Updates in using a molecular classifier to identify usual interstitial pneumonia in conventional transbronchial lung biopsy samples

The most common fibrosing interstitial lung disease (ILD) is idiopathic pulmonary fibrosis (IPF), with an incidence of 14–60 cases per 100 000 inhabitants per year in North America [1] and 3–9 cases per 100 000 per year in Europe [2]. IPF is a chronic, progressive fibrosing interstitial lung disease characterised by continued scarring of the lung parenchyma and associated with a steady worsening of respiratory symptoms, quality of life and pulmonary function, ultimately leading to death [1, 3], and a median survival of 3–5 years from the time of diagnosis [4, 5]. A precise diagnosis of the underlying ILD entity is essential for prognostication and choice of therapy as treatments differ between ILD subtypes, including that some drugs may be detrimental to an IPF patient. However, the diagnosis of ILD is sometimes difficult, partly imprecise, and frequently characterised by delay, misdiagnosis, use of costly and invasive procedures, and high use of healthcare resources.

In the absence of an alternative cause of interstitial pneumonia, IPF is defined by the presence of a usual interstitial pneumonia (UIP) pattern on a high-resolution computed tomography (HRCT) of the lungs or on biopsy [1, 6]. In the correct clinical context, an UIP or probable UIP pattern on HRCT may suffice for diagnosis [1, 7]. However, the combination of clinical information and HRCT pattern does not allow for a secure diagnosis in nearly half of the patients [8]. Current guidelines advocate the use of surgical lung biopsy (SLB) in such cases, if patients are eligible for SLB [6]. However, SLB might be associated with a meaningful mortality and complication rate as well as costs [9]. Further difficulties include interrater disagreement and the possibility of sampling error [10].

Several alternative approaches are being investigated. As the diagnostic yield with transbronchial lung biopsy (TBLB) is low, current research has assessed the role of transbronchial lung cryobiopsy (TBLC). TBLC was shown to be a relatively safe method that provided significantly larger lung samples, where UIP can be observed with high confidence and a good overall agreement between observers [9]. Therefore, TBLC may be an adequate substitute for SLB in centres with expertise performing TBLC in the histopathological diagnosis of ILDs, as it has shown a relevant diagnostic value in the context of an experienced interdisciplinary team and may enable histopathological evaluation even in patients with a more severe disease not suitable for SLB [11].

Another important endoscopic technique is bronchoalveolar lavage (BAL) as it can provide additional information. The main indication for BAL is to obtain cell morphology, differential cytology and microbiology. In IPF, neutrophilia and eosinophilia are found commonly, while a lymphocytosis might be associated with, for example, hypersensitivity pneumonitis. However, BAL cytology results are unspecific [12].

Finally, multidisciplinary diagnosis is associated with great improvements in the diagnostic confidence, and evidence-based guidelines recommend an interdisciplinary approach involving clinicians, radiologists and pathologists to achieve a final diagnosis with the greatest confidence [10].

The aim of this paper is to provide an update on the role of molecular analysis in the diagnosis of IPF, using a machine learning algorithm in less-invasive TBLB samples, as a first step into precision medicine in this disease [13].

Biomarkers

Genomic, transcriptomic, metabolomic, proteomic and epigenetic biomarkers, gene co-expression networks, drug–gene interaction testing, lung microbiome and mitochondrial DNA analyses, gene expression in BAL assays, and quantitative CT and machine learning algorithms are helping understanding of pathomechanisms, detecting significant features, and determining predictive value of disease risk, outcome and mortality.

Improvements in TBLC, new endobronchial procedures and improvement of radiological techniques have all emerged as promising ways to increase diagnostic sensitivity and confidence for IPF in expert referral institutions. New biomarkers may provide a less invasive approach and an objective tool to define the UIP pattern from a TBLB specimen and reach a confident diagnosis of IPF.

Many of the biomarker studies are small, single-centre, retrospective trials with a small sample; however, the first longitudinal, prospective biomarker study (PROFILE) has been recently published [14]. Although, reliable predictive diagnostic, therapeutic and prognostic biomarkers are still missing, genetic testing has resulted in important findings in recent years.

Genetic variants of TOLLIP and MUC5B are associated with a susceptibility for IPF, outcomes, and perhaps, with therapy responsiveness. Shortening of telomere-length is associated with poor prognosis [15].

Lung microbiome studies provided information on IPF subjects with progressive or stable disease, suggesting that the bacterial burden itself may be important for disease progression [15–17].

Previous transcriptomic studies have mainly been performed using mass tissue. With the emergence of novel technologies, the regulatory and transcriptional networks can examine the lung from a variety of viewpoints using available mass data and profiles of disease and single cell microenvironments. This will enable the integration of information about the lung affected by IPF and thus improve diagnostics and therapy [18]. Subsequent transcriptomic studies on lung and peripheral blood samples revealed the role of genes involved in alveolar epithelial injury and remodelling in IPF pathogenesis. It has been shown that pathways in IPF are aberrantly stimulated and that different phenotypes vary in their gene expression patterns [15]. Mitochondrial aberrations have been proven to have an effect on the extent of the alterations in microRNA expression and a gene signature consisting of 52 differently expressed genes could effectively categorise patients at high versus low mortality risk [15]. Furthermore, the possibility of using transcriptomic data to reconstruct the UIP pattern has recently been established [7]. Thus, the transcriptomic signature, which may also predict the biological response to antifibrotic therapy, could be very promising to implement personalised medicine in IPF [15].

Machine learning

Machine learning is a method of constructing a process or set of rules (an algorithm) learned on a data record that can be used to make accurate predictions on a new sample of data. These techniques have been widely used to overcome medical challenges and have improved our understanding of signalling pathways, significant phenotypes and help to estimate disease risk. In machine learning, large samples (usually several thousand) from medical imaging data and serial data are needed, while clinical trials in IPF often have a smaller sample size because of the difficulty recruiting patients with rare diseases such as IPF.

In 2015, Kim et al. [19] used machine learning to develop a genomic data algorithm from surgical biopsy samples to detect a molecular signature for UIP that is concordant with histopathological diagnoses derived from the same tissue.

In this context, very recently Raghu et al. [13] studied the utility and validity of a molecular UIP signature, which can be identified by a machine learning algorithm, in less-invasive TBLB samples.

Summary

A molecular classifier using a machine-learning algorithm based on genomic data could provide an objective method to help clinicians and MDTs to establish the diagnosis of IPF in less-invasive TBLB, particularly for patients without a clear radiological diagnosis.