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TRACERx Consortium,

Publications and source records attributed to TRACERx Consortium,.

3 recordsLinked to original sources

Genomic evolution of non-small cell lung cancer patient-derived xenograft models

Patient-derived xenograft (PDX) models of cancer, developed through injection of patient tumour cells into immunocompromised mice, have been widely adopted in preclinical studies, as well as in precision oncology approaches. However, the extent to which PDX models represent the underlying genetic diversity of a patients tumour and the extent of on-going genomic evolution in PDX models are incompletely understood, particularly in the context of heterogeneous cancers such as non-small cell lung cancer (NSCLC). To investigate the depiction of intratumour heterogeneity by PDX models, we derived 47 new subcutaneous multi-region PDX models from 22 patients with primary NSCLC enrolled in the clinical longitudinal cohort study TRACERx. By analysing whole exome sequencing data from primary tumours and PDX models, we find that PDX establishment creates a genomic bottleneck, with 76% of PDX models being derived from a single primary tumour subclone. Despite this, multiple primary tumour subclones were capable of PDX establishment in regional PDX models, indicating that PDX libraries derived from multiple tumour regions can capture intratumour heterogeneity. Acquisition of somatic mutations continued during PDX model expansion, and was associated with APOBEC- or mismatch repair deficiency-induced mutational signatures in a subset of models. Overall, while NSCLC PDX models retain truncal genomic alterations, the absence of subclonal heterogeneity representative of the primary tumour is a major limitation. Our results emphasise the importance of characterising and monitoring intratumour heterogeneity in the context of pre-clinical cancer studies.

cancer biology↗

Copy number-aware deconvolution of tumor-normal DNA methylation profiles

Aberrant methylation is a hallmark of cancer, but bulk tumor data is confounded by admixed normal cells and copy number changes. Here, we introduce Copy number-Aware Methylation Deconvolution Analysis of Cancers (CAMDAC; https://github.com/VanLoo-lab/CAMDAC), which outputs tumor purity, allele-specific copy number and deconvolved methylation estimates. We apply CAMDAC to 122 multi-region samples from 38 TRACERx non-small cell lung cancers profiled by reduced representation bisulfite sequencing. CAMDAC copy number profiles parallel those derived from genome sequencing and highlight widespread chromosomal instability. Deconvolved polymorphism-independent methylation rates enable unbiased tumor-normal and tumor-tumor differential methylation calling. Read-phasing validates CAMDAC methylation rates and directly links genotype and epitype. We show increased epigenetic instability in adenocarcinoma vs. squamous cell carcinoma, frequent hypermethylation at sites carrying somatic mutations, and parallel copy number losses and methylation changes at imprinted loci. Unlike bulk methylomes, CAMDAC profiles recapitulate tumor phylogenies and evidence distinct patterns of epigenetic heterogeneity in lung cancer.

genomics↗

Whole Genome Doubling mitigates Muller’s Ratchet in Cancer Evolution

Whole genome doubling (WGD) is a prevalent macro-evolutionary event in cancer, involving a doubling of the entire chromosome complement. However, despite its prevalence and clinical prognostic relevance, the evolutionary selection pressures for WGD have not been investigated. Here, we explored whether WGD may act to mitigate the irreversible, inexorable ratchet-like, accumulation of deleterious mutations in essential genes. Utilizing 1050 tumor regions from 816 non-small cell lung cancers (NSCLC), we temporally dissect mutations to determine their temporal acquisition in relation to WGD. We find evidence for strong negative selection against homozygous loss of essential cancer genes prior to WGD. However, mutations in essential genes occurring after duplication were not subject to significant negative selection, consistent with WGD providing a buffering effect, decreasing the likelihood of homozygous loss. Finally, we demonstrate that loss of heterozygosity and temporal dissection of mutations can be exploited to identify signals of positive selection in lung, breast, colorectal cancer and other cancer types, enabling the elucidation of novel tumour suppressor genes and a deeper characterization of known cancer genes.

genomics↗