Performance evaluation of DNA copy number segmentation methods

A number of bioinformatic or biostatistical methods are available for analyzing DNA copy number profiles measured from microarray or sequencing technologies. In the absence of rich enough gold standard data sets, the performance of these methods is generally assessed using unrealistic simulation studies, or based on small real data analyses. We have designed and implemented a framework to generate realistic DNA copy number profiles of cancer samples with known truth. These profiles are generated by resampling real SNP microarray data from genomic regions with known copy-number state. The original real data have been extracted from dilutions series of tumor cell lines with matched blood samples at several concentrations. Therefore, the signal-to-noise ratio of the generated profiles can be controlled through the (known) percentage of tumor cells in the sample. In this paper, we describe this framework and illustrate some of the benefits of the proposed data generation approach on a practical use case: a comparison study between methods for segmenting DNA copy number profiles from SNP microarrays. This study indicates that no single method is uniformly better than all others. It also helps identifying pros and cons for the compared methods as a function of biologically informative parameters, such as the fraction of tumor cells in the sample and the proportion of heterozygous markers. Availability: R package jointSeg: http://r-forge.r-project.org/R/?group\_id=156

A combinatorial approach to gene expression analysis: DNA microarrays.

The microarray technology is based on analytical tools that parallelize the quantitative and qualitative analysis of nucleic acids, proteins and tissue sections one of its more recent evolutions-. By miniaturizing the size of the reaction and sensing area, microarrays allow to assess at the activity of thousands of genes in a given tissue or cell line at once in a rapid and quantitative way, and to carry out serial comparative tests in multiple samples. These tools, that stem from the innovations resulting from the technological improvements and knowledge arising from the genome sequencing projects, can be considered as a combinatorial technique that can rapidly provide significant information about complex cellular pathways and processes within one or few ‘‘mass scale’’ and comprehensive testing of a biological sample’s composition

Microchips and their significance in isolation of circulating tumor cells and monitoring of cancers

In micro-fluid systems, fluids are injected into extremely narrow polymer channels in small amounts such as micro-, nano-, or pico-liter scales. These channels themselves are embedded on tiny chips. Various specialized structures in the chips including pumps, valves, and channels allow the chips to accept different types of fluids to be entered the channel and along with flowing through the channels, exert their effects in the framework of different reactions. The chips are generally crystal, silicon, or elastomer in texture. These highly organized structures are equipped with discharging channels through which products as well as wastes of the reactions are secreted out. A particular advantage regarding the use of fluids in micro-scales over macro-scales lies in the fact that these fluids are much better processed in the chips when they applied as micro-scales. When the laboratory is miniaturized as a microchip and solutions are injected on a micro-scale, this combination makes a specialized construction referred to as "lab-on-chip". Taken together, micro-fluids are among the novel technologies which further than declining the costs; enhancing the test repeatability, sensitivity, accuracy, and speed; are emerged as widespread technology in laboratory diagnosis. They can be utilized for monitoring a wide spectrum of biological disorders including different types of cancers. When these microchips are used for cancer monitoring, circulatory tumor cells play a fundamental role

Capturing the ‘ome’ : the expanding molecular toolbox for RNA and DNA library construction

All sequencing experiments and most functional genomics screens rely on the generation of libraries to comprehensively capture pools of targeted sequences. In the past decade especially, driven by the progress in the field of massively parallel sequencing, numerous studies have comprehensively assessed the impact of particular manipulations on library complexity and quality, and characterized the activities and specificities of several key enzymes used in library construction. Fortunately, careful protocol design and reagent choice can substantially mitigate many of these biases, and enable reliable representation of sequences in libraries. This review aims to guide the reader through the vast expanse of literature on the subject to promote informed library generation, independent of the application

환자맞춤형 치료를 위한 체외 항암제 스크리닝용 바이오칩

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 권성훈.정밀의학(Precision Medicine) 혹은 개인맞춤의학(Personalized Medicine)은 개개인의 최적화된 치료방법을 결정하는 것을 목표로 하는 의학의 패러다임이다. 특히, 임상종양학에서는 차세대염기서열분석(NGS), 전사체서열분석, 그리고 질량분석법들을 통한 환자의 분자 프로파일(molecular profile) 방법이 발전해오고 있으며, 이를 바탕으로 환자를 세분화하여 맞춤형 치료를 구현하려고 노력해오고 있다. 하지만, 여전히 현 수준에서 이해되지 못하는 수준의 종양 이질성(tumor heterogeneity)과 오랜 처방기록을 가진 환자군들의 항암제 획득내성(acquired resistance) 등의 원인으로 맞춤형 환자 처방은 쉽지 않은 경우가 많다. 이러한 경우 환자로부터 얻어진 암세포, 조직으로부터 얻어진 일차세포 혹은 체외 배양된 세포, 스페로이드, 장기유사체 등을 이용하여 고속다중약물스크리닝기술을 통한 맞춤형 항암제를 선별해내는 체외 약물진단 기술을 생각해낼 수 있는데, 이는 기존의 유전체 기반의 시도와 병행되어 개개의 환자들에게 더욱 적합한 치료방법을 찾는 것이 가능하게 한다. 하지만 이러한 목적의 고속다중약물스크리닝기술은 높은 활용가능성에도 불구하고, 광범위한 보급과 활용이 되기에는 제약점이 많았다. 기존의 고속다중약물스크리닝기술은 많은 양의 샘플이 소모되고, 값비싼 시약의 소모량도 적지 않았다. 게다가, 수천 가지 이상의 서로 다른 물질들을 탐색하기 위해 반드시 필요한 고가의 자동화된 액체 운반기(liquid handler) 등이 필요하였는데, 이러한 문제로 대형 제약사, 연구소 등을 제외하고는 도입이 쉽지가 않아 기술접근성이 제한되어 있었다. 본 연구에서는 반도체공정에서의 노광기술을 이용하여 개개의 식별할 수 있는 코드를 가지고 있는 코드화된 하이드로젤 기반의 광경화성폴리머 미세입자를 만들어, 이를 원하는 암세포에 약물 스크리닝을 해보고자 하는 다양한 약물라이브러리를 이용 각각의 코드화된 미세입자에 흡수시켜 약물-미세입자 라이브러리를 제작한다. 그후, 값비싼 어레이 제작용 스포터 혹은 디스펜서 장비없이 간단한 자기조립을 통해 대규모의 다양한 약물-하이드로젤 어레이를 제작할 수 있는 기술을 개발하였다. 또한, 소량의 세포들 만으로도 미세우물(microwell) 기반의 세포칩에 도포하는 방식을 개발하였으며, 이를통해 약물-하이드로젤 어레이와 미세우물기반의 세포칩의 결합으로 수백-수천의 다양한 어세이를 적은 수의 샘플만으로도 한번에 수행할 수 있는 고속다중약물스크리닝 기술을 수행할 수 있게 만들었다. 본 연구에서 제시한 소형화된 체외 항암제 스크리닝용 약물플랫폼은 다음과 같은 의의를 가진다. 적은 수의 환자세포 혹은 샘플의 양에 적용할 수 있는, 사용하기 손쉬운 기술로서, 기존의 값비싼 장비, 시약의 사용량을 획기적으로 줄일 수 있는 기술이다. 본 연구에서 제안된 기술을 통해 기존의 장비를 사용할 때 시약의 값이 비싸거나, 장비의 가격이 비싸서, 혹은 다루고자 하는 샘플의 양이 제한적이어서 기존에 접근하기 힘들었던 다양한 학술연구에 적용할 수 있으며, 병원에서의 임상연구 및 실제 환자맞춤형 치료에 사용 될 수 있는 접근성을 획기적으로 높일 수 있다. 특히, 비교적 중,소 규모의 연구환경에서도 다양한 희귀한 환자유래세포 혹은 환자유래오가노이드 등과 접목하여 사용된다면 본 플랫폼의 가능성을 더욱 극대화 할 수 있을 것으로 기대한다.Precision or Personalized Medicine is a medical paradigm aimed to determine optimal therapy for individual patient. In particular, clinical oncology has been using methods of molecular profiling for each patient through next-generation sequencing (NGS), mRNA-sequencing, and mass spectrometry, and has been trying to implement personalized treatment. However, personalized treatment based on molecular profiling to each patient is not always possible due to the high level of heterogeneity of tumor that is still not fully understood at the current level and acquired resistance of anti-cancer drug due to cumulative targeted therapy. In such cases, in vitro drug testing platform using primary cells obtained from patients, or patient-derived cells, spheroids, and organoids can make it possible to find a more appropriate treatment for each individual patient. However, though high-throughput drug screening technology for this purpose is of the utmost importance in saving lives, there were many limitations to its wide use in many hospitals. The existing high-throughput drug combination screening technology consumes a large number of samples and consumes a considerable amount of expensive reagents. In addition, expensive automated liquid handlers, which were essential for exploring thousands of different pipetting, were not easy to introduce except for large-sized pharmaceutical companies and research institutes, which limited access to technology. In this study, I construct a heterogeneous drug-loaded microparticle library by fabricating encoded photocurable polymer particle that has individually identifiable codes to track loaded drug. and I load various drug molecules, which I want to test to target cells, into each coded microparticle. Then, I developed to produce heterogeneous drug-laden microparticle arrays through simple self-assembly without the need for a microarray spotter or dispensing machine for generating microarray. I also have developed cell seeding method of seeding small-volume samples into the microwell-based cell chip. By utilizing the drug-laden microparticle hydrogel array and microwell-based cell chip technology, hundreds to thousands of different assays can be done at once with just a small number of samples and low cost. Through the implemented platform, the anti-cancer drug sequential combination screening was conducted on the triple-negative breast cooler (TNBC) cells, which are generally known to be difficult to treat due to lack of known drug target, and the results of screening were analyzed by establishing a library of drugs in the EGFR inhibitory type and drugs in the genotoxin type. In addition, another study was conducted to find optimal drug combinations using patient-derived cells derived from tumors in patients with non-small cell lung cancer that have obtained acquired resistance. Finally, as the growing need for three-dimensional culture, such as spheroid and organoid for having a similar response to in vivo drug testing, it was also developed that microwell-based cell chip that is capable of 3D culture with low-cost and small-volume of cells. The miniaturized in vitro anticancer drug screening platform presented in this study has the following significance. An easy-to-use technique that can be applied to a small number of patient cells or samples, which can dramatically reduce the use of conventional expensive equipment, reagents. The proposed technology in this study can be applied to a variety of academic studies previously inaccessible to high-throughput screening due to the high cost of reagents, the high price of equipment, or the limited amount of samples in conventional drug screening. and this platform can also dramatically increase access to clinical research in hospitals for personalized treatments. In particular, it is expected that the possibility of this platform will be further maximized if it is used in a relatively small and medium-sized research environment by the combined use of various rare samples such as patient-derived cells or patient-derived organoids.Chapter 1 Introduction １ 1.1 Motivation of this research ２ 1.2 Competing technologies and Previous works ８ 1.3 Main Concept: In vitro drug testing using miniaturized encoded drug-laden hydrogel array technology １５ Chapter 2 Platform Development of Drug Releasing Hydrogel Microarray ２０ 2.1 Encoded Drug-Laden Hydrogel & Library construction ２１ 2.2 Array generation of heterogenous drug-laden microparticles. ３４ 2.3 Cell Culturing on Cell Chip and bioassay ３６ Chapter 3 Sequential Drug Combination Screening Assy on TNBC ４０ 3.1 Background : Sequential Drug Combination as promising therapeutic option ４１ 3.2 Experimental design with sequential drug treatment assay ４３ 3.3 Technical Issue & its engineering solution ４４ 3.4 Assay Result ４９ Chapter 4 Drug Combination Assay on Patient-Derived Cells ５８ 4.1 Background : Simultaneous Combination Treatment using Patient-Derived Cells ５９ 4.2 Improvement of Platform for facilitating translational study ６２ 4.3 Study Design for small-volume drug combinatorial screening with NSCLC patient derived cell ６５ 4.4 Assay Result ６９ Chapter 5 Development of platform for 3D culture model ７２ 5.1 3D culturable platform ７３ 5.2 Development of 3D culture platform based Matrigel scaffold. ７８ 5.3 Advantage over conventional 3D culture-based drug testing platform. ８５ Chapter 6 Conclusion ８７ Bibliography ９０ Abstract in Korean ９７Docto

Extracting Gene Networks for Low-Dose Radiation Using Graph Theoretical Algorithms

Genes with common functions often exhibit correlated expression levels, which can be used to identify sets of interacting genes from microarray data. Microarrays typically measure expression across genomic space, creating a massive matrix of co-expression that must be mined to extract only the most relevant gene interactions. We describe a graph theoretical approach to extracting co-expressed sets of genes, based on the computation of cliques. Unlike the results of traditional clustering algorithms, cliques are not disjoint and allow genes to be assigned to multiple sets of interacting partners, consistent with biological reality. A graph is created by thresholding the correlation matrix to include only the correlations most likely to signify functional relationships. Cliques computed from the graph correspond to sets of genes for which significant edges are present between all members of the set, representing potential members of common or interacting pathways. Clique membership can be used to infer function about poorly annotated genes, based on the known functions of better-annotated genes with which they share clique membership (i.e., “guilt-by-association”). We illustrate our method by applying it to microarray data collected from the spleens of mice exposed to low-dose ionizing radiation. Differential analysis is used to identify sets of genes whose interactions are impacted by radiation exposure. The correlation graph is also queried independently of clique to extract edges that are impacted by radiation. We present several examples of multiple gene interactions that are altered by radiation exposure and thus represent potential molecular pathways that mediate the radiation response

The emergence and diffusion of DNA microarray technology

The network model of innovation widely adopted among researchers in the economics of science and technology posits relatively porous boundaries between firms and academic research programs and a bi-directional flow of inventions, personnel, and tacit knowledge between sites of university and industry innovation. Moreover, the model suggests that these bi-directional flows should be considered as mutual stimulation of research and invention in both industry and academe, operating as a positive feedback loop. One side of this bi-directional flow – namely; the flow of inventions into industry through the licensing of university-based technologies – has been well studied; but the reverse phenomenon of the stimulation of university research through the absorption of new directions emanating from industry has yet to be investigated in much detail. We discuss the role of federal funding of academic research in the microarray field, and the multiple pathways through which federally supported development of commercial microarray technologies have transformed core academic research fields. Our study confirms the picture put forward by several scholars that the open character of networked economies is what makes them truly innovative. In an open system innovations emerge from the network. The emergence and diffusion of microarray technologies we have traced here provides an excellent example of an open system of innovation in action. Whether they originated in a startup company environment that operated like a think-tank, such as Affymax, the research labs of a large firm, such as Agilent, or within a research university, the inventors we have followed drew heavily on knowledge resources from all parts of the network in bringing microarray platforms to light. Federal funding for high-tech startups and new industrial development was important at several phases in the early history of microarrays, and federal funding of academic researchers using microarrays was fundamental to transforming the research agendas of several fields within academe. The typical story told about the role of federal funding emphasizes the spillovers from federally funded academic research to industry. Our study shows that the knowledge spillovers worked both ways, with federal funding of non-university research providing the impetus for reshaping the research agendas of several academic fields

Computational Hybrid Systems for Identifying Prognostic Gene Markers of Lung Cancer

Lung cancer is the most fatal cancer around the world. Current lung cancer prognosis and treatment is based on tumor stage population statistics and could not reliably assess the risk for developing recurrence in individual patients. Biomarkers enable treatment options to be tailored to individual patients based on their tumor molecular characteristics. To date, there is no clinically applied molecular prognostic model for lung cancer. Statistics and feature selection methods identify gene candidates by ranking the association between gene expression and disease outcome, but do not account for the interactions among genes. Computational network methods could model interactions, but have not been used for gene selection due to computational inefficiency. Moreover, the curse of dimensionality in human genome data imposes more computational challenges to these methods.;We proposed two hybrid systems for the identification of prognostic gene signatures for lung cancer using gene expressions measured with DNA microarray. The first hybrid system combined t-tests, Statistical Analysis of Microarray (SAM), and Relief feature selections in multiple gene filtering layers. This combinatorial system identified a 12-gene signature with better prognostic performance than published signatures in treatment selection for stage I and II patients (log-rank P\u3c0.04, Kaplan-Meier analyses). The 12-gene signature is a more significant prognostic factor (hazard ratio=4.19, 95% CI: [2.08, 8.46], P\u3c0.00006) than other clinical covariates. The signature genes were found to be involved in tumorigenesis in functional pathway analyses.;The second proposed system employed a novel computational network model, i.e., implication networks based on prediction logic. This network-based system utilizes gene coexpression networks and concurrent coregulation with signaling pathways for biomarker identification. The first application of the system modeled disease-mediated genome-wide coexpression networks. The entire genomic space were extensively explored and 21 gene signatures were discovered with better prognostic performance than all published signatures in stage I patients not receiving chemotherapy (hazard ratio\u3e1, CPE\u3e0.5, P \u3c 0.05). These signatures could potentially be used for selecting patients for adjuvant chemotherapy. The second application of the system modeled the smoking-mediated coexpression networks and identified a smoking-associated 7-gene signature. The 7-gene signature generated significant prognostication specific to smoking lung cancer patients (log-rank P\u3c0.05, Kaplan-Meier analyses), with implications in diagnostic screening of lung cancer risk in smokers (overall accuracy=74%, P\u3c0.006). The coexpression patterns derived from the implication networks in both applications were successfully validated with molecular interactions reported in the literature (FDR\u3c0.1).;Our studies demonstrated that hybrid systems with multiple gene selection layers outperform traditional methods. Moreover, implication networks could efficiently model genome-scale disease-mediated coexpression networks and crosstalk with signaling pathways, leading to the identification of clinically important gene signatures