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

Nature Medicine volume 25, pages 517–525 (2019)Cite this article

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Abstract

The molecular alterations that occur in cells before cancer is manifest are largely uncharted. Lung carcinoma in situ (CIS) lesions are the pre-invasive precursor to squamous cell carcinoma. Although microscopically identical, their future is in equipoise, with half progressing to invasive cancer and half regressing or remaining static. The cellular basis of this clinical observation is unknown. Here, we profile the genomic, transcriptomic, and epigenomic landscape of CIS in a unique patient cohort with longitudinally monitored pre-invasive disease. Predictive modeling identifies which lesions will progress with remarkable accuracy. We identify progression-specific methylation changes on a background of widespread heterogeneity, alongside a strong chromosomal instability signature. We observed mutations and copy number changes characteristic of cancer and chart their emergence, offering a window into early carcinogenesis. We anticipate that this new understanding of cancer precursor biology will improve early detection, reduce overtreatment, and foster preventative therapies targeting early clonal events in lung cancer.

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Fig. 1: Analysis of pre-invasive lung CIS lesions.
Fig. 2: Genomic aberrations in pre-invasive lung CIS lesions.
Fig. 3: Altered methylation and gene expression in lung CIS lesions.
Fig. 4: CIS gene expression and methylation profiles are predictive of progression to cancer.
Fig. 5: Chromosomal instability is associated with progression to cancer.

Code availability

All code used in our analysis will be made available at http://github.com/ucl-respiratory/preinvasive on publication. All software dependencies, full version information, and parameters used in our analysis can be found here.Unless otherwise specified, all analyses were performed in an R statistical environment (v3.5.0; www.r-project.org/) using Bioconductor32 version 3.7.

Data availability

Whole-genome sequencing data have been deposited at the European Genome Phenome Archive (https://www.ebi.ac.uk/ega/ at the EBI) with accession number EGAD00001003883. All gene expression and methylation microarray data reported in this study have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) public repository, and they are accessible through GEO accession number GSE108124.

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Acknowledgements

We thank all of the patients who participated in this study and K. Pearce, G. Chennel, D. Chambers, P. Mercer and K. Gowers for technical help and proofreading. We thank P. Rabbitts, A. Banerjee and C. Read for their early development of the study. The results published here are in part based on data generated by a TCGA pilot project established by the National Cancer Institute and National Human Genome Research Institute. Information about TCGA and the investigators and institutions that constitute the TCGA research network can be found at http://cancergenome.nih.gov. R.E.H., N.M., P.J.C., and S.M.J. are supported by the Wellcome Trust fellowships. S.M.J. is also supported by the Rosetrees Trust, the Welton Trust, the Garfield Weston Trust, the Stoneygate Trust and UCLH Charitable Foundation. V.T., C.P., R.E.H. and S.M.J. have been funded by the Roy Castle Lung Cancer Foundation. A.P. is funded by a Wellcome Trust clinical PhD training fellowship. H.L.-S. is funded by the Wellcome Trust Sanger Institute non-clinical PhD studentship. C.T. was a CRUK Clinician Scientist. This work was partially undertaken at UCLH/UCL, who received a proportion of funding from the Department of Health’s NIHR Biomedical Research Centre’s funding scheme (S.M.J. and N.N.). R.E.H., N.M., C.S., and S.M.J. are part of the CRUK Lung Cancer Centre of Excellence. A.S., C.S., and S.M.J. are supported by Stand Up to Cancer. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Vitor H. Teixeira, Christodoulos P. Pipinikas and Adam Pennycuick.

Authors and Affiliations

  1. Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK

    Vitor H. Teixeira, Christodoulos P. Pipinikas, Adam Pennycuick, Deepak Chandrasekharan, Paschalis Ntolios, Robert E. Hynds, James M. Brown, Neal Navani, Ricky M. Thakrar & Sam M. Janes

  2. Research Department of Cancer Biology and Medical Genomics Laboratory, UCL Cancer Institute, University College London, London, UK

    Christodoulos P. Pipinikas, Tiffany J. Morris, Anna Karpathakis, Andrew Feber, Charles E. Breeze, Dirk S. Paul, Stephan Beck & Christina Thirlwell

  3. The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK

    Henry Lee-Six & Peter J. Campbell

  4. Department of Medicine, Boston University School of Medicine, Boston, MA, USA

    Jennifer Beane & Avrum Spira

  5. CRUK Lung Cancer Centre of Excellence, UCL Cancer Institute, London, UK

    Robert E. Hynds, Nicholas McGranahan & Charles Swanton

  6. Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK

    Robert E. Hynds & Charles Swanton

  7. Department of Pathology, University College London Hospitals NHS Trust, London, UK

    Mary Falzon & Arrigo Capitanio

  8. Department of Thoracic Medicine, University College London Hospital, London, UK

    Bernadette Carroll, Georgia Hardavella, Neal Navani, Ricky M. Thakrar, Phillip Jeremy George & Sam M. Janes

  9. Center for Inflammation and Tissue Repair, UCL Respiratory, University College London, London, UK

    Pascal F. Durrenberger & Rachel C. Chambers

  10. Computational Biology and Statistics Laboratory, Cancer Research UK Cambridge Institute, Cambridge, UK

    Andy G. Lynch & Henry Farmery

  11. School of Medicine/School of Mathematics and Statistics, University of St Andrews, St Andrews, UK

    Andy G. Lynch

  12. Johnson and Johnson Innovation, Cambridge, MA, USA

    Avrum Spira

Authors
  1. Vitor H. Teixeira
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  2. Christodoulos P. Pipinikas
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  32. Sam M. Janes
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Contributions

V.H.T., C.P.P., and A.P. contributed equally to this work. S.M.J., P.J.C., V.H.T., A.P., R.E.H., H.L.-S., and C.P.P. co-wrote the manuscript. S.M.J., P.J.C., C.T., V.H.T., and C.P.P. conceived the study design. S.M.J., P.J.C., C.T., V.H.T., C.P.P., and A.P. designed the study protocols. V.H.T. performed gene expression, qPCR and LCM experiments, and analyzed and integrated clinicopathological data and gene expression data. C.P.P. performed methylation and LCM experiments, and analyzed and integrated clinicopathological data and methylation data. A.P. analyzed and integrated clinicopathological data, WGS data, gene expression data, and methylation data. H.L.-S., A.G.L., and H.F. analyzed WGS data. D.C. and P.N. performed LCM experiments. J.B. analyzed gene expression data. T.J.M., A.K., A.F., C.E.B., and D.S.P. analyzed methylation data. M.F. and A.C. conducted the pathological review. P.J.G., B.C., N.N., G.H., J.M.B., and R.M.T. performed bronchoscopies and collected the CIS and control biopsies. P.F.D. performed histological experiments. R.E.H., R.C.C., N.M., C.S., S.B., and A.S. gave advice and reviewed the manuscript. S.M.J. provided overall study oversight.

Corresponding author

Correspondence to Sam M. Janes.

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Competing interests

A.S. is an employee of Johnson and Johnson. Discoveries within this manuscript have led S.M.J. to lead on Patent Applications 1819453.0 and 1819452.2 filed with the UK Intellectual Property Office through UCL Business PLC.

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Extended data

Extended Data Fig. 1 Experimental workflow.

Flow diagram illustrating which profiling techniques were applied to which samples. Biopsies taken from index CIS lesions were stored as fresh frozen (FF) and formalin-fixed paraffin embedded (FFPE). DNA was extracted from FF biopsies. The first 54 samples studied that had sufficient extracted DNA passing quality control (QC) underwent first methylation profiling, then whole-genome sequencing (WGS) when sufficient remaining DNA was available. Due to the low DNA quantity extracted from some biopsies, the methylation dataset (n = 54) was larger than the WGS data set (n = 29), therefore the subsequent 10 samples underwent WGS directly without methylation profiling. RNA was extracted from FFPE samples and underwent gene expression profiling when RNA passed QC. To ensure validity of our conclusions across orthogonal platforms we used Illumina microarrays to profile a discovery set of 33 samples, then subsequently used Affymetrix microarrays to profile an independent validation set of 18 further samples.

Extended Data Fig. 2 Mutational signatures of CIS lesions.

a–d, The contribution of each of five pre-selected mutational signatures to each lesion is shown. These five mutational signatures, associated with CpG deamination (1), APOBEC (2 and 13), tobacco (4) and unknown aetiology (5), were selected based on an initial run using all 30 mutational signatures, which showed that these were present in the data and in signature extractions from lung squamous cell cancer (LUSC) datasets. The number of substitutions attributed to each signature is shown (a-b) as well as the proportion of mutations attributed to each mutational signature (c-d). Samples from the same patient share the same identifier except for the final letter; for example, PD21883a and PD21883d are two samples from the same patient. e, Comparison of the mutational signatures of CIS lesions to those found in lung squamous cell cancer (LUSC). LUSC data were downloaded from TCGA and mutations called with our algorithms. All mutations from all samples from each cancer type were pooled for this analysis. The colour scale indicates the proportion of substitutions in each sample that are attributed to each signature. f-j, Comparison of the relative proportion of mutations attributed to each signature between progressive (red; n = 29) and regressive (green; n = 10) CIS samples. P values were calculated using likelihood ratio tests of a mixed effects model with outcome (progressive or regressive) included as a fixed effect versus a model that was identical but for the fact that outcome was not included as a fixed effect. Only signature 4 (smoking-associated) was significantly different between the two groups. Boxplots are generated using the R boxplot function, which displays the first and third quartile as hinges and places whiskers at the most extreme data point that is no more than 1.5 times the length of the box away from the box.

Extended Data Fig. 3 Genome-wide copy number changes of CIS lesions.

Visualization of copy number changes for 39 whole-genome-sequenced CIS samples. Rows represent samples, genomic position is represented on the x-axis. Local copy number gains are illustrated in red, losses in blue. We observe widespread changes in progressive CIS samples and a subset of regressive samples.

Extended Data Fig. 4 Documentation of biopsy history and chronology of lesion appearance in three misclassified regressive cases.

a, Case 1 (PD21893a) appeared to regress from a CIS lesion (07/2012) to squamous metaplasia (SqM; 11/2012). However, again, CIS was subsequently reconfirmed by biopsy (05/2013). b, Case 2 (PD21884a) had a lobectomy for T1N0 lung squamous cell cancer (LUSC) in the left upper lobe (LUL) and was under surveillance for carcinoma-in-situ (CIS) at the resection margins. A subsequent, high-grade CIS lesion (08/2009) profiled for genome-wide DNA methylation changes was considered regressive since a follow-up biopsy on the same anatomical site demonstrated the presence of a low-grade, moderately dysplastic (MoD) lesion (11/2009). A subsequent biopsy, however, was classified as CIS (02/2011) and the lesion then remained static for 26 months but eventually progressed into invasive cancer (04/2014). c, Case 3 (PD38326a) had an initial diagnosis of CIS (11/2015) followed by regression to normal epithelium (03/2016). CIS was subsequently identified at the same site (03/2017), with invasive cancer diagnosed on subsequent biopsy (07/2017).

Extended Data Fig. 5 Genomic aberrations in pre-invasive lung CIS lesions.

Comparisons of the number of substitutions (a), small insertions and deletions (b), genome rearrangements (c) and copy number changes (d), showing significantly more genomic changes in progressive (n = 29) than regressive (n = 10) lesions. Although there were more clonal substitutions in progressive than regressive lesions (e), the proportion of substitutions that were clonal and the number of clones were similar (f-g). Progressive lesions had more putative driver mutations (h). Telomere lengths (base pairs) were similar between the two groups (i). To confirm an association between CIN gene expression and copy number change we correlated Weighted Genome Integrity Index (wGII) with mean CIN gene expression for the CIS samples in which we have both gene expression and whole-genome sequencing data (n = 11). Pearson correlation coefficient r2 = 0.473 (j). All P values were calculated using likelihood ratio tests of a mixed effects model with outcome (progressive or regressive) included as a fixed effect versus a model that was identical but for the fact that outcome was not included as a fixed effect. Boxplots are generated using the R boxplot function, which displays the first and third quartile as hinges and places whiskers at the most extreme data point that is no more than 1.5 times the length of the box away from the box.

Extended Data Fig. 6 Subclonal mutational structure in progressive and regressive CIS lesions.

Heatmap showing the proportion of overlapping mutations between samples taken from the same patient. For four patients with lesions that would ultimately progress to cancer (denoted ‘P’), over half the mutations were shared between any two given samples, suggesting that the lesions were derived from a common ancestral clone. By contrast, for two patients with lesions that would ultimately regress (denoted ‘R’), almost no mutations were shared, suggesting that the lesions arose independently. Samples from the same patient are shown in the same color; PD38321a and PD38322a do belong to the same patient and were mislabelled during processing.

Extended Data Fig. 7 Differential molecular changes between progressive and regressive lesions.

Visualization of differential changes across the genome. A, shows all identified differentially methylated regions (DMRs) (hypermethylated regions in yellow, hypomethylated in blue) alongside a similar analysis comparing cancer and control samples from The Cancer Genome Atlas. We observe that 58% of DMRs identified in our progressive vs regressive analysis are also identified in cancer vs control. B, shows copy number changes across the genome in regressive CIS, progressive CIS and TCGA cancer samples. We observe congruency of copy number change, suggesting similar processes in the two cohorts.

Extended Data Fig. 8 Principal component analysis investigating effect of various biological, clinical and technical factors affecting correct case segregation for all DMPs and gene expression data.

a-f, Principal component analysis based on all methylation probes (n = 87; 36 progressive, 18 regressive, 33 control). (a) Smoking history (pack years). (b) Chronic obstructive pulmonary disease (COPD) status. (c) Previous lung cancer history referring to the presence of lung squamous cell cancer (LUSC) prior to identification of pre-invasive lesions. (d) Age at bronchoscopy (years); age of individual when pre-invasive lesion was first biopsied. (e) Gender. (f) Sentix ID. g-k, Principal component analysis for all gene expression data. (g) Smoking history (pack years). (h) COPD status. (i) Previous lung cancer history referring to the presence of LUSC prior to identification of pre-invasive lesions. (j) Age at bronchoscopy (years); age of individual when pre-invasive lesion was first biopsied. (k) Gender. P-values were calculated using multivariate ANOVA.

Extended Data Fig. 9 Predictive modeling and ROC analytics of gene expression and CNA data.

ROC and precision-recall curves for the predictive model based on gene expression data shown in Fig. 4A-C. Curves are shown for the CIS discovery set (a-b), CIS validation set (c-d) and application to TCGA LUSC data (e-f). Using an analogous method to gene expression and methylation we used copy number data derived from methylation arrays to predict lesion outcome. Probe-level copy number changes were aggregated over cytogenetic bands; these data were used as input to Prediction Analysis of Microarrays (PAM). g-i, Probability plot based on a 154 cytogenetic band signature for correct class prediction (red circles indicate progressive lesions, green circles indicate regressive lesions). The area under the curve for the 154-cytogenetic band signature is 0.86. j-l, Application of our predictive model to previously published data (van Boerdonk et al.) replicates their result, classifying all regressive and 9/12 progressive samples correctly. This dataset included pre-invasive samples of various histological grades, rather than only CIS. m-o, Application of our predictive model to TCGA copy number data. Samples were correctly classified into TCGA LUSC and TCGA control samples with an AUC of 0.98.

Extended Data Fig. 10 Predictive modeling of methylation data.

In addition to the predictive modeling based on probe variation shown in Fig. 5, we used differentially expressed methylation probes to create a predictor using a Prediction Analysis for Microarrays (PAM) method. The model was trained on a training set (a-c) consisting of 26 progressive samples, 11 regressive samples and 23 control samples, shown in red, green and blue, respectively. A predictor based on 141 DMPs was created. This was applied to a validation set of 10 progressive, 7 regressive and 10 control samples (d-f), predicting outcome with AUC = 0.99. g-i, Application of our predictive model to TCGA methylation data. Samples were correctly classified into TCGA LUSC and TCGA control samples with AUC = 0.99. j-m, ROC analytics and precision-recall curves for Methylation Heterogeneity Index (MHI) model presented in Fig. 4. Curves apply to cancer vs control (j-k) and progressive vs regressive (l-m), respectively. n, Histogram of AUC values using MHI model with random samples of 2000 probes, applied to progressive vs regressive data. This demonstrates that a similar AUC is achieved with a random sample of probes as when using the entire array.

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Teixeira, V.H., Pipinikas, C.P., Pennycuick, A. et al. Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions. Nat Med 25, 517–525 (2019). https://doi.org/10.1038/s41591-018-0323-0

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