Biology subjects

Graham, T. A.

Publications and source records attributed to Graham, T. A..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Phenotypic plasticity and genetic control in colorectal cancer evolution

Cancer evolution is driven by natural selection acting upon phenotypic trait variation. However, the extent to which phenotypic variation within a tumour is a consequence of intra-tumour genetic heterogeneity remains undetermined. Here we show that colorectal cancer cells frequently have highly plastic phenotypic traits in vivo in patient tumours. We measured the degree to which trait variation reflects genetic ancestry by quantifying the phylogenetic signal of gene expression across 297 samples with multi-region paired whole genome and transcriptome sequencing collected from 27 primary colorectal cancers. Within-tumour phylogenetic signal for genes and pathways was detected only infrequently, suggesting that the majority of intra-tumour variation in gene expression programmes was not strongly heritable. Expression quantitative trait loci analyses (eQTL) identified a small number of putative mechanisms of genetic control of gene expression due to the cis-acting coding, non-coding and structural genetic alteration, but most gene expression variation was not explained by our genetic analysis. Leveraging matched chromatin-accessibility sequencing data, enhancer mutations with cis regulatory effects on gene expression were associated with a change in chromatin accessibility, indicating that non-coding variation can have phenotypic consequence through modulation of the 3D architecture of the genome. This study maps the evolution of transcriptional variation during cancer evolution, highlighting that intra-tumour phenotypic plasticity is pervasive in colorectal malignancies, and may play key roles in further tumour evolution, from metastasis to therapy resistance.

cancer biology↗

bioRxiv2021

Assessment of the evolutionary consequence of putative driver mutations in colorectal cancer with spatial multiomic data

Cancer genomic medicine relies on targeting driver genes. However, current catalogues of cancer drivers are mostly based on indirect measurements of mutation frequencies, positions or types, rather than their effect on clonal expansions in vivo. Moreover, non-genetic drivers are largely unknown, as are the epigenetic and transcriptomic effects of genetic drivers. Here we perform spatial computational inference on multiomic data with matched whole-genome sequencing, ATAC-seq and RNA-seq. Using 436 samples, we directly quantify the contribution, or lack thereof, of putative driver genes to subclonal expansions in vivo in 30 colorectal carcinomas (4-33 samples per patient, median=15). Although subclonal neutral evolution was widespread (13/26 cases with sufficient data), there were cases with clear evidence of subclonal selection (6/26) in which we measured epigenetic and transcriptomic differences between subclones in vivo. In 7/26 cases we could not distinguish between neutral or selective evolution with the available data. We identified expanding subclones that were not driven by known genetic alterations, and propose candidate epigenetic drivers. We identified the distinguishing patterns of genomic heterogeneity produced in fast, exponentially growing tumours (7/26) versus neoplasms growing only at the periphery (19/26), as well as identifying clonally intermixed (16/28 cases with sufficient data) versus segregated malignancies (10/28). Our model-based approach measures genetic and non-genetic subclonal selection, or lack thereof, in space and time and allows in vivo comparisons of the emergent phenotypic properties of subclones within human tumours.

cancer biology↗

bioRxiv2021

The co-evolution of the genome and epigenome in colorectal cancer

Colorectal malignancies are a leading cause of cancer death. Despite large-scale genomic efforts, DNA mutations do not fully explain malignant evolution. Here we study the co-evolution of the genome and epigenome of colorectal tumours at single-clone resolution using spatial multi-omic profiling of individual glands. We collected 1,373 samples from 30 primary cancers and 9 concomitant adenomas and generated 1,212 chromatin accessibility profiles, 527 whole-genomes and 297 whole-transcriptomes. We found positive selection for DNA mutations in chromatin modifier genes and recurrent chromatin changes in regulatory regions of cancer drivers with otherwise no mutation. Genome-wide alterations in transcription factor binding accessibility involved CTCF, downregulation of interferon, and increased accessibility for SOX and HOX, indicating developmental genes reactivation. Epigenetic aberrations were heritable, distinguishing adenomas from cancers. Mutational signature analysis showed the epigenome influencing DNA mutation accumulation. This study provides a map of (epi)genetic tumour heterogeneity, with fundamental implications for understanding colorectal cancer biology.

cancer biology↗

bioRxiv2021

Reconstruction of Contemporary Human Stem Cell Dynamics with Oscillatory Molecular Clocks

Molecular clocks record cellular ancestry. However, currently used clocks tick too slowly to measure the short-timescale dynamics of cellular renewal in adult tissues. Here we develop rapidly oscillating DNA methylation clocks where ongoing (de)methylation causes the clock to tick-tock back-and-forth between methylated and unmethylated states like a pendulum. We identify oscillators using standard methylation arrays and develop a mathematical modelling framework to quantitatively measure human adult stem cell dynamics from these data. Small intestinal crypts were inferred to contain slightly more stem cells than colon (6.5 {+/-} 1.0 vs 5.8 {+/-} 1.7 stem cells/crypt) with slower stem cell replacement in small intestine (0.79 {+/-} 0.5 vs 1.1 {+/-} 0.8 replacements/stem cell/year). Germline APC mutation increased the number of replacements per crypt (13.0 {+/-} 2.4 replacements/crypt/year vs 6.9 {+/-} 4.6 for healthy colon). In blood, we measure rapid expansion of acute leukaemia and slower growth of chronic disease. Rapidly oscillating molecular clocks are a new methodology to quantitatively measure human somatic cell dynamics.

bioinformatics↗

bioRxiv2021

The mutational signatures of formalin fixation on the human genome

BackgroundFormalin fixation and paraffin embedding (FFPE) of patient material remains standard practice in clinical pathology labs around the world. Clinical archives of patient material near-exclusively consist of FFPE blocks. The ability to perform high quality genome sequencing on FFPE-derived DNA would accelerate a broad spectrum of medical research. However, formalin is a recognised mutagen and sequencing of DNA derived from FFPE material is known to be riddled with artefactual mutations. ResultsHere we derive genome-wide mutational signatures caused by formalin fixation, and provide a computational method to correct mutational profiles for these formalin-induced artefacts. We show that the FFPE-signature is dominated by C>T transitions caused by cytosine deamination, and has very high similarity to COSMIC signature SBS30 (base excision repair deficiency due to inactivation mutations in NTHL1). Further, we demonstrate that chemical repair of formalin-induced DNA lesions, a process that is routinely performed as part of sequencing library preparation, leads to a signature highly similar to COSMIC signature SBS1 (spontaneous deamination of methylated cytosine). Next, we design FFPEsig, a computational method to remove the formalin-induced artefacts from mutational counts. We prove the efficacy of this method by generating synthetic FFPE samples using 2,780 cancer genomes from the Pan-Cancer Analysis of Whole Genome (PCAWG) project, and via analysis of FFPE-derived genome sequencing data from colorectal cancers. ConclusionsFormalin fixation leaves a predictable mutational footprint across the genome. The application of our FFPEsig software corrects the mutational profile for the influence of formalin, enabling robust mutational signature analysis in FFPE-derived patient material.

bioinformatics↗

bioRxiv2021

LiquidCNA: tracking subclonal evolution from longitudinal liquid biopsies using somatic copy number alterations

Cell-free DNA (cfDNA) measured via liquid biopsies provides a way for minimally-invasive monitoring of tumour evolutionary dynamics during therapy. Here we present liquidCNA, a method to track subclonal evolution from longitudinally collected cfDNA samples based on somatic copy number alterations (SCNAs). LiquidCNA utilises SCNA profiles derived through cost-effective low-pass whole genome sequencing to automatically and simultaneously genotype and quantify the size of the dominant subclone without requiring prior knowledge of the genetic identity of the emerging clone. We demonstrate the accuracy of liquidCNA in synthetically generated sample sets and in vitro and in silico mixtures of cancer cell lines. Application in vivo in patients with metastatic lung cancer reveals the progressive emergence of a novel tumour sub-population. LiquidCNA is straightforward to use, computationally inexpensive and enables continuous monitoring of subclonal evolution to understand and control therapy-induced resistance.

bioinformatics↗

bioRxiv2020

Platinum resistance induces diverse evolutionary trajectories in high grade serous ovarian cancer

Resistance to therapy is an enduring challenge in cancer care. Here we interrogate this critical unmet need using high grade serous ovarian cancer (HGSC) as a disease model. We have generated a unique panel of platinum-resistant HGSC models and shown that they share multiple transcriptomic features with relapsed human HGSC. Moreover, they evolve diverse in vivo phenotypes reflecting the human disease. We previously characterised copy number signatures in HGSC that correlate with patient survival and now provide the first evidence that these signatures undergo recurrent alterations during platinum therapy. Furthermore, specific, resistance-associated signature change is associated with functionally relevant gene expression differences. For example, reduced signature 3 (BRCA1/2-related homologous recombination deficiency) is associated with increased expression of homologous recombination repair genes (Rad51C, Rad51D, BRCA1) and DNA recombination pathway enrichment. Our mechanistic examination therefore provides new and clinically relevant insights into the genomic evolution of platinum-resistant cancers.

cancer biology↗

bioRxiv2020

dN/dS dynamics quantify tumour immunogenicity and predict response to immunotherapy

Immunoediting is a major force during cancer evolution that selects for clones with low immunogenicity (adaptation), or clones with mechanisms of immune evasion (escape). However, quantifying immunogenicity in the cancer genome and how the tumour-immune coevolutionary dynamics impact patient outcomes remain unexplored. Here we show that the ratio of nonsynonymous to synonymous mutations (dN/dS) in the immunopeptidome quantifies tumor immunogenicity and differentiates between adaptation and escape. We analysed 8,543 primary tumors from TCGA and validated immune dN/dS as a measure of selection associated with immune infiltration in immune-adapted tumours. In a cohort of 308 metastatic patients that received immunotherapy, pre-treatment lesions in non-responders showed increased immune selection (dN/dS<1), whereas responders did not and instead harboured a higher proportion of genetic escape mechanisms. Ultimately, these findings highlight the potential of evolutionary genomic measures to predict clinical response to immunotherapy.

genomics↗

bioRxiv2020

Stabilising selection causes grossly altered but stable karyotypes in metastatic colorectal cancer

Aneuploidy, the loss and gain of whole and part chromosomes, is near-ubiquitous in cancer genomes and likely defines cancer cell biology. However, the temporal evolutionary dynamics that select for aneuploidy remain uncharacterised. Here we perform longitudinal genomic analysis of 755 samples from a total of 167 patients with colorectal-derived neoplastic lesions that represent distinct stages of tumour evolution through metastasis and treatment. Adenomas typically had few copy number alterations (CNAs) and most were subclonal, whereas cancers had many clonal CNAs, suggesting that progression goes through a CNA bottleneck. Individual CRC glands from the same tumour typically had very similar karyotypes, despite evidence of ongoing instability at the cell level in patient tumours, cell lines and organoids. CNAs in metastatic lesions sampled from liver and other organs, after chemotherapy or targeted therapies, and in late recurrences were typically similar to the primary tumour. Mathematical modelling and statistical inference indicated that these data are consistent with the action of negative selection on CNAs that traps cancer cell genomes on a fitness peak defined by the specific pattern of chromosomal aberrations. These data suggest that the initial progression of colorectal cancer requires the traversal of a rugged fitness landscape and subsequent CNA evolution, including metastatic dissemination and therapeutic resistance, is constrained by negative selection.

cancer biology↗

bioRxiv2019

Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios

The distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells, however the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that yields the selective coefficients from individual driver mutations from dN/dS estimates, and then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1-5%). Accurate measurement of the per-gene DFE in cancer evolution is precluded by the quality of currently available sequencing data. This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and reveals the DFE for mutations in human tissues.

bioinformatics↗

bioRxiv2019

Niche engineering drives early passage through an immune bottleneck in progression to colorectal cancer

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/623959v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (55K):\norg.highwire.dtl.DTLVardef@1c48cf5org.highwire.dtl.DTLVardef@1026747org.highwire.dtl.DTLVardef@ca92f1org.highwire.dtl.DTLVardef@1753cd5_HPS_FORMAT_FIGEXP M_FIG C_FIG Colorectal cancer develops from its precursor lesion, the adenoma. The immune system is hypothesized to be key in modulating progression, but tumor-immune eco-evolutionary dynamics remain uncharacterized. Here, we demonstrate a key role for immune evasion in the progression of human benign disease to colorectal cancer. We constructed a mathematical model of tumor-immune eco-evolutionary dynamics that predicted ecological succession, from an \"immune-hot\" adenoma immune ecology rich in T cells to an \"immune-cold\" carcinoma ecology, deficient in T cells and rich in immunosuppressive cells. Using a cross-sectional cohort of adenomas and carcinomas, we validated this prediction by direct measurement of the tumor-immune ecology using whole-slide 10-marker immunohistochemistry (IHC), and analysis of neoantigen clonal architecture multi-region exome sequencing data. Changes in immune ecology relax selection against antigens with high recognition potentials. This study indicates that immune surveillance represents a key evolutionary bottleneck in the evolution of colon cancer.

cancer biology↗

bioRxiv2019

Macrophage-mediated immunoediting drives ductal carcinoma evolution: Space is the game changer

Under normal conditions, the immune system is capable of rapidly detecting and eliminating potentially dangerous entities, including tumor cells. Due to intense selection pressure imposed by the immune response, tumor cells often evolve strategies to avoid elimination in a process known as immunoediting. It is less known how the evolutionary response to immune predation is altered by context. We explore the evolution of immune escape strategies in ductal cancers, a natural case in which to study evolution in different contexts: inside and outside of ducts. We highlight the role of macrophages as a source of \"public goods,\" releasing diffusible factors (reactive oxygen species and growth factors). Immunohistochemistry reveals differences between macrophage densities of invasive ductal carcinomas and non-invasive ductal carcinomas in situ. For the first time, immunohistochemistry (IHC) imaging data comparing DCIS to IDC were used to initialize mechanistic agent-based models of evolutionary dynamics. By using IHC to map the initial conditions of a growing tumor, we show that spatial competition and structure influence transient dynamics during invasion. These dynamics are context-dependent, a conclusion that may be missed from interpreting imaging or non-spatial modeling alone. Before invasion, the presence of macrophages correlate with shorter ductal breach times. After invasion, tumors may employ a \"pioneer-engineer\" strategy where pioneering immunoresistant cells on the tumors edge stimulate the release of M1-macrophage-derived reactive oxygen species, degrading surrounding stroma. Behind the invasive edge, the engineering immunosuppressive cells promote the release of M2-macrophage-derived growth factors, providing a long-term immune escape strategy. Together, mathematical modeling and image analysis highlight the crucial role tumor-associated macrophages play in immune escape and invasion, both inside and outside of ducts.

cancer biology↗

bioRxiv2019

Model-based tumor subclonal reconstruction

The vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors.

bioinformatics↗

bioRxiv2019

Measuring single cell divisions in human cancers from multi-region sequencing data

Cancer is driven by complex evolutionary dynamics involving billions of cells. Increasing effort has been dedicated to sequence single tumour cells, but obtaining robust measurements remains challenging. Here we show that multi-region sequencing of bulk tumour samples contains quantitative information on single-cell divisions that is accessible if combined with evolutionary theory. Using high-throughput data from 16 human cancers, we measured the in vivo per-cell point mutation rate (mean: 1.69x10-8 bp per cell division) and per-cell survival rate (mean: 0.57) in individual patient tumours from colon, lung and renal cancers. Per-cell mutation rates varied 50-fold between individuals, and per-cell survival rates were between nearly-homeostatic and almost perfect cell doublings, equating to tumour ages between 1 and 19 years. Furthermore, reanalysing a recent dataset of 89 whole-genome sequenced healthy haematopoietic stem cells, we find 1.14 mutations per genome per cell division and near perfect cell doublings (per-cell survival rate: 0.96) during early haematopoietic development. Our analysis measures in vivo the most fundamental properties of human cancer and healthy somatic evolution at single-cell resolution within single individuals.

cancer biology↗

bioRxiv2019

Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data. SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

bioinformatics↗

bioRxiv2019

Evolutionary dynamics of neoantigens in growing tumours

Cancer evolution is driven by the acquisition of somatic mutations that provide cells with a beneficial phenotype in a changing microenvironment. However, mutations that give rise to neoantigens, novel cancer-specific peptides that elicit an immune response, are likely to be disadvantageous. Here we show how the clonal structure and immunogenotype of growing tumours is shaped by negative selection in response to neoantigenic mutations. We construct a mathematical model of neoantigen evolution in a growing tumour, and verify the model using genomic sequencing data. The model predicts that, in the absence of active immune escape mechanisms, tumours either evolve clonal neoantigens (antigen- hot), or have no clonally- expanded neoantigens at all (antigen- cold), whereas antigen- warm tumours (with high frequency subclonal neoantigens) form only following the evolution of immune evasion. Counterintuitively, strong negative selection for neoantigens during tumour formation leads to an increased number of antigen- warm or - hot tumours, as a consequence of selective pressure for immune escape. Further, we show that the clone size distribution under negative selection is effectively- neutral, and moreover, that stronger negative selection paradoxically leads to more neutral- like dynamics. Analysis of antigen clone sizes and immune escape in colorectal cancer exome sequencing data confirms these results. Overall, we provide and verify a mathematical framework to understand the evolutionary dynamics and clonality of neoantigens in human cancers that may inform patient- specific immunotherapy decision- making.

cancer biology↗

bioRxiv2018

NeoPredPipe: High-Throughput Neoantigen Prediction and Recognition Potential Pipeline

Next generation sequencing has yielded an unparalleled means of quickly determining the molecular make-up of patient tumors. In conjunction with emerging, effective immunotherapeutics for a number of cancers, this rapid data generation necessitates a paired high-throughput means of predicting and assessing neoantigens from tumor variants that may stimulate immune response. Here we offer NeoPredPipe (Neoantigen Prediction Pipeline) as a contiguous means of predicting putative neoantigens and their corresponding recognition potentials for both single and multi-region tumor samples. NeoPredPipe is able to quickly provide summary information for researchers, and clinicians alike, on neoantigen burdens while providing high-level insights into tumor heterogeneity given somatic mutation calls and, optionally, patient HLA haplotypes. Given an example dataset we show how NeoPredPipe is able to rapidly provide insights into neoantigen heterogeneity, burden, and immune stimulation potential. Through the integration of widely adopted tools for neoantigen discovery NeoPredPipe offers a contiguous means of processing single and multi-region sequence data. NeoPredPipe is user-friendly and adaptable for high-throughput performance. NeoPredPipe is freely available at https://github.com/MathOnco/NeoPredPipe.

bioinformatics↗

bioRxiv2017

Detecting repeated cancer evolution in human tumours from multi-region sequencing data

Carcinogenesis is an evolutionary process driven by the accumulation of genomic aberrations. Recurrent sequences of genomic changes, both between and within patients, reflect repeated evolution that is valuable for anticipating cancer progression. Multi-region sequencing and phylogenetic analysis allow inference of the partial temporal order of genomic changes within a patients tumour. However, the inherent stochasticity of the evolutionary process makes phylogenetic trees from different patients appear very distinct, preventing the robust identification of recurrent evolutionary trajectories. Here we present a novel quantitative method based on a machine learning approach called Transfer Learning (TL) that allows overcoming the stochastic effects of cancer evolution and highlighting hidden recurrences in cancer patient cohorts. When applied to multi-region sequencing datasets from lung, breast and renal cancer (708 samples from 160 patients), our method detected repeated evolutionary trajectories that determine novel patient subgroups, which reproduce in large singlesample cohorts (n=2,641) and have prognostic value. Our method provides a novel patient classification measure that is grounded in the cancer evolution paradigm, and which reveals repeated evolution during tumorigenesis, with implications for our ability to anticipate malignant evolution.

bioinformatics↗