Multiregion whole-genome sequencing depicts intratumour heterogeneity and punctuated evolution in ovarian clear cell carcinoma

Yin, Xiang; Bi, Rui; Ma, Ping; Zhang, Shengzhe; Zhang, Yang; Sun, Yunheng; Zhang, Yi; Jing, Ying; Yu, Miao; Wang, Wenjing; Li, Tan; Di, Wen; Zhuang, Guanglei; Cai, Mei-Chun

doi:10.1136/jmedgenet-2019-106418

Cited by 15 publications

(11 citation statements)

References 19 publications

(24 reference statements)

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“…In terms of mutational profiles, our analyses suggest the involvement of cytidine deaminases and kataegis in ATC. This is a recently described localized mutational aggregation in which multiple same-strand substitutions are clustered over kilobase-sized DNA regions [ 30 , 41 , 42 ]. As kataegis can result from AID/APOBEC-catalyzed cytidine deamination, the finding of an ATC-prevalent mutational profile suggesting dysregulation of APOBEC is highly interesting [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Pan-Genomic Sequencing Reveals Actionable CDKN2A/2B Deletions and Kataegis in Anaplastic Thyroid Carcinoma

Stenman

Yang

Paulsson

et al. 2021

Cancers

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Anaplastic thyroid carcinoma (ATC) is a lethal malignancy characterized by poor response to conventional therapies. Whole-genome sequencing (WGS) analyses of this tumor type are limited, and we therefore interrogated eight ATCs using WGS and RNA sequencing. Five out of eight cases (63%) displayed cyclin-dependent kinase inhibitor 2A (CDKN2A) abnormalities, either copy number loss (n = 4) or truncating mutations (n = 1). All four cases with loss of the CDKN2A locus (encoding p16 and p14arf) also exhibited loss of the neighboring CDKN2B gene (encoding p15ink4b), and displayed reduced CDKN2A/2B mRNA levels. Mutations in established ATC-related genes were observed, including TP53, BRAF, ARID1A, and RB1, and overrepresentation of mutations were also noted in 13 additional cancer genes. One of the more predominant mutational signatures was intimately coupled to the activity of Apolipoprotein B mRNA-editing enzyme, the catalytic polypeptide-like (APOBEC) family of cytidine deaminases implied in kataegis, a focal hypermutation phenotype, which was observed in 4/8 (50%) cases. We corroborate the roles of CDKN2A/2B in ATC development and identify kataegis as a recurrent phenomenon. Our findings pinpoint clinically relevant alterations, which may indicate response to CDK inhibitors, and focal hypermutational phenotypes that may be coupled to improved responses using immune checkpoint inhibitors.

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Section: Discussionmentioning

confidence: 99%

Pan-Genomic Sequencing Reveals Actionable CDKN2A/2B Deletions and Kataegis in Anaplastic Thyroid Carcinoma

Stenman

Yang

Paulsson

et al. 2021

Cancers

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“…Those distances were further segmented using constant fitting to find regions of constant intermutation distances. Putative regions of kataegis were identified as those segments containing six or more consecutive mutations with an average intermutation distance of ≤1000 bp [21].…”

Section: Discussionmentioning

confidence: 99%

“…For somatic signature assessment, data was analyzed by NMF decomposition, Residual Sum of Squares (RSS) and explained variance statistics. All statistical analyses were performed in R environment [18], with implementation of SomaticSignatures [23], YAPSA [24] and KataegisPortal [21] packages.…”

Section: Discussionmentioning

confidence: 99%

Genomic Sub-Classification of Ovarian Clear Cell Carcinoma Revealed by Distinct Mutational Signatures

Oliveira

Schnack

Poulsen

et al. 2021

Cancers

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Ovarian clear cell carcinoma (OCCC) is characterized by dismal prognosis, partially due to its low sensitivity to standard chemotherapy regimen. It is also well-known for presenting unique molecular features in comparison to other epithelial ovarian cancer subtypes. Here, we aim to identify potential subgroups of patients in order to (1) determine their molecular features and (2) characterize their mutational signature. Furthermore, we sought to perform the investigation based on a potentially clinically relevant setting. To that end, we assessed the mutational profile and genomic instability of 55 patients extracted from the Gynecologic Cancer Database (DGCD) by using a panel comprised of 409 cancer-associated genes and a microsatellite assay, respectively; both are currently used in our routine environment. In accordance with previous findings, ARID1A and PIK3CA were the most prevalent mutations, present in 49.1% and 41.8%, respectively. From those, the co-occurrence of ARID1A and PIK3CA mutations was observed in 36.1% of subjects, indicating that this association might be a common feature of OCCC. The microsatellite instability frequency was low across samples. An unbiased assessment of signatures identified the presence of three subgroups, where “PIK3CA” and “Double hit” (with ARID1A and PIK3CA double mutation) subgroups exhibited unique signatures, whilst “ARID1A” and “Undetermined” (no mutations on ARID1A nor PIK3CA) subgroups showed similar profiles. Those differences were further indicated by COSMIC signatures. Taken together, the current findings suggest that OCCC presents distinct mutational landscapes within its group, which may indicate different therapeutic approaches according to its subgroup. Although encouraging, it is noteworthy that the current results are limited by sample size, and further investigation on a larger group would be crucial to better elucidate them.

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“…Many of these approaches utilize a piece-wise linear regression to segment each chromosome, which, in most cases, is optimized for calling larger strand-coordinated kataegic events ( Supplementary Fig. 1 ) (Alexandrov, et al, 2013; Lin, et al, 2021; Yin, et al, 2020). Most existing methods have also ignored confounding effects attributed to localized differences in mutation rates, copy number alterations, or the mutational burden across each chromosome within a given sample leading to an accumulation of false positive clustered events ( Supplementary Fig.…”

Section: Introductionmentioning

confidence: 99%

Examining clustered somatic mutations with SigProfilerClusters

Bergstrom

Kundu

Tbeileh

et al. 2022

Preprint

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Clustered mutations are found in the human germline as well as in the genomes of cancer and normal somatic cells. Clustered events can be imprinted by a multitude of mutational processes, and they have been implicated in both cancer evolution and development disorders. Prior tools for identifying clustered mutations have been optimized for a particular subtype of clustered event and, in most cases, relied on a predefined inter-mutational distance (IMD) cutoff combined with a piecewise linear regression analysis. Here we present SigProfilerClusters, an automated tool for detecting all types of clustered mutations by calculating a sample-dependent IMD threshold using a simulated background model that takes into account extended sequence context, transcriptional strand asymmetries, and regional mutation densities. SigProfilerClusters disentangles all clustered events from non-clustered mutations and annotates each clustered event into an established subclass, including the widely used classes of doublet-base substitutions, multi-base substitutions, omikli, and kataegis. SigProfilerClusters outputs non-clustered mutations and clustered events using standard data formats as well as provides multiple visualizations for exploring the distributions and patterns of clustered mutations across the genome. Availability: SigProfilerClusters is freely available at https://github.com/AlexandrovLab/SigProfilerClusters with support across most operating systems and extensive documentation at https://osf.io/qpmzw/wiki/home/.

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Multiregion whole-genome sequencing depicts intratumour heterogeneity and punctuated evolution in ovarian clear cell carcinoma

Cited by 15 publications

References 19 publications

Pan-Genomic Sequencing Reveals Actionable CDKN2A/2B Deletions and Kataegis in Anaplastic Thyroid Carcinoma

Pan-Genomic Sequencing Reveals Actionable CDKN2A/2B Deletions and Kataegis in Anaplastic Thyroid Carcinoma

Genomic Sub-Classification of Ovarian Clear Cell Carcinoma Revealed by Distinct Mutational Signatures

Examining clustered somatic mutations with SigProfilerClusters

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