Biology subjects

Boutros, P. C.

Publications and source records attributed to Boutros, P. C..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Germline determinants of the prostate tumor genome

A persons germline genome strongly influences their risk of developing cancer. Yet the molecular mechanisms linking the host genome to the specific somatic molecular phenotypes of individual cancers are largely unknown. We quantified the relationships between germline polymorphisms and somatic mutational features in prostate cancer. Across 1,991 prostate tumors, we identified 23 co-occurring germline and somatic events in close 2D or 3D spatial genomic proximity, affecting 10 cancer driver genes. These driver quantitative trait loci (dQTLs) overlap active regulatory regions, and shape the tumor epigenome, transcriptome and proteome. Some dQTLs are active in multiple cancer types, and information content analyses imply hundreds of undiscovered dQTLs. Specific dQTLs explain at least 16.7% ancestry-biases in rates of TMPRSS2-ERG gene fusions and 67.3% of ancestry-biases in rates of FOXA1 point mutations. These data reveal extensive influences of common germline variation on somatic mutational landscapes.

cancer biology↗

bioRxiv2022

A rapid platform for 3D patient-derived cutaneous neurofibroma organoid establishment and screening

Localized cutaneous neurofibromas (cNFs) are benign tumors that arise in the dermis of patients affected by Neurofibromatosis Type 1 syndrome (NF1). cNFs are fundamentally benign lesions: they do not undergo malignant transformation or metastasize. Nevertheless, in NF1 patients, they can cover a significant proportion of the body, with some individuals developing hundreds to thousands of lesions. cNFs can cause pain, itching, and disfigurement with substantial socio-emotional repercussions. To date, surgical removal or laser desiccation are the only treatment options, but can result in scarring and the leave a potential for regrowth. To support drug discovery efforts focused on identifying effective systemic therapies for cNF, we introduce an approach to routinely establish and screen cNF tumor organoids. We optimized conditions to support ex vivo growth of genomically-diverse cNFs. Patient-derived cNF organoids closely recapitulate the molecular and cellular heterogeneity of these tumors as measured by immunohistopathology, DNA methylation, RNA-seq and flow cytometry. Our tractable patient-derived cNF organoid platform enables rapid screening of hundreds of compounds in a patient- and tumor-specific manner.

systems biology↗

bioRxiv2022

Crowd-sourced benchmarking of single-sample tumour subclonal reconstruction

Tumours are dynamically evolving populations of cells. Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumour evolution, allowing assessment of how cancers initiate, progress and respond to selective pressures. A plethora of subclonal reconstruction algorithms have been created, but their relative performance across the varying biological and technical features of real-world cancer genomic data is unclear. We therefore launched the ICGC-TCGA DREAM Somatic Mutation Calling -- Tumour Heterogeneity and Evolution Challenge. This seven-year community effort used cloud-computing to benchmark 31 containerized subclonal reconstruction algorithms on 51 simulated tumours. Each algorithm was scored for accuracy on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance significantly more than tumour features, but purity-adjusted read-depth, copy number state and read mappability were associated with performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were surprisingly unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumour evolution.

bioinformatics↗

bioRxiv2021

High-speed live cell interferometry for screening bioprinted organoids

High-throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms for high-throughput screening use two-dimensional cultures of immortalized cell lines which do not accurately reflect the biology of human tumors. More clinically relevant model systems, such as three-dimensional tumor organoids, can be difficult to screen and scale. For example, manually seeded organoids coupled to destructive endpoint assays allow for the characterization of response to treatment, but do not capture the transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We therefore developed a pipeline to generate bioprinted tumor organoids linked to label-free, real-time imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D organoid structures that preserve tumor histology and gene expression. HSLCI imaging in tandem with machine learning-based image segmentation and organoid classification tools enables accurate, label-free parallel mass measurements for thousands of bioprinted organoids. We demonstrate that our method quantitatively identifies individual organoids as insensitive, transiently sensitive, or persistently sensitive to specific treatments. This opens new avenues for rapid, actionable therapeutic selection using automated tumor organoid screening.

bioengineering↗

bioRxiv2021

The Telomere Length Landscape of Localized Prostate Cancer

Replicative immortality is a hallmark of cancer, and can be achieved through telomere lengthening and maintenance. We report telomere lengths (TLs) of 392 localized prostate cancer tumours and characterize their relationship to genomic, transcriptomic and proteomic features. Shorter tumour TLs were associated with elevated genomic instability, including single-nucleotide variants, indels and structural variants. Genes involved in cell proliferation and signaling were correlated with tumour TL at all levels of the central dogma. TL was also associated with multiple clinical features of a tumour. Longer TLs in non-tumour samples were associated with a lower rate of biochemical relapse after definitive local therapy. Our analysis integrates multi-omics data to illuminate the relationship of specific genomic alterations in a tumour and TL in prostate cancer. Although the role of telomere length in cancer has been well studied, its association to genomic features is less well known. We describe the multi-level integration of telomere length, genomics, transcriptomics and proteomics in localized prostate cancer. Patient Summary We examined the association between telomere length and multiple omics-level data in prostate cancer. We observed that traditional telomere mutations are rare in prostate cancer and that telomere length is associated with multiple measure of genomic instability.

genomics↗

bioRxiv2021

iSubGen: Integrative Subtype Generation by Pairwise Similarity Assessment

There are myriad types of biomedical data- genetics, transcriptomics, clinical, imaging, wearable devices and many more. When a group of patients with the same underlying disease exhibit similarities across multiple types of data, this is called a subtype. Disease subtypes can reflect etiology and sometimes predict clinical behaviour. Existing subtyping approaches struggle to simultaneously handle multiple diverse data types, particularly when there is missing information, as is common in most real-world clinical datasets. To improve subtype discovery, we exploited changes in the correlation-structure between different data types to create iSubGen, an algorithm for integrative subtype generation. iSubGen can combine arbitrary data types for subtype discovery, such as merging molecular, mutational signature, pathway and micro-environmental data. iSubGen recapitulates known subtypes across multiple diseases, even in the face of substantial missing data. It identifies groups of patients with divergent clinical outcomes, and can combine arbitrary data types for subtype discovery, such as merging molecular, mutational signature, pathway and micro-environmental data. iSubGen can accommodate any feature that can be compared with a similarity-metric, and provides a versatile approach for creating subtypes. It is available at https://CRAN.R-project.org/package=iSubGen.

bioinformatics↗

bioRxiv2021

Cis-Regulatory Element Hijacking by Structural Variants Overshadows Topological Changes in Primary Prostate Cancer

Prostate cancer is a heterogeneous disease whose progression is linked to genome instability. However the impact of this instability on the three-dimensional chromatin organization and how this drives progression is unclear. Using primary benign and tumour tissue, we find a high concordance in the higher-order three-dimensional genome organization across normal and prostate cancer cells. This concordance argues for constraints to the topology of prostate tumour genomes. Nonetheless, we identify changes to focal chromatin interactions and show how structural variants can induce these changes to guide cis-regulatory element hijacking. Such events result in opposing differential expression on genes found at antipodes of rearrangements. Collectively, our results argue that cis-regulatory element hijacking from structural variant-induced altered focal chromatin interactions overshadows higher-order topological changes in the development of primary prostate cancer.

cancer biology↗

bioRxiv2020

NOX4 links metabolic regulation in pancreatic cancer to endoplasmic reticulum redox vulnerability and dependence on PRDX4

Pancreatic ductal adenocarcinoma (PDAC) is an almost universally fatal malignancy and there is an urgent need for new therapeutic targets. PDAC cells harbor genetic alterations and display metabolic changes that render them vulnerable to perturbations in redox homeostasis. Peroxiredoxin 4 (PRDX4) supports redox homeostasis by metabolizing H2O2 in the endoplasmic reticulum (ER). Based on functional genomics, we found that PDAC cell lines are dependent on PRDX4 for their growth and survival. We validated this dependency in established and primary PDAC cells, as well as 3D models and orthotopic xenografts. Cell death induced by PRDX4 depletion was accompanied by increased levels of reactive oxygen species (ROS), DNA damage and a DNA-PKcs-governed DNA damage response. As such, PRDX4 depletion also sensitized cells and tumors to ionizing radiation. The source of ROS that created a dependency on PRDX4 was attributed to NADPH oxidase 4 (NOX4), which localizes to the ER membrane. The functional requirement for PRDX4 was correlated with cellular NADPH levels across different models of PDAC and could be rescued by depletion of NOX4 or NADPH. As such, this study has identified NOX4 as a link between metabolic deregulation and ER-specific redox vulnerability in PDAC. Since PRDX4 is not an essential gene in normal tissues, our work also suggests that PRDX4 represents a novel therapeutic target for pancreatic cancer that may be particularly potent in combination with radiotherapy.

cancer biology↗

bioRxiv2020

Differing total mRNA expression shapes the molecular and clinical phenotype of cancer

Cancers can vary greatly in their transcriptomes. In contrast to alterations in specific genes or pathways, differences in tumor cell total mRNA content have not been comprehensively assessed. Technical and analytical challenges have impeded examination of total mRNA expression at scale across cancers. To address this, we developed a model for quantifying tumor-specific total mRNA expression (TmS) from bulk sequencing data, which performs transcriptomic deconvolution while adjusting for mixed genomes. We used single-cell RNA sequencing data to demonstrate total mRNA expression as a feature of tumor phenotype. We estimated and validated TmS in 5,015 patients across 15 cancer types identifying significant inter-individual variability. At a pan-cancer level, high TmS is associated with increased risk of disease progression and death. Cancer type-specific patterns of genetic alterations, intra-tumor genetic heterogeneity, as well as pan-cancer trends in metabolic dysregulation and hypoxia contribute to TmS. Taken together, our results suggest that measuring cell-type specific total mRNA expression offers a broader perspective of tracking cancer transcriptomes, which has important biological and clinical implications.

genomics↗

bioRxiv2020

Ancestry Influences on the Molecular Presentation of Tumours

Epidemiological studies have identified innumerable ways in which cancer presentation and behaviour is associated with patient ancestry. The molecular bases for these relationships remain largely unknown. We analyzed ancestry associations in the somatic mutational landscape of 12,774 tumours across 33 tumour-types, including 2,562 with whole-genome sequencing. Ancestry influences both the number of mutations in a tumour and the evolutionary timing of when they occur. Specific mutational signatures are associated with ancestry, reflecting potential differences in exogenous and endogenous oncogenic processes. A subset of known cancer driver genes was mutated in ancestry-associated patterns, with transcriptomic consequences. Cancer genome sequencing data is not well-balanced in epidemiologic factors; these data suggest ancestry strongly shapes the somatic mutational landscape of cancer, with potential functional implications.

cancer biology↗

bioRxiv2020

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗

bioRxiv2019

A robust benchmark for germline structural variant detection

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls [≥]50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.

genomics↗

bioRxiv2019

Identifying TCDD-resistance genes via murine and rat comparative genomics and transcriptomics

The aryl hydrocarbon receptor (AHR) mediates many of the toxic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). However, the AHR alone is insufficient to explain the widely different outcomes among organisms. Attempts to identify unknown factor(s) have been confounded by genetic variability of model organisms. Here, we evaluated three transgenic mouse lines, each expressing a different rat AHR isoform (rWT, DEL, and INS), as well as C57BL/6 and DBA/2 mice. We supplement these with whole-genome sequencing and transcriptomic analyses of the corresponding rat models: Long-Evans (L-E) and Han/Wistar (H/W) rats. These integrated multi-species genomic and transcriptomic data were used to identify genes associated with TCDD-response phenotypes.\n\nWe identified several genes that show consistent transcriptional changes in both transgenic mice and rats. Hepatic Pxdc1 was significantly repressed by TCDD in C57BL/6, rWT mice, and in L-E rat. Three genes demonstrated different AHRE-1 (full) motif occurrences within their promoter regions: Cxxc5 had fewer occurrences in H/W, as compared with L-E; Sugp1 and Hgfac (in either L-E or H/W respectively). These genes also showed different patterns of mRNA abundance across strains.\n\nThe AHR isoform explains much of the transcriptional variability: up to 50% of genes with altered mRNA abundance following TCDD exposure are associated with a single AHR isoform (30% and 10% unique to DEL and rWT respectively following 500 g/kg TCDD). Genomic and transcriptomic evidence allowed identification of genes potentially involved in phenotypic outcomes: Pxdc1 had differential mRNA abundance by phenotype; Cxxc5 had altered AHR binding sites and differential mRNA abundance.\n\nAuthor SummaryEnvironmental contaminants such as dioxins cause many toxic responses, anything from chloracne (common in humans) to death. These toxic responses are mostly regulated by the Ahr, a ligand-activated transcription factor with roles in drug metabolism and immune responses, however other contributing factors remain unclear. Studies are complicated by the underlying genetic heterogeneity of model organisms. Our team evaluated a number of mouse and rat models, including two strains of mouse, two strains of rat and three transgenic mouse lines which differ only at the Ahr locus, that present widely different sensitivities to the most potent dioxin: 2,3,7,8 tetrachlorodibenzo-p-dioxin (TCDD). We identified a number of changes to gene expression that were associated with different toxic responses. We then contrasted these findings with results from whole-genome sequencing of the H/W and L-E rats and found some key genes, such as Cxxc5 and Mafb, which might contribute to TCDD toxicity. These transcriptomic and genomic datasets will provide a valuable resource for future studies into the mechanisms of dioxin toxicities.

genomics↗

bioRxiv2019

Divergent mutational processes distinguish hypoxic and normoxic tumours

Many primary tumours have low levels of molecular oxygen (hypoxia). Hypoxic tumours are more likely to metastasize to distant sites and respond poorly to multiple therapies. Surprisingly, then, the pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited understanding of its associations with specific mutational processes, non-coding driver genes and evolutionary features. To fill this gap, we quantified hypoxia in 1,188 tumours spanning 27 cancer types. We show that elevated hypoxia is associated with increased mutational load across cancers, irrespective of the underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology are directly associated with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in tumours with high hypoxia, and mutations in PTEN interact with hypoxia to direct the evolutionary trajectory of tumours. Overall, this work demonstrates that hypoxia plays a critical role in shaping the genomic landscape of cancer.

cancer biology↗

bioRxiv2019

Sex Differences in Mutational Processes

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here, we describe the first pan-cancer analysis of sex differences in whole genomes of 1,983 tumours of 28 subtypes from the ICGC Pan-Cancer Analysis of Whole Genomes project. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in cancer research. Sex disparities in cancer epidemiology include an increased overall cancer risk in males corresponding with higher incidence in most tumor types, even after adjusting for known risk factors1,2. Cancer mortality is also higher in males, due in part to better survival for female patients in many cancer types, including those of the colon and head & neck3. Interestingly, female colorectal cancer patients respond better to surgery4 and adjuvant chemotherapy, though this is partially due to biases in tumour location and microsatellite instability5. Similarly, premenopausal female nasopharyngeal cancer patients have improved survival regardless of tumour stage, radiation or chemotherapy regimen6. There is a growing body of evidence for sex differences in cancer genomics7-13, but their molecular origins and clinical implications remain largely elusive. Previous studies have mostly focused on protein coding regions, leaving the vast majority of the genome unexplored. We hypothesized that there are uncharacterized sex differences in the non-coding regions of the genome. Using whole genome sequencing data from the Pan-cancer Analysis of Whole Genomes (PCAWG) project14, we performed a survey of sex-biased mutations in 1,983 samples (1,213 male, 770 female) from 28 tumour subtypes, excluding those of the sex organs (Supplementary Table 1). We also excluded the X and Y chromosomes to focus on autosomal sex differences in cancers affecting both men and women, but there are known to be significant X-chromosome mutational differences between tumours arising in men and women15. Our analysis revealed sex differences in both genome-wide phenomena and in specific genes. These sex-biases occur not only at the pan-cancer level across all 1,983 samples, but also in individual tumour subtypes.

cancer biology↗

bioRxiv2018

The Inter and Intra-Tumoural Heterogeneity of Subclonal Reconstruction

Whole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluated sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

cancer biology↗

bioRxiv2018

HPCI: A Perl module for writing cluster-portable bioinformatics pipelines

BackgroundMost biocomputing pipelines are run on clusters of computers. Each type of cluster has its own API (application programming interface). That API defines how a program that is to run on the cluster must request the submission, content and monitoring of jobs to be run on the cluster. Sometimes, it is desirable to run the same pipeline on different types of cluster. This can happen in situations including when:\n\nO_LIdifferent labs are collaborating, but they do not use the same type of cluster\nC_LIO_LIa pipeline is released to other labs as open source or commercial software\nC_LIO_LIa lab has access to multiple types of cluster, and wants to choose between them for scaling, cost or other purposes\nC_LIO_LIa lab is migrating their infrastructure from one cluster type to another\nC_LIO_LIduring testing or travelling, it is often desired to run on a single computer\nC_LI\n\nHowever, since each type of cluster has its own API, code that runs jobs on one type of cluster needs to be re-written if it is desired to run that application on a different type of cluster. To resolve this problem, we created a software module to generalize the submission of pipelines across computing environments, including local compute, clouds and clusters.\n\nResultsHPCI (High Performance Computing Interface) is a Perl module that provides the interface to a standardized generic cluster.\n\nWhen the HPCI module is used, it accepts a parameter to specify the cluster type. The HPCI module uses this to load a driver HPCD:: . This is used to translate the abstract HPCI interface to the specific software interface.\n\nSimply by changing the cluster parameter, the same pipeline can be run on a different type of cluster with no other changes.\n\nConclusionThe HPCI module assists in writing Perl programs that can be run in different lab environments, with different site configuration requirements and different types of hardware clusters. Rather than having to re-write portions of the program, it is only necessary to change a configuration file.\n\nUsing HPCI, an application can manage collections of jobs to be runs, specify ordering dependencies, detect success or failure of jobs run and allow automatic retry of failed jobs (allowing for the possibility of a changed configuration such as when the original attempt specified an inadequate memory allotment).

bioinformatics↗

bioRxiv2018

Integrative pathway enrichment analysis of multivariate omics data

Multi-omics datasets quantify complementary aspects of molecular biology and thus pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple omics datasets using a statistical data fusion approach, rationalizes contributing evidence and highlights associated genes. We demonstrate its utility by analyzing coding and non-coding mutations from 2,583 whole cancer genomes, revealing frequently mutated hallmark pathways and a long tail of known and putative cancer driver genes. We also studied prognostic molecular pathways in breast cancer subtypes by integrating genomic and transcriptomic features of tumors and tumor-adjacent cells and found significant associations with immune response processes and anti-apoptotic signaling pathways. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations.

bioinformatics↗