’s trajectory of decline and the overall prognosis is highly dependent on his or her clinical diagnosis, lumping all subgroups of progressive ILD may hinder the ability to identify and develop targeted therapies for each group (24). Early management decisions will be even more relevant given the increasing number of clinical trials of both antifibrotic medications and immunosuppressive therapies targeted to specific ILD diagnoses (23). Future studies may include the Envisia Classifier in differentiating patients with a UIP pattern to aid in the appropriate management of patients at the onset and later in the course of PF-ILD.
Current guidelines for the diagnosis of IPF conditionally recommend SLB for a pathologic diagnosis when HRCT and clinical factors are not definitive for IPF (4). However, SLB is not without risk and has a reported in-hospital mortality of 1.7% for elective procedures and 16% for nonelective procedures (5). In addition, there may be select patients who are at high risk for complications related to SLB and who, therefore, may be unable or unwilling to undergo SLB because of these additional risks. The Envisia Genomic Classifier is obtained via bronchoscopy with TBBx, an easier and safer procedure that can be performed in the outpatient setting by general pulmonologists worldwide. As a result, the classifier can be expeditiously obtained while avoiding the risks and complications of SLB.
Histopathology obtained from both SLB and cryobiopsies have appreciable interobserver variability in the interpretation of ILD patterns even among expert ILD pathologists (25–27). A recent meta-analysis of more than 10,000 patients with ILD showed that 12% of all patients with ILD remain unclassifiable despite many undergoing SLB, highlighting the heterogeneity and diagnostic uncertainty of ILD pathology (28). Although the recent prospective study demonstrated a good interobserver results in histopathology diagnostic accuracy with cryobiopsy compared with SLB (29), cryobiopsy requires the procedure to be done by experienced experts in specialized centers, whereas TBBx is a routine procedure by pulmonologists in the ambulatory setting, and its safety is well established worldwide. Interestingly, in our study, there were more patients with a probable UIP pattern on histopathology in the classifier FN biopsies (i.e., pathology ultimately showed UIP and the classifier result showed non-UIP). In addition, among the FN classifier results, there was an enrichment of discordance in the lung lobar pathological diagnosis (i.e., UIP and a non-UIP diagnosis in different lobes in a patient). These findings may highlight the inherent challenge in achieving a clear ILD diagnosis.
It is important to note that although UIP detected by either molecular, radiographic, or histopathologic identification is present in patients with IPF, it is not synonymous with IPF and may be present in patients with other types of ILD, including chronic HP, connective tissue disease–associated ILD, and ILD associated with other exposures, such as asbestosis exposure. The recognition of a UIP pattern by the Envisia Classifier should be interpreted in the context of the HRCT image patterns and clinical features, ideally in the presence of a multidisciplinary team discussion, and does not always confer a diagnosis of IPF. Similarly, making a diagnosis of chronic HP is often complex and is based on clinical features, exposure history, and specific radiographic pattern with or without histopathology. A UIP pattern from the Envisia Classifier in patients with suspected chronic HP can be challenging and should be interpreted in the context of these clinical and radiographic features.
There are a few limitations of this study. First, the generalizability of the patient population is limited to patients without a recognized definitive HRCT UIP pattern that required lung biopsy with diagnostic pathology to obtain a UIP diagnosis. However, given the multicenter prospective accrual of patients from both community and academic centers across the United States, with two centers in Europe, we believe that the patients with ILD in this validation cohort are heterogeneous and representative of a broad spectrum of patients with ILD. In addition, the accuracy of the Envisia Genomic Classifier performance is unknown in patients with ILD who had a nondiagnostic pathology in lung biopsy. This study did not mandate the type of procedure that was required to obtain lung biopsies, only that these biopsies met the criteria set by two central expert ILD pathologists of having an adequate tissue specimen to make an ILD diagnosis. Biopsies that did not meet this level of rigor were not included in this study. This study was not designed to assess or compare lung biopsies obtained by different diagnostic techniques (29, 30). Lastly, the investigators acknowledge the limitation related to the interpretation of local HRCT reports per the Fleischner criteria by two independent reviewers. Given the importance of capturing a real-world experience of local pulmonologists receiving local radiology reports, these summary diagnoses of the radiology features in a consistent manner and according to guidelines should provide a representative picture of the patient radiology in this study cohort.
In conclusion, the results of this study support the sustained accuracy and reproducibility of the molecular diagnosis of UIP by the Envisia Genomic Classifier in a second independent validation cohort. The study highlights the potential utility of increased identification of UIP by the Envisia Genomic Classifier when HRCT interpretation according to guidelines remains inconclusive for a diagnosis of ILD. The combined validity and utility of the Envisia Genomic Classifier has the ability to identify a UIP pattern of lung fibrosis in patients with ILD of unclear etiology, potentially facilitating an earlier IPF diagnosis in the correct clinical context and an earlier initiation of treatment without the need for an SLB.
The authors thank the following members of their team for their assistance with the coordination of the clinical and biological sample collection, processing, and scoring: Grażyna Fedorowicz, Eric Morrie, Jeremy Burbanks-Ivey, Germaine Ng, Hannah Neville, Srinivas Jakkula, Jianchang Ning, Xinwu Yang, Marla Johnson, and the clinical laboratory scientists of the Veracyte Clinical Laboratory Improvement Amendments (CLIA) laboratory. They also thank the patients and their families who made this study possible.
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* G.J.C. is Associate Editor and F.J.M. is Deputy Editor of AJRCCM. Their participation complies with American Thoracic Society requirements for recusal from review and decisions for authored works.
Supported by Veracyte, Inc. (BRAVE study and development of the Envisia Genomic Classifier).
Author Contributions: L.R., M.B.S., D.A.L., T.V.C., J.L.M., S.D.G., J.H.C., S.B., S.D.N., J.R.D., S.L.S., L.H., D.S., J. Hetzel, G.J.C., A.H.C., M.R., K.C., U.A.G., N.M.P., L.L., Y.C., D.G.P., P.S.W., L.R.L., J. Huang, S.M.B., G.C.K., F.J.M., and G.R. participated in the design of the study. L.R., M.B.S., Y.C., D.G.P., P.S.W., L.R.L., J. Huang, S.M.B., G.C.K., and G.R. guided the clinical analysis and interpretation of the data. D.A.L. and J.H.C. performed the central radiology review. T.V.C., J.L.M., and S.D.G. performed the central pathology review. S.D.N., J.R.D., S.L.S., L.H., D.S., J. Hetzel, G.J.C., A.H.C., M.R., K.C., S.M.B., and U.A.G. enrolled study participants and collected biopsy specimens. G.C.K., Y.C., D.G.P., P.S.W., L.R.L., and J. Huang designed laboratory experiments, developed the molecular classification algorithm, and performed the classifier analysis. L.R.L. supervised data collection for the BRAVE study. L.R., M.B.S., N.M.P., L.L., Y.C., D.G.P., P.S.W., L.R.L., J. Huang, S.M.B., G.C.K., and G.R. interpreted the data analysis, drafted the manuscript, created the figures and tables, and prepared, revised, and submitted the manuscript. All study authors had access to the study data and reviewed this manuscript. The corresponding author reviewed and approved the submission of the final manuscript.
This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.
Originally Published in Press as DOI: 10.1164/rccm.202003-0877OC on July 28, 2020
Author disclosures are available with the text of this article at www.atsjournals.org.
