tracking the evolution of non-small-cell lung cancer the evolution of non–small-cell lung cancer...

Tracking the Evolution of Non-Small-Cell LungCancerJamal-hanjani, Mariam; Wilson, Gareth A.; Mcgranahan, Nicholas; Birkbak, Nicolai J.;Watkins, Thomas B.k.; Veeriah, Selvaraju; Shafi, Seema; Johnson, Diana H.; Mitter, Richard;Rosenthal, Rachel; Salm, Max; Horswell, Stuart; Escudero, Mickael; Matthews, Nik; Rowan,Andrew; Chambers, Tim; Moore, David A.; Turajlic, Samra; Xu, Hang; Lee, Siow-mingDOI:10.1056/NEJMoa1616288

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Citation for published version (Harvard):Jamal-hanjani, M, Wilson, GA, Mcgranahan, N, Birkbak, NJ, Watkins, TB, Veeriah, S, Shafi, S, Johnson, DH,Mitter, R, Rosenthal, R, Salm, M, Horswell, S, Escudero, M, Matthews, N, Rowan, A, Chambers, T, Moore, DA,Turajlic, S, Xu, H, Lee, S, Forster, MD, Ahmad, T, Hiley, CT, Abbosh, C, Falzon, M, Borg, E, Marafioti, T,Lawrence, D, Hayward, M, Kolvekar, S, Panagiotopoulos, N, Janes, SM, Thakrar, R, Ahmed, A, Blackhall, F,Summers, Y, Shah, R, Joseph, L, Quinn, AM, Crosbie, PA, Naidu, B, Middleton, G, Langman, G, Trotter, S,Nicolson, M, Remmen, H, Kerr, K, Chetty, M, Gomersall, L, Fennell, DA, Nakas, A, Rathinam, S, Anand, G,Khan, S, Russell, P, Ezhil, V, Ismail, B, Irvin-sellers, M, Prakash, V, Lester, JF, Kornaszewska, M, Attanoos, R,Adams, H, Davies, H, Dentro, S, Taniere, P, O?sullivan, B, Lowe, HL, Hartley, JA, Iles, N, Bell, H, Ngai, Y,Shaw, JA, Herrero, J, Szallasi, Z, Schwarz, RF, Stewart, A, Quezada, SA, Le Quesne, J, Van Loo, P, Dive, C,Hackshaw, A & Swanton, C 2017, 'Tracking the Evolution of Non-Small-Cell Lung Cancer' New England Journalof Medicine, vol 376, no. 22, pp. 2109-2121. DOI: 10.1056/NEJMoa1616288Link to publication on Research at Birmingham portal

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established in 1812 June 1, 2017 vol. 376 no. 22

The authors’ full names, academic de-grees, and affiliations are listed in the Appendix. Address reprint requests to Dr. Swanton at the Translational Cancer Therapeutics Laboratory, Francis Crick Institute, 3rd Fl. SW, 1 Midland Rd., Lon-don NW1 1AT, United Kingdom, or at charles . swanton@ crick . ac . uk.

* A complete list of investigators in the Tracking Non–Small-Cell Lung Cancer Evolution through Therapy (TRACERx) Consortium is provided in Supplemen-tary Appendix 1, available at NEJM.org.

Drs. Jamal-Hanjani, Wilson, McGranahan, Birkbak, and Veeriah and Mr. Watkins con-tributed equally to this article.

This article was published on April 26, 2017, at NEJM.org.

N Engl J Med 2017;376:2109-21.DOI: 10.1056/NEJMoa1616288Copyright © 2017 Massachusetts Medical Society.

BACKGROUNDAmong patients with non–small-cell lung cancer (NSCLC), data on intratumor heterogeneity and cancer genome evolution have been limited to small retrospective cohorts. We wanted to prospectively investigate intratumor heterogeneity in relation to clinical outcome and to determine the clonal nature of driver events and evolutionary processes in early-stage NSCLC.

METHODSIn this prospective cohort study, we performed multiregion whole-exome sequencing on 100 early-stage NSCLC tumors that had been resected before systemic therapy. We sequenced and analyzed 327 tumor regions to define evolutionary histories, obtain a census of clonal and subclonal events, and assess the relationship between intratumor heterogeneity and recurrence-free survival.

RESULTSWe observed widespread intratumor heterogeneity for both somatic copy-number altera-tions and mutations. Driver mutations in EGFR, MET, BRAF, and TP53 were almost always clonal. However, heterogeneous driver alterations that occurred later in evolution were found in more than 75% of the tumors and were common in PIK3CA and NF1 and in genes that are involved in chromatin modification and DNA damage response and repair. Genome doubling and ongoing dynamic chromosomal instability were associated with intratumor heterogeneity and resulted in parallel evolution of driver somatic copy-number alterations, including amplifications in CDK4, FOXA1, and BCL11A. Elevated copy-number heterogeneity was associated with an increased risk of recurrence or death (hazard ratio, 4.9; P = 4.4×10−4), which remained significant in multivariate analysis.

CONCLUSIONSIntratumor heterogeneity mediated through chromosome instability was associated with an increased risk of recurrence or death, a finding that supports the potential value of chromosome instability as a prognostic predictor. (Funded by Cancer Research UK and others; TRACERx ClinicalTrials.gov number, NCT01888601.)

a bs tr ac t

Tracking the Evolution of Non–Small-Cell Lung CancerM. Jamal-Hanjani, G.A. Wilson, N. McGranahan, N.J. Birkbak, T.B.K. Watkins, S. Veeriah, S. Shafi, D.H. Johnson, R. Mitter, R. Rosenthal, M. Salm, S. Horswell, M. Escudero, N. Matthews, A. Rowan, T. Chambers, D.A. Moore,

S. Turajlic, H. Xu, S.-M. Lee, M.D. Forster, T. Ahmad, C.T. Hiley, C. Abbosh, M. Falzon, E. Borg, T. Marafioti, D. Lawrence, M. Hayward, S. Kolvekar, N. Panagiotopoulos, S.M. Janes, R. Thakrar, A. Ahmed, F. Blackhall, Y. Summers, R. Shah, L. Joseph, A.M. Quinn, P.A. Crosbie, B. Naidu, G. Middleton, G. Langman, S. Trotter, M. Nicolson, H. Remmen, K. Kerr, M. Chetty, L. Gomersall, D.A. Fennell, A. Nakas, S. Rathinam, G. Anand,

S. Khan, P. Russell, V. Ezhil, B. Ismail, M. Irvin-Sellers, V. Prakash, J.F. Lester, M. Kornaszewska, R. Attanoos, H. Adams, H. Davies, S. Dentro, P. Taniere, B. O’Sullivan, H.L. Lowe, J.A. Hartley, N. Iles, H. Bell, Y. Ngai, J.A. Shaw, J. Herrero, Z. Szallasi, R.F. Schwarz, A. Stewart, S.A. Quezada, J. Le Quesne, P. Van Loo, C. Dive,

A. Hackshaw, and C. Swanton, for the TRACERx Consortium*

The New England Journal of Medicine Downloaded from nejm.org at UNIVERSITY OF BIRMINGHAM on June 22, 2017. For personal use only. No other uses without permission.

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T h e n e w e ngl a nd j o u r na l o f m e dic i n e

Lung cancer is the leading cause of cancer-related death worldwide,1,2 with non–small-cell lung cancer (NSCLC) being the

most common type. Large-scale sequencing stud-ies have revealed the complex genomic landscape of NSCLC3-6 and genomic differences between lung adenocarcinomas and lung squamous-cell carcino-mas.7 However, in-depth exploration of NSCLC intratumor heterogeneity (which provides the fuel for tumor evolution and drug resistance) and can-cer genome evolution has been limited to small retrospective cohorts.8,9 Therefore, the clinical significance of intratumor heterogeneity and the potential for clonality of driver events to guide therapeutic strategies have not yet been defined.

Tracking Non–Small-Cell Lung Cancer Evolu-tion through Therapy (TRACERx)10 is a multi-center, prospective cohort study, which began recruitment in April 2014 with funding from Cancer Research UK. The target enrollment is 842 patients from whom samples will be obtained for high-depth, multiregion whole-exome se-quencing of surgically resected NSCLC tumors in stages IA through IIIA. One primary objective of TRACERx is to investigate the hypothesis that in-tratumor heterogeneity — in terms of mutations (single or dinucleotide base substitutions or small insertions and deletions) or somatic copy-number alterations (reflecting gains or losses of chromo-some segments) — is associated with clinical outcome. Here, we report on the first 100 patients who were prospectively recruited in the study.

Me thods

Patients and Tumor Samples

We collected tumor samples from 100 patients with NSCLC who had not received previous sys-temic therapy (Fig. 1A; and Fig. S1 in Supplemen-tary Appendix 1, available with the full text of this article at NEJM.org). Identifiers of patients were reassigned to protect anonymity and were ordered according to intratumor heterogeneity and histologic subtype. Eligible patients were at least 18 years of age and had received a diagno-sis of NSCLC in stages IA through IIIA (except Patient CRUK0035, whose tumor was classified as stage IIIB on the basis of postoperative histo-logic analysis). The cohort was representative of a population of patients with NSCLC who were eligible for curative resection. Histologic data were confirmed on central review by a lung pa-

thologist. (Details regarding the study design are provided in the protocol, available at NEJM.org.)

To assess intratumor heterogeneity, samples of at least two tumor regions that were separated by a margin of 0.3 cm to 1 cm (depending on the size of the tumor) had to be available for study. None of the tumors carried a translocation in ALK, ROS1, or RET on the basis of sequencing. This finding was confirmed for ALK and ROS1 with the use of immunohistochemical testing. All the patients provided written informed con-sent. The clinical characteristics of the patients and the study criteria are provided in Tables S1 and S2 and in the Experimental Procedures sec-tion in Supplementary Appendix 1.

Multiregion Whole-Exome Sequencing

We used the Illumina HiSeq to perform whole-exome sequencing on multiple regions collected from each tumor. We sequenced 327 tumor re-gions (323 primary tumor regions and 4 lymph-node metastases) and 100 matched germline sam-ples derived from whole blood (median number, 3 regions per tumor; range, 2 to 8), to a median depth of 426× (Table S3 in Supplementary Ap-pendix 1). Orthogonal validation was performed (Table S4 and Fig. S2 in Supplementary Appen-dix 1). All sequencing data have been deposited in the European Genome–Phenome Archive under accession number EGAS00001002247.

R esult s

Intratumor Heterogeneity in NSCLC

Genetic diversity within tumors can act as a substrate for natural selection and tumor evolu-

Figure 1 (facing page). Overview of the Demographic and Clinical Characteristics of the Patients in the TRACERx Study.

Panel A shows the demographic and clinical character-istics of the 100 patients in the study, including diag-nosis, tumor stage, and smoking status. Panel B shows how multiregion sequencing was performed on surgi-cally resected tumors to analyze somatic mutations and copy-number alterations, which facilitated the as-sessment of intratumor heterogeneity and phylogenetic reconstruction. Stars on the schematic chromosomes indicate mutations, where yellow represents clonal pre-genome doubling mutations, pink represents clonal postgenome doubling mutations, and red represents subclonal mutations. Panel C shows the key clinical questions that were addressed in the study.




Tr acking the Evolution of Non–Small-Cell Lung Cancer

A TRACERx 100 Cohort

B Multiregion Intratumor Heterogeneity Analysis

C Clinical Questions

R2R2R2R2R1

R2R3

R4

Multiregion Sampling

Multiregion Mutation andCopy-Number Analysis

Clonal Hierarchy and Phylogeny Surgery with

Curative Intent

Intratumor Heterogeneity and Survival Causes of Intratumor Heterogeneity Census of Clonal and Subclonal Drivers

R1 R2 R3 R4

Genomedoubling

Subclonalmutations

Late clonalmutationsEarly clonalmutations

Genome doublingMutational heterogeneity and survival Clonal status of targetable alterations

Chromosomal instability

Mutational processes

GCGATCACGACCGCTAGTGCTG

GCGATTACGACCGCTAATGCTG

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Copy-number heterogeneity and survival

Time

% A

live

Time

% A

live

Never smoked (N=12) Former smoker (N=48) Current or recent smoker (N=40) 62 Men, 38 Women

1B

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2B

Lung Adenocarcinoma (N=61)

Stage 1A (N=26) Stage 1B (N=36) Stage 2A (N=13) Stage 2B (N=11) Stage 3A (N=13) Stage 3B (N=1)

Other (N=7)

Lung Squamous-Cell Carcinoma (N=32)





tion. We performed multiregion whole-exome sequencing on 100 TRACERx tumors and classi-fied somatic mutations, which were defined as coding and noncoding single-nucleotide variants, and copy-number alterations, which were measured as a percentage of the genome affected by such alterations, as clonal (present in all cancer cells) or subclonal (present in a subset of cancer cells) (Fig. 1).

We observed extensive intratumor heteroge-neity, with a median of 30% (range, 0.5 to 93) of somatic mutations identified as subclonal and a median of 48% (range, 0.3 to 88) of copy-number alterations as subclonal (Fig. 2A, and Fig. S3 in Supplementary Appendix 1). This finding sug-gests that genomic-instability processes at the mutational and chromosomal level are ongoing during tumor development. Considerable varia-tion in intratumor heterogeneity among tumors was also observed, with the number of subclonal mutations ranging from 2 to 2310 and the per-centage of the genome affected by subclonal copy-number alterations ranging from 0.06 to 81% (Fig. 2A). Without multiregion whole-exome se-quencing, 76% of subclonal mutations could have appeared to be clonal, which suggests the selec-tion of subclones within individual tumor regions (Fig. S4 in Supplementary Appendix 1). Signifi-cantly more mutations were identified with the use of multiregion whole-exome sequencing than with single-sample analysis (median number, 517 vs. 398; P = 0.009) or with the use of single NSCLC samples obtained from the Cancer Genome Atlas (median number, 207; P<0.001) (Fig. S5 in Supple-mentary Appendix 1). The Cancer Genome Atlas research network (http://cancergenome . nih . gov) was retrieved through dbGaP authorization acces-sion number phs000178.v9.p8.

Squamous-cell carcinomas carried significant-ly more clonal mutations than did adenocarcino-mas (P = 0.003) (Fig. S6 in Supplementary Ap-pendix 1). This finding potentially reflects differences in smoking history, with a median of 32 pack-years for adenocarcinomas and 41 pack-years for squamous-cell carcinomas (P = 0.047) (Fig. S7 in Supplementary Appendix 1). There were no significant differences between squamous-cell carcinomas and adenocarcinomas in the number or proportion of subclonal mutations (P = 0.72) (Fig. S6 in Supplementary Appendix 1) or within specific adenocarcinoma histopathological sub-types (Fig. S8 in Supplementary Appendix 1). In

squamous-cell carcinomas, no significant rela-tionship was observed between intratumor hetero-geneity and clinical variables (Table S5 in Sup-plementary Appendix 1).

In adenocarcinomas, tumor stage positively correlated with the proportion of subclonal copy-number alterations, and Ki67 staining positively correlated with the burden of both clonal and subclonal mutations, as well as with the propor-tion of subclonal copy-number alterations (Table S5 in Supplementary Appendix 1). Furthermore, in adenocarcinomas, a significantly higher clonal and subclonal mutational burden was observed in smokers than in patients who had never smoked (Fig. S9 in Supplementary Appendix 1).

There was no significant association between the proportion of subclonal mutations (median in the cohort, 30%) and relapse-free survival (Fig. 2B). However, in this preliminary analysis, patients who had tumors with a high proportion of sub-clonal copy-number alterations (≥48%, the co-hort median) were at higher risk for recurrence or death than those with a low proportion (haz-ard ratio, 4.9; 95% confidence interval [CI], 1.8 to 13.1; P = 4.4×10−4) (Fig. 2C). The median time until recurrence or death was 24.4 months in the higher risk group of patients compared with a median that was not reached in the lower risk group. This finding remained significant in a multivariate analysis after adjustment for age, pack-years of smoking, histologic subtype, adju-

Figure 2 (facing page). Genomic Heterogeneity of Tumors Obtained from Patients with Non–Small- Cell Lung Cancer (NSCLC).

Panel A shows the number of coding and noncoding mutations that were detected in each tumor region in the study, according to tumor stage, smoking history, outcome of recurrence or death, and number of regions affected. The percentages of somatic mutations and copy-number alterations that were found to be clonal or subclonal in each tumor are shown below the num-ber of mutations. The percentages of study patients who were disease-free over a 30-month period are shown according to whether the patients had a high proportion (above the median) or a low proportion (below the median) of subclonal mutations (Panel B) or of subclonal copy-number alterations (Panel C). There was no significant association between the pro-portion of subclonal mutations and relapse-free sur-vival (P = 0.70), but patients who had tumors with a high proportion of subclonal copy-number alterations were at significantly higher risk for recurrence or death than those with a low proportion (P = 4.4×10−4).





vant therapy, and tumor stage (hazard ratio, 3.70; 95% CI, 1.29 to 10.65; P = 0.01) (Table S6 in Supplementary Appendix 1). A static measure

of chromosome disruption (describing the mean proportion of the genome that was aberrant across tumor regions) was not associated with

No.

of C

odin

g an

d N

onco

ding

Mut

atio

ns p

er T

umor

4000

2000

3000

1000

0

Cop

y N

umbe

r,Pe

rcen

tage

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lona

l

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Recurrence or DeathNo. of Regions

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60

80

40

20

0

Mut

atio

n,Pe

rcen

tage

Subc

lona

l

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60

80

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A Intratumor Heterogeneity

Subclonal

Clonal

No Yes

Dis

ease

-free

Sur

viva

l (%

)

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60

80

40

20

00 5 10 15 20 25 30

Months to Death or Recurrence

B Disease-free Survival According to Percentage of Subclonal Mutations

Hazard ratio, 0.86 (95% CI, 0.40 –1.85)P=0.70

No. at RiskLowHigh

4951

4049

3643

3135

2121

74

00

Low

High

Low

High

Dis

ease

-free

Sur

viva

l (%

)

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40

20

00 5 10 15 20 25 30

Months to Death or Recurrence

C Disease-free Survival According to Percentage of SubclonalCopy-Number Alterations

Hazard ratio, 4.9 (95% CI, 1.8 –13.1)P=4.4×10 −

4

No. at RiskLowHigh

4745

4240

4032

3624

1918

54

00

13001a 1b 2 3 4 5 6 7 8

Adenocarcinoma Squamous-Cell Carcinoma Other

Patients 1–61 Patients 62–93 Patients 94–100

Tumor Stage Pack-Years Recurrence or Death No. of Regions2a 2b 3a 3b





survival, which suggests that the rate of ongoing dynamic chromosomal instability, rather than the state of the genome, is prognostic (Fig. S10 in Supplementary Appendix 1).

Evolutionary Histories and Tumor Clonal Architecture in NSCLC

The number or proportion of subclonal muta-tions does not fully capture the extent of intra-tumor heterogeneity, since these measures do not reflect the number or prevalence of genetically distinct subclones that evolve in space and time. To elucidate subclones within regions and map the evolutionary history of each tumor, we clus-tered mutations according to their cellular prev-alence. Each cluster represents a node on the phylogenetic tree of the tumor and a subclone that is present in the tumor population or has existed during its evolutionary history (Table S7, Figs. S11 and S12, and the Experimental Proce-dures section in Supplementary Appendix 1).

We identified 525 mutation clusters, with a median of 5 per tumor (range, 2 to 15). Most tumor regions (86%) were found to carry sub-clones from only a single branch of the phyloge-netic tree, which emphasizes the limitations of a single diagnostic biopsy sample in accurately capturing the true extent of intratumor hetero-geneity. Without the use of multiregion whole-exome sequencing, 65% of branched subclone clusters could have erroneously appeared to be clonal.

Causes of Intratumor Heterogeneity in NSCLCMutational Processes

Understanding how mutational processes shape tumor evolution may inform strategies to limit tumor adaptation in the clinical setting.11 Using published mutational signatures,12 we analyzed clonal and subclonal mutations to determine which mutational processes contributed to intra-tumor heterogeneity.

The number of early mutations (accumulated before genome doubling or copy-number change) significantly correlated with the burden of muta-tions associated with smoking (mutational sig-nature 4), with Spearman’s rank correlations of 0.90 (P<1.1×10−16) for adenocarcinomas and of 0.84 (P = 3.9×10−9) for squamous-cell carcinomas. This finding was consistent with the identifica-tion of mutations induced by tobacco carcino-gens as being a key influence on trunk length

(i.e., the number of mutations found in the most recent common ancestor of all cancer cells) and was reflected in the significant correlation be-tween pack-years and truncal signature 4 muta-tions in adenocarcinomas (Spearman’s rank cor-relation, 0.63; P = 5.3×10−8). In samples obtained from 7 of 12 patients with adenocarcinomas who were long-term former smokers (with >20 years since last tobacco exposure), a smoking signa-ture could be detected in late clonal mutations (>30% with signature 4). This finding was sug-gestive of a long period of tumor latency in the evolution of lung adenocarcinomas before clini-cal presentation.

In squamous-cell carcinomas, no significant correlation was observed between pack-years and smoking-related signature 4 (Spearman’s rank correlation, 0.10; P = 0.57), and the timing of genome doubling (ratio of the number of early mutations to the number of late mutations) was significantly later than in adenocarcinomas (Fig. S13 in Supplementary Appendix 1). Intriguingly, Patient CRUK0093, who had squamous-cell car-cinoma, had a large burden of clonal signature 4 mutations (>1000) despite having been identi-fied as a lifelong nonsmoker. This patient’s oc-cupational history indicated exposure to chemicals that included arsenic, benzene, bisphenol, and polybrominated diphenyl ethers and coal tar, which may mimic the mutagenic effects of to-bacco exposure.

There were significant correlations between the subclonal mutation burden and the number of subclonal mutations that were classified as clocklike signatures 1A (spontaneous deamina-tion of methylated cytosines) and 5 (of unknown cause).13 The number of subclonal mutations was also significantly correlated with signatures 2 and 13 (induced by APOBEC, a family of cytidine deaminase enzymes involved in messenger RNA editing) but not with signature 4 (smoking)12 (Fig. S14 in Supplementary Appendix 1). (APOBEC cy-tidine deaminases, which are usually involved in innate immunity and RNA editing, have been found to be enriched in several tumor types and act as an important source of mutagenesis.14) Tumors with the largest subclonal mutation bur-den had extensive APOBEC-mediated mutagene-sis (e.g., those obtained from Patients CRUK0001, CRUK0006, CRUK0020, and CRUK0063), and spatial heterogeneity in APOBEC mutations was observed in 15 tumors (Figs. S11 and S14 in





Supplementary Appendix 1). Tumors obtained from 19 patients had subclonal driver mutations that could be attributed to APOBEC activity, which illustrates how APOBEC mutagenesis may frequently induce a subclonal driver event that may contribute to subclonal expansions.

Chromosomal Instability and Genome DoublingGiven the association between intratumor hetero-geneity characterized by copy-number alterations and shorter relapse-free survival, we further ex-plored the dynamics of chromosomal alterations in different tumor regions and the extent to which chromosomal instability may drive intra-

tumor heterogeneity. By leveraging germline heterozygous single-nucleotide polymorphisms in tumors by means of multiregion whole-exome sequencing, it is possible to determine whether the same or distinct parental alleles are gained or lost in distinct subclones on different branches of the phylogenetic tree of a tumor. Specifically, if the maternal allele is gained or lost in a sub-clone in one region, yet the paternal allele is gained or lost in a different subclone in another region, it will result in a mirrored subclonal al-lelic imbalance profile (Fig. 3A and 3B). Such an imbalance, which indicates additional ongoing chromosomal instability, may also reflect parallel

Figure 3. Drivers of Intratumor Heterogeneity.

Panel A shows an example of mirrored subclonal allelic imbalance. This occurs when the maternal allele is gained or lost in a subclone in one region and the paternal allele is gained or lost in a different subclone in another region. Such imbalance indicates additional ongoing chromosomal instability and can be inferred through multiregion whole-exome sequencing by using the frequencies at which heterozy-gous germline single-nucleotide polymorphisms (SNPs) (termed B-allele frequency [BAF]) are detected. The BAF of heterozygous SNPs is plotted in the same color as their parental chromosome of origin. Panel B shows the BAF profile across the genome of a tumor sample obtained from Patient CRUK0062. Areas of BAF in regions (including tumor regions R1 through R7 and a germline [GL] reference region) that have mirrored subclonal allelic imbalance are highlighted in blue or orange. Events that showed mirrored subclonal allelic imbalance were identified in more than 40% of the genome. Panel C shows phylogenetic trees that indicate parallel evolution of driver amplifica-tions detected through the observation of mirrored subclonal allelic imbalance (arrows). Subclones that are colored blue carry a cancer driver event, and those that are colored gray carry no driver event; black outlining of the circles indicates that the subclone appears to be clonal in at least one tumor region.

A Mirrored Subclonal Allelic Imbalance B BAF Profile in a Single Tumor Sample

C Phylogenetic Trees Indicating Parallel Evolution of Driver Amplifications

12

34

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CRUK0012

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CCNB1IP1CHD8NKX2-1FOXA1amp

CCNB1IP1CHD8NKX2-1FOXA1amp

CRUK0072

BCL11ARELXPO1amp

BCL11ARELXPO1amp

CRUK0009

RHOHPHOX2Bamp

RHOHPHOX2Bamp

CRUK0001

CDK4LRIG3amp

CDK4LRIG3amp

CDK4LRIG3amp





evolution involving multiple distinct events con-verging on the same genes in different subclones (Fig. S15 in Supplementary Appendix 1). This phenomenon was observed in 62% of 92 tumors with copy-number data on multiregion whole-exome sequencing (found in 30 adenocarcino-mas, 23 squamous-cell carcinomas, and 4 other samples). In total, we detected 375 mirrored subclonal allelic imbalance events that varied in size from focal to whole chromosome and in-volved 1 to 43% of affected tumor genomes (Fig. S16 in Supplementary Appendix 1).

Chromosomal instability may also directly con-tribute to mutational heterogeneity through loss of genomic segments carrying clonal mutations. Overall, a median of 13% of subclonal mutations (range, 0 to 56) per sample are probably sub-clonal as a result of loss events associated with copy-number alterations, which suggests that chromosomal instability may be an initiator of both copy-number and mutational heterogeneity (Fig. S17 in Supplementary Appendix 1).

Accumulating evidence suggests that genome-doubling events are associated with the propaga-tion of chromosomal instability by cancer cells and may predict a poor prognosis.15-17 Genome-doubling events were identified in 76% of tumors and appeared to be clonal in all but three of these tumors (from Patients CRUK0011, CRUK0062, and CRUK0063), which suggests that whole-genome duplication is an early event in NSCLC evolution. In adenocarcinomas, we observed a significant association between genome doubling and the frequency of both subclonal mutations (P = 0.02) and subclonal copy-number alterations (P = 0.003) (Fig. S18 in Supplementary Appendix 1). Moreover, mirrored subclonal allelic imbalance was significantly enriched in genome-doubled tumors (P = 0.004 by Fisher’s exact test) (Fig. S16 in Supplementary Appendix 1).

Selection and Parallel Evolution

Deciphering evidence of ongoing selection in tu-mors may shed light on evolutionary constraints, which may identify therapeutic targets. Con-straints and selection are exemplified by the oc-currence of parallel evolution, in which somatic events in distinct branches within a single tumor converge on the same gene, protein complex, or pathway.

No evidence of parallel evolution was found

at the mutational level. However, focal amplifi-cations of different parental alleles in distinct subclones occurred in 5 tumors and affected known cancer genes, including MUC1, CDK4, CHD8, and NKX2-1 (Fig. 3C, and Fig. S19 in Sup-plementary Appendix 1). At the chromosome-arm level, potential parallel evolution was observed in 13 tumors (5 adenocarcinomas, 6 squamous-cell carcinomas, and 2 other tumors). Most parallel evolution of chromosome-arm gains (in 10 of 11 samples) and losses (in 6 of 8 samples) have been previously classified as significantly gained or lost in NSCLC,3,7 a finding that is consistent with positive selection operating later in tumor evolution (Fig. S20, S21, and S22 in Supplemen-tary Appendix 1).

To empirically estimate positive selection at the mutational level, we used a ratio of substitu-tion rates at nonsynonymous sites to those at synonymous sites (dN/dS) that accounts for the trinucleotide context of each mutation and de-termines whether there is an enrichment of protein-altering mutations as compared with the background mutation rate.18 Evidence for positive selection (dN/dS, >1) was observed when all exonic missense mutations were considered (Table S8 in Supplementary Appendix 1). This finding sug-gests that mutations may be shaped by selection in NSCLC. However, when mutations were tem-porally dissected, significant positive selection was observed for late, but not early, mutations. Consistent with this finding, nonsense mutations were found to be depleted (dN/dS, <1) early but not late in tumor development. These data fur-ther suggest that selection is persistent in NSCLC evolution and that constraints shape evolution-ary trajectories. Depletion of early nonsense mu-tations (dN/dS, <1) was greater in squamous-cell carcinomas than in adenocarcinomas, and the rate of acquisition of clonal driver mutations (as determined by the ratio of driver mutations to passenger mutations) was significantly greater in adenocarcinomas than in squamous-cell car-cinomas (P = 0.001 by the Wilcoxon test).

Clonal and Subclonal Driver Alterations and Timing of Genomic Events

Determining whether a cancer driver event oc-curs early or late can indicate whether it is in-volved in tumor initiation or maintenance, and its clonality may inform potential therapeutic





strategies, since subclonal alterations will be present in only a proportion of cells and when targeted may result in reduced treatment effi-cacy.19 We identified 795 driver events (range in adenocarcinomas, 1 to 19; range in squamous-cell carcinomas, 2 to 21). Of these events, 219 in 77 tumors were found to be subclonal (range in adenocarcinomas, 0 to 10; range in squamous-cell carcinomas, 0 to 12) and 576 to be clonal (range in adenocarcinomas, 1 to 18; range in squamous-cell carcinomas, 1 to 14) (Fig. S23 in Supplementary Appendix 1 and Table S9 in Sup-plementary Appendix 2). Significantly more driver alterations were identified with the use of multi-region whole-exome sequencing than with single-sample analysis (P = 0.004 by the Wilcoxon test) (Fig. S24 in Supplementary Appendix 1).

Alterations in certain cancer genes were not only primarily clonal but almost always occurred before genome duplication, which suggests in-volvement in tumor initiation (Fig. 4). In adeno-carcinomas, these alterations included targeta-ble mutations or amplifications in EGFR, MET, and BRAF, as well as amplifications in TERT, 8p loss, and 5p gain. In squamous-cell carcinomas, muta-tions in NOTCH1, amplifications in FGFR1 and in the 3q region (which includes SOX2 and PIK3CA), and loss of 3p, 4p, 5q, and 17p were early clonal events. Mutations in TP53 were predominantly clonal and early for both subtypes. Conversely, other driver events, including mutations in KMT2C and COL5A2 in adenocarcinomas and in PIK3CA in squamous-cell carcinomas, while predomi-nantly clonal, often occurred after genome du-plication, which suggests their involvement in tumor maintenance or progression. Except for alterations in TP53, ATM, CHEK2, and MDM2, 51% of 72 driver alterations affecting chromatin remodeling, histone methylation, or DNA dam-age response and repair were subclonal or late in both histologic subtypes (23 of 41 events in ad-enocarcinomas and 14 of 31 events in squamous-cell carcinomas) (Fig. S25 in Supplementary Appendix 1). UBR5, with a known role in dif-ferentiation and DNA damage response, was one of the most frequently altered genes later in evolu-tion in both adenocarcinomas and squamous-cell carcinomas. Other genes that were subject to frequent subclonal or late alterations in adeno-carcinomas included NF1 and NOTCH1, along with 3p, 13q, and 21p loss and 7q and 8q gain,

whereas in squamous-cell carcinomas, alterations in MLH1 and KRAS, along with 10q loss and 7p, 8q, and 20q gain, were late events.

Driver mutations that occurred early showed a significantly greater tendency to occur in estab-lished histologic-subtype–specific cancer genes than did late or subclonal driver mutations, which affected a broader selection of pan-cancer genes20 (Fig. S26 in Supplementary Appendix 1). These data are consistent with the dN/dS muta-tion-selection analysis and suggest that constraints inherent in cancer evolution vary as tumors de-velop, which potentially renders more evolution-ary paths permissive for progression.

Overall, 86 of the 100 tumors in our study had alterations that are being investigated in NSCLC in genomically profiled drug studies, in-cluding the National Lung Matrix Trial (NLMT)21 and the Molecular Analysis for Therapy Choice (MATCH) trial.22 Of these 86 tumors, 17 (20%) had subclonal targetable mutations and copy-number alterations. In 12 of these 17 tumors (71%), both a clonal and a subclonal targetable alteration were present, which indicates how tar-gets might be prioritized for therapeutic inter-vention (Fig. S27 in Supplementary Appendix 1).

Discussion

Intratumor heterogeneity provides the fuel for tumor evolution and drug resistance.23 Here, we have provided an analysis of NSCLC evolution, which has shown that intratumor heterogeneity and branched evolution are almost universal across the cohort. We also observed a common pattern of early clonal genome doubling, followed by extensive subclonal diversification.

These data may have important implications for our understanding of tumor biology and therapeutic control in NSCLC. Certain targetable driver mutations, including those in EGFR, MET, and BRAF, were almost exclusively clonal and early, which explains the robust and uniform responses that are often seen across multiple sites of disease when these alterations are tar-geted.24-26 However, more than 75% of the tumors in our study carried a subclonal driver alteration, including in genes such as PIK3CA, NF1, KRAS, TP53, and NOTCH family members. Moreover, a large fraction of subclonal driver mutations ap-peared to be clonal in a single region but were





Gen

ome

doub

ling

Late

driv

ers

Mut

atio

nsC

opy-

Num

ber

Even

ts

Cop

y-N

umbe

r Ev

ents

Cop

y-N

umbe

r Ev

ents

Mut

atio

ns

Mut

atio

ns

Mut

atio

nal P

roce

sses

Mut

atio

nal P

roce

sses

Mut

atio

nal P

roce

sses

7q8q3p13q

21p9p15q7p17p9q1q5p8p

14/1

812

/16

12/1

912

/20

12/2

1

11/2

011

/20

17/3

16/

157/

205/

16

4/22

2/16

PPFI

BP1

(12

p)EI

F3E

(8q)

KR

AS

(12p

)C

OX

6C (

8q)

RSP

O2

(8q)

NK

X2−

1 (1

4q)

HEY

1 (8

q)TE

RT

(5p)

EGFR

(7p

)FO

XA

1 (1

4q)

2/6

2/6

2/7

2/7

1/6

1/6

1/7

1/10

1/10

0/7

CS

ESN

S

CS

ESN

S

CS

ESN

SC

ICEP

300

FLT4

NO

TCH

1PT

PRC

SMA

D4

DN

M2

PASK

UB

R5

KM

T2C

BA

P1PL

XN

B2

AR

ID2

CTN

NB

1N

CO

R1

NC

OA

6C

OL2

A1

CO

L5A

2U

2AF1

KM

T2D

PIK

3CA

NF1

MG

AD

OT1

LFU

BP1

CR

EBB

PPR

F1R

ASA

1W

RN

NO

TCH

2N

RA

SR

NF4

3SM

AR

CA

4A

RH

GA

P35

RA

D21

SER

PIN

B13

RB

1A

TRX

KD

M5C

WA

SFA

NC

MA

RID

1BST

K11

ATM

2/2

2/2

2/2

2/2

2/2

2/2

2/2

2/2

2/2

3/3

3/4

3/4

3/4

2/3

2/3

2/3

2/3

2/3

2/3

2/3

3/5

4/7

5/9

1/2

1/2

1/2

1/2

1/2

1/2

2/4

1/2

1/2

1/2

2/4

1/2

1/2

2/5

1/3

1/3

1/3

1/3

1/3

2/7

1/4

FAT1

EGFR

TP53

KR

AS

KEA

P1B

CO

RR

BM

10A

PCC

HEK

2FB

XW

7PH

OX

2BTS

C2

BR

AF

CM

TR2

MET

PRD

M1

1/5

2/13

4/29

3/26

1/9

0/2

0/5

0/4

0/2

0/2

0/2

0/2

0/4

0/3

0/2

0/2

Pre-

GD

in

itiat

ing

driv

ers

Post

-GD

clon

al/

subc

lona

l

Gen

ome

doub

ling

Mut

atio

nsC

opy-

Num

ber

Even

ts

Mut

atio

nal P

roce

sses

Mut

atio

nal P

roce

sses

Mut

atio

nal P

roce

sses

2p7p8q15q

12p

20q

16q7q22q1p10q

9/9

10/1

012

/13

7/8

6/7

9/11

8/10

5/7

7/10

6/10

8/14

5p20p4q9p10p

18p

13q9q11p

18q

21p

21q

5q17p3q3p

10/2

06/

125/

107/

148/

165/

107/

155/

113/

73/

76/

154/

10

4/17

3/13

2/9

4/18

4p2/

13

CD

KN

2A (

9p)

BC

L11A

(2p

)FI

P1L1

(4q

)

IL7R

(5p

)A

KT2

(19

q)C

D79

A (

19q)

FOX

L2 (

3q)

CC

ND

1 (1

1q)

MYC

(8q

)FG

FR1

(8p)

PIK

3CA

(3q

)SO

X2

(3q)

2/3

2/3

2/3

LSM

14A

(19

q)TE

RT

(5p)

REL

(2p

)IK

BK

B (

8p)

CEB

PA (

19q)

LIFR

(5p

)H

OO

K3

(8p)

2/5

2/5

1/3

1/4

1/4

1/5

1/5

1/6

1/6

0/3

0/3

0/4

0/4

0/8

0/13

0/14

CS

ES

CS

ES

CS

ES

CYL

DK

RA

SM

LH1

UB

R5

CB

LBPI

K3C

AM

GA

NC

OA

6PL

XN

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ERC

C5

CU

L3C

OL2

A1

NO

TCH

2C

OL5

A2

FAT1

CD

KN

2AB

RIP

1D

NM

2FA

NC

MW

RN

CU

X1

DIC

ER1

NFE

2L2

RA

SA1

NO

TCH

1TP

53C

REB

BP

KEA

P1LA

TS1

SMA

D4

KM

T2D

FBX

W7

PTEN

WT1

2/2

2/2

2/2

4/4

2/2

5/7

2/3

2/3

2/3

1/2

1/2

1/2

1/2

2/4

4/8

4/8

1/2

1/2

1/2

1/2

1/2

1/2

3/7

2/5

1/5

2/27

0/2

0/2

0/2

0/2

0/3

0/2

0/3

0/2

Late

driv

ers

Pre-

GD

initi

atin

gdr

iver

s

Post

-GD

clon

al/

subc

lona

l

Pre-

GD

clo

nal

som

atic

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ntU

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ed c

lona

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mat

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Post

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nal

som

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som

atic

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ure

Unc

lass

ified

Sign

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gnat

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4(s

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(unk

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Squa

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nom

a





absent or subclonal in other regions, which con-firmed the limitations of sampling single tumor regions and emphasized the ability of multi-region whole-exome sequencing to define the clonality of driver events for prioritization of drug targets.

Late mutations in tumor-suppressor genes that occur after genome doubling often affected only one allele, which potentially left the wild-type alleles intact. Although this finding could indi-cate that late tumor-suppressor mutations are often passenger events that do not contribute to tumor progression, it is also plausible that germ-line defects, subclonal copy-number loss, haplo-insufficiency, or transcriptional regulation may act to limit wild-type expression. In contrast to early mutations, late driver mutations were not specific to the NSCLC subtype and often occurred in cancer genes that have been identified in other tumor types; a high proportion occurred in genes that are involved in the maintenance of genome integrity through DNA damage response and repair, chromatin remodeling, and histone methylation. Such mutations may remove tissue-specific constraints on the cancer genome and provide advantages to emerging subclones later in evolution. However, the observation of paral-lel evolution of driver copy-number alterations that were identified through mirrored subclonal allelic imbalance, including in CDK4, FOXA1, and BCL11A, suggests that despite extensive diversity, specific constraints, which could be therapeuti-

cally exploited, may operate later in tumor evo-lution.

Tumors with the highest subclonal mutational burden had extensive APOBEC-mediated muta-genesis, and 19 tumors carried subclonal driver mutations within an APOBEC context. This find-ing suggests that targeting the enzymatic activ-ity of APOBEC may provide a means of limiting subclone diversification. The clonal mutation burden was significantly enriched in patients with a smoking history. Conceivably, this find-ing could be exploited for therapeutic benefit through the use of peptide vaccines or adoptive cell therapy against clonal neoantigens that are present in every tumor cell. However, the obser-vation that clonal mutations can be lost owing to later copy-number events could limit the efficacy of such strategies, especially in tumors with high chromosome instability.

Finally, although a single sample can provide a static measure of chromosomal complexity,27 the use of multiregion whole-exome sequencing enables the assessment of dynamic chromosome instability, which may lead to differences in chro-mosomal karyotypes between NSCLC subclones. The onset of chromosome instability appears to have a considerable effect on the evolution of NSCLC; such instability appears to be the pre-dominant driver of parallel evolution and can lead to both mutational and copy-number diver-sity among subclones. Elevated copy-number het-erogeneity was associated with shorter relapse-free survival, which suggests that patients who have early-stage tumors with high levels of copy-number heterogeneity may represent a high-risk group who may benefit from close monitoring and early therapeutic intervention during follow-up. We are continuing to assess this association in the next 742 patients enrolled in TRACERx. Whether noninvasive prognostic approaches, such as liquid biopsy, can be used to prospectively assess the levels of chromosomal instability in the clinical setting warrants further attention.28 In addition to ongoing efforts to target single genetic alterations, there is a need to develop a greater understanding of chromosomal instabil-ity, which can alter the copy number of a multi-tude of genes simultaneously. Indeed, therapeu-tic efforts that can attenuate this process may limit the ensuing heterogeneity and tumor evolu-tion that drive poor rates of relapse-free survival. In the analysis presented here, we provide a

Figure 4 (facing page). Timing of Somatic Events in NSCLC Evolution.

A diagram of tumor evolution in adenocarcinoma (Panel A) and squamous-cell carcinoma (Panel B) shows the approximate timing of genomic aberrations with respect to the cancer life history. The timing of mutations and copy-number events is shown as bars indicating whether the events are clonal or subclonal. Clonal mutations and chromosome-arm events are fur-ther timed as early or late with respect to genome dou-bling (GD). The frequency of mutations and copy-num-ber alterations (subclonal and total) is indicated on the right side of the bars. Pie charts show the fraction of estimated mutations for each signature, averaged across current smokers or recent ex-smokers (CS), long-term (>20 years) former smokers (ES), and life-long never smokers (NS) at three different time points. Only genes that were mutated in at least two patients or that had copy-number alterations in at least 20% of the patients in the cohort are shown.





census of driver events in early-stage NSCLC in relation to clonality and show that chromo-somal instability is not only a significant driver of parallel evolution but also a predictor of poor outcome.

Supported by Cancer Research UK (CRUK), the CRUK Lung Cancer Centre of Excellence, the University College London Hos-pitals Biomedical Research Centre, the CRUK University College London Experimental Cancer Medicine Centre, the Rosetrees Trust, the Francis Crick Institute (which receives its core fund-ing from CRUK [FC001169 and FC001202]), the U.K. Medical Research Council (FC001169 and FC001202), and the Wellcome

Trust (FC001169 and FC001202). Dr. Swanton is a Royal Society Napier Chair in Oncology and is funded by CRUK (TRACERx and CRUK Cancer Immunotherapy Catalyst Network), the Na-tional Institute for Health Research, Novo Nordisk Foundation (ID16584), the European Research Council, and PloidyNet (a Marie Curie Initial Training Network). Dr. Van Loo is a Winton Group Leader in recognition of the support of the Winton Charitable Foundation in the establishment of the Francis Crick Institute.

Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.

We thank all the patients who participated in this study and representatives of Illumina and Agilent who provided sequenc-ing infrastructure support.

AppendixThe authors’ full names and academic degrees are as follows: Mariam Jamal-Hanjani, M.D., Ph.D., Gareth A. Wilson, Ph.D., Nicholas McGranahan, Ph.D., Nicolai J. Birkbak, Ph.D., Thomas B.K. Watkins, M.C.I.T., Selvaraju Veeriah, Ph.D., Seema Shafi, Ph.D., Diana H. Johnson, B.Sc., Richard Mitter, M.Sc., Rachel Rosenthal, M.Sc., Max Salm, Ph.D., Stuart Horswell, M.Math., Mickael Escudero, M.Sc., Nik Matthews, B.Sc., Andrew Rowan, B.Sc., Tim Chambers, M.Sc., David A. Moore, M.D., Samra Turajlic, M.D., Ph.D., Hang Xu, Ph.D., Siow-Ming Lee, M.D., Ph.D., Martin D. Forster, M.D., Ph.D., Tanya Ahmad, M.D., Crispin T. Hiley, M.D., Ph.D., Christopher Abbosh, M.D., Mary Falzon, M.D., Elaine Borg, M.D., Teresa Marafioti, M.D., David Lawrence, M.D., Martin Hayward, M.D., Shyam Kolvekar, M.D., Nikolaos Panagiotopoulos, M.D., Sam M. Janes, M.D., Ph.D., Ricky Thakrar, M.D., Asia Ahmed, M.D., Fiona Blackhall, M.D., Ph.D., Yvonne Summers, M.D., Ph.D., Rajesh Shah, M.D., Leena Joseph, M.D., Anne M. Quinn, M.D., Ph.D., Phil A. Crosbie, M.D., Ph.D., Babu Naidu, M.D., Gary Middleton, M.D., Gerald Langman, M.D., Simon Trotter, M.D., Marianne Nicolson, M.D., Hardy Rem-men, M.D., Keith Kerr, M.D., Mahendran Chetty, M.D., Lesley Gomersall, M.D., Dean A. Fennell, M.D., Ph.D., Apostolos Nakas, M.D., Sridhar Rathinam, M.D., Girija Anand, M.D., Sajid Khan, M.D., Peter Russell, M.D., Ph.D., Veni Ezhil, M.D., Babikir Ismail, M.D., Melanie Irvin-Sellers, M.D., Vineet Prakash, M.D., Jason F. Lester, M.D., Malgorzata Kornaszewska, M.D., Ph.D., Richard Attanoos, M.D., Haydn Adams, M.D., Helen Davies, M.D., Stefan Dentro, M.Sc., Philippe Taniere, M.D., Ph.D., Brendan O’Sullivan, B.Sc., Helen L. Lowe, Ph.D., John A. Hartley, Ph.D., Natasha Iles, Ph.D., Harriet Bell, M.Sc., Yenting Ngai, B.Sc., Jacqui A. Shaw, Ph.D., Javier Herrero, Ph.D., Zoltan Szallasi, M.D., Roland F. Schwarz, Ph.D., Aengus Stewart, M.Sc., Sergio A. Quezada, Ph.D., John Le Quesne, M.D., Ph.D., Peter Van Loo, Ph.D., Caroline Dive, Ph.D., Allan Hackshaw, M.Sc., and Charles Swanton, M.D., Ph.D.

The authors’ affiliations are as follows: the Cancer Research UK Lung Cancer Centre of Excellence (M.J.-H., G.A.W., N. McGranahan, N.J.B., S.V., S.S., D.H.J., R.R., S.-M.L., M.D.F., C.A., S.M.J., C.D., C.S.), London and Manchester, Good Clinical Laboratory Practice Facility, University College London (UCL) Experimental Cancer Medicine Centre (H.L.L., J.A.H.), Bill Lyons Informatics Centre (J.H.), and Cancer Immunology Unit (S.A.Q.), UCL Cancer Institute, the Translational Cancer Therapeutics Laboratory (G.A.W., N. McGranahan, N.J.B., T.B.K.W., A.R., T.C., S. Turajlic, H.X., C.T.H., C.S.), Department of Bioinformatics and Biostatistics (R.M., M.S., S.H., M.E., A.S.), Advanced Sequencing Facility (N. Matthews), and Cancer Genomics Laboratory (S.D., P.V.L.), Francis Crick Institute, the Renal and Skin Units, Royal Marsden Hospital (S. Turajlic), the Departments of Medical Oncology (M.J.-H., S.-M.L., M.D.F., T.A., C.A., C.S.), Pathology (M.F., E.B., T.M.), Cardiothoracic Surgery (D.L., M.H., S. Kolvekar, N.P.), Respiratory Medicine (S.M.J., R.T.), and Radiol-ogy (A.A.), UCL Hospitals, Lungs for Living, UCL Respiratory, UCL (S.M.J.), the Department of Radiotherapy, North Middlesex Univer-sity Hospital (G.A.), the Department of Respiratory Medicine, Royal Free Hospital (S. Khan), and UCL Cancer Research UK and Cancer Trials Centre (N.I., H.B., Y.N., A.H.), London, Cancer Studies, University of Leicester (D.A.M., D.A.F., J.A.S., J.L.Q.), the Department of Thoracic Surgery, Glenfield Hospital (A.N., S.R.), and the Medical Research Center Toxicology Unit (J.L.Q.), Leicester, the Institute of Cancer Studies, University of Manchester (F.B.), the Christie Hospital (F.B., Y.S.), the Departments of Cardiothoracic Surgery (R.S.) and Pathology (L.J., A.M.Q.) and the North West Lung Centre (P.A.C.), University Hospital of South Manchester, and Cancer Research UK Manchester Institute (C.D.), Manchester, the Departments of Thoracic Surgery (B.N.) and Cellular Pathology (G.L., S. Trotter), Birmingham Heartlands Hospital, Molecular Pathology Diagnostic Services, Queen Elizabeth Hospital (P.T., B.O.), and Institute of Im-munology and Immunotherapy, University of Birmingham (G.M.), Birmingham, the Departments of Medical Oncology (M.N.), Cardio-thoracic Surgery (H.R.), Pathology (K.K.), Respiratory Medicine (M.C.), and Radiology (L.G.), Aberdeen University Medical School and Aberdeen Royal Infirmary, Aberdeen, the Department of Respiratory Medicine, Barnet and Chase Farm Hospitals, Barnet (S. Khan), the Department of Respiratory Medicine, Princess Alexandra Hospital, Harlow (P.R.), the Department of Clinical Oncology, St. Luke’s Cancer Centre, Guildford (V.E.), the Departments of Pathology (B.I.), Respiratory Medicine (M.I.-S.), and Radiology (V.P.), Ashford and St. Peters’ Hospitals, Surrey, the Department of Clinical Oncology, Velindre Hospital (J.F.L.), the Departments of Radiology (H.A.) and Respiratory Medicine (H.D.), University Hospital Llandough, the Departments of Pathology (R.A.) and Cardiothoracic Surgery (M.K.), University Hospital of Wales, and Cardiff University (R.A.), Cardiff, and Wellcome Trust Sanger Institute, Hinxton, and Big Data Insti-tute, University of Oxford, Oxford (S.D.) — all in the United Kingdom; the Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby (Z.S.); the Computational Health Informatics Program, Boston Children’s Hospital and Harvard Medical School, Boston (Z.S.); MTA-SE-NAP, Brain Metastasis Research Group, 2nd Department of Pathology, Semmelweis University, Budapest, Hungary (Z.S.); Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin (R.F.S.); and the Department of Human Genetics, University of Leuven, Leuven, Belgium (P.V.L.).

References1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin 2013; 63: 11-30.2. Jemal A, Bray F, Center MM, Ferlay J,

Ward E, Forman D. Global cancer statis-tics. CA Cancer J Clin 2011; 61: 69-90.3. Cancer Genome Atlas Research Net-work. Comprehensive molecular profiling

of lung adenocarcinoma. Nature 2014; 511: 543-50.4. Cancer Genome Atlas Research Net-work. Comprehensive genomic character-





ization of squamous cell lung cancers. Nature 2012; 489: 519-25.5. Imielinski M, Berger AH, Hammer-man PS, et al. Mapping the hallmarks of lung adenocarcinoma with massively par-allel sequencing. Cell 2012; 150: 1107-20.6. Govindan R, Ding L, Griffith M, et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012; 150: 1121-34.7. Campbell JD, Alexandrov A, Kim J, et al. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat Genet 2016; 48: 607-16.8. de Bruin EC, McGranahan N, Mitter R, et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 2014; 346: 251-6.9. Zhang J, Fujimoto J, Zhang J, et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multi-region sequencing. Science 2014; 346: 256-9.10. Jamal-Hanjani M, Hackshaw A, Ngai Y, et al. Tracking genomic cancer evolution for precision medicine: the lung TRACERx study. PLoS Biol 2014; 12(7): e1001906.11. Alexandrov LB, Nik-Zainal S, Siu HC, Leung SY, Stratton MR. A mutational sig-nature in gastric cancer suggests thera-peutic strategies. Nat Commun 2015; 6: 8683.12. Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational pro-cesses in human cancer. Nature 2013; 500: 415-21.

13. Alexandrov LB, Jones PH, Wedge DC, et al. Clock-like mutational processes in human somatic cells. Nat Genet 2015; 47: 1402-7.14. Roberts SA, Lawrence MS, Klimczak LJ, et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in hu-man cancers. Nat Genet 2013; 45: 970-6.15. Carter SL, Cibulskis K, Helman E, et al. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotech-nol 2012; 30: 413-21.16. Dewhurst SM, McGranahan N, Burrell RA, et al. Tolerance of whole-genome doubling propagates chromosomal insta-bility and accelerates cancer genome evo-lution. Cancer Discov 2014; 4: 175-85.17. Fujiwara T, Bandi M, Nitta M, Ivanova EV, Bronson RT, Pellman D. Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature 2005; 437: 1043-7.18. Martincorena I, Roshan A, Gerstung M, et al. Tumor evolution: high burden and pervasive positive selection of somat-ic mutations in normal human skin. Sci-ence 2015; 348: 880-6.19. Lohr JG, Stojanov P, Carter SL, et al. Widespread genetic heterogeneity in mul-tiple myeloma: implications for targeted therapy. Cancer Cell 2014; 25: 91-101.20. Lawrence MS, Stojanov P, Mermel CH, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Na-ture 2014; 505: 495-501.21. Middleton G, Crack LR, Popat S, et al.

The National Lung Matrix Trial: translat-ing the biology of stratification in ad-vanced non-small-cell lung cancer. Ann Oncol 2015; 26: 2464-9.22. Abrams J, Conley B, Mooney M, et al. National Cancer Institute’s Precision Medi-cine Initiatives for the new National Clin-ical Trials Network. Am Soc Clin Oncol Educ Book 2014; : 71-6.23. Greaves M. Evolutionary determi-nants of cancer. Cancer Discov 2015; 5: 806-20.24. Chapman PB, Hauschild A, Robert C, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med 2011; 364: 2507-16.25. Mok TS, Wu Y-L, Thongprasert S, et al. Gefitinib or carboplatin–paclitaxel in pul-monary adenocarcinoma. N Engl J Med 2009; 361: 947-57.26. Cao Y, Xiao G, Qiu X, Ye S, Lin T. Ef-ficacy and safety of crizotinib among Chi-nese EML4-ALK-positive, advanced-stage non-small cell lung cancer patients. PLoS One 2014; 9(12): e114008.27. McGranahan N, Burrell RA, Endes-felder D, Novelli MR, Swanton C. Cancer chromosomal instability: therapeutic and diagnostic challenges. EMBO Rep 2012; 13: 528-38.28. Ni X, Zhuo M, Su Z, et al. Reproducible copy number variation patterns among single circulating tumor cells of lung can-cer patients. Proc Natl Acad Sci U S A 2013; 110: 21083-8.Copyright © 2017 Massachusetts Medical Society.

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