Biology subjects

Hatzigeorgiou, A.

Publications and source records attributed to Hatzigeorgiou, A..

3 recordsLinked to original sources

bioRxiv2022

The Radiogenomic and Spatiogenomic Landscapes of Glioblastoma, and Their Relationship to Oncogenic Drivers

Glioblastoma (GBM) is well-known for its molecular and spatial heterogeneity, which poses a challenge for precision therapies and clinical trial stratification. Here, in a comprehensive radiogenomics study of 358 GBMs, we investigated the associations between the imaging and spatial characteristics of the tumors with their cancer gene mutation status, as well as with the cross-sectionally inferred likely order of mutational events. We show that cross-validated machine learning analysis of multi-parametric MRI scans results in distinctive in vivo imaging signatures of several mutations, which are relatively more distinctive in homogeneous tumors which harbor only one of these mutations. These imaging signatures offer mechanistic insights into how various mutations influence the phenotype of the tumor and its surrounding infiltrated brain tissue via neovascularization and vascular leakage, increased cell density, invasion and migration, and other characteristics captured by respective imaging features. Furthermore, we found that spatial location and tumor distribution vary, depending on the GBMs molecular characteristics. Finally, distinct imaging and spatial characteristics were associated with cross-sectionally estimated evolutionary trajectories of the tumors. Collectively, our study establishes a panel of in vivo and clinically accessible imaging-AI biomarkers of GBM that reflect their molecular composition and oncogenic drivers.

bioengineering↗

bioRxiv2020

IWS1 phosphorylation promotes cell proliferation and predicts poor prognosis in EGFR mutant lung adenocarcinoma patients, through the cell cycle-regulated U2AF2 RNA splicing.

The authors have withdrawn their manuscript. While attempting to reproduce the data on the alternative splicing of exon 2 of U2AF2, they observed that the proposed splicing mechanism could not give rise to a functional U2AF2 protein. In addition, they observed evidence of manipulation in the electropherogram of the splicing junction between exons 1 and 3 and in the primary data on which this electropherogram was based, which were deposited in Mendeley by the first author. These observations raise questions on the integrity of the reported results. In light of this information, the authors have no confidence in the key findings of the paper, and therefore, do not wish it to be cited. If you have any questions, please contact the corresponding author.

cancer biology↗

bioRxiv2019

Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for analysis of CAGE data

Cap Analysis of Gene Expression (CAGE) experimental protocol has emerged as a powerful experimental technique for assisting in the identification of transcription start sites (TSSs). There is strong evidence that CAGE also identifies capping sites along various other locations of transcribed loci such as splicing byproducts, alternative isoforms and capped molecules overlapping introns and exons. We present ADAPT-CAGE, a Machine Learning framework which is trained to distinguish between CAGE signal derived from TSSs and transcriptional noise. ADAPT-CAGE provides annotation-agnostic, highly accurate and single-nucleotide resolution experimentally derived TSSs on a genome-wide scale. It has been specifically designed aiming for flexibility and ease-of-use by only requiring aligned CAGE data and the underlying genomic sequence. When compared to existing algorithms, ADAPT-CAGE exhibits improved performance on every benchmark that we designed based on both annotation- and experimentally-driven strategies. This performance boost brings ADAPT-CAGE in the spotlight as a computational framework that is able to assist in the refinement of gene regulatory networks, the incorporation of accurate information of gene expression regulators and alternative promoter usage in both physiological and pathological conditions.

bioinformatics↗