Biology subjects

McPherson, A.

Publications and source records attributed to McPherson, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Noise Optimization of Basic Signal Component Extraction for Cryogenic and On-Scalp Magnetoencephalography (MEG)

Magnetoencephalography (MEG) measures human neural activity non-invasively with spatio-temporal precision, and has been foundational in enabling impactful discoveries in cognitive neuroscience. New on-scalp MEG sensor technologies, such as OPM-MEG, offer the opportunity to capture more information about the neuronal magnetic fields with higher sensitivity to more complex, higher order spatial components, leading to improved source localization. The accuracy of MEG and OPM-MEG source localization relies on data preprocessing techniques to isolate the neuronal fields from other magnetic and biomagnetic sources through signal space separation, rejection, and suppression methods. Current preprocessing methods risk rejecting brain signals of interest, or can spread sensor noise artifacts unknowingly. Here we propose a novel preprocessing method for MEG, and test the extent to which it overcomes limitations of prior methods. Specifically, we derive, apply, and assess a novel signal space separation (SSS) method with Fosters inverse, a weighted matrix inversion protocol that can utilize information about the MEG sensor noise and artifacts to reconstruct neuronal activity. With simulations, phantom head cryogenic MEG recordings, and subject recordings with two OPM-MEG systems, we show that Fosters inverse with SSS offers a more robust and stable reconstruction of the neuronal magnetic fields, especially in the face of sensor noise and artifacts. As the field of cognitive neuroscience continues to embrace MEG and OPM-MEG, Fosters inverse with SSS offers a robust and powerful data preprocessing technique for reducing noise and improving source localization of the underlying neuronal currents.

neuroscience↗

bioRxiv2026

Inter-organ communication shapes human metabolic tissue states and resolves anti-diabetic drug response modes in a six-tissue microphysiological system

Systemic glucose regulation depends on coordinated signaling among metabolically specialized tissues, yet most human in vitro models capture only limited portions of this network. Here, we developed and benchmarked a perfused human six-tissue MPS by combining AnthroHive, a recirculating perfusion platform, with MOTIVE-6, a six-compartment Multiorgan Tissue Interaction Vessel, to culture human gut epithelium, pancreatic islets, liver organoids, adipocytes, skeletal muscle, and midbrain-patterned brain organoids in a microphysiological system. Shared perfusion redirected engineered tissue states toward tissue-aligned metabolic, endocrine, absorptive, contractile, and neural-associated programs while reducing selected isolation-associated stress and remodeling signatures. Under High nutrient conditions, however, multi-tissue interaction shifted liver and islet responses toward inflammatory and nutrient-stress-associated gene expression, indicating context-dependent effects of cross-compartment signaling. Graded nutrient exposure resolved a staged circuit trajectory: Low nutrient conditions supported maintenance-associated programs, Mid nutrient exposure induced compensatory endocrine and anabolic remodeling with declining net glucose depletion, and High nutrient exposure shifted the system toward stress-associated metabolic dysfunction. Under High conditions, metformin and semaglutide produced distinct response modes. Metformin preserved circuit-level glucose handling without increasing insulin or C-peptide accumulation, while semaglutide remodeled gut, brain organoid, islet, and liver organoid transcriptional programs linked to nutrient sensing, epithelial maintenance, endocrine signaling, and neurometabolic state. Together, this study establishes a benchmarked human six-tissue MPS resource, paired with tissue-resolved transcriptomic, shared-media metabolomic, functional, endocrine, and inflammatory datasets, for investigating how tissue interaction, nutrient availability, and metabolic therapies reshape glucose-regulatory networks. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/726943v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@181cdfforg.highwire.dtl.DTLVardef@fb211eorg.highwire.dtl.DTLVardef@13b7284org.highwire.dtl.DTLVardef@1db7543_HPS_FORMAT_FIGEXP M_FIG C_FIG Created in BioRender. Trapecar, M. (2026) https://BioRender.com/a4tl7nv

bioengineering↗

bioRxiv2025

End-to-end proteogenomics for discovery of cryptic and non-canonical cancer proteoforms using long-read transcriptomics and multi-dimensional proteomics

Modern mass spectrometry now permits genome-scale quantitative measurements of biological proteomes. However, current approaches remain hindered by incomplete representation of biological variability of protein sequences in canonical reference proteomes, limiting studies to already annotated proteoforms. This, combined with reliance on tryptic peptides and limited sample complexity reduction techniques render detection of variant proteoforms missing from current reference annotations virtually impossible. Here, we report a method for end-to-end proteogenomics which integrates (i) sample-specific transcriptome assembly using long-read sequencing with (ii) multi-protease, multi-dimensional (MPMD) trapped ion mobility spectrometry (TIMS) parallel accumulation-serial fragmentation (PASEF) mass spectrometry. Termed ProteomeGenerator3, we benchmark its sensitivity and specificity for unbiased human cancer proteome discovery. Using long-read transcriptome sequencing of human Ewing sarcoma cancer cells, we detected over 40,000 full-length transcript isoforms, including aberrantly expressed neogenes, splice isoforms, and gene fusions. Combined with MPMD-TIMS-PASEF mass spectrometry, our proteogenomics workflow enabled the detection of 12,189 unique proteoforms with an average sequence coverage exceeding 50%, including 3,216 putative, non-canonical proteoforms, constituting one of the most complete human proteomes to date. Likewise, we report the proteogenomic performance metrics for current state-of-the-art implementations of DIA-NN, Spectronaut and diaTracer-MSFragger algorithms for data-independent acquisition (DIA) and PASEF mass spectral data, including optimal Double Rainbow-diaPASEF parameters for non-tryptic proteomes. In all, end-to-end integrative ProteomeGenerator3 provides a versatile platform for deep sample-specific proteogenomic discovery, suitable for the identification of disease biomarkers and therapeutic targets, and is available open-source from https://github.com/kentsislab/proteomegenerator3.

bioinformatics↗

bioRxiv2025

Complementary modes of resistance to EGFR TKI in lung adenocarcinoma through MAPK activation and cellular plasticity

EGFR-mutant lung adenocarcinoma (LUAD) represents 20% of all non-small cell lung carcinomas, with most patients presenting with incurable metastatic disease. Treatment with mutant-selective EGFR tyrosine kinase inhibitors (TKIs) results in initial tumor reduction, yet nearly all patients eventually relapse. The mechanisms driving drug resistance are incompletely understood, creating significant barriers to curing metastatic disease. We integrated clinical genomic and single-nuclei RNA (snRNA) sequencing from a cohort of 62 EGFR-mutant LUAD patients treated with the third generation EGFR TKI, osimertinib, and compared treatment-naive (TN), minimal residual disease (MRD), and progressive disease (PD) tumors. We found that disease progression is associated with a marked decrease in alveolar lineage fidelity, coincident with reduced MAPK signaling and adenocarcinoma identity. PD tumors with sustained MAPK pathway activity, such as those with EGFR or MET amplifications, tended to retain adenocarcinoma identity. In contrast, MAPK-low tumors were more likely to undergo histological transformation to squamous or neuroendocrine lineages. Remarkably, we observed rare tumor cell populations prior to treatment that were poorly differentiated, in some cases with neuroendocrine or squamous features. At progression, these histologically divergent tumor cells increased in prevalence, both in cases with overt histological transformation, and in others with sub-clinical histological plasticity. These findings suggest that pre-existing capacity for histologic plasticity may be a substrate for therapy induced selection. Taken together, our results illuminate genomically encoded MAPK signaling and lineage plasticity as complementary mechanisms of acquired resistance to EGFR TKI in lung adenocarcinoma.

cancer biology↗

bioRxiv2025

Disrupted priming within draining lymph nodes drives immune quiescence in gastric cancer

The gastric mucosa is characterized by continuous innate immune surveillance and inflammatory signaling, yet a high proportion of gastric carcinomas (GCs) are recalcitrant to immune-directed therapies. The mechanisms by which GCs evade adaptive immune surveillance within the highly antigenic microenvironment of the gastric mucosa remains unknown. To address this, we collected patient-matched tumor tissue, distant normal tissue, metastasis, and draining lymph nodes to generate a large-scale single-cell immune profiling dataset from 64 patients (n=179 samples, >150,000 cells). From single cell analysis, we identified two distinct sources of impaired tumor surveillance within tumor draining lymph nodes. First, we observed that a significant fraction of tumor draining lymph nodes had undergone cytokine-driven reprogramming, leading to reduced dendritic cell homing and limited T cell priming. Second, T cells undergoing successful activation exhibited limited expansion and constrained differentiation, marked by expression of the quiescence-associated transcription factor Kruppel-like Factor 2 (KLF2). Overexpression of KLF2 in primary T cells limited both their differentiation and cytotoxic capacity. These findings implicate both impaired T cell priming and KLF2-dependent T cell quiescence in limiting T cell immunity in gastric adenocarcinoma. We suggest these findings represent an emerging model for immune silencing in tumors developing from tissues with chronic inflammation.

immunology↗

bioRxiv2025

Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints

Ongoing mutagenesis in cancer drives genetic diversity throughout the natural history of cancers. As the activities of mutational processes are dynamic throughout evolution, distinguishing the mutational signatures of active and historical processes has important implications for studying how tumors evolve. This can aid in understanding mutagenic states at the time of presentation, and in associating active mutational process with therapeutic resistance. As bulk sequencing primarily captures historical mutational processes, we studied whether ultra-low-coverage single-cell whole-genome sequencing (scWGS), which measures the distribution of mutations across hundreds or thousands of individual cells, could enable the distinction between historical and active mutational processes. While technical challenges and data sparsity have limited mutation analysis in scWGS, we show that these data contain valuable information about dynamic mutational processes. To robustly interpret single nucleotide variants (SNVs) in scWGS, we introduce ArtiCull, a method to identify and remove SNV artifacts by leveraging evolutionary constraints, enabling reliable detection of mutations for signature analysis. Applying this approach to scWGS data from pancreatic ductal adenocarcinoma (PDAC), triple-negative breast cancer (TNBC), and high-grade serous ovarian cancer (HGSOC), we uncover temporal and spatial patterns in mutational processes. In PDAC, we observe a temporal increase in mismatch repair deficiency (MMRd). In cisplatin-treated TNBC patient-derived xenografts, we identify therapy-induced mutagenesis and inactivation of APOBEC3 activity. In HGSOC, we show distinct patterns of APOBEC3 mutagenesis, including late tumor-wide activation in one case and clade-specific enrichment in another. Additionally, we detect a clone-specific increase in SBS17 activity, in a clone previously linked to recurrence. Our findings establish ultra-low-coverage scWGS as a powerful approach for studying active mutational processes that may influence ongoing clonal evolution and therapeutic resistance.

cancer biology↗

bioRxiv2024

Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1, RAB25, NOTCH3, and ERBB2. Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.

cancer biology↗

bioRxiv2024

Luminal breast epithelial cells from wildtype and BRCA mutation carriers harbor copy number alterations commonly associated with breast cancer

Cancer-associated mutations have been documented in normal tissues, but the prevalence and nature of somatic copy number alterations and their role in tumor initiation and evolution is not well understood. Here, using single cell DNA sequencing, we describe the landscape of CNAs in >42,000 breast epithelial cells from women with normal or high risk of developing breast cancer. Accumulation of individual cells with one or two of a specific subset of CNAs (e.g. 1q gain and 16q, 22q, 7q, and 10q loss) is detectable in almost all breast tissues and, in those from BRCA1 or BRCA2 mutations carriers, occurs prior to loss of heterozygosity (LOH) of the wildtype alleles. These CNAs, which are among the most common associated with ductal carcinoma in situ (DCIS) and malignant breast tumors, are enriched almost exclusively in luminal cells not basal myoepithelial cells. Allele-specific analysis of the enriched CNAs reveals that each allele was independently altered, demonstrating convergent evolution of these CNAs in an individual breast. Tissues from BRCA1 or BRCA2 mutation carriers contain a small percentage of cells with extreme aneuploidy, featuring loss of TP53, LOH of BRCA1 or BRCA2, and multiple breast cancer-associated CNAs in addition to one or more of the common CNAs in 1q, 10q or 16q. Notably, cells with intermediate levels of CNAs are not detected, arguing against a stepwise gradual accumulation of CNAs. Overall, our findings demonstrate that chromosomal alterations in normal breast epithelium partially mirror those of established cancer genomes and are chromosome- and cell lineage-specific.

cancer biology↗

bioRxiv2024

VICTree - a Variational Inference method for Clonal Tree reconstruction

Clonal tree inference brings crucial insights to the analysis of tumor heterogeneity and cancer evolution. Recent progress in single cell sequencing has prompted a demand for more advanced probabilistic models of copy number evolution, coupled with inference methods which can account for the noisy nature of the data along with dependencies between adjacent sites in copy number profiles. We present VICTree, a variational inference based algorithm for joint Bayesian inference of clonal trees, together with a novel Tree-structured Mixture Hidden Markov Model (TSMHMM) which combines HMMs related through a tree with a mixture model. For the tree inference, we introduce a new algorithm, LARS, for sampling directed labeled multifurcating trees. To evaluate our proposed method, we conduct experiments on simulated data and on samples of multiple myeloma and breast cancer. We demonstrate VICTrees capacity for reliable clustering, clonal tree reconstruction, copy number evolution and the utility of the ELBO for model selection. Lastly, VICTrees results are compared in terms of quality and speed of inference to other state-of-the-art methods. The code for VICTree is available on GitHub: github.com/Lagergren-Lab/victree.

cancer biology↗

bioRxiv2023

Modeling single cell DNA replication dynamics and aneuploidy in genomically unstable cancers

Dysregulated DNA replication is both a cause and a consequence of aneuploidy, yet the dynamics of DNA replication in aneuploid cell populations remains understudied. We developed a new method, PERT, for inferring cell-specific DNA replication states from single-cell whole genome sequencing, and investigated clone-specific DNA replication dynamics in >50,000 cells obtained from a collection of aneuploid and clonally heterogeneous cell lines, xenografts and primary cancer tissues. Clone replication timing (RT) profiles correlated with future copy number changes in serially passaged cell lines. Cell type was the strongest determinant of RT heterogeneity, while whole genome doubling and mutational process were associated with accumulation of late S-phase cells and weaker RT associations. Copy number changes affecting chromosome X had striking impact on RT, with loss of the inactive X allele shifting replication earlier, and loss of inactive Xq resulting in reactivation of Xp. Finally, analysis of time series xenografts illustrate how cell cycle distributions approximate clone proliferation, recapitulating expected relationships between proliferation and fitness in treatment-naive and chemotherapeutic contexts.

cancer biology↗

bioRxiv2023

Exploiting allele-specific transcriptional effects of subclonal copy number alterations for genotype-phenotype mapping in cancer cell populations

Somatic copy number alterations drive aberrant gene expression in cancer cells. In tumors with high levels of chromosomal instability, subclonal copy number alterations (CNAs) are a prevalent feature which often result in heterogeneous cancer cell populations with distinct phenotypes1. However, the extent to which subclonal CNAs contribute to clone-specific phenotypes remains poorly understood, in part due to the lack of methods to quantify how CNAs influence gene expression at a subclone level. We developed TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population and explicitly models gene dosage effects from subclonal alterations. We show through quantitative benchmarking data and application to human cancer data with single cell DNA and RNA libraries that TreeAlign accurately encodes clone-specific transcriptional effects of subclonal CNAs, the impact of allelic imbalance on allele-specific transcription, and obviates the need to arbitrarily define genotypic clones from a phylogenetic tree a priori. Combined, these advances lead to highly granular definitions of clones with distinct copy-number driven expression programs with increased resolution and accuracy over competing methods. The resulting improvement in assignment of transcriptional phenotypes to genomic clones enables clone-clone gene expression comparisons and explicit inference of genes that are mechanistically altered through CNAs, and identification of expression programs that are genomically independent. Our approach sets the stage for dissecting the relative contribution of fixed genomic alterations and dynamic epigenetic processes on gene expression programs in cancer.

cancer biology↗

bioRxiv2022

TCRi: Information theoretic metrics for single cell RNA and TCR sequencing in cancer

Single-cell T cell repertoire sequencing can pair both T cell receptor (TCR) and gene expression sequence data, providing an enriched view of T cell behavior. This powerful tool can identify and characterize specific clonotypes and phenotypes as well as track their changes in response to therapy, such as immune checkpoint blockade (ICB). We present a novel information theoretic framework called TCRi for characterizing single cell T cell repertoires by formalizing the relationship between clonotype and phenotype in a joint probability distribution. Our strategy allows for the identification of subpopulations of T cells and jointly quantifies their TCR and expression profiles in response to stimuli, in addition the framework tracks the phenotypic changes in individual T cell clones over time. We applied this framework to four datasets of T cells sequenced from cancer patients treated with anti-PD-(L)1 ICB immunotherapies and examined evolution of T cell responses pre- and post-treatment. Quantitative of phenotypic and clonotypic entropy analysis with TCRi demonstrated improvements in characterization of the transcriptional signature of clonotypes. Furthermore, TCRi highlighted the importance of phenotypic flux and specific T-cell phenotypes as determinants of therapeutic response.

bioinformatics↗

bioRxiv2022

Adenovirus-Vectored SARS-CoV-2 Vaccine Expressing S1-N Fusion Protein

Additional COVID-19 vaccines that are safe, easy to manufacture, and immunogenic are needed for global vaccine equity. Here, we developed a recombinant type 5 adenovirus vector encoding for the SARS-CoV-2-S1 subunit antigen and nucleocapsid as a fusion protein (Ad5.SARS-CoV-2-S1N) delivered to BALB/c mice through multiple vaccine administration routes. A single subcutaneous (S.C.) immunization with Ad5.SARS-CoV-2-S1N induced a similar humoral response, along with a significantly higher S1-specific cellular response, as a recombinant type 5 adenovirus vector encoding for S1 alone (Ad5.SARS-CoV-2-S1). Immunogenicity was improved by homologous prime boost strategies, using either S.C. or intranasal (I.N.) delivery of Ad5.SARS-CoV-2-S1N, and further improved through heterologous prime boost, with traditional intramuscular (I.M.) injection, using subunit recombinant S1 protein. Priming with low dose (1x1010 v.p.) of Ad5.SARS-CoV-2-S1N and boosting with either wildtype recombinant rS1 or B.1.351 recombinant rS1 induced a robust neutralizing response, that was sustained against immune evasive Beta and Gamma SARS-CoV-2 variants, along with a long-lived plasma cell response in the bone marrow 29 weeks post vaccination. This novel Ad5-vectored SARS-CoV-2 vaccine candidate showed promising immunogenicity in mice and supports the further development of COVID-19 based vaccines incorporating the nucleoprotein as a target antigen.

immunology↗

bioRxiv2022

Chemotherapy Signatures Map Evolution of Therapy-Related Myeloid Neoplasms

Patients treated with cytotoxic therapies, including autologous stem cell transplantation, are at risk for developing therapy-related myeloid neoplasms1, 2. Pre-leukemic clones (i.e., clonal hematopoiesis) are detectable years before the development of these aggressive malignancies3-5, though the genomic events leading to transformation and expansion are not well-defined. Here, leveraging distinctive chemotherapy-associated mutational signatures6-12 from whole-genome sequencing data and targeted sequencing of pre-chemotherapy samples, we reconstruct the evolutionary life-history of 39 therapy-related myeloid malignancies. A dichotomy is revealed, in which neoplasms with evidence of chemotherapy-induced mutagenesis from platinum and melphalan are relatively hypermutated and enriched for complex structural variants (i.e., chromothripsis), while neoplasms with alternative exposures bear a similar profile to de novo acute myeloid leukemia. Using chemotherapy-associated mutational signatures as a temporal barcode in each patients life, we estimate that several complex events and genomic drivers are acquired after chemotherapy exposure. In the case of treatment with high-dose melphalan and autologous stem cell transplantation, we demonstrate that the procedure allows clonal hematopoiesis to escape chemotherapy exposure entirely, and to be reinfused to expand to malignancy. This information reveals a novel mode of malignant progression for therapy-related malignancies that is not reliant on direct mutagenesis or even exposure to chemotherapy, itself, and prompts further investigation into leukemia-permissive effects of cytotoxic drugs.

cancer biology↗

bioRxiv2022

GeneVector: Identification of transcriptional programs using dense vector representations defined by mutual information.

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotypespecific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.

bioinformatics↗

bioRxiv2022

Genetic Analysis Suggests a Surface of PAT-4 (ILK) that Interacts with UNC-112 (kindlin)

The transmembrane protein integrin plays a crucial role in the attachment of cells to the extracellular matrix. Integrin recruits many proteins intracellularly, including a four protein complex (Kindlin, Integrin linked kinase (ILK), PINCH, and parvin). C. elegans muscle provides an excellent model to study integrin adhesion complexes. In C. elegans, UNC-112 (Kindlin) binds to the cytoplasmic tail of PAT-3 ({beta}-integrin) and to PAT-4 (ILK). We previously reported that PAT-4 binding to UNC-112 is essential for the binding of UNC-112 to PAT-3. Although there are crystal structures for ILK and a kindlin, there is no co-crystal structure available. To understand the molecular interaction between PAT-4 (ILK) and UNC-112 (Kindlin), we took a genetic approach. First, we isolated mutant PAT-4 proteins that cannot bind to UNC-112. Then, we isolated suppressor mutant UNC-112 proteins that restore interaction with mutant PAT-4 proteins. Second, these mutant PAT-4 proteins cannot localize to attachment structures in nematode muscle, but upon co-expression of an UNC-112 suppressor mutant protein, the mutant PAT-4 protein could localize to attachment structures. Third, overexpression of a mutant PAT-4 protein results in disorganization of the adhesion plaques at muscle cell boundaries, and co-expression of the UNC-112 supressor mutant protein alleviates this defect. Thus, we demonstrate that UNC-112 binding to PAT-4 is required for the localization and function of PAT-4 in integrin adhesion complexes in vivo. The missense mutations were mapped onto homology models of PAT-4 and UNC-112, and taking into account previously isolated mutations, we suggest a surface of PAT-4 that binds to UNC-112.

genetics↗

bioRxiv2021

Evolutionary tracking of cancer haplotypes at single-cell resolution

Cancer genomes exhibit extensive chromosomal copy number changes and structural variation, yet how allele specific alterations drive cancer genome evolution remains unclear. Here, through application of a new computational approach we report allele specific copy number alterations in 11,097 single cell whole genomes from genetically engineered mammary epithelial cells and 21,852 cells from high grade serous ovarian and triple negative breast cancers. Resolving single cell copy number profiles to individual alleles uncovered genomic background distributions of gains, losses and loss of heterozygosity, yielding evidence of positive selection of specific chromosomal alterations. In addition specific genomic loci in maternal and paternal alleles were commonly found to be altered in parallel with convergent phenotypic transcriptional effects. Finally we show that haplotype specific alterations trace the cyclical etiology of high level amplifications and reveal clonal haplotype decomposition of complex structures. Together, our results illuminate how allele and haplotype specific alterations, here determined across thousands of single cell cancer genomes, impact the etiology and evolution of structural variations in human tumours.

cancer biology↗

bioRxiv2021

The impact of mutational processes on structural genomic plasticity in cancer cells

Structural genome alterations are determinants of cancer ontogeny and therapeutic response. While bulk genome sequencing has enabled delineation of structural variation (SV) mutational processes which generate patterns of DNA damage, we have little understanding of how these processes lead to cell-to-cell variations which underlie selection and rates of accrual of different genomic lesions. We analysed 309 high grade serous ovarian and triple negative breast cancer genomes to determine their mutational processes, selecting 22 from which we sequenced >22,000 single cell whole genomes across a spectrum of mutational processes. We show that distinct patterns of cell-to-cell variation in aneuploidy, copy number alteration (CNA) and segment length occur in homologous recombination deficiency (HRD) and fold-back inversion (FBI) phenotypes. Widespread aneuploidy through induction of HRD through BRCA1 and BRCA2 inactivation was mirrored by continuous whole genome duplication in HRD tumours, contrasted with early ploidy fixation in FBI. FBI tumours exhibited copy number distributions skewed towards gains, widespread clone-specific variation in amplitude of high-level amplifications, often impacting oncogenes, and break-point variability consistent with progressive genomic diversification, which we termed serriform structural variation (SSV). SSVs were consistent with a CNA-based molecular clock reflecting a continual and distributed process across clones within tumours. These observations reveal previously obscured genome plasticity and evolutionary properties with implications for cancer evolution, therapeutic targeting and response.

cancer biology↗