Biology subjects

Steele, C. D.

Publications and source records attributed to Steele, C. D..

3 recordsLinked to original sources

bioRxiv2021

Signatures of copy number alterations in human cancer

The gains and losses of DNA that emerge as a consequence of mitotic errors and chromosomal instability are prevalent in cancer. These copy number alterations contribute to cancer initiaition, progression and therapeutic resistance. Here, we present a conceptual framework for examining the patterns of copy number alterations in human cancer using whole-genome sequencing, whole-exome sequencing, and SNP6 microarray data making it widely applicable to diverse datasets. Deploying this framework to 9,873 cancers representing 33 human cancer types from the TCGA project revealed a set of 19 copy number signatures that explain the copy number patterns of 93% of TCGA samples. 15 copy number signatures were attributed to biological processes of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, and chromothripsis. The aetiology of four copy number signatures are unexplained and some cancer types have unique patterns of amplicon signatures associated with extrachromosomal DNA, disease-specific survival, and gains of proto-oncogenes such as MDM2. In contrast to base-scale mutational signatures, no copy number signature associated with known cancer risk factors. The results provide a foundation for exploring patterns of copy number changes in cancer genomes and synthesise the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes giving rise to copy number changes.

cancer biology↗

bioRxiv2020

Therapeutic vulnerability to PARP1/2 inhibition in RB1-mutant osteosarcoma

BackgroundLoss-of-function mutations of the retinoblastoma tumour suppressor RB1 are key drivers in cancer, with prominent involvement in the natural history of Osteosarcoma (OS). RB1 loss-of-function compromises genome maintenance in cells and hence could yield vulnerability to therapeutics targeting such processes. MethodWe assessed the response to Poly-ADP-Polymerase1/2 inhibitors (PARPi) in histiotype-matched cancer cell lines differing in RB1 status including an extended panel of OS lines, measuring viability, clonogenic activity and inhibition of xenograft growth in vivo. We used mutational signature analysis and RAD51 immunostaining to assess competence for homologous repair defect (HRd). ResultsWe report selective hypersensitivity to clinically-approved PARPi in OS lines with RB1 mutation, which extends to other cancer histiotypes and is induced in RB1-normal OS following engineered RB1 loss. PARPi treatment caused extensive cell death in RB1-mutated OS and extended survival of mice carrying human RB1-mutated OS grafts. Sensitivity in OS with natural or engineered RB1 loss surpassed that seen in BRCA-mutated backgrounds where PARPi are showing clinical benefit. PARPi sensitivity was not associated with loss of RAD51 recruitment and HRd-linked mutational signatures, which predict PARPi sensitivity in cancers with BRCA1/2 loss, but linked to rapid activation of replication checkpoint signalling with S phase transit critical for the death response observed. ConclusionOur work demonstrates that mutations in RB1 causes clinically relevant hypersensitivity to approved PARP1/2-targeting therapeutics and advocates PARP1/2 inhibition as a novel, genome lead strategy for RB1-mutated osteosarcoma.

cancer biology↗

bioRxiv2020

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 34 distinct scenarios encompassing 2,500 simulated signatures operative in more than 60,000 unique synthetic genomes and 20,000 synthetic exomes demonstrates that SigProfilerExtractor outperforms thirteen other tools across all datasets with and without noise. For genome simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome sequenced and 19,184 whole-exome sequenced cancers reveals four previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.

bioinformatics↗