Multiplatform biomarker identification using a data-driven approach enables single-sample classification

Background: High-throughput gene expression profiles have allowed discovery of potential biomarkers enabling early diagnosis, prognosis and developing individualized treatment. However, it remains a challenge to identify a set of reliable and reproducible biomarkers across various gene expression platforms and laboratories for single sample diagnosis and prognosis. We address this need with our Data-Driven Reference (DDR) approach, which employs stably expressed housekeeping genes as references to eliminate platform-specific biases and non-biological variabilities. Results: Our method identifies biomarkers with “built-in” features, and these features can be interpreted consistently regardless of profiling technology, which enable classification of single-sample independent of platforms. Validation with RNA-seq data of blood platelets shows that DDR achieves the superior performance in classification of six different tumor types as well as molecular target statuses (such as MET or HER2-positive, and mutant KRAS, EGFR or PIK3CA) with smaller sets of biomarkers. We demonstrate on the three microarray datasets that our method is capable of identifying robust biomarkers for subgrouping medulloblastoma samples with data perturbation due to different microarray platforms. In addition to identifying the majority of subgroup-specific biomarkers in CodeSet of nanoString, some potential new biomarkers for subgrouping medulloblastoma were detected by our method. Conclusions: In this study, we present a simple, yet powerful data-driven method which contributes significantly to identification of robust cross-platform gene signature for disease classification of single-patient to facilitate precision medicine. In addition, our method provides a new strategy for transcriptome analysis

Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium.

Adult diffuse gliomas are a diverse group of brain neoplasms that inflict a high emotional toll on patients and their families. The Cancer Genome Atlas and similar projects have provided a comprehensive understanding of the somatic alterations and molecular subtypes of glioma at diagnosis. However, gliomas undergo significant cellular and molecular evolution during disease progression. We review the current knowledge on the genomic and epigenetic abnormalities in primary tumors and after disease recurrence, highlight the gaps in the literature, and elaborate on the need for a new multi-institutional effort to bridge these knowledge gaps and how the Glioma Longitudinal Analysis Consortium (GLASS) aims to systemically catalog the longitudinal changes in gliomas. The GLASS initiative will provide essential insights into the evolution of glioma toward a lethal phenotype, with the potential to reveal targetable vulnerabilities and, ultimately, improved outcomes for a patient population in need

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Glioma Through the Looking GLASS: Molecular Evolution of Diffuse Gliomas and the Glioma Longitudinal AnalySiS Consortium

Adult diffuse gliomas are a diverse group of brain neoplasms that inflict a high emotional toll on patients and their families. The Cancer Genome Atlas (TCGA) and similar projects have provided a comprehensive understanding of the somatic alterations and molecular subtypes of glioma at diagnosis. However, gliomas undergo significant cellular and molecular evolution during disease progression. We review the current knowledge on the genomic and epigenetic abnormalities in primary tumors and after disease recurrence, highlight the gaps in the literature, and elaborate on the need for a new multi-institutional effort to bridge these knowledge gaps and how the Glioma Longitudinal AnalySiS Consortium (GLASS) aims to systemically catalog the longitudinal changes in gliomas. The GLASS initiative will provide essential insights into the evolution of glioma toward a lethal phenotype, with the potential to reveal targetable vulnerabilities, and ultimately, improved outcomes for a patient population in need

Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium.

Adult diffuse gliomas are a diverse group of brain neoplasms that inflict a high emotional toll on patients and their families. The Cancer Genome Atlas and similar projects have provided a comprehensive understanding of the somatic alterations and molecular subtypes of glioma at diagnosis. However, gliomas undergo significant cellular and molecular evolution during disease progression. We review the current knowledge on the genomic and epigenetic abnormalities in primary tumors and after disease recurrence, highlight the gaps in the literature, and elaborate on the need for a new multi-institutional effort to bridge these knowledge gaps and how the Glioma Longitudinal Analysis Consortium (GLASS) aims to systemically catalog the longitudinal changes in gliomas. The GLASS initiative will provide essential insights into the evolution of glioma toward a lethal phenotype, with the potential to reveal targetable vulnerabilities and, ultimately, improved outcomes for a patient population in need

Metabolic phenotyping of diet and dietary Intake

Nutrition provides the building blocks for growth, repair, and maintenance of the body and is key to maintaining health. Exposure to fast foods, mass production of dietary components, and wider importation of goods have challenged the balance between diet and health in recent decades, and both scientists and clinicians struggle to characterize the relationship between this changing dietary landscape and human metabolism with its consequent impact on health. Metabolic phenotyping of foods, using high-density data-generating technologies to profile the biochemical composition of foods, meals, and human samples (pre- and postfood intake), can be used to map the complex interaction between the diet and human metabolism and also to assess food quality and safety. Here, we outline some of the techniques currently used for metabolic phenotyping and describe key applications in the food sciences, ending with a broad outlook at some of the newer technologies in the field with a view to exploring their potential to address some of the critical challenges in nutritional science

Enhanced separation in ambient mass spectrometry imaging:towards quantification of pharmaceuticals

In the pharmaceutical industry, the development and application of good separation methods is important to study the distribution and the effect of a drug candidate. Mass spectrometry imaging is a technique that is often applied to study the distribution of a drug candidate. This technique is not always able to separate the drug candidate from other molecules that are present in the sample. Therefore, we need better separation techniques in addition to mass spectrometry imaging. This PhD research investigates novel technological developments as possible tools for the pharmaceutical industry to use. The techniques presented in this thesis allow for better separation of molecules that are structurally alike. Because these techniques separate the drug candidate better from other molecules in the sample, their addition to mass spectrometry imaging is used for quantification of two drug candidates in the last chapter of this thesis

Advances on thirdhand smoke using targeted and untargeted approaches

El fum de tabac residual (thirdhand smoke en anglès, THS) és una via d'exposició a compostos tòxics de fum tabac poc estudiada fins ara. El THS es produeix per la deposició de parEcules i gasos en superFcies i pols, on es poden reemetre i/o reaccionar produint nous compostos tòxics, alguns d'ells carcinògens. Malgrat les creixents evidències, els riscos inherents a l'exposició a THS encara no s'han descrit completament. L'objecKu principal d'aquesta tesi és avançar en la caracterització química del THS i dels efectes per a la salut derivats d'aquesta exposició mitjançant l'aplicació de mètodes analíKcs dirigits i no dirigits. Aquesta tesi presenta el desenvolupament de un nou mètode analíKc per determinar simultàniament tòxics del tabac en pols domèsKca, mitjançant cromatografia líquida (UHPLC). En aquesta tesi, també s'ha desenvolupat un mètode d'anàlisi no dirigit basat en l'adquisició de mostres per UHPLC acoblada a espectrometria de masses d'alta resolució (HR-MS), amb l'aplicació d'estratègies avançades de processament de dades, la priorització estadísKca d’ions rellevants i una nova estratègia per a l'anotació de compostos. La combinació d'aquests dos mètodes va proporcionar per primera vegada l'anotació de dotzenes de tòxics relacionats amb la contaminació per THS adherits a la pols domèsKca. Pel que fa als efectes sobre la salut, presentem el primer estudi metabolòmic no dirigit en fetge de ratolins exposats a THS. L'aplicació de les tècniques UHPLC-HRMS i ressonància magnèKca nuclear (RMN) va permetre idenKficar dotzenes de metabòlits hepàKcs alterats, mentre que les imatges d'espectrometria de masses (MSI) van mostrar la distribució espacial diferencial de lípids en fetge induïda per THS. Aquests resultats confirmen els perills de l'exposició a THS i el paper clau de la introducció de noves estratègies metodològiques en la invesKgació en salut ambiental.El humo de tabaco residual (thirdhand smoke en inglés, THS) es una vía de exposición a compuestos tóxicos del humo del tabaco poco estudiada hasta la fecha. El THS se produce por la deposición de parBculas y gases en superficies y polvo, dónde se pueden reemiEr y/o reaccionar produciendo nuevos compuestos tóxicos, algunos de ellos carcinógenos. A pesar de las crecientes evidencias, los riesgos inherentes a la exposición a THS aún no se han descrito por completo. El objeEvo principal de esta tesis es avanzar en la caracterización química del THS y de los efectos para la salud derivados de esta exposición mediante la aplicación de métodos analíEcos dirigidos y no dirigidos. Esta tesis presenta el desarrollo de un nuevo método analíEco para determinar simultáneamente tóxicos del tabaco en polvo domésEco mediante cromatograMa líquida (UHPLC). En esta tesis, también se ha desarrollado un método de análisis no dirigido basado en la adquisición de muestras por UHPLC acoplada a espectrometría de masas de alta resolución (HR-MS), con la aplicación de estrategias avanzadas de procesamiento de datos, la priorización estadísEca de iones relevantes y una nueva estrategia para la anotación de compuestos. La combinación de estos dos métodos proporcionó por primera vez la anotación de docenas de tóxicos relacionados con la contaminación por THS adheridos al polvo domésEco.Respecto a los efectos sobre la salud, presentamos el primer estudio metabolómico no dirigido en hígado de ratones expuestos a THS. La aplicación de las técnicas UHPLC-HRMS y resonancia magnéEca nuclear (RMN) permiEó idenEficar docenas de metabolitos hepáEcos alterados, mientras que las imágenes de espectrometría de masas (MSI) mostraron la distribución espacial diferencial de lípidos en hígado inducida por THS. Estos resultados confirman los peligros de la exposición a THS y el papel clave de nuevos enfoques metodológicos en la invesEgación en salud ambiental.Thirdhand tobacco smoke (THS) is a novel and poorly understood pathway of tobacco exposure produced by the deposi=on and ageing of tobacco smoke par=cles and toxicants in surfaces and dust. This aged tobacco smoke could reemit into the air or react with other atmospheric chemicals to yield new toxicants, including carcinogens and becoming increasingly toxic with age. Although growing evidences of THS hazards, its chemical characteriza=on and the related health effects remain to be fully elucidated. Hence, this thesis aims to advance on the current knowledge on THS chemical characteriza=on and on the health effects derived from THS exposure by applying novel targeted and untargeted approaches. To advance on THS chemical characteriza=on, we have developed an efficient, quick and robust analy=cal method for simultaneously determining tobacco-specific compounds in household dust by ultra-highperformance liquid-chromatography coupled to tandem mass spectrometry (UHPLC-MSMS). We applied this target method in combina=on with untargeted strategies for a comprehensive characteriza=on of THS toxicants aNached to household dust. The developed untargeted workflow combines the sample acquisi=on by UHPLC coupled to high-resolu=on mass spectrometry (HR-MS) with the applica=on of advanced data processing strategies, the sta=s=cal priori=za=on of relevant features and a novel strategy for compound annota=on. The combina=on of these two approaches provided for the first =me the annota=on of dozens of toxicants related to THS contamina=on. To advance on the health effects, this thesis presents the first mul=plaQorm untargeted metabolomics study to unravel the molecular altera=ons of liver from mice exposed to THS. UHPLC-HRMS and nuclear magne=c resonance (NMR) revealed dozens of hepa=c metabolites altered in THS-exposed mice whilst mass spectrometry imaging (MSI) showed the differen=al spa=al distribu=on of lipids induced by THS. The results presented here confirm the hazards of THS exposure and the key role of combined untargeted and targeted methods in environmental health research

Non-invasive, innovative and promising strategy for breast cancer diagnosis based on metabolomic profile of urine, cancer cell lines and tissue

The work presented in this thesis aimed to establish the metabolomic profile of urine and breast cancer (BC) tissue from BC patients (samples cordially provided by Funchal Hospital), in addition to BC cell lines (MCF-7, MDA-MB-231, T-47D) as a powerful strategy to identify metabolites as potential BC biomarkers, helping on the development of non-invasive approaches for BC diagnosis and management. To achieve the main goal and obtain a deeper and comprehensive knowledge on BC metabolome, different analytical platforms, namely headspace solid-phase microextraction (HSSPME) combined with gas chromatography-quadrupole mass spectrometry (GC-qMS) and nuclear magnetic ressonance (1H NMR) spectroscopy were used. The application of multivariate statistical methods - principal component analysis (PCA) and orthogonal partial least square – discriminant analysis (OPLS-DA), to data matrix obtained from the different target samples allowed to find a set of highly sensitive and specific metabolites metabolites, namely, 4-heptanone, acetic acid and glutamine, able to be used as potential biomarkers in BC diagnosis. Significant group separation was observed in OPLS-DA score plot between BC and CTL indicating intrinsic metabolic alterations in each group. To attest the robustness of the model, a random permutation test with 1000 permutations was performed with OPLS-DA. The permutation test yielded R2 (represents goodness of fit) and Q2 values (represents predictive ability) with values higher than 0.717 and 0.691, respectively. Several metabolic pathways were dysregulated in BC considering the analytical approaches used. The main pathways included pyruvate, glutamine and sulfur metabolisms, indicating that there might be an association between the metabolites arising from the type of biological sample of the same donor used to perform the investigation. The integration of data obtained from different analytical platforms (GC-qMS and 1H NMR) for urinary and tissue samples revealed that five metabolites (e.g., acetone, 3-hexanone, 4-heptanone, 2methyl-5-(methylthio)-furan and acetate), were found significant using a dual analytical approach.O trabalho apresentado nesta tese teve como objetivo estabelecer o perfil metabolómico da urina e do tecido da mama de doentes com cancro de mama (BC) (amostras cordialmente fornecidas pelo Hospital do Funchal), além das linhas celulares de BC (MCF-7, MDA-MB-231, T -47D) como uma poderosa estratégia para identificar metabolitos como potenciais biomarcadores de BC, auxiliando no desenvolvimento de abordagens não invasivas para o diagnóstico e a gestão da patologia. Para obter um conhecimento mais profundo e abrangente do metaboloma de BC, diferentes plataformas analíticas, nomeadamente a microextração em fase sólida em modo headspace (HS-SPME) combinada com a cromatografia em fase gasosa acoplada à espectrometria de massa (GC-qMS) e espectroscopia de ressonância magnética nuclear (1H RMN), foram usadas para atingir o objetivo principal. A aplicação de métodos estatísticos multivariados - análise de componentes principais (PCA) e análise discriminante de mínimos quadrados parciais ortogonais (OPLS-DA) à matriz de dados obtida a partir das diferentes amostras alvo, permitiu estabelecer um grupo de metabolitos sensíveis e específicos, nomeadamente a 4-heptanona, o ácido acético e a glutamina, possíveis de serem utilizados como potenciais biomarcadores no diagnóstico de BC. Uma separação significativa entre os grupos BC e CTL foi observada pelo OPLS-DA, indicando alterações metabólicas em cada grupo. Para verificar a robustez do modelo, foi realizado um teste de permutação aleatória com 1000 permutações com o sistema OPLS-DA. Valores de R2 (representa o ajuste) e Q2 (representa a capacidade preditiva) superiores a 0,717 e 0,691, foram obtidos utilizando o teste da permutação. Diversas vias metabólicas estavam desreguladas no BC considerando as abordagens analíticas utilizadas. As principais vias incluíram os metabolismos do piruvato e glutamina, indicando que poderá haver uma associação entre os metabolitos derivados do tipo de amostra biológica do mesmo doador utilizado para realizar a investigação. A integração de dados obtidos pelas diferentes plataformas analíticas (GC-qMS e 1H RMN) para amostras urinárias e de tecido revelou cinco metabolitos significativos usando a dupla abordagem analítica. (i.e., acetona, 3-hexanona, 4-heptanona, 2-metil-5- (metiltio) - furano e acetato)

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