1,108 research outputs found
To metabolomics and beyond: a technological portfolio to investigate cancer metabolism
Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies
Infrared molecular fingerprinting of blood-based liquid biopsies for the detection of cancer
Recent omics analyses of human biofluids provide opportunities to probe selected species of biomolecules for disease diagnostics. Fourier-transform infrared (FTIR) spectroscopy investigates the full repertoire of molecular species within a sample at once. Here, we present a multi-institutional study in which we analysed infrared fingerprints of plasma and serum samples from 1639 individuals with different solid tumours and carefully matched symptomatic and non-symptomatic reference individuals. Focusing on breast, bladder, prostate, and lung cancer, we find that infrared molecular fingerprinting is capable of detecting cancer: training a support vector machine algorithm allowed us to obtain binary classification performance in the range of 0.78–0.89 (area under the receiver operating characteristic curve [AUC]), with a clear correlation between AUC and tumour load. Intriguingly, we find that the spectral signatures differ between different cancer types. This study lays the foundation for high-throughput onco-IR-phenotyping of four common cancers, providing a cost-effective, complementary analytical tool for disease recognition
MACHINE LEARNING APPROACHES FOR BIOMARKER IDENTIFICATION AND SUBGROUP DISCOVERY FOR POST-TRAUMATIC STRESS DISORDER
Post-traumatic stress disorder (PTSD) is a psychiatric disorder caused by environmental and genetic factors resulting from alterations in genetic variation, epigenetic changes and neuroimaging characteristics. There is a pressing need to identify reliable molecular and physiological biomarkers for accurate diagnosis, prognosis, and treatment, as well to deepen the understanding of PTSD pathophysiology. Machine learning methods are widely used to infer patterns from biological data, identify biomarkers, and make predictions. The objective of this research is to apply machine learning methods for the accurate classification of human diseases from genome-scale datasets, focusing primarily on PTSD.The DoD-funded Systems Biology of PTSD Consortium has recruited combat veterans with and without PTSD for measurement of molecular and physiological data from blood or urine samples with the goal of identifying accurate and specific PTSD biomarkers. As a member of the Consortium with access to these PTSD multiple omics datasets, we first completed a project titled Clinical Subgroup-Specific PTSD Classification and Biomarker Discovery. We applied machine learning approaches to these data to build classification models consisting of molecular and clinical features to predict PTSD status. We also identified candidate biomarkers for diagnosis, which improves our understanding of PTSD pathogenesis. In a second project, entitled Multi-Omic PTSD Subgroup Identification and Clinical Characterization, we applied methods for integrating multiple omics datasets to investigate the complex, multivariate nature of the biological systems underlying PTSD. We identified an optimal 2 PTSD subgroups using two different machine learning approaches from 82 PTSD positive samples, and we found that the subgroups exhibited different remitting behavior as inferred from subjects recalled at a later time point. The results from our association, differential expression, and classification analyses demonstrated the distinct clinical and molecular features characterizing these subgroups.Taken together, our work has advanced our understanding of PTSD biomarkers and subgroups through the use of machine learning approaches. Results from our work should strongly contribute to the precise diagnosis and eventual treatment of PTSD, as well as other diseases. Future work will involve continuing to leverage these results to enable precision medicine for PTSD
The Translational Status of Cancer Liquid Biopsies
Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research. Lay Summary: Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research
IMass time: The future, in future!
Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Quantitative extraction of high-dimensional mineable data from medical images
is a process known as radiomics. Radiomics is foreseen as an essential
prognostic tool for cancer risk assessment and the quantification of
intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying
tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET
and CT images of 300 patients from four different cohorts were analyzed for the
risk assessment of locoregional recurrences (LR) and distant metastases (DM) in
head-and-neck cancer. Prediction models combining radiomic and clinical
variables were constructed via random forests and imbalance-adjustment
strategies using two of the four cohorts. Independent validation of the
prediction and prognostic performance of the models was carried out on the
other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).
Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the
potential of radiomics for assessing the risk of specific tumour outcomes using
multiple stratification groups. This could have important clinical impact,
notably by allowing for a better personalization of chemo-radiation treatments
for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7
figures, 8 table
Developing methods for the context-specific reconstruction of metabolic models of cancer cells
Dissertação de mestrado em BioinformáticaThe recent advances in genome sequencing technologies and other high-throughput methodologies
allowed the identification and quantification of individual cell components. These efforts led to the
development of genome-scale metabolic models (GSMMs), not only for humans but also for several
other organisms. These models have been used to predict cellular metabolic phenotypes under a
variety of physiological conditions and contexts, proving to be useful in tasks such as drug discovery,
biomarker identification and interactions between hosts and pathogens. Therefore, these models
provide a useful tool for targeting diseases such as cancer, Alzheimer or tuberculosis.
However, the usefulness of GSSMs is highly dependent on their capabilities to predict phenotypes
in the array of different cell types that compose the human body, making the development of
tissue/context-specific models mandatory. To address this issue, several methods have been
proposed to integrate omics data, such as transcriptomics or proteomics, to improve the phenotype
prediction abilities of GSSMs. Despite these efforts, these methods still have some limitations. In most
cases, their usage is locked behind commercially licensed software platforms, or not available in a
user-friendly fashion, thus restricting their use to users with programming or command-line
knowledge.
In this work, an open-source tool was developed for the reconstruction of tissue/context-specific
models based on a generic template GSMM and the integration of omics data. The Tissue-Specific
Model Reconstruction (TSM-Rec) tool was developed under the Python programming language and
features the FASTCORE algorithm for the reconstruction of tissue/context-specific metabolic models.
Its functionalities include the loading of omics data from a variety of omics databases, a set of filtering
and transformation methods to adjust the data for integration with a template metabolic model, and
finally the reconstruction of tissue/context-specific metabolic models.
To evaluate the functionality of the developed tool, a cancer related case-study was carried. Using
omics data from 314 glioma patients, the TSM-Rec tool was used to reconstruct metabolic models of
different grade gliomas. A total of three models were generated, corresponding to grade II, III and IV
gliomas. These models were analysed regarding their differences and similarities in reactions and
pathways. This comparison highlighted biological processes common to all glioma grades, and
pathways that are more prominent in each glioma model. The results show that the tool developed
during this work can be useful for the reconstruction of cancer metabolic models, in a search for
insights into cancer metabolism and possible approaches towards drug-target discovery.Os avanços recentes nas tecnologias de sequenciação de genomas e noutras metodologias
experimentais de alto rendimento permitiram a identificação e quantificação dos diversos
componentes celulares. Estes esforços levaram ao desenvolvimento de Modelos Metabólicos à Escala
Genómica (MMEG) não só de humanos, mas também de diversos organismos. Estes modelos têm
sido utilizados para a previsão de fenótipos metabólicos sob uma variedade de contextos e condições
fisiológicas, mostrando a sua utilidade em áreas como a descoberta de fármacos, a identificação de
biomarcadores ou interações entre hóspede e patógeno. Desta forma, estes modelos revelam-se
ferramentas úteis para o estudo de doenças como o cancro, Alzheimer ou a tuberculose.
Contudo, a utilidade dos MMEG está altamente dependente das suas capacidades de previsão
de fenótipos nos diversostipos celulares que compõem o corpo humano, tornando o desenvolvimento
de modelos específicos de tecidos uma tarefa obrigatória. Para resolver este problema, vários
métodos têm proposto a integração de dados ómicos como os de transcriptómica ou proteómica
para melhorar as capacidades preditivas dos MMEG. Apesar disso, estes métodos ainda sofrem de
algumas limitações. Na maioria dos casos o seu uso está confinado a plataformas ou softwares com
licenças comerciais, ou não está disponível numa ferramenta de fácil uso, limitando a sua utilização
a utilizadores com conhecimentos de programação ou de linha de comandos.
Neste trabalho, foi desenvolvida uma ferramenta de acesso livre para a reconstrução de modelos
metabólicos específicos para tecidos tendo por base um MMEG genérico e a integração de dados
ómicos. A ferramenta TSM-Rec (Tissue-Specific Model Reconstruction), foi desenvolvida na linguagem
de programação Python e recorre ao algoritmo FASTCORE para efetuar a reconstrução de modelos
metabólicos específicos. As suas funcionalidades permitem a leitura de dados ómicos de diversas
bases de dados ómicas, a filtragem e transformação dos mesmos para permitir a sua integração
com um modelo metabólico genérico e por fim, a reconstrução de modelos metabólicos específicos.
De forma a avaliar o funcionamento da ferramenta desenvolvida, esta foi aplicada num caso de
estudo de cancro. Recorrendo a dados ómicos de 314 pacientes com glioma, usou-se a ferramenta
TSM-Rec para a reconstrução de modelos metabólicos de gliomas de diferentes graus. No total, foram
desenvolvidos três modelos correspondentes a gliomas de grau II, grau III e grau IV. Estes modelos
foram analisados no sentido de perceber as diferenças e as similaridades entre as reações e as vias
metabólicas envolvidas em cada um dos modelos. Esta comparação permitiu isolar processos
biológicos comuns a todos os graus de glioma, assim como vias metabólicas que se destacam em cada um dos graus. Os resultados obtidos demonstram que a ferramenta desenvolvida pode ser útil
para a reconstrução de modelos metabólicos de cancro, na procura de um melhor conhecimento do
metabolismo do cancro e possíveis abordagens para a descoberta de fármacos
Steps to Improve Precision Medicine in Epilepsy
Precision medicine is an old concept, but it is not widely applied across human health conditions as yet. Numerous attempts have been made to apply precision medicine in epilepsy, this has been based on a better understanding of aetiological mechanisms and deconstructing disease into multiple biological subsets. The scope of precision medicine is to provide effective strategies for treating individual patients with specific agent(s) that are likely to work best based on the causal biological make-up. We provide an overview of the main applications of precision medicine in epilepsy, including the current limitations and pitfalls, and propose potential strategies for implementation and to achieve a higher rate of success in patient care. Such strategies include establishing a definition of precision medicine and its outcomes; learning from past experiences, from failures and from other fields (e.g. oncology); using appropriate precision medicine strategies (e.g. drug repurposing versus traditional drug discovery process); and using adequate methods to assess efficacy (e.g. randomised controlled trials versus alternative trial designs). Although the progress of diagnostic techniques now allows comprehensive characterisation of each individual epilepsy condition from a molecular, biological, structural and clinical perspective, there remain challenges in the integration of individual data in clinical practice to achieve effective applications of precision medicine in this domain
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