Biomedical subjects

Jan Budczies

Publications and source records attributed to Jan Budczies.

6 recordsLinked to original sources

PubMed2026

Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.

Tumor heterogeneity-encompassing diverse cellular phenotypes, genomic alterations, and microenvironmental contexts-is a principal barrier to effective cancer therapy. Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve this complexity by capturing transcriptomes at single-cell resolution. Here, we review the technical foundations required for high-quality scRNA-seq studies. We then trace the evolution of scRNA-seq platforms from manual micromanipulation to high-throughput systems, and describe the computational pipelines that enable reliable data interpretation. The application of scRNA-seq is exemplarily shown in the context of lung cancer, where single-cell profiling has revealed (i) the clonal and sub-clonal architecture of tumors, (ii) extensive remodeling of the immune microenvironment, iii) key mechanisms underlying resistance to targeted agents and immune-checkpoint blockade, and (iv) the dynamics of neo-antigen-specific T-cell responses. Integrating machine-learning techniques-such as deep-learning classifiers and graph-based models-with single-cell transcriptomic data has markedly sped up biomarker discovery, produced more accurate risk-stratification scores, and enabled the generation of patient-specific therapeutic predictions. We surveyed the major trial registry ClinicalTrials.gov and identified ∼380 ongoing or completed studies that explicitly incorporate scRNA-seq as a correlative or pharmacodynamic endpoint. Overall, the analysis shows that scRNA-seq becomes an increasingly important component of modern trials, providing high-resolution cellular and molecular readouts that complement conventional imaging and bulk-omics endpoints. While key challenges remain, ranging from costs, scalability and need for rigorous validation before routine clinical deployment, ongoing technological advances continue to expand the potential of scRNA-seq as a cornerstone of precision medicine.

Humans↗

PubMed2006

Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors.

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. We have used a metabolite profiling approach to test the hypothesis that quantitative signatures of primary metabolites can be used to characterize molecular changes in ovarian tumor tissues. Sixty-six invasive ovarian carcinomas and nine borderline tumors of the ovary were analyzed by gas chromatography/time-of-flight mass spectrometry (GC-TOF MS) using a novel contamination-free injector system. After automated mass spectral deconvolution, 291 metabolites were detected, of which 114 (39.1%) were annotated as known compounds. By t test statistics with P < 0.01, 51 metabolites were significantly different between borderline tumors and carcinomas, with a false discovery rate of 7.8%, estimated with repeated permutation analysis. Principal component analysis (PCA) revealed four principal components that were significantly different between both groups, with the highest significance found for the second component (P = 0.00000009). PCA as well as additional supervised predictive models allowed a separation of 88% of the borderline tumors from the carcinomas. Our study shows for the first time that large-scale metabolic profiling using GC-TOF MS is suitable for analysis of fresh frozen human tumor samples, and that there is a consistent and significant change in primary metabolism of ovarian tumors, which can be detected using multivariate statistical approaches. We conclude that metabolomics is a promising high-throughput, automated approach in addition to functional genomics and proteomics for analyses of molecular changes in malignant tumors.

Cluster Analysis↗

PubMed2006

DNA damage-induced expression of p53 suppresses mitotic checkpoint kinase hMps1: the lack of this suppression in p53MUT cells contributes to apoptosis.

DNA damage induced by the topoisomerase I inhibitor irinotecan (CPT-11) triggers in p53(WT) colorectal carcinoma cells a long term cell cycle arrest and in p53MUT cells a transient arrest followed by apoptosis (Magrini, R., Bhonde, M. R., Hanski, M. L., Notter, M., Scherübl, H., Boland, C. R., Zeitz, M., and Hanski, C. (2002) Int. J. Cancer 101, 23-31; Bhonde, M. R., Hanski, M. L., Notter, M., Gillissen, B. F., Daniel, P. T., Zeitz, M., and Hanski, C. (2006) Oncogene 25, 165-175). The mechanism of the p53-independent apoptosis still remains largely unclear. Here we used five p53WT and five p53MUT established colon carcinoma cell lines to identify gene expression alterations associated with apoptosis in p53MUT cells after treatment with SN-38, the irinotecan metabolite. After treatment, 16 mitosis-related genes were found to be expressed at least 2-fold stronger in the apoptosis-executing p53MUT cells than in the cell cycle-arrested p53WT cells by oligonucleotide microarray analysis. One of the genes whose strong post-treatment expression was associated with apoptosis was the mitotic checkpoint kinase hMps1 (human ortholog of the yeast monopolar spindle 1 kinase). hMps1 mRNA and protein expression were suppressed by the treatment-induced and by the exogenous adenovirus-coded p53 protein. The direct suppression of hMps1 on RNA level or inhibition of its activity by a dominant-negative hMps1 partly suppressed apoptosis. Together, these data indicate that the high expression of mitotic genes in p53MUT cells after SN-38 treatment contributes to DNA damage-induced apoptosis, whereas their suppression in p53WT cells acts as a safeguard mechanism preventing mitosis initiation and the subsequent apoptosis. hMps1 kinase is one of the mitotic checkpoint proteins whose expression after DNA damage in p53MUT cells activates the checkpoint and contributes to apoptosis.

Antineoplastic Agents, Phytogenic↗

PubMed2005

Zinc-sensitive genes as potential new target genes of the metal transcription factor-1 (MTF-1).

Zinc is an essential trace element that serves as a structural constituent of a large number of transcription factors, which explains its pivotal role in the control of gene expression. Previous studies investigating the effect of zinc deficiency and zinc supplementation on gene expression in the human adenocarcinoma cell line HT-29 led to the identification of a considerable number of genes responding to alterations in cellular zinc status with changes in steady state mRNA levels. For 9 of 20 genes from these previous screenings that were studied in more detail, mRNA steady state levels responded to both high and low media zinc concentrations. As they are primarily zinc-dependent, we assessed whether these genes are controlled by the zinc-finger metal transcription factor MTF-1. To test this hypothesis we generated a doxycyline-inducible Tet-On HT-29 cell line overexpressing MTF-1. Using this conditional expression system, we present evidence that Kruppel-like factor 4 (klf4), hepatitis A virus cellular receptor 1 (hhav), and complement factor B (cfbp) are 3 potential new target genes of MTF-1. To support this, we used in silico analysis to screen for metal-responsive elements (MREs) within promotors of zinc-sensitive genes. We conclude that zinc responsiveness of klf4, hhav, and cfbp in HT-29 cells is mediated at least in part by MTF-1.

Anti-Bacterial Agents↗

PubMed2004

Optimization models for cancer classification: extracting gene interaction information from microarray expression data.

MOTIVATION: Microarray data appear particularly useful to investigate mechanisms in cancer biology and represent one of the most powerful tools to uncover the genetic mechanisms causing loss of cell cycle control. Recently, several different methods to employ microarray data as a diagnostic tool in cancer classification have been proposed. These procedures take changes in the expression of particular genes into account but do not consider disruptions in certain gene interactions caused by the tumor. It is probable that some genes participating in tumor development do not change their expression level dramatically. Thus, they cannot be detected by simple classification approaches used previously. For these reasons, a classification procedure exploiting information related to changes in gene interactions is needed. RESULTS: We propose a MAximal MArgin Linear Programming (MAMA) method for the classification of tumor samples based on microarray data. This procedure detects groups of genes and constructs models (features) that strongly correlate with particular tumor types. The detected features include genes whose functional relations are changed for particular cancer types. The proposed method was tested on two publicly available datasets and demonstrated a prediction ability superior to previously employed classification schemes. AVAILABILITY: The MAMA system was developed using the linear programming system LINDO http://www.lindo.com. A Perl script that specifies the optimization problem for this software is available upon request from the authors.

Algorithms↗

PubMed2004

Tumor-associated E-cadherin mutations do not induce Wnt target gene expression, but affect E-cadherin repressors.

E-cadherin is a cell-cell adhesion molecule and tumor invasion suppressor gene that is frequently altered in human cancers. It interacts through its cytoplasmic domain with beta-catenin which in turn interacts with the Wnt (wingless) signaling pathway. We have compared the effects of different tumor-derived E-cadherin variants with those of normal E-cadherin on Wnt signaling and on genes involved in epithelial mesenchymal transition. We established an in-house cDNA microarray composed of 1105 different, sequence verified cDNA probes corresponding to 899 unique genes that represent the majority of genes known to be involved in cadherin-dependent cell adhesion and signaling ('Adhesion/Signaling Array'). The expression signatures of E-cadherin-negative MDA-MB-435S cancer cells transfected with E-cadherin variants (in frame deletions of exon 8 or 9, D8 or D9, respectively, or a point mutation in exon 8 (D370A)) were compared to that of wild-type E-cadherin (WT) transfected cells. From the differentially expressed genes, we selected 38 that we subsequently analyzed by quantitative real-time RT-PCR and/or Northern Blot. A total of 92% of these were confirmed as differentially expressed. Most of these genes encode proteins of the cytoskeleton, cadherins/integrins, oncogenes and matrix metalloproteases. No significant expression differences of genes downstream of the Wnt-pathway were found, except in E-cadherin D8 transfected cells where upregulation of three Tcf/Lef-transcribed genes was seen. One possible reason for the lack of expression differences of the Tcf/Lef-regulated genes is upregulation of SFRP1 and SFRP3; both of which are competitive inhibitors of the Wnt proteins. Interestingly, known E-cadherin transcriptional repressors, such as SLUG (SNAI2), SIP1 (ZEB2), TWIST1, SNAIL (SNAI1) and ZEB1 (TCF8), but not E12/E47 (TCF3), had a lack of upregulation in cells expressing mutated E-cadherin compared to WT. In conclusion, E-cadherin mutations have no influence on expression of genes involved in Wnt-signaling, but they may promote their own expression by blocking upregulation of E-cadherin repressors.

Animals↗