Biology subjects

Bartha, A.

Publications and source records attributed to Bartha, A..

5 recordsLinked to original sources

bioRxiv2026

Pan-cancer proteogenomic interrogation of the Ubiquitin Proteasome System

Somatic mutations rewire the ubiquitin-proteasome system (UPS) to support tumor growth, but the proteome-wide consequences of cancer-driver alterations on UPS composition remain incompletely understood. Using harmonized proteogenomic data from up to 11 CPTAC cohorts, we performed an integrated pan-cancer analysis of UPS protein dysregulation, prognostic associations, and mutation-driven remodeling. We show that mRNA poorly predicts UPS protein abundance, that a defined set of E3 ligases is recurrently dysregulated across cancers, and that somatic mutations (most strikingly TP53 loss) produce coherent UPS protein-quantitative trait locus (pQTL) signatures. Two case studies (UBR5 and TRIM28) illustrate orthogonal modes of UPS rewiring: a mutation-driven axis in which TP53-mutant tumors elevate UBR5 to support replication stress tolerance, and a lineage-driven axis in which TRIM28 engages tissue-restricted regulatory networks with opposing prognostic effects in glioblastoma versus head and neck cancer. Each axis exposes context-specific therapeutic vulnerabilities, including sensitivity to DNA damage response inhibitors (UBR5-high) and lineage-specific drug responses (TRIM28-high). Together, these analyses define a mechanistic framework for how cancer-driver mutations reshape proteostasis through the UPS and nominate mutation- and lineage-defined dependencies for precision degrader therapy. The harmonized pan-tissue atlas and the UbiDash interactive resource that underpin parts of this analysis are reported in our companion paper1. HighlightsO_LIProteome-centric profiling reveals UPS dysregulation invisible at the transcript level across human cancers C_LIO_LIPan-cancer differential expression identifies recurrently dysregulated E3 ligases with prognostic value C_LIO_LIGenome-wide pQTL analysis uncovers mutation-driven UPS remodeling, with TP53 loss as a dominant driver C_LIO_LIUBR5 and TRIM28 define orthogonal mutation- and lineage-driven axes of UPS rewiring with distinct therapeutic vulnerabilities C_LI

systems biology↗

bioRxiv2024

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making. O_FIG O_LINKSMALLFIG WIDTH=164 HEIGHT=200 SRC="FIGDIR/small/592032v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@754668org.highwire.dtl.DTLVardef@1616263org.highwire.dtl.DTLVardef@827009org.highwire.dtl.DTLVardef@1899ba7_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Identifying Ferroptosis Inducers, HDAC, and RTK Inhibitor Sensitivity in Melanoma Subtypes through Unbiased Drug Target Prediction

The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem. Here we mine large scale MM proteogenomic data integrating it with MM cell line dependency screen, and drug sensitivity data to identify druggable targets and forecast treatment efficacy and resistance. Leveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC and AKT1, across five distinct MM subtypes. These proteins serve as potential drug targets applicable to one or multiple MM subtypes. By analyzing transcriptomic data from 48 publicly accessible melanoma cell lines sourced from Achilles and CRISPR dependency screens, we forecasted 162 potentially targetable genes. We also identified genetic resistance in 260 genes across at least one melanoma subtype. In addition, we employed publicly available compound sensitivity data (Cancer Therapeutics Response Portal, CTRPv2) on the cell lines to assess the correlation of compound effectiveness within each subtype. We have identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype. Remarkably, employing this unbiased approach, we have uncovered compounds targeting ferroptosis, that demonstrate a striking 30x fold difference in sensitivity among different subtypes. This implies that the proteogenomic classification of melanoma has the potential to predict sensitivity to ferroptosis compounds. Our results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy. Highlights(1) Proteogenomic subtype classification can define the landscape of genetic dependencies in melanoma (2) Nine proteins from molecular subtypes were identified as potential drug targets for specified MM patients (3) 20 compounds identified that show potential effectiveness in at least one melanoma subtype (4) Proteogenomics can predict specific ferroptosis inducers, HDAC, and RTK Inhibitor sensitivity in melanoma subtypes O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/579424v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@73dd77org.highwire.dtl.DTLVardef@d0cb8eorg.highwire.dtl.DTLVardef@1e4117forg.highwire.dtl.DTLVardef@38b727_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

cancer biology↗

bioRxiv2023

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA Damage Response inhibitors

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC= 0.62, AUC=0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell avatars capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.

cancer biology↗

bioRxiv2020

TNMplot.com: a web tool for the comparison of gene expression in normal, tumor and metastatic tissues

Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation, and can serve as biomarkers of progression or as therapy targets with minimal off-target effects. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web-platform enabling mining of this database by comparing normal, tumor and metastatic data across all genes in real time. We utilized data generated by either gene arrays or RNA-seq. Gene array data were manually selected from NCBI-GEO. RNA sequencing data was downloaded from the TCGA, TARGET, and GTEx repositories. TCGA and TARGET contain predominantly tumor and metastatic samples from adult and pediatric patients, while GTEx samples are from healthy tissues. Statistical significance was computed using Mann-Whitney or Kruskall-Wallis tests. The entire database contains 56,938 samples including 33,520 samples from 3,180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3,691 normal samples), 11,010 samples from TCGA (394 metastatic, 9,886 tumorous and 730 normal), 1,193 samples from TARGET (1 metastatic, 1,180 tumor, 12 normal) and 11,215 normal samples from GTEx. The most consistently up-regulated genes across multiple tumor types were TOP2A (mean FC=7.8), SPP1 (FC=7.0) and CENPA (FC=6.03) and the most consistently down-regulated gene was ADH1B (mean FC=0.15). Validation of differential expression using equally sized training and test sets confirmed reliability of the database in breast, colon, and lung cancer (p<0.0001). The online analysis platform enables unrestricted mining of the database and is accessible at www.tnmplot.com.

bioinformatics↗