Integrative analyses of transcriptome sequencing identify novel functional lncRNAs in esophageal squamous cell carcinoma.

Long non-coding RNAs (lncRNAs) have a critical role in cancer initiation and progression, and thus may mediate oncogenic or tumor suppressing effects, as well as be a new class of cancer therapeutic targets. We performed high-throughput sequencing of RNA (RNA-seq) to investigate the expression level of lncRNAs and protein-coding genes in 30 esophageal samples, comprised of 15 esophageal squamous cell carcinoma (ESCC) samples and their 15 paired non-tumor tissues. We further developed an integrative bioinformatics method, denoted URW-LPE, to identify key functional lncRNAs that regulate expression of downstream protein-coding genes in ESCC. A number of known onco-lncRNA and many putative novel ones were effectively identified by URW-LPE. Importantly, we identified lncRNA625 as a novel regulator of ESCC cell proliferation, invasion and migration. ESCC patients with high lncRNA625 expression had significantly shorter survival time than those with low expression. LncRNA625 also showed specific prognostic value for patients with metastatic ESCC. Finally, we identified E1A-binding protein p300 (EP300) as a downstream executor of lncRNA625-induced transcriptional responses. These findings establish a catalog of novel cancer-associated functional lncRNAs, which will promote our understanding of lncRNA-mediated regulation in this malignancy

A bioinformatic analysis identifies circadian expression of splicing factors and time-dependent alternative splicing events in the HD-MY-Z cell line

The circadian clock regulates key cellular processes and its dysregulation is associated to several pathologies including cancer. Although the transcriptional regulation of gene expression by the clock machinery is well described, the role of the clock in the regulation of post-transcriptional processes, including splicing, remains poorly understood. In the present work, we investigated the putative interplay between the circadian clock and splicing in a cancer context. For this, we applied a computational pipeline to identify oscillating genes and alternatively spliced transcripts in time-course high-throughput data sets from normal cells and tissues, and cancer cell lines. We investigated the temporal phenotype of clock-controlled genes and splicing factors, and evaluated their impact in alternative splice patterns in the Hodgkin Lymphoma cell line HD-MY-Z. Our data points to a connection between clock-controlled genes and splicing factors, which correlates with temporal alternative splicing in several genes in the HD-MY-Z cell line. These include the genes DPYD, SS18, VIPR1 and IRF4, involved in metabolism, cell cycle, apoptosis and proliferation. Our results highlight a role for the clock as a temporal regulator of alternative splicing, which may impact malignancy in this cellular model

A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk.

BackgroundTumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway.ResultTo develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior.ConclusionThese results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field

Cancer systems biology: a network modeling perspective

Cancer is now appreciated as not only a highly heterogenous pathology with respect to cell type and tissue origin but also as a disease involving dysregulation of multiple pathways governing fundamental cell processes such as death, proliferation, differentiation and migration. Thus, the activities of molecular networks that execute metabolic or cytoskeletal processes, or regulate these by signal transduction, are altered in a complex manner by diverse genetic mutations in concert with the environmental context. A major challenge therefore is how to develop actionable understanding of this multivariate dysregulation, with respect both to how it arises from diverse genetic mutations and to how it may be ameliorated by prospective treatments. While high-throughput experimental platform technologies ranging from genomic sequencing to transcriptomic, proteomic and metabolomic profiling are now commonly used for molecular-level characterization of tumor cells and surrounding tissues, the resulting data sets defy straightforward intuitive interpretation with respect to potential therapeutic targets or the effects of perturbation. In this review article, we will discuss how significant advances can be obtained by applying computational modeling approaches to elucidate the pathways most critically involved in tumor formation and progression, impact of particular mutations on pathway operation, consequences of altered cell behavior in tissue environments and effects of molecular therapeutics

Nuclear receptor networks in the normal breast and breast cancer

Nuclear receptors (NRs) have been targets of intensive drug development for decades due to their roles as key regulators of multiple developmental, physiological and disease processes. In the normal breast, a number of NRs are reported to be differentially expressed in different epithelial breast cell lineages and likely play a role in the differentiation and maintenance of the normal breast epithelial cell lineages. In breast cancer, expression of the estrogen and progesterone receptors remains clinically important in predicting prognosis and determining therapeutic strategies. More recently, there is growing evidence suggesting the involvement of multiple nuclear receptors other than the estrogen and progesterone receptors, in the regulation of various processes important to the initiation and progression of breast cancer. Identification of key NRs and the pathways they govern in the normal breast and breast cancer is important to our understanding of normal breastdevelopment and pave the way for rational design of prognostic and therapeutic targets for breast cancer. This thesis systematically investigates the expression and co-expression networks of NRs in the normal breast and how they are perturbed in breast cancer with a focus on the identification of network-based prognostic markers for breast cancer. This is done through analysis of multiple expression datasets, both publicly available and in-house generated, of primary normal breast and breast cancer tissues. Among the main findings of this work is the identification of NRs differentially expressed in normal breast epithelial cells at single cell level and the observation that there are major changes in the NR co-expression networks in breast cancer compared to the normal breast. We showed that cancer associated changes in NR co-expression networks are clinically relevant and that these changes can be used to identify NRs with prognostic values in estrogen receptor negative (ER-), HER2 and Basal subgroups of breast cancer. In addition, we demonstrated the utility of co-expression analysis in the identification of potential crosstalk in the signalling networks of different NRs by investigating the potential crosstalk of of MR and RARB in the normal breast and breast cancer

Simulating non-small cell lung cancer with a multiscale agent-based model

Background The epidermal growth factor receptor (EGFR) is frequently overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In silcio modeling is considered to be an increasingly promising tool to add useful insights into the dynamics of the EGFR signal transduction pathway. However, most of the previous modeling work focused on the molecular or the cellular level only, neglecting the crucial feedback between these scales as well as the interaction with the heterogeneous biochemical microenvironment. Results We developed a multiscale model for investigating expansion dynamics of NSCLC within a two-dimensional in silico microenvironment. At the molecular level, a specific EGFR-ERK intracellular signal transduction pathway was implemented. Dynamical alterations of these molecules were used to trigger phenotypic changes at the cellular level. Examining the relationship between extrinsic ligand concentrations, intrinsic molecular profiles and microscopic patterns, the results confirmed that increasing the amount of available growth factor leads to a spatially more aggressive cancer system. Moreover, for the cell closest to nutrient abundance, a phase-transition emerges where a minimal increase in extrinsic ligand abolishes the proliferative phenotype altogether. Conclusions Our in silico results indicate that, in NSCLC, in the presence of a strong extrinsic chemotactic stimulus, and depending on the cell's location, downstream EGFR-ERK signaling may be processed more efficiently, thereby yielding a migration-dominant cell phenotype and overall, an accelerated spatio-temporal expansion rate.Comment: 37 pages, 7 figure

정량 단백체학 및 생물정보학을 이용한 공격적인 유방암 바이오 마커의 발굴

학위논문(박사) -- 서울대학교대학원 : 의과대학 의학과, 2022.2. 유한석.서론: 질량분석기 기반 단백체학은 대규모 분자생물학과 세포생물학을 단백질 수준에서 다루는 기술이다. 대량 단백질의 동정 및 정량으로 단백체학 분석기법은 단백질의 서열, 발현, 전사 후 변형 및 단백질-단백질 상호작용 등을 해석할 수 있도록 한다. 세포주부터 제한된 양의 임상 시료인 체액, 신선한 냉동 조직, 파라핀 포매 (FFPE) 조직 등으로부터 단백질을 추출한다. 높은 처리량과 감도를 가진 차세대 고속 질량분석기 기반 분석으로 수천 개의 단백질을 동시에 정량 하여 대량의 데이터를 생산한다. 생물정보학 분석 기법을 활용하여 질병의 상태, 예후, 치료에 따른 효과에 따른 단백질 발현 수준의 차이를 감지할 수 있고, 더 나아가 질병의 생물학적 메커니즘을 제시할 수 있다. 방법: 1장에서, 가장 공격적인 삼중 음성 (TNBC) 유방암 하위 유형인 클라우딘 낮은 (Claudin-low) 하위 유형에서 암 줄기세포 마커인 CD44의 역할을 규명하였다. 유전자 조작 기법을 통해 CD44 발현을 조절한 세포주를 구축하였다. CD44의 발현을 감소시켰을 때, 단백질 발현 양상의 변화를 분석하여 분자생물학적 역할을 입증하였다. 2장에서, 유방암 환자 중 타장기로의 원격 전이 고위험군 환자에 대한 예후 예측 바이오 마커를 발굴하기 위하여 동일 병기 28명의 환자 (조기 원격전이: 9명, 지연 원격전이: 9명, 비원격전이: 10명) FFPE 종양 조직을 수집하였다. 제한적인 양의 시료를 분석하기 위한 단백체 분석법을 확립하였다. 원격전이 예후예측을 위한 바이오 마커 후보군을 발굴하였고, 전사체 외부 데이터에서 회귀 모델을 개발하여 검증하였다. 결과: 1장에서, Cluain-low 하위유형 유방암 세포주 MDA-MB-231에서 7396개, Hs578T 에서 6567개의 단백질을 동정하였다. 통계적으로 유의한 발현의 차이를 나타낸 MDA-MB-231의 4908개 단백질, Hs578T의 855개 단백질을 생물정보학 분석 (gene ontology, 단백질-단백질 상호작용 네트워크 분석) 하여 세포 증식, 대사과정, 유전자의 발현 조절을 통한 암화 과정을 제시하였다. 생물학적 메커니즘의 확인을 위해 기능 연구를 수행하였고, CD44가 대량의 단백질의 발현을 조절하여 세포 증식과 이동을 조절하는 것을 검증하였다. 2장에서, 유방암 FFPE 슬라이드에서 종양 부분만을 선별하여 분리하여 질량 분석하여 9455개의 단백질을 동정하였다. 원 발암 진단 후 원격전이가 일어난 기간에 따라 발현의 유의한 차이가 있는 단백질 중 비교 분석, 상관관계 네트워크 분석, 머신 러닝 기반 특성 추출, 생존분석을 통해 7개의 최종 바이오 마커 후보군을 발굴하였다. 7개의 마커 후보군으로 외부데이터를 활용하여 Cox 비례 위험 회귀 모델을 구축하여 원격전이 예측할 수 있음을 확인하였다. 결론: 1장에서 2가지 Cluain-low 하위유형 유방암 세포주에서 암 줄기세포 마커인 CD44 발현을 감소시킨 단백체 발현 비교 데이터를 생성하였다. 이를 분석하여 CD44가 암세포의 유전적 발현, 대사, 부착을 유기적으로 조절하여 핵심 암화 과정인 세포 증식, 이동에 영향을 주는 것을 확인하였다. 이를 통해 공격적인 삼중 음성 유방암의 핵심 조절인자인 CD44의 생물학적 기전을 분자적 수준에서 이해할 수 있도록 도왔으며, 더 나아가 Cluain-low 하위유형 유방암의 잠재적 치료의 표적 물질이 될 수 있음을 확인하였다. 2장에서 유방암 환자의 파라핀 포매 종양 조직 단백체 분석을 통해 심층적인 단백체 데이터를 생성하였고, 원격전이 예측을 위한 잠재적 바이오 마커를 발굴하였다. 이러한 원격전이 예후 예측 바이오 마커의 개발과 생물 정보학 분석을 통한 분자 생물학적 기전의 규명은 정밀의학 실현의 핵심 근거자료로 활용할 수 있을 것이며, 유방암 환자의 효과적인 치료 계획 수립에 도움을 줄 것으로 기대한다.Mass spectrometry (MS)-based proteomics covers large-scale molecular and cellular biology at the protein level. Through the identification and quantification of proteins, the proteome analysis can interpret protein sequence, post-transcriptional modification and protein-protein interactions. This allows us to profile new disease biomarkers. From the cell lines to the limited amount of samples (body fluids, fresh frozen tissues, and FFPE tissues), thousands of proteins were discovered simultaneously to detect changes in expression level with disease status. The resulting expression data are complex and ambiguous patterns. Therefore, exquisite bioinformatics algorithms have to be applied to determine these unique biomarker patterns. A proteomic study discovers a list of biomarkers and helps elucidate the biological mechanisms. In Chapter Ⅰ, mass spectrometry-based proteomics was performed using breast cancer cells. To discover global proteome changes induced by CD44 expression levels, we regulated CD44 transcription by siRNA in two claudin-low breast cancer cell lines. For deep coverage of proteome, we used tandem mass tag-based MS analysis. We discovered 2736 proteins were upregulated and 2172 proteins were downregulated in CD44-knockdown MDA-MB-231 cells. For Hs 578T CD44-knockdown cells, 412 proteins were upregulated and 443 were downregulated. Informatics (Gene ontology and protein-protein interaction network) analysis demonstrated altered oncogenic cellular processes including proliferation, metabolism, and gene expression regulations. To confirm the changes of biology patterns, functional studies were conducted. As a result, we discovered that CD44-regulated proteome of claudin-low breast cancer cells, revealing changes that mediate cell proliferation and migration. In Chapter Ⅱ, label free-based MS proteomic analysis of clinical FFPE tissues. To discover candidate prognosis markers for distant metastasis of breast cancer, 10 no-metastasis, 9 late-metastasis, and 9 early-metastasis patients’ primary tumor samples were analyzed. To achieve an in-depth proteome in the minimum of FFPE slides per sample, we performed well-defined proteomic strategies with high-resolution quadrupole Orbitrap LC-MS/MS. We identified a total of 9,455 protein groups using FFPE slides at 1% of the peptide and protein FDR level. Five biomarker candidates were differentially expressed using pair-wise comparison, and correlation network analysis filtered five candidates into two no metastasis specific and one late metastasis specific proteins. In addition, machine learning-based feature selection detected ten early metastasis classifier proteins, and the system biology method filtered into seven proteins. For external validation, we used published mRNA data of breast primary tumor. Consequently, we suggested seven prognosis protein marker candidates that can help patients who need active treatment.General Abstract i Table of Contents iii Lists of Tables and Figures iv List of Abbreviations x General Introduction 1 Chapter Ⅰ Quantitative Proteomics Reveals Knockdown of CD44 Promotes Proliferation and Migration in Claudin-Low MDA-MB-231 and Hs 578T Breast Cancer Cell Lines 4 Abstract 5 Introduction 6 Material and Methods 8 Results 16 Discussion 37 Chapter Ⅱ In-depth proteome profiling of breast cancer formalin-fixed paraffin-embedded tissue for distant metastasis 41 Abstract 42 Introduction 44 Material and Methods 46 Results 51 Discussion 72 General Discussion 77 Refernece 82 Abstract in Korean 93박

Cellular interactions in the tumor microenvironment: the role of secretome

Over the past years, it has become evident that cancer initiation and progression depends on several components of the tumor microenvironment, including inflammatory and immune cells, fibroblasts, endothelial cells, adipocytes, and extracellular matrix. These components of the tumor microenvironment and the neoplastic cells interact with each other providing pro and antitumor signals. The tumor-stroma communication occurs directly between cells or via a variety of molecules secreted, such as growth factors, cytokines, chemokines and microRNAs. This secretome, which derives not only from tumor cells but also from cancer-associated stromal cells, is an important source of key regulators of the tumorigenic process. Their screening and characterization could provide useful biomarkers to improve cancer diagnosis, prognosis, and monitoring of treatment responses.Agência financiadora Fundação de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) FAPESP 10/51168-0 12/06048-2 13/03839-1 National Council for Scientific and Technological Development (CNPq) CNPq 306216/2010-8 Fundacao para a Ciencia e a Tecnologia (FCT) UID/BIM/04773/2013 CBMR 1334info:eu-repo/semantics/publishedVersio