Biology subjects

McDermott, U.

Publications and source records attributed to McDermott, U..

15 recordsLinked to original sources

bioRxiv2026

Comparative Single-Cell Profiling of CRISPR Knockout and Interference Defines Modality-Specific Strengths in Functional Genomics

Pooled CRISPR screens coupled with single-cell RNA sequencing enable high-throughput functional interrogation of gene regulatory networks, yet systematic comparisons of CRISPR knockout (CRISPRko) and CRISPR interference (CRISPRi) remain limited. We established a single-cell CRISPRko workflow and benchmarked it against CRISPRi using 87 sgRNAs targeting 29 unfolded protein response genes. CRISPRko generated transcriptional phenotypes are highly concordant with CRISPRi, and induced comparable pathway-level responses. While CRISPRi allows direct assessment of target gene repression, CRISPRko provides an effective complementary approach for complete loss-of-function studies, expanding the toolkit for single-cell functional genomics.

genomics↗

bioRxiv2025

Predicting Personalised Therapeutic Combinations in Non-Small Cell Lung Cancer Using In Silico Modelling

The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with around a million new cases diagnosed globally each year, and a 5-year survival rate of less than 20%. A lack of therapeutic options personalized to individual patient genetics, and the targeted therapies that exist quickly succumbing to resistance, leads to high variation in survival. Patient stratification combined with greater personalisation of therapies have the potential to improve outcomes, however, the wide variation in mutations found in NSCLC adenocarcinoma patients mean that experimentally determining suitable treatment combinations is time-consuming and expensive. Here we present an in silico model encompassing tumour intrinsic key oncogenic signalling pathways, including EGFR, AKT, JAK/STAT and WNT for efficiently predicting rational drug-drug and drug-radiotherapy combination therapies in NSCLC. Using this model, we simulate diverse genetic profiles and test over 10,000 therapeutic combinations to identify optimal strategies to overcome resistance mechanisms specific to genetic profiles and p53 status. Our in silico model reproduces drug additivity experiments, predicts radio-sensitising genes validated in a CRISPR screen and identifies 53BP1 as a potential drug target that improves the therapeutic window during radiotherapy, as well as potential to use ATM inhibitors to overcome p53 loss-of-function driven radiotherapy resistance. We further use the in silico model to identify a 19-gene signature to stratify patients most likely to benefit from radiotherapy and validated this using TCGA data. These results further demonstrate the utility of in silico mechanistic modelling and present a bespoke computational resource for large-scale screening of personalised therapies applied to NSCLC.

cancer biology↗

bioRxiv2024

The long-term effects of chemotherapy on normal blood cells

In developed countries, [~]10% of individuals are exposed to systemic chemotherapy for cancer and other diseases. Many chemotherapeutic agents act by increasing DNA damage in cancer cells, triggering cell death. However, there is limited understanding of the extent and long-term consequences of collateral DNA damage to normal tissues. To investigate the impact of chemotherapy on mutation burdens and cell population structure of a normal tissue we sequenced blood cell genomes from 23 individuals, aged 3-80 years, treated with a range of chemotherapy regimens. Substantial additional mutation loads with characteristic mutational signatures were imposed by some chemotherapeutic agents, but there were differences in burden between different classes of agent, different agents of the same class and different blood cell types. Chemotherapy also induced premature changes in the cell population structure of normal blood, similar to those of normal ageing. The results constitute an initial survey of the long-term biological consequences of cytotoxic agents to which a substantial fraction of the population is exposed during the course of their disease management, raising mechanistic questions and highlighting opportunities for mitigation of adverse effects.

cell biology↗

bioRxiv2024

A benchmark comparison of CRISPRn guide-RNA design algorithms and generation of small single and dual-targeting libraries to boost screening efficiency

Genome-wide CRISPR sgRNA libraries have emerged as transformative tools to systematically probe gene function. While these libraries have been iterated over time to be more efficient, their large size limits their use in some applications. Here, we benchmarked publicly available genome-wide single-targeting sgRNA libraries and evaluated dual targeting as a strategy for pooled CRISPR loss-of-function screens. We leveraged this data to design two minimal genome-wide human CRISPR-Cas9 libraries that are 50% smaller than other libraries and that preserve specificity and sensitivity, thus enabling broader deployment at scale.

molecular biology↗

bioRxiv2024

10x GENOMICS GENE EXPRESSION FLEX IS A POWERFUL TOOL FOR SINGLE-CELL TRANSCRIPTOMICS OF XENOGRAFT MODELS

The 10x Genomics Gene Expression Flex protocol allows profiling of fixed or frozen material, greatly simplifying the logistics of sample collection, storage and transfer prior to single -cell sequencing. The method makes single-cell transcriptomics possible for existing fresh-frozen or FFPE tissue samples, but also facilitates the logistics of the sampling process, allowing instant preservation of samples. The technology relies on species-specific probes available for human and mouse. Nevertheless, processing of patient-derived (PDX) or cell line (CDX) xenografts, which contain mixed human and mouse cells, is currently not supported by this protocol due to the high degree of homology between the probe sets. Here we show that it is feasible to simultaneously profile populations containing both human and mouse cells by mixing the transcriptome probe sets of both species. Cellranger outputs a count table for each of the species allowing evaluation of the performance of the different probe sets. Cross-reactive probes are greatly outperformed by the specific probe hybridizations leading to a clear difference in the recovery of UMIs and unique genes per cell. Furthermore, we developed a pipeline that removes cross-reactive signal from the data and provides species-specific count tables for further downstream analysis. Hence, the 10x Genomics Gene Expression Flex protocol can be used to process xenograft samples without the need for separation of human and mouse cells by flow sorting and allows analysis of the human and mouse single-cell transcriptome from each sample. We anticipate it will be increasingly used for single-cell sequencing of cancer cell line and patient-derived xenografts, facilitating the preservation of the samples and allowing the interrogation of both the (human) xenograft and the (mouse) tumor microenvironment at single-cell resolution.

genomics↗

bioRxiv2023

Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer PDXs

AO_SCPLOWBSTRACTC_SCPLOWPatient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. We characterised 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic level and measured their response to cetuximab, an EGFR inhibitor in clinical use for metastatic colorectal cancer. After assessing PDXs quality, stability, and molecular concordance with publicly available patient cohorts, we trained, interpreted, and validated an integrated ensemble classifier (CeSta) which takes in input the PDXs multi-omic characterisation and predicts their sensitivity to cetuximab treatment (AUROC > 0.9). Our study shows that large PDX collections can be used to train accurate, interpretable models of drug sensitivity, which 1) better recapitulate patient-derived therapeutic biomarkers than other models trained on CCL data, 2) can be robustly validated across independent PDX cohorts, and 3) can be used for the development of novel therapeutic biomarkers.

bioinformatics↗

bioRxiv2021

Knowledge Graph-based Recommendation Framework Identifies Novel Drivers of Resistance in EGFR mutant Non-small Cell Lung Cancer

Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify high value hits resulting from the CRISPR screen involves significant manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete. To find key drivers of resistance faster we built a hybrid recommendation system on top of a heterogeneous biomedical knowledge graph integrating preclinical, clinical, and literature evidence. Genes were ranked based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identified 36 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identified novel resistance mechanisms that we prospectively validated.

cancer biology↗

bioRxiv2021

Life without mismatch repair

Mismatch repair (MMR) is a critical defence against mutation, but we lack quantification of its activity on different DNA lesions during human life. We performed whole-genome sequencing of normal and neoplastic tissues from individuals with constitutional MMR deficiency to establish the roles of MMR components, tissue type and disease state in somatic mutation rates. Mutational signatures varied extensively across genotypes, some coupled to leading-strand replication, some to lagging-strand replication and some independent of replication, implying that the various MMR components engage different forms of DNA damage. Loss of MSH2 or MSH6 (MutS), but not MLH1 or PMS2 (MutL), caused 5-methylcytosine-dependent hypermutation, indicating that MutS is the pivotal complex for repairing spontaneous deamination of methylated cytosines in humans. Neoplastic change altered the distribution of mutational signatures, particularly accelerating replication-coupled indel signatures. Each component of MMR repairs 1-10 lesions/day per normal human cell, and many thousands of additional events during neoplastic transformation. HighlightsO_LIMMR repairs 1-10 lesions/day in every normal cell and thousands more in tumor cells C_LIO_LIMMR patterns and rates are shaped by genotype, tissue type and malignant transformation C_LIO_LIMSH2 and MSH6 are pivotal for repairing spontaneous deamination of methylated cytosine C_LIO_LIReplication indels and substitutions vary by leading versus lagging strand and genotype C_LI

genomics↗

bioRxiv2020

Human essentiality genes and targeted oncology therapies

Human essentiality genes are significantly enriched in targeted therapies successfully used in oncology. Embedding human essentiality metrics into discovery pipelines could optimise the delivery of highly effective targeted therapies among clinical development strategies.

genomics↗

bioRxiv2019

High-sensitivity monitoring of ctDNA by patient-specific sequencing panels and integration of variant reads

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Patients with small tumors have few copies of ctDNA in plasma, resulting in limited sensitivity to detect low-volume or residual disease. We show that sampling limitations can be overcome and sensitivity for ctDNA detection can be improved by massively parallel sequencing when hundreds to thousands of mutations are identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) analysis pipeline, which combines patient-specific mutation lists with both custom error-suppression methods and signal enrichment based on biological features of ctDNA. In this framework, the sensitivity can be estimated independently for each sample based on the number of informative reads, which is the product of the number of mutations analyzed and the average depth of unique sequencing reads. We applied INVAR to deep sequencing data generated by custom hybrid-capture panels, and showed that when ~106 informative reads were obtained INVAR allowed detection of tumor-derived DNA fractions to parts per million (ppm). In serial samples from patients with advanced melanoma on treatment, we detected ctDNA when imaging confirmed tumor volume of ~1cm3. In patients with resected early-stage melanoma, ctDNA was detected in 40% of patients who later relapsed, with higher rates of detection when more informative reads were obtained. We further demonstrated that INVAR can be generalized and allows improved detection of ctDNA from whole-exome and low-depth whole-genome sequencing data.

cancer biology↗

bioRxiv2019

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi- omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.

cancer biology↗

bioRxiv2019

Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR/Cas9 screening

Many gene fusions have been reported in tumours and for most their role remains unknown. As fusions can be used clinically for diagnostic and prognostic purposes, and are targets for treatment, it is crucial to assess their functional implications in cancer. To investigate the role of fusions in tumor cell fitness, we developed a systematic analysis utilising RNA-sequencing data from 1,011 human cancer cell lines to functionally link 8,354 gene fusion events with genomic data, sensitivity to >350 anti-cancer drugs and CRISPR-Cas9 loss-of-fitness information. Established clinically-relevant fusions were readily identified. Overall, functional fusions were rare, including those involving cancer driver genes, suggesting that many fusions are dispensable for tumor cell fitness. Novel therapeutically actionable fusions involving RAF1, BRD4 and ROS1 were verified in new histologies. In addition, recurrent YAP1-MAML2 fusions were identified as activators of Hippo-pathway signaling in multiple cancer types, supporting therapeutic targeting of Hippo signalling. Our approach discriminates functional fusions, identifying new drivers of carcinogenesis and fusions that could have important clinical implications. SignificanceWe identify fusions as new potential candidates for drug repurposing and drivers of carcinogenesis. These results support histology agnostic marker-driven precision cancer medicine. Most fusions are not functional with implications for interpreting cancer fusions reported from clinical sequencing studies.

cancer biology↗

bioRxiv2017

A screen for combination therapies in BRAF/NRAS wild type melanoma identifies nilotinib plus MEK inhibitor as a synergistic combination

Despite recent therapeutic advances in the management of BRAFV600-mutant melanoma, there is still a compelling need for more effective treatments for patients who developed BRAF/NRAS wild type disease. Since the activity of single targeted agents is limited by innate and acquired resistance, we performed a high-throughput drug screen using 180 drug combinations to generate over 18,000 viability curves, with the aim of identifying agents that synergise to kill BRAF/NRAS wild type melanoma cells. From this screen we observed strong synergy between the tyrosine kinase inhibitor nilotinib and MEK inhibitors and validated this combination in an independent cell line collection. We found that AXL expression was associated with synergy to the nilotinib/MEK inhibitor combination, and that both drugs work in concert to suppress pERK. This finding was supported by genome-wide CRISPR screening which revealed that resistance mechanisms converge on regulators of the MAPK pathway. Finally, we validated the synergy of nilotinib/trametinib combination in vivo using patient-derived xenografts. Our results indicate that a nilotinib/MEK inhibitor combination may represent an effective therapy in BRAF/NRAS wild type melanoma patients.

cancer biology↗

bioRxiv2017

An integrated genomic analysis of anaplastic meningioma identifies prognostic molecular signatures

Anaplastic meningioma is a rare and aggressive brain tumor characterised by intractable recurrences and dismal outcomes. Here, we present an integrated analysis of the whole genome, transcriptome and methylation profiles of primary and recurrent anaplastic meningioma. A key finding was the delineation of two distinct molecular subgroups that were associated with diametrically opposed survival outcomes. Relative to lower grade meningiomas, anaplastic tumors harbored frequent driver mutations in SWI/SNF complex genes, which were confined to the poor prognosis subgroup. Our analyses discern two biologically distinct variants of anaplastic meningioma with potential prognostic and therapeutic significance.

genomics↗

bioRxiv2016

Genomic determinants of protein abundance variation in colorectal cancer cells

Assessing the extent to which genomic alterations compromise the integrity of the proteome is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling and tribrid mass spectrometry to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the relationships between genomic and proteomic variation. The robust quantification of 12,000 proteins and 27,000 phosphopeptides revealed how protein symbiosis translates to a co-variome which is subjected to a hierarchical order and exposes the collateral effects of somatic mutations on protein complexes. Targeted depletion of key chromatin modifiers confirmed the transmission of variation and the directionality as characteristics of protein interactions. Protein level variation was leveraged to build drug response predictive models towards a better understanding of pharmacoproteomic interactions in colorectal cancer. Overall, we provide a deep integrative view of the molecular structure underlying the variation of colorectal cancer cells.\n\nHighlightsO_LIThe cancer cell functional \"co-variome\" is a strong attribute of the proteome.\nC_LIO_LIMutations can have a direct impact on protein levels of chromatin modifiers.\nC_LIO_LITransmission of genomic variation is a characteristic of protein interactions.\nC_LIO_LIPharmacoproteomic models are strong predictors of response to DNA damaging agents.\nC_LI\n\nAbbreviations

systems biology↗