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. 2014 Oct 10;346(6206):251-6.
doi: 10.1126/science.1253462.

Spatial and temporal diversity in genomic instability processes defines lung cancer evolution

Elza C de Bruin  1 , Nicholas McGranahan  2 , Richard Mitter  3 , Max Salm  3 , David C Wedge  4 , Lucy Yates  5 , Mariam Jamal-Hanjani  1 , Seema Shafi  1 , Nirupa Murugaesu  1 , Andrew J Rowan  3 , Eva Grönroos  3 , Madiha A Muhammad  1 , Stuart Horswell  3 , Marco Gerlinger  3 , Ignacio Varela  6 , David Jones  4 , John Marshall  4 , Thierry Voet  7 , Peter Van Loo  7 , Doris M Rassl  8 , Robert C Rintoul  8 , Sam M Janes  9 , Siow-Ming Lee  10 , Martin Forster  10 , Tanya Ahmad  11 , David Lawrence  11 , Mary Falzon  11 , Arrigo Capitanio  11 , Timothy T Harkins  12 , Clarence C Lee  12 , Warren Tom  12 , Enock Teefe  12 , Shann-Ching Chen  12 , Sharmin Begum  3 , Adam Rabinowitz  3 , Benjamin Phillimore  3 , Bradley Spencer-Dene  3 , Gordon Stamp  3 , Zoltan Szallasi  13 , Nik Matthews  3 , Aengus Stewart  3 , Peter Campbell  4 , Charles Swanton  14
Affiliations

Spatial and temporal diversity in genomic instability processes defines lung cancer evolution

Elza C de Bruin et al. Science. .

Abstract

Spatial and temporal dissection of the genomic changes occurring during the evolution of human non-small cell lung cancer (NSCLC) may help elucidate the basis for its dismal prognosis. We sequenced 25 spatially distinct regions from seven operable NSCLCs and found evidence of branched evolution, with driver mutations arising before and after subclonal diversification. There was pronounced intratumor heterogeneity in copy number alterations, translocations, and mutations associated with APOBEC cytidine deaminase activity. Despite maintained carcinogen exposure, tumors from smokers showed a relative decrease in smoking-related mutations over time, accompanied by an increase in APOBEC-associated mutations. In tumors from former smokers, genome-doubling occurred within a smoking-signature context before subclonal diversification, which suggested that a long period of tumor latency had preceded clinical detection. The regionally separated driver mutations, coupled with the relentless and heterogeneous nature of the genome instability processes, are likely to confound treatment success in NSCLC.

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Figures

Fig. 1
Fig. 1. Intratumor heterogeneity of somatic mutations in human NSCLC
(A) Heat maps show the regional distribution of all nonsilent mutations; presence (blue) or absence (gray) of each mutation is indicated for every tumor region. Cartoons depict the location of each tumor. Column next to heat map shows the intratumor heterogeneity; mutation present in all regions (blue), in more than one but not all (yellow), or in one region (red). Mutations are ordered on tumor driver category with categories 1 to 3 indicated in the right column in black, dark gray, and light gray, respectively (details in table S3). Total number of nonsilent mutations is provided below each tumor with percentage of heterogeneous mutations in brackets. In L001, the mutation marked by an asterisk (*) is additional to the germline MEN1 mutation. LN, lymph node; R, region. (B) Two-dimensional Dirichlet plots show the cancer cell fraction (CCF) of the mutations in all regions of tumors L004; increasing intensity of red indicates the location of a high posterior probability of a cluster. In region R5, the majority of heterogeneous mutations are subclonal, and a cluster of mutations with a CCF below 1 can be observed.
Fig. 2
Fig. 2. Intratumor heterogeneity of chromosomal alterations in human NSCLC
(A) Distribution of potential tumor driver copy number alterations is indicated for each tumor region. The upper heat maps show the regional distribution of recurrently amplified (left) or deleted (right) chromosomal segments based on TCGA LUAD data, and the lower heat maps show the regional distribution of recurrently amplified or deleted chromosomal segments based on TCGA LUSC data. For each region, gain (red) or loss (blue) was determined relative to the mean ploidy. (B) Circos plots depicting inter- and intrachromosomal translocations, as well as deletions and insertions for regions R1 and R3 for L002 (upper) and L008 (lower); shared events are indicated in blue, events private to region R1 are indicated in red, and private to region R3 in green. The outer circle represents the integer copy number data for R1 and the inner circle for R3 for each tumor sample; copy number segments with an integer value greater than mean ploidy are in red and those less than mean ploidy in blue.
Fig. 3
Fig. 3. Temporal and spatial dissection of mutation spectra in LUAD and LUSC samples
(A) Fraction of early mutations (trunk) and late mutations (branch) accounted for by each of the six mutation types in all M-seq samples. (B) Beeswarm plots showing the fraction of early mutations and late mutations accounted for by each of the six mutation types in every TCGA former smoker or current smoker with both early and late mutations. Significance is indicated. (C) APOBEC mutation enrichment odds ratio for early (trunk, blue bars) and late (branch, red bars) mutations for M-seq samples. The APOBEC signature encompasses C>T and C>G mutations in a TpC context (16).The 95% confidence intervals for Fisher’s exact test are indicated. (D and E) Three mutation types (C>A; C>G and C>T) at all 16 possible trinucleotide contexts for L002 (D) and L008 (E). For both samples, trunk mutations as well as branch mutations from two regions are depicted.
Fig. 4
Fig. 4. A model of the evolutionary history of NSCLC
Evolutionary histories of tumors from patients L002 (A) and L008 (B) are depicted. Genomic instability processes defining NSCLC evolution have been placed on their phylogenetic trees. Driver mutations occurring in an APOBEC context are highlighted with a blue box, and those occurring in a smoking context with a gray box. In each case, the timing of genome-doubling events is indicated with an arrow. CIN, chromosomal instability; muts, mutations.
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