Biology subjects

Boutros, P. C.

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

At least 19 recordsLinked to original sources

bioRxiv2026

The impact of sex and ancestry on the molecular landscape of pancreatic adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is projected to become the second leading cause of cancer-related mortality, with an increasing incidence and dismal 10% survival at 5 years despite best available care. PDAC is characterized by low tumour cellularity, recurrent driver mutation alterations, and substantial inter and intra-tumoural heterogeneity. The influence of individual genetic features such as sex and ancestry on tumour heterogeneity remains poorly understood. Methods: We assembled a cohort of PDAC across five datasets for a total of 7,527 tumours. Measures of genomic instability were assessed across sex and ancestry on a global level (SNV density and percent genome altered by copy number events), and at a gene level for recurrent events. KRAS variant frequencies, ploidy and mRNA abundance were also evaluated. Ancestry was assigned using ADMIXTURE from germline genotypes where available, and patient reported otherwise. Results: We observed ancestry-specific mutation patterns in global point mutation density, with higher SNV burden in African PDAC tumours and lower SNV burden in Asian PDAC tumours as compared to European tumours. We also identified an ancestry bias in the driver gene, GNAS, in which driver SNVs in GNAS was detected more frequently in those of Asian ancestry, and less frequently in those of African ancestry. No sex- or ancestry-associations were found for KRAS, but individual KRAS variants showed a trend towards ancestry-related variation. Conclusions: These findings demonstrate that ancestry contributes to the genomic heterogeneity of PDAC, shaping both global and gene-specific mutation patterns. This work underscores the importance of including diverse ancestries in PDAC molecular profiling efforts which may inform the development of tailored molecular diagnostics and targeted therapies.

genomics↗

bioRxiv2025

Somatic evolution of prostate cancer: mutation, selection, and epistasis across disease stages

BackgroundSomatic mutations involved in prostate cancer tumorigenesis and disease progression have been identified, but their evolutionary dynamics--including differential selective pressures across oncogenesis and metastatic spread--remain poorly understood. No prior study has systematically quantified the adaptive landscape from prostate organogenesis through tumor initiation and progression to metastatic castrate-resistant prostate cancer (mCRPC), nor characterized the selective epistatic interactions that structure this evolutionary trajectory. Methods and FindingsTo address this gap, we analyzed 2,704 low- and high-risk primary tumors and metastatic castration-resistant prostate cancers to quantify the mutation rates, mutational processes, and scaled selection coefficients of somatic mutations across disease stages. Trinucleotide mutational patterns were stable, but both mutation load and mutation rates increased with progression. In parallel, selective pressures on specific somatic mutations changed substantially, revealing a dynamic adaptive landscape. Stage-specific selective effects were associated with significant synergistic and antagonistic selective epistasis among key driver genes. Early selection on SPOP mutations in the BRD3 binding domain were under strong positive selection, and they increased selection for subsequent RHOA mutations while decreasing selection for TP53 mutations. Antagonistic selective epistasis was evident between mutations of CUL3 and both SPOP and PIK3CA. Mutations in KMT2C increased the selection for mutations in TP53, consistent with their frequent co-occurrence. Synergistic epistatic interactions between mutations of PTEN and both PIK3CA and AR support a strong therapeutic rationale for combined inhibition of PI3K/AR pathway in PTEN-deficient prostate cancers. ConclusionsThese findings provide a comprehensive map of the evolving selective and epistatic forces that shape prostate cancer progression across clinical stages. By distinguishing shifts in selection from changes in mutation rate and revealing the extents of cooperative and conflicting relationships among driver mutations, our work identifies critical points of vulnerability and informs that design of therapeutic strategies that anticipate and intercept the somatic evolutionary trajectory of prostate cancer.

cancer biology↗

bioRxiv2025

Ancestry-Dependent Immunologic and Prognostic Effects Characterize the Prostate Cancer Urinary Proteome

Urine is an attractive biomarker analyte for non-invasive longitudinal monitoring of health and disease, particularly for diseases of the genitourinary tract, like prostate and bladder cancer. The composition of an individuals urine reflects both genetic and lifestyle characteristics that differ across geographies and populations, like diet, hydration and other socio-economic factors. While men of African ancestry have elevated prostate cancer risk, it is unclear to what extent this influences urinary biomarkers. We therefore quantified the urinary proteomes of 329 localized prostate cancer patients: 135 self-identifying as White and 194 self-identifying as Black. We identified 110 proteins that significantly differed between these groups after controlling for age, PSA, and cISUP. Immune pathways were particularly dysregulated. The urinary proteome of Black patients harboured more features of aggressive cancers than those of grade- and PSA-matched White patients. These observations highlight the importance of controlling for race- and ancestry-associated differences in the development of urinary biomarkers.

cancer biology↗

bioRxiv2025

OGDHL regulates nucleotide metabolism, tumor growth, and neuroendocrine marker expression in prostate cancer

As cancer cells evade therapeutic pressure and adopt alternate lineage identities not commonly observed in the tissue of origin, they likely adopt alternate metabolic programs to support their evolving demands. Targeting these alternative metabolic programs in distinct molecular subtypes of aggressive prostate cancer may lead to new therapeutic approaches to combat treatment-resistance. We identify the poorly studied metabolic enzyme Oxoglutarate Dehydrogenase-Like (OGDHL), named for its structural similarity to the tricarboxylic acid (TCA) cycle enzyme Oxoglutarate Dehydrogenase (OGDH), as an unexpected regulator of tumor growth, treatment-induced lineage plasticity, and DNA Damage in prostate cancer. While OGDHL has been described as a tumor-suppressor in various cancers, we find that its loss impairs prostate cancer cell proliferation and tumor formation. Loss of OGDHL profoundly alters Androgen Receptor inhibition-induced plasticity, including suppressing the neuroendocrine markers DLL3 and HES6, induces accumulation of the DNA damage response marker {gamma}H2AX, and reduces nucleotide synthesis. Our data suggest that OGDHL has minimal impact on TCA cycle activity, and that mitochondrial localization is not required for its regulation of prostate cancer plasticity and nucleotide metabolism. Finally, we demonstrate that OGDHL expression is tightly correlated with neuroendocrine differentiation in clinical prostate cancer. These findings underscore the importance of investigating poorly characterized metabolic genes as potential regulators of distinct molecular subtypes of aggressive cancer.

cancer biology↗

bioRxiv2025

Sex Differences in the Cancer Proteome

Proteins play a central role in cancer biology: they are the most common drug targets and biomarkers. Sex influences the proteome in many diseases, ranging from neurological to cardiovascular. In cancer, sex is associated with incidence, progression and therapeutic response, as well as characteristics of the tumour genome and transcriptome. The extent to which sex differences impact the cancer proteome remains largely unknown. To fill this gap, we quantified sex differences across 1,590 proteomes from eight cancer types, identifying 901 genes with sex-differential proteins abundance in adenocarcinomas of the lung, and 20 genes across five other tumour types: squamous cell carcinoma of the lung, hepatocellular carcinomas, clear cell cancers of the kidney, adenocarcinomas of the pancreas and glioblastoma. A subset of these protein differences could be rationalized by sex-differential copy number aberrations. Pathway analysis showed that male-biased proteins in lung adenocarcinoma were enriched in MYC and E2F target pathways. These findings highlight the modest impact of sex on the cancer proteome, but the very limited power of existing proteomics cohorts for these analyses.

cancer biology↗

bioRxiv2025

RNA Transcripts Serve as a Template for Double-Strand Break Repair in Human Cells

Double-strand breaks (DSBs) are toxic lesions that lead to genome instability. While canonical DSB repair pathways typically operate independently of RNA, emerging evidence suggests that RNA:DNA hybrids and transcripts near damaged sites can influence repair outcomes. However, a direct role for transcript RNA as a template during DSB repair in human cells is yet to be established. In this study, we designed fluorescent- and sequencing-based assays, which demonstrated that RNA-containing oligonucleotides and messenger RNA serve as templates to promote DSB repair. We conducted a CRISPR/Cas9-based genetic screen to identify factors that promote RNA-templated DSB repair (RT-DSBR), and of the candidate polymerases, we identified DNA polymerase-zeta (Pol{zeta}) as the potential reverse transcriptase that facilitates RT-DSBR. Furthermore, by analyzing sequencing data from cancer genomes, we identified the presence of whole intron deletions, a unique genomic scar reflective of RT-DSBR activity generated when spliced mRNA serves as the repair template. These findings highlight RT-DSBR as an alternative pathway for repairing DSBs in transcribed genes, with potential mutagenic consequences.

molecular biology↗

bioRxiv2025

Deconvolution of Sample Identity in Single-Cell RNA Sequencing via Genome Imputation

BackgroundDroplet based single-cell RNA sequencing (scRNA-seq) is a powerful tool for measuring RNA abundance profiles at cell-specific resolution. Droplet-based barcoding technology allows sample multiplexing, thereby facilitating high-scale of single cell sequencing. The resulting processing complexity, sample contamination and the underlying chemistries can all contribute to cell mis-labelling and consequent spurious cell-to-sample assignment. Approaches for barcode-free de-multiplexing which leverage natural genetic variation have been developed, but generally require an external source of genotype information. ResultsWe propose a novel method to exploit genome imputation and clustering to assign cells to inferred donor groups in the absence of a priori genetic information. Using tumor-derived single-cell RNA-sequencing (scRNA-seq) data, our workflow successfully assigned individual cells to donor-of-origin with high concordance. ConclusionsThis imputation-clustering approach represents a quality-assessment and quality-control strategy for barcode-free single cell donor-origin deconvolution with the capacity to resolve cases of sample cross-contamination.

bioinformatics↗

bioRxiv2025

The Landscape of Prostate Tumour Methylation

Prostate cancer is characterized by profound clinical and molecular heterogeneity. While its genomic heterogeneity is well-characterized, its epigenomic heterogeneity remains less understood. We therefore created a compendium of 3,001 multi-ancestry prostate methylomes spanning normal tissue through localized disease of all grades to poly-metastatic disease. A subset of 884 samples had multi-omic DNA and/or RNA characterization. We identify four epigenomic subtypes that risk-stratify patients and reflect distinct evolutionary trajectories. We demonstrate extensive regulatory interplay between DNA copy number and methylation, with transcriptional consequences that vary across genes and disease stages. We define epigenetic dysregulation signatures for 15 important clinico-molecular features, creating predictive models for each. For example, we identify specific epigenetic features that predict patient outcome and are synergistic with clinical prognostic features. These results define a complex interplay between tumour genetics and epigenetics that converges to modify gene-expression programs and clinical presentation, in part through modulation of epigenetic aging.

cancer biology↗

bioRxiv2025

Sex Differences in Cancer Functional Genomics: Gene Dependency and Drug Sensitivity

Patient sex influences a wide range of cancer phenotypes, including prevalence, response to therapy and survival endpoints. Molecular sex differences across the central dogma have been identified that may drive these phenotypic differences. Despite a growing catalog of specific genomic, transcriptomic and proteomic sex differences in a range of cancer types, their functional consequences remain unclear. To assess how patient sex impacts cancer cell function, we evaluated 1,209 cell lines subjected to CRISPR knockout, RNAi knockdown or drug exposures. Despite limited statistical power, we identified pan- and per-cancer sex differences in gene essentiality in six sex-linked and fourteen autosomal genes, and in drug sensitivity for two compounds. These data expand our understanding of the propagation of sex-differing molecular features to functional outcomes, and their role in influencing patient phenotypes. They highlight the importance of considering sex-specific effects in mechanistic and functional studies.

cancer biology↗

bioRxiv2024

A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.

Somatic mosaicism is an important cause of disease, but mosaic and somatic variants are often challenging to detect because they exist in only a fraction of cells. To address the need for benchmarking subclonal variants in normal cell populations, we developed a benchmark containing mosaic variants in the Genome in a Bottle Consortium (GIAB) HG002 reference material DNA from a large batch of a normal lymphoblastoid cell line. First, we used a somatic variant caller with high coverage (300x) Illumina whole genome sequencing data from the Ashkenazi Jewish trio to detect variants in HG002 not detected in at least 5% of cells from the combined parental data. These candidate mosaic variants were subsequently evaluated using >100x BGI, Element, and PacBio HiFi data. High confidence candidate SNVs with variant allele fractions above 5% were included in the HG002 draft mosaic variant benchmark, with 13/85 occurring in medically relevant gene regions. We also delineated a 2.45 Gbp subset of the previously defined germline autosomal benchmark regions for HG002 in which no additional mosaic variants >2% exist, enabling robust assessment of false positives. The variant allele fraction of some mosaic variants is different between batches of cells, so using data from the homogeneous batch of reference material DNA is critical for benchmarking these variants. External validation of this mosaic benchmark showed it can be used to reliably identify both false negatives and false positives for a variety of technologies and detection algorithms, demonstrating its utility for optimization and validation. By adding our characterization of mosaic variants in this widely-used cell line, we support extensive benchmarking efforts using it in simulation, spike-in, and mixture studies.

bioinformatics↗

bioRxiv2024

StableLift: Optimized Germline and Somatic Variant Detection Across Genome Builds

Reference genomes are foundational to modern genomics. Our growing understanding of genome structure leads to continual improvements in reference genomes and new genome "builds" with incompatible coordinate systems. We quantified the impact of genome build on germline and somatic variant calling by analyzing tumour-normal whole-genome pairs against the two most widely used human genome builds. The average individual had a build-discordance of 3.8% for germline SNPs, 8.6% for germline SVs, 25.9% for somatic SNVs and 49.6% for somatic SVs. Build-discordant variants are not simply false-positives: 47% were verified by targeted resequencing. Build-discordant variants were associated with specific genomic and technical features in variant- and algorithm-specific patterns. We leveraged these patterns to create StableLift, an algorithm that predicts cross-build stability with AUROCs of 0.934 {+/-} 0.029. These results call for significant caution in cross-build analyses and for use of StableLift as a computationally efficient solution to mitigate inter-build artifacts.

genomics↗

bioRxiv2024

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline

SummaryThe price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data- visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data. AvailabilityMetapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is available at https://github.com/uclahs-cds/metapipeline-DNA.

bioinformatics↗

bioRxiv2024

moPepGen: Rapid and Comprehensive Proteoform Identification

Proteogenomics is limited by challenges of modeling the complexities of gene expression. We create moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization.

bioinformatics↗

bioRxiv2024

Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms

BackgroundIntra-tumoural heterogeneity complicates cancer prognosis and impairs treatment success. One of the ways subclonal reconstruction (SRC) quantifies intra-tumoural heterogeneity is by estimating the number of subclones present in bulk DNA sequencing data. SRC algorithms are probabilistic and need to be initialized by a random seed. However, the seeds used in bioinformatics algorithms are rarely reported in the literature. Thus, the impact of the initializing seed on SRC solutions has not been studied. To address this gap, we generated a set of ten random seeds to systematically benchmark the seed sensitivity of three probabilistic SRC algorithms: PyClone-VI, DPClust, and PhyloWGS. ResultsWe characterized the seed sensitivity of three algorithms across fourteen whole-genome sequences of head and neck squamous cell carcinoma and nine SRC pipelines, each composed of a single nucleotide variant caller, a copy number aberration caller and an SRC algorithm. This led to a total of 1470 subclonal reconstructions, including 1260 single-region and 210 multi-region reconstructions. The number of subclones estimated per patient vary across SRC pipelines, but all three SRC algorithms show substantial seed sensitivity: subclone estimates vary across different seeds for the same set of input using the same SRC algorithm. No seed consistently estimated the mode number of subclones across all patients for any SRC algorithm. ConclusionsThese findings highlight the variability in quantifying intra-tumoural heterogeneity introduced by the seed sensitivity of probabilistic SRC algorithms. We recommend that authors, reviewers and editors adopt guidelines to both report and randomize seed choices. It may also be valuable to consider seed-sensitivity in the benchmarking of newly developed SRC algorithms. These findings may be of interest in other areas of bioinformatics where seeded probabilistic algorithms are used and suggest consideration of formal seed reporting standards to enhance reproducibility.

bioinformatics↗

bioRxiv2023

Small variant benchmark from a complete assembly of X and Y chromosomes

The sex chromosomes contain complex, important genes impacting medical phenotypes, but differ from the autosomes in their ploidy and large repetitive regions. To evaluate variant detection on chromosomes X and Y, we created an 111,725 variant benchmark for the Genome in a Bottle HG002 reference material. We show how complete assemblies can expand benchmarks to difficult regions, but highlight remaining challenges benchmarking complex gene conversions, copy number variable gene arrays, and human satellites.

genomics↗

bioRxiv2023

Prostate Cancer Reshapes the Secreted and Extracellular Vesicle Urinary Proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions, and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome, and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

systems biology↗

bioRxiv2023

The landscape of drug sensitivity and resistance in sarcoma

Sarcomas are a family of rare malignancies composed of over 100 distinct histological subtypes. The rarity of sarcoma poses significant challenges in conducting clinical trials to identify effective therapies, to the point that many rarer subtypes of sarcoma do not have standard-of-care treatment. Even for established regimens, there can be substantial heterogeneity in responses. Overall, novel, personalized approaches for identifying effective treatments are needed to improve patient out-comes. Patient-derived tumor organoids (PDTOs) are clinically relevant models representative of the physiological behavior of tumors across an array of malignancies. Here, we use PDTOs as a tool to better understand the biology of individual tumors and characterize the landscape of drug resistance and sensitivity in sarcoma. We collected n=194 specimens from n=126 sarcoma patients, spanning 24 distinct subtypes. We characterized PDTOs established from over 120 biopsy, resection, and metastasectomy samples. We leveraged our organoid high-throughput drug screening pipeline to test the efficacy of chemotherapeutics, targeted agents, and combination therapies, with results available within a week from tissue collection. Sarcoma PDTOs showed patient-specific growth characteristics and subtype-specific histopathology. Organoid sensitivity correlated with diagnostic subtype, patient age at diagnosis, lesion type, prior treatment history, and disease trajectory for a subset of the compounds screened. We found 90 biological pathways that were implicated in response to treatment of bone and soft tissue sarcoma organoids. By comparing functional responses of organoids and genetic features of the tumors, we show how PDTO drug screening can provide an orthogonal set of information to facilitate optimal drug selection, avoid ineffective therapies, and mirror patient outcomes in sarcoma. In aggregate, we were able to identify at least one effective FDA-approved or NCCN-recommended regimen for 59% of the specimens tested, providing an estimate of the proportion of immediately actionable information identified through our pipeline. HighlightsO_LIStandardized organoid culture preserve unique sarcoma histopathological features C_LIO_LIDrug screening on patient-derived sarcoma organoids provides sensitivity information that correlates with clinical features and yields actionable information for treatment guidance C_LIO_LIHigh-throughput screenings provide orthogonal information to genetic sequencing C_LIO_LISarcoma organoid response to treatment correlates with patient response to therapy C_LIO_LILarge scale, functional precision medicine programs for rare cancers are feasible within a single institution C_LI

cancer biology↗

bioRxiv2022

Increased mRNA expression of CDKN2A is a transcriptomic marker of clinically aggressive meningiomas

BackgroundHomozygous loss of CDKN2A/B is a genetic alteration found in many cancer types including meningiomas, where it is associated with poor clinical outcome. It is now also a diagnostic criterion for grade 3 meningiomas in the 2021 WHO classification for central nervous system tumors. However, as in other cancers, the relationship between copy number loss of CDKN2A/B and expression of its gene product is unclear and may be either commensurate or paradoxical in nature. Therefore, we aimed to investigate the association of CDKN2A mRNA expression with clinical prognosis, WHO grade, and other molecular biomarkers in meningiomas such as DNA methylation, molecular group, and proteomics. MethodsWe used multidimensional molecular data of 490 meningioma samples from 4 independent cohorts to examine the relationship between mRNA expression of CDKN2A and copy number status, its correlation to clinical outcome, the transcriptomic pathways altered in differential CDKN2A expression, and its relationship with DNA methylation, and proteomics using an integrated molecular approach. ResultsMeningiomas without any copy number loss were dichotomized into high (CDKN2Ahigh) and low (CDKN2Alow) CDKN2A mRNA expression groups. Patients with CDKN2Ahigh meningiomas had poorer progression free survival (PFS) compared to those with CDKN2Alow meningiomas. CDKN2A mRNA expression was increased in more aggressive molecular groups, and in higher WHO grade meningiomas across all cohorts. CDKN2Ahigh meningiomas and meningiomas with CDKN2A copy number loss shared common up-regulated cell cycling pathways. CDK4 mRNA expression was increased in CDKN2Ahigh meningiomas and both p16 and CDK4 protein were more abundant in CDKN2Ahigh meningiomas. CDKN2Ahigh meningiomas were frequently hypermethylated at the gene body and UTR compared to CDKN2Alow meningiomas and found be more commonly Rb-deficient. ConclusionsAn intermediate level of CDKN2A mRNA expression appears to be optimal as significantly low (CDKN2A deleted) or high expression (CDKN2Ahigh) are associated with poorer outcomes clinically. Though CDK4 is elevated in CDKN2Ahigh meningiomas, Rb-deficiency may be more common in this group, leading to lack of response to CDK inhibitors.

cancer biology↗