File Name: intratumor heterogeneity and branched evolution revealed by multiregion sequencing .zip
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MSeq data and evolution analysis of single region-data from 64 MSI GOAs show that chromosome 8 gains are early genetic events and that the hypermutator-phenotype remains active during progression.
MSeq may be necessary for biomarker development in these heterogeneous cancers. Comparison with other MSeq-analysed tumour types reveals mutation rates and their timing to determine phylogenetic tree morphologies.
Gastro-oesophageal adenocarcinomas GOAs are one of the commonest causes of cancer mortality worldwide 1. These tumours are characterized by a hypermutator-phenotype leading to high mutation loads and a large fraction of small insertions and deletions indels , predominantly in homopolymer and dinucleotide repeats.
This has been attributed to a large number of mutation-encoded neoantigens, which enable recognition by the adaptive immune system. Consistent with the notion of high immunogenicity, dMMR cancers are among the tumour types most sensitive to checkpoint-inhibiting immunotherapy However, not all tumours respond to immunotherapy and some acquire resistance after initial benefit.
Chemotherapy and anti-angiogenic drugs are the only other systemic treatment options for dMMR GOAs and the identification of novel therapeutics is important to improve outcomes. Genetic intratumour heterogeneity ITH and ongoing cancer evolution have been demonstrated in multiple cancer types 7.
The ability to evolve is thought to foster cancer progression, drug resistance and poor outcomes 8. High mutation rates may fuel evolvability by generating an abundance of novel phenotypes which selection can act upon 9. A pan-cancer study indeed demonstrated large numbers of subclonal mutations within single tumour regions of MSI cancers Our previous work in kidney cancer for example showed that most driver mutations are located in subclones Subclonal driver mutations are poor therapeutic targets as co-existing wild-type subclones remain untargeted They furthermore hinder effective biomarker development as the analysis of single tumour regions incompletely profiles the genomic landscape of the entire tumour.
However whether they are truncal or subclonal within individual tumours is unknown. Multi-region exome sequencing MSeq reconstructs cancer evolution by comparing mutational profiles from spatially separated tumour regions. MSeq found that mutations often appear to be present in all cancer cells i. Seven primary tumour regions from each of four GOAs Fig.
Two lymph node metastases were included from each of two cases. TNM-stage was assessed but no other clinical information was available as the samples had to be anonymised to comply with local ethics and research legislation.
The grey line labelled Z marks the gastro-oesophageal junction. The table shows the number of heterogeneous Het and ubiquitous Ub mutations identified in each tumour and their percentage of the total non-silent mutation count of the tumour.
About — median: non-silent mutations were identified per case Fig. The number of ubiquitous non-silent mutations that were detected across all sequenced regions per tumour ranged from to median: This exceeded the number of ubiquitous non-silent mutations reported for clear cell renal cell carcinomas ccRCC, median: 28 11 , and even for lung cancers median: 16 and melanomas median: 19 , which are among the most highly mutated cancer types 20 Fig. MSeq-identified ubiquitous mutations are likely to define the mutations that were present in the founding cell of each tumour before diversification into subclones occurred These high numbers hence reveal that the dMMR-phenotype was likely acquired in the precancerous cell lineage considerably earlier than malignant transformation of the founding cell.
Malignant transformation shortly after dMMR acquisition which was then followed by selective sweeps is an alternative explanation. Yet, it appears unlikely that this would have left no trace of the early subclones in any tumour. A median of mutations were only detectable in some but not in all analysed tumour regions per case and hence heterogeneous.
This significantly exceeded the heterogeneous mutation burden detected by MSeq in ccRCC 11 by fold, in lung cancer 16 by fold, and in melanoma 19 by fold Fig. Importantly, the median mutation load per region in these MSeq series was similar to those reported by the TCGA for the respective cancer type Fig. High mutation and neoantigen loads are associated with immunotherapy benefit.
Between and of these were clonal. This is higher than clonal neoantigen loads reported for most lung cancers or melanomas It is conceivable that this high clonal neoantigen burden explains the immunotherapy sensitivity of dMMR tumours We next investigated mutational signatures by counting the number of all possible base substitution in their trinucleotide contexts Supplementary Fig.
Signature 1 mutations reflect the spontaneous deamination of methylated cytosine, a mutational process active in most normal tissues. A fraction of these were likely acquired in the normal cells over the lifetime of these patients. However, based on the estimated mutation rate in normal gastro-oesophageal epithelium, only 0. It is hence likely that the dMMR-phenotype also contributes to the generation of signature 1 mutations. A total of Tumour 3 harboured a POLD1 mutation but this was subclonal and could not explain the presence of clonal signature 14 mutations.
The absence of signature 14 from subclonal mutations furthermore suggested that this is a passenger mutation. No other mutational signatures contributed substantially to the heterogeneous mutations, confirming that the MSI-phenotype remains active during cancer progression and is the primary mechanism generating these large numbers of subclonal mutations.
Tumour 4 showed highly aberrant near-tetraploid profiles in all regions. A high number of mutations were present on all copies of the major allele of most gained chromosomes Fig. CIN was confirmed by the weighted genome integrity index wGII that measures the proportion of all chromsomes with copy number states that differ from the ploidy of a sample and where values above 0.
Near-diploid and near-triploid CNA profiles were found in distinct regions of Tumour 1. Profiles showing chromosomal instability CIN are labelled with a black bar on the right. Among the small number of ubiquitous gains, only Chr8q and Chr20q were gained in more than one tumour.
To further time the acquisition of these recurrent truncal CNAs, we mapped ubiquitous mutations onto the allele-specific CNA profiles. Copy number gains that occurred early can be identified if the majority of mutations in that region have a mutation copy number 23 which is lower than that of the gained allele.
We next deconvoluted the subclonal composition of individual regions and reconstructed the phylogenetic tree for each tumour Fig. Similar to MSeq analyses of other tumour types 11 , 16 , 19 , this revealed branched evolution. Comparison of the phylogenetic trees with the mutation heatmaps showed some phylogenetic conflicts. Inspection of the CNA status of the mutated DNA positions showed that most conflicts could be explained by losses of chromosome copies in individual regions marked in green in Fig.
Thus, subclones can lose a small proportion of mutations during cancer evolution. Trees were reconstructed from non-silent and synonymous mutations and trunk and branch lengths are proportional to the number of mutations acquired.
Trees are rooted at the germline DNA sequence, determined by exome sequencing of DNA from tumour adjacent normal tissue. Subclones that define the tips of the tree are labelled with the tumour region in which they were identified. Numbers were added where several subclones were identified by the phylogenetic deconvolution algorithm within a tumour region, with 1 defining the largest intra-regional subclone and 2, 3 increasingly smaller subclones.
Likely driver mutations and relevant loss of heterozygosity LOH events were mapped onto the branch of the trees where they likely occurred. Genes affected by more than one genetic aberration within a tumour are labelled with the genetic aberration type that occurred.
Phylogenetically closely related clones were usually located in close physical proximity Supplementary Fig. Importantly, each of the two lymph node metastases analysed in Tumours 2 and 3 had evolved from distinct subclones rather than being seeded by the same subclone or sequentially from one node to the other Fig.
Dissemination hence propagated subclonal diversity from the primary tumour to metastatic sites. We next assessed the evolution of putative driver mutations and of corresponding LOH of tumour suppressor genes and mapped them onto the phylogenetic trees Fig. Tumours 2—4 furthermore harboured a truncal frameshift mutation in MSH6.
We could not formally demonstrate that the two mutations affected both alleles of the ARID1A tumour suppressor gene but biallelic inactivation is likely as all mutations were disrupting in nature, suggesting evolutionary selection for inactivating events.
Truncal mutations in TP53 were found in three tumours. Moreover, both showed truncal Chr18q loss which promotes CIN in colorectal cancer A third ARID1A mutation was subclonal and affected recurrently mutated amino acids AAdel located proximally to the truncal frameshift mutations. This may be functionally relevant if ARID1A had retained some residual activity despite the more distal mutations. The most parsimonious explanation for this phylogenetic conflict is that the same mutation independently evolved twice, once in AL and once in the ancestor cell of P1 and Y1.
It is conceivable that two cells independently acquire the same mutation in some tumours of this size. The tumour suppressor gene PRDM2 harboured frameshift mutations on the trunks of Tumours 2 and 3 and a second frameshift mutation was acquired in subclones of each tumour, potentially leading to biallelic inactivation.
Given the high burden of mutations caused by dMMR, it is possible that several mutations which we classified as likely drivers are passengers without significant fitness effects. Tumour 2 harboured a truncal JAK2 frameshift mutation. In addition, a subclonal JAK2 splice-site mutation evolved in one clade and a frameshift mutation in region AE.
Another subclone had acquired a JAK1 frameshift mutation but no evidence for biallelic inactivation was found. One clade in Tumour 2 furthermore acquired two disrupting mutations in B2M. Inspecting short read sequencing data confirmed that these were not located on the same allele but conferred biallelic inactivation which abrogates MHC Class I antigen presentation Supplementary Fig. Although several primary tumour regions in Tumours 2 and 3 showed biallelic B2M inactivation this was not propagated to any of the four lymph node metastases Fig.
The two tumours with evidence of immune evasion events, which also had the highest truncal and subclonal mutation burdens, showed higher T-cell infiltrates than the other two cases Fig.
Black bar: median; p -values Spearman rank test are shown for significant differences. Together with the identification of parallel evolution in Tumours 2 and 3, this suggests that these tumours are under selection pressure and adaptive mutations continue to evolve. Our results show that MSeq allows to dissect the temporal dynamics of selection in dMMR tumours and this can be used to reveal what genetic alterations are selected for or against in larger series.
The total number of synonymous and non-synonymous mutations available in each category for the analysis are shown beneath the plot.
We first used our MSeq dataset to assess which information can be robustly generated by single sample deconvolution and which ones are more likely to be gained by MSeq. Moreover, the number of mutations identified as clonal in a single region varied highly between samples from the same tumour.
Recent advances in next-generation sequencing have opened a new prospect in the field of cancer genomics and cancer evolution. Using next-generation sequencing, researchers have revealed that the occurrence of somatic events such as mutations and copy number alterations is associated with tumor evolution and genomic diversity in tumors, which is known as tumor heterogeneity 1. Tumor heterogeneity includes interpatient tumor heterogeneity 2 , intratumoral heterogeneity 3 , 4 , intermetastatic heterogeneity 5 - 8 , and intrametastatic heterogeneity 9. Tumor heterogeneity is a key factor associated with heterogeneous treatment responses including cancer progression, relapse and drug resistance Primary lung cancers harbor a larger mutational burden compared to other cancers, which might be a reason of poor treatment outcomes in patients with primary lung cancer 1 , 11 -
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Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing To examine intratumor heterogeneity, we performed exome sequencing, chromosome Supplementary appendix. Click here to view.(34M, pdf). Go to.
Tumor Heterogeneity View all 7 Articles. Today, clinical evaluation of tumor heterogeneity is an emergent issue to improve clinical oncology. In particular, intra-tumor heterogeneity ITH is closely related to cancer progression, resistance to therapy, and recurrences. It is interconnected with complex molecular mechanisms including spatial and temporal phenomena, which are often peculiar for every single patient.
Adrenocortical carcinoma is a rare cancer with poor but heterogeneous prognosis. The main prognostic factors used in clinical practice at present are the tumor extension, best reflected by the ENSAT stage 2 , and the tumor proliferation, estimated either by mitotic count 3 or Ki67 proliferation index 4 , 5. However, the prognosis still varies widely among tumors with the same tumor stage and proliferation index 6. Recently, pan-genomic studies have identified molecular subtypes closely associated with prognosis 7 , 8. This subgroup is associated with very poor outcome.
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