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Zoltan Szallasi

Publications and source records attributed to Zoltan Szallasi.

13 recordsLinked to original sources
PubMed2026

Crosstalk between S-nitrosylation and glycation defines a metabolic vulnerability in liver and renal cancers.

Metabolic reprogramming is a defining feature of cancer; however, how it contributes to therapeutic resistance remains incompletely understood. Here we show that loss of aldo-ketoreductase 1A1 (AKR1A1) in renal cell carcinoma (RCC) and hepatocellular carcinoma (HCC) disrupts terminal glycolytic flux and lactate production through S-nitrosylation-mediated inhibition of pyruvate kinase, resulting in the accumulation of methylglyoxal (MGO). In multiple AKR1A1-deficient models, but not in those endogenously expressing the C423/424 A mutant of pyruvate kinase M2, elevated MGO triggers autophagic degradation of Kelch-like ECH-associated protein 1, leading to Nuclear factor erythroid 2-Related Factor 2 (NRF2) activation and transcriptional reprogramming. This NRF2-driven response enhances chemoresistance and promotes tumor cell migration, two hallmarks of aggressive cancer. Therapeutically, we demonstrate that pharmacological inhibition of the glyoxalase system-the major pathway for MGO detoxification-restores drug sensitivity in patient-derived cells and xenograft models, revealing a context-dependent metabolic vulnerability in AKR1A1 loss conditions. These findings identify AKR1A1 as a metabolic tumor suppressor and uncover crosstalk between S-nitrosylation and glycation as a key regulatory axis linking metabolic reprogramming to NRF2-driven therapy resistance, offering glyoxalase inhibition as a potential precision treatment strategy for RCC and HCC.

Humans↗
PubMed2006

A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers.

We developed a computational method to characterize aneuploidy in tumor samples based on coordinated aberrations in expression of genes localized to each chromosomal region. We summarized the total level of chromosomal aberration in a given tumor in a univariate measure termed total functional aneuploidy. We identified a signature of chromosomal instability from specific genes whose expression was consistently correlated with total functional aneuploidy in several cancer types. Net overexpression of this signature was predictive of poor clinical outcome in 12 cancer data sets representing six cancer types. Also, the signature of chromosomal instability was higher in metastasis samples than in primary tumors and was able to stratify grade 1 and grade 2 breast tumors according to clinical outcome. These results provide a means to assess the potential role of chromosomal instability in determining malignant potential over a broad range of tumors.

Aneuploidy↗
PubMed2006

A survey of methods for classification of gene expression data using evolutionary algorithms.

The rapid increase in the quantity of available biologic data over the last decade, brought about by the introduction of massively parallel methods for gene expression measurements, has highlighted the need for more efficient computational techniques for analysis. This paper reviews the use of evolutionary algorithms (EAs) in connection with classification based on gene expression data matrices. Brief introductions to data classification methods and EAs are given, followed by a survey of studies dealing with the application of evolutionary algorithms to various (cancer related) data sets. The general conclusion, based on the published results surveyed here, is that EAs may constitute an efficient method for optimal gene selection, and can also help in reducing the size (number of features used) of classifiers. In many cases, the classification accuracy obtained using EAs, often in conjunction with other methods, represents a significant improvement over results obtained without the use of EAs. However, long-term, independent clinical follow-up studies will be essential to validate prognostic markers identified by the use of EA-based methods.

Algorithms↗
PubMed2005

Reliability and reproducibility issues in DNA microarray measurements.

DNA microarrays enable researchers to monitor the expression of thousands of genes simultaneously. However, the current technology has several limitations. Here we discuss problems related to the sensitivity, accuracy, specificity and reproducibility of microarray results. The existing data suggest that for relatively abundant transcripts the existence and direction (but not the magnitude) of expression changes can be reliably detected. However, accurate measurements of absolute expression levels and the reliable detection of low abundance genes are difficult to achieve. The main problems seem to be the sub-optimal design or choice of probes and some incorrect probe annotations. Well-designed data-analysis approaches can rectify some of these problems.

Animals↗
PubMed2005

Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements.

BACKGROUND: Comparison of data produced on different microarray platforms often shows surprising discordance. It is not clear whether this discrepancy is caused by noisy data or by improper probe matching between platforms. We investigated whether the significant level of inconsistency between results produced by alternative gene expression microarray platforms could be reduced by stringent sequence matching of microarray probes. We mapped the short oligo probes of the Affymetrix platform onto cDNA clones of the Stanford microarray platform. Affymetrix probes were reassigned to redefined probe sets if they mapped to the same cDNA clone sequence, regardless of the original manufacturer-defined grouping. The NCI-60 gene expression profiles produced by Affymetrix HuFL platform were recalculated using these redefined probe sets and compared to previously published cDNA measurements of the same panel of RNA samples. RESULTS: The redefined probe sets displayed a substantially higher level of cross-platform consistency at the level of gene correlation, cell line correlation and unsupervised hierarchical clustering. The same strategy allowed an almost complete correspondence of breast cancer subtype classification between Affymetrix gene chip and cDNA microarray derived gene expression data, and gave an increased level of similarity between normal lung derived gene expression profiles using the two technologies. In total, two Affymetrix gene-chip platforms were remapped to three cDNA platforms in the various cross-platform analyses, resulting in improved concordance in each case. CONCLUSION: We have shown that probes which target overlapping transcript sequence regions on cDNA microarrays and Affymetrix gene-chips exhibit a greater level of concordance than the corresponding Unigene or sequence matched features. This method will be useful for the integrated analysis of gene expression data generated by multiple disparate measurement platforms.

Breast Neoplasms↗
PubMed2004

Robustness of cellular functions.

Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is a long-recognized key property of living systems. Owing to intimate links to cellular complexity, however, its molecular and cellular basis has only recently begun to be understood. Theoretical approaches to complex engineered systems can provide guidelines for investigating cellular robustness because biology and engineering employ a common set of basic mechanisms in different combinations. Robustness may be a key to understanding cellular complexity, elucidating design principles, and fostering closer interactions between experimentation and theory.

Animals↗
PubMed2004

Increased measurement accuracy for sequence-verified microarray probes.

Microarrays have been extensively used to investigate genome-wide expression patterns. Although this technology has been tremendously successful, it has suffered from suboptimal individual measurement precision. Significant improvements in this respect have been recently made. In an effort to further explore the underlying variability, we have attempted to globally assess the accuracy of individual probe sequences used to query gene expression. For mammalian Affymetrix microarrays, we identify an unexpectedly large number of probes (greater than 19% of the probes on each platform) that do not correspond to their appropriate mRNA reference sequence (RefSeq). Compared with data derived from inaccurate probes, we find that data derived from sequence-verified probes show 1) increased precision in technical replicates, 2) increased accuracy translating data from one generation microarray to another, 3) increased accuracy translating data from oligonucleotide to cDNA microarrays, and 4) improved capture of biological information in human clinical specimens. The logical conclusion of this work is that probes containing the most reliable sequence information provide the most accurate results. Our data reveal that the identification and removal of inaccurate probes can significantly improve this technology.

Breast↗
PubMed2004

Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements.

Cancer derived microarray data sets are routinely produced by various platforms that are either commercially available or manufactured by academic groups. The fundamental difference in their probe selection strategies holds the promise that identical observations produced by more than one platform prove to be more robust when validated by biology. However, cross-platform comparison requires matching corresponding probe sets. We are introducing here sequence-based matching of probes instead of gene identifier-based matching. We analyzed breast cancer cell line derived RNA aliquots using Agilent cDNA and Affymetrix oligonucleotide microarray platforms to assess the advantage of this method. We show, that at different levels of the analysis, including gene expression ratios and difference calls, cross-platform consistency is significantly improved by sequence- based matching. We also present evidence that sequence-based probe matching produces more consistent results when comparing similar biological data sets obtained by different microarray platforms. This strategy allowed a more efficient transfer of classification of breast cancer samples between data sets produced by cDNA microarray and Affymetrix gene-chip platforms.

Breast↗
PubMed2004

Simultaneous suppression of epidermal growth factor receptor and c-erbB-2 reverses aneuploidy and malignant phenotype of a human ovarian carcinoma cell line.

Coexpression of epidermal growth factor receptor (EGFR) and c-erbB-2 in 47-68% of ovarian cancer cells indicate their strong association with tumor formation. We examined the effects of simultaneous antisense- or immunosuppression of EGFR and c-erbB-2 expression on the invasive phenotype, aneuploidy, and genotype of cultured human ovarian carcinoma cells (NIH:OVCAR-8). We report here that suppression of both EGFR and c-erbB-2 results in regression of aneuploidy and genomic imbalances in NIH:OVCAR-8 cells, restores a more normal phenotype, and results in a more normal gene expression profile. Combined with cytogenetic analysis, our data demonstrate that the regression of aneuploidy is due to the selective apoptosis of double antisense transfected cells with highly abnormal karyotype.

Aneuploidy↗
PubMed2002

Assessing the significance of consistently mis-regulated genes in cancer associated gene expression matrices.

MOTIVATION: The simplest level of statistical analysis of cancer associated gene expression matrices is aimed at finding consistently up- or down-regulated genes within a given set of tumor samples. Considering the high level of gene expression diversity detected in cancer, one needs to assess the probability that the consistent mis-regulation of a given gene is due to chance. Furthermore, it is important to determine the required sample number that will ensure the meaningful statistical analysis of massively parallel gene expression measurements. RESULTS: The probability of consistent mis-regulation is calculated in this paper for binarized gene expression data, using combinatorial considerations. For practical purposes, we also provide a set of accurate approximate formulas for determining the same probability in a computationally less intensive way. When the pool of mis-regulatable genes is restricted, the probability of consistent mis-regulation can be overestimated. We show, however, that this effect has little practical consequences for cancer associated gene expression measurements published in the literature. Finally, in order to aid experimental design, we have provided estimates on the required sample number that will ensure that the detected consistent mis-regulation is not due to chance. Our results suggest that less than 20 sufficiently diverse tumor samples may be enough to identify consistently mis-regulated genes in a statistically significant manner. AVAILABILITY: An implementation using Mathematica (tm) of the main equation of the paper, (4), is available at www.me.chalmers.se/~mwahde/bioinfo.html.

Algorithms↗