Signatures of Mutational Processes and Associated Risk Factors in Esophageal Squamous Cell Carcinoma: A Geographically Independent Stratification Strategy?

Cancer Research UKThis is the accepted manuscript. The final version is available at http://www.sciencedirect.com/science/article/pii/S001650851600370X

Simulation-based model selection for dynamical systems in systems and population biology

Computer simulations have become an important tool across the biomedical sciences and beyond. For many important problems several different models or hypotheses exist and choosing which one best describes reality or observed data is not straightforward. We therefore require suitable statistical tools that allow us to choose rationally between different mechanistic models of e.g. signal transduction or gene regulation networks. This is particularly challenging in systems biology where only a small number of molecular species can be assayed at any given time and all measurements are subject to measurement uncertainty. Here we develop such a model selection framework based on approximate Bayesian computation and employing sequential Monte Carlo sampling. We show that our approach can be applied across a wide range of biological scenarios, and we illustrate its use on real data describing influenza dynamics and the JAK-STAT signalling pathway. Bayesian model selection strikes a balance between the complexity of the simulation models and their ability to describe observed data. The present approach enables us to employ the whole formal apparatus to any system that can be (efficiently) simulated, even when exact likelihoods are computationally intractable.Comment: This article is in press in Bioinformatics, 2009. Advance Access is available on Bioinformatics webpag

Molecular effects of Lapatinib in the treatment of HER2 overexpressing oesophago-gastric adenocarcinoma.

BACKGROUND: Lapatinib, a dual EGFR and HER2 inhibitor has shown disappointing results in clinical trials of metastatic oesophago-gastric adenocarcinomas (OGAs), and in vitro studies suggest that MET, IGFR, and HER3 confer resistance. This trial applied Lapatinib in the curative neoadjuvant setting and investigated the feasibility and utility of additional endoscopy and biopsy for assessment of resistance mechanisms ex vivo and in vivo. METHODS: Patients with HER2 overexpressing OGA were treated for 10 days with Lapatinib monotherapy, and then in combination with three cycles of Oxaliplatin and Capecitabine before surgery. Endoscopic samples were taken for molecular analysis at: baseline including for ex vivo culture +/- Lapatinib to predict in vivo response, post-Lapatinib monotherapy and at surgery. Immunohistochemistry (IHC) and proteomic analysis was performed to assess cell kinetics and signalling activity. RESULTS: The trial closed early (n=10) due to an anastomotic leak in two patients for which a causative effect of Lapatinib could not be excluded. The reduction in Phosphorylated-HER2 (P-HER2) and P-EGFR in the ex vivo-treated biopsy demonstrated good correlation with the in vivo response at day 10. Proteomic analysis pre and post-Lapatinib demonstrated target inhibition (P-ERBB2, P-EGFR, P-PI3K, P-AKT, and P-ERK) that persisted until surgery. There was also significant correlation between the activation of MET with the level of P-Erk (P=0.0005) and P-PI3K : T-PI3K (total PI3K) ratio (P=0.0037). There was no significant correlation between the activation status of IGFR and HER3 with downstream signalling molecules. CONCLUSIONS: Additional endoscopy and biopsy sampling for multiple biomarker endpoints was feasible and confirmed in vitro data that MET is likely to be a significant mechanism of Lapatinib resistance in vivo.This research was funded by the Medical Research Council [Grant SK002].This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/bjc.2015.34

HistoMIL: A Python package for training multiple instance learning models on histopathology slides

Hematoxylin and eosin (H&E) stained slides are widely used in disease diagnosis. Remarkable advances in deep learning have made it possible to detect complex molecular patterns in these histopathology slides, suggesting automated approaches could help inform pathologists’ decisions. Multiple instance learning (MIL) algorithms have shown promise in this context, outperforming transfer learning (TL) methods for various tasks, but their implementation and usage remains complex. We introduce HistoMIL, a Python package designed to streamline the implementation, training and inference process of MIL-based algorithms for computational pathologists and biomedical researchers. It integrates a self-supervised learning module for feature encoding, and a full pipeline encompassing TL and three MIL algorithms: ABMIL, DSMIL, and TransMIL. The PyTorch Lightning framework enables effortless customization and algorithm implementation. We illustrate HistoMIL's capabilities by building predictive models for 2,487 cancer hallmark genes on breast cancer histology slides, achieving AUROC performances of up to 85%

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation, and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.Comment: Submitted to 'Systems Medicine' as a book chapte

Visualization and analysis strategies for dynamic gene-phenotype relationships and their biological interpretation

The complexity of biological systems is one of their most fascinating and, at the same time, most cryptic aspects. Despite the progress of technology that has enabled measuring biological parameters at deeper levels of detail in time and space, the ability to decipher meaning from these large amounts of heterogeneous data is limited. In order to address this challenge, both analysis and visualization strategies need to be adapted to handle this complexity. At system-wide level, we are still limited in our ability to infer genetic and environmental causes of disease, or consistently compare and link phenotypes. Moreover, despite the increasing availability of time-resolved experiments, the temporal context is often lost. In my thesis, I explored a series of analysis and visualization strategies to compare and connect dynamic phenotypic outcomes of cellular perturbations in a genetic and network context. More specifically, in the first part of my thesis, I focused on the cell cycle as one of the best examples of a complex, highly dynamic process. I applied analysis and data integration methods to investigate phenotypes derived from cell division failure. I examined how such phenotypes may arise as a result of perturbations in the underlying network. To this purpose, I investigated the role of short structural elements at binding interfaces of proteins, called linear motifs, in shaping the cell division network. I assessed their association to different phenotypes, in the context of local perturbations and of disease. This analysis enabled a more detailed understanding of the regulatory mechanisms beyond the malfunctioning of cell division processes, but the ability to compare phenotypes and track their evolution was limited. Exploring large-scale, time-resolved phenotypic screens is still a bottleneck, especially in the visualization area. To help address this question, in the subsequent parts of the thesis I proposed novel visualization approaches that would leverage pattern discovery in such heterogeneous, dynamic datasets and enable the generation of new hypotheses. First, I extended an existing visualization tool, Arena3D, to enable the comparison of phenotypes in a genetic and network context. I used this tool to continue the exploration of phenotype-wide differences between outcomes of gene function suppression within mitosis. I also applied it to an investigation of systemic changes in the network of embryonic stem cell fate determinants upon downregulation of the pluripotency factor Nanog. Second, time-resolved tracking of phenotypes opens up new possibilities in exploring how genetic and phenotypic connections evolve through time, an aspect that is largely missing in the visualization area. I developed a novel visualization approach that uses 2D/3D projections to enable the discovery of genetic determinants linking phenotypes through time. I used the resulting tool, PhenoTimer, to investigate the patterns of transitions between phenotypes in cell populations upon perturbation of cell division and the timing of cancer-relevant transcriptional events. I showed the potential of discovering drug synergistic effects by visual mapping of similarities in their mechanisms of action. Overall, these approaches help clarify aspects of the consequences of cell division failure and provide general visualization frameworks that should be of interest to the wider scientific community, for use in the analysis of multidimensional phenotypic screens

Pan-Cancer Survey of Tumor Mass Dormancy and Underlying Mutational Processes

Tumor mass dormancy is the key intermediate step between immune surveillance and cancer progression, yet due to its transitory nature it has been difficult to capture and characterize. Little is understood of its prevalence across cancer types and of the mutational background that may favor such a state. While this balance is finely tuned internally by the equilibrium between cell proliferation and cell death, the main external factors contributing to tumor mass dormancy are immunological and angiogenic. To understand the genomic and cellular context in which tumor mass dormancy may develop, we comprehensively profiled signals of immune and angiogenic dormancy in 9,631 cancers from the Cancer Genome Atlas and linked them to tumor mutagenesis. We find evidence for immunological and angiogenic dormancy-like signals in 16.5% of bulk sequenced tumors, with a frequency of up to 33% in certain tissues. Mutations in the CASP8 and HRAS oncogenes were positively selected in dormant tumors, suggesting an evolutionary pressure for controlling cell growth/apoptosis signals. By surveying the mutational damage patterns left in the genome by known cancer risk factors, we found that aging-induced mutations were relatively depleted in these tumors, while patterns of smoking and defective base excision repair were linked with increased tumor mass dormancy. Furthermore, we identified a link between APOBEC mutagenesis and dormancy, which comes in conjunction with immune exhaustion and may partly depend on the expression of the angiogenesis regulator PLG as well as interferon and chemokine signals. Tumor mass dormancy also appeared to be impaired in hypoxic conditions in the majority of cancers. The microenvironment of dormant cancers was enriched in cytotoxic and regulatory T cells, as expected, but also in macrophages and showed a reduction in inflammatory Th17 signals. Finally, tumor mass dormancy was linked with improved patient survival outcomes. Our analysis sheds light onto the complex interplay between dormancy, exhaustion, APOBEC activity and hypoxia, and sets directions for future mechanistic explorations

Distinct patterns of proteostasis network gene expression are associated with different prognoses in melanoma patients

The proteostasis network (PN) is a collection of protein folding and degradation pathways that spans cellular compartments and acts to preserve the integrity of the proteome. The differential expression of PN genes is a hallmark of many cancers, and the inhibition of protein quality control factors is an effective way to slow cancer cell growth. However, little is known about how the expression of PN genes differs between patients and how this impacts survival outcomes. To address this, we applied unbiased hierarchical clustering to gene expression data obtained from primary and metastatic cutaneous melanoma (CM) samples and found that two distinct groups of individuals emerge across each sample type. These patient groups are distinguished by the differential expression of genes encoding ATP-dependent and ATP-independent chaperones, and proteasomal subunits. Differences in PN gene expression were associated with increased levels of the transcription factors, MEF2A, SP4, ZFX, CREB1 and ATF2, as well as markedly different survival outcomes. However, surprisingly, similar PN alterations in primary and metastatic samples were associated with discordant survival outcomes in patients. Our findings reveal that the expression of PN genes demarcates CM patients and highlights several new proteostasis sub-networks that could be targeted for more effective suppression of CM within specific individuals

A Comparison of Low Read Depth QuantSeq 3 ' Sequencing to Total RNA-Seq in FUS Mutant Mice

Transcriptomics is a developing field with new methods of analysis being produced which may hold advantages in price, accuracy, or information output. QuantSeq is a form of 3′ sequencing produced by Lexogen which aims to obtain similar gene-expression information to RNA-seq with significantly fewer reads, and therefore at a lower cost. QuantSeq is also able to provide information on differential polyadenylation. We applied both QuantSeq at low read depth and total RNA-seq to the same two sets of mouse spinal cord RNAs, each comprised by four controls and four mutants related to the neurodegenerative disease amyotrophic lateral sclerosis. We found substantial differences in which genes were found to be significantly differentially expressed by the two methods. Some of this difference likely due to the difference in number of reads between our QuantSeq and RNA-seq data. Other sources of difference can be explained by the differences in the way the two methods handle genes with different primary transcript lengths and how likely each method is to find a gene to be differentially expressed at different levels of overall gene expression. This work highlights how different methods aiming to assess expression difference can lead to different results

Genomic and microenvironmental heterogeneity shaping epithelial-to-mesenchymal trajectories in cancer

The epithelial to mesenchymal transition (EMT) is a key cellular process underlying cancer progression, with multiple intermediate states whose molecular hallmarks remain poorly characterised. To fill this gap, we present a method to robustly evaluate EMT transformation in individual tumours based on transcriptomic signals. We apply this approach to explore EMT trajectories in 7180 tumours of epithelial origin and identify three macro-states with prognostic and therapeutic value, attributable to epithelial, hybrid E/M and mesenchymal phenotypes. We show that the hybrid state is relatively stable and linked with increased aneuploidy. We further employ spatial transcriptomics and single cell datasets to explore the spatial heterogeneity of EMT transformation and distinct interaction patterns with cytotoxic, NK cells and fibroblasts in the tumour microenvironment. Additionally, we provide a catalogue of genomic events underlying distinct evolutionary constraints on EMT transformation. This study sheds light on the aetiology of distinct stages along the EMT trajectory, and highlights broader genomic and environmental hallmarks shaping the mesenchymal transformation of primary tumours