Biology subjects

Bruce, J. P.

Publications and source records attributed to Bruce, J. P..

3 recordsLinked to original sources

bioRxiv2022

Transcriptional immunogenomic analysis reveals distinct immunological clusters in pediatric nervous system tumours

To inform immunotherapy approaches in children, we performed an immunogenomic analysis of RNA-seq data from 925 treatment-naive pediatric nervous system tumours (pedNST) spanning 12 cancer types from three public data sets. Within pedNST, we uncovered four broad immune clusters: Pediatric Inflamed (10%), Myeloid Predominant (30%), Immune Neutral (43%) and Immune Excluded (17%). We validated these clusters using immunohistochemistry, methylation immune inference, and segmentation analysis of tissue images. We report shared biology of these immune clusters within and across cancer types, and characterization of specific immune-cell frequencies as well as T- and B-cell repertoires. We found no associations between immune infiltration levels and tumour mutational burden, although molecular cancer entities were enriched within specific immune clusters. Given the heterogeneity within pedNST, our findings suggest personalized immunogenomic profiling is needed to guide selection of immunotherapeutic strategies.

genomics↗

bioRxiv2021

Non-coding mutations reveal cancer driver cistromes in luminal breast cancer

Whole genome sequencing of primary breast tumors enabled the identification of cancer driver genes 1,2 and non-coding cancer driver plexuses from somatic mutations 3-6. However, differentiating driver and passenger events among non-coding genetic variants remains a challenge to understand the etiology of cancer and inform delivery of personalized cancer medicine. Herein, we reveal an enrichment of non-coding mutations in cis-regulatory elements that cover a subset of transcription factors linked to tumor progression in luminal breast cancers. Using a cohort of 26 primary luminal ER+PR+ breast tumors, we compiled a catalogue of [~]100,000 unique cis-regulatory elements from ATAC-seq data. Integrating this catalogue with somatic mutations from 350 publicly available breast tumor whole genomes, we identified four recurrently mutated individual cis-regulatory elements. By then partitioning the non-coding genome into cistromes, defined as the sum of binding sites for a transcription factor, we uncovered cancer driver cistromes for ten transcription factors in luminal breast cancer, namely CTCF, ELF1, ESR1, FOSL2, FOXA1, FOXM1 GATA3, JUND, TFAP2A, and TFAP2C in luminal breast cancer. Nine of these ten transcription factors were shown to be essential for growth in breast cancer, with four exclusive to the luminal subtype. Collectively, we present a strategy to find cancer driver cistromes relying on quantifying the enrichment of non-coding mutations over cis-regulatory elements concatenated into a functional unit drawn from an accessible chromatin catalogue derived from primary cancer tissues.

cancer biology↗

bioRxiv2017

Bamgineer: Introduction of simulated allele-specific copy number variants into exome and targeted sequence data sets

Somatic copy number variations (CNVs) play a crucial role in development of many human cancers. The broad availability of next-generation sequencing (NGS) data has enabled the development of algorithms to computationally infer CNV profiles from a variety of data types including exome and targeted sequence data; currently the most prevalent types of cancer genomics data. However, systemic evaluation and comparison of these tools remains challenging due to a lack of ground truth reference sets. To address this need, we have developed Bamgineer, a tool written in Python to introduce user-defined haplotype-phased allele-specific copy number events into an existing Binary Alignment Mapping (BAM) file, with a focus on targeted and exome sequencing experiments. As input, this tool requires a read alignment file (BAM format), lists of non-overlapping genome coordinates for introduction of gains and losses (bed file), and an optional file defining known haplotypes (vcf format). To improve runtime performance, Bamgineer introduces the desired CNVs in parallel using queuing and parallel processing on a local machine or on a high-performance computing cluster. As proof-of-principle, we applied Bamgineer to a single high-coverage (mean: 220X) exome sequence file from a blood sample to simulate copy number profiles of 3 exemplar tumours from each of 10 tumour types at 5 tumour cellularity levels (20-100%, 150 BAM files in total). In addition to these reference sets, we expect Bamgineer to be of use for systematic benchmarking of CNV calling algorithms using their own data and expected tumour content for a variety of applications. The source code and reference datasets are freely available at http://github.org/pughlab/bamgineer.

bioinformatics↗