Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, the issue is addressed as a consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, a new concept of performance-based consistency is proposed in this thesis.An interesting finding in our previous experiments is that by using a proper set of informative genes, we significantly improved the consistency characteristic of microarray data. Therefore, how to select genes in terms of consistency modelling becomes an interesting topic. Many previously published gene selection methods perform well in the cancer diagnosis domain, but questions are raised because of the irreproducibility of experimental results. Motivated by this, two new gene selection methods based on the proposed performance-based consistency concept, GAGSc (Genetic Algorithm Gene Selection method in terms of consistency) and LOOLSc (Leave-one-out Least-Square bound method with consistency measurement) were developed in this study with the purpose of identifying a set of informative genes for achieving replicable results of microarray data analysis.The proposed consistency concept was investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy.As an implementation of the proposed performance-based consistency, GAGSc and LOOLSc are capable of providing a small set of informative genes. Comparing with those traditional gene selection methods without using consistency measurement, GAGSc and LOOLSc can provide more accurate classification results. More importantly, GAGSc and LOOLSc have demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments.

Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, the issue is addressed as a consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, a new concept of performance-based consistency is proposed in this thesis.An interesting finding in our previous experiments is that by using a proper set of informative genes, we significantly improved the consistency characteristic of microarray data. Therefore, how to select genes in terms of consistency modelling becomes an interesting topic. Many previously published gene selection methods perform well in the cancer diagnosis domain, but questions are raised because of the irreproducibility of experimental results. Motivated by this, two new gene selection methods based on the proposed performance-based consistency concept, GAGSc (Genetic Algorithm Gene Selection method in terms of consistency) and LOOLSc (Leave-one-out Least-Square bound method with consistency measurement) were developed in this study with the purpose of identifying a set of informative genes for achieving replicable results of microarray data analysis.The proposed consistency concept was investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy.As an implementation of the proposed performance-based consistency, GAGSc and LOOLSc are capable of providing a small set of informative genes. Comparing with those traditional gene selection methods without using consistency measurement, GAGSc and LOOLSc can provide more accurate classification results. More importantly, GAGSc and LOOLSc have demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments.

O câncer é caracterizado pelo crescimento anormal de células em consequência ao acúmulo de alterações no DNA. Diferentes tecidos apresentam variadas incidências de tumores. Em 2015, Tomasetti & Vogelstein demonstraram uma forte correlação positiva (r = 0.804) entre o número de divisões de células tronco e o risco de câncer em diferentes tecidos, em que tecidos com maior número de divisões de células tronco estão mais susceptíveis aos efeitos estocásticos da replicação do DNA, e, assim, mais propícios a desenvolverem tumores. Assim, esta correlação é justificada pelo número de mutações. Neste trabalho, propomos e testamos in silico um novo conceito para justificar parte desta correlação positiva entre o número de divisões de células tronco e o risco de câncer entre diferentes tecidos, o qual denominamos concentração de ancestrais (AC). Em nossa hipótese, tecidos com alta taxa de proliferação concentram mais seus ancestrais, amplificando as chances de perpetuar células ancestrais mutadas, e, por isso, estão relacionados a maiores riscos de câncer. Assim, tecidos com altos valores de divisão de células tronco apresentam um perfil de alto AC e tecidos com baixo número de divisão de células tronco apresentam um perfil de baixo AC Para comprovar nossa hipótese, simulamos a evolução tumoral através do software esi- Cancer e aplicamos diferentes valores de proliferação e morte (CTOR). Os resultados demonstraram uma correlação positiva de 0.995 entre o valor de CTOR e o perfil de AC (P = 0:002). Como esperado, demonstramos que maiores CTORs estão relacionados a maiores médias de gerações com esiTumors para valores totais de mutações por divisão iguais. Entretanto, esta relação se mantém quando aplicadas valores corrigidos conforme o número de divisões para os diferentes CTORs, a fim de o número de mutações totais ser igual. Logo, apenas variações não são suficientes para explicar a incidência observada em diferentes tecidos. Nossos resultados demonstram que tecidos com maior número de divisões de células tronco apresentam um perfil de alto AC, o qual amplifica as chances de concentrar ancestrais mutados, aumentando as chances de desenvolver tumores. Assim, justificando parte da correlação encontrada por Tomasetti & Vogelstein (2015). / Cancer is characterized by an abnormal replication of somatic cells as a result of DNA alterations. Different types of tissues present differences in cancer incidence. Tomasetti & Vogelstein (2015) have shown that lifetime cancer risk of different tissues presents a strong correlation of 0.804 with the number of stem cells divisions, in which tissues with higher number of stem cells divisions are more susceptible to stochastic effects of DNA replication and thus more likely to develop cancer. Thus, the number of mutations was used to explain this correlation. In our work, we propose and test in silico a new concept to explain this positive correlation, which we denominated ancestral concentration (AC). In our hypothesis, a tissue with high proliferation rates concentrates more their ancestral cells and increases the chance of a mutated ancestral to persist; which result in a higher risk of cancer. Tissues with a high number of stem cells divisions presents a high AC profile whereas tissues with a low number of stem cells divisions presents a low AC profile To prove our hypothesis, we simulated tumor evolution using esiCancer software and applied different initial rates of proliferation and death (CTOR). We observed a positive correlation of 0.995 between CTOR values and the AC profile (P = 0:002). Besides, higher CTOR values are associated to higher mean generations with esiTumors when equal mutation rates are applied. Nevertheless, this association still exist in simulations with mutation rates corrected by total number of divisions, whereas the total mutation rate is similar for different CTORs. This way, modifications of mutations solely are not sufficient to explain the observed cancer risks in different tissues. Our results showed that tissues with higher number of stem cells divisions present a high AC profile, which rises the probabilities of concentrate mutated ancestral cells, increasing the tumor risk. In this way, justifying partly the correlation that was founded by Tomasetti & Vogelstein (2015).

O câncer é caracterizado pelo crescimento anormal de células em consequência ao acúmulo de alterações no DNA. Diferentes tecidos apresentam variadas incidências de tumores. Em 2015, Tomasetti & Vogelstein demonstraram uma forte correlação positiva (r = 0.804) entre o número de divisões de células tronco e o risco de câncer em diferentes tecidos, em que tecidos com maior número de divisões de células tronco estão mais susceptíveis aos efeitos estocásticos da replicação do DNA, e, assim, mais propícios a desenvolverem tumores. Assim, esta correlação é justificada pelo número de mutações. Neste trabalho, propomos e testamos in silico um novo conceito para justificar parte desta correlação positiva entre o número de divisões de células tronco e o risco de câncer entre diferentes tecidos, o qual denominamos concentração de ancestrais (AC). Em nossa hipótese, tecidos com alta taxa de proliferação concentram mais seus ancestrais, amplificando as chances de perpetuar células ancestrais mutadas, e, por isso, estão relacionados a maiores riscos de câncer. Assim, tecidos com altos valores de divisão de células tronco apresentam um perfil de alto AC e tecidos com baixo número de divisão de células tronco apresentam um perfil de baixo AC Para comprovar nossa hipótese, simulamos a evolução tumoral através do software esi- Cancer e aplicamos diferentes valores de proliferação e morte (CTOR). Os resultados demonstraram uma correlação positiva de 0.995 entre o valor de CTOR e o perfil de AC (P = 0:002). Como esperado, demonstramos que maiores CTORs estão relacionados a maiores médias de gerações com esiTumors para valores totais de mutações por divisão iguais. Entretanto, esta relação se mantém quando aplicadas valores corrigidos conforme o número de divisões para os diferentes CTORs, a fim de o número de mutações totais ser igual. Logo, apenas variações não são suficientes para explicar a incidência observada em diferentes tecidos. Nossos resultados demonstram que tecidos com maior número de divisões de células tronco apresentam um perfil de alto AC, o qual amplifica as chances de concentrar ancestrais mutados, aumentando as chances de desenvolver tumores. Assim, justificando parte da correlação encontrada por Tomasetti & Vogelstein (2015). / Cancer is characterized by an abnormal replication of somatic cells as a result of DNA alterations. Different types of tissues present differences in cancer incidence. Tomasetti & Vogelstein (2015) have shown that lifetime cancer risk of different tissues presents a strong correlation of 0.804 with the number of stem cells divisions, in which tissues with higher number of stem cells divisions are more susceptible to stochastic effects of DNA replication and thus more likely to develop cancer. Thus, the number of mutations was used to explain this correlation. In our work, we propose and test in silico a new concept to explain this positive correlation, which we denominated ancestral concentration (AC). In our hypothesis, a tissue with high proliferation rates concentrates more their ancestral cells and increases the chance of a mutated ancestral to persist; which result in a higher risk of cancer. Tissues with a high number of stem cells divisions presents a high AC profile whereas tissues with a low number of stem cells divisions presents a low AC profile To prove our hypothesis, we simulated tumor evolution using esiCancer software and applied different initial rates of proliferation and death (CTOR). We observed a positive correlation of 0.995 between CTOR values and the AC profile (P = 0:002). Besides, higher CTOR values are associated to higher mean generations with esiTumors when equal mutation rates are applied. Nevertheless, this association still exist in simulations with mutation rates corrected by total number of divisions, whereas the total mutation rate is similar for different CTORs. This way, modifications of mutations solely are not sufficient to explain the observed cancer risks in different tissues. Our results showed that tissues with higher number of stem cells divisions present a high AC profile, which rises the probabilities of concentrate mutated ancestral cells, increasing the tumor risk. In this way, justifying partly the correlation that was founded by Tomasetti & Vogelstein (2015).

O câncer é caracterizado pelo crescimento anormal de células em consequência ao acúmulo de alterações no DNA. Diferentes tecidos apresentam variadas incidências de tumores. Em 2015, Tomasetti & Vogelstein demonstraram uma forte correlação positiva (r = 0.804) entre o número de divisões de células tronco e o risco de câncer em diferentes tecidos, em que tecidos com maior número de divisões de células tronco estão mais susceptíveis aos efeitos estocásticos da replicação do DNA, e, assim, mais propícios a desenvolverem tumores. Assim, esta correlação é justificada pelo número de mutações. Neste trabalho, propomos e testamos in silico um novo conceito para justificar parte desta correlação positiva entre o número de divisões de células tronco e o risco de câncer entre diferentes tecidos, o qual denominamos concentração de ancestrais (AC). Em nossa hipótese, tecidos com alta taxa de proliferação concentram mais seus ancestrais, amplificando as chances de perpetuar células ancestrais mutadas, e, por isso, estão relacionados a maiores riscos de câncer. Assim, tecidos com altos valores de divisão de células tronco apresentam um perfil de alto AC e tecidos com baixo número de divisão de células tronco apresentam um perfil de baixo AC Para comprovar nossa hipótese, simulamos a evolução tumoral através do software esi- Cancer e aplicamos diferentes valores de proliferação e morte (CTOR). Os resultados demonstraram uma correlação positiva de 0.995 entre o valor de CTOR e o perfil de AC (P = 0:002). Como esperado, demonstramos que maiores CTORs estão relacionados a maiores médias de gerações com esiTumors para valores totais de mutações por divisão iguais. Entretanto, esta relação se mantém quando aplicadas valores corrigidos conforme o número de divisões para os diferentes CTORs, a fim de o número de mutações totais ser igual. Logo, apenas variações não são suficientes para explicar a incidência observada em diferentes tecidos. Nossos resultados demonstram que tecidos com maior número de divisões de células tronco apresentam um perfil de alto AC, o qual amplifica as chances de concentrar ancestrais mutados, aumentando as chances de desenvolver tumores. Assim, justificando parte da correlação encontrada por Tomasetti & Vogelstein (2015). / Cancer is characterized by an abnormal replication of somatic cells as a result of DNA alterations. Different types of tissues present differences in cancer incidence. Tomasetti & Vogelstein (2015) have shown that lifetime cancer risk of different tissues presents a strong correlation of 0.804 with the number of stem cells divisions, in which tissues with higher number of stem cells divisions are more susceptible to stochastic effects of DNA replication and thus more likely to develop cancer. Thus, the number of mutations was used to explain this correlation. In our work, we propose and test in silico a new concept to explain this positive correlation, which we denominated ancestral concentration (AC). In our hypothesis, a tissue with high proliferation rates concentrates more their ancestral cells and increases the chance of a mutated ancestral to persist; which result in a higher risk of cancer. Tissues with a high number of stem cells divisions presents a high AC profile whereas tissues with a low number of stem cells divisions presents a low AC profile To prove our hypothesis, we simulated tumor evolution using esiCancer software and applied different initial rates of proliferation and death (CTOR). We observed a positive correlation of 0.995 between CTOR values and the AC profile (P = 0:002). Besides, higher CTOR values are associated to higher mean generations with esiTumors when equal mutation rates are applied. Nevertheless, this association still exist in simulations with mutation rates corrected by total number of divisions, whereas the total mutation rate is similar for different CTORs. This way, modifications of mutations solely are not sufficient to explain the observed cancer risks in different tissues. Our results showed that tissues with higher number of stem cells divisions present a high AC profile, which rises the probabilities of concentrate mutated ancestral cells, increasing the tumor risk. In this way, justifying partly the correlation that was founded by Tomasetti & Vogelstein (2015).

Bladder cancer (BCa) is one of the most common malignancies and has a relatively poor outcome worldwide. However, the molecular mechanisms and processes of BCa development and progression remain poorly understood. Therefore, the present study aimed to identify candidate genes in the carcinogenesis and progression of BCa. Five GEO datasets and TCGA-BLCA datasets were analyzed by statistical software R, FUNRICH, Cytoscape, and online instruments to identify differentially expressed genes (DEGs), to construct protein‒protein interaction networks (PPIs) and perform functional enrichment analysis and survival analyses. In total, we found 418 DEGs. We found 14 hub genes, and gene ontology (GO) analysis revealed DEG enrichment in networks and pathways related to cell cycle and proliferation, but also in cell movement, receptor signaling, and viral carcinogenesis. Compared with noncancerous tissues, TPM1, CRYAB, and CASQ2 were significantly downregulated in BCa, and the other hub genes were significant upregulated. Furthermore, MAD2L1 and CASQ2 potentially play a pivotal role in lymph nodal metastasis. CRYAB and CASQ2 were both significantly correlated with overall survival (OS) and disease-free survival (DFS). The present study highlights an up to now unrecognized possible role of CASQ2 in cancer (BCa). Furthermore, CRYAB has never been described in BCa, but our study suggests that it may also be a candidate biomarker in BCa.

No description available.

The tumour microenvironment comprises the non-cancerous cells present in the tumour mass (fibroblasts, endothelial, and immune cells), as well as signalling molecules and extracellular matrix. Tumour growth, invasion, metastasis, and response to therapy are influenced by the tumour microenvironment. Therefore, characterising the cellular and molecular components of the tumour microenvironment, and understanding how they influence tumour progression, represent a crucial aim for the success of cancer therapies. High-grade serous ovarian cancer provides an excellent opportunity to systematically study the tumour microenvironment due to its clinical presentation of advanced disseminated disease and debulking surgery being standard of care. This thesis first presents a case report of a long-term survivor (>10 years) of metastatic high-grade serous ovarian cancer who exhibited concomitant regression/progression of the metastatic lesions (5 samples). We found that progressing metastases were characterized by immune cell exclusion, whereas regressing metastases were infiltrated by CD8+ and CD4+ T cells. Through a T cell - neoepitope challenge assay we demonstrated that pre- dicted neoepitopes were recognised by the CD8+ T cells obtained from blood drawn from the patient, suggesting that regressing tumours were subjected to immune attack. Immune excluded tumours presented a higher expression of immunosuppressive Wnt signalling, while infiltrated tumours showed a higher expression of the T cell chemoattractant CXCL9 and evidence of immunoediting. These findings suggest that multiple distinct tumour immune microenvironments can co-exist within a single individual and may explain in part the hetero- geneous fates of metastatic lesions often observed in the clinic post-therapy. Second, this thesis explores the prevalence of intra-patient tumour microenvironment het- erogeneity in high-grade serous ovarian cancer at diagnosis (38 samples from 8 patients), as well as the effect of chemotherapy on the tumour microenvironment (80 paired samples from 40 patients). Whole transcriptome analysis and image-based quantification of T cells from treatment-naive tumours revealed highly prevalent variability in immune signalling and distinct immune microenvironments co-existing within the same individuals at diagnosis. ConsensusTME, a method that generates consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods that predict immune cell populations using bulk RNA data was developed. ConsensusTME improved accuracy and sensitivity of T cell and leukocyte deconvolutions in ovarian cancer samples. As previously observed in the case report, Wnt signalling expression positively correlated with immune cell exclusion. To evaluate the effect of chemotherapy on the tumour microenvironment, we compared site-matched and site-unmatched tumours before and after neoadjuvant chemotherapy. Site- matched samples showed increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, unlike site-unmatched samples where heterogeneity could not be accounted for. In addition, low levels of immune activation pre-chemotherapy were found to be correlated with immune activation upon chemotherapy treatment. These results cor- roborate that the tumour-immune interface in advanced high-grade serous ovarian cancer is intrinsically heterogeneous, and that chemotherapy induces an immunogenic effect mediated by cytotoxic cells. Finally, the different deconvolution methods were benchmarked along with ConsensusTME in a pan-cancer setting by comparing deconvolution scores to DNA-based purity scores, leukocyte methylation data, and tumour infiltrating lymphocyte counts from image analysis. In so far as it has been benchmarked, unlike the other methods, ConsensusTME performs consistently among the top three methods across cancer-related benchmarks. Additionally, ConsensusTME provides a dynamic and evolvable framework that can integrate newer de- convolution tools and benchmark their performance against itself, thus generating an ever updated version. Overall, this thesis presents a systematic characterisation of the tumour microenvironment of high grade serous ovarian cancer in treatment-naive and chemotherapy treated samples, and puts forward the development of an integrative computational method for the systematic analysis of the tumour microenvironment of different tumour types using bulk RNA data.

Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It “portrays” the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers.