Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution

  • A Corrigendum to this article was published on 20 December 2017


The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies.

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Figure 1: Phylogenetic ctDNA tracking.
Figure 2: Clinicopathological predictors of ctDNA detection.
Figure 3: Tumour volume predicts plasma VAF.
Figure 4: Postoperative ctDNA detection predicts and characterizes NSCLC relapse.
Figure 5: Phylogenetic trees incorporating relapse tissue sequencing data.
Figure 6: ctDNA tracking of lethal cancer subclones in CRUK0063.

Change history

  • 19 December 2017

    Please see accompanying Corrigendum ( In the original Article, Figs 2a, 3a and b, Extended Data Figs 3d and 4c–f, Extended Data Table 2b, Supplementary Table 1 and parts of the text have been updated to include correct tumour volumetric and positron emission tomography data for 6 of 96 patients (CRUK0014, CRUK0030, CRUK0048, CRUK0059, CRUK0096 and CRUK0097) in the study. Correlation coefficients presented in the figures improve; levels of significance and conclusions remain unchanged. In addition, there were some formatting errors in the affiliations and corrected Source Data for Fig. 2 have been uploaded. The original Article has been corrected online.


  1. 1

    Jemal, A. et al. Global cancer statistics. CA Cancer J. Clin. 61, 69–90 (2011)

    Article  Google Scholar 

  2. 2

    Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2017. CA Cancer J. Clin. 67, 7–30 (2017)

    Article  Google Scholar 

  3. 3

    Pignon, J.-P. et al. Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group. J. Clin. Oncol. 26, 3552–3559 (2008)

    Article  Google Scholar 

  4. 4

    Landau, D. A . et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013)

    CAS  Article  Google Scholar 

  5. 5

    Beaver, J. A. et al. Detection of cancer DNA in plasma of patients with early-stage breast cancer. Clin. Cancer Res. 20, 2643–2650 (2014)

    CAS  Article  Google Scholar 

  6. 6

    Garcia-Murillas, I . et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci. Transl. Med. 7, 302ra133 (2015)

    Article  Google Scholar 

  7. 7

    Tie, J . et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci. Transl. Med. 8, 346ra92 (2016)

    Article  Google Scholar 

  8. 8

    Jamal-Hanjani, M. et al. Tracking genomic cancer evolution for precision medicine: the lung TRACERx study. PLoS Biol. 12, e1001906 (2014)

    Article  Google Scholar 

  9. 9

    Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. (2017)

  10. 10

    Jamal-Hanjani, M. et al. Detection of ubiquitous and heterogeneous mutations in cell-free DNA from patients with early-stage non-small-cell lung cancer. Ann. Oncol. 27, 862–867 (2016)

    CAS  Article  Google Scholar 

  11. 11

    Diaz, L. A. Jr & Bardelli, A. Liquid biopsies: genotyping circulating tumor DNA. J. Clin. Oncol. 32, 579–586 (2014)

    Article  Google Scholar 

  12. 12

    Caruso, R. et al. Histologic coagulative tumour necrosis as a prognostic indicator of aggressiveness in renal, lung, thyroid and colorectal carcinomas: a brief review. Oncol. Lett. 3, 16–18 (2012)

    Article  Google Scholar 

  13. 13

    Vesselle, H. et al. Lung cancer proliferation correlates with [F-18]fluorodeoxyglucose uptake by positron emission tomography. Clin. Cancer Res. 6, 3837–3844 (2000)

    CAS  PubMed  Google Scholar 

  14. 14

    Higashi, K. et al. FDG PET measurement of the proliferative potential of non-small cell lung cancer. J. Nucl. Med. 41, 85–92 (2000)

    CAS  PubMed  Google Scholar 

  15. 15

    Murtaza, M. et al. Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer. Nat. Commun. 6, 8760 (2015)

    ADS  Article  Google Scholar 

  16. 16

    Newman, A. M. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 20, 548–554 (2014)

    CAS  Article  Google Scholar 

  17. 17

    Del Monte, U. Does the cell number 109 still really fit one gram of tumor tissue? Cell Cycle 8, 505–506 (2009)

    CAS  Article  Google Scholar 

  18. 18

    Peters, S. & Zimmermann, S. Targeted therapy in NSCLC driven by HER2 insertions. Transl. Lung Cancer Res. 3, 84–88 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Livasy, C. A. et al. Phenotypic evaluation of the basal-like subtype of invasive breast carcinoma. Mod. Pathol. 19, 264–271 (2006)

    CAS  Article  Google Scholar 

  20. 20

    Keam, B. et al. Prognostic impact of clinicopathologic parameters in stage II/III breast cancer treated with neoadjuvant docetaxel and doxorubicin chemotherapy: paradoxical features of the triple negative breast cancer. BMC Cancer 7, 203 (2007)

    Article  Google Scholar 

  21. 21

    Rhee, J. et al. The clinicopathologic characteristics and prognostic significance of triple-negativity in node-negative breast cancer. BMC Cancer 8, 307 (2008)

    Article  Google Scholar 

  22. 22

    Aberle, D. R. et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365, 395–409 (2011)

    Article  Google Scholar 

  23. 23

    Newman, A. M. et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat. Biotechnol. 34, 547–555 (2016)

    CAS  Article  Google Scholar 

  24. 24

    Hofheinz, F. et al. An investigation of the relation between tumor-to-liver ratio (TLR) and tumor-to-blood standard uptake ratio (SUR) in oncological FDG PET. EJNMMI Res. 6, 19 (2016)

    Article  Google Scholar 

  25. 25

    Bütof, R. et al. Prognostic value of pretherapeutic tumor-to-blood standardized uptake ratio in patients with esophageal carcinoma. J. Nucl. Med. 56, 1150–1156 (2015)

    Article  Google Scholar 

  26. 26

    Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014)

    CAS  Article  Google Scholar 

  27. 27

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)

    Article  Google Scholar 

  28. 28

    Malikic, S., McPherson, A. W., Donmez, N. & Sahinalp, C. S. Clonality inference in multiple tumor samples using phylogeny. Bioinformatics 31, 1349–1356 (2015)

    CAS  Article  Google Scholar 

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We acknowledge R. Macina for developing the collaboration between Natera and TRACERx. We thank S. Navarro and A. Tin for facilitating the PEACE ctDNA analysis. We thank the members of the TRACERx and PEACE consortia for participating in this study. C.S. is Royal Society Napier Research Professor. This work is supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001169, FC001202), the UK Medical Research Council (FC001169, FC001202) and the Wellcome Trust (FC001169, FC001202). 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 Trust, NovoNordisk Foundation (ID 16584), the Prostate Cancer Foundation, the Breast Cancer Research Foundation and the European Research Council (THESEUS), and support was provided to C.S. by the National Institute for Health Research, the University College London Hospitals Biomedical Research Centre and the Cancer Research UK University College London Experimental Cancer Medicine Centre. 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. The TRACERx study ( 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 (grant number C11496/A17786) and coordinated through the Cancer Research UK and UCL Cancer Trials Centre. The PEACE study ( is sponsored by University College London (UCL/13/0165) and has been approved by an independent Research Ethics Committee (13/LO/0972). PEACE is funded by Cancer Research UK (C416/A21999) and coordinated through the Cancer Research UK and UCL Cancer Trials Centre.

Author information





C.A., N.J.B., G.A.W., M.J.-H., T.C., R.Sa. and J.L.Q. contributed equally to this work. D.A.M. and S.V. contributed equally to this work as joint second authors. C.A. and C.S. co-wrote the manuscript. C.A., M.J.-H. and C.S. conceived the study design. C.A., N.J.B., G.A.W. and R.R. integrated clinicopathological data, exome data and ctDNA data. M.R., B.G.Z., C.J.L., T.C., R.S., E.K., N.S., D.H., A.Nai. and A.G. conducted and analysed multiplex-PCR NGS experimental work for ctDNA data. N.J.B., G.A.W., T.B.K.W., M.A.B., R.R. and N.M. conducted M-seq analyses of exome data. J.L.Q., T.M. and D.A.M. conducted the pathological review. F.F., R.E. and F.Z. conducted radiological review of PET scans. H.J.W.L.A., W.L.B., F.M.F. and N.J.B. conducted radiomic analyses. S.V., D.J., J.L., S.S., J.C.-K., A.R., T.C., D.O. and A.U.A. conducted TRACERx sample processing. G.E., S.W., N.M. and G.A.W. conducted exome sequencing. L.M., J.R. and J.A.S. conducted ctDNA cross-platform validation. M.J.-H., C.D., J.A.S. and C.S. designed the study protocols. C.H., S.M.L., M.D.F., T.A., M.F., E.B., D.L., M.H., S.Ko., N.P., S.M.J., R.T., A.A., F.B., Y.S., R.Sh., L.J., A.M.Q., P.A.C., B.N., G.M., G.L., S.T., M.N., H.R., K.K., M.C., L.G., D.A.F., A.Nak., S.R., G.A., S.Kh., P.R., V.E., B.I., M.I.-S., V.P., J.F.L., M.K., R.A., H.A., H.D. and S.L. are clinical members of TRACERx study sites. J.A.H. and H.L.L. run the UCL GCLP facility. A.H., H.B., N.I. and Y.N. were involved in study oversight. J.A.S., J.L.Q., Z.S., E.G., S.Ka., S.T., M.A.B., R.F.S., J.H., A.S., S.A.Q., P.V.L., C.D., C.J.L. and B.G.Z. gave advice and reviewed the manuscript. A.H. gave statistical advice. C.S. provided overall study oversight.

Corresponding author

Correspondence to Charles Swanton.

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Competing interests

T.C., R.S., E.K., N.S., D.H., A.Nai., A.G., S.Ka., J.L., B.G.Z. and M.R. are all employees or former employees of Natera Inc. and own stock and/or options to purchase stock. C.A., M.J.-H., G.W., C.S. and Natera Inc. employees are co-inventors of or contributors to unpublished patent applications based on the work in this manuscript, filed by UCL Business Plc and Natera Inc. C.S. has had the following financial relationships in the last 36 months: Boehringer Ingelheim, consulting and speaker fees; Novartis, consulting and speaker fees; Eli Lilly, speaker fees; Roche, consulting and speaker fees; GlaxoSmithKline, speaker fees; Pfizer, speaker fees; Celgene, speaker fees; Servier, speaker fees; Grail, scientific advisory board and stock options; APOGEN Biotechnologies, scientific advisory board and stock options; EPIC Biosciences, scientific advisory board and stock options; Achilles Therapeutics, founder and stock options. In 2015 C.S. was a member of a Natera advisory board and received an honorarium.

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Reviewer Information Nature thanks S. Lippman, R. Rosell and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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A list of participants and their affiliations appears in the Supplementary Information.

Extended data figures and tables

Extended Data Figure 1 Multiplex-PCR NGS platform analytical validation.

a, Analytical validation of the multiplex-PCR NGS platform was performed by spiking synthetic SNVs into control cfDNA. Sensitivity and specificity of the platform at different spike concentrations was ascertained, 95% binomial confidence interval displayed as error bars. b, Specificity of ctDNA detection based on a one-SNV and two-SNV call threshold taking into account parallel testing of multiple SNVs. c, The median read depth across a position did not vary depending on whether an SNV position was called or not called using the platform error model. Wilcoxon rank-sum test, P = 0.786. Median read depth at uncalled positions, 45,777; range, 0–146,774; n = 3,745. Median read depth at called positions, 45,478; range, 1,354–152,974; n = 1,124. Whiskers represent 1.5× the interquartile range, two-sided test.

Extended Data Figure 2 Study construction and assay panel design.

a, The preoperative study phase cohort consisted of 100 TRACERx patients present in the first 100 patient TRACERx cohort in April 2016. Preoperative plasma samples were profiled for 96 patients for the reasons listed. b, Contents of patient-specific assay panels designed in the preoperative study phase for LUSC, LUAD and other. c, The longitudinal study phase cohort consisted of patients with confirmed NSCLC relapse and patients without relapse. d, Contents of patient-specific assay panels designed in the longitudinal phases of this study. e, SNV type that was targeted.

Extended Data Figure 3 Clinicopathological predictors of ctDNA detection.

a, 96 patients in the preoperative cohort stratified by pathological tumour, node and metastasis (TNM) stage. b, LUSCs and ctDNA-positive LUADs are significantly more necrotic that ctDNA-negative LUADs. Significant differences in necrosis between groups: LUSCs (median necrosis, 40%; n = 31), ctDNA-positive LUADs (median necrosis, 15%; n = 11) and ctDNA-negative LUADs (median necrosis, 2%; n = 47), Kruskal–Wallis test, P < 0.001, two-sided pairwise comparisons were performed using Dunn’s procedure with Bonferroni correction. c, Univariate (left) and multivariate analyses (right) were performed, by logistic regression to determine significant predictors of ctDNA detection in early-stage NSCLC. ctDNA detection was defined as detection of two or more SNVs in preoperative plasma samples. Details regarding multivariable analysis methodology are in the Methods. d, Receiver operating characteristic (ROC) curve analysis of preoperative PET scan FDG avidity (normalized to tumour background ratio (TBR), see Methods), as a predictor of ctDNA detection (92 out of 96 PET scans were available for central review), determined by the area under the curve (AUC). Median PET TBR of detected tumours = 9.01, n = 45. Median PET TBR of undetected tumours = 3.64, n = 47. P value based on Wilcoxon rank-sum test. e, LUAD subtype analyses based on ctDNA detection and the presence of an EGFR, KRAS or TP53 driver mutation.

Extended Data Figure 4 Predictors of plasma VAF.

a, Plasma VAFs of SNVs detected in plasma in 46 patients who were ctDNA-positive (two or more SNVs detected). Clonal (blue) and subclonal (red) VAFs are indicated, mean is shown as a horizontal line. Driver variants are shown as triangles. b, Mean clonal VAF correlated with maximum tumour size measured in a post-surgical specimen (pathologic size, n = 46). Grey vertical bars represent range of clonal VAF; shaded red background indicates the 95% confidence interval. c, Filtering steps taken to define a group of ctDNA-positive patients with volumetric data considered adequate to model tumour volume and plasma VAF. d, Scatter plot showing mean clonal VAF relative to tumour volume for TRACERx (blue dots and fitted blue line, n = 37) and VAF relative to volume for previously published data16 based on CAPP-seq analysis of ctDNA (orange dots and orange fitted line, n = 9). Orange shaded background indicates the 95% confidence interval based on CAPP-seq data. e, Mean clonal VAF correlated with tumour volume × tumour purity (cancer cell volume), n = 37. Shaded red background indicates the 95% confidence interval. f, Association between the number of cancer cells and VAF of clonal SNVs in plasma based on linear modelling of Extended Data Fig. 4e and the assumption that a cancer cell volume of 1 cm3 contains 9.4 × 107 cells17. g, Detected subclonal SNVs were mapped back to M-seq-derived tumour phylogenetic trees (process illustrated in the key). Detected private subclones (subclones identified within only a single tumour region) are coloured red. Shared subclones (subclones detected in more than one tumour region) are light blue. Subclonal nodes were sized on the basis of the maximum recorded CCF. The top row of the phylogenetic trees represent subclonal nodes targeted by primers within that patient’s assay panel, the bottom row represent subclonal nodes detected in ctDNA, within this row grey subclonal nodes represent subclones that were not detected in ctDNA.

Extended Data Figure 5 Longitudinal ctDNA profiling, remaining relapse cases.

a, Kaplan–Meier curve demonstrating relapse-free survival for patients in whom ctDNA was detected versus patients in whom ctDNA was not detected. bh, Longitudinal cfDNA profiling. ctDNA detection in plasma was defined as the detection of two tumour-specific SNVs. Relapse was based on imaging-confirmed NSCLC relapse, imaging performed as clinically indicated. Detected clonal (circles, light blue) and subclonal (triangles, colours indicate different subclones) SNVs from each patient-specific assay panel are plotted coloured by M-seq-derived tumour phylogenetic nodes. Mean clonal (blue) and mean subclonal (red) VAFs are indicated on graphs. Preoperative and relapse M-seq-derived phylogenetic trees represented by ctDNA are illustrated above each graph in cases where subclonal SNVs were detected.

Extended Data Figure 6 Longitudinal ctDNA profiling, non-relapse cases.

aj, Detected clonal (circles, light blue) and subclonal (red triangles) SNVs from each patient-specific assay panel are plotted. Mean clonal (blue) and mean subclonal (red) VAFs are indicated.

Extended Data Figure 7 Heat maps illustrating detection of SNVs in bespoke panels at each sampled time point.

a, cf, Bespoke assay panels for CRUK0063 (a), CRUK0035 (c), CRUK0044 (d), CRUK0041 (e) and CRUK0013 (f). Colours indicate originating subclonal cluster based on the phylogenetic trees above the heat map. Light blue indicates clonal mutation cluster. Full panel with cluster colour is shown below each heat map. Filled squares indicate detection of a given variant in plasma ctDNA. The y axis shows day of sampling; y axis labels appended with [R] indicate day of clinical relapse. The x axis indicates SNVs targeted by the assay panel. b, Re-examination of primary tumour regions from CRUK0063 with a lowered threshold to potentially identify SNVs private to the sequenced relapse biopsy. 16 out 88 variants were found at very low VAF in region 3, indicating that this region from the primary probably gave rise to the metastasis.

Extended Data Figure 8 Heat map illustrating the content of the metastatic bespoke panel designed for patient CRUK0063, demonstrating detection status of SNVs across all sampled time points.

Colours indicate originating subclonal cluster based on patient CRUK0063’s phylogenetic tree above the heat map. Light blue indicates clonal mutation cluster. Full SNV panel with cluster colour shown below each heat map. Filled squares indicate detection of a given variant in plasma ctDNA. The y axis shows the postoperative day of sampling.

Extended Data Table 1 Patient characteristics
Extended Data Table 2 Cross-platform validation using a generic approach to ctDNA profiling

Supplementary information

Supplementary Information

This file contains the full list of authors and affiliations for the PEACE Consortium. (PDF 102 kb)

Supplementary Data

This file contains Supplementary Tables 1-8. (XLSX 1675 kb)

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Abbosh, C., Birkbak, N., Wilson, G. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).

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