Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours

Abstract

SUMMARY Analysis of molecular aberrations across multiple cancer types, known as pan-cancer analysis, identifies commonalities and differences in key biological processes dysregulated in cancer cells from diverse lineages. Pan-cancer analyses have been performed for adult1–4 but not pediatric cancers, which commonly occur in developing mesodermic rather than adult epithelial tissues5. Here we present a pan-cancer study of somatic alterations, including single nucleotide variants (SNVs), small insertion/deletions (indels), structural variations (SVs), copy number alterations (CNAs), gene fusions and internal tandem duplications (ITDs), in 1,699 pediatric leukemia and solid tumours across six histotypes, with whole-genome (WGS), whole-exome (WES) and transcriptome (RNA-seq) sequencing data processed under a uniform analytical framework (Online Methods and Extended Data Fig. 1). We report 142 driver genes in pediatric cancers, of which only 45% matched those found in adult pan-cancer studies and CNAs and SVs constituted the majority (62%) of events. Eleven genome-wide mutational signatures were identified, including one attributed to ultraviolet-light exposure in eight aneuploid leukemias. Transcription of the mutant allele was detectable for 34% of protein-coding mutations, and 20% exhibited allele-specific expression. These data provide a comprehensive genomic architecture for pediatric cancers and emphasize the need for pediatric cancer-specific development of precision therapies.

The landscape of genomic alterations across childhood cancers

“…5b). In line with the accompanying paediatric pan-cancer study 9 , only around 30% of paediatric SMGs overlapped with adult SMGs (Extended Data Fig. 5c).…”

“…Although some alteration types and rarer entities are still under-represented and significance analyses are probably limited, this dataset of nearly 1,000 tumours (which can be explored at http://www.pedpancan. com) provides an unprecedented data resource for paediatric cancer research, further complemented by the accompanying pan-cancer study 9 (https://pecan.stjude.org/proteinpaint/study/pan-target). The multiple differences found compared to previous studies of adult tumours emphasize the need to consider paediatric cancers separately, further demonstrating a need for mechanism-of-action driven drug development for paediatric indications 47 .…”

“…1a). This study is biased towards central nervous system tumours, and is complemented by an additional study of a non-overlapping paediatric cohort with mainly leukaemias and extracranial solid tumours 9 .We compiled paired-end Illumina-based sequencing data for 961 tumours (914 individual patients) from previous cancer-type specific studies (see Methods and Supplementary Note 1) including 547 whole-genome sequences (WGS, median coverage 37× ) and 414 whole-exome sequences (WES, 121× ) partially complemented by low-coverage whole genomes (Supplementary Tables 1, 2). Tumour and matched germline samples were processed with standardized pipelines to detect single nucleotide variants (SNVs), short insertions and A list of authors and affiliations appears at the end of the paper.…”

“…1a). This study is biased towards central nervous system tumours, and is complemented by an additional study of a non-overlapping paediatric cohort with mainly leukaemias and extracranial solid tumours 9 .…”

Adapting crowdsourced clinical cancer curation in CIViC to the ClinGen minimum variant level data community‐driven standards

“…The PSWG has defined a set of pediatric cancer genes in specific childhood tumor types, and has identified and prioritized variants in disease‐gene pairs using Mastermind, a literature mining search tool (https://mastermind.genomenon.com/). A gene‐disease search in Mastermind produces a list of variants, and these are prioritized based on the following: (1) overall absence of curated data in CIViC, (2) the number of article hits against the Mastermind search, (3) the number of hits against a PubMed search, and (4) review of variants in pediatric‐relevant datasets (Chakravarty et al., 2017; Ma et al., 2018). The variants are discussed and vetted with experts on the WG call.…”