Neoantigen-directed immune escape in lung cancer evolution

Abstract

The interplay between an evolving cancer and a dynamic immune microenvironment remains unclear. Here we analyse 258 regions from 88 early-stage, untreated non-small-cell lung cancers using RNA sequencing and histopathology-assessed tumour-infiltrating lymphocyte estimates. Immune infiltration varied both between and within tumours, with different mechanisms of neoantigen presentation dysfunction enriched in distinct immune microenvironments. Sparsely infiltrated tumours exhibited a waning of neoantigen editing during tumour evolution, indicative of historical immune editing, or copy-number loss of previously clonal neoantigens. Immune-infiltrated tumour regions exhibited ongoing immunoediting, with either loss of heterozygosity in human leukocyte antigens or depletion of expressed neoantigens. We identified promoter hypermethylation of genes that contain neoantigenic mutations as an epigenetic mechanism of immunoediting. Our results suggest that the immune microenvironment exerts a strong selection pressure in early-stage, untreated non-small-cell lung cancers that produces multiple routes to immune evasion, which are clinically relevant and forecast poor disease-free survival.

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Fig. 1: Heterogeneity of immune infiltration in NSCLC.
Fig. 2: Immune editing at the DNA level.
Fig. 3: Transcriptional neoantigen depletion.
Fig. 4: Immune-evasion capacity in early-stage, untreated NSCLC.

Data availability

Sequence data used during the study has been deposited at the European Genome–phenome Archive (EGA), which is hosted by The European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG) under the accession codes: EGAS00001003458 (RNA-seq) and EGAS00001003484 (RRBS). Further information about EGA can be found at https://ega-archive.org. Any other relevant data can be obtained from the corresponding authors upon reasonable request.

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Acknowledgements

We thank the members of the TRACERx consortium for participating in this study. C.S. is Royal Society Napier Research Professor. C.S. is supported by the Francis Crick Institute, which receives its core funding from the Medical Research Council (FC001169), the Wellcome Trust (FC001169), and Cancer Research UK (FC001169). C.S. is funded by Cancer Research UK (TRACERx and CRUK Cancer Immunotherapy Catalyst Network), the CRUK Lung Cancer Centre of Excellence, Stand Up 2 Cancer (SU2C), the Rosetrees and Stoneygate Trusts, NovoNordisk Foundation (ID 16584), the Breast Cancer Research Foundation (BCRF), the European Research Council Consolidator Grant (FP7-THESEUS-617844), European Commission ITN (FP7-PloidyNet-607722), Chromavision (this project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 665233), National Institute for Health Research (NIHR), the University College London Hospitals Biomedical Research Centre (BRC) and the Cancer Research UK University College London Experimental Cancer Medicine Centre. N.M. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (211179/Z/18/Z), and also receives funding from CRUK Lung Cancer Centre of Excellence, Rosetrees and the University College London Hospitals Biomedical Research Centre (BRC) and the Cancer Research UK University College London Experimental Cancer Medicine Centre. E.L.C., J.D. and P.V.L. are supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001202), the UK Medical Research Council (FC001202), and the Wellcome Trust (FC001202). P.V.L. is a Winton Group Leader in recognition of the Winton Charitable Foundation’s support towards the establishment of The Francis Crick Institute. J.D. is a postdoctoral fellow of the Research Foundation - Flanders (FWO). S.A.Q. is funded by a CRUK Senior Cancer Research Fellowship (C36463/A22246), a CRUK Biotherapeutic Program Grant (C36463/A20764), the Cancer Immunotherapy Accelerator Award (CITA-CRUK) (C33499/A20265) and Rosetrees. M.T. received funding from the People Programme Marie Curie Actions (FP7/2007-2013/WHRI-ACADEMY-608765) and the Danish Council for Strategic Research (1309-00006B). The TRACERx study (Clinicaltrials.gov no: NCT01888601) is sponsored by University College London (UCL/12/0279) and has been approved by an independent Research Ethics Committee (13/LO/1546). TRACERx is funded by Cancer Research UK (C11496/A17786) and coordinated through the Cancer Research UK and UCL Cancer Trials Centre. For the RRBS methylation data, we acknowledge technical support from the CRUK–UCL Centre-funded Genomics and Genome Engineering Core Facility of the UCL Cancer Institute and grant support from the NIHR BRC (BRC275/CN/SB/101330). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The results published here are based in part upon data generated by The Cancer Genome Atlas pilot project established by the NCI and the National Human Genome Research Institute. The data were retrieved through database of Genotypes and Phenotypes (dbGaP) authorization (accession number phs000178.v9.p8). Information about TCGA and the constituent investigators and institutions the TCGA research network can be found at http://cancergenome.nih.gov/.

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Nature thanks Lynette Sholl and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Contributions

R.R. created the bioinformatics analysis pipeline and wrote the manuscript. R.S., M.A.B., D.A.M., C.T.H. and T.L. jointly analysed pathology TIL estimates. J.L.R., J.Y.H. and E.G. performed flow cytometry experiments for validating immune signatures. K.J. performed T cell receptor sequencing experiments for validating immune signatures. S.V. performed sample preparation and RNA extraction. E.L.C., J.D., A.F., G.A.W. and M.T. generated and analysed RRBS data. E.L.C. and J.D. performed DNA methylation analyses and neoantigen methylation analyses, under supervision of S.B. and P.V.L. N.J.B. gave advice on immune signatures, conducted analyses of multiregion sequencing exome data and reviewed the manuscript. M.J.-H. designed study protocols and helped to analyse patient clinical characteristics. Z.S., S.L. and M.D.H. helped to direct avenues of bioinformatics and pathology TIL analysis. B.C., J.H. and S.A.Q. provided data analysis support and supervision. N.M. and C.S. jointly supervised the study and helped to write the manuscript.

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Correspondence to Nicholas McGranahan or Charles Swanton.

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Competing interests

C.S. receives grant support from Pfizer, AstraZeneca, BMS and Ventana. C.S. has consulted for Boehringer Ingelheim, Eli Lilly, Servier, Novartis, Roche-Genentech, GlaxoSmithKline, Pfizer, BMS, Celgene, AstraZeneca, Illumina and the Sarah Cannon Research Institute. C.S. is a shareholder of Apogen Biotechnologies, Epic Bioscience, GRAIL, and has stock options in and is co-founder of Achilles Therapeutics. S.A.Q. is a co-founder of Achilles Therapeutics. R.R., N.M. and G.A.W. have stock options in and have consulted for Achilles Therapeutics. M.A.B has consulted for Achilles Therapeutics. M.D.H. receives research funding from Bristol-Myers Squibb; is paid consultant to Merck, Bristol-Myers Squibb, AstraZeneca, Genentech/Roche, Janssen, Nektar, Syndax, Mirati, and Shattuck Labs; received travel support/honoraria from AztraZeneca and BMS; and a patent has been filed by the Memorial Sloan Kettering Cancer Center related to the use of tumour mutation burden to predict response to immunotherapy (PCT/US2015/062208), which has received licensing fees from PGDx.

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Extended data figures and tables

Extended Data Fig. 1 Determination of robust immune infiltration approach.

ad, The expression of the genes used in each of the immune-signature definitions is correlated against tumour purity (a, b) and tumour copy number (c, d). Random genes (n = 1,000), or genes in the TIMER8 (n = 575), EPIC7 (n = 98), Danaher et al.11 (n = 60), Rooney et al.16 (n = 100) and Davoli et al.6 (n = 75) immune-signature definitions, are plotted. The Spearman’s ρ value of the correlation is plotted for the immune genes that comprise each signature definition, coloured by the P value of the association. The comparisons are performed separately for lung adenocarcinoma and lung squamous cell carcinoma. The median ρ value for the immune-signature set is indicated by the red line. The fraction of genes with an expression value that is significantly correlated with purity or tumour copy number is shown, and compared to a set of random genes. For every immune signature we considered, there was significant enrichment of genes with expression that was negatively correlated with tumour purity (as compared to the random selection of genes) and a significant enrichment of genes with expression that was positively correlated with tumour copy number (as compared to the random selection of genes). e, Scatter plots show the Spearman correlation between pathology TIL estimates and CD8+ T cells as measured by the Danaher et al.11 approach (n = 140), between flow CD8+ T cell estimates and Danaher et al.11 CD8+ T cells (n = 36), T cell receptor sequencing abundance and Danaher et al.11 CD8+ T cells (n = 72), normalized live-flow CD8+ T cell estimates and Danaher et al.11 CD8+ T cells (n = 39) and normalized live-flow CD8+ T cell to Treg ratio and Danaher et al.11 CD8+ cell to Treg ratio estimates (n = 38). Blue dots indicate regions from a lung adenocarcinoma tumour; red dots indicate regions from a lung squamous cell carcinoma tumour. Spearman ρ values, and P values are given for all tumour regions (black), lung adenocarcinoma tumour regions (blue) and lung squamous cell carcinoma tumour regions (red). f, A scatter plot showing the correlation between pathology TIL estimates and CD8+ estimates from each of the immune-infiltration methods is shown (n = 140 tumour regions). Lung adenocarcinoma tumour regions are shown in blue; lung squamous cell carcinoma tumour regions are shown in red. Below, the top six correlations between pathology TIL estimates and an immune-cell subset is shown for each method. Blue boxes indicate positive correlation; red boxes indicate negative correlation. P values were corrected for false discovery rate. g, Example of CD8 T cell quantification in a representative TRACERx TIL sample. TILs were isolated from the tumour regions of surgical resections, as previously described5, and cryopreserved. Thawed samples were stained with a custom-designed 20-marker antibody panel to measure T cell activation, dysfunction and differentiation by flow cytometry.

Extended Data Fig. 2 TRACERx 100 sample selection and patient characteristics.

a, CONSORT diagram showing the selection of TRACERx 100 patients for RNA-seq and/or pathology TIL estimate analysis. b, Patient characteristics for the TRACERx 100 cohort are shown. Patient characteristics can also be found in Supplementary Table 1.

Extended Data Fig. 3 Difference in immune infiltration by histology.

The distribution of Danaher et al.11 estimated CD8+ T cell infiltrate is displayed for lung adenocarcinomas (adeno.) and lung squamous cell carcinomas (squam.) (n = 145 tumour regions). The minimum and maximum are indicated by the extreme points of box plot; the median is indicated by a thick horizontal line; and the first and third quartiles are indicated by box edges. A two-sided Wilcoxon rank-sum test is used.

Extended Data Fig. 4 Incorporating tumour regions that lack RNA-seq data by using pathology TIL estimates.

a, The difference in pathology TIL estimates is shown by immune clusters derived from RNA-seq (n = 139). b, All regional pathology TIL estimates are plotted for each tumour sample (lung adenocarcinoma, n = 121; lung squamous cell carcinoma, n = 90). If a region also had RNA-seq information available, the immune cluster to which that region belonged is also shown as immune high (red) or immune low (blue). Immune clusters for tumour regions without RNA-seq data are annotated as grey. The immune class for the patients is also provided as high (red), low (blue), heterogeneous (orange) or unknown (grey). For all box plots, the minimum and maximum are indicated by the extreme points of the plot; the median is indicated by a thick horizontal line; and first and third quartiles are indicated by box edges. A two-sided Wilcoxon rank-sum test is used for comparisons. c, The number of patients in each immune classification is plotted as inferred from using RNA-seq data alone, or by also incorporating pathology TIL estimates. d, A correlation matrix of the Danaher et al.11 immune-cell estimates with the Jiang et al.12 immunosuppressive cell subsets is shown (Spearman’s test). Positive correlations are indicated in blue and negative correlations are indicated in red. Correlations are significant unless marked with a black X. e, The Jiang et al.12 immune-infiltration estimates are shown for tumour-associated macrophage M2 (TAM M2) and myeloid-derived suppressor cells (MDSC), split by immune cluster (n = 163). f, Tumour purity is shown for the regions of high and low tumour mutational burden (TMB) for every tumour with heterogeneous mutation burdens (n = 12).Two-sided paired t-test is used for comparison. No corrections were made for multiple comparisons.

Extended Data Fig. 5 Heterogeneity of biomarkers that predict responses to checkpoint blockade.

a, The TIDE gene-signature score of each tumour region is shown per patient, for patients with more than one region available (n = 39). Using a threshold shown by the dashed line, patients are classified as having high TIDE (light blue), low TIDE (dark blue) or heterogeneous TIDE (orange) scores. b, The IPRES gene-signature score of each tumour region is shown per patient, for patients with more than one region available (n = 39). Using the previously defined threshold13 (shown by the dashed line), patients are classified as having low IPRES (light blue), high IPRES (dark blue) or heterogeneous IPRES (orange) scores. c, The expanded Ayers et al.14 IFN signature is shown for each tumour region per patient, for patients with more than one region available (n = 38). For ac, the immune classification of the patient is also given. d, The greatest difference in the expanded Ayers et al.14 IFN signature between tumour regions from the same tumour is plotted, according to whether or not the tumour has heterogeneous levels of immune infiltration (n = 38). A two-sided Wilcoxon rank-sum test is used for comparison. e, Tumour mutational burden of each tumour region is shown per patient (n = 93). Using a threshold of ten mutations per megabase (dashed line), patients are classified as having a low (light blue), high (dark blue) or heterogeneous tumour mutational burden (orange). For all box plots, the minimum and maximum are indicated by extreme points of the plot; the median is indicated by a thick horizontal line; and the first and third quartiles are indicated by box edges. f, A summary of the tumour histology, immune classification, tumour mutational burden status, TIDE category and IPRES category is shown for each tumour (n = 93). There is an enrichment for heterogeneously immune infiltrated tumours to have heterogeneous tumour mutational burden status and heterogeneous TIDE scores (Fisher’s exact test). No corrections were made for multiple comparisons.

Extended Data Fig. 6 Relationship between immune infiltration and tumour-region diversity.

a, The pairwise copy number (cn) and immune distances between every two tumour regions from the same patient are compared for lung adenocarcinoma (n = 91) and lung squamous cell carcinoma (n = 60). b, c, For each tumour region, the CD8+ T cell score is plotted against the Shannon diversity score. Lung adenocarcinomas (n = 89) (b) and lung squamous cell carcinomas (n = 50) (c) are shown. d, The correlation between pathology TIL estimates and tumour purity is shown for lung adenocarcinoma (n = 120) (blue) and lung squamous cell carcinoma (n = 90) (red) regions. No relationship for either histology is observed. Spearman’s test is used to determine the relationship. e, The Shannon diversity score per lung adenocarcinoma tumour region (n = 137) is plotted by immune classification, as determined solely by pathology TIL estimates. A two-sided Wilcoxon rank-sum test is used for comparison. f, A comparison of the observed:expected immunoediting score between lung adenocarcinoma and lung squamous cell carcinoma tumours (n = 92) is shown. A two-sided Wilcoxon rank-sum test is used for comparison. g, The observed:expected immunoediting score is shown by number of unique HLAs present in the tumour (patients heterozygous at HLA-A, HLA-B and HLA-C will have six unique HLA alleles) (n = 90). For all box plots, the minimum and maximum are indicated by the extreme points of the plot; the median is indicated by a thick horizontal line; and the first and third quartiles are indicated by box edges. h, The odds ratio and 95% confidence interval of transcriptional neoantigen depletion is shown for strongly binding neoantigens, calculated with Fisher’s exact test. Values <1 indicate that putative neoantigens are less likely to be expressed, as compared to non-synonymous mutations that are not putative neoantigens. Tumours are broken down by HLA LOH status and their immune classification. i, The enrichment for neoantigens and strongly binding neoantigens to occur in non-expressed genes (as compared to non-synonymous non-neoantigens) is shown, calculated with Fisher’s exact test. No corrections were made for multiple comparisons.

Extended Data Fig. 7 Components of immune-evasion mechanisms in NSCLC.

a, Each of the potential immune-evasion mechanisms explored in Fig. 4 are shown, divided into their component genes. Patients are split according to their immune-evasion capacity status. Copy-number losses are shown in blue, and mutations are shown in green. b, A schematic of how LOH of the HLA-C locus in HLA-C1 and HLA-C2 heterozygous tumours may lead to destruction mediated by natural killer cells is shown. c, The level of natural killer cell infiltration divided by the total TIL estimate (using the method of Danaher et al.11) is shown for tumour regions with (n = 45) and without (n = 90) HLA-C LOH, according to their HLA-C1 and HLC-C2 heterozygosity status. A two-sided Wilcoxon rank-sum test is used for comparison.

Extended Data Fig. 8 Relationship between clonal neoantigen burden, immune infiltration and patient prognosis.

a, c, e, Kaplan–Meier curves are shown for lung adenocarcinoma and lung squamous cell carcinoma. The curves are split on the basis of the upper quartile of clonal neoantigen burden (a), on the upper quartile of subclonal neoantigen burden (c) and on the upper quartile of total neoantigen burden (e). For all survival curves, the number of patients in each group for every time point is indicated below the time point, and significance is determined using a log-rank test. b, d, The hazard ratio is shown for each threshold value of clonal neoantigen (b) and subclonal neoantigen (d) load, which indicates that a high clonal neoantigen burden remains significantly prognostic across a wide range of thresholds. Significant associations are indicated in red; non-significant associations are plotted in black. f, Clonal neoantigen load and immune-infiltration classification are incorporated in a multivariate analysis: this combination is more significant as a predictor of prognosis than either metric individually. Other tumour and clinical characteristics are also controlled for in the multivariate analysis. Hazard ratios of each variable with a 95% confidence interval are shown on the horizontal axis. Significance is calculated using a Cox proportional hazards model. All statistical tests were two-sided.

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Supplementary Information

This file contains the full list of names of The TRACERx consortium members and affiliations.

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Rosenthal, R., Cadieux, E.L., Salgado, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019). https://doi.org/10.1038/s41586-019-1032-7

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