Biology subjects

McGranahan, N.

Publications and source records attributed to McGranahan, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Odon: An ultra-fast viewer for spatial proteomics

Multiplexed spatial proteomics and spatial transcriptomics generate large, high-dimensional imaging datasets that are challenging to visualize efficiently, particularly at whole-slide and cohort scale. Visualization is an essential step for rapid detection of staining artefacts, such as protein aggregates or non-specific staining. Here, we present Odon, a native Rust desktop viewer designed for rapid, interactive exploration of multiplex imaging data on a standard laptop. Odon is primarily built around the OME-Zarr imaging format, and supports annotations via GeoJSON and GeoParquet, with secondary support for SpatialData, Xenium containers, and TIFF. Data can be stored locally or streamed directly from HTTP or S3-compatible object storage using viewport-driven tile loading. Odon incorporates a highly optimized rendering engine that substantially outperforms existing viewers. In benchmarking, Odon loaded a 32 GB, 36-plex whole-slide OME-Zarr image in under 1 second, compared with 10.14 seconds for QuPath and 35 seconds for Napari. Its GPU-based compositing pipeline also enables smooth rendering and interaction with more than 1,000,000 segmented cells, exceeding the practical limits of many existing tools. Odon further supports integrated visual analytics, including live thresholding and cell selection, and a mosaic mode for simultaneous viewing of hundreds of regions of interest in cohort and tissue microarray studies. Together, these features establish Odon as a high-performance platform for scalable visualization of spatial proteomics data.

bioinformatics↗

bioRxiv2025

Agent-based simulations of lung tumour evolution suggest that ongoing cell competition drives realistic clonal expansions

Computational simulations of tumour evolution are increasingly used to infer the rules underlying cancer growth, with the goal of one day recommending tailored treatments. To make reliable inferences, such models must be able to reflect the properties of real tumours. Recent work has shown that lung tumours undergo frequent and late subclonal expansions, which are associated with poor prognosis. This paper tests three candidate simulations of three-dimensional tumour growth, which make different assumptions about the nature of competition between cells, for their ability to replicate these late expansions. Only a model which assumes stringent competition between tumour cells for existing tissue space, after the tumour has reached a fixed size, can produce multiregion sequencing data realistic to lung tumours. This work also assesses the influence of these assumptions on the inferred selection strength of individual tumours in a large cohort of lung cancers, and finds that inferences from a well-tailored model produce much higher estimates than poorly-tailored models of the effects of driver mutations on cell fitness. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/686578v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@40fae9org.highwire.dtl.DTLVardef@18f2d2org.highwire.dtl.DTLVardef@1edec55org.highwire.dtl.DTLVardef@136d1da_HPS_FORMAT_FIGEXP M_FIG Computational simulations of tumour evolution are increasingly used to infer the rules underlying cancer growth, with the goal of one day recommending tailored treatments. Here we show that the properties of lung cancer sequencing data are best replicated by a model which assumes that cells compete both to proliferate and survive. C_FIG

cancer biology↗

bioRxiv2025

Recovery of human upper airway epithelium after smoking cessation is driven by a slow-cycling stem cell population and immune surveillance

The upper airway epithelium in humans is maintained in homeostasis by a resident population of basal stem cells. In the presence of tobacco smoke these gain mutations that significantly increase their risk of transformation to lung squamous cell carcinoma. Previous studies show that a small proportion of stem cells avoid the mutational damage caused by carcinogens in tobacco and are more abundant in the lungs of former smokers than ongoing smokers, indicating unexplained tissue-level genomic recovery. This mirrors epidemiological risk, which falls rapidly after quitting smoking. Somatic evolutionary mechanistic hypotheses have been proposed to explain these observations. Here, we present a computational framework to model each of these hypotheses within the upper airway epithelial stem cell population over the entire patient lifetimes of a cohort with diverse smoking histories. Applying a mechanistic learning approach based on a set of biologically informed metrics to single cell-derived whole-genome sequencing data, we identified subtle differences between epithelia modelled under different combinations of hypotheses. A slow-cycling subpopulation of stem cells, combined with suppression of immune predation of highly mutated stem cells while smoking, best matched observed data, a result converged upon by multiple distinct machine learning methodologies. Our findings, drawing on an evolutionary model of mutagen exposure at a whole-lifetime scale that is not feasible to model in vivo, reveal the mechanisms behind reduction in lung squamous cell carcinoma risk on cessation of smoking and inform future therapeutic interventions to prevent lung cancer initiation.

evolutionary biology↗

bioRxiv2025

LIMD1 Loss as an Early Driver of PD-L1 Upregulation and Immune Evasion in Lung Cancer

LIMD1 is a tumour suppressor gene frequently lost in non-small cell lung cancer (NSCLC), but its role in cancer-immune cell interactions remains unexplored. Here, we demonstrate that LIMD1 loss results in upregulation of the key immune checkpoint protein PD-L1. Using multi-region sequencing from the TRACERx dataset, we identify that LIMD1 loss is clonal in over 80% of squamous cell carcinoma (LUSC) and 40% of lung adenocarcinoma (LUAD) cases, correlating with increased PD-L1 expression. LIMD1 deficiency results in upregulation of basal and IFN{gamma}-induced PD-L1 expression in NSCLC cells and, consistent with its early loss during oncogenesis, in primary human small airway epithelial cells. Mechanistically, we demonstrate that LIMD1 interacts with the E3 ubiquitin ligase ARIH1 to mediate efficient PD-L1 ubiquitination and degradation, a process that is significantly impaired in LIMD1-deficient cells, resulting in increased PD-L1 stability. As a consequence, LIMD1-deficient tumour cells suppressed CD8+ T cell activation in vitro, and blockade of PD-L1 reversed this suppression. Clinically, we show that LIMD1 loss is associated with enhanced response to immune checkpoint inhibitors (ICIs) in NSCLC patient cohorts, revealing a novel cancer cell-intrinsic correlation of ICI efficacy. Our results uncover a tumour suppressor-mediated mechanism of PD-L1 expression and pave the way for stratified immunotherapy approaches in LIMD1-/- NSCLC.

cancer biology↗

bioRxiv2024

Characterizing the Solution Space of Migration Histories of Metastatic Cancers with MACH2

Understanding the migration history of cancer cells is essential for advancing metastasis research and developing therapies. Existing migration history inference methods often rely on parsimony criteria such as minimizing migrations, comigrations, and seeding locations. Importantly, existing methods either yield a single optimal solution or are probabilistic algorithms without guarantees on optimality nor comprehensiveness of the returned solution space. As such, current methods are unable to capture the full extent of uncertainty inherent to the data. To address these limitations, we introduce MACH2, a method that systematically enumerates all plausible migration histories by exactly solving the PO_SCPLOWARSIMONIOUSC_SCPLOW MO_SCPLOWIGRATIONC_SCPLOW HO_SCPLOWISTORYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWWITHC_SCPLOW TO_SCPLOWREEC_SCPLOW RO_SCPLOWEFINEMENTC_SCPLOW (PMH-TR). In addition to the migration and comigration criteria, MACH2 employs a novel parsimony criterion that minimizes the number of clones unobserved in their inferred location of origin. MACH2 allows one to specify both the order and the set of criteria to include during optimization, allowing users to adapt the model to specific analysis needs. MACH2 also includes a summary graph to identify high-confidence migrations. Finally, we introduce MACH2-viz, an interactive webtool for visualizing and exploring MACH2 solution spaces. Using simulated tumors with known ground truth, we show that MACH2, especially the version that prioritizes the new unobserved clone criterion, outperforms existing methods. On real data, MACH2 detects extensive uncertainty in non-small cell lung, ovarian, and prostate cancers, and infers migration histories consistent with orthogonal experimental data.

cancer biology↗

bioRxiv2023

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomic data. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through the analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability that explains the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME, and ultimately to determine its unfolding and fate during immunotherapy with immune checkpoint blockers.

bioinformatics↗

bioRxiv2023

RNA allelic frequencies of somatic mutations encode substantial functional information in cancers

A central goal of cancer research is the identification of cancer genes that drive tumour growth and progression. Existing approaches to this problem typically leverage frequentist approaches based on patterns of somatic mutagenesis in DNA. Here, we interrogate RNA variant allele frequencies to identify putative cancer genes with a novel computational tool, RVdriver, from bulk genomic-transcriptomic data within 7,948 paired exomes and transcriptomes across 30 cancer types. An elevated RNA VAF reflects a signal from multiple biological features: clonal mutations; mutations retained or gained during somatic copy-number alterations; mutations favoured by allele-specific expression; and mutations in genes expressed preferentially by the tumour compartment of admixed bulk samples. RVdriver, a statistical approach that classifies RNA VAFs of nonsynonymous mutations relative to a synonymous mutation background, leverages this information to identify known, as well as putatively novel, cancer genes, with comparable performance to DNA-based approaches. Furthermore, we demonstrate RNA VAFs of individual mutations are able to distinguish driver from passenger mutations within established cancer genes. Low-RNA VAF EGFR mutations otherwise annotated as drivers of glioblastoma by DNA tools harbour a phenotype of reduced EGFR signalling, whilst high-RNA VAF KDM6A mutations otherwise annotated as passengers exhibit a driver-like H3K27me3 expression profile, demonstrating the value of our approach in phenotyping tumours. Overall, our study showcases a novel approach for cancer gene discovery, and highlights the potential value of multi-omic and systems-biology approaches in finding novel therapeutic vulnerabilities in cancer to bring about patient benefit.

cancer biology↗

bioRxiv2023

Genomic evolution of non-small cell lung cancer patient-derived xenograft models

Patient-derived xenograft (PDX) models of cancer, developed through injection of patient tumour cells into immunocompromised mice, have been widely adopted in preclinical studies, as well as in precision oncology approaches. However, the extent to which PDX models represent the underlying genetic diversity of a patients tumour and the extent of on-going genomic evolution in PDX models are incompletely understood, particularly in the context of heterogeneous cancers such as non-small cell lung cancer (NSCLC). To investigate the depiction of intratumour heterogeneity by PDX models, we derived 47 new subcutaneous multi-region PDX models from 22 patients with primary NSCLC enrolled in the clinical longitudinal cohort study TRACERx. By analysing whole exome sequencing data from primary tumours and PDX models, we find that PDX establishment creates a genomic bottleneck, with 76% of PDX models being derived from a single primary tumour subclone. Despite this, multiple primary tumour subclones were capable of PDX establishment in regional PDX models, indicating that PDX libraries derived from multiple tumour regions can capture intratumour heterogeneity. Acquisition of somatic mutations continued during PDX model expansion, and was associated with APOBEC- or mismatch repair deficiency-induced mutational signatures in a subset of models. Overall, while NSCLC PDX models retain truncal genomic alterations, the absence of subclonal heterogeneity representative of the primary tumour is a major limitation. Our results emphasise the importance of characterising and monitoring intratumour heterogeneity in the context of pre-clinical cancer studies.

cancer biology↗

bioRxiv2022

Refphase: Multi-sample reference phasing reveals haplotype-specific copy number heterogeneity

Most computational methods that infer somatic copy number alterations (SCNAs) from bulk sequencing of DNA analyse tumour samples individually. However, the sequencing of multiple tumour samples from a patients disease is an increasingly common practice. We introduce Refphase, an algorithm that leverages this multi-sampling approach to infer haplotype-specific copy numbers through multi-sample reference phasing. We demonstrate Refphases ability to infer haplotype-specific SCNAs and characterise their intra-tumour heterogeneity, to uncover previously undetected allelic imbalance in low purity samples, and to identify parallel evolution in the context of whole genome doubling in a pan-cancer cohort of 336 samples from 99 tumours.

bioinformatics↗

bioRxiv2022

cloneMap: a R package to visualise clonal heterogeneity

Genomic evolution and clonal heterogeneity in the soma are widespread and are involved in various pathologies with devastating consequences to human health, most prominently in neoplasia. Tools to interrogate and visualise the diversity of clonal populations in malignant or non-malignant tissues are currently lacking. cloneMap is a tool to represent clonal heterogeneity in a sample or set of samples, accounting for the clone size and phylogenetic relationships between clones. We apply cloneMap to several publicly available cancer sequencing datasets to demonstrate its ability to provide biological inferences of clonal heterogeneity. cloneMap is an R package available on github (https://github.com/amf71/cloneMap).

bioinformatics↗

bioRxiv2021

Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lung cancer

Although cancer immunotherapy with PD-(L)1 blockade is now routine treatment for patients with lung cancer, remarkably little is known about acquired resistance. We examined 1,201 patients with NSCLC treated with PD-(L)1 blockade to clinically characterize acquired resistance, finding it to be common (occurring in more than 60% of initial responders), with persistent but diminishing risk over time, and with distinct metastatic and survival patterns compared to primary resistance. To examine the molecular phenotype and potential mechanisms of acquired resistance, we performed whole transcriptome and exome tumor profiling in a subset of NSCLC patients (n=29) with acquired resistance. Systematic immunogenomic analysis revealed that tumors with acquired resistance generally had enriched signals of inflammation (including IFN{gamma} signaling and inferred CD8+ T cells) and could be separated into IFN{gamma} upregulated and stable subsets. IFN{gamma} upregulated tumors had putative routes of resistance with signatures of dysfunctional interferon signaling and mutations in antigen presentation genes. Transcriptomic profiling of cancer cells from a murine model of acquired resistance to PD-(L)1 blockade also showed evidence of dysfunctional interferon signaling and acquired insensitivity to in vitro interferon gamma treatment. In summary, we characterized clinical and molecular features of acquired resistance to PD-(L)1 blockade in NSCLC and found evidence of ongoing but dysfunctional IFN response. The persistently inflamed, rather than excluded or deserted, tumor microenvironment of acquired resistance informs therapeutic strategies to effectively reprogram and reverse acquired resistance.

cancer biology↗

bioRxiv2020

Targeted cancer therapy induces APOBEC fuelling the evolution of drug resistance

Introductory paragraphThe clinical success of targeted cancer therapy is limited by drug resistance that renders cancers lethal in patients1-4. Human tumours can evolve therapy resistance by acquiring de novo genetic alterations and increased heterogeneity via mechanisms that remain incompletely understood1. Here, through parallel analysis of human clinical samples, tumour xenograft and cell line models and murine model systems, we uncover an unanticipated mechanism of therapy-induced adaptation that fuels the evolution of drug resistance. Targeted therapy directed against EGFR and ALK oncoproteins in lung cancer induced adaptations favoring apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC)-mediated genome mutagenesis. In human oncogenic EGFR-driven and ALK-driven lung cancers and preclinical models, EGFR or ALK inhibitor treatment induced the expression and DNA mutagenic activity of APOBEC3B via therapy-mediated activation of NF-{kappa}B signaling. Moreover, targeted therapy also mediated downregulation of certain DNA repair enzymes such as UNG2, which normally counteracts APOBEC-catalyzed DNA deamination events. In mutant EGFR-driven lung cancer mouse models, APOBEC3B was detrimental to tumour initiation and yet advantageous to tumour progression during EGFR targeted therapy, consistent with TRACERx data demonstrating subclonal enrichment of APOBEC-mediated mutagenesis. This study reveals how cancers adapt and drive genetic diversity in response to targeted therapy and identifies APOBEC deaminases as future targets for eliciting more durable clinical benefit to targeted cancer therapy.

cancer biology↗

bioRxiv2020

Copy number-aware deconvolution of tumor-normal DNA methylation profiles

Aberrant methylation is a hallmark of cancer, but bulk tumor data is confounded by admixed normal cells and copy number changes. Here, we introduce Copy number-Aware Methylation Deconvolution Analysis of Cancers (CAMDAC; https://github.com/VanLoo-lab/CAMDAC), which outputs tumor purity, allele-specific copy number and deconvolved methylation estimates. We apply CAMDAC to 122 multi-region samples from 38 TRACERx non-small cell lung cancers profiled by reduced representation bisulfite sequencing. CAMDAC copy number profiles parallel those derived from genome sequencing and highlight widespread chromosomal instability. Deconvolved polymorphism-independent methylation rates enable unbiased tumor-normal and tumor-tumor differential methylation calling. Read-phasing validates CAMDAC methylation rates and directly links genotype and epitype. We show increased epigenetic instability in adenocarcinoma vs. squamous cell carcinoma, frequent hypermethylation at sites carrying somatic mutations, and parallel copy number losses and methylation changes at imprinted loci. Unlike bulk methylomes, CAMDAC profiles recapitulate tumor phylogenies and evidence distinct patterns of epigenetic heterogeneity in lung cancer.

genomics↗

bioRxiv2020

Somatic chromosomal number alterations affecting driver genes inform in-vitro and clinical drug response in high-grade serous ovarian cancer

The genomic complexity and heterogeneity of high-grade serous ovarian cancer (HGSOC) has hampered the realisation of successful therapies and effective personalised treatment is an unmet clinical need. Here we show that primary HGSOC spheroid models can be used to predict drug response and use them to demonstrate that somatic copy number alterations (SCNAs) in frequently amplified HGSOC cancer genes significantly correlate with gene expression and drug response. These genes are often located in areas of the genome with frequent clonal SCNAs. MYC chromosomal copy number is associated with ex-vivo and clinical response to paclitaxel and ex-vivo response to mTORC1/2 inhibition. Activation of the mTOR survival pathway in the context to MYC-amplified HGSOC is mostly due to increased prevalence of SCNAs in genes from the PI3K pathway. These results suggest that SCNAs encompassing driver genes could be used to inform therapeutic response in the context of clinical trials testing personalised medicines.

cancer biology↗

bioRxiv2019

Immune surveillance in clinical regression of pre-invasive squamous cell lung cancer

Before squamous cell lung cancer develops, pre-cancerous lesions can be found in the airways. From longitudinal monitoring, we know that only half of such lesions become cancer, whereas a third spontaneously regress. While recent studies have described the presence of an active immune response in high-grade lesions, the mechanisms underpinning clinical regression of pre-cancerous lesions remain unknown. Here, we show that host immune surveillance is strongly implicated in lesion regression. Using bronchoscopic biopsies from human subjects, we find that regressive carcinoma in-situ lesions harbour more infiltrating immune cells than those that progress to cancer. Moreover, molecular profiling of these lesions identifies potential immune escape mechanisms specifically in those that progress to cancer: antigen presentation is impaired by genomic and epigenetic changes, TGF-beta signalling is overactive, and the immunomodulator TNFSF9 is downregulated. Changes appear intrinsic to the CIS lesions as the adjacent stroma of progressive and regressive lesions are transcriptomically similar. This study identifies mechanisms by which pre-cancerous lesions evade immune detection during the earliest stages of carcinogenesis and forms a basis for new therapeutic strategies that treat or prevent early stage lung cancer.

cancer biology↗

bioRxiv2019

High-dose oncogenic PIK3CA drives constitutive cellular stemness through self-sustained TGFβ pathway activation

Activating PIK3CA mutations are known "drivers" of human cancer and developmental overgrowth syndromes. We recently demonstrated that the "hotspot" PIK3CAH1047R variant exerts unexpected allele dose-dependent effects on stemness in human pluripotent stem cells (hPSCs). In the present study, we combine high-depth transcriptomics, total proteomics and reverse-phase protein arrays to reveal potentially disease-related alterations in heterozygous cells, and to assess the contribution of activated TGF{beta} signalling to the stemness phenotype of PIK3CAH1047R homozygous cells. We demonstrate signalling rewiring as a function of oncogenic PI3K signalling dose, and provide experimental evidence that self-sustained stemness is causally related to enhanced autocrine NODAL/TGF{beta} signalling. A significant transcriptomic signature of TGF{beta} pathway activation in PIK3CAH1047R heterozygous was observed but was modest and was not associated with the stemness phenotype seen in homozygous mutants. Notably, the stemness gene expression in PIK3CAH1047R homozygous iPSCs was reversed by pharmacological inhibition of TGF{beta} signalling, but not by pharmacological PI3K pathway inhibition. Altogether, this provides the first in-depth analysis of PI3K signalling in human pluripotent stem cells and directly links dose-dependent PI3K activation to developmental NODAL/TGF{beta} signalling.

cancer biology↗

bioRxiv2019

Whole Genome Doubling mitigates Muller’s Ratchet in Cancer Evolution

Whole genome doubling (WGD) is a prevalent macro-evolutionary event in cancer, involving a doubling of the entire chromosome complement. However, despite its prevalence and clinical prognostic relevance, the evolutionary selection pressures for WGD have not been investigated. Here, we explored whether WGD may act to mitigate the irreversible, inexorable ratchet-like, accumulation of deleterious mutations in essential genes. Utilizing 1050 tumor regions from 816 non-small cell lung cancers (NSCLC), we temporally dissect mutations to determine their temporal acquisition in relation to WGD. We find evidence for strong negative selection against homozygous loss of essential cancer genes prior to WGD. However, mutations in essential genes occurring after duplication were not subject to significant negative selection, consistent with WGD providing a buffering effect, decreasing the likelihood of homozygous loss. Finally, we demonstrate that loss of heterozygosity and temporal dissection of mutations can be exploited to identify signals of positive selection in lung, breast, colorectal cancer and other cancer types, enabling the elucidation of novel tumour suppressor genes and a deeper characterization of known cancer genes.

genomics↗

bioRxiv2018

Oncogenic PIK3CA promotes cellular stemness in an allele dose-dependent manner

The PIK3CA gene, which encodes the p110 catalytic subunit of PI3-kinase (PI3K), is mutationally activated in cancer and in overgrowth disorders known as PIK3CA-related overgrowth spectrum (PROS). To determine the consequences of genetic PIK3CA activation in a developmental context of relevance to both PROS and cancer, we engineered isogenic human induced pluripotent stem cells (iPSCs) with heterozygous or homozygous knock-in of PIK3CAH1047R. While heterozygous iPSCs remained largely similar to wild-type cells, homozygosity for PIK3CAH1047R caused widespread, cancer-like transcriptional remodeling, partial loss of epithelial morphology, upregulation of stemness markers and impaired differentiation to all three germ layers in vitro and in vivo. Genetic analysis of PIK3CA-associated cancers revealed that 64 % had multiple oncogenic PIK3CA copies (39 %) or additional PI3K signaling pathway-activating \"hits\" (25 %). This contrasts with the prevailing view that PIK3CA mutations occur heterozygously in cancer. Our findings suggest that a PI3K activity threshold determines pathological consequences of oncogenic PIK3CA activation and provide the first insight into the specific role of this pathway in human pluripotent stem cells.

cancer biology↗