Biology subjects

Karakatsoulis, G.

Publications and source records attributed to Karakatsoulis, G..

3 recordsLinked to original sources

bioRxiv2026

Lineage-associated tumor cell states predict outcome and guide immunotherapeutic target selection in pediatric osteosarcoma

Osteosarcoma is a highly heterogeneous primary bone malignancy that predominantly affects adolescents and young adults, and robust prognostic biomarkers and effective targeted therapies remain lacking. While single-cell transcriptomics has begun to resolve tumor composition, a tumor cell-intrinsic framework linking transcriptional states to clinical outcome and therapeutic targeting is not established. Here, we present a single-cell transcriptomic analysis of pediatric osteosarcoma, integrating 22 samples across 18 patients spanning six diagnostic, six post-treatment, and ten metastatic lesions profiled using complementary sequencing platforms. Using non-negative matrix factorization, we identify recurrent lineage-associated transcriptional programs that define osteoblastic-like, chondroblastic-like, and fibroblastic-like tumor cell states. These programs show overlapping activity across tumor cells, indicating that tumor cell states are not strictly discrete. Strikingly, combined osteoblastic-like and chondroblastic-like program activity is associated with poorer overall survival, a finding validated across two independent bulk RNA-sequencing cohorts. These tumor cell states are further linked to distinct immune microenvironmental compositions. Mapping candidate immunotherapeutic targets at single-cell resolution reveals variable expression across tumor cell states, supporting state-informed strategies for CAR T cell targeting. Together, our findings establish a tumor cell state framework that links osteosarcoma heterogeneity to clinical outcome and provides a basis for patient stratification and the development of state-informed therapeutic strategies in pediatric osteosarcoma.

Cancer Biology↗

bioRxiv2024

Synth4bench: a framework for generating synthetic genomics data for the evaluation of tumor-only somatic variant calling algorithms

MotivationSomatic variant calling is a key activity towards identifying genomic alterations; yet, the evaluation of the respective tools remains challenging due to the scarcity of high quality ground truth datasets. To overcome this limitation, we developed synth4bench, a synthetic data generation pipeline for robust benchmarking. Using a systematic process to create distinct synthetic datasets, we thoroughly evaluated five variant callers (Mutect2, FreeBayes, VarDict, VarScan2 and LoFreq). We compared tool outputs against our synthetic ground truth across key sequencing aspects (such as depth and read length) to assess their capacities and shed light on their underlying algorithmic principles. ResultsSynth4bench is an approach for evaluating tumor-only somatic variant callers that relies on a systematic definition of fully controlled ground-truth datasets. Our analysis revealed significant inconsistencies among the tool outputs and a strong dependence of caller performance on sequencing parameters. Indels remain the hardest-to-call variant type, driven by errors at low allele frequencies. Algorithmic choice is also critical; the most robust callers displayed the highest precision in allele frequency estimation, while the most sensitive caller was best for maximizing true positive recovery. Conversely, the least suitable caller exhibited systematic errors along with the poorest overall performance. These findings indicate that there isnt a one-solution-fit-all; sequencing optimization together with caller selection are necessary to maximize sensitivity and reliability. Furthermore, the pronounced inconsistencies suggest that current algorithms are not yet able to capture all mutational mechanisms adequately, with the modeling of the underlying processes remaining an open challenge. Availabilitycode: https://github.com/sfragkoul/synth4bench/ and data: https://zenodo.org/records/16524193 Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/582313v2_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1ac0fe5org.highwire.dtl.DTLVardef@1479cddorg.highwire.dtl.DTLVardef@8b7d1borg.highwire.dtl.DTLVardef@1c2a81b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

MotivationComputational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking. ResultsWe introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests co-expression and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19. Availability and ImplementationAPNets R, Python scripts and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet Contactggeorav@certh.gr Supplementary informationSupplementary information can be accessed in Zenodo (10.5281/zenodo.10438830).

systems biology↗