A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Molecular Inverse Comorbidity between Alzheimer’s Disease and Lung Cancer: New Insights from Matrix Factorization

International audienceMatrix factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology. Here, we challenge MF in depicting the molecular bases of epidemiologically described disease-disease (DD) relationships. As a use case, we focus on the inverse comorbidity association between Alzheimer's disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To this day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities. To this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which we confirm the involvement of processes related to the immune system and mitochondrial metabolism. We then distinguish mechanisms specific to LC from those shared with other cancers through a pan-cancer analysis. Additionally, new candidate molecular players, such as estrogen receptor (ER), cadherin 1 (CDH1) and histone deacetylase (HDAC), are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, also suggesting the existence of heterogeneity across patients in the context of inverse comorbidity

NetCore: a network propagation approach using node coreness

We present NetCore, a novel network propagation approach based on node coreness, for phenotype–genotype associations and module identification. NetCore addresses the node degree bias in PPI networks by using node coreness in the random walk with restart procedure, and achieves improved re-ranking of genes after propagation. Furthermore, NetCore implements a semi-supervised approach to identify phenotype-associated network modules, which anchors the identification of novel candidate genes at known genes associated with the phenotype. We evaluated NetCore on gene sets from 11 different GWAS traits and showed improved performance compared to the standard degree-based network propagation using cross-validation. Furthermore, we applied NetCore to identify disease genes and modules for Schizophrenia GWAS data and pan-cancer mutation data. We compared the novel approach to existing network propagation approaches and showed the benefits of using NetCore in comparison to those. We provide an easy-to-use implementation, together with a high confidence PPI network extracted from ConsensusPathDB, which can be applied to various types of genomics data in order to obtain a re-ranking of genes and functionally relevant network modules

From Correlation to Causality: Does Network Information improve Cancer Outcome Prediction?

Motivation: Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. A widely used approach is high-throughput experiments that aim to explore predictive signature genes which would provide identification of clinical outcome of diseases. Microarray data analysis helps to reveal underlying biological mechanisms of tumor progression, metastasis, and drug-resistance in cancer studies. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. The experimental or computational noise in data and limited tissue samples collected from patients might furthermore reduce the predictive power and biological interpretability of such signature genes. Nevertheless, signature genes predicted by different studies generally represent poor similarity; even for the same type of cancer. Integration of network information with gene expression data could provide more efficient signatures for outcome prediction in cancer studies. One approach to deal with these problems employs gene-gene relationships and ranks genes using the random surfer model of Google's PageRank algorithm. Unfortunately, the majority of published network-based approaches solely tested their methods on a small amount of datasets, questioning the general applicability of network-based methods for outcome prediction. Methods: In this thesis, I provide a comprehensive and systematically evaluation of a network-based outcome prediction approach -- NetRank - a PageRank derivative -- applied on several types of gene expression cancer data and four different types of networks. The algorithm identifies a signature gene set for a specific cancer type by incorporating gene network information with given expression data. To assess the performance of NetRank, I created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and one in-house dataset. Results: NetRank performs significantly better than classical methods such as foldchange or t-test as it improves the prediction performance in average for 7%. Besides, we are approaching the accuracy level of the authors' signatures by applying a relatively unbiased but fully automated process for biomarker discovery. Despite an order of magnitude difference in network size, a regulatory, a protein-protein interaction and two predicted networks perform equally well. Signatures as published by the authors and the signatures generated with classical methods do not overlap -- not even for the same cancer type -- whereas the network-based signatures strongly overlap. I analyze and discuss these overlapping genes in terms of the Hallmarks of cancer and in particular single out six transcription factors and seven proteins and discuss their specific role in cancer progression. Furthermore several tests are conducted for the identification of a Universal Cancer Signature. No Universal Cancer Signature could be identified so far, but a cancer-specific combination of general master regulators with specific cancer genes could be discovered that achieves the best results for all cancer types. As NetRank offers a great value for cancer outcome prediction, first steps for a secure usage of NetRank in a public cloud are described. Conclusion: Experimental evaluation of network-based methods on a gene expression benchmark dataset suggests that these methods are especially suited for outcome prediction as they overcome the problems of random gene signatures and noisy expression data. Through the combination of network information with gene expression data, network-based methods identify highly similar signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. In general allows the integration of additional information in gene expression analysis the identification of more reliable, accurate and reproducible biomarkers and provides a deeper understanding of processes occurring in cancer development and progression.:1 Definition of Open Problems 2 Introduction 2.1 Problems in cancer outcome prediction 2.2 Network-based cancer outcome prediction 2.3 Universal Cancer Signature 3 Methods 3.1 NetRank algorithm 3.2 Preprocessing and filtering of the microarray data 3.3 Accuracy 3.4 Signature similarity 3.5 Classical approaches 3.6 Random signatures 3.7 Networks 3.8 Direct neighbor method 3.9 Dataset extraction 4 Performance of NetRank 4.1 Benchmark dataset for evaluation 4.2 The influence of NetRank parameters 4.3 Evaluation of NetRank 4.4 General findings 4.5 Computational complexity of NetRank 4.6 Discussion 5 Universal Cancer Signature 5.1 Signature overlap – a sign for Universal Cancer Signature 5.2 NetRank genes are highly connected and confirmed in literature 5.3 Hallmarks of Cancer 5.4 Testing possible Universal Cancer Signatures 5.5 Conclusion 6 Cloud-based Biomarker Discovery 6.1 Introduction to secure Cloud computing 6.2 Cancer outcome prediction 6.3 Security analysis 6.4 Conclusion 7 Contributions and Conclusion

Proteomic analyses reveal distinct chromatin-associated and soluble transcription factor complexes.

The current knowledge on how transcription factors (TFs), the ultimate targets and executors of cellular signalling pathways, are regulated by protein-protein interactions remains limited. Here, we performed proteomics analyses of soluble and chromatin-associated complexes of 56 TFs, including the targets of many signalling pathways involved in development and cancer, and 37 members of the Forkhead box (FOX) TF family. Using tandem affinity purification followed by mass spectrometry (TAP/MS), we performed 214 purifications and identified 2,156 high-confident protein-protein interactions. We found that most TFs form very distinct protein complexes on and off chromatin. Using this data set, we categorized the transcription-related or unrelated regulators for general or specific TFs. Our study offers a valuable resource of protein-protein interaction networks for a large number of TFs and underscores the general principle that TFs form distinct location-specific protein complexes that are associated with the different regulation and diverse functions of these TFs

Falsifiable Network Models. A Network-based Approach to Predict Treatment Efficacy in Ulcerative Colitis

This work is focused on understanding the treatment efficacy of patients with ulcerative colitis (UC) using a network-based approach. UC is one of two forms of inflammatory bowel disease (IBD) along with Crohn’s disease. UC is a debilitating condition characterized by chronic inflammation and ulceration of the colon and rectum. UC symptoms occur gradually rather than abruptly, and the degree of symptoms differs across UC patients. Only around 20% of all UC cases can be explained by known genetic variations, implying a more ambiguous aetiology that is yet not fully understood but is thought to involve a complex interplay between genetic and environmental factors. The available therapy for UC substantially reduces symptoms and achieves long-term remission. However, about one-third of UC patients fail to respond to anti-TNFα therapy and consequently develop long-term side effects due to medication. Non-response to existing antibody-based therapies in subgroups of UC patients is a major challenge and incurs a healthcare burden. Therefore, the disease markers for predicting therapy response to assist individualized therapy decisions are needed. To date, no quantitative computational framework is available to predict treatment response in UC. We developed a quantitative framework that uses gene expression data and existing biological background information on signalling pathways to quantify network connectivity from receptors to transcription factors (TF) that are involved in UC pathogenesis. Variations in network connectivity in UC patients can be used to identify responders and non-responders to anti-TNFα and anti-Integrin treatment. Our findings allow us to summarize the effect of small gene expression changes on the overall connectivity of a signalling network and estimate the effect this will have on the individual patients' responses. Estimating the network connectivity associated with varied drug responses may provide an understanding of individualized treatment outcomes. Our model could be used to generate testable hypotheses about how individual genes act together in networks to cause inflammation in UC as well as other immune-inflammatory diseases such as psoriasis, asthma, and rheumatoid arthritis

RNA 시퀀싱 데이터의 해독과 활용을 위한 기계학습 기법

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 생물정보학전공,2019. 8. 김선.진핵 세포 시스템에서는 mRNA 분자가 전사된 이후 완전히 처리되어 단백질로 번역될 때까지 여러 단계의 전사 후 조절 과정을 거치게 된다. 전사 후 조절 과정은 RNA 편집, 선택적 접합, 선택적 아데닐화 등을 포함한다. 즉 어느 한 시점에서 전사체를 들여다보면 그 내부는 다양한 중간체들의 혼합물로 구성되어 있는 것이다. 이러한 복잡한 조절 시스템 때문에 전사체를 전체적인 수준에서 이해하기가 쉽지 않다. 본 학위 연구는 RNA 시퀀싱 데이터를 해독하고 활용하기 위한 기계학습 기법들에 대한 연구이며 RNA 편집, 선택적 접합 및 유전자 발현의 관점에서 수행된 세 가지 연구로 구성된다. RNA 편집은 ADAR(A=>I) 과 APOBEC(C=>U) 두 가지 효소에 의해 촉매 되는 전사 후 RNA 서열 조절 기작이다. RNA 편집은 단백질 활성도, 선택적 접합 및 miRNA 표적 조절 등 다양한 세포 기작을 제어하는 것으로 알려진 중요한 새포 내 조절 시스템이다. RNA 시퀀싱을 이용해 RNA 편집 현상을 검출하는 것은 RNA 편집 현상의 생물학적 기능을 이해하는 데에 매우 중요하다. 문제는 이 과정에서 상당한 양의 위양성이 발생한다는 점이다. 샘플당 수만 개 이상 발생하는 RNA 편집 잔기들 모두를 실험적으로 검증할 수 없기 때문에 이를 걸러내기 위한 전산학적 모델이 요구된다. RDDpred는 RNA 시퀀싱 데이터로부터 RNA 편집 현상을 검출하는 과정에서 발생하는 위양성 잔기들을 기계학습 기술에 기반하여 구분하는 모델이다. RDDpred는 두 개의 기 발표된 RNA 편집 연구 데이터를 이용하여 검증되었다. RNA 시퀀싱 기술이 활용될 수 있는 또 하나의 복잡한 문제로 접합체 차원에서의 종양 이질성 (ITH) 측정 문제가 있다. ITH는 암 조직을 구성하는 세포 집단의 다양성의 지표이며, 최근 출판된 연구들의 결과는 유전자 발현량 데이터에 기반하여 측정된 전사체 수준에서의 ITH가 암 환자의 예후예측에 유용함을 시사한다. 접합체는 유전자 발현량과 함께 전사체를 구성하는 주요 요소 중 하나이며 따라서 접합체 수준에서 ITH를 측정하는 것은 보다 전체적인 수준에서 전사체 ITH를 연구하기 위한 자연스러운 흐름이다. RNA 시퀀싱 데이터를 이용하여 암 접합체 수준에서 ITH를 측정하는 과정에는 복잡한 접합 패턴과 광범위한 인트론 연장 변이 및 짧은 시퀀싱 판독 길이 등의 심각한 기술적 난관들이 있다. SpliceHetero는 이러한 문제들을 고려하여 접합체 수준에서의 ITH (즉, sITH)를 측정하기 위한 도구이며 내부적으로 정보이론을 활용한다. SpliceHetero는 시뮬레이션 데이터, 이종이식 종양 데이터 및 TCGA pan-cancer 데이터 등을 활용하여 광범위하게 검증되었으며 ITH를 잘 반영하는 것으로 확인되었다. 이뿐 아니라 sITH는 암의 진행과 암 환자의 예후 및 PAM50와 같은 잘 알려진 분자 아형들과도 높은 상관관계를 가지는 것으로 확인되었다. 마지막 연구 주제는 유전자 발현량 데이터에 기반하여 특정 암 표현형에 특이적인 환자 부분 공간을 정의하는 기계학습 알고리즘을 개발하는 것이다. RNA 시퀀싱 데이터는 암 환자의 유전자 발현량 프로파일을 얻는 데에 유용한 도구이지만, 2만 개 이상의 차원을 가진 매우 고차원의 데이터이기 때문에 실질적인 용도로 사용되기 위해서는 그 차원의 크기를 축소할 필요가 있다. 이때 각 유전자들은 복잡하지만 고유한 방식으로 서로 상호작용한다는 점을 이용할 수 있다. 실험적으로 검증된 단백질 간의 상호작용 정보를 모아 네트워크 형태로 묶은 것을 단백질 상호작용 네트워크 (혹은 PIN)라 부른다. 이 PIN을 활용하여 RNA 시퀀싱 데이터의 차원을 줄이면서도 데이터로부터 생물학적으로 유의미한 특징들을 추출할 수 있다. Tumor2Vec은 이렇게 추출된 PIN 수준의 특징들을 활용하여 특정 암 표현형에 특이적인 환자 부분 공간을 정의한다. Tumor2Vec은 조기 구강 암에서 림프절 전이를 예측하기 위한 파일럿 연구에 적용되었으며 그 결과 RNA 시퀀싱 데이터의 차원을 줄여 림프절 전이 예측 모델을 생성했고 이 과정에서 암 표현형을 잘 설명하는 PIN 수준의 특징들을 보존하는 데에도 성공했다.In eukaryotic cells, there are several post-transcriptional modification steps such as RNA editing and alternative splicing, until mRNA molecules are fully matured and translated into proteins. Thus, the transcriptome is a complex mixture of various intermediates that are processed in multiple steps. This complex regulatory structure makes it difficult to fully understand the landscape of transcriptome. My doctoral study consists of three studies that enable RNA-seq to be decoded and utilized in terms of RNA editing, alternative splicing, and gene expression. RNA editing is a post-transcriptional RNA sequence modification performed by two catalytic enzymes ADAR (A-to-I) and APOBEC (C-to-U). RNA editing is considered an important regulatory system that controls a variety of cellular functions such as protein activation, alternative splicing, and miRNA targeting. Therefore, detecting RNA editing events in RNA-seq data is important for understanding its biological functions. However, it is known that a significant amount of false-positives occur when detecting RNA editing in RNA-seq. Since it is not possible to experimentally validate all RNA editing residues extracted from RNA-seq, a computational model is needed to filter potential false-positive RNA editing calls. RDDpred, an RNA editing predictor based on machine learning techniques, was developed to filter out false-positive RNA editing calls in RNA-seq. It uses prior knowledge bases to collect training instances directly from the input data, and then trains the random forest (RF) predictors that are specific to the input data. RDDpred was tested using two publicly available datasets of RNA editing studies and has shown good performance. Another complex problem in RNA-seq decoding is spliceomic intratumor heterogeneity (ie, sITH). Intratumor heterogeneity (ITH) represents the diversity of cell populations that make up the cancer tissue. Recent studies have identified ITH at the transcriptome level and suggested that ITH at gene expression levels is useful for predicting prognosis. Measuring ITH levels at the spliceome level is a natural extension. There is a serious technical challenge in measuring sITH from bulk tumor RNA-seq, such as complex splicing patterns, widespread intron retentions, and short sequencing read lengths. SpliceHetero, an information-theoretic method for measuring the sITH of a tumor, was developed to address the aforementioned technical problems. SpliceHetero was extensively tested in experiments using synthetic data, xenograft tumor data and TCGA pan-cancer data and measured sITH successfully. Also, sITH was shown to be closely related to cancer progression and clonal heterogeneity, along with clinically significant features such as cancer stage, survival outcome, and PAM50 subtype. The last research topic is to develop a machine learning algorithm that defines patient subspaces specific to particular cancer phenotypes based on gene expression data. Since RNA-seq data is high-dimensional data composed of 20,000 or more genes in general, it is not easy to apply a machine learning algorithm. A network that collects information of experimentally verified interaction of proteins is called a Protein Interaction Network (PIN). Tumor2Vec defines the patient subspace by defining the subnetwork communities that interact with each other by applying the Graph Embedding technique to PIN. Tumor2Vec proposed a clinical model by defining a subspace for patients with different lymph node metastases in early oral cancer and found biologically significant features in the PIN subnetwork unit in the process.Chapter 1 Introduction 1 1.1 Biological background . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Challenges in decoding and utilizing RNA-seq data . . . . . . . . 5 1.2.1 false-positives in RNA editing calls . . . . . . . . . . . . . 6 1.2.2 Absence of a model for measuring spliceomic intratumor heterogeneity considering complex cancer spliceome . . . 6 1.2.3 Lack of biological interpretation of dimension reduction techniques using gene expression . . . . . . . . . . . . . . 8 1.3 Machine learning techniques to solve difficulties in using RNA-seq 9 1.4 Outline of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 2 RDDpred: A condition specific machine learning model for filtering false-positive RNA editing calls in RNAseq data 11 2.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 A preliminary study . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.5.1 Design of experiments for evaluation . . . . . . . . . . . . 18 2.5.2 Evaluation using data from Bahn et al. . . . . . . . . . . 19 2.5.3 Evaluation using data from Peng et al. . . . . . . . . . . . 19 2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Chapter 3 SpliceHetero: An information-theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq data 24 3.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3 A preliminary study . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.1 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.2 Xenograft tumor data . . . . . . . . . . . . . . . . . . . . 36 3.5.3 TCGA pan-cancer data . . . . . . . . . . . . . . . . . . . 38 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Chapter 4 Tumor2Vec: A supervised learning algorithm for extracting subnetwork representations of cancer RNAseq data using protein interaction networks 48 4.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.1 Lymph node metastasis in early oral cancer . . . . . . . . 57 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Chapter 5 Conclusion 62 초록 78Docto