6,458 research outputs found
Blood Vessel Tortuosity Selects against Evolution of Agressive Tumor Cells in Confined Tissue Environments: a Modeling Approach
Cancer is a disease of cellular regulation, often initiated by genetic
mutation within cells, and leading to a heterogeneous cell population within
tissues. In the competition for nutrients and growth space within the tumors
the phenotype of each cell determines its success. Selection in this process is
imposed by both the microenvironment (neighboring cells, extracellular matrix,
and diffusing substances), and the whole of the organism through for example
the blood supply. In this view, the development of tumor cells is in close
interaction with their increasingly changing environment: the more cells can
change, the more their environment will change. Furthermore, instabilities are
also introduced on the organism level: blood supply can be blocked by increased
tissue pressure or the tortuosity of the tumor-neovascular vessels. This
coupling between cell, microenvironment, and organism results in behavior that
is hard to predict. Here we introduce a cell-based computational model to study
the effect of blood flow obstruction on the micro-evolution of cells within a
cancerous tissue. We demonstrate that stages of tumor development emerge
naturally, without the need for sequential mutation of specific genes.
Secondly, we show that instabilities in blood supply can impact the overall
development of tumors and lead to the extinction of the dominant aggressive
phenotype, showing a clear distinction between the fitness at the cell level
and survival of the population. This provides new insights into potential side
effects of recent tumor vasculature renormalization approaches
Quantification of tumour heterogenity in MRI
Cancer is the leading cause of death that touches us all, either directly or indirectly.
It is estimated that the number of newly diagnosed cases in the Netherlands will increase
to 123,000 by the year 2020. General Dutch statistics are similar to those in
the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised
at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence
per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup
Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer
INTRODUCTION
Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice.
METHODS
More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer 'stem' cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account.
RESULTS
The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working.
CONCLUSIONS
With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years
Assessment of a Novel VEGF Targeted Agent Using Patient-Derived Tumor Tissue Xenograft Models of Colon Carcinoma with Lymphatic and Hepatic Metastases
The lack of appropriate tumor models of primary tumors and corresponding metastases that can reliably predict for response to anticancer agents remains a major deficiency in the clinical practice of cancer therapy. It was the aim of our study to establish patient-derived tumor tissue (PDTT) xenograft models of colon carcinoma with lymphatic and hepatic metastases useful for testing of novel molecularly targeted agents. PDTT of primary colon carcinoma, lymphatic and hepatic metastases were used to create xenograft models. Hematoxylin and eosin staining, immunohistochemical staining, genome-wide gene expression analysis, pyrosequencing, qRT-PCR, and western blotting were used to determine the biological stability of the xenografts during serial transplantation compared with the original tumor tissues. Early passages of the PDTT xenograft models of primary colon carcinoma, lymphatic and hepatic metastases revealed a high degree of similarity with the original clinical tumor samples with regard to histology, immunohistochemistry, genes expression, and mutation status as well as mRNA expression. After we have ascertained that these xenografts models retained similar histopathological features and molecular signatures as the original tumors, drug sensitivities of the xenografts to a novel VEGF targeted agent, FP3 was evaluated. In this study, PDTT xenograft models of colon carcinoma with lymphatic and hepatic metastasis have been successfully established. They provide appropriate models for testing of novel molecularly targeted agents
Current challenges in glioblastoma : intratumour heterogeneity, residual disease and models to predict disease recurrence
Glioblastoma (GB) is the most common malignant primary brain tumour, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumour recurrence. Technological advances are continually improving our understanding of the disease and in particular our knowledge of clonal evolution, intratumour heterogeneity and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modelling (including neural networks), and strategies such as multiple-sampling during tumour resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB
Artificial intelligence for breast cancer precision pathology
Breast cancer is the most common cancer type in women globally but is associated with a
continuous decline in mortality rates. The improved prognosis can be partially attributed to
effective treatments developed for subgroups of patients. However, nowadays, it remains
challenging to optimise treatment plans for each individual. To improve disease outcome and
to decrease the burden associated with unnecessary treatment and adverse drug effects, the
current thesis aimed to develop artificial intelligence based tools to improve individualised
medicine for breast cancer patients.
In study I, we developed a deep learning based model (DeepGrade) to stratify patients that
were associated with intermediate risks. The model was optimised with haematoxylin and eosin
(HE) stained whole slide images (WSIs) with grade 1 and 3 tumours and applied to stratify
grade 2 tumours into grade 1-like (DG2-low) and grade 3-like (DG2-high) subgroups. The
efficacy of the DeepGrade model was validated using recurrence free survival where the
dichotomised groups exhibited an adjusted hazard ratio (HR) of 2.94 (95% confidence interval
[CI] 1.24-6.97, P = 0.015). The observation was further confirmed in the external test cohort
with an adjusted HR of 1.91 (95% CI: 1.11-3.29, P = 0.019).
In study II, we investigated whether deep learning models were capable of predicting gene
expression levels using the morphological patterns from tumours. We optimised convolutional
neural networks (CNNs) to predict mRNA expression for 17,695 genes using HE stained WSIs
from the training set. An initial evaluation on the validation set showed that a significant
correlation between the RNA-seq measurements and model predictions was observed for
52.75% of the genes. The models were further tested in the internal and external test sets.
Besides, we compared the model's efficacy in predicting RNA-seq based proliferation scores.
Lastly, the ability of capturing spatial gene expression variations for the optimised CNNs was
evaluated and confirmed using spatial transcriptomics profiling.
In study III, we investigated the relationship between intra-tumour gene expression
heterogeneity and patient survival outcomes. Deep learning models optimised from study II
were applied to generate spatial gene expression predictions for the PAM50 gene panel. A set
of 11 texture based features and one slide average gene expression feature per gene were
extracted as input to train a Cox proportional hazards regression model with elastic net
regularisation to predict patient risk of recurrence. Through nested cross-validation, the model
dichotomised the training cohort into low and high risk groups with an adjusted HR of 2.1
(95% CI: 1.30-3.30, P = 0.002). The model was further validated on two external cohorts.
In study IV, we investigated the agreement between the Stratipath Breast, which is the
modified, commercialised DeepGrade model developed in study I, and the Prosigna® test.
Both tests sought to stratify patients with distinct prognosis. The outputs from Stratipath Breast
comprise a risk score and a two-level risk stratification whereas the outputs from Prosigna®
include the risk of recurrence score and a three-tier risk stratification. By comparing the number
of patients assigned to ‘low’ or ‘high’ risk groups, we found an overall moderate agreement
(76.09%) between the two tests. Besides, the risk scores by two tests also revealed a good
correlation (Spearman's rho = 0.59, P = 1.16E-08). In addition, a good correlation was observed
between the risk score from each test and the Ki67 index. The comparison was also carried out
in the subgroup of patients with grade 2 tumours where similar but slightly dropped correlations
were found
The Molecular Characterisation of Circulating Tumour Cells in Neuroendocrine Neoplasms
Identification of the molecular alterations that drive cancer is critical for precision oncology. Profiling of a single tissue biopsy is insufficient to interrogate the full spectrum of molecular heterogeneity that exists within a patient’s tumour, and is not without risk to the patient. The analysis of CTCs as part of a liquid biopsy circumvents this issue and allows single-cell analysis as well as longitudinal monitoring over time and in response to therapy. The aim of this thesis is to perform the first molecular characterisation of CTCs derived from NEN patients with a view to evaluating therapeutic targets and characterising tumour heterogeneity and evolution at the single-cell level. Firstly, I developed an assay to enable detection of the therapeutic targets SSTR2 and SSTR5 on individual NEN CTCs. Applied to a cohort of 31 metastatic NEN patients, I identified an SSTR+ subpopulation in 33% of patients and demonstrated significant intra- and inter-patient heterogeneity of SSTR expression. Next, I evaluated the size-based Parsortix platform for CTC enrichment against the gold standard EpCAM-dependent CellSearch in a pilot study of NEN patients, demonstrating that a higher number of CTCs could be isolated in a greater proportion of NEN patients using this technique. Furthermore, the presence of CTCs with low and absent EpCAM expression was observed for the first time in NEN alongside significant intra-patient heterogeneity in EpCAM expression. In order to fully dissect the heterogeneity observed in this early work, I developed DEPArraybased workflows to allow the single-cell evaluation of CTC copy number profiles using next-generation sequencing. The developed methodologies were subsequently tested in a representative cohort of NEN patients. By performing comprehensive copy number profiling of 125 single CTCs, I was able to identify recurrent and therapeutically relevant rearrangements, such as the amplification of CDK4/6, MET and BRAF and loss of BRCA2. Unsupervised hierarchical clustering demonstrated CTCs with distinct clonal lineages and significant heterogeneity was seen in CNV profiles between and within patient samples. In conclusion, this thesis describes successful workflows for the genomic analysis of CTCs at the single-cell level and is a step towards the implementation of precision oncology in neuroendocrine patients. This analysis has identified CTC heterogeneity at the single-cell level with implications for the identification of therapeutic targets, mechanisms of resistance to therapy, tracking of evolutionary change and biomarker refinement in NEN
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
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Novel approaches to MRI of glioma
Gliomas are extremely heterogeneous, both morphologically and biologically, which contributes to a very poor prognosis. Current imaging of glioma is insufficient for a thorough diagnosis, therapy assessment and prognosis prediction. Moreover, refined and more sophisticated imaging technique could help in furthering our knowledge of gliomas.
In order to facilitate proliferation, cancer cells undergo a change in structure and an increase in metabolism that results in distortion and disruption of tissue architecture. Gliomas are characterised by an increase in cells of variable sizes, as well as changes in the tissue microstructure. Diffusion-Weighted Imaging (DWI) and the apparent diffusion coefficient (ADC), have been extensively studied as potential imaging biomarkers for cellularity and tissue architecture. However, several studies have shown partial overlap in the measured values between tumour subtypes. Moreover, ADC is influenced by several factors and does not provide detailed information on the tissue microstructure. The Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours (VERDICT) is a novel diffusion model that infers tissue microstructure compartment from conventional DWI measurements. This model derives metrics for the intracellular, intravascular and extracellular– extravascular spaces providing a more detailed interpretation of the tissue microstructure. To date, VERDICT has been applied to xenograft models of colorectal cancer, patient studies of prostate cancer and recently its feasibility in glioma has been shown. In this PhD I have applied a shortened version of the VERDICT method to image intratumoral and intertumoral heterogeneity in glioma. The results have also been validated with histology as part of a prospective study.
Gliomas also exhibit a significant increase in mitotic activity within the tumour. The increased number of mitosis alters cell density which, in turn, affects the total concentration of tissue sodium as the concentration of tissue sodium is approximately ten-fold higher in the extracellular compared to the intracellular space. In addition, there is a decrease in Na+/K+-ATPase activity in tumours due to ATP depletion, which contributes to disturb sodium homeostasis. Non-invasive detection of 23Na with MRI has the potential to quantify sodium concentration and therefore could be an imaging probe of cell morphology and membrane function within the tumour microenvironment, as well as a method of probing tissue heterogeneity. During my PhD, a novel 23Na-MRI technique has been used to evaluate sodium distribution within glioma and in the surrounding tissue.
Metabolic reprogramming is one of the major driving forces for determining glioma growth and invasion. Therefore, the non-invasive characterization of metabolic intratumoral, peritumoral and intertumoral heterogeneity in vivo could help to better stratify patients and to develop novel therapeutic strategies targeting cancer-specific metabolic pathways. 13C magnetic resonance imaging (MRI) using dynamic nuclear polarization (DNP) is a novel technique that allows non-invasive assessment of the metabolism of hyperpolarized (HP) 13C-labelled molecules in vivo, such as the exchange of [1-13C]pyruvate to [1-13C]lactate in tumours (Warburg effect). Part of my PhD has focused on developing and translating HP [1-13C]pyruvate MRI to explore metabolic reprogramming in glioma and the surrounding microenvironment.
The overall aim of my PhD has been to develop novel approaches to imaging glioma with MRI to probe both the architectural and metabolic changes of Glioma. The preliminary evidence suggests that these tools can more deeply phenotype tumours than conventional imaging approaches. Although the main focus of this work has been gliomas, the techniques developed and presented here may be applied to study other pathological conditions within the brain, which raises the possibility of other potential clinical applications for this work
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