Author
Martin Granzow
Other affiliations: German Cancer Research Center, University Hospital Heidelberg, Weingarten Realty Investors
Bio: Martin Granzow is an academic researcher from Heidelberg University. The author has contributed to research in topics: Exome sequencing & Comparative genomic hybridization. The author has an hindex of 25, co-authored 53 publications receiving 4947 citations. Previous affiliations of Martin Granzow include German Cancer Research Center & University Hospital Heidelberg.
Papers published on a yearly basis
Papers
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TL;DR: The results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods.
Abstract: The integrity of RNA molecules is of paramount importance for experiments that try to reflect the snapshot of gene expression at the moment of RNA extraction. Until recently, there has been no reliable standard for estimating the integrity of RNA samples and the ratio of 28S:18S ribosomal RNA, the common measure for this purpose, has been shown to be inconsistent. The advent of microcapillary electrophoretic RNA separation provides the basis for an automated high-throughput approach, in order to estimate the integrity of RNA samples in an unambiguous way. A method is introduced that automatically selects features from signal measurements and constructs regression models based on a Bayesian learning technique. Feature spaces of different dimensionality are compared in the Bayesian framework, which allows selecting a final feature combination corresponding to models with high posterior probability. This approach is applied to a large collection of electrophoretic RNA measurements recorded with an Agilent 2100 bioanalyzer to extract an algorithm that describes RNA integrity. The resulting algorithm is a user-independent, automated and reliable procedure for standardization of RNA quality control that allows the calculation of an RNA integrity number (RIN). Our results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods.
2,406 citations
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TL;DR: It is suggested that in breast cancer, tumor cells may disseminate in a far less progressed genomic state than previously thought, and that they acquire genomic aberrations typical of metastatic cells thereafter.
Abstract: According to the present view, metastasis marks the end in a sequence of genomic changes underlying the progression of an epithelial cell to a lethal cancer. Here, we aimed to find out at what stage of tumor development transformed cells leave the primary tumor and whether a defined genotype corresponds to metastatic disease. To this end, we isolated single disseminated cancer cells from bone marrow of breast cancer patients and performed single-cell comparative genomic hybridization. We analyzed disseminated tumor cells from patients after curative resection of the primary tumor (stage M0), as presumptive progenitors of manifest metastasis, and from patients with manifest metastasis (stage M1). Their genomic data were compared with those from microdissected areas of matched primary tumors. Disseminated cells from M0-stage patients displayed significantly fewer chromosomal aberrations than primary tumors or cells from M1-stage patients (P < 0.008 and P < 0.0001, respectively), and their aberrations appeared to be randomly generated. In contrast, primary tumors and M1 cells harbored different and characteristic chromosomal imbalances. Moreover, applying machine-learning methods for the classification of the genotypes, we could correctly identify the presence or absence of metastatic disease in a patient on the basis of a single-cell genome. We suggest that in breast cancer, tumor cells may disseminate in a far less progressed genomic state than previously thought, and that they acquire genomic aberrations typical of metastatic cells thereafter. Thus, our data challenge the widely held view that the precursors of metastasis are derived from the most advanced clone within the primary tumor.
619 citations
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17 Aug 2009
TL;DR: A Practical Approach to Microarray Data Analysis is intended for students, teachers, researchers, and research managers who want to understand the state of the art and of the presented methodologies and the areas in which gaps in knowledge demand further research and development.
Abstract: A Practical Approach to Microarray Data Analysis is for all life scientists, statisticians, computer experts, technology developers, managers, and other professionals tasked with developing, deploying, and using microarray technology including the necessary computational infrastructure and analytical tools. The book addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools. It is intended for students, teachers, researchers, and research managers who want to understand the state of the art and of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. The book is designed to be used by the practicing professional tasked with the design and analysis of microarray experiments or as a text for a senior undergraduate- or graduate level course in analytical genetics, biology, bioinformatics, computational biology, statistics and data mining, or applied computer science. Key topics covered include: -Format of result from data analysis, analytical modeling/experimentation; -Validation of analytical results; -Data analysis/Modeling task; -Analysis/modeling tools; -Scientific questions, goals, and tasks; -Application; -Data analysis methods; -Criteria for assessing analysis methodologies, models, and tools.
457 citations
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TL;DR: The high-risk chromosomal aberrations del(17p13), t(4;14), and +1q21 are adverse prognostic factors in SMM just as they are in active myeloma, independent of tumor mass.
Abstract: Purpose The aim of this study was to analyze chromosomal aberrations in terms of frequency and impact on time to progression in patients with smoldering multiple myeloma (SMM) on the background of clinical prognostic factors. Patients and Methods The chromosomal abnormalities 1q21, 5p15/5q35, 9q34, 13q14.3, 15q22, 17p13, t(11;14)(q13;q32), and t(4;14)(p16.3;q32) were assessed in CD138-purified myeloma cells by interphase fluorescent in situ hybridization (iFISH) alongside clinical parameters in a consecutive series of 248 patients with SMM. Results The high-risk aberrations in active myeloma (ie, del(17p13), t(4;14), and +1q21) present in 6.1%, 8.9%, and 29.8% of patients significantly confer adverse prognosis in SMM with hazard ratios (HRs) of 2.90 (95% CI, 1.56 to 5.40), 2.28 (95% CI, 1.33 to 3.91), and 1.66 (95% CI, 1.08 to 2.54), respectively. Contrary to the conditions in active myeloma, hyperdiploidy, present in 43.3% of patients, is an adverse prognostic factor (HR, 1.67; 95% CI, 1.10 to 2.54). Per...
176 citations
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TL;DR: Previous cytogenetic results from primary Hodgkin's tumors suggest an important pathogenic role of REL and JAK2 in this disease, and provide evidence for a novel cytogenetics pathomechanism leading to increased copy numbers of putative oncogenes from terminal chromosomal regions, most probably in the course of chromosomal stabilization by telomeric capture.
Abstract: Four Hodgkin's lymphoma cell lines (KM-H2, HDLM-2, L428, L1236) were analyzed for cytogenetic aberrations, applying multiplex fluorescence in situ hybridization, chromosome banding and comparative genomic hybridization. Each line was characterized by a highly heterogeneous pattern of karyotypic changes with a large spectrum of different translocated chromosomes (range 22–57). A recurrent finding in all cell lines was the presence of chromosomal rearrangements of the short arm of chromosome 2 involving the REL oncogene locus. Furthermore, multiple translocated copies of telomeric chromosomal segments were frequently detected. This resulted in a copy number increase of putative oncogenes, e.g., JAK2 (9p24) in 3 cell lines, FGFR3 (4p16) and CCND2 (12p13) in 2 cell lines as well as MYC (8q24) in 1 cell line. Our data confirm previous cytogenetic results from primary Hodgkin's tumors suggesting an important pathogenic role of REL and JAK2 in this disease. In addition, they provide evidence for a novel cytogenetic pathomechanism leading to increased copy numbers of putative oncogenes from terminal chromosomal regions, most probably in the course of chromosomal stabilization by telomeric capture. © 2002 Wiley-Liss, Inc.
168 citations
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
13,246 citations
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TL;DR: An in-depth analysis of 15 diverse human tissue and cell line transcriptomes on the basis of deep sequencing of complementary DNA fragments yielding a digital inventory of gene and mRNA isoform expression suggested common involvement of specific factors in tissue-level regulation of both splicing and polyadenylation.
Abstract: Through alternative processing of pre-messenger RNAs, individual mammalian genes often produce multiple mRNA and protein isoforms that may have related, distinct or even opposing functions. Here we report an in-depth analysis of 15 diverse human tissue and cell line transcriptomes on the basis of deep sequencing of complementary DNA fragments, yielding a digital inventory of gene and mRNA isoform expression. Analyses in which sequence reads are mapped to exon-exon junctions indicated that 92-94% of human genes undergo alternative splicing, 86% with a minor isoform frequency of 15% or more. Differences in isoform-specific read densities indicated that most alternative splicing and alternative cleavage and polyadenylation events vary between tissues, whereas variation between individuals was approximately twofold to threefold less common. Extreme or 'switch-like' regulation of splicing between tissues was associated with increased sequence conservation in regulatory regions and with generation of full-length open reading frames. Patterns of alternative splicing and alternative cleavage and polyadenylation were strongly correlated across tissues, suggesting coordinated regulation of these processes, and sequence conservation of a subset of known regulatory motifs in both alternative introns and 3' untranslated regions suggested common involvement of specific factors in tissue-level regulation of both splicing and polyadenylation.
4,711 citations
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TL;DR: Experimental data demonstrating the role of the microenvironment in metastasis is described, areas for future research are identified and possible new therapeutic avenues are suggested.
Abstract: Metastasis is a multistage process that requires cancer cells to escape from the primary tumour, survive in the circulation, seed at distant sites and grow. Each of these processes involves rate-limiting steps that are influenced by non-malignant cells of the tumour microenvironment. Many of these cells are derived from the bone marrow, particularly the myeloid lineage, and are recruited by cancer cells to enhance their survival, growth, invasion and dissemination. This Review describes experimental data demonstrating the role of the microenvironment in metastasis, identifies areas for future research and suggests possible new therapeutic avenues.
3,332 citations
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TL;DR: The invasion-metastasis cascade is a multistep cell-biological process that involves dissemination of cancer cells to anatomically distant organ sites and their subsequent adaptation to foreign tissue microenvironments as mentioned in this paper.
3,150 citations