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Projeto de investigação
OLISSIPO – Fostering Computational Biology Research and Innovation in Lisbon
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Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications
Publication . Coletti, Roberta; Leiria de Mendonça, Mónica; Vinga, Susana; Lopes, Marta B.; CMA - Centro de Matemática e Aplicações; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; SAGE Publications
Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official World Health Organization (WHO) classification of the central nervous system (CNS). These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on The Cancer Genome Atlas (TCGA) glioma RNA-sequencing data set updated according to the 2016 and 2021 WHO guidelines, we proposed a 2-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularized Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.
ROSIE
Publication . Jensch, Antje; Lopes, Marta B.; Vinga, Susana; Radde, Nicole; NOVALincs; CMA - Centro de Matemática e Aplicações; SAGE Publications
The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy. Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods. We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of (Formula presented.) genes and more than (Formula presented.) samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.
Updating TCGA glioma classification through integration of molecular data following the latest WHO guidelines
Publication . de Mendonça, Mónica Leiria; Coletti, Roberta; Gonçalves, Céline S.; Martins, Eduarda P.; Costa, Bruno M.; Vinga, Susana; Lopes, Marta B.; CMA - Centro de Matemática e Aplicações; Faculdade de Ciências e Tecnologia (FCT); UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; DM - Departamento de Matemática; Nature Research
The understanding of glioma disease has significantly advanced through the application of genetic and molecular profiling techniques on brain tumour tissue. Molecular biomarkers have gained a crucial role in glioma diagnosis, driving groundbreaking changes in the disease classification as standardised by the 2016 and 2021 World Health Organisation (WHO) Classification of Tumours of the Central Nervous System. Recent insights from large-scale multi-omics databases, such as The Cancer Genome Atlas (TCGA), have enriched our comprehension of this cancer type. However, given the evolution of glioma classification, retrospective databases may contain outdated annotations, suboptimal for research. To address this issue, we propose two methods for updating the tumor classification of TCGA glioma samples according to the 2016 and 2021 WHO guidelines, through the integration of open-access curated molecular profiling data. Respectively, our Method-2016 and Method-2021 allowed for the diagnostic update of 98% and 87% of cases. The proposed reclassification pipelines, provided in R scripts, enable straightforward reproduction or customisation upon new WHO guideline releases.
Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization
Publication . Peixoto, Carolina; Lopes, Marta B.; Martins, Marta; Casimiro, Sandra; Sobral, Daniel; Grosso, Ana Rita; Abreu, Catarina; Macedo, Daniela; Costa, Ana Lúcia; Pais, Helena; Alvim, Cecília; Mansinho, André; Filipe, Pedro; Costa, Pedro Marques da; Fernandes, Afonso; Borralho, Paula; Ferreira, Cristina; Malaquias, João; Quintela, António; Kaplan, Shannon; Golkaram, Mahdi; Salmans, Michael; Khan, Nafeesa; Vijayaraghavan, Raakhee; Zhang, Shile; Pawlowski, Traci; Godsey, Jim; So, Alex; Liu, Li; Costa, Luís; Vinga, Susana; NOVALincs; CMA - Centro de Matemática e Aplicações; UCIBIO - Applied Molecular Biosciences Unit; DCV - Departamento de Ciências da Vida; BioMed Central (BMC)
Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods (decision trees, linear and radial kernel support vector machines, logistic regression, and random forest) using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner—a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods’ accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes (OPCML-IT2), to be related to resistance to cancer therapies (RAC1P3), or to be involved in several cancer processes such as genome stability (XRCC6P2), tumor growth and metastasis (MIR602) and regulation of gene transcription (NME2P2). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models’ predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients’ groups based on RNA-seq data.
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
951970