Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma

Abstract Glioblastoma multiforme (GBM) is a highly malignant form of cancer that lacks effective treatment options or well‐defined strategies for personalized cancer therapy. The disease has been stratified into distinct molecular subtypes; however, the underlying regulatory circuitry that gives rise to such heterogeneity and its implications for therapy remain unclear. We developed a modular computational pipeline, Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of Susceptibility in Cancer (inTRINSiC), to dissect subtype‐specific regulatory programs and predict genetic dependencies in individual patient tumors. Using a multilayer network consisting of 518 transcription factors (TFs), 10,733 target genes, and a signaling layer of 3,132 proteins, we were able to accurately identify differential regulatory activity of TFs that shape subtype‐specific expression landscapes. Our models also allowed inference of mechanisms for altered TF behavior in different GBM subtypes. Most importantly, we were able to use the multilayer models to perform an in silico perturbation analysis to infer differential genetic vulnerabilities across GBM subtypes and pinpoint the MYB family member MYBL2 as a drug target specific for the Proneural subtype

생물학적 사전 지식을 활용한 고차원의 다중 오믹스 관계를 찾는 컴퓨터 공학적 접근 방법

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2021.8. 김선.세포가 어떻게 기능하고 외부 자극에 반응하는지 이해하는 것은 생물학, 의학에서 가장 중요한 관심사 중 하나이다. 기술의 발전으로 과학자들은 단일 생물학적 실험으로 세포의 변화요인들을 쉽게 측정할 수 있게 되었다. 주목할만한 예시로 게놈 시퀀싱, 유전자 발현량 측정, 유전자 발현을 조절하는 후성 유전체 측정 같은 다중 오믹스 데이터가 있다. 세포의 상태를 더 자세히 이해하기 위해서 다중 오믹스 조절자와 유전자 사이의 조절 관계를 알아내는 것이 중요하다. 하지만 다중 오믹스 조절 관계는 매우 복잡하고 모든 세포 상태 특이적인 관계를 실험적으로 검증하는 것은 불가능하다. 따라서, 서로 다른 유형의 고차원 오믹스 데이터로부터 관계를 예측하기 위한 효율적인 컴퓨터 공학적 접근방법이 요구된다. 이러한 고차원 데이터를 처리하는 한 가지 방법은 다양한 데이터베이스에서 선별된 유전자의 기능과 오믹스 간의 관계와 같은 외부 생물학적 지식을 통합하여 활용하는 것이다. 본 박사학위 논문은 생물학적 사전 지식을 활용하여 다중 오믹스 데이터로부터 유전자의 발현을 조절하는 관계를 예측하기 위한 세 가지 컴퓨터 공학적인 접근법을 제안하였다. 첫 번째는 마이크로 알엔에이와 유전자의 일대다 관계를 예측하기 위한 기법이다. 마이크로 알엔에이 표적 예측 문제는 가능한 표적 유전자의 개수가 너무 많으며 거짓 양성과 거짓음성의 비율을 조절해야 하는 문제가 있다. 이러한 문제를 해결하기 위해 마이크로 알엔에이-유전자와 데이터의 맥락 사이의 연관성을 문헌 지식을 활용하여 결정하고 마이크로 알엔에이-유전자 관계를 예측하기 위한 ContextMMIA를 개발하였다. ContextMMIA는 통계적 유의성과 문헌 관련성을 기반으로 마이크로 알엔에이-유전자 관계의 점수를 계산하여 관계의 우선순위를 결정한다. 예후가 다른 유방암 데이터에 대한 실험에서 ContextMMIA는 예후가 나쁜 유방암에서 활성화된 마이크로 알엔에이-유전자 관계를 예측하였고 기존 실험적으로 검증된 관계가 높은 우선순위로 예측되었으며 해당 유전자들이 유방암 관련 경로에 관여하는 것으로 알려졌다. 두 번째는 약물 반응을 일으키는 유전자의 다대일 조절 관계를 예측하기 위한 기법이다. 약물 반응 예측을 위해서 약물 반응 매개 유전자를 결정해야 하며 이를 위해 20,000개 유전자의 다중 오믹스 데이터를 통합 분석하는 방법이 필요하다. 이 문제를 해결하기 위해 저차원 임베딩 방법, 약물-유전자 연관성에 대한 문헌 지식 및 유전자-유전자 상호 작용 지식을 활용하여 약물 반응을 예측하기 위한 DRIM을 개발하였다. DRIM은 오토인코더, 텐서 분해, 약물-유전자 연관성을 이용하여 다중 오믹스 데이터에서 다대일 관계를 결정한다. 결정된 매개 유전자의 조절 관계를 유전자-유전자 상호 작용 지식과 약물 반응 시계열 유전자 발현 데이터의 상호 상관관계를 이용하여 결정한다. 유방암 세포주 데이터에 대한 실험에서 DRIM은 라파티닙이 표적으로 하는 PI3K-Akt 패스웨이에 관여하는 유전자들의 약물 반응 조절 관계를 예측하였고 라파티닙 반응성과 관련된 매개 유전자를 예측하였다. 그리고 예측된 조절 관계가 세포주 특이적인 패턴을 보이는 것을 확인하였다. 세 번째는 세포의 상태를 설명하는 조절자와 유전자의 다대다 조절 관계를 예측하기 위한 기법이다. 다대다 관계 예측을 위해 관찰된 유전자 발현 값과 유전자 조절 네트워크로부터 추정된 유전자 발현 값 사이의 차이를 측정하는 목적 함수를 만들었다. 목적 함수를 최소화하기 위하여 조절인자와 유전자의 수에 따라 기하급수적으로 증가하는 검색 공간을 탐색해야 한다. 이 문제를 해결하기 위해 조절자-유전자 상호 작용 지식을 활용하여 두 가지 연산을 반복하여 조절 관계를 찾는 최적화 기법을 개발하였다. 첫 번째 단계는 네트워크에 간선을 추가하기 위해 강화 학습 기반 휴리스틱을 통해 조절자를 선택하는 다대일 유전자 중심 관계를 탐색하는 단계이다. 두 번째 단계는 네트워크에서 간선을 제거하기 위해 유전자를 확률적으로 선택하는 일대다 조절자 중심 관계를 탐색하는 단계이다. 유방암 세포주 데이터에 대한 실험에서 제안된 방법은 이전의 최적화 방법보다 더 정확한 유전자 발현량 추정을 하였고 조절자 및 유전자 발현 데이터로 유방암 아형 특이적 네트워크를 구성하였다. 또한, 유방암 아형 관련 실험 검증된 조절 관계를 예측하였다. 요약하면, 본 박사학위 논문은 다중 오믹스 조절자와 유전자의 사이의 일대다, 다대일, 다대다 관계를 예측하기 위하여 생물학적 지식을 활용한 컴퓨터 공학적 접근법을 제안하였다. 제안된 방법은 증가하고 있는 분자 생물학 데이터를 분석하여 유전자 조절 상호 작용을 이해함으로써 세포 기능에 대한 심층적인 이해를 도와줄 수 있을 것으로 기대된다.Understanding how cells function or respond to external stimuli is one of the most important questions in biology and medicine. Thanks to the advances in instrumental technologies, scientists can routinely measure events within cells in single biological experiments. Notable examples are multi-omics data: sequencing of genomes, quantifications of gene expression, and identification of epigenetic events that regulate expression of genes. In order to better understand cellular mechanisms, it is essential to identify regulatory relationships between multi-omics regulators and genes. However, regulatory relationships are very complex and it is infeasible to validate all condition-specific relationships experimentally. Thus, there is an urgent need for an efficient computational method to extract relationships from different types of high-dimensional omics data. One way to address these high-dimensional data is to incorporate external biological knowledge such as relationships between omics and functions of genes curated in various databases. In my doctoral study, I developed three computational approaches to identify the regulatory relationships from multi-omics data utilizing biological prior knowledge. The first study proposes a method to predict one-to-m relationships between miRNA and genes. The computational challenge of miRNA target prediction is that there are many miRNA target candidates, and the ratio of false positives to false negatives needs to be adjusted. This challenge is addressed by utilizing literature knowledge for determining the association between miRNA-gene and a given context. In this study, I developed ContextMMIA to predict miRNA-gene relationships from miRNA and gene expression data. ContextMMIA computes scores of miRNA-gene relationships based on statistical significance and literature relevance and prioritizes the relationships based on the scores. In experiments on breast cancer data with different prognosis, ContextMMIA predicted differentially activated miRNA-gene relationships in invasive breast cancer. The experimentally verified miRNA-gene relationships were predicted with high priority and those genes are known to be involved in breast cancer-related pathways. The second study proposes a method to predict n-to-one relationships between regulators and gene on drug response. The computational challenge of drug response prediction is how to integrate multi-omics data of 20,000 genes for determining drug response mediator genes. This challenge is addressed by utilizing low-dimensional embedding methods, literature knowledge of drug-gene associations, and gene-gene interaction knowledge. For this problem, I developed DRIM to predict drug response relationships from the multi-omics data and drug-induced time-series gene expression data. DRIM uses autoencoder, tensor decomposition, and drug-gene association to determine n-to-one relationships from multi-omics data. Then, regulatory relationships of mediator genes are determined by gene-gene interaction knowledge and cross-correlation of drug-induced time-series gene expression data. In experiments on breast cancer cell line data, DRIM extracted mediator genes relevant to drug response and regulatory relationships of genes involved in the PI3K-Akt pathway targeted by lapatinib. In addition, DRIM revealed distinguished patterns of relationships in breast cancer cell lines with different lapatinib resistance. The third study proposes a method to predict n-to-m relationships between regulators and genes. In order to predict n-to-m relationships, this study formulated an objective function that measures the deviation between observed gene expression values and estimated gene expression values derived from gene regulatory networks. The computational challenge of minimizing the objective function is to navigate the search space of relationships exponentially increasing according to the number of regulators and genes. This challenge is addressed by the iterative local optimization with regulator-gene interaction knowledge. In this study, I developed a two-step iterative RL-based method to predict n-to-m relationships from regulator and gene expression data. The first step is to explore the n-to-one gene-oriented step that selects regulators by reinforcement learning based heuristic to add edges to the network. The second step is to explore the one-to-m regulator-oriented step that stochastically selects genes to remove edges from the network. In experiments on breast cancer cell line data, the proposed method constructed breast cancer subtype-specific networks from the regulator and gene expression profiles with a more accurate gene expression estimation than previous combinatorial optimization methods. Moreover, regulatory relationships involved in the networks were associated with breast cancer subtypes. In summary, in this thesis, I proposed computational methods for predicting one-to-m, n-to-one, and n-to-m relationships between multi-omics regulators and genes utilizing external domain knowledge. The proposed methods are expected to deepen our knowledge of cellular mechanisms by understanding gene regulatory interactions by analyzing the ever-increasing molecular biology data such as The Cancer Genome Atlas, Cancer Cell Line Encyclopedia.Chapter 1 Introduction 1 1.1 Biological background 1 1.1.1 Multi-omics analysis 1 1.1.2 Multi-omics relationships indicating cell state 2 1.1.3 Biological prior knowledge 4 1.2 Research problems for the multi-omics relationship 6 1.3 Computational challenges and approaches in the exploring multiomics relationship 6 1.4 Outline of the thesis 12 Chapter 2 Literature-based condition-specific miRNA-mRNA target prediction 13 2.1 Computational Problem & Evaluation criterion 14 2.2 Related works 15 2.3 Motivation 17 2.4 Methods 20 2.4.1 Identifying genes and miRNAs based on the user-provided context 22 2.4.2 Omics Score 23 2.4.3 Context Score 24 2.4.4 Confidence Score 26 2.5 Results 26 2.5.1 Pathway analysis 27 2.5.2 Reproducibility of validated targets in humans 31 2.5.3 Sensitivity tests when different keywords are used 33 2.6 Summary 34 Chapter 3 DRIM: A web-based system for investigating drug response at the molecular level by condition-specific multi-omics data integration 36 3.1 Computational Problem & Evaluation criterion 37 3.2 Related works 38 3.3 Motivation 42 3.4 Methods 44 3.4.1 Step 1: Input 45 3.4.2 Step 2: Identifying perturbed sub-pathway with time-series 45 3.4.3 Step 3: Embedding multi-omics for selecting potential mediator genes 47 3.4.4 Step 4: Construct TF-regulatory time-bounded network and identify regulatory path 52 3.4.5 Step 5: Analysis result on the web 52 3.5 Case study: Comparative analysis of breast cancer cell lines that have different sensitivity with lapatinib 54 3.5.1 Multi-omics analysis result before drug treatment 56 3.5.2 Time-series gene expression analysis after drug treatment 57 3.6 Summary 61 Chapter 4 Combinatorial modeling and optimization using iterative RL search for inferring sample-specific regulatory network 63 4.1 Computational Problem & Evaluation criterion 64 4.2 Related works 64 4.3 Motivation 66 4.4 Methods 68 4.4.1 Formulating an objective function 68 4.4.2 Overview of an iterative search method 70 4.4.3 G-step for exploring n-to-one gene-oriented relationship 73 4.4.4 R-step for exploring one-to-m regulator-oriented relationship 79 4.5 Results 80 4.5.1 Cancer cell line data 80 4.5.2 Hyperparameters 81 4.5.3 Quantitative evaluation 82 4.5.4 Qualitative evaluation 83 4.6 Summary 86 Chapter 5 Conclusions 88 국문초록 111박

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships

Aberrantly Expressed CeRNAs Account for Missing Genomic Variability of Cancer Genes via MicroRNA-Mediated Interactions

There is growing evidence that RNAs compete for binding and regulation by a finite pool of microRNAs (miRs), thus regulating each other through a competing endogenous RNA (ceRNA) mechanism. My dissertation work focused on systematically studying ceRNA interactions in cancer by reverse-engineering context-specific miR-RNA interactions and ceRNA regulatory interactions across multiple tumor types and study the effects of these interactions in cancer. I attempted to use ceRNA interactions to explain how genetic and epigenetic alterations are propagated to target established drivers of tumorigenesis. Using bioinformatics analysis of primary tumor samples and experimental validation in cell lines, I have investigated the roles that mRNAs and noncoding RNAs can play in tumorigenesis via ceRNA interactions. Specifically, I studied how RNAs target tumor-suppressors and oncogenes as ceRNAs, and attempted to accounting for some of the missing genomic variability in tumors

Integrative methods for reconstruction of dynamic networks in chondrogenesis

Application of human mesenchymal stem cells represents a promising approach in the field of regenerative medicine. Specific stimulation can give rise to chondrocytes, osteocytes or adipocytes. Investigation of the underlying biological processes which induce the observed cellular differentiation is essential to efficiently generate specific tissues for therapeutic purposes. Upon treatment with diverse stimuli, gene expression levels of cultivated human mesenchymal stem cells were monitored using time series microarray experiments for the three lineages. Application of gene network inference is a common approach to identify the regulatory dependencies among a set of investigated genes. This thesis applies the NetGenerator V2.0 tool, which is capable to deal with multiple time series data, which investigates the effect of multiple external stimuli. The applied model is based on a system of linear ordinary differential equations, whose parameters are optimised to reproduce the given time series datasets. Several procedures in the inference process were adapted in this new version in order to allow for the integration of multiple datasets. Network inference was applied on in silico network examples as well as on multi-experiment microarray data of mesenchymal stem cells. The resulting chondrogenesis model was evaluated on the basis of several features including the model adaptation to the data, total number of connections, proportion of connections associated with prior knowledge and the model stability in a resampling procedure. Altogether, NetGenerator V2.0 has provided an automatic and efficient way to integrate experimental datasets and to enhance the interpretability and reliability of the resulting network. In a second chondrogenesis model, the miRNA and mRNA time series data were integrated for the purpose of network inference. One hypothesis of the model was verified by experiments, which demonstrated the negative effect of miR-524-5p on downstream genes

Integrative microRNA and mRNA deep-sequencing expression profiling in endemic Burkitt lymphoma

BACKGROUND: Burkitt lymphoma (BL) is characterized by overexpression of the c-myc oncogene, which in the vast majority of cases is a consequence of an IGH/MYC translocation. While myc is the seminal event, BL is a complex amalgam of genetic and epigenetic changes causing dysregulation of both coding and non-coding transcripts. Emerging evidence suggest that abnormal modulation of mRNA transcription via miRNAs might be a significant factor in lymphomagenesis. However, the alterations in these miRNAs and their correlations to their putative mRNA targets have not been extensively studied relative to normal germinal center (GC) B cells. METHODS: Using more sensitive and specific transcriptome deep sequencing, we compared previously published small miRNA and long mRNA of a set of GC B cells and eBL tumors. MiRWalk2.0 was used to identify the validated target genes for the deregulated miRNAs, which would be important for understanding the regulatory networks associated with eBL development. RESULTS: We found 211 differentially expressed (DE) genes (79 upregulated and 132 downregulated) and 49 DE miRNAs (22 up-regulated and 27 down-regulated). Gene Set enrichment analysis identified the enrichment of a set of MYC regulated genes. Network propagation-based method and correlated miRNA-mRNA expression analysis identified dysregulated miRNAs, including miR-17~95 cluster members and their target genes, which have diverse oncogenic properties to be critical to eBL lymphomagenesis. Central to all these findings, we observed the downregulation of ATM and NLK genes, which represent important regulators in response to DNA damage in eBL tumor cells. These tumor suppressors were targeted by multiple upregulated miRNAs (miR-19b-3p, miR-26a-5p, miR-30b-5p, miR-92a-5p and miR-27b-3p) which could account for their aberrant expression in eBL. CONCLUSION: Combined loss of p53 induction and function due to miRNA-mediated regulation of ATM and NLK, together with the upregulation of TFAP4, may be a central role for human miRNAs in eBL oncogenesis. This facilitates survival of eBL tumor cells with the IGH/MYC chromosomal translocation and promotes MYC-induced cell cycle progression, initiating eBL lymphomagenesis. This characterization of miRNA-mRNA interactions in eBL relative to GC B cells provides new insights on miRNA-mediated transcript regulation in eBL, which are potentially useful for new improved therapeutic strategies

Integrative approaches for systematic reconstruction of regulatory circuits in mammals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 141-149).The reconstruction of regulatory networks is one of the most challenging tasks in systems biology. Although some models for inferring regulatory networks can make useful predictions about the wiring and mechanisms of molecular interactions, these approaches are still limited and there is a strong need to develop increasingly universal and accurate approaches for network reconstruction. This problem is particularly challenging in mammals, due to the higher complexity of mammalian regulatory networks and limitations in experimental manipulation. In this thesis, I present three systematic approachs to reconstruct, analyse and refine models of gene regulation. In Chapter 1, I devise a method for deriving an observational model from temporal genomic profiles. I use it to choose targets for perturbation experiments in order to determine a network controlling the responses of mouse primary dendritic cells to stimulation with pathogen components. In Chapter 2, I introduce the algorithm Exigo, for identifying essential interactions in regulatory networks reconstructed from experimental data where regulators have been silenced, using a network reduction strategy. Exigo outperforms previous approaches on simulated data, uncovers the core network structure when applied to real networks derived from perturbation studies in mammals, and improves the performance of network inference methods. Lastly, I introduce in Chapter 3 an approach to learn a module network from multiple highthroughput assays. Analysis of a diffuse large B-cell lymphoma dataset identifies candidate regulator genes, microRNAs and copy number aberrations with biological, and possibly therapeutic, importance.by Ana Paula Santos Botelho Oliveira Leite.Ph.D