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Twiner: correlation-based regularization for identifying common cancer gene signatures
Publication . Lopes, Marta B.; Casimiro, Sandra; Vinga, Susana
Background: Breast and prostate cancers are typical examples of hormone-dependent cancers, showing remarkable similarities at the hormone-related signaling pathways level, and exhibiting a high tropism to bone. While the identification of genes playing a specific role in each cancer type brings invaluable insights for gene therapy research by targeting disease-specific cell functions not accounted so far, identifying a common gene signature to breast and prostate cancers could unravel new targets to tackle shared hormone-dependent disease features, like bone relapse. This would potentially allow the development of new targeted therapies directed to genes regulating both cancer types, with a consequent positive impact in cancer management and health economics.
Results: We address the challenge of extracting gene signatures from transcriptomic data of prostate adenocarcinoma (PRAD) and breast invasive carcinoma (BRCA) samples, particularly estrogen positive (ER+), and androgen positive (AR+) triple-negative breast cancer (TNBC), using sparse logistic regression. The introduction of gene network information based on the distances between BRCA and PRAD correlation matrices is investigated, through the proposed twin networks recovery (twiner) penalty, as a strategy to ensure similarly correlated gene features in two diseases to be less penalized during the feature selection procedure.
Conclusions: Our analysis led to the identification of genes that show a similar correlation pattern in BRCA and PRAD transcriptomic data, and are selected as key players in the classification of breast and prostate samples into ER+ BRCA/AR+ TNBC/PRAD tumor and normal tissues, and also associated with survival time distributions. The results obtained are supported by the literature and are expected to unveil the similarities between the diseases, disclose common disease biomarkers, and help in the definition of new strategies for more effective therapies.
TCox : correlation-based regularization applied to colorectal cancer survival data
Publication . Peixoto, Carolina; Lopes, Marta B.; Martins, Marta; Costa, Luis; Vinga, Susana
Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. Being a heterogeneous disease, cancer therapy and prognosis represent a significant challenge to medical care. The molecular information improves the accuracy with which patients are classified and treated since similar pathologies may show different clinical outcomes and other responses to treatment. However, the high dimensionality of gene expression data makes the selection of novel genes a problematic task. We propose TCox, a novel penalization function for Cox models, which promotes the selection of genes that have distinct correlation patterns in normal vs. tumor tissues. We compare TCox to other regularized survival models, Elastic Net, HubCox, and OrphanCox. Gene expression and clinical data of CRC and normal (TCGA) patients are used for model evaluation. Each model is tested 100 times. Within a specific run, eighteen of the features selected by TCox are also selected by the other survival regression models tested, therefore undoubtedly being crucial players in the survival of colorectal cancer patients. Moreover, the TCox model exclusively selects genes able to categorize patients into significant risk groups. Our work demonstrates the ability of the proposed weighted regularizer TCox to disclose novel molecular drivers in CRC survival by accounting for correlation-based network information from both tumor and normal tissue. The results presented support the relevance of network information for biomarker identification in high-dimensional gene expression data and foster new directions for the development of network-based feature selection methods in precision oncology.
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
9471 - RIDTI
Número da atribuição
PTDC/CCI-CIF/29877/2017