Lung squamous cell carcinoma (LUSC) is a type of non-small cell lung cancer that accounts for 20–30% of all lung cancer cases (Chen et al., 2014; Sung et al., 2021; Bray et al., 2021). Despite being the second most frequent type of lung cancer (Torre et al., 2016), our knowledge regarding the biology of this disease as well as the therapeutic modalities to treat it remain far behind the most frequent type of lung cancer, lung adenocarcinoma (LUAD) (Hirsch et al., 2016; Khuder, 2001).

LUAD genetics is dominated by mutations (that are often druggable) that activate the RTK/RAS pathway, including EGFR and KRAS mutations (The Cancer Genome Atlas Research Network, 2012; Jamal-Hanjani et al., 2017). However, the genetic landscape of LUSC is more complex, with multiple pathways altered in subsets of patients and a lack of actionable mutations (Campbell et al., 2016; The Cancer Genome Atlas Research Network, 2012; Kim et al., 2014), precluding the development of new therapies. Hence, the only pharmacological therapies available to treat LUSC patients are immune checkpoint inhibitor monotherapy or in combination with chemotherapy (Mok et al., 2019; Paz-Ares et al., 2018; Weinberg and Gadgeel, 2019). Furthermore, the National Lung Cancer Matrix Trial and The Lung Master Protocol, the largest personalized medicine trials in lung cancer, have not shown clear therapeutic benefits with targeted agents in LUSC (; Middleton et al., 2020; Redman et al., 2020). LUSC also has a worse prognosis than LUAD independent of stage at detection, with a 5-year overall survival (OS) of 6.2% for patients diagnosed with distant metastasis (9.5% for LUAD). However, patients diagnosed with localized disease are eligible for curative surgery and the 5-year OS is 50% (National Cancer Institute and DCCPS, 2018). Therefore, early detection is currently the most valuable tool to prevent deaths by LUSC as evidenced by several ongoing programmes of lung cancer early detection (Crosbie et al., 2019a; Crosbie et al., 2019b; Aberle et al., 2011). These initiatives make use of low-dose CT scans in high-risk populations, but in spite of the frequent detection of localized lung cancer eligible for resection with curative intent, 40% of early diagnosed patients die within 5 years.

LUSC progresses through a series of premalignant stages characterized by alterations of the normal bronchial epithelium (Figure 1; Ishizumi et al., 2010; Kadara et al., 2016; Thakrar et al., 2017; Pennycuick et al., 2020). These endobronchial premalignant lesions (PMLs) are classified as low-grade (squamous metaplasia, mild and moderate dysplasia) and high-grade (severe dysplasia, and carcinomas in situ [CIS]) (Figure 1). However, not all PMLs progress to LUSC. Although obvious differences exist between the multiple studies published on the topic, high-grade PMLs have a higher risk of progression than low grade, and high levels of chromosomal instability (CIN) are also predictive of progressive PMLs (Thakrar et al., 2017; van Boerdonk et al., 2014; Teixeira et al., 2019; Merrick et al., 2016). This potential role of CIN as biomarker of PMLs progression has been observed in low- and high-grade PMLs (van Boerdonk et al., 2014; Teixeira et al., 2019). These reports showed that high levels of copy number variations was the best predictor of progressive PMLs (van Boerdonk et al., 2014; Teixeira et al., 2019) and that immune response is the most likely cause of regression as these lesions contained higher levels of immune infiltration (Pennycuick et al., 2020; Beane et al., 2019). These results are supported by transcriptomic analysis of PMLs that indicate immune evasion in the transition to invasiveness (Mascaux et al., 2019).

Figure 1

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The detection of PMLs cannot be carried out by routine patient imaging techniques such as CT or PET scans as the morphological change that they cause in the airway does not result in radiological contrast. Alternatively, ablation of high-grade PMLs detected by autofluorescence bronchoscopy using minimally invasive endoscopic procedures in high-risk populations is an innovative and interesting strategy to prevent LUSC (Guibert et al., 2016). Nonetheless autofluorescence bronchoscopy is an expensive and complex technique of limited use in large screening programmes. Therefore, simpler, more cost effective, and scalable methods of high-grade PML detection are needed to prevent deaths by LUSC. Improving the detection of PMLs requires a better understanding of their biology and the validation of adequate biomarkers of progressive lesions that can be translated into new technologies for large screening initiatives. Cell surface proteins, metabolites, nasal-based biomarkers, blood and sputum/bronchoalveolar lavage biomarkers are examples of biomarkers that can be used to improve and/or complement current diagnostic techniques (such as CT and PET scans) or develop new ones. A user-friendly application to interrogate gene expression from studies focusing on precancerous LUSC stages will enhance the advances in PML biology. However, such application does not exist.

Recently, scientific interest in the biology of preinvasive LUSC stages has motivated the publication of several articles characterizing PML transcriptomes from various perspectives (Figure 1). Two reports published by Beane et al., 2019, and Mascaux et al., 2019, showed stage-by-stage gene expression analyses of PMLs. These studies provided the most detailed transcriptomic characterization of all PML stages so far and identified changes in the immune microenvironment associated with invasive transformation. Additionally, longitudinal studies by Beane et al., 2019, Merrick et al., 2018, and Teixeira et al., 2019, contained samples with known progression potential. Beane et al., 2019, identified molecular subtypes with specific biological traits whereas articles published by Merrick et al., 2018, and Teixeira et al., 2019 focused on gene expression of specific preinvasive stages (dysplasias and CIS, respectively) with the objective of identifying predictors of PML progression and the biomolecular processes involved (Figure 1). These studies provide a valuable source of gene expression data to identify candidate biomarkers for the detection of high-risk PMLs and/or early stage LUSC as well as to investigate the biology of premalignant LUSC progression.

Transparent and straightforward accessibility to transcriptomic databases is a key requirement for the open science philosophy. Applications that integrate multiple databases focusing on the same biological and clinical problem, such as camcAPP (Dunning et al., 2017), allow cross-comparisons between independent studies, strengthen the robustness of results obtained, and allow the selection of high-confidence data. In this report, we provide an overall description of XTABLE (Exploring Transcriptomes of Bronchial Lesions) and provide examples of its functions. XTABLE is a new open-source application that will enable scientists to interrogate currently four LUSC PML transcriptomic datasets in a versatile manner that can be adapted to the needs of each researcher. Specifically, LUSC prevention and diagnosis are the main areas that can benefit the most from XTABLE, but its versatility and multiple functions lend themselves to the exploration of a variety of research questions. Without XTABLE, researchers would have to put together all the packages, data processing steps themselves for each analysis they wished to run. In this report, we provide an overall explanation of all the functions of XTABLE as well as a detailed description of the most important analysis modules, including two-group comparisons, gene-of-interest analyses, and interrogation of transcriptional signatures. Additionally, we explored the use of CIN-related signatures as a biomarker of progressive PMLs and mapped the onset of the most important LUSC pathways to its developmental stages.

In this report, we have described XTABLE, an open source bioinformatic tool to explore gene expression in LUSC PMLs using four different transcriptomic datasets. The most novel aspect of this application is the emphasis on preinvasive disease (to our knowledge, the first bioinformatic tool focusing on PMLs), and the possibility of multiple sample stratifications by parameters that correlate with high risk of malignant progression (stage, progression status, and CIN). Treating lung cancer is complex. Chemotherapies, radiotherapy, targeted therapies, and immunotherapies save lives. However, the progress in lung cancer patient survival during the last 20 years is disappointing (National Cancer Institute and DCCPS, 2018). Redirecting research efforts to prevent lung cancer and to detect its more treatable premalignant stages is the most efficient way to prevent lung cancer deaths to date. XTABLE offers researchers the possibility of accessing the most relevant transcriptomic databases on LUSC PMLs to assist in the understanding of PML biology and identify biomarkers for LUSC early detection. Biomarker identification, investigating the evolution of signalling pathways in multiple developmental LUSC stages, identification of immunomodulatory signals, changes in transcriptional signatures, and exploring the causes and consequences of CIN in PMLs are amongst the multiple examples of promising applications of XTABLE for basic and translational biologists.

Whereas multiple open-source applications for generic processing of transcriptomic data have been developed since the advent of microarray and RNAseq technologies, none of those applications integrate analysis capabilities directed to prevention and early detection discovery research. Furthermore, these applications lack versatility for downstream analysis, automatically apply data transformation (log-transformation) to dataset that do not require it, lack of update for years, compatibility issues with current operating systems, require reformatting and renaming of expression datasets (not practical for large sample numbers), and do not evaluate the diagnostic performance of classifiers such as ROC analysis. XTABLE overcomes these limitations and unifies different packages in a manner that facilitates precancerous biology, prevention, and early detection research.

XTABLE also contributes to a more open, accessible, and inclusive science. Research laboratories often have restricted access to bioinformatic support due to funding constraints or lack of adequate collaborations. This limitation can be a major hurdle in the competitiveness of research groups as it may prevent hypothesis generation, validation of experimental results in patient cohorts, or acquisition of preliminary results. XTABLE and similar applications can contribute to addressing those disadvantages with an accessible and versatile platform for gene expression analysis. This application also contributes to the open science philosophy as it promotes the dissemination of data, accessibility, and transparency. Although XTABLE is unlikely to offer all the possibilities of analysis required by the scientific community, the code can be obtained by the users to adapt it to their research questions. Additionally, XTABLE allows the download of results that can be subject to additional downstream analyses.

Although integrative analyses of two or more datasets as well as integration of other genomic platforms would be desirable capabilities of XTABLE, the different nature of the studies, methodologies, and platforms make these integrations hardly achievable. For instance, stratifications that apply to all datasets and setting up the same thresholds for ROC analysis is not possible. Moreover, implementing separate analyses is the most scientifically sound alternative to prevent wrong conclusions. Nevertheless, by analysing the four datasets under a unified interface, and at the same time, implementing analyses on individual datasets, XTABLE achieves an optimal balance between clarity, speed of analysis, user friendliness, and scientific rigor while avoiding overcomplications. Additionally, only one study (GSE108105) also published genomic and epigenomic data.

Another potential limitation of XTABLE is that it cannot be used to interrogate future datasets in a simple manner for non-computational scientists. This limitation arises from the fact that the characteristics of future studies are hardly predictable, which hampers the design of stratification and analysis options. However, the open-source nature of this application allows the future modification of the code to add new datasets by other users. Moreover, the analysis modules for each of the current databases provide a template that will be re-coded to integrate future databases.

Our analysis of CIN signatures and their correlation with the known progression potential of PMLs is one of the examples of the use of XTABLE to obtain a wider view of LUSC PML biology. CIN5 was a good predictor of CIS progression in the GSE108124 study, as already described by the authors of the study (Teixeira et al., 2019). However, CIN5 did not perform as well in other studies. Conceivably, the most plausible reason for this discrepancy is the different definition of progression status in the four studies. Whereas GSE108124 provides a binary classification of PMLs (progressive and regressive), the other studies show a more complex classification that includes progressive and persistent lesions under the same category. The endpoint in the definition of the progression status also differs between studies. Specifically, the endpoint in GSE108124 is progression to invasive LUSC whereas the other studies define progression as transition to a higher grade. Conceivably, CIN might not play the same role in the transition between PMLs as it does in malignant transformation. Additionally, GSE108124 focuses on CIS, the precursor lesions of invasive carcinomas, and uses microdissected samples. The other two studies with progression status information focus on different stages and do not perform enrichment of the tumour component. GSE114489 investigates dysplasias, an earlier LUSC stage and lacks further subdivision into mild, moderate, and severe dysplasic lesions. This limitation might result in data with more noise and poorer correlation with progression status. Nevertheless, this cohort did show an increase in CIN5 signature in persistent dysplasias when compared with regressive lesions, a difference that is not observed in GSE109743 (a cohort that does not separate PMLs by stage). These discrepancies point at several limitations of the use of CIN and its associated signatures as surrogates of progression potential. The use of CIN and more specifically CIN-scores as a bona fide predictor of progression might be limited to microdissected samples and CIS lesions. For example, the presence of tumour stroma could result in an underestimated CIN-score. Additionally, CIS lesions are the most advanced premalignant stage and show the highest levels of CIN5 signatures, thereby contributing to a higher CIN-related signal. Comparisons with microdissected PMLs of earlier stages are necessary to address the extent of these limitations.

Regardless, CIN gene expression signatures present a very robust correlation with genomic instability indexes in cancer, and specifically in lung cancer (Carter et al., 2006). Although we have shown that the correlation of CIN signatures with progression potential varies between databases, they can be used to investigate other biological questions such as identification of CIN-tolerance and CIN-driver genes, identification of changes in the immune microenvironment associated with CIN (in light of the new role of CIN in modulating tumour immunity Tijhuis et al., 2019), and interrogation of genes of interest for the user in CIN-high vs. CIN-low PMLs.

Using XTABLE, we have mapped the most important signalling pathways targeted in LUSC to the developmental stages. We found that the onset activation of squamous differentiation, PI3K/Akt pathways occur in the transition from low- to high-grade PMLs. Comprehensive genomic characterizations of PMLs have not been undertaken so far, and therefore, we are not able to map the onset of LUSC pathways with the genomic profiles of each premalignant stage. However, our observations suggest that the genomic alterations targeting those pathways should be more frequent in high-grade PMLs than low-grade. Another explanation is that those mutations are present in low-grade PMLs but their effect on the pathways is only unleashed in the transition to high-grade lesions. Co-ocurring somatic alterations and microenvironment changes could explain this. Nevertheless, our results strongly indicate that squamous differentiation and PI3K/Akt pathways are unlikely to play a role in the earliest developmental stages (hyperplasias, metaplasias, and mild dysplasias). On the other hand, our observations with the CDK4/cyclin-D1 and E2F signatures indicate an earlier onset (metaplasias and moderate dysplasias). Both signatures can be influenced by CDKN2A inactivation and cyclin-D1 (CCND1) amplification, two alterations frequently observed in LUSC, and known to override oncogene-induced senescence (Serrano et al., 1997). It is conceivable that CDKN2A inactivation and CCND1 amplification occur earlier than activation of oncogenic in order to prevent oncogene-induced senescence. For instance, SOX2 overexpression has a negative effect in cell fitness in multiple experimental scenarios (Cho et al., 2013; Correia et al., 2017; Cox et al., 2012; Wuebben et al., 2016). Early activation of the CDK4/cyclin-D1 pathway could be key to avoid this toxicity in LUSC. A similar scenario is also likely to occur with PI3K/Akt signalling, as this pathway needs to be exquisitely regulated to avoid senescence (Astle et al., 2012). However, mechanisms that modify CDK4/cyclin-D1 and E2F activities independently of CDKN2A inactivation cannot be ruled out as multiple oncogenic pathways can regulate the cell cycle. Furthermore, the existence of CDKN2A inactivation in low-grade PML is not known.

The activation of the squamous differentiation, PI3K/Akt pathways in high-grade PMLs could be very useful as biomarkers to develop new modalities of early detection using multiple approaches. High-grade PMLs frequently undergo malignant progression (Ishizumi et al., 2010) and therefore, detection of these lesions and removal is an optimal strategy to prevent deaths by LUSC. Design of appropriate molecular probes and theragnostic technologies that identify lesions with high-level pathway activation, and secreted proteins that are associated with those pathways are amongst the strategies to exploit pathway activation in early detection.

Finally, the aim of this article is not to prioritize any of the studies included in XTABLE, but to provide a tool for the simple and quick analysis of a large amount of biologically relevant data on PML biology. Each study has its own advantages and limitations. Therefore, the user is ultimately responsible for choosing the datasets that are more adequate to interrogate their research questions, compare results between databases considering the different scientific contexts of each study, and interpret them in light of the different designs of each study.

The current manuscript makes use of previously published databases, so no data have been generated for this manuscript. All analyses shown in the manuscript has been carried out using XTABLE and can be reproduced easily by any user.

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Author details

1. Matthew Roberts

   1. Cancer Biomarker Centre, Cancer Research UK Manchester Institute, The University of Manchester, Macclesfield, United Kingdom
   2. Cancer Research UK Lung Cancer Centre of Excellence, Alderley Park, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Julia Ogden

   1. Cancer Research UK Lung Cancer Centre of Excellence, Alderley Park, United Kingdom
   2. Translational Lung Cancer Biology Laboratory, Cancer Research UK Manchester Institute, University of Manchester, Macclesfield, United Kingdom

   Contribution

   Conceptualization, Validation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. AS Mukarram Hossain

   1. Cancer Biomarker Centre, Cancer Research UK Manchester Institute, The University of Manchester, Macclesfield, United Kingdom
   2. Cancer Research UK Lung Cancer Centre of Excellence, Alderley Park, United Kingdom

   Contribution

   Software, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Anshuman Chaturvedi

   1. Cancer Research UK Lung Cancer Centre of Excellence, Alderley Park, United Kingdom
   2. Department of Histopathology, The Christie Hospital, Manchester, United Kingdom

   Contribution

   Data curation, Formal analysis, Validation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Alastair RW Kerr

   1. Cancer Biomarker Centre, Cancer Research UK Manchester Institute, The University of Manchester, Macclesfield, United Kingdom
   2. Cancer Research UK Lung Cancer Centre of Excellence, Alderley Park, United Kingdom

   Contribution

   Software, Supervision, Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9207-6050

6. Caroline Dive

   1. Cancer Biomarker Centre, Cancer Research UK Manchester Institute, The University of Manchester, Macclesfield, United Kingdom
   2. Cancer Research UK Lung Cancer Centre of Excellence, Alderley Park, United Kingdom

   Contribution

   Supervision, Funding acquisition, Investigation

   Competing interests

   No competing interests declared

7. Jennifer Ellen Beane

   Boston University School of Medicine, Boston, United States

   Contribution

   Conceptualization, Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. Carlos Lopez-Garcia

   1. Cancer Research UK Lung Cancer Centre of Excellence, Alderley Park, United Kingdom
   2. Translational Lung Cancer Biology Laboratory, Cancer Research UK Manchester Institute, University of Manchester, Macclesfield, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing

   For correspondence

   carlos.lopezgarcia@cruk.manchester.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9848-8216

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