Cancer drivers and clonal dynamics in acute lymphoblastic leukaemia subtypes

doi:10.21203/rs.3.rs-366981/v1

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Cancer drivers and clonal dynamics in acute lymphoblastic leukaemia subtypes

https://doi.org/10.21203/rs.3.rs-366981/v1

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To obtain a comprehensive picture of composite genetic drivers events and clonal dynamics in subtypes of paediatric acute lymphoblastic leukaemia (ALL) we analysed tumour-normal whole genome sequencing and expression data from 361 newly diagnosed patients. We report the identification of both novel coding and structural drivers as well as recurrent non-coding variation in promoters and cis-regulatory regions. The transcriptional profile of histone gene cluster 1 and CTCF altered tumours shared hallmarks of hyperdiploid ALL suggesting a ‘hyperdiploid like’ subtype. ALL subtypes are driven by distinct mutational processes with AID mutagenesis being confined to ETV6-RUNX1 tumours. Subclonality is a ubiquitous feature of ALL, consistent with Darwinian evolution driving selection and expansion of tumours. Driver mutations in B-cell developmental genes (IKZF1, PAX5, ZEB2) tend to be clonal and RAS/RTK mutations subclonal. In addition to identifying new avenues for therapeutic exploitation this analysis highlights that targeted therapies should take into account composite mutational profile and clonality.

Cancer Biology

Oncology

Acute lymphoblastic leukaemia

paediatric

whole genome sequencing

driver mutations

non-coding variation

clonality

Acute lymphoblastic leukaemia (ALL) is the most common childhood cancer, with around 80% of ALL cases derived from B-cell precursors (BCP-ALL)¹. The disease is characterised by initiating genetic lesions resulting in characteristic patterns of whole chromosome gain (hyperdiploidy) or loss (hypodiploidy) and the formation of specific fusion genes. Recurrent fusion genes include t(12;21) ETV6-RUNX1, t(1;19) TCF3-PBX1 and t(9;22) BCR-ABL1¹. In addition to these events, copy number changes in RUNX1 as a consequence of intra-chromosomal amplification (iAMP21), and ERG deletion, have more recently been recognised as initiating events². The biological differences between these subtypes is reflected in their clinical behaviour^3–5.

Current first line therapy for ALL is dominated by use of chemotherapeutic and steroidal agents. While their use has driven 5-year survival to > 90%⁶ this is at the expense of significant morbidity. Despite these improvements, survival for relapsed ALL is still only 21%-39%^7,8. Strategies for developing novel therapies for ALL have largely focused on monoclonal antibodies or CAR-T cells. Such therapies are expensive; for example when licenced the anti-cd19 monoclonal blinatumomab was the most expensive cancer therapy ever brought to market⁹. It is therefore desirable to develop additional targeted small molecule therapies to reduce treatment associated morbidity and relapse associated treatment cost. Such developments are likely to require more precise molecular characterisation and risk stratification; both informed by our understanding of ALL genomics.

Precancerous lesions harbouring initiating events can be undetected for years usually requiring the acquisition of additional genetic lesions for symptomatic disease. Most commonly secondary lesions impact genes regulating the cell cycle (CDKN2A, RB1), B-cell development (PAX5, IKZF1, EBF1) and the RAS/RTK pathway (NRAS, KRAS, FTL1)¹. However, the full complement of molecular lesions sufficient to cause ALL, and explain its diversity is unknown.

To obtain a more comprehensive picture of the composite genetic events acting in concert in each of the BCP-ALL subtypes, we performed a genomic analysis of diagnostic samples from 361 ALL patients (Supplementary Fig. 1). As well as novel coding, we identify non-coding and copy number drivers. Our analysis also reveals differences in the mutational and biological pathways influencing the initiation and progression of disease subtypes.

Cases, data and sequencing

Matched tumour-normal whole genome sequencing (WGS) data from 361 treatment-naïve cases of paediatric (< 18 years old) BCP-ALL were obtained from St. Jude Research Hospital (https://www.stjude.cloud/). Data were accessed and analysed through the DNAnexus cloud computing platform. Ethical permission was not required as all data were in the public domain.

WGS data were generated using 100bp paired-end libraries sequenced to an average read depth of 45× and 62× for normal and tumour samples respectively, using Illumina (San Diego, USA) HiSeq2000 technology. Raw sequencing data were aligned with BWAmem v0.7.17¹⁰ to GRCh38. Alignments were performed by Google Genomics. Cross contamination was assessed using GATK v4.0.0.1; no sample having > 2.6%. Tumour RNA sequencing (RNA-seq) on 222 (post quality control [QC]) of the 361 cases was performed on 125bp paired-end libraries using Illumina HiSeq technology to an average number of 55 x 10⁶ reads. RNA-seq fastq files were analysed using FastQC and aligned to GRCh38 using STAR v2.6.1¹¹, discarding samples with < 20% of reads aligning to the genome. Transcript abundance was calculated in transcripts per million (TPM) using RSEM v1.3.0¹² based on GENCODEv30 annotation and was adjusted for batch effects using ComBat-seq¹³.

Fusion genes were identified from RNA-seq data using STAR-Fusion v1.5.018 and FusionInspector¹⁴. Candidate fusions were retained when fusion genes were separated by > 1MB and fusions were absent from normal blood or bone marrow (1000 Genomes [n = 465] and HPA [n = 200] RNA sequencing projects).

The transcriptional impact of histone gene cluster 1 (chr6:26122685–26239852) deletion and CTCF alteration (deletion or mutation) were assessed using DESeq2 v1.329¹⁵, with default settings. Differentially expressed genes were identified after removing tumours with: (1) > 2 copies of the corresponding interval; (2) Alteration of both CTCF and the histone gene cluster 1 (n = 1). This resulted in 9 tumours with CTCF alterations and 8 tumours with histone cluster deletion.

Variant calling

Somatic single nucleotide variants (SNVs) and indels were called using Strelka v2.8.4¹⁶ adopting default parameters. QC filtering of somatic variants comprised: (1) Retaining only variants marked as ‘PASS’; (2) Excluding variants seen in panel of 160 matched germline samples; (3) Excluding variants in repetitive regions (extracted from UCSC) or in homopolymer runs of > 7 nucleotides; (4) Excluding variants with a POPMAX allele frequency > 0.001 in GnomAD v3. In-silico predictions of variant effects on protein function were made using Polyphen2 HumDif scores as per¹⁷. Driver mutation plot generated using Maftools¹⁸.

Mutational signatures

De novo extraction of signatures was performed using SigProfilerExtractor v1.0.18¹⁹. Extracted signatures were assigned to reference signatures from Catalogue of Somatic Mutations in Cancer (COSMIC) v3.1 using a cosine similarity threshold of 0.9. Mutation enrichment testing was performed on tumours with > 30% of mutations from either SBS7a or SBS2 and SBS13 and accounted for subtype applying Benjamini-Hochberg correction.

Tumour subtyping

Tumour chromosomal ploidy was based on copy number data. Tumours with a total chromosome number > 50 were classified as hyperdiploid and those with < 45, hypodiploid. Near haploid tumours (n = 11) chromosome number 24–30 are included the hypodiploid subtype unless otherwise stated. iAMP21 status was called as per Harrison et al ²⁰ on the basis of chromosome 21 ploidy and RUNX1 copy number. Subtypes defined by driver fusion events (e.g. ETV6-RUNX1, BCR-ABL1) were assigned on the basis of a clonal SVs consistent with fusion gene expression and corresponding RNA-seq identified fusions. Cases without an established initiating driver event were designated as unclassified/other. The subtype composition of the cohort is detailed in Supplementary Fig. 2.

Identification of cancer drivers and Pathways

Identification of SNV/indel drivers in coding regions was based on a consensus-based approach. Per gene P-values were calculated combining the output of MutSigCV v1.3.01²¹, dndsCV v0.0.1²² and OncodriveFML²³ using Harmonic means²⁴ and Benjamini–Hochberg correction. Variants were classified as non-silent using variant effect annotator (VEP)²⁵ annotations (Supplementary Table 3).

Driver CNVs were called using GISTIC2 v2.0.23²⁶ run in focal mode (excluding arm level events) with default parameters imposing a Q-value cut-off of 0.01. Genome regions were excluded if they: (1) overlapped a immunoglobulin locus, (2) contained no protein coding genes, (3) contained no genes expressed in the corresponding RNA-seq data (excluding deleted cases) or (4) the region was both significantly amplified and deleted.

To identify driver mutations in enhancer regions we adopted the strategy of Orlando et al²⁷. Cis-regulatory elements (CREs) were identified from promoter capture chromatin confirmation (PHi-C) contacts (CHiCAGO score > 5) from naïve B-cells²⁸. CRE-specific mutation probabilities for each tumour were generated by fitting a logistic regression model using the glm R package, accounting for base composition, mutation rate, replication timing, and coverage. Mean replication timing was extracted for the lymphoblastoid cell lines (GM12878, GM12813, GM12812, GM12801, GM06990). The Poibin R package was used for approximation of Poisson binomial to derive empiric P-values regions as per Melton et al²⁹.

Mutational clustering within CREs was tested to infer functionality. Regulatory regions harbouring > 5 mutations were permuted 10,000 times and tested assuming uniform mutation distribution, deriving empiric P-values. Frequency and clustering P-values were combined using Fisher’s method and adjusted for multiple testing using Benjamini–Hochberg correction. Genome regions with a Q-value < 0.1 were examined for transcriptional effects. Expression of genes whose promoter were captured by an interaction were compared between mutated and non-mutated samples using Benjamin-Hochberg corrected Wilcox rank-sum test. Tumours with CNVs at either the target gene or CRE were not considered.

Mutation burden in promoters and UTR regions was assessed using OncodriveFML²³. Promoters (defined from the transcription start site − 2000bp) were extracted from GENCODE v30 GRCh38.p12. Where genes had multiple transcription start sites all promoter sequences were evaluated jointly. Promoters were filtered for any overlapping coding or UTR sequence.

Driver genes were manually assigned to biological pathways. Gene – pathway assignments: RAS/RTK; NRAS, KRAS, PTPN11, FLT3, NF1, ABL1. B-cell development; PAX5, IKZF1, ETV6, ZEB2, RUNX1, TCF3, RAG1, RAG2, EBF1. Chromatin regulation; SETD2, HDAC7, NSD2, CTCF, KMT2A, STAG2, histone gene cluster 1. Cytokine signalling; JAK2, IL7R, CRLF2. Gene regulation; CREBBP, MLLT1, MLLT3, AFF1, BTG1, ERG, TCF4, NCOA6. Signal transduction; TBL1XR1, TBL1X, PBX1, PAG1. Cell cycle regulation; CDKN2A, CDKN2B, RB1. Immune regulation; BTLA, HLA-DRB5.

Identification of copy number and structural variants

Somatic copy number variation (CNV) was called using CNVkit v0.9.5.3³⁰. Tumour WGS data were called against a pooled reference, generated from 45 depth representative matched germline samples (23 male, 22 female). CNVkit segment specific coverage log2 ratios were adjusted for tumour cell purity, estimated by cpgBattenberg³¹. Contiguous segments with the same copy number were merged. CNVs were annotated as ‘arm’ level when a copy state occupied > 80% of the mappable length of a chromosome arm differed from chromosome copy number. Other variant regions defined as ‘focal’.

Structural variants (SVs) were called using Manta v1.5³², Lumpy v0.2.13³³ and Delly2 v0.8.1³⁴. Manta and Delly2 were run using default parameters. Lumpy was run using the wrapper Smoove v0.2.3. Variants were excluded if they were located in centromeric, telomeric or heterochromatic regions, had a variant allele frequency (VAF) < 0.1, or were seen in a panel of matched normals, generated using the corresponding method. Remaining variants were merged as per Li et al³⁵, retaining only those called by 2 or more methods.

SV cancer cell fractions were estimated using SVclone³⁶ and SV clustering examined using ClusterSV³⁵. Regions of chromothripsis were identified using ShatterSeek v0.4³⁷, based on thresholds of > 3 adjacent segments of oscillating copy number involving > 5 interleaved SVs. Candidate chromothripsis events were manually reviewed.

SV breakpoint motif enrichment was performed using HOMER v4.10.4³⁸, by extracting two 100bp sequences (± 50bp) from each breakpoint, excluding SVs where both break points mapped to immunoglobulin regions (Supplementary Table 1). HOMER extracted motif sequences are annotated using the most similar sequence, based on Pearson correlation coefficient, from the JASPAR³⁹ database. Annotated HOMER motifs were further processed with reference to motifs of candidate mutagenic drivers of ALL (Supplementary Table 2). Where the Pearson correlation between a HOMER motif and a candidate mutagenic motif exceeded the most similar JASPAR annotation they were substituted. Motifs with a correlation of < 0.85 were excluded from analysis.

To jointly analyse CNV and SV, regions called by GISTIC were additionally filtered, retaining only those with an enrichment of overlapping SVs. For each variant region only simple SVs (not part of a complex rearrangement called by SVClust) of the corresponding type (deletion/amplification) were used. For each GISTIC region chromosome-arm-specific background SV rates were estimated by permuting (n = 1000) arm-specific SVs. P-values were then computed as the proportion of permutations where the simulated SVs overlapping the locus was greater than or equal to the number of observed SVs overlapping the locus. Variant regions significantly enriched (P < 0.01) for overlapping SVs were retained. Additional copy number variant regions were identified using HMMcopy v1.32 calculating GC and mappability normalised tumour/normal log2 coverage ratios.

Clonality and tumour evolution

Tumour ploidy and SNV cancer cell fractions (CCF) were estimated using cpgBattenberg v3.5.0³¹, adopting default parameters with the exception of minimum ploidy, which was thresholded at 1.1. Single nucleotide polymorphisms alleles from the 1000 Genomes Project (v3, GRCh38) were counted in tumour and normal samples, and genotypes phased using impute2⁴⁰. Purity-corrected copy number segments were used to compute SNV/indel CCF estimates and subclones identified by DPClust v2.28³¹. Variants were assigned to most likely clusters. For each tumour the cluster with the highest CCF > 0.9 and < 1.1 was considered clonal, others considered subclonal. Samples were excluded based on the following criteria: (1) a variant cluster with CCF > 1.1; (2) no clonal variant cluster (CCF 0.9–1.1); (3) copy number state-specific SNVs which failed to cluster at predicted VAFs; (4) copy number solutions with homozygous deletions > 3Mb. In the first instance samples were analysed using Battenberg derived purity estimates, when resulting copy number solutions failed QC CCube v1.0⁴¹ estimates were used. 280 samples satisfying QC criteria were retained. Heterogeneity was estimated using the Simpson Index (probability that two individuals/cells, selected from a population/tumour, are from the same species/clone), calculated using VEGAN⁴². Evidence to support neutral evolution was sought using MOBSTER v0.1.1⁴³, as per authors recommendations (retaining only SNVs and indels in diploid regions). MOBSTER identifies variants with a VAF distribution consistent with neutral processes, termed a “neutral tail”. Variants belonging to a neutral tail, subclonal or clonal clusters were also analysed using dNdSCV and by calculating mutation rate (number of non-synonymous variants/all non-synonymous sites/total number of mutations) for ALL drivers (Supplementary Table 8).

As previously documented, the burden of SNVs and indels was low (median 0.6 Mb^− 1, range 0.04–5.31) when compared to the majority of solid cancers. Mutation burdens differed significantly across subtypes (P_{Kruskal−Wallis}=2.2x10^− 16), with iAMP21 and KTM2D (MML1) positive tumours having the highest and lowest mutational burdens respectively (Fig. 1A). The most common chromosome-arm level aberrations were loss of 9p (containing CDKN2A/CDKN2B) and gain of 21q (containing RUNX1), both occurring in 8% of cases (Supplementary Fig. 3). 9p loss preferentially occurred in TCF3-PBX1 translocated tumours (P_Fisher= 0.043), and 21q gain in hypodiploid tumours (P_Fisher= 0.012) (Supplementary Fig. 4A and 4B).

The median number of SVs was eight per tumour (2x10^− 3 Mb^− 1), with iAMP21 tumours possessing the highest number (Fig. 1B). The rate of SVs on chromosome 21 (0.027 Mb^− 1) was 10-fold higher than other chromosomes, largely accounted for by iAMP21 tumours (Fig. 1C). Since iAMP21 tumours are defined by RUNX1 copy number, we examined the distribution of SVs on chromosome 21, finding no clustering evident (Supplementary Fig. 5). Chromothripsis did not account for elevated SV rates in iAMP21 tumours as no events were observed on chromosome 21.

Identification of driver genes

We searched for drivers of ALL by first considering the following classes of somatic coding alterations; single nucleotide variants (SNVs)/indels, copy number variants (CNVs), structural variants (SVs) and loss of heterozygosity (LOH). In addition to established drivers, we identified a number of novel ALL drivers, including HLA-DRB5, the histone gene cluster 1, ZEB2, CTCF and MAP1B.

Consistent with previous reports^1,44, the most frequently altered genes included CDKN2A/B, PAX5, ETV6, ERG, RUNX1, NRAS, KRAS and IKZF1 (Fig. 2). By combining CNV and SV data we identified two novel regions of recurrent alteration. Firstly, a 120 kb region of HLA (6p21; 32,442,465 − 32,554,750 bps) was deleted in 17% of tumours (Fig. 3A). Within this region only HLA − DRB5 was expressed and deletion was associated with significantly reduced gene expression (P = 3.7x10^− 4). We further evaluated read depth data in tumours with an HLA SV but no CNV using an additional copy segmentation algorithm⁴⁵, finding evidence of a corresponding change number change within 2,000bp an SV breakpoints in every tumour (Supplementary Fig. 6). Secondly, a 117 kb region of 6p22.2 overlapping histone gene cluster 1 (26,122,685 − 26,239,852 bps) was deleted in 10% of tumours (Fig. 3B), within which deletion was associated with reduced expression of HIST1H4E (P = 0.034) and HIST1H2AE (P = 0.023). The cancer cell fraction (CCF) of SVs in the region suggested the majority of these variants are clonal.

Non-silent SNVs or indels in ZEB2, CTCF and MAP1B were seen in 2.2%, 1.7%, and 1.4% of tumours respectively (Supplementary Table 4 and Supplementary Fig. 7). ZEB2 missense mutations were clustered at three base positions, consistent with oncogenic activation (Supplementary Fig. 8). A further eight tumours had focal ZEB2 amplifications. In addition to truncating and damaging mutations in CTCF, an additional 20 tumours had CTCF deletions, consistent with gene inactivation. None of the MAP1B mutations were recurrent and all were predicted to be damaging.

Next we sought to identify non-coding driver mutations. We observed a significant excess of promoter mutations for BTLA (4.2%, Q = 0.002) and CHID1 (2.2%, Q = 0.049). BTLA promoter mutations were clustered within a 27 bp region, and were associated with 5-fold reduced BTLA expression (P_{Mann−Whitney}=0.056), the small number of tumours with corresponding expression data presumably preventing this relationship from attaining significance (Fig. 4A). Mutations were predicted to alter the affinity of various transcription factors (TFs) with evidence of binding from ChIP-sEq. Each BTLA promoter mutant possessed a variant predicted to disrupt the binding of an interacting TF, most frequently RUNX1/3, GATA3 and MYB (Supplementary Table 5). Of 14 CHID1 promoter mutations 12 clustered within a 12bp region 1kb upstream of the transcription start site within an AGO1 binding site, corresponding RNA-seq was consistent with mutation conferring reduced CHID1 expression (Fig. 4B).

To search for significantly mutated cis-regulatory elements (CREs) we restricted our analysis to sequences interacting with promoters through chromatin looping. A CRE interacting with the USP22 promoter was mutated in 9.1% of tumours and these were associated with reduced USP22 expression (Q_{Mann−Whitney}=0.009) (Fig. 4C). Mutations were not uniformly distributed and occupied a number of TF binding sites. A CRE interacting with XRCC2 was mutated in 4% of tumours, mutations were associated with elevated XRCC2 expression (Q_{Mann−Whitney}=0.046) (Fig. 4D).

We found no evidence of recurrent mutations within UTRs or non-coding RNAs when imposing a threshold of at least five effected tumours.

Mutated pathways

In addition to documented enrichment of NRAS and KRAS mutations in hyperdiploid ALL and TP53 mutations in hypodiploid/near haploid ALL, we identified a number of additional associations (Supplementary Table 6). Notably, TBL1XR1 and ZEB2 mutations were enriched in ERG-deleted ALL (present in 21% and 14% of tumours respectively). iAMP21 tumours were characterised by an excess of RB1 deletions (40%) and IL7R mutations (20%). NF1 mutations were largely confined to near haploid tumours occurring in 45%. ETV6-RUNX1 positive tumours were associated with enrichment for the deletion of TBLXR1 and RAG1/RAG2. Finally undefined tumours (included in other) showed an excess of IKZF1 deletions.

Given the identification of alterations in both CTCF and the histone gene cluster 1 we explored their transcriptional impacts, performing differential expression analysis. We identified five differentially expressed genes in both sets of mutated tumours (P_binomial= 1.5x10^− 8), including CLIC5 and IGF2BP1 (Supplementary Table 7 and Supplementary Fig. 9). Whilst CLIC5 and IGF2BP1 are markers of hyperdiploid ALL⁴⁶, none of the tumours harbouring these mutations were of this subtype. In total 60 tumours (17%) harboured alterations (deletions or mutations) in either CTCF or the histone gene cluster 1.

To produce a composite picture of somatic events we clustered drivers by biological pathways (Fig. 5). The most frequently altered pathway featured B-cell developmental genes, altered in 70% of tumours. This analysis confirmed the importance of RAS/RTK alterations in hyperdiploid biology and highlighted a number of other key pathways, including secondary alterations affecting cytokine signalling in iAMP21, where 37% of tumours possessed a secondary hit in either IL7R, JAK2 or CRLF2 (including 3/5 cases of P2RY8-CRLF2 translocation). BCR-ABL tumours were characterised by recurrent alteration of genes regulating the cell cycle, whilst hypodiploid tumours were typified by disruption of transcriptional (gene) regulation. As well as disruption of B-cell development genes, driven by loss of the second ETV6 alleles, 56% of ETV6-RUNX1 tumours were effected by alterations in chromatin and histone modification genes.

We assessed the clonality of driver gene mutations, finding most occur both clonally and sub-clonally (Fig. 6A and Supplementary Fig. 10). Exceptions to this included ZEB2 mutations which were always clonal, moreover mutations of B-cell development and haematopoiesis genes (IKZF1, PAX5 and ZEB2) tended to be clonal. Conversely the majority of RAS/RTK gene mutations were subclonal (65%; P_Fisher=0.001). This was especially true of ERG-deleted tumours where 44% possessed a subclonal RAS/RTK variant (accounting for 89% of these mutations in the subtype) compared to only 8% with a clonal variant. Conversely RAS/RTK mutations in hyperdiploid tumours were usually clonal (60%), occurring in 44% of tumours compared to 20% with only a subclonal variant.

To assess the molecular mechanisms promoting tumorigenesis we used non-negative matrix factorization (NMF) to extract COSMIC single base signatures (SBS). Ten signatures were seen contributing at least 1% of mutations (Supplementary Fig. 11). SBS5 (aetiology unknown but clock-like) accounted for the most mutations (41%) and was seen in all tumours (Supplementary Figs. 12 and 15). SBS2 and SBS13 (AID/APOBEC) were almost exclusively confined to ETV-RUNX1 tumours (Q_{Man−Whitney}=2.3x10^− 33 and Q_{Man−Whitney}=1.1x10^− 36 respectively), whilst SBS7a (UV exposure) was highly enriched in iAMP21 tumours (Q_{Man−Whitney}=5.3x10^− 12) (Supplementary Figs. 13 and 15). SBS7a was associated with the highest mutation rate, 10-fold higher than SBS1 (Supplementary Fig. 16) and was largely responsible for the increased mutation rate in iAMP21 tumours (Supplementary Fig. 17).

It has been reported that SVs in ETV6-RUNX1 positive tumours bear the hallmarks for RAG1 and RAG2 activity⁴⁷. We searched for recurrent DNA motifs at SV breakpoints, firstly agnostically using motif enrichment performed using HOMER, and secondly by assessing the similarity of discovered motifs to those of candidate mutagenic drivers (Supplementary Table 2). Overall the most enriched motifs were the RAG heptamer (P < 1x10^− 200), RAG nonamer (P < 1x10^− 200) and PRDM9 (P = 1x10^− 121), found at 8.8%, 7.2% and 1.5% of breakpoints respectively. With the exception of ETV6-RUNX1 positive tumours the most frequent enriched motifs were the RAG hepamer and RAG nonamer, however in ETV6-RUNX1 the most common motif was PRDM9 contained in 28% of breakpoints (P = 1x10^− 162) (Fig. 6B). Overall RAG heptamers were observed at both breakpoints of 3% of SVs.

We also sought evidence of activation induced deaminase (AID) activity at SV breakpoints. Due to the degenerate nature of AID motifs we used the number of repeats of core AID recognition sequences (Supplementary Table 2) as a proxy of activity. After comparing SVs in immunoglobulin regions we established a cut-off of > 10 repeats as suggestive of AID activity (Supplementary Fig. 18). AID signatures were detected in the breakpoints of 2% of all SVs, but 17% of SVs in TCF3-PBX1 positive tumours (P_Fisher=8x10^− 9) (Supplementary Fig. 18).

Clonal architecture

The presence of subclonal populations in tumours was almost universal (observed in 98% of tumours; Fig. 7A). Most commonly tumours possessed two subclones, however ERG-deleted tumours tended to have a higher number of subclones (Q_{Mann−Whitney} =0.008) and KMT2A translocated lower (Q_{Mann−Whitney}=0.038) (Supplementary Fig. 20). The distribution of subclone CCF was similar across subtypes, with the exception of hyperdiploid tumours whose subclones tended to have higher CCFs (Q_{Mann−Whitney}=0.004), 50% having a subclone with a CCF between 0.7 and 0.8, compared to 9% of other tumours (Supplementary Fig. 21).

The diversity of cell populations (i.e. heterogeneity) varied across subtypes, hypodiploid and ERG-deleted tumours were the most heterogeneous (median Simpson index = 0.61 and 0.62; Q_{Mann−Whitney}=1.7x10^− 3, 1.13x10^− 3), while hyperdiploid tumours exhibited lower heterogeneity (Simpson index = 0.45; Q_{Mann−Whitney}=2.8x10^− 7).

Accounting for mutational frequency, we found subclones were enriched for driver mutations (P_binomal=1.8x10^− 5) relative to clonal populations. To examine the processes influencing tumour evolution we compared the frequency of driver gene alteration in subclones. ERG-deleted subclones were the most likely to possess a mutation in an ALL driver gene (35%; Q_binomal=0.0016), whereas BCR-ABL1 positive tumour subclones contained the lowest frequency of driver alterations (4%; Q_binomal= 0.052) (Supplementary Fig. 22).

To explore the possible contribution of neutral evolution to tumour heterogeneity we used MOBSTER⁴³, which models variant distribution under neutrality. MOBSTER called neutral tails in the majority of tumours, fitting a median of 12% (SNVs) and 16% (SNVs and indels) of variants (Supplementary Fig. 23). Evidence of positive selection was sought using dNdSCV, revealing that tail compartments were enriched in NRAS (Q = 3.4x10^− 8) and KRAS (Q = 1.9x10^− 3) mutations. Additionally rates of non-synonymous substitution in NRAS, KRAS, FLT3, NSD2 were higher in tail compartments than clonal groups (Supplementary Fig. 24).

By analysing whole genome sequencing and transcriptome data from a large series of ALL patients we provide for an enhanced understanding of ALL subtype genetics identifying novel candidate coding, non-coding and copy number drivers. Our analysis reveals differences in the mutational and biological pathways processes influencing the initiation and progression of disease. We also provide evidence of ongoing selection of subclonal mutations as an important feature of ALL evolution.

Around half of ETV6-RUNX1 and iAMP21 tumours are characterised both by an increased mutation rate and enrichment for specific COSMIC single base signatures. AID/APOBEC related signatures, SBS2 and SBS13, were largely confined to ETV6-RUNX1 ALL, while UV associated SBS7a was highly enriched in iAMP21 positive tumours. SBS7a has previously been reported to occur in ALL tumours at a similar rate¹⁹. Moreover SBS7a it occurs at similar rates in a number of tumours types lacking this exposure¹⁹. These observations provide evidence for an additional mechanistic origin of SBS7a suggesting either an unknown germline genetic influence or environmental exposure promoting this subtype.

As well as identifying novel coding drivers, we also provide the first evidence of non-coding mutations influencing ALL leukaemogenesis. Both hotspot mutation and amplification impacted ZEB2, consistent with oncogenic activation. ZEB2 is a zinc-finger, E-box–binding transcriptional repressor and is highly expressed in haematopoietic stem cells and splenic B-cells⁴⁸ where it regulates haematopoiesis. ZEB2 deficient mice have decreased B-cells populations⁴⁹ and overexpression of the gene promotes proliferation⁵⁰ in AML. Collectively this suggests ZEB2 drives tumourigenesis by altering normal haematopoiesis and promoting B-cell proliferation, further ZEB2 variants were clonal implying that, in those tumours, it was required for transformation.

Deletions of the gene encoding B and T-lymphocyte attenuator (BTLA) have previously been reported in ALL⁵¹, which typically overlap CD200, however the functional mediator has yet to be elucidated. The identification of BTLA promoter variants suggest loss of this genes as opposed CD200 confers selective advantage. Mutated CREs on chromosomes 17 and 7 were associated with reduced USP22 and increased XRCC2 expression respectively. USP22 has been implicated in T-cell activation however its activity as a deubiquitinase of core histones suggests a wider role in transcriptional regulation⁵². XRCC2 is a key regulator of double strand break repair. How XRCC2 promotes ALL is unknown, although the importance of double stand breaks inducing enzymes (RAG, AID) in leukaemogenesis may offer an explanation.

We additionally report recurrent copy and structural variation impacting HLA-DRB5. Although the selective basis of these lesions is unclear, genome-wide association studies in chronic lymphocytic leukaemia⁵³ and lymphoma⁵⁴ have identified germline variants in HLA-DRB5 influencing disease risk.

Around 10% of tumours possessed a deletion overlapping the histone gene cluster 1, which contains 16 different histone isoforms, including at least two of each core histone. Recurrent histone H1 mutations have also been reported in around 30%-50% of lymphomas causing alterations in 3D chromatin architecture inducing stem cell like transcriptional profiles⁵⁵. Further functional characterisation will be required in order to determine the functional gene(s) within these lesions. We also show that tumours harbouring histone gene cluster 1 deletions and CTCF alterations share a common transcriptional profile, with down-regulation of IGF2BP1 and CLIC5 occurring in both groups. Interestingly both genes were recently identified alongside CTCF as markers hyperdiploid tumours⁴⁶. No hyperdiploid tumours were included in this analysis precluding a co-variant effect driving this relationship. Mutations these genes were however enriched in tumours with no assigned subtype. Alteration of CTCF/histone gene cluster 1 was common, occurring in 17% of tumours. Collectively these data raise the possibility of a ‘hyperdiploid-like’ subtype of ALL.

While there is commonality in disruption of pathways between ALL subtypes there are clear distinctions not only in the particular biological pathways harbouring mutations but also the clonal distribution of these mutations. These differences have implications both for choice of potential targeted therapies and determining which patients will benefit most from their use. As targeting activated oncogenes is generally more tractable than tumour suppressors the biological pathway from most relevance for ALL are RAS/RTK and IL7 signalling. Importantly, RAS/RTK mutations in hyperdiploid tumours were typically clonal whereas in ERG deleted ALL mutations were almost exclusively subclonal, suggesting the efficacy of RAS/RTK inhibitors will differ between subtypes. Alteration of IL7 signalling was common in iAMP21 tumours suggesting that JAK2 inhibitors may have utility in this group.

Variants identified as neutrally occurring by MOBSTER were enriched in ALL drivers, indicating that neutral evolution is not a major contributor to genetic heterogeneity in ALL, this may be reflective of the low mutation rate of the disease comparative to most solid cancers. We show that subclonality in ALL is common suggesting Darwinian evolution drives the selection and expansion of mutations and subclones. Consequently the use novel targeted therapies should take account of the clonality and heterogeneity of tumours.

DATA AVAILABILITY

Data available from DNAnexus (DNAnexus.com) subject to application from St Jude hospital.

WEB RESOURCES

Repetitive genomic loci used for variant filtering were downloaded from hgdownload.cse.ucsc.edu/ goldenpath/hg38/database/simpleRepeat.txt.gz.

Smooth the wrapper for structural variant caller Lumpy is available from github.com/brentp/smoove.

Structural variant positional filtering was based on https://github.com/dellytools/delly/blob/master /excludeTemplates/human.hg38.excl.tsv.

FusionInspector the RNAseq fusion gene detection software is available from github.com/FusionInspector.

Control RNAseq data for GETex and HPA were downloaded from ebi.ac.uk/arrayexpress/experiments/E-GEUV-1/samples/ and ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR315.

Replication timing was downloaded from 2.replicationdomain.com/.

Promoters were defined using genode v30 downloaded from ftp://ftp.ebi.ac.uk/pub/databases /gencode/Gencode_human/release_30/ gencode.v30.annotation.gtf.gz.

VEGAN package for calculating population diversity is available from github.com/vegandevs/vegan.

HMMcopy is available from http://www.bioconductor.org/packages/release/bioc/manuals/HMMcopy/ man/HMMcopy.pdf

ACKNOWLEDGMENTS

This work was supported by Cancer Research UK (C1298/A8362) and Blood Cancer UK.

The authors declare no competing financial interests.

AUTHOR CONTRIBUTIONS

JS, AC, PL performed bioinformatic and statistical analyses. PH and AC provided additional bioinformatics support. JS and RH drafted the manuscript.

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SupplementaryFigures.pdf
Supplementary Figure 1. Study overview. Supplementary Figure 2. Molecular subtype composition. Due to small numbers near haploid (chromosome copy number 23-30) tumours were grouped with hypodiploid tumours and BCR-ABL like with unclassified/other. Supplementary Figure 3. Chromosomal arm aberrations. Stacked bar charts of alteration frequency. Left Y-axis - percent of tumours. Right Y-axis number of tumours. Supplementary Figure 4. Frequency of large-scale chromosomal aberrations. (a) 9p deletion; (b) 21q amplification. Supplementary Figure 5. Density and regional genetic plot of structural variant (SV) distribution across chromosome 21. Ideogram cytobands based on GRCh38 coordinates. The location of RUNX1 and ERG are shown for reference. Lower horizontal line depict individual variants, blue – deletions; red – amplifications; purple – inversions. Supplementary Figure 6. Copy number changes around structural variant breakpoints overlapping HLA-DRB5. Tumours with structural variants overlapping chr6-32,442,465-32,554,750 but lacking supporting CNVs were analysed using an additional CNV caller (HMMcopy). Dots show the mappability and GC normalised log2 tumour/normal coverage ratio for 1000bp bins. Horizontal blue lines show copy number segments called by HMMcopy and vertical red line show the location of SV breakpoints. Supplementary Figure 7. Mutation plots of (A) ZEB2, (B) CTCF and (C) MAP1B. Lollipops represent the position of mutations within the protein. Lollipop height indicates mutation count and text shows the specific amino acid substitutions, plot produced using Maftools6. Supplementary Figure 8. Mutations in ZEB2, IL7R and JAK2 cluster into hotspots, consistent with oncogenic activation. OncodriveCLUST plot, x axis shows the frequency of non-silent variants located with clusters. Y axis –log10FDR corrected P-value. Supplementary Figure 9. Expression of genes in CTCF and histone gene clusters 1 altered tumours. Normalised RNAseq counts of CLIC5 (a) and IGF2BP1 (b) extracted by DESeq2. Supplementary Figure 10. Driver gene mutation (SNV/indels) clonality. Proportion of non-silent variants assigned to clonal clusters. Horizontal red line depicts the clonal proportion of all driver gene mutations. Supplementary Figure 11. Proportion of mutations attributed to mutational signatures. Legend details COSMIC single base signatures (extracted using SigProfilerExtractor) and putative mechanism. Supplementary Figure 12. Prevalence of mutational signatures. Y-axis, the proportion of all tumours in which COSMIC mutation signatures observed. Supplementary Figure 13. Mutation count attributed to each COSMIC signature, by tumour subtype. Supplementary Figure 14. Subtype mutation count, by COSMIC signature. Supplementary Figure 15. Mutation signature profile in all 361 tumours. Each bar represents a tumour. Upper pane shows the proportion of mutations assigned to signatures. Lower pane shows the mutation count per signature. Lower multi-coloured horizontal bar denotes tumour subtype. Supplementary Figure 16. Signature mutation burden and frequency distribution. Solid horizontal black line denotes the median mutation count per tumour. Y-axis; mutation count. X-axis; cumulative frequency, 0-100% from left to right. Each dot represents a tumour and colour denotes subtype. The lower number in bold - number of tumours with signature mutations. Supplementary Figure 17. Mutational signatures SBS7a and SBS13/SBS2 drive higher mutations rates in iAMP21 and ETV6-RUNX1 tumours respectively. Box and whiskers plot of mutation count. Each dot denotes a tumour. Purple outline denotes mutations per tumour after remove variants attributed to (a) SBS7a (iAMP21) and (b) SBS13/SBS2 (ETV6-RUNX1). Control group contains all tumours after the removal of (a) iAMP21 and (b) ETV6-RUNX1. Supplementary Figure 18. The number of repeats of core AID motifs in structural variant (SV) breakpoints in immunoglobulin regions and non Ig loci. 500bp sequences flanking SV breakpoints was extracted and the number of AID core motifs enumerated. Y-axis density and X-axis motif number per SV. Supplementary Figure 19. The proportion of SVs with an AID motif count >10. Red horizontal red line shows the average for all tumours. Supplementary Figure 20. Subclonal reconstruction showing the number of clones in tumour subtypes. Bar chart of cluster counts (clonal and subclonal) in each tumour. Y-axis; proportion of tumours. X-axis; number of clusters. Supplementary Figure 21. Subclone frequency distribution. Density plot of subclone cancer cell fraction (CCF). For each subtype the subclone CCF is plotted. Y-axis; density. X-axis; subclone CFF. Supplementary Figure 22. Proportion of subclones with a driver mutation based on Supplementary table 8. Supplementary Figure 23. Violin plots of variants fitted as neutral by MOBSTER43; (a) proportion of variants fitted per tumour (b) VAF of fitted variants. Supplementary Figure 24. Non-synonymous mutation rates of ALL drivers in clonal, subclonal and neutral compartments. Line graph shows the rate of nonsynonymous mutations. Variants; (a) SNVs and (b) SNVs and indels consistent with neutral evolution were identified using MOBSTER43.
SupplementaryTables.pdf
Supplementary Table 1. Immunoglobulin regions used for filtering somatic variants. Supplementary Table 2. Motifs of candidate mutagenic drivers in tumours, used for the motif enrichment in SV breakpoint analysis. Supplementary Table 3. Non-silent variant effect predictor annotations. Supplementary Table 4. Driver genes identified from short variant analysis. Table compiled from the output of dndsCV, MutSigCV and ONCOdriveFML using SNVs and indels retaining only genes with significant P-values in ≥2. Q-values calculated using FDR correction (Benjamini-Hochberg). Undescribed genes in bold. Supplementary Table 5. Disrupted transcription motifs in the promoter of BTLA. 15 tumours possessed BTLA-promoter mutations which were predicted to disrupt the binding of overlapping transcription factors. Table shows number of tumours with a mutation predicted to disrupt the motif of a binding transcription. Supplementary Table 6. Frequency of genetic alteration in tumour subtypes, excluding subtype-initiating lesions. Supplementary Table 7. Differentially expressed genes in histone gene cluster 1 deleted and CTCF altered tumours identified by DESeq2. Rank refers to the P-value ranked position of that gene in the list of most significantly differentially expressed genes within each data set. Supplementary Table 8. Previously reported ALL driver genes.

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Cancer drivers and clonal dynamics in acute lymphoblastic leukaemia subtypes

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