Periodic Reporting for period 1 - LINKER (Elucidating transcriptional rewiring on hematological malignancies via computational methods)
Okres sprawozdawczy: 2020-03-01 do 2022-02-28
Genes and their corresponding pathways form networks that regulate various cellular functions critical in tumor development and response to therapy. These networks, termed gene regulatory networks, define the regulatory relationships among genes and provide a concise representation of the transcriptional regulatory landscape of the cell. Therefore, the construction and exploration of the topology of such networks and their constituents can help us understand the biological mechanisms of cancer. The rapid growth of biomedical cancer datasets has created an urgent need for highly scalable computational tools that can make sense of these biomedical “big data” and extract biological and medical insights from them. Hence, it is of uttermost importance to i) develop robust and efficient methods to uncover gene regulatory networks from high throughput sequencing data; and ii) given those networks, develop efficient differential network analysis methods that will shed light into the transcriptional rewiring associated with a particular phenotype.
To achieve these goals, in this action we extended and optimized a computational method, termed LINKER that builds Gene Regulatory Networks from high-throughput sequencing data, and scores them for evidence of significant transcriptional rewiring between phenotype groups. We then validated the developed method using publicly available resources, and finally, we applied it to elucidate biomarkers associated with disease progression on hematological malignancies. Therefore, the overarching goal achienved on this proposal was to build computational models to elucidate the transcriptional rewiring associated with disease progression on hematological malignancies. This goal was achieved through the development of computational methods to uncover dynamically-regulated GRNs, and applied to elucidate transcriptional rewiring on hematological malignancies.
To achieve these goals, in this action we extended and optimized a computational method, termed LINKER that builds Gene Regulatory Networks from high-throughput sequencing data, and scores them for evidence of significant transcriptional rewiring between phenotype groups. We then validated the developed method using publicly available resources, and finally, we applied it to elucidate biomarkers associated with disease progression on hematological malignancies. Therefore, the overarching goal achienved on this proposal was to build computational models to elucidate the transcriptional rewiring associated with disease progression on hematological malignancies. This goal was achieved through the development of computational methods to uncover dynamically-regulated GRNs, and applied to elucidate transcriptional rewiring on hematological malignancies.
We developed sparseGMM, the next evolution of the previously developed GRN inference method called LINKER , a module network approach in a Bayesian framework, whereby the clustering of target genes and the assignment of regulators are combined in one step, which allows genes to be associated to multiple modules simultaneously. The assignment of a regulator to its modules can be thus calculated with a confidence interval. We show an improved performance in sparsity, compared to previous methods, choosing fewer genes as true regulators, and confirming biological knowledge of the scale-free nature of gene networks. Further, our probabilistic assignment approach is potentially superior for modeling genes with multiple biological functions. Thus, we define the entropy of a gene to be the entropy of the estimated module-assignment probability and show that it can then be used as an indicator of a multifunctional biological role based on joint membership to two or more modules. These multifunctional genes could in turn translate to multifunctional proteins having central roles in the crosstalk between two or more pathways in cancer cells, and, thus become attractive targets for overcoming drug resistance through compensation mechanisms.
We show that high-entropy genes are more common in cancer samples than in healthy tissue, and we associate them to crosstalk between several pathways including TP53, interferon gamma and TNF alpha. Our analysis of high entropy genes exemplifies ways in which major cancer pathways share key multifunctional components.
Evaluating the quality and accuracy of GRNs is not a straightforward task, as there are no clear metrics for evaluation. In addition, GRNs are difficult to interpret and visualize. While synthetic data is typically used to evaluate their goodness of fit to the data, it has been shown that GRNs excelling on synthetic data do not necessarily provide more accurate biological insights. To address this issue, we developed a novel methodology to evaluate the altered regulatory dynamics of the different TFs across the different cell phenotypes. The developed SimiC workflow can generate a heatmap that shows the regulatory dissimilarities for all regulons and all cell clusters. which allows us to uncover shifts in regulatory activity that are associated with different conditions, environments, or developmental states.
When applied to hematological malignancies, we found specific biological processes for each hematological differentiation trajectory that are potentially abrogated in disease with respect to healthy samples. Example of these are the Heme-metabolism and gas transport in the Erythroid-Megakaryocyte differentiation trajectory, or Neutrophil activation and degranulation in the Monocytic-Granulocytic differentiation trajectory. In addition, we highlighted two transcription factors: ZNF350 and ZMAT2. These TFs significantly downregulated all their target genes in Myelodisplastic síndromes, whereas in the healthy condition no significant correlations were recorded.
These results were presented at both national (SEHH 21) and international (ASH 2021) clinical haematology conferences, and the computational model were presented at the International Conference of Intelligence Systems for Molecular Biology (ISMB 2021). The action also resulted in several publications. Finally, since all developed computational models can be applied to a myriad of biomedical problems, all developed methods are openly available in open source repositories to facilitate their use by the wider biomedical research community.
We show that high-entropy genes are more common in cancer samples than in healthy tissue, and we associate them to crosstalk between several pathways including TP53, interferon gamma and TNF alpha. Our analysis of high entropy genes exemplifies ways in which major cancer pathways share key multifunctional components.
Evaluating the quality and accuracy of GRNs is not a straightforward task, as there are no clear metrics for evaluation. In addition, GRNs are difficult to interpret and visualize. While synthetic data is typically used to evaluate their goodness of fit to the data, it has been shown that GRNs excelling on synthetic data do not necessarily provide more accurate biological insights. To address this issue, we developed a novel methodology to evaluate the altered regulatory dynamics of the different TFs across the different cell phenotypes. The developed SimiC workflow can generate a heatmap that shows the regulatory dissimilarities for all regulons and all cell clusters. which allows us to uncover shifts in regulatory activity that are associated with different conditions, environments, or developmental states.
When applied to hematological malignancies, we found specific biological processes for each hematological differentiation trajectory that are potentially abrogated in disease with respect to healthy samples. Example of these are the Heme-metabolism and gas transport in the Erythroid-Megakaryocyte differentiation trajectory, or Neutrophil activation and degranulation in the Monocytic-Granulocytic differentiation trajectory. In addition, we highlighted two transcription factors: ZNF350 and ZMAT2. These TFs significantly downregulated all their target genes in Myelodisplastic síndromes, whereas in the healthy condition no significant correlations were recorded.
These results were presented at both national (SEHH 21) and international (ASH 2021) clinical haematology conferences, and the computational model were presented at the International Conference of Intelligence Systems for Molecular Biology (ISMB 2021). The action also resulted in several publications. Finally, since all developed computational models can be applied to a myriad of biomedical problems, all developed methods are openly available in open source repositories to facilitate their use by the wider biomedical research community.
In the last decade omics data has emerged as a very valuable tool towards improved diagnosis and treatment. Nevertheless, computational methods to efficiently handle and analyze these big data are still being developed, especially as new sequencing technologies, such as single-cell technologies, and data types emerge. The developed computational tools will provide biomedical researchers and clinicians with a new easy-to-use framework for the novel characterization of the regulatory dynamics of cancer. We envision this new uncovered information to be of high value for the clinicians towards an improved personalized medicine.
In this context, during the execution of this project we exemplified the capabilities of our developed tools on hematological neoplasms. Over the last years, improvements in the healthcare systems have led to a notable aging of the population in modern countries and, consequently, to an increase in age-related diseases, such hematological neoplasms. Thus, the developed AI tools that make sense of huge amount of new omic data towards the identification of altered regulatory mechanisms that contribute to the development of these diseases is of fundamental interest as it could allow us to develop preventive treatments.
Moreover, survival rates of MDS patients have not improved significantly over the last years, which can be due to ineffective therapeutic drugs being currently used. Thus, the novel therapeutic targets uncovered during this action (e.g. the transcription factors ZNF350 or ZMAT2) could ultimately lead to the development of novel biomarkers that will greatly contribute to the prognosis and treatment of these patients, overall improving their quality of life.
In this context, during the execution of this project we exemplified the capabilities of our developed tools on hematological neoplasms. Over the last years, improvements in the healthcare systems have led to a notable aging of the population in modern countries and, consequently, to an increase in age-related diseases, such hematological neoplasms. Thus, the developed AI tools that make sense of huge amount of new omic data towards the identification of altered regulatory mechanisms that contribute to the development of these diseases is of fundamental interest as it could allow us to develop preventive treatments.
Moreover, survival rates of MDS patients have not improved significantly over the last years, which can be due to ineffective therapeutic drugs being currently used. Thus, the novel therapeutic targets uncovered during this action (e.g. the transcription factors ZNF350 or ZMAT2) could ultimately lead to the development of novel biomarkers that will greatly contribute to the prognosis and treatment of these patients, overall improving their quality of life.