Lung squamous cell carcinoma is the second most common lung cancer. However, very little is known about how normal tissues in the lung develop in to these tumours. Like many cancers, this transformation comprises of an intermediate phase where healthy cells begin to form lesions that may (or may not) progress in to tumours. Understanding the biology of these lesions in lung squamous cell carcinoma may help clinicians detect them before they become cancerous.

Knowing which genes are switched on and off during this intermediary phase can provide clues as to how these lesions form. There are already some publicly available transcriptional datasets showing the activity of tens of thousands of genes in pre-cancerous lesions extracted from patients with lung squamous cell carcinoma. But not every laboratory has the bioinformatic tools and skills required to interrogate these extensive databases.

To address this, Roberts et al. built an open-source platform called XTABLE (short for Exploring Transcriptomes of Bronchial Lesions) which can analyse transcriptional datasets in multiple ways depending on the needs of the user. For instance, the tool can stratify the data into groups based on different parameters, such as the lesions potential to progress in to cancer, to see how the genes of the groups compare. It can also analyse the activity of individual genes and sets of genes involved in the same biological processes.

Using XTABLE, Roberts et al. showed that a biological process linked to lung squamous cell carcinoma is also involved in the formation of pre-cancerous lesions. This suggests that molecules and genes associated with this process could potentially help scientists design prevention strategies.

XTABLE will help researchers to better understand the biology of pre-cancerous lesions and how they develop in to tumours. Moreover, it will make it easier for scientists to validate their hypotheses using data collected from patients. The tool could also be useful for scientists interested in other types of lung cancers that share a similar biology.

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.

1. 1. Mascaux C

   (2014) NCBI Gene Expression Omnibus

   ID GSE33479. Molecular characterisation of the multistep process of lung squamous carcinogenesis by gene expression profiling.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33479
2. 1. Beane JE
   2. Spira A

   (2019) NCBI Gene Expression Omnibus

   ID GSE109743. Bronchial premalignant lesions have distinct molecular subtypes associated with future histologic progression.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109743
3. 1. Merrick D

   (2018) NCBI Gene Expression Omnibus

   ID GSE114489. Altered Cell-Cycle Control, Inflammation and Adhesion in High-Risk Persistent Bronchial Dysplasia.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114489
4. 1. Teixeira VH

   (2019) NCBI Gene Expression Omnibus

   ID GSE108124. Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108124

1. 1. Aberle DR
   2. Adams AM
   3. Berg CD
   4. Black WC
   5. Clapp JD
   6. Fagerstrom RM
   7. Gareen IF
   8. Gatsonis C
   9. Marcus PM
   10. Sicks JD
   11. National Lung Screening Trial Research Team

   (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening

   The New England Journal of Medicine 365:395–409.

   https://doi.org/10.1056/NEJMoa1102873

   • PubMed
   • Google Scholar
2. 1. Astle MV
   2. Hannan KM
   3. Ng PY
   4. Lee RS
   5. George AJ
   6. Hsu AK
   7. Haupt Y
   8. Hannan RD
   9. Pearson RB

   (2012) Akt induces senescence in human cells via mTORC1 and p53 in the absence of DNA damage: implications for targeting mTOR during malignancy

   Oncogene 31:1949–1962.

   https://doi.org/10.1038/onc.2011.394

   • PubMed
   • Google Scholar
3. 1. Beane JE
   2. Mazzilli SA
   3. Campbell JD
   4. Duclos G
   5. Krysan K
   6. Moy C
   7. Perdomo C
   8. Schaffer M
   9. Liu G
   10. Zhang S
   11. Liu H
   12. Vick J
   13. Dhillon SS
   14. Platero SJ
   15. Dubinett SM
   16. Stevenson C
   17. Reid ME
   18. Lenburg ME
   19. Spira AE

   (2019) Molecular subtyping reveals immune alterations associated with progression of bronchial premalignant lesions

   Nature Communications 10:1856.

   https://doi.org/10.1038/s41467-019-09834-2

   • PubMed
   • Google Scholar
4. 1. Ben-Porath I
   2. Thomson MW
   3. Carey VJ
   4. Ge R
   5. Bell GW
   6. Regev A
   7. Weinberg RA

   (2008) An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors

   Nature Genetics 40:499–507.

   https://doi.org/10.1038/ng.127

   • PubMed
   • Google Scholar
5. Book

   1. Bray F
   2. Colombet M M
   3. Mery L L
   4. Piñeros M M
   5. Znaor A A
   6. Ferlay J J
   7. Zanetti

   (2021)

   Cancer Incidence in Five Continents XI

   International Agency for Research on Cancer.

   • Google Scholar
6. 1. Campbell JD
   2. Alexandrov A
   3. Kim J
   4. Wala J
   5. Berger AH
   6. Pedamallu CS
   7. Shukla SA
   8. Guo G
   9. Brooks AN
   10. Murray BA
   11. Imielinski M
   12. Hu X
   13. Ling S
   14. Akbani R
   15. Rosenberg M
   16. Cibulskis C
   17. Ramachandran A
   18. Collisson EA
   19. Kwiatkowski DJ
   20. Lawrence MS
   21. Weinstein JN
   22. Verhaak RGW
   23. Wu CJ
   24. Hammerman PS
   25. Cherniack AD
   26. Getz G
   27. Cancer Genome Atlas Research Network
   28. Artyomov MN
   29. Schreiber R
   30. Govindan R
   31. Meyerson M

   (2016) Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas

   Nature Genetics 48:607–616.

   https://doi.org/10.1038/ng.3564

   • PubMed
   • Google Scholar
7. 1. Carter SL
   2. Eklund AC
   3. Kohane IS
   4. Harris LN
   5. Szallasi Z

   (2006) A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers

   Nature Genetics 38:1043–1048.

   https://doi.org/10.1038/ng1861

   • PubMed
   • Google Scholar
8. 1. Chen Z
   2. Fillmore CM
   3. Hammerman PS
   4. Kim CF
   5. Wong KK

   (2014) Non-Small-Cell lung cancers: a heterogeneous set of diseases

   Nature Reviews. Cancer 14:535–546.

   https://doi.org/10.1038/nrc3775

   • PubMed
   • Google Scholar
9. 1. Cho Y-Y
   2. Kim DJ
   3. Lee HS
   4. Jeong C-H
   5. Cho E-J
   6. Kim M-O
   7. Byun S
   8. Lee K-Y
   9. Yao K
   10. Carper A
   11. Langfald A
   12. Bode AM
   13. Dong Z

   (2013) Autophagy and cellular senescence mediated by Sox2 suppress malignancy of cancer cells

   PLOS ONE 8:e57172.

   https://doi.org/10.1371/journal.pone.0057172

   • PubMed
   • Google Scholar
10. 1. Correia LL
    2. Johnson J-A
    3. McErlean P
    4. Bauer J
    5. Farah H
    6. Rassl DM
    7. Rintoul RC
    8. Sethi T
    9. Lavender P
    10. Rawlins EL
    11. Littlewood TD
    12. Evan GI
    13. McCaughan FM

    (2017) Sox2 drives bronchial dysplasia in a novel organotypic model of early human squamous lung cancer

    American Journal of Respiratory and Critical Care Medicine 195:1494–1508.

    https://doi.org/10.1164/rccm.201510-2084OC

    • PubMed
    • Google Scholar
11. 1. Cox JL
    2. Wilder PJ
    3. Desler M
    4. Rizzino A

    (2012) Elevating Sox2 levels deleteriously affects the growth of medulloblastoma and glioblastoma cells

    PLOS ONE 7:e44087.

    https://doi.org/10.1371/journal.pone.0044087

    • PubMed
    • Google Scholar
12. 1. Crosbie PA
    2. Balata H
    3. Evison M
    4. Atack M
    5. Bayliss-Brideaux V
    6. Colligan D
    7. Duerden R
    8. Eaglesfield J
    9. Edwards T
    10. Elton P
    11. Foster J
    12. Greaves M
    13. Hayler G
    14. Higgins C
    15. Howells J
    16. Irion K
    17. Karunaratne D
    18. Kelly J
    19. King Z
    20. Lyons J
    21. Manson S
    22. Mellor S
    23. Miller D
    24. Myerscough A
    25. Newton T
    26. O’Leary M
    27. Pearson R
    28. Pickford J
    29. Sawyer R
    30. Screaton NJ
    31. Sharman A
    32. Simmons M
    33. Smith E
    34. Taylor B
    35. Taylor S
    36. Walsham A
    37. Watts A
    38. Whittaker J
    39. Yarnell L
    40. Threlfall A
    41. Barber PV
    42. Tonge J
    43. Booton R

    (2019a) Second round results from the manchester “ lung health check” community-based targeted lung cancer screening pilot

    Thorax 74:700–704.

    https://doi.org/10.1136/thoraxjnl-2018-212547

    • PubMed
    • Google Scholar
13. 1. Crosbie PA
    2. Balata H
    3. Evison M
    4. Atack M
    5. Bayliss-Brideaux V
    6. Colligan D
    7. Duerden R
    8. Eaglesfield J
    9. Edwards T
    10. Elton P
    11. Foster J
    12. Greaves M
    13. Hayler G
    14. Higgins C
    15. Howells J
    16. Irion K
    17. Karunaratne D
    18. Kelly J
    19. King Z
    20. Manson S
    21. Mellor S
    22. Miller D
    23. Myerscough A
    24. Newton T
    25. O’Leary M
    26. Pearson R
    27. Pickford J
    28. Sawyer R
    29. Screaton NJ
    30. Sharman A
    31. Simmons M
    32. Smith E
    33. Taylor B
    34. Taylor S
    35. Walsham A
    36. Watts A
    37. Whittaker J
    38. Yarnell L
    39. Threlfall A
    40. Barber PV
    41. Tonge J
    42. Booton R

    (2019b) Implementing lung cancer screening: baseline results from a community-based “ lung health check” pilot in deprived areas of manchester

    Thorax 74:405–409.

    https://doi.org/10.1136/thoraxjnl-2017-211377

    • PubMed
    • Google Scholar
14. 1. Dunning MJ
    2. Vowler SL
    3. Lalonde E
    4. Ross-Adams H
    5. Boutros P
    6. Mills IG
    7. Lynch AG
    8. Lamb AD

    (2017) Mining human prostate cancer datasets: the “ camcapp ” shiny APP

    EBioMedicine 17:5–6.

    https://doi.org/10.1016/j.ebiom.2017.02.022

    • PubMed
    • Google Scholar
15. 1. Guibert N
    2. Mhanna L
    3. Droneau S
    4. Plat G
    5. Didier A
    6. Mazieres J
    7. Hermant C

    (2016) Techniques of endoscopic airway tumor treatment

    Journal of Thoracic Disease 8:3343–3360.

    https://doi.org/10.21037/jtd.2016.11.49

    • PubMed
    • Google Scholar
16. 1. Hirsch FR
    2. Suda K
    3. Wiens J
    4. Bunn PA

    (2016) New and emerging targeted treatments in advanced non-small-cell lung cancer

    Lancet 388:1012–1024.

    https://doi.org/10.1016/S0140-6736(16)31473-8

    • PubMed
    • Google Scholar
17. 1. Ishizumi T
    2. McWilliams A
    3. MacAulay C
    4. Gazdar A
    5. Lam S

    (2010) Natural history of bronchial preinvasive lesions

    Cancer Metastasis Reviews 29:5–14.

    https://doi.org/10.1007/s10555-010-9214-7

    • PubMed
    • Google Scholar
18. 1. Jamal-Hanjani M
    2. Wilson GA
    3. McGranahan N
    4. Birkbak NJ
    5. Watkins TBK
    6. Veeriah S
    7. Shafi S
    8. Johnson DH
    9. Mitter R
    10. Rosenthal R
    11. Salm M
    12. Horswell S
    13. Escudero M
    14. Matthews N
    15. Rowan A
    16. Chambers T
    17. Moore DA
    18. Turajlic S
    19. Xu H
    20. Lee S-M
    21. Forster MD
    22. Ahmad T
    23. Hiley CT
    24. Abbosh C
    25. Falzon M
    26. Borg E
    27. Marafioti T
    28. Lawrence D
    29. Hayward M
    30. Kolvekar S
    31. Panagiotopoulos N
    32. Janes SM
    33. Thakrar R
    34. Ahmed A
    35. Blackhall F
    36. Summers Y
    37. Shah R
    38. Joseph L
    39. Quinn AM
    40. Crosbie PA
    41. Naidu B
    42. Middleton G
    43. Langman G
    44. Trotter S
    45. Nicolson M
    46. Remmen H
    47. Kerr K
    48. Chetty M
    49. Gomersall L
    50. Fennell DA
    51. Nakas A
    52. Rathinam S
    53. Anand G
    54. Khan S
    55. Russell P
    56. Ezhil V
    57. Ismail B
    58. Irvin-Sellers M
    59. Prakash V
    60. Lester JF
    61. Kornaszewska M
    62. Attanoos R
    63. Adams H
    64. Davies H
    65. Dentro S
    66. Taniere P
    67. O’Sullivan B
    68. Lowe HL
    69. Hartley JA
    70. Iles N
    71. Bell H
    72. Ngai Y
    73. Shaw JA
    74. Herrero J
    75. Szallasi Z
    76. Schwarz RF
    77. Stewart A
    78. Quezada SA
    79. Le Quesne J
    80. Van Loo P
    81. Dive C
    82. Hackshaw A
    83. Swanton C
    84. TRACERx Consortium

    (2017) Tracking the evolution of non-small-cell lung cancer

    The New England Journal of Medicine 376:2109–2121.

    https://doi.org/10.1056/NEJMoa1616288

    • PubMed
    • Google Scholar
19. 1. Kadara H
    2. Scheet P
    3. Wistuba II
    4. Spira AE

    (2016) Early events in the molecular pathogenesis of lung cancer

    Cancer Prevention Research 9:518–527.

    https://doi.org/10.1158/1940-6207.CAPR-15-0400

    • PubMed
    • Google Scholar
20. 1. Khuder SA

    (2001) Effect of cigarette smoking on major histological types of lung cancer: a meta-analysis

    Lung Cancer 31:139–148.

    https://doi.org/10.1016/s0169-5002(00)00181-1

    • PubMed
    • Google Scholar
21. 1. Kim Y
    2. Hammerman PS
    3. Kim J
    4. Yoon J
    5. Lee Y
    6. Sun J-M
    7. Wilkerson MD
    8. Pedamallu CS
    9. Cibulskis K
    10. Yoo YK
    11. Lawrence MS
    12. Stojanov P
    13. Carter SL
    14. McKenna A
    15. Stewart C
    16. Sivachenko AY
    17. Oh I-J
    18. Kim HK
    19. Choi YS
    20. Kim K
    21. Shim YM
    22. Kim K-S
    23. Song S-Y
    24. Na K-J
    25. Choi Y-L
    26. Hayes DN
    27. Kim J
    28. Cho S
    29. Kim Y-C
    30. Ahn JS
    31. Ahn M-J
    32. Getz G
    33. Meyerson M
    34. Park K

    (2014) Integrative and comparative genomic analysis of lung squamous cell carcinomas in East Asian patients

    Journal of Clinical Oncology 32:121–128.

    https://doi.org/10.1200/JCO.2013.50.8556

    • PubMed
    • Google Scholar
22. 1. Liberzon A
    2. Subramanian A
    3. Pinchback R
    4. Thorvaldsdóttir H
    5. Tamayo P
    6. Mesirov JP

    (2011) Molecular signatures database

    Bioinformatics 27:1739–1740.

    https://doi.org/10.1093/bioinformatics/btr260

    • PubMed
    • Google Scholar
23. 1. Liberzon A
    2. Birger C
    3. Thorvaldsdóttir H
    4. Ghandi M
    5. Mesirov JP
    6. Tamayo P

    (2015) The molecular signatures database (msigdb) hallmark gene set collection

    Cell Systems 1:417–425.

    https://doi.org/10.1016/j.cels.2015.12.004

    • PubMed
    • Google Scholar
24. 1. Mascaux C
    2. Angelova M
    3. Vasaturo A
    4. Beane J
    5. Hijazi K
    6. Anthoine G
    7. Buttard B
    8. Rothe F
    9. Willard-Gallo K
    10. Haller A
    11. Ninane V
    12. Burny A
    13. Sculier J-P
    14. Spira A
    15. Galon J

    (2019) Immune evasion before tumour invasion in early lung squamous carcinogenesis

    Nature 571:570–575.

    https://doi.org/10.1038/s41586-019-1330-0

    • PubMed
    • Google Scholar
25. 1. Merrick DT
    2. Gao D
    3. Miller YE
    4. Keith RL
    5. Baron AE
    6. Feser W
    7. Kennedy TC
    8. Blatchford PJ
    9. Braudrick S
    10. Hirsch FR
    11. Heasley L
    12. Bunn PA
    13. Franklin WA

    (2016) Persistence of bronchial dysplasia is associated with development of invasive squamous cell carcinoma

    Cancer Prevention Research 9:96–104.

    https://doi.org/10.1158/1940-6207.CAPR-15-0305

    • PubMed
    • Google Scholar
26. 1. Merrick DT
    2. Edwards MG
    3. Franklin WA
    4. Sugita M
    5. Keith RL
    6. Miller YE
    7. Friedman MB
    8. Dwyer-Nield LD
    9. Tennis MA
    10. O’Keefe MC
    11. Donald EJ
    12. Malloy JM
    13. van Bokhoven A
    14. Wilson S
    15. Koch PJ
    16. O’Shea C
    17. Coldren C
    18. Orlicky DJ
    19. Lu X
    20. Baron AE
    21. Hickey G
    22. Kennedy TC
    23. Powell R
    24. Heasley L
    25. Bunn PA
    26. Geraci M
    27. Nemenoff RA

    (2018) Altered cell-cycle control, inflammation, and adhesion in high-risk persistent bronchial dysplasia

    Cancer Research 78:4971–4983.

    https://doi.org/10.1158/0008-5472.CAN-17-3822

    • PubMed
    • Google Scholar
27. 1. Middleton G
    2. Fletcher P
    3. Popat S
    4. Savage J
    5. Summers Y
    6. Greystoke A
    7. Gilligan D
    8. Cave J
    9. O’Rourke N
    10. Brewster A
    11. Toy E
    12. Spicer J
    13. Jain P
    14. Dangoor A
    15. Mackean M
    16. Forster M
    17. Farley A
    18. Wherton D
    19. Mehmi M
    20. Sharpe R
    21. Mills TC
    22. Cerone MA
    23. Yap TA
    24. Watkins TBK
    25. Lim E
    26. Swanton C
    27. Billingham L

    (2020) The National lung matrix trial of personalized therapy in lung cancer

    Nature 583:807–812.

    https://doi.org/10.1038/s41586-020-2481-8

    • PubMed
    • Google Scholar
28. 1. Mok TSK
    2. Wu YL
    3. Kudaba I
    4. Kowalski DM
    5. Cho BC
    6. Turna HZ
    7. Castro G
    8. Srimuninnimit V
    9. Laktionov KK
    10. Bondarenko I
    11. Kubota K
    12. Lubiniecki GM
    13. Zhang J
    14. Kush D
    15. Lopes G
    16. KEYNOTE-042 Investigators

    (2019) Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial

    Lancet 393:1819–1830.

    https://doi.org/10.1016/S0140-6736(18)32409-7

    • PubMed
    • Google Scholar
29. 1. Molenaar JJ
    2. Ebus ME
    3. Koster J
    4. van Sluis P
    5. van Noesel CJM
    6. Versteeg R
    7. Caron HN

    (2008) Cyclin D1 and CDK4 activity contribute to the undifferentiated phenotype in neuroblastoma

    Cancer Research 68:2599–2609.

    https://doi.org/10.1158/0008-5472.CAN-07-5032

    • PubMed
    • Google Scholar
30. Report

    1. National Cancer Institute
    2. DCCPS

    (2018)

    Surveillance, Epidemiology, and End Results (SEER) Program

    National Cancer Institute.

    • Google Scholar
31. 1. Paz-Ares L
    2. Luft A
    3. Vicente D
    4. Tafreshi A
    5. Gümüş M
    6. Mazières J
    7. Hermes B
    8. Çay Şenler F
    9. Csőszi T
    10. Fülöp A
    11. Rodríguez-Cid J
    12. Wilson J
    13. Sugawara S
    14. Kato T
    15. Lee KH
    16. Cheng Y
    17. Novello S
    18. Halmos B
    19. Li X
    20. Lubiniecki GM
    21. Piperdi B
    22. Kowalski DM
    23. KEYNOTE-407 Investigators

    (2018) Pembrolizumab plus chemotherapy for squamous non-small-cell lung cancer

    The New England Journal of Medicine 379:2040–2051.

    https://doi.org/10.1056/NEJMoa1810865

    • PubMed
    • Google Scholar
32. 1. Pennycuick A
    2. Teixeira VH
    3. AbdulJabbar K
    4. Raza SEA
    5. Lund T
    6. Akarca AU
    7. Rosenthal R
    8. Kalinke L
    9. Chandrasekharan DP
    10. Pipinikas CP
    11. Lee-Six H
    12. Hynds RE
    13. Gowers KHC
    14. Henry JY
    15. Millar FR
    16. Hagos YB
    17. Denais C
    18. Falzon M
    19. Moore DA
    20. Antoniou S
    21. Durrenberger PF
    22. Furness AJ
    23. Carroll B
    24. Marceaux C
    25. Asselin-Labat M-L
    26. Larson W
    27. Betts C
    28. Coussens LM
    29. Thakrar RM
    30. George J
    31. Swanton C
    32. Thirlwell C
    33. Campbell PJ
    34. Marafioti T
    35. Yuan Y
    36. Quezada SA
    37. McGranahan N
    38. Janes SM

    (2020) Immune surveillance in clinical regression of preinvasive squamous cell lung cancer

    Cancer Discovery 10:1489–1499.

    https://doi.org/10.1158/2159-8290.CD-19-1366

    • PubMed
    • Google Scholar
33. 1. Redman MW
    2. Papadimitrakopoulou VA
    3. Minichiello K
    4. Hirsch FR
    5. Mack PC
    6. Schwartz LH
    7. Vokes E
    8. Ramalingam S
    9. Leighl N
    10. Bradley J
    11. Miao J
    12. Moon J
    13. Highleyman L
    14. Miwa C
    15. LeBlanc ML
    16. Malik S
    17. Miller VA
    18. Sigal EV
    19. Adam S
    20. Wholley D
    21. Sigman C
    22. Smolich B
    23. Blanke CD
    24. Kelly K
    25. Gandara DR
    26. Herbst RS

    (2020) Biomarker-driven therapies for previously treated squamous non-small-cell lung cancer (lung-MAP SWOG S1400): a biomarker-driven master protocol

    The Lancet. Oncology 21:1589–1601.

    https://doi.org/10.1016/S1470-2045(20)30475-7

    • PubMed
    • Google Scholar
34. Software

    1. Roberts M

    (2022) XTABLE, version swh:1:rev:243f763aa1924915f88468588fe369b1327273a8

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:b0269a0dc038c24edaf0556ee09cef4c9d613d6a;origin=https://gitlab.com/cruk-mi/xtable.git;visit=swh:1:snp:2a4ba655d4f67031cf3baf2cc537e48123544e9f;anchor=swh:1:rev:243f763aa1924915f88468588fe369b1327273a8
35. 1. Serrano M
    2. Lin AW
    3. McCurrach ME
    4. Beach D
    5. Lowe SW

    (1997) Oncogenic Ras provokes premature cell senescence associated with accumulation of p53 and p16INK4a

    Cell 88:593–602.

    https://doi.org/10.1016/s0092-8674(00)81902-9

    • PubMed
    • Google Scholar
36. 1. Subramanian A
    2. Tamayo P
    3. Mootha VK
    4. Mukherjee S
    5. Ebert BL
    6. Gillette MA
    7. Paulovich A
    8. Pomeroy SL
    9. Golub TR
    10. Lander ES
    11. Mesirov JP

    (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles

    PNAS 102:15545–15550.

    https://doi.org/10.1073/pnas.0506580102

    • PubMed
    • Google Scholar
37. 1. Sung H
    2. Ferlay J
    3. Siegel RL
    4. Laversanne M
    5. Soerjomataram I
    6. Jemal A
    7. Bray F

    (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

    CA 71:209–249.

    https://doi.org/10.3322/caac.21660

    • PubMed
    • Google Scholar
38. 1. Teixeira VH
    2. Pipinikas CP
    3. Pennycuick A
    4. Lee-Six H
    5. Chandrasekharan D
    6. Beane J
    7. Morris TJ
    8. Karpathakis A
    9. Feber A
    10. Breeze CE
    11. Ntolios P
    12. Hynds RE
    13. Falzon M
    14. Capitanio A
    15. Carroll B
    16. Durrenberger PF
    17. Hardavella G
    18. Brown JM
    19. Lynch AG
    20. Farmery H
    21. Paul DS
    22. Chambers RC
    23. McGranahan N
    24. Navani N
    25. Thakrar RM
    26. Swanton C
    27. Beck S
    28. George PJ
    29. Spira A
    30. Campbell PJ
    31. Thirlwell C
    32. Janes SM

    (2019) Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions

    Nature Medicine 25:517–525.

    https://doi.org/10.1038/s41591-018-0323-0

    • PubMed
    • Google Scholar
39. 1. Thakrar RM
    2. Pennycuick A
    3. Borg E
    4. Janes SM

    (2017) Preinvasive disease of the airway

    Cancer Treatment Reviews 58:77–90.

    https://doi.org/10.1016/j.ctrv.2017.05.009

    • PubMed
    • Google Scholar
40. 1. The Cancer Genome Atlas Research Network

    (2012) Comprehensive genomic characterization of squamous cell lung cancers

    Nature 489:519–525.

    https://doi.org/10.1038/nature11404

    • PubMed
    • Google Scholar
41. 1. Tijhuis AE
    2. Johnson SC
    3. McClelland SE

    (2019) The emerging links between chromosomal instability (CIN), metastasis, inflammation and tumour immunity

    Molecular Cytogenetics 12:17.

    https://doi.org/10.1186/s13039-019-0429-1

    • PubMed
    • Google Scholar
42. 1. Torre LA
    2. Siegel RL
    3. Jemal A

    (2016) Lung cancer statistics

    Advances in Experimental Medicine and Biology 893:1–19.

    https://doi.org/10.1007/978-3-319-24223-1_1

    • PubMed
    • Google Scholar
43. 1. van Boerdonk RAA
    2. Daniels JMA
    3. Snijders PJF
    4. Grünberg K
    5. Thunnissen E
    6. van de Wiel MA
    7. Ylstra B
    8. Postmus PE
    9. Meijer CJLM
    10. Meijer GA
    11. Smit EF
    12. Sutedja TG
    13. Heideman DAM

    (2014) Dna copy number aberrations in endobronchial lesions: a validated predictor for cancer

    Thorax 69:451–457.

    https://doi.org/10.1136/thoraxjnl-2013-203821

    • PubMed
    • Google Scholar
44. 1. Weinberg F
    2. Gadgeel S

    (2019) Combination pembrolizumab plus chemotherapy: a new standard of care for patients with advanced non-small-cell lung cancer

    Lung Cancer 10:47–56.

    https://doi.org/10.2147/LCTT.S176391

    • PubMed
    • Google Scholar
45. 1. Wuebben EL
    2. Wilder PJ
    3. Cox JL
    4. Grunkemeyer JA
    5. Caffrey T
    6. Hollingsworth MA
    7. Rizzino A

    (2016) Sox2 functions as a molecular rheostat to control the growth, tumorigenicity and drug responses of pancreatic ductal adenocarcinoma cells

    Oncotarget 7:34890–34906.

    https://doi.org/10.18632/oncotarget.8994

    • PubMed
    • Google Scholar

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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