Multi-Region Whole-Exome Sequencing Uncovers the Genetic Evolution and Mutational

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Author Manuscript Published OnlineFirst on May 23, 2016; DOI: 10.1158/0008-5472.CAN-15-3476 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Multi-region whole-exome sequencing uncovers the genetic evolution and mutational

heterogeneity of early-stage metastatic melanoma

Katja Harbst1,5, Martin Lauss1,5, Helena Cirenajwis1, Karolin Isaksson2, Frida Rosengren1,

Therese Törngren1, Anders Kvist1, Maria C Johansson1, Johan Vallon-Christersson1, Bo

Baldetorp1, Åke Borg1, Håkan Olsson1,3, Christian Ingvar2,4, Ana Carneiro1,3,5, Göran Jönsson1,5

1. Division of Oncology and Pathology, Department of Clinical Sciences, Lund University,

Lund, Sweden

2. Department of Surgery, Skåne University Hospital, Lund, Sweden

3. Department of Oncology, Skåne University Hospital, Lund, Sweden

4. Division of Surgery, Department of Clinical Sciences, Lund University, Lund, Sweden

5. These authors contributed equally to this work

Corresponding author: Göran Jönsson, PhD, Division of Oncology and Pathology,

Department of Clinical Sciences, Lund University, Lund, Sweden

Email:[email protected]

Tel: +46 46 222 1444

Running title: Genetic evolution of early-stage metastatic melanoma

Authors have no conflict of interest

Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2016 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 23, 2016; DOI: 10.1158/0008-5472.CAN-15-3476 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Abstract

Cancer genome sequencing has shed light on the underlying genetic aberrations that drive

tumorigenesis. However, current sequencing-based strategies, which focus on a single tumor

biopsy, fail to take into account intratumoral heterogeneity. To address this challenge and

elucidate the evolutionary history of melanoma, we performed whole-exome and

transcriptome sequencing of 41 multiple melanoma biopsies from eight individual tumors.

This approach revealed heterogeneous somatic mutations in the range of 3-38% in individual

tumors. Known mutations in melanoma drivers BRAF and NRAS were always ubiquitous

events. Using RNA sequencing, we found that the majority of mutations were not expressed

or were expressed at very low levels, and preferential expression of a particular mutated

allele did not occur frequently. In addition, we found that the proportion of ultraviolet B

(UVB) radiation-induced C>T transitions differed significantly (P<0.001) between early and

late mutation acquisition, suggesting that different mutational processes operate during the

evolution of metastatic melanoma. Finally, clinical history reports revealed that patients

harboring a high degree of mutational heterogeneity were associated with more aggressive

disease progression. In conclusion, our multi-region tumor sequencing approach highlights

the genetic evolution and non-UVB mutational signatures associated with melanoma

development and progression, and may provide a more comprehensive perspective of

patient outcome.

Introduction

Intratumor heterogeneity (ITH) has recently been thoroughly examined in cancer using

whole-exome sequencing (WES) of multiple regions of a sample (1-8). ITH could have major

implications for clinical strategies concerning biopsy representation of the tumor, clonality

of alterations in targetable genes and drug resistance. In renal cell carcinoma substantial ITH

was demonstrated with up to 70% of mutations occurring in a heterogeneous pattern across

spatially separated regions in individual tumors. Several known cancer gene mutations were

confined to distinct regions (2). Lung adenocarcinomas have also been analyzed using a

similar multi-region approach (3,4). Although lung adenocarcinomas are also characterized

by ITH, 76% of all mutations were identified in all tumor regions. Furthermore, somatic

mutations acquired during the progression of the tumors tended to be less smoking induced

and instead had evidence of being acquired through upregulation of APOBEC cytidine

deaminases (9). Other studies using multi-region sequencing of tumors revealed different

extent of heterogeneity depending on the tumor type, for example, close to 100%

heterogeneous genetic alterations in multifocal prostate cancer (6,7), 55% in esophageal

adenocarcinoma (8) and 49% in ovarian cancer (5). Overall, these data point to the fact that

single-biopsy strategies may be inadequate to inform on mutation status and may lead to

significant molecular information being missed.

We recently found evidence of intra-patient tumor heterogeneity, using targeted gene

sequencing and gene expression microarray analysis, in treatment-naive metastatic

melanoma patients (10). Furthermore, individual metastases have been shown to be

founded by multiple cell populations in the primary melanoma (11) highlighting the

complexity of metastatic progression. Although few treatments have been available in

metastatic melanoma, the recent development of inhibitors targeting BRAF and MEK

changed the clinical outcome of patients with metastatic disease (12). However, in most

cases, the success is limited by development of resistance in the majority of patients within

6 - 9 months from treatment initiation. Several groups have studied whether genetic

heterogeneity can explain therapy resistance (4,13,14) and further highlighting the complex

heterogeneity of therapy resistance, it has been shown that different mechanisms of

resistance occur simultaneously in a single melanoma patient (15). Whether these genetic

lesions exist in small subclones prior to treatment initiation still remains unclear.

To address these challenges, we applied multi-region DNA and RNA sequencing to

melanoma tumors from eight patients. Our findings shed light on the extent of ITH in

treatment-naïve melanoma, help to separate early from late genetic events and decipher

mutational spectra occurring during the course of early metastatic disease.

Materials and Methods

Patient selection

Patients (n=8) (Table 1) were selected for the study by surgeon or oncologist based on the

size of the tumor to allow for sampling of each quadrant of the tumor (see Supplementary

Information). The biopsies were taken at surgery and immediately stored at -80oC (see

Supplementary Figure 1 for study workflow). We collected frozen tissue from multiple

spatially separated tumor regions (Supplementary Table 1). Six patients had metastatic

disease at the time of surgery, and for two patients (Mm1765, Mm1837) the primary

melanoma was biopsied. In one case (Mm1742), the inguinal metastasis was removed during

Temozolomide treatment due to local progress and should be considered separately in all

subsequent analyses. All other cases were untreated at the time of surgical removal of

tumor.

For patient Mm1702, only three different core needle biopsies were analysed. Blood

samples were available from all patients except for Mm1837, for whom normal skin was

used as matched normal control. All patients consented and the study was approved by the

Regional Ethical Committee (Dnr. 101/2013).

Nucleic acid extraction

DNA and RNA were extracted from tumor and normal skin using AllPrep DNA/RNA Mini Kit

(Qiagen). DNA was extracted from blood using QIAamp DNA Blood Mini Kit (Qiagen).

Whole-exome sequencing (WES)

Tumor and matched normal DNA samples were subjected to library preparation. All sample

libraries except Mm1641 were prepared according to SureSelect Target Enrichment System

for Illumina Paired-End Sequencing Library Protocol (Agilent Technologies), using Clinical

Research Exome (CRE) capture oligo panel. For Mm1641, libraries were prepared using the

following three platforms: SureSelect All Exon V5+UTR (Agilent Technologies);

TruSeqProtocol with TruSeq Exome Enrichment (Illumina); Nextera Rapid Capture Protocol

with Expanded Exome (Illumina). All biopsies were sequenced with a physical coverage of at

least 120x with the exception of Mm1641 (coverage 220x). Libraries were sequenced on a

HiSeq 2500 using normal or rapid run mode.

WES data analysis

Reads were mapped to the human reference genome (hg19) with decoy using Novoalign

(Novocraft Technologies). Post-alignment processing is described in the Supplementary

Information. For variant calling, MuTect (16) and VarScan (17) were used, and consensus

calls between the two in the coding parts of the genes by Annovar annotation (18) were

retained (see Supplementary Information). Copy number log-ratios of sequenced exons

were generated from bam files of tumor-normal pairs using CONTRA 2.03, with default

parameters (19). Exons with insufficient coverage in the normal sample were removed. Copy

number data was segmented using GLAD (20).

Phylogenetic and subclonality analysis

Phylogenetic trees of single nucleotide variation (SNV) data were generated using the

maximum parsimony method. ABSOLUTE (21) was used for identification of cancer cell

fractions of SNVs with support from segmented copy number data and FACS ploidy analysis

(for details see Supplementary Information).

Gene expression analysis

Gene expression levels of multi-region samples were assayed using the Illumina HT12

microarray platform according to the manufacturer's instructions at the SCIBLU facility in

Lund. Data analysis included integration of the multi-region samples with a reference set of

214 melanomas (22) and subtype classification (Supplementary Information).

Allele-specific expression at mutation sites

RNA-seq was performed using the Illumina NextSeq for the multi-region samples largely as

described by Saal and colleagues (23) but using Illumina TruSeq Stranded mRNA Library Prep

Kit and NextSeq500. Mm1641:R1 and Mm1749:R6 were omitted due to lack of input mRNA.

For details on RNA-seq and allele-specific expression analysis see Supplementary

Information.

Results

Analysis of intratumor heterogeneity in melanoma tumors

WES was performed on 41 tumor samples obtained from eight patients with an average

target depth of 120x per sample (220x for Mm1641). Overall, three samples were excluded

due to high normal tissue admixture (see Supplementary Information). We called a mutation

being present in a tumor if a nucleotide substitution was present in at least 10% of reads in

at least one tumor region. We then determined the regional distribution of non-synonymous

mutations based on the WES data. We found on average 489 (range 45-814) non-

synonymous mutations per tumor whereof 13% (range 2.7-37.8%) of the non-synonymous

somatic mutations were heterogeneous, i.e., not detected across all sampled regions in the

individual tumors (Figure 1). Notably, mutational ITH was not obvious at the morphologic

level, with uniform appearance of the tumor throughout its regions (Supplementary Figure

2). In melanoma, three genes (BRAF, NRAS and NF1), all key components of the MAPK

pathway, are frequently mutated and overall demonstrate a mutually exclusive pattern (24).

We found BRAF V600E mutation in two patients, three cases harbored NRAS Q61 mutation,

one case had both NRAS Q61 and NF1 mutation and one case had both KIT and NF1

mutation. KIT is rarely mutated in cutaneous melanoma, however it is a known oncogenic

driver in acral and mucosal melanoma (25). These mutations were present across the tumor

regions at similar allele frequencies (Supplementary Table 1). The Mm1767 tumor, which did

not receive treatment regimens prior to surgical removal, had the lowest degree of

mutational heterogeneity with 546 non-synonymous somatic mutations of which only 2.7%

were not detected in all spatially separated regions. Three patients (Mm1837, Mm1641,

Mm1702) died of their disease within six months of the analysed biopsy (Table 1). These had

higher degree of mutational heterogeneity as compared to the other patients (P=0.03, t-

test). Seven spatially separated regions from a lymph node metastasis from patient Mm1641

exhibited 14.2% mutational heterogeneity. At the time of surgery the patient had an

aggressive disease and developed lung metastases. The patient subsequently received BRAF

inhibitor therapy, however after six months on therapy the patient had a progressive disease

course. From Mm1702 three core-needle biopsies in a lymph node metastasis were analyzed

and revealed 20.9% mutational heterogeneity. In addition, FACS ploidy analysis revealed one

hypo- and one hyper-tetraploid subclone in all tumor regions (Supplementary Figure 3,

Supplementary Table 2). At the time of diagnosis the patient presented with distant

metastases to the lungs and shortly thereafter the patient developed meningeal

carcinomatosis. The third patient (Mm1837) had 45 somatic mutations, which exhibited

37.8% mutational heterogeneity. The patient was diagnosed with a large primary melanoma

(Breslow thickness 18mm) of a non-defined pathological subtype that was MLANA negative,

and developed an aggressive metastatic disease six months after initial diagnosis. Two

patients are currently alive of which one received both Ipilumumab and Pembrolizumab.

These two patients had the lowest degree of heterogeneous mutations (2.7% and 5.4%,

respectively) (Table 1). Though intriguing, larger series are required to determine the usage

of ITH as a biomarker in melanoma.

Analysis of regional subclones and mapping melanoma tumor evolution by phylogenetic tree

analysis

Next, we mapped driver genes from Catalogue of Somatic Mutations in Cancer (COSMIC)

Cancer Gene Census list (26) to determine whether mutations in previously identified cancer

driver genes were predominantly trunk or branch mutations within the phylogenetic tree.

We found 129 mutations in cancer driver genes with 113 mutations (88%) being trunk

mutations while only 16 (12%) were branch mutations (Figure 2). Mutations in the

melanoma driver genes, BRAF, NRAS and NF1, were always present on the trunk consistent

with the critical role of constitutive activation of the MAPK pathway in the majority of

melanomas. Although the majority of driver mutations detected in these melanomas appear

to be present in all spatially separated regions (Figure 1, 2A-B), we also observed parallel

evolution of subclones within the melanoma. For example, Mm1641 harbored three distinct

and spatially separated activating mutations in PIK3CA (encoding p.R38H, p.V344G and

p.H1047R). Furthermore, a PIK3R1 (encoding p.Q384X) truncating mutation was detected as

part of the dominant clone in a distinct region (R5) wild-type for the three different PIK3CA

mutations. In addition, a mutation in the phosphatase domain of PTEN (p.K125E) was

present at high allele frequencies in all regions except R1. This mutation alters a highly

conserved amino acid residue and was predicted “probably damaging” by PolyPhen-2 with a

score of 0.999. Importantly, R1 harbored the H1047R PIK3CA kinase domain mutation

frequently found to be mutually exclusive to PTEN mutations (27). The R7 region

demonstrated the highest degree of subclonality that presumably includes the clones from

R5 and R1 due to subclonal presence of mutations in PIK3CA (p.H1047R), PTEN and PIK3R1

(Figure 2C, Supplementary Figures 4-8). Overall, parallel evolution converging on distinct

genes in the same pathway indicates a strong selection for aberrations in these genes in the

progression of melanoma. Although the presence of private mutations was observed in all

analyzed tumors, there was no evidence of subclonality of cancer driver genes in the other

patients.

It is well established that most somatic mutations acquired in melanomas are C>T transitions

preceded by a pyrimidine representing UVB-induced mutations. We found that

approximately 80% of all trunk mutations harbored the UVB signature, while there was a

span of 20-70% of branch mutations having the UVB signature (Figure 3; P<0.001). On the

other hand, T>G transversions characteristic of UVA mutation signature (28) were

significantly increased among the branch mutations as compared to trunk mutations

(P<0.001, Fishers Exact test). These findings indicate that different mutational processes

are operative during the progression of melanoma (Supplementary Figure 9). Notably, the

only case biopsied during treatment (Mm1742) did not show a decrease of UVB- or increase

of UVA-induced mutations (Figure 3).

Finally, we constructed phylogenetic trees for these tumors by maximum parsimony analysis

(Figure 4). We observed a branched evolutionary pattern in tumor Mm1641 where seven

distinct regions were subjected to WES. By contrast, Mm1749 showed limited degree of

branching, even though these were different in-transit metastases removed simultaneously.

Inclusion of three additional asynchronous in-transit metastases supported the limited

branched evolution in Mm1749. In the phylogenetic tree it is clearly observed that M1-3 are

closely intermingled with R1-6. In conclusion, the branched evolutionary pattern indicates

that a linear progression model does not sufficiently explain melanoma evolution.

Transcriptome analysis of intratumor heterogeneity

We then performed microarray-based gene expression profiling of all samples to determine

the transcriptional heterogeneity within individual tumors. Importantly, we pooled the data

from the multi-region samples with a reference set of 214 metastatic melanoma tumors

previously profiled using the same platform, allowing to relate the gene expression levels of

the ITH regions in relation to the full melanoma expression space (22). We determined the

intratumoral expression of a subtype signature shown to classify melanomas into four

molecular subgroups: high-immune (associated with a good prognosis), microphthalmia-

associated transcription factor (MITF)-low proliferative (associated with a poor prognosis),

MITF-high pigmentation (associated with a poor prognosis) and normal-like. In four out of

eight patients we could observe gene expression based ITH when taking into account genes

that have been used to develop the subtype classification. Inconsistent with the

phylogenetic tree analysis we observed that two regions evolutionary similar by mutation

analysis could be assigned to different gene expression subtypes (Figure 5). For example,

based on phylogenetic tree analysis in Mm1641, R1 and R7 displayed genetic divergence.

However, based on gene expression subtype analysis, R3 and R5 demonstrated different

gene expression classification. Such differences were also observed patients Mm1702,

Mm1749 and Mm1742.

Next, we investigated mRNA levels of 10 genes involved in melanocyte differentiation (MITF,

MLANA, TYR), immune checkpoint (CTLA4, PD1), T cell functions (CD3, CD247, CD8A) and cell

proliferation (CCNE1, AURKA) (Figure 5). MITF and its target genes (MLANA and TYR)

displayed intratumor gene expression differences and showed agreement with the subtype

classification. Furthermore, mRNA levels of immune checkpoint genes (CTLA4 and PD1)

showed some consistency with the phylogenetic tree analysis. For example, R1 and R2 from

Mm1765 had decreased levels of CTLA4 and these two regions formed a distinct branch that

seemed to have evolved later than R3 and R4, suggesting that some clones may evolve to

escape the immune response (Figure 3, 5). Gene copy number values extracted from the

WES data were used to determine an additional layer of ITH and unveiled additional

information using GISTIC regions from the melanoma Cancer Genome Atlas study (24). The

amount of copy number changes per tumor varied between patients. For example, Mm1765

harbored copy number changes exclusively on chromosomes 1q, 6p24.3 and 9p23 with a

heterogeneous pattern at chromosome 9p23 (Figure 5). In Mm1749 several copy number

changes were identified with three in-transit metastases lacking a CDKN2A homozygous

deletion detected in all the other six metastases. Mm1837 harbored the highest degree of

mutational ITH, however we found no gene copy number differences. Overall, this suggests

that ITH may independently be dictated by mutations, DNA copy number changes or gene

expression.

RNA-seq reveals allele specific expression

Finally, we performed RNA sequencing to determine spatially restricted mutation allele

specific expression. The majority of mutated genes had low or absent expression, as only

about 40% of mutated genes were expressed and <20% had >20 reads (Figure 6A). To

determine allele-specific expression, we used the >20 read cutoff to obtain robust

frequencies of the mutated allele, and correlated the variant allele frequency (VAF) of the

RNA to the VAF of the DNA. In line with Castle et al. (29), we observed a good correlation

between RNA and DNA VAF for the majority of mutations, i.e., the mutated allele was

expressed according to its frequency in the DNA (Figure 6B). As an exception from this rule

and in accordance with the concept of nonsense-mediated mRNA decay, we found that

alleles harboring stop mutations had significantly lower expression as compared to alleles

harboring synonymous or missense mutations (Figure 6C). As expected, a significant fraction

of genes with mutations at chromosomal loci undergoing loss of heterozygosity (LOH)

demonstrated preferential expression of the mutated allele. Subsequently, we used a

binomial test to identify genes with preferential expression of the mutated allele and

identified a limited number of genes. Only TNC had a branch mutation with preferential

expression of the mutated allele in three different in-transit metastases from patient

Mm1749. The vast majority of the genes harboring non-trunk mutations were expressed at

similar mRNA levels in tumor regions with and without the mutation (Supplementary Figure

10). We found several genes with trunk mutations, however with a preferential expression

of mutated allele in only one of the spatially separated regions. For example, Mm1765

harbored a KIT D816V mutation found at equal DNA allele frequencies in all spatially

separated regions, with preferential over-expression of the mutated allele only in R3 (Figure

6D, Supplementary Figure 11-15). Collectively, our data suggest that preferential expression

of the mutated allele occurs however it does not seem to be a general mechanism of

melanomagenesis.

Discussion

We have identified ITH of somatic mutations, DNA copy number and transcriptomic changes

in multiple regions from eight melanoma tumors and demonstrated that 3-38% of somatic

mutations were not identified in all regions of the tumors. In contrast to renal cell cancer,

where approximately 75% of cancer driver mutations are affected by ITH (2), we found only

12% of cancer driver mutations to be affected by ITH, indicating that heterogeneous

mutations were predominantly passenger mutations. Moreover, ITH seemed to be

independent of the number of biopsies analyzed, as the tumor with only three regions

analyzed showed the highest degree of ITH, suggesting that the degree of ITH could vary in

melanoma.

In line with other melanoma sequencing studies (9,30), we detected UVB-induced C>T

transitions. Our analysis confirms that this signature is enriched early in melanoma tumor

evolution occurring in all tumor regions. We found a significant reduction of the UVB-

induced mutational signature in branch (non-trunk) mutations, suggesting that mutations

arising later in the progression of melanoma may occur as a result of other mutational

processes. This is in line with recent studies in lung cancer, where later acquired mutations

lack the tobacco-smoking signature (3,4). Furthermore, with the exception of one patient in

current study none received any systemic treatment prior to surgical removal of tumor;

therefore, the switch in mutational processes is likely due to evolutionary changes occurring

during melanoma progression and not due to extrinsic factors. This further highlights the

benefit of multi-region sequencing as different mutational processes operate during tumor

evolution.

We confirmed previous findings consistent with activating somatic mutations in the MAPK

pathway being driver events in the majority of melanomas (24). We showed that

constitutive activation of BRAF, KIT and NRAS through somatic mutations was ubiquitous in

seven patients. The last patient was wildtype for BRAF, NRAS, NF1 and KIT, however the

tumor harbored a non-kinase domain PDGFRA mutation. By contrast, we found evidence of

genetic mutations in the PI3K pathway occurring as non-trunk events. In particular, we

found parallel evolution of three distinct activating PIK3CA mutations (p.V344G, p.R38H and

p.H1047R) and a stop mutation in PIK3R1 in a large lymph node metastasis (Mm1641).

Mutational convergence affecting different PI3K members in single melanoma tumors

implicate that activation of this pathway is involved in later stages of metastatic melanoma

progression. In addition, activation of the PI3K pathway via somatic mutations may be a

reason for acquired resistance to BRAF inhibition as previously described (31). Indeed,

patient Mm1641 received BRAF inhibition post surgery but subsequently developed

resistance to the therapy.

Although it is difficult to discern the full extent of ITH at all levels using multi-region

sequencing only, we observed that patients having melanoma tumors with high degree of

ITH as revealed by our approach experienced a more aggressive disease course. These

findings are further supported by data from similar studies in lung cancer, esophageal

adenocarcinoma and pediatric tumors (4,8,32). However, given that this study included a

limited number of patients all treated differently, additional larger studies are required to

further confirm these data given the significance of prognostic markers in melanoma.

Addressing an additional layer of genomic heterogeneity we investigated the expression of

mutated alleles using RNA sequencing. In line with the results of Castle and colleagues (29)

we found a good correlation (corr.=0.57) between DNA and RNA VAFs. Our molecular

exploration of ITH in melanoma further showed that there is considerable variation in

prognostic gene expression signatures within individual melanoma tumors in line with

results from studies in renal cell cancer (2). However, the analyzed gene signatures are partly

dependent on the tumor microenvironment, highlighting another layer of genomic

complexity within melanoma tumors.

Taken together, we have used a multi-region sequencing approach in early-stage treatment-

naïve metastatic melanomas and showed that ITH was directed by either mutational or gene

expression heterogeneity, suggesting a dual layer of genomic complexity in the

heterogeneity of melanoma tumors. This study represents the first comprehensive genomic

investigation of intratumor heterogeneity in metastatic melanoma and complements the

findings by Shain and colleagues on genetic evolution of melanoma from precursor lesions

(33). Although the majority of mutations in cancer driver genes such as BRAFV600E occurred

ubiquitously throughout the tumor, mutations in the PI3K pathway demonstrated a

heterogeneous pattern suggesting that genetic events occurring in this pathway represent

later acquired mutations in metastatic melanoma evolution. Furthermore, Shain and

colleagues (33) found that UVB is the main factor in both the initiation of precursor lesions

and their progression to primary melanoma; however our results implicate that there are

other mutational processes operating during the evolution of metastatic melanoma. Thus,

early melanoma progression may be dependent on UVB-induced mutagenesis, while later

metastatic evolution is associated with additional non-UVB mutational processes.

Importantly, our results further indicate that mutation based ITH may be a prognostic

biomarker, as patients with high degree of mutational heterogeneity presented with an

aggressive disease course. Extension of research to epigenetic and clinical assessment in

much larger cohorts, preferably with repeated biopsies at relapse after treatment, is needed

to fully understand the clinical and biological impact of ITH in melanoma.

Author contributions

KH, ML, AC and GJ conceived the study and drafted the manuscript. KH, HC, FR, TT, MCJ, BB

performed laboratory analyses. KH, ML, AK, JVC, JH performed bioinformatic analyses. KI,

HO, CI and AC collected clinical information. KH, ML, HC, KI, FR, TT, AK, MCJ, JVC, JH, BB, ÅB,

HO, CI, AC and GJ read and approved the final manuscript.

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Figure legends

Figure 1. Regional distribution of mutations found in multi-tumor regions of eight melanoma

patients. Presence (yellow) or absence (blue) of each mutation is marked for each tumor

region. Insets provide gene symbols for the heterogeneous mutations in each case. Cancer

Gene Census genes are marked in red. The table shows numbers of non-synonymous

mutations and percentage of heterogeneous mutations in each case. Notably, for Mm1742

the melanoma metastasis was removed during Temozolomide treatment.

Figure 2. Analysis of cancer gene mutations and subclonality in melanoma tumors. A) Bar-

plot displaying the number of mutations in cancer census genes (n=129). Mutations are

divided in trunk (T, shared mutations present in all regions) and non-trunk (NT, mutations

not present in all regions). B) Fractions of cancer gene mutations among trunk (T) and non-

trunk (NT) mutations demonstrating that only a fraction of all T and NT mutations are

alterations in cancer driver genes. C) Mutation-plot demonstrating the subclonality analysis

in different regions of tumor Mm1641 that displayed the most distinct subclonal pattern.

The subclonal fraction is obtained from the ABSOLUTE analysis. Subclonal fractions of all

mutations in two regions are plotted on the x- and y-axis. Melanoma driver mutations such

as BRAF mutations and mutations in the PI3K pathway are indicated in the plots.

Figure 3. Mutational signatures in melanoma revealed by multi-region sequencing. Fraction

of each kind of base substitution is shown for trunk (shared) and branch (heterogeneous)

single nucleotide variations (SNVs) for each patient. The two left columns represent

mutations combined from all patients. A Fishers exact test was used to determine

differences in mutational spectrum between trunk and branch mutations. Color codes

reflect the different base substitutions, with C>T mutations divided into two groups: Y-C>T

denotes C-to-T mutations at sites preceded by a pyrimidine (Y), and R-C>T denotes those

preceded by a purine (R). * denotes the patient treated with Temozolomide at the time of

surgery.

Figure 4. Phylogenetic trees of mutation evolution from different regions of eight melanoma

patients generated by maximum parsimony analysis from exome data. The number of

somatic mutations common to all tumor regions are indicated at the top of each tree. The

different tumor regions (R) are indicated at the ends of the branches. In case Mm1749 M1-3

represent three asynchronous in-transit metastases. Length of branch reflects number of

mutations. Blue color indicates the stem of the tree with all shared mutations, yellow

indicates branches shared by at least two regions and red branch represents individual

regions. Also, indicated in the phylogenetic tree are mutations in melanoma cancer driver

genes.

Figure 5. Gene expression and DNA copy number aberrations (CNA) analysis for the eight

melanoma patients. For each tumor site/patient (A), the following information is shown

(panels from top): B) molecular subtype (high-immune (orange), pigmentation (green),

proliferative (red), normal-like (blue) and unclassified (grey)) (22); C) expression of

pigmentation, immune response and proliferation genes (green denotes down- regulation

whereas red shows up-regulation); D) copy number levels of GISTIC regions defined by the

TCGA-melanoma study(24). For CNAs, blue denotes loss of genetic material and red denotes

gain thereof.

Figure 6. RNA sequencing of melanoma samples from eight patients. A) Fraction of all

mutations present in >1 read (left panel) and >20 reads (right panel). B) Correlation plot

between DNA and RNA variant allele frequency (VAF) across all samples. C) Analysis of VAF

expression vs expected expression for synonymous, non-synonymous and stop mutations in

all samples. D) Correlation plots in individual regions in Mm1765 that was the only sample

displaying preferential expression of a cancer driver gene. Preferential expression of the KIT

mutated allele observed only in R3 is indicated in the plots. Red color indicates significant

values using a binomial test as described in Supplemental information. Cor. denotes

Spearman correlation value between DNA and RNA VAF.

Table 1. Clinical features of patients included in the study.

Patient Disease state at Treatment Overall Patient Number of surgery (post-surgery) survival status samples (months from surgery) Mm1641 Lymph node and BRAF inhibitor 6 Dead of 7 lung metastases disease Mm1702 Lymph node and Dacarbazine 5 Dead of 3 lung metastases disease Mm1765 Primary No treatment NA NA 4 melanoma Mm1767 Lymph node No treatment 12 Alive 4 metastasis Mm1772 Lymph node and Ipilimumab, 11 Alive 4 skeletal Pembrolizumab metastases Mm1837** Primary Nivolumab 6 Dead of 3 melanoma disease Mm1749* In-transit Ipilumumab 48 Dead of 9 metastasis disease Mm1742# Lymph-node Temozolomide, 26 Dead of 3 Pembrolizumab disease Ipilimumab * Nine in-transit metastases were obtained from this patient. **This patient died within one month of stage IV diagnosis. # This patient was treated with Temozolomide during surgery.

Figure 1 Mm1749 Mm1641 Mm1765 Mm1702 Mm1767 Mm1742 1 2 3 4 5 6 M1M2M3 1 2 3 4 5 6 7 1 2 3 4 1 2 3 1 2 3 4 1 2 3 TNFRSF17 MUC4 ILVBL GATA1 TP53 XPC NUTM2A MED12 WWC2 NEXN RPL5 ERC1 NCAN CIC KAT6A PRDM16 EPPK1 ROS1 PTEN GPHN TPR C1QTNF2 EWRS1 KIAA1549 NF1 KIAA0355 CDKN2A TBX3 CDK12 NFKMIE CDK4 CDRT15L2 NRAS TSPAN2 FRMPD3 HCLS1 ARID2 MAML2 NF1 TM7SF2 PPP6C KMT2D VPS52 BRAF NDUFA13 CREB1 PIP5K1C PTPRB SMARCA4 OR56A1 BRAF ITK ZBTB16 FGFR2 LRIG3 RET PTPLAD2 BRAF IKBKB NRAS ZFP92 PKDREJ ARID1A CIITA ADAT1 MED1 CBLC JAK3 IKZF1 CRTC3 CIITA MYH6 LCK NEB WT1 CNBD1 KIAA1549 BBX ASXL1 PTPRB TOPAZ1 PML AHNAK2 TPRX1 PTPRB SYK CRELD1 CRTC1 CNOT2 CRYBG3 ATP2B3 UTP14A TBC1D8B CDH11 DNASE1L1 GRIN2A TRANK1 MAFB CSF2RA PARP15 GRPR TMEM5 AIFM1 STAG2 RB1 ITGB6 CARD11 FBXW7 POU6F1 ROS1 NOP14 FAM111A GRIN2A PPP2R3C PVRL4 SND1 ANKRD30B BCOR LSS ELF4 CCNE1 PER3 PIK3CA HIST1H3B PER3 SLC25A5 UBR5 GRIA4 GAB3 NPHS1 G6PD WDFY4 BHLHB9 FGFR2 EP300 IVL PTPRK FRMD7 BRCA1 BEND3 PNMA5 TET1 GPRASP1 MED12 MITF CA7 USP26 HEATR1 TAF1 CCDC120 GNAS UBQLN2 FGFR4 JAK3 PTPRB FRMPD4 NF1 IGFBP2 FANCB HDGFRP2 SETBP1 PORCN LIFR RFPL4A DRP2 MYH11 ACSS3 NWD1 REPS2 UBAP2L GOPC RBM5 C2orf44 CXorf67 FGFR2 CFAP44 NOTCH2 NRAS PLS3 FAM115A WDR45 DSPP NEBL JAK3 TBK1 OVGP1 PATE1 SLC45A3 IMPA1 LRRC45 ELN OPN5 SLC22A24 LRRCC1 TNFSF12 CPAMD8 SATB1 KIT LRCOL1 HSPG2 COL3A1 RPL5 TIAM2 FIZ1 ALK MMS22L TMEM63C GLP1R ZNF385C Mm1772 TNNT3 FAM170A PLXNA4 PRDM16 CSMD1 KPNB1 VPS13D HDAC2 1 2 3 4 GLYATL2 TCF12 CUL1 PPFIBP1 IMPG1 CACNA1I FOXO3 ABCA2 ELMO3 OR10G9 ZFYVE27 PIK3CA PKD1 NYAP2 MSN CUX2 FBRSL1 MYH11 UTP6 CECR1 CCDC30 KIF19 ZIC5 LRRC7 TBCA ZNF730 CDK4 SLC35A1 FAM153B DPP9 HAUS3 PLD3 TDG ANKZF1 EFCC1 POU3F1 PLEKHN1 FAM178B RPAP3 CNTNAP2 SFT2D3 DNAH6 CBL BPTF PIK3CA ROCK2 RBMXL1 C20orf144 RBM27 CRIPAK IDH1 KIAA2018 GPD2 NWD2 TCHH NF1 SLC25A27 ARL6IP4 ANKRD62 DPYSL4 ADCK1 NOTCH2 ADAM32 NHSL2 NEUROG3 SLC38A11 RBM5 ADAMTS19 PCDHB10 ZNF831 PIK3R1 PTPRC MED24 DND1 SLC2A3 PODXL Wildtype LRRIQ3 CELSR1 LTBP3 SLC2A146 USH2A B3GALT6 SERF1A INPP4A GUCY2C POU3F3 HGS ELN Mutation CELA1 MST1 GALNT8 NKX2-4 EXOC6B PLBD1 PRR23A PSKH2 SLITRK2 KCNS2 TGFBR2 TAS2R7 SEL1L2 ZNF529 Mm1837 DEDD PBRM1 APC2 GRXCR1 1 2 3 CHRNE DESI2 PCDHAC2 KMTD2 TNC GAD2 CCDC88C NOTCH2 RANBP17 ADM5 DTX3L AARS2 PHF2 ABCA13 NRAS ZNF23 CARD11 PCDHA6 AEBP1 ANK3 EDEM1 LTN1 ORT27 FCGBP MYH3 CELA1 MSGN1 IGSF3 LGALS12 TWIST1 MSGN1 SSTR1 BCR NR1D1 NCKAP5L KRTAP4-11 C9orf50 DSE IL37 PCDHB10 ZNF681 SEZ6 MTMR4 RNPC3 FRMD3 PRKRA CDK2 ALMS1 NPAS3 COL8A2 KCNC1 SCN8A ZIC2 RBM15 NUMBL ANKRD34C FAM83H ADAD1 DCTN5 IFFO1 ANKLE1 HMMR CHTF18 PLXNA2 SCAF1 NLRP4 UBE2Q2 SLC38A3 DDHD1 SIPA1L1 IL7R ADAM21 FAM169A XKR4 TBC1D2 EPS8L2 NPY2R VCY COQ6 TLDC1 CNOT1 MAP3K9 C9orf43 ALDH2 AFAP1L1 NAGK PCDHA13 MPHOSPH8 HMCN1 SLC35F4 MED15 USP25 NDC1 SEC16A LRIG3 JAK2 DNAH6 SALL3 SESTD1 ACE2 PPP2R2B ADRM1 LAMA1 TBP GDF1 NOC3L PDGFRA AKAP9 ZBBX MYH14 ANKRD31 DCC VPS13C NUMA1 JAKMIP2 VTN ILF3 Case Nonsynonomous Heterogeneous LPA HEXIM1 EXOC3L4 CGREF1 mutations mutations (%) DTX3L NEURL1B HERC1 TPP2 TOP1 FGF23 Mm1749 814 7.2 TRIM43 SLC2A3 LRBA FGFRL1 COL7A1 CRIPAK Mm1641 380 14.2 APOBR ZNF358 MX2 Mm1772 367 5.4 TIAM1 KRTAP11-1 KRT8 Mm1765 742 5.9 PLA2G4D SLC26A10 Mm1702 541 20.9 GIT2 ATP13A3 KDM2B Mm1767 546 2.7 FAM71D Downloaded from cancerres.aacrjournals.org on SeptemberMm1742 26, 2021. 473 © 2016 American 6.3 Association for Cancer Research.Mm1837 45 37.8 Author Manuscript Published OnlineFirst on May 23, 2016; DOI: 10.1158/0008-5472.CAN-15-3476 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 2

A) Number of cancer driver B) Fraction of trunk vs non-trunk C) gene mutations cancer driver gene mutations Subclonality analysis of Mm1641

Cancer gene mutations PIK3CA (V344G) Non-trunk mutations PTEN PTEN PIK3CA (V344G) BRAF Other somatic mutations BRAF 120 Trunk mutations 100

100 80 R4 subclonal fraction R4 subclonal fraction

PIK3CA (H1047R) PIK3R1 PIK3CA (H1047R) PIK3CA (R38H) PIK3R1 PIK3CA (R38H) 0.0 0.2 0.4 0.6 0.8 1.0 80 0.0 0.2 0.4 0.6 0.8 1.0 60 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 R1 subclonal fraction R2 subclonal fraction

60 PTEN BRAF PIK3R1 Fraction (%) Fraction 40 BRAF

40 Number cancer driver mutations driver cancer Number

PIK3CA (H1047R) 20 20 PTEN R5 subclonal fraction R7 subclonal fraction PIK3R1 PIK3CA (H1047R) PIK3CA (V344G) PIK3CA (V344G) PIK3CA (R38H) PIK3CA (R38H) 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0 0 All Mm1749Mm1641Mm1772Mm1765Mm1702Mm1767Mm1837Mm1742 T NT T NT T NT T NT T NT T NT T NT T NT T NT 0.0 0.2 0.4 0.6 0.8 1.0 All Mm1749 Mm1641Mm1772 Mm1765Mm1702 Mm1767 Mm1837Mm1742 0.0 0.2 0.4 0.6 0.8 1.0 R1 subclonal fraction R2 subclonal fraction

Branches Downloaded from Author manuscriptshavebeenpeerreviewedandacceptedforpublicationbutnotyetedited. Mm1641 2 364137 644 43 322

Trunk Author ManuscriptPublishedOnlineFirstonMay23,2016;DOI:10.1158/0008-5472.CAN-15-3476

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Trunk 13 51

Branches 4 32 641 Mm1742* Trunk

Branches T>G T>A C>G C>A T>C R−C>T Y−C>T Author Manuscript Published OnlineFirst on May 23, 2016; DOI: 10.1158/0008-5472.CAN-15-3476 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 4

Mm1749 Mm1742 Mm1641 Mm1767

1149 641 476 813

BRAF NRAS NRAS FBXW7 TP53 NF1 ARID2 BRAF MITF CDKN2A PIK3CA (R38H) PPP6C BRCA1 PTEN HMZ R5 R2 R4 PIK3R1 R3 R6 R2 R6 R1 PIK3CA (H1047R) IDH1 R7 R1 R3 R5 R2 R5 R4 M1 R3 R4 R2 R1 M3 M2 Mm1837 Mm1772 42 531 R1

Mm1765 1086

NRAS Mm1702 KMTD2 PDGFRA CDKN2A hmz 643

NF1 KIT CDK4 NRAS CDK4 RB1 TOP1

R4 R3 R3 R4 R5 R2 R2 R1 R1 JAK2 R3 NOTCH2 R4 R4 R2

DNA copy number Gene expression

R4 Mm1641

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% mutations with reads>1 Figure 6 100 B) 20 40 60 80

0 0.2 0.4 0.6 0.8 1.0 0 . . . . 1.0 0.8 0.6 0.4 0.2 0

T KIT

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V Mm1767 T AF ofDNA Mm1767 NT Mm1772 T cor=0.28 Mm1772 NT Mm1837 T Mm1837 NT Author Manuscript Published OnlineFirst on May 23, 2016; DOI: 10.1158/0008-5472.CAN-15-3476 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Multi-region whole-exome sequencing uncovers the genetic evolution and mutational heterogeneity of early-stage metastatic melanoma

Katja Harbst, Martin Lauss, Helena Cirenajwis, et al.

Cancer Res Published OnlineFirst May 23, 2016.

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