Implementation of an hybrid machine learning methodology for pharmacological modeling

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática) Universidade de Lisboa, Faculdade de Ciências, 2017Hoje em dia, especialmente na area biomedica, os dados contem milhares de variaveis de fontes diferentes e com apenas algumas instancias ao mesmo tempo. Devido a este facto, as abordagens da aprendizagem automatica enfrentam dois problemas, nomeadamente a questao da integracao de dados heterogeneos e a selecao das caracteristicas. Este trabalho propoe uma solucao eficiente para esta questao e proporciona uma implementacao funcional da metodologia hibrida. A inspiracao para este trabalho veio do desafio proposto no ambito da competicao AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge em 2016, e da solucao vencedora desenvolvida por Yuanfang Guan. Relativamente a motivacao do concurso, e observado que os tratamentos combinatorios para o cancro sao mais eficientes do que as terapias habituais de agente unico, desde que tem potencial para superar as desvantagens dos outros (limitado espetro de acao e desenvolvimento de resistencia). No entanto, o efeito combinatorio de drogas nao e obvio, produzindo possivelmente o resultado aditivo, sinergico ou antagonico. Assim, o objetivo da competicao era prever in vitro a sinergia dos compostos, sem ter acesso aos dados experimentais da terapia combinatoria. No ambito da competicao foram fornecidos ficheiros de varias fontes, contendo o conhecimento farmacologico tanto experimental como obtido de ajustamento das equacoes, a informacao sobre propriedades quimicas e estruturais de drogas, e por fim, os perfis moleculares de celulas, incluindo expressao de RNA, copy variants, sequencia e metilacao de DNA. O trabalho referido envolveu uma abordagem muito bem sucedida de integração dos dados heterogeneos, estendendo o modelo com conhecimento disponivel dentro do projeto The Cancer Cell Line Encyclopedia, e tambem introduzindo o passo decisivo de simulacao que permite imitar o efeito de terapia combinatoria no cancro. Apesar das descricoes pouco claras e da documentacao da solucao vencedora ineficiente, a reproducao da abordagem de Guan foi concluida, tentando ser o mais fiel possivel. A implementacao funcional foi escrita nas linguagens R e Python, e o seu desempenho foi verificado usando como referencia a matriz submetida no concurso. Para melhorar a metodologia, o workflow de selecao dos caracteristicas foi estabelecido e executado usando o algoritmo Lasso. Alem disso, o desempenho de dois metodos alternativos de modelacao foi experimentado, incluindo Support Vector Machine and Multivariate Adaptive Regression Splines (MARS). Varias versoes da equacao de integracao foram consideradas permitindo a determinacao de coeficientes aparentemente otimos. Como resultado, a compreensao da melhor solucao de competição foi desenvolvida e a implementacao funcional foi construida com sucesso. As melhorias foram propostas e no efeito o algoritmo SVM foi verificado como capaz de superar os outros na resolução deste problema, a equacao de integracao com melhor desempenho foi estabelecida e finalmente a lista de 75 variaveis moleculares mais informativas foi fornecida. Entre estes genes, poderiam ser encontrados possiveis candidatos de biomarcadores de cancro.Nowadays, especially in the biomedical field, the data sets usually contain thousands of multi-source variables and with only few instances in the same time. Due to this fact, Machine Learning approaches face two problems, namely the issue of heterogenous data integration and the feature selection. This work proposes an efficient solution for this question and provides a functional implementation of the hybrid methodology. The inspiration originated from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge from 2016 and the winning solution by Yuanfang Guan. Regarding to the motivation of competition, the combinatory cancer treatments are believed to be more effective than standard single-agent therapies since they have a potential to overcome others weaknesses (narrow spectrum of action and development of the resistance). However, the combinatorial drug effect is not obvious bringing possibly additive, synergistic or antagonistic treatment result. Thus, the goal of the competition was to predict in vitro compound synergy, without the access to the experimental combinatory therapy data. Within the competition, the multi-source files were supplied, encompassing the pharmacological knowledge from experiments and equation-fitting, the information on chemical properties and structure of drugs, finally the molecular cell profiles including RNA expression, copy variants, DNA sequence and methylation. The referred work included very successful approach of heterogenous data integration, extending additionally the model with prior knowledge outsourced from The Cancer Cell Line Encyclopedia, as well as introduced a key step of simulation that allows to imitate effect of a combinatory therapy on cancer. Despite unexplicit descriptions and poor documentation of the winning solution, as accurate as possible, reproduction of Guan’s approach was accomplished. The functional implementation was written in R and Python languages, and its performance was verified using as a reference the submitted in challenge prediction matrix. In order to improve the methodology feature selection workflow was established and run using a Lasso algorithm. Moreover, the performance of two alternative modeling methods was experimented including Support Vector Machine and Multivariate Adaptive Regression Splines (MARS). Several versions of merging equation were considered allowing determination of apparently optimal coefficients. As the result, the understanding of the best challenge solution was developed and the functional implementation was successfully constructed. The improvements were proposed and in the effect the SVM algorithm was verified to surpass others in solving this problem, the best-performing merging equation was established, and finally the list of 75 most informative molecular variables was provided. Among those genes, potential cancer biomarker candidates could be found

Epigenetic Landscape of Pain in Diabetic Neuropathy

The available treatments for neuropathic pain are still unsatisfactory – they cope with low efficiency and serious side effects. To our knowledge, this is a pioneering study evaluating whole-genome DNA methylation in unique populations of type 2 diabetes mellitus patients that were histopalogically diagnosed with diabetic neuropathy, with or without neuropathic pain. We provided an evidence on the significant differences in methylation patterns between painful and painless phenotypes. Epigenomes of patients from two independent cohorts were assessed in whole blood samples with Infinium Methylation EPIC BeadChip. We performed differential analysis and identified epigenetic signals that highlight the dissimilarities between painful and painless subjects. We estimated epigenetic age of the patients and evaluated eventual acceleration of biological age in painful and painless phenotypes using set of epigenetic predictors. With the differential analysis we identified 27 CpG sites that reached the level of statistical significance in both studied cohorts, presented the methylation change between painful and painless diabetic neuropathy > 1% in one of the populations and had the direction of methylation change concordant between the two cohorts. 19 of selected probes were genic and resulted in a list of 19 unique genes. Multidimensional scaling analysis confirmed the potential of generated set of CpG sites to separate painful and painless subjects and to highlight the dissimilarities between two phenotypes. Evaluation of biological age showed that there was no association between painful phenotype and acceleration of biological age expressed by any of the assessed epigenetic clocks. DNA methylation based prediction of telomere length was found to vary between painful and painless groups in both studied cohorts. Obtained results confirmed the presence of epigenetic differences between painful and painless diabetic neuropathy patients. Promising genes were identified that may be linked to neuropathic pain through DNA methylation mechanisms

TP53 polymorphism in plasma cell myeloma

Introduction. Significant and accessible predictive factors for bortezomib treatment in plasma cell myeloma (PCM) are still lacking. TP53 codon 72 polymorphism (P72R) results in proline (P) or arginine (R) at 72 amino acid position, which causes synthesis of proteins with distinct functions. The aims of our study were to: 1) analyze whether this polymorphism is associated with an increased risk of PCM; 2) study whether the P72R polymorphism affects overall survival (OS) among PCM patients; 3) assess the possible association of the P72R polymorphism with sensitivity to bortezomib in cell cultures derived from PCM patients. Material and methods. Genomic DNA from newly diagnosed 59 patients (without IgVH gene rearrangements and TP53 deletions) and 50 healthy blood donors were analyzed by RFLP-PCR to identify TP53 polymorphism. Chromosomal aberrations were detected by use of cIg-FISH. The lymphocyte cell cultures from a subgroup of 40 PCM patients were treated with bortezomib (1, 2 and 4 nM). Results. The P allele of the P72R polymorphism was more common than the R allele in PMC patients compared to controls (39% vs. 24%), and the difference was significant (p = 0.02). The PP and PR genotypes (in combina­tion) were more frequent among cases than in controls (65% vs. 42%, OR = 2.32, p = 0.04). At the cell culture level and 2 nM bortezomib concentration the PP genotype was associated with higher necrosis rates (10.5%) compared to the PR genotype (5.7%, p = 0.006) or the RR genotype (6.3%, p = 0.02); however, no effect of genotypes was observed at bortezomib concentrations of 1 and 4 nM. The shortest OS (12 months) was observed in patients with the PP genotype compared to patients with the PR or RR genotypes (20 months) (p = 0.04). Conclusions. The results suggest that P72R polymorphisms may be associated with an increased PCM risk and may affect OS of PCM patients. However, we saw no consistent results of the polymorphism effect on apoptosis and necrosis in cell cultures derived from PCM patients. Further studies are need in this regard

Children and adolescents with pulmonary arterial hypertension : baseline and follow-up data from the polish registry of pulmonary hypertension (BNP-PL)

We present the results from the pediatric arm of the Polish Registry of Pulmonary Hypertension. We prospectively enrolled all pulmonary arterial hypertension (PAH) patients, between the ages of 3 months and 18 years, who had been under the care of each PAH center in Poland between 1 March 2018 and 30 September 2018. The mean prevalence of PAH was 11.6 per million, and the estimated incidence rate was 2.4 per million/year, but it was geographically heterogeneous. Among 80 enrolled children (females, n = 40; 50%), 54 (67.5%) had PAH associated with congenital heart disease (CHD-PAH), 25 (31.25%) had idiopathic PAH (IPAH), and 1 (1.25%) had portopulmonary PAH. At the time of enrolment, 31% of the patients had significant impairment of physical capacity (WHO-FC III). The most frequent comorbidities included shortage of growth (n = 20; 25%), mental retardation (n = 32; 40%), hypothyroidism (n = 19; 23.8%) and Down syndrome (n = 24; 30%). The majority of children were treated with PAH-specific medications, but only half of them with double combination therapy, which improved after changing the reimbursement policy. The underrepresentation of PAH classes other than IPAH and CHD-PAH, and the geographically heterogeneous distribution of PAH prevalence, indicate the need for building awareness of PAH among pediatricians, while a frequent coexistence of PAH with other comorbidities calls for a multidisciplinary approach to the management of PAH children

Implementation of an hybrid machine learning methodology for pharmacological modeling

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática) Universidade de Lisboa, Faculdade de Ciências, 2017Hoje em dia, especialmente na area biomedica, os dados contem milhares de variaveis de fontes diferentes e com apenas algumas instancias ao mesmo tempo. Devido a este facto, as abordagens da aprendizagem automatica enfrentam dois problemas, nomeadamente a questao da integracao de dados heterogeneos e a selecao das caracteristicas. Este trabalho propoe uma solucao eficiente para esta questao e proporciona uma implementacao funcional da metodologia hibrida. A inspiracao para este trabalho veio do desafio proposto no ambito da competicao AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge em 2016, e da solucao vencedora desenvolvida por Yuanfang Guan. Relativamente a motivacao do concurso, e observado que os tratamentos combinatorios para o cancro sao mais eficientes do que as terapias habituais de agente unico, desde que tem potencial para superar as desvantagens dos outros (limitado espetro de acao e desenvolvimento de resistencia). No entanto, o efeito combinatorio de drogas nao e obvio, produzindo possivelmente o resultado aditivo, sinergico ou antagonico. Assim, o objetivo da competicao era prever in vitro a sinergia dos compostos, sem ter acesso aos dados experimentais da terapia combinatoria. No ambito da competicao foram fornecidos ficheiros de varias fontes, contendo o conhecimento farmacologico tanto experimental como obtido de ajustamento das equacoes, a informacao sobre propriedades quimicas e estruturais de drogas, e por fim, os perfis moleculares de celulas, incluindo expressao de RNA, copy variants, sequencia e metilacao de DNA. O trabalho referido envolveu uma abordagem muito bem sucedida de integração dos dados heterogeneos, estendendo o modelo com conhecimento disponivel dentro do projeto The Cancer Cell Line Encyclopedia, e tambem introduzindo o passo decisivo de simulacao que permite imitar o efeito de terapia combinatoria no cancro. Apesar das descricoes pouco claras e da documentacao da solucao vencedora ineficiente, a reproducao da abordagem de Guan foi concluida, tentando ser o mais fiel possivel. A implementacao funcional foi escrita nas linguagens R e Python, e o seu desempenho foi verificado usando como referencia a matriz submetida no concurso. Para melhorar a metodologia, o workflow de selecao dos caracteristicas foi estabelecido e executado usando o algoritmo Lasso. Alem disso, o desempenho de dois metodos alternativos de modelacao foi experimentado, incluindo Support Vector Machine and Multivariate Adaptive Regression Splines (MARS). Varias versoes da equacao de integracao foram consideradas permitindo a determinacao de coeficientes aparentemente otimos. Como resultado, a compreensao da melhor solucao de competição foi desenvolvida e a implementacao funcional foi construida com sucesso. As melhorias foram propostas e no efeito o algoritmo SVM foi verificado como capaz de superar os outros na resolução deste problema, a equacao de integracao com melhor desempenho foi estabelecida e finalmente a lista de 75 variaveis moleculares mais informativas foi fornecida. Entre estes genes, poderiam ser encontrados possiveis candidatos de biomarcadores de cancro.Nowadays, especially in the biomedical field, the data sets usually contain thousands of multi-source variables and with only few instances in the same time. Due to this fact, Machine Learning approaches face two problems, namely the issue of heterogenous data integration and the feature selection. This work proposes an efficient solution for this question and provides a functional implementation of the hybrid methodology. The inspiration originated from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge from 2016 and the winning solution by Yuanfang Guan. Regarding to the motivation of competition, the combinatory cancer treatments are believed to be more effective than standard single-agent therapies since they have a potential to overcome others weaknesses (narrow spectrum of action and development of the resistance). However, the combinatorial drug effect is not obvious bringing possibly additive, synergistic or antagonistic treatment result. Thus, the goal of the competition was to predict in vitro compound synergy, without the access to the experimental combinatory therapy data. Within the competition, the multi-source files were supplied, encompassing the pharmacological knowledge from experiments and equation-fitting, the information on chemical properties and structure of drugs, finally the molecular cell profiles including RNA expression, copy variants, DNA sequence and methylation. The referred work included very successful approach of heterogenous data integration, extending additionally the model with prior knowledge outsourced from The Cancer Cell Line Encyclopedia, as well as introduced a key step of simulation that allows to imitate effect of a combinatory therapy on cancer. Despite unexplicit descriptions and poor documentation of the winning solution, as accurate as possible, reproduction of Guan’s approach was accomplished. The functional implementation was written in R and Python languages, and its performance was verified using as a reference the submitted in challenge prediction matrix. In order to improve the methodology feature selection workflow was established and run using a Lasso algorithm. Moreover, the performance of two alternative modeling methods was experimented including Support Vector Machine and Multivariate Adaptive Regression Splines (MARS). Several versions of merging equation were considered allowing determination of apparently optimal coefficients. As the result, the understanding of the best challenge solution was developed and the functional implementation was successfully constructed. The improvements were proposed and in the effect the SVM algorithm was verified to surpass others in solving this problem, the best-performing merging equation was established, and finally the list of 75 most informative molecular variables was provided. Among those genes, potential cancer biomarker candidates could be found

Neutrophil extracellular traps generation and degradation in patients with granulomatosis with polyangiitis and systemic lupus erythematosus

Neutrophils are one of the first cells to arrive at the site of infection, where they apply several strategies to kill pathogens: degranulation, respiratory burst, phagocytosis, and release of neutrophil extracellular traps (NETs). Recent discoveries try to connect NETs formation with autoimmune diseases, like systemic lupus erythematosus (SLE) or granulomatosis with polyangiitis (GPA) and place them among one of the factors responsible for disease pathogenesis. The aim of the study was to assess the NETotic capabilities of neutrophils obtained from freshly diagnosed autoimmune patients versus healthy controls. Further investigation involved assessing NETs production among treated patients. In the latter step, NETs degradation potency of collected sera from non-treated patients was checked. Lastly, the polymorphisms of the DNASE I gene among tested subjects were checked. NETs formation was measured in a neutrophil culture by fluorometry, while degradation assessment was performed with patients’ sera and extracellular source of DNA. Additionally, Sanger sequencing was used to check potential SNP mutations between patients. About 121 subjects were enrolled into this study, 54 of them with a diagnosed autoimmune disorder. Neutrophils stimulated with NETosis inducers were able to release NETs in all cases. We have found that disease affected patients produce NETs more rapidly and in larger quantities than control groups, with up to 82.5% more released. Most importantly, we showed a difference between the diseases themselves. NETs release was 68.5% higher in GPA samples when compared to SLE ones while stimulated with Calcium Ionophore. Serum nucleases were less effective at degrading NETs in both autoimmune diseases, with a reduction in degradation of 20.9% observed for GPA and 18.2% for SLE when compared with the controls. Potential therapies targeting neutrophils and NETs should be specifically tailored to the type of the disease. Since there are significant differences between NETs release and disease type, a standard neutrophil targeted therapy could prevent over-generation of traps in some cases, while in others it would deplete the cells, leaving the immune system unresponsive to primary infections

High frequency of heterozygous rare variants of the SLC34A1 and SLC9A3R1 genes in patients with atypical femur fracture

Objective: Atypical femur fractures (AFFs) are rare fragility fractures originating at the lateral cortex of the femur, affecting the subtrochanteric or diaphyseal area of thebone with a transverse morphology. Occurrence of AFF is specifically associated with a small number of rare monogenic congenital metabolic bone disorders, such as hypophosphatasia, and with long-term treatment with antiresorptiondrugs. The exact pathogenesis of these fractures remains poorly understood and, except for cases of diagnosed HPP or other AFF-causing bone diseases, it is not possible to assess which patients are at higher riskof developing AFFs as a consequence of anti-resorption therapy. Design: We genetically screened 25 unrelated patients who had developed at least one AFF. Intervention: Genetic screening was performed through a nextgeneration sequencing analysis with a customized panel containing 76 human genes involved in the regulation of the mineralization processWe genetically screened 25 unrelated patients who had developed at least one AFF. Results: We found a relatively high frequency (32.0%) of heterozygous rare variants inthe SLC34A1 and SLC9A3R1 genes, two genes whose heterozygous inactivating mutations have been respectively associated with autosomal dominant hypophosphatemic nephrolithiasis/osteoporosis types 1 and 2 (NPHLOP1and NPHLOP2). Other heterozygous rare variants were found in the BMPR1B, CYP27B1, FBN1, MEPE, PIGO, and PHOSPHO1 genes, each in a single AFF case (4.0%). Conclusions and relevance: Our findings suggest that rarevariants of SLC34A1 and SLC9A3R1 could represent a possible genetic risk factor for the occurrence of AFFs. On the other hand, AFFs could represent an unsuspected clinical manifestation and/or an anti-resorption therapycorrelatedadverse event in patients with NPHLOP disorders

Heterogeneity of Cellular Senescence: Cell Type-Specific and Senescence Stimulus-Dependent Epigenetic Alterations

The aim of the present study was to provide a comprehensive characterization of whole genome DNA methylation patterns in replicative and ionizing irradiation- or doxorubicin-induced premature senescence, exhaustively exploring epigenetic modifications in three different human cell types: in somatic diploid skin fibroblasts and in bone marrow- and adipose-derived mesenchymal stem cells. With CpG-wise differential analysis, three epigenetic signatures were identified: (a) cell type- and treatment-specific signature; (b) cell type-specific senescence-related signature; and (c) cell type-transversal replicative senescence-related signature. Cluster analysis revealed that only replicative senescent cells created a distinct group reflecting notable alterations in the DNA methylation patterns accompanying this cellular state. Replicative senescence-associated epigenetic changes seemed to be of such an extent that they surpassed interpersonal dissimilarities. Enrichment in pathways linked to the nervous system and involved in the neurological functions was shown after pathway analysis of genes involved in the cell type-transversal replicative senescence-related signature. Although DNA methylation clock analysis provided no statistically significant evidence on epigenetic age acceleration related to senescence, a persistent trend of increased biological age in replicative senescent cultures of all three cell types was observed. Overall, this work indicates the heterogeneity of senescent cells depending on the tissue of origin and the type of senescence inducer that could be putatively translated to a distinct impact on tissue homeostasis

A Targeted Epigenetic Clock for the Prediction of Biological Age

Epigenetic clocks were initially developed to track chronological age, but accumulating evidence indicates that they can also predict biological age. They are usually based on the analysis of DNA methylation by genome-wide methods, but targeted approaches, based on the assessment of a small number of CpG sites, are advisable in several settings. In this study, we developed a targeted epigenetic clock purposely optimized for the measurement of biological age. The clock includes six genomic regions mapping in ELOVL2, NHLRC1, AIM2, EDARADD, SIRT7 and TFAP2E genes, selected from a re-analysis of existing microarray data, whose DNA methylation is measured by EpiTYPER assay. In healthy subjects (n = 278), epigenetic age calculated using the targeted clock was highly correlated with chronological age (Spearman correlation = 0.89). Most importantly, and in agreement with previous results from genome-wide clocks, epigenetic age was significantly higher and lower than expected in models of increased (persons with Down syndrome, n = 62) and decreased (centenarians, n = 106; centenarians’ offspring, n = 143; nutritional intervention in elderly, n = 233) biological age, respectively. These results support the potential of our targeted epigenetic clock as a new marker of biological age and open its evaluation in large cohorts to further promote the assessment of biological age in healthcare practice