Biomedical subjects

Aniko Szabo

Publications and source records attributed to Aniko Szabo.

4 recordsLinked to original sources

PubMed2026

MEK inhibitor-based genomically matched combinatorial targeted therapies in metastatic pancreatic adenocarcinoma with KRAS alterations.

INTRODUCTION: Pancreatic Ductal Adenocarcinoma (PDAC) is often caused by mutations in multiple genes including KRAS (activating the Ras-Raf-MEK-ERK pathway). This study evaluated the role of MEK inhibitor (MEKi)-based combinatorial targeted therapies in patients with PDAC. Methods. This is a retrospective/prospective observational, single institution study, including 29 patients with metastatic PDAC with KRAS alterations, treated with MEKi therapies between 2022-2024. RESULTS: Ten patients had KRAS G12R (34.5%), ten G12D (34.5%), and nine G12V (31%). Majority of patients received MEKi therapy as third-line and beyond (KRAS G12R/G12D/G12V 60%/50%/78%, respectively). Median overall survival from MEKi initiation for KRAS G12R/G12D/G12V was 8.2/5.1/4.7 months (P = 0.5), respectively, and median progression-free survival was 4.4/2.3/1.4 months (P = 0.11). Six (21%) patients discontinued at least one drug in the treatment combination due to toxicity. CONCLUSIONS: MEKi-based combinatorial therapies had modest disease control in patients with KRAS G12R, and minimal disease control in patients with KRAS G12D/V in the late-line setting.

KRAS↗

PubMed2003

Multivariate exploratory tools for microarray data analysis.

The ultimate success of microarray technology in basic and applied biological sciences depends critically on the development of statistical methods for gene expression data analysis. The most widely used tests for differential expression of genes are essentially univariate. Such tests disregard the multidimensional structure of microarray data. Multivariate methods are needed to utilize the information hidden in gene interactions and hence to provide more powerful and biologically meaningful methods for finding subsets of differentially expressed genes. The objective of this paper is to develop methods of multidimensional search for biologically significant genes, considering expression signals as mutually dependent random variables. To attain these ends, we consider the utility of a pertinent distance between random vectors and its empirical counterpart constructed from gene expression data. The distance furnishes exploratory procedures aimed at finding a target subset of differentially expressed genes. To determine the size of the target subset, we resort to successive elimination of smaller subsets resulting from each step of a random search algorithm based on maximization of the proposed distance. Different stopping rules associated with this procedure are evaluated. The usefulness of the proposed approach is illustrated with an application to the analysis of two sets of gene expression data.

Algorithms↗

PubMed2002

Estimating an oncogenetic tree when false negatives and positives are present.

Human solid tumors are believed to be caused by a sequence of genetic abnormalities arising in the tumor cells. The understanding of these sequences is extremely important for improving cancer treatment. Models for the occurrence of the abnormalities include linear structure and a recently proposed tree-based structure. In this paper we extend the pure oncogenetic tree model by introducing false positive and false negative observations. We state conditions sufficient for the reconstruction of the generating tree. As an example we analyze a comparative genomic hybridization data set and show that addition of the error model significantly improves the ability of the model to describe the data.

Adenocarcinoma, Clear Cell↗

PubMed2002

Identification of gene expression profiles that segregate patients with childhood leukemia.

To identify genes whose expression correlated with biological features of childhood leukemia, we prospectively analyzed the expression profiles of 4608 genes using cDNA microarrays in 51 freshly processed bone marrow samples from children with acute leukemia, over a 24-month period, at a single institution. Two supervised methods of analysis were used to identify the 20 best discriminating genes between the following cohorts: acute myelogenous leukemia (AML) versus acute lymphoblastic leukemia (ALL); B-lineage versus T-lineage ALL; newly diagnosed B-lineage standard-risk versus high-risk ALL; and B-lineage leukemia harboring the TEL-AML 1 fusion versus patients without a molecularly characterized translocation. These methods identified overlapping sets of genes that segregated patients within described subgroups. Cross-validation demonstrated that the majority of patients could be correctly classified based on these genes alone, and hierarchical clustering grouped patients with similar clinical and biological disease features. The potential for select genes to discriminate patients was validated using real-time PCR in samples that were analyzed by microarray profiling and in other uniformly processed leukemic marrow samples. As expected, microarray technology can successfully segregate patients defined by traditional measures such as immunophenotype and cytogenetic alterations. However, among specific subgroups, this preliminary analysis also suggests that microarrays can identify unanticipated similarities and diversity in individual patients and thus may be useful in augmenting risk-group stratification in the future.

Child↗