Biology subjects

Buljan, M.

Publications and source records attributed to Buljan, M..

9 recordsLinked to original sources

bioRxiv2026

Decoding molecular programs that define macrophage responses to tumor-derived cues

Tumor-associated macrophages (TAMs) comprise functionally diverse states that can suppress anti-tumor immunity and promote tumor progression, yet the tumor microenvironmental cues and signaling programs that generate these states remain incompletely defined. Here, we systematically stimulate primary human monocyte-derived macrophages with a panel of cytokines and metabolites abundant in the tumor microenvironment (TME), and profile their transcriptomic and phosphoproteomic responses to resolve stimulus-specific molecular programs. We observe that potassium (K+) and adenosine (Ado) stimulation, which accumulate in necrotic tumor cores, downregulate antigen-presentation genes and their master regulator CIITA. K+ stimulation results in the upregulated fibronectin 1 expression, associated with immunosuppressive, metastasis-promoting TAM subsets. Ado induces upregulated expression of tryptophan (Trp) catabolism genes, myeloid checkpoints and metallothioneins (MTs). Although MT-high TAM states have been recurrently observed across tumor single cell RNA sequencing studies, their function remains poorly defined. We show that elevated MT expression in tumor tissue is associated with shorter overall survival. By aligning in vitro transcriptomes with single-cell RNA sequencing (scRNA-seq) signatures from a pan-cancer TAM atlas, we identify significant similarities between several in vitro states and clinically observed TAM populations, with Ado-stimulated macrophages closely resembling a MT-expressing TAM cluster. Overall, this work provides a systematic molecular context linking tumor microenvironmental cues to clinically relevant TAM states and offers a framework for recapitulating their functions in vitro. STATEMENT OF SIGNIFICANCEThis study explores how cytokines and metabolites from the tumor microenvironment shape macrophage molecular phenotypes and lead to the upregulation of clinically relevant marker genes and recapitulation of functional states of interest.

immunology↗

bioRxiv2025

RIPPLET: Mutation-Only Gene and Pathway Profiling for Precision Oncology

Clinical implementation of comprehensive genomic profiling, via whole-genome (WGS) or whole-exome sequencing (WES), is constrained by sparse mutation burdens and analytic pipelines reliant on matched transcriptomes. Currently, gene-centric analysis prevails, but overlooks the complex, multigene and pathway perturbations shaping tumor biology. We introduce RIPPLET, a DNA-only framework converting somatic variants into quantitative gene-impact scores and topology-aware pathway-perturbation profiles. By integrating tissue-specific protein-protein interaction networks with cohort-informed reweighting, RIPPLET prioritizes likely functionally relevant alterations. Applied across 33 TCGA cancer types, RIPPLET surpasses four state-of-the-art multi-omic driver-prioritization tools in recovering cancer type-specific drivers. In a cohort of metastatic cutaneous melanomas, it identifies pathway signatures that predict drug response, provide prognostic insight and distinguish immune-infiltration phenotypes without RNA data, independently validated on an in-house cohort. RIPPLET enables DNA-only inference of tumor-specific gene and pathway dysregulation, aligning with clinical sequencing workflows and offering a scalable precision-oncology strategy in transcriptome-limited settings.

bioinformatics↗

bioRxiv2025

Mapping 3D Heterogeneity of Thyroid Tumors Using Micro-CTbased Radiomics

Tumor heterogeneity plays a central role in treatment resistance, disease progression, and diagnostic uncertainty. However, it may be overlooked by traditional 2D histology. Accurate 3D assessment of tumor microarchitecture is therefore essential for capturing its spatial complexity. Micro-CT, an emerging imaging modality that offers high-resolution 3D virtual histology of soft tissues, provides a promising alternative. Combined with radiomics--a computational approach for interpretable quantification of tissue phenotypes--this technique enables a deeper understanding of tumor biology beyond visual inspection. In this study, we analyzed a large cohort of thyroid tumors (418 patients) using micro-CT imaging of next-generation tissue microarrays, from which we extracted radiomics features. We achieved robust classification of (i) neoplastic versus non-neoplastic thyroid tissues, (ii) papillary versus follicular thyroid carcinoma, and (iii) BRAF V600E mutation status. Feature interpretation using Shapley additive explanations revealed key visual traits driving these classification decisions. Preliminary results also indicated radiomic patterns associated with TERT promoter mutations, suggesting the existence of potential surrogate imaging biomarkers. Overall, micro-CT radiomics shows strong potential as a complementary tool for improving diagnostic and prognostic accuracy in thyroid cancer and offers a novel platform for quantitative pathology into the 3D spatial complexity of neoplastic tissues.

pathology↗

bioRxiv2025

Deep visual multi-omics profiling reveals mechanisms that underly cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) exhibits significant intra-tumoral heterogeneity (ITH) at both morphological and genetic levels, complicating treatment and contributing to disease progression. Among these, ccRCCs with focal rhabdoid differentiation stand out as highly aggressive tumors distinguished by cells with unique morphological features. However, the correlation between distinct morphological phenotypes, specific molecular alterations, and their influence on tumor behavior remains poorly understood. In this study, we integrated advanced AI-based image analysis with single-cell isolation and multi-omics profiling to dissect the link between clinically relevant morphological and molecular features of ccRCC cells. Using a novel digital pathology workflow, we quantified low-grade, high-grade, and rhabdoid morphologies in ccRCC diagnostic images with unprecedented precision. Subsequently, isolation of two sets of 1,000 morphologically distinct cells for detailed mRNA and protein expression analyses, revealed significant increasing dysregulation associating with higher histopathological grades. Rhabdoid ccRCC cells (grade 4) demonstrated unique molecular profiles, including upregulated FOXM1-driven proliferation, disrupted cell-matrix interactions, and enhanced immune evasion pathways. Despite high T-cell infiltration in rhabdoid areas, we identified a rhabdoid-specific immunosuppressive network driven by cytokines, IFN-beta, and integrin signaling, likely contributing to T-cell exhaustion. Rhabdoid ccRCC cells develop a distinct immunosuppressive signaling network, involving PD-L1 and novel immunomodulatory factors such as CD38 and ITGB2. These findings provide a basis for novel therapeutic strategies targeting these pathways in combination with immunotherapy to improve outcomes for patients with aggressive rhabdoid ccRCC. Key PointsO_LIccRCC is characterized by well-established morphological heterogeneity but the correlation with the underlying molecular aberrations remained elusive. C_LIO_LIBy integrating AI-based image analysis with single cell isolation and deep multi-omics profiling, we dissect the molecular intricacies of ccRCC, from targeted collection of 1,000 morphologically distinct cells. C_LIO_LIOur results demonstrate significant dysregulation of gene and protein expression correlating with higher histopathological grades in ccRCC. C_LIO_LIAggressive ccRCC cells with rhabdoid differentiation (grade 4) display distinct molecular profiles, as they upregulate FOXM1-mediated proliferation, ECM remodeling and the immune evasion responses, suggesting new therapeutic avenues enhancing ICI efficacy in these patients. C_LI

cancer biology↗

bioRxiv2024

NOODAI: A webserver for network-oriented multi-omics data analysis and integration pipeline

Omics profiling has proven of great use for unbiased and comprehensive identification of key features that define biological phenotypes and underlie medical conditions. While each omics profile assists characterization of specific molecular components relevant for the studied phenotype, their joint evaluation can offer deeper insights into the overall mechanistic functioning of biological systems. Here, we introduce an approach where starting from representative traits (e.g., differentially expressed elements) obtained for each omics profile, we construct and analyze joint interaction networks. The resulting networks rely on the existing knowledge of confident interactions among biological entities. We use these maps to identify and describe central elements, which connect multiple entities characteristic for the studied phenotypes and we leverage MONET network decomposition tool in order to highlight functionally connected network modules. In order to enable broad usage of this approach, we developed the NOODAI software platform, which enables integrative omics analysis through a user-friendly interface. The analysis outcomes are presented both as raw output tables as well as high-quality summary plots and written reports. Since the MONET tool enables the use of algorithms with strong performance in identifying disease-relevant modules, the NOODAI software platform can be of a high value for the analysis of clinical multi-omics datasets. Availability and ImplementationThe platform is available as a web application freely accessible at https://omics-oracle.com. The source code is freely available from GitHub under the GPL3 license at: https://github.com/TotuTiberiu/NOODAI.

bioinformatics↗

bioRxiv2024

Mapping the Molecular Landscape of Thyroid Neoplasms: A Comprehensive Proteomic and Phosphoproteomic Analysis Across Tumors of Follicular Origin

Thyroid nodules are a widespread phenomenon, with follicular cell-derived thyroid tumors being the most prevalent type of endocrine tumor, spanning from benign through low grade malignant to aggressive neoplasms with dismal prognosis. In clinical practice, histopathological criteria are primarily used to determine malignancy and aggressiveness. Therefore, accurate classification may result in surgical procedures for diagnostic reasons, associated with an imbalanced risk/benefit ratio. In recent years, the use of integrated proteomic approaches has proven valuable in expanding the molecular understanding of thyroid neoplasms, with implications in classification, yet remains understudied in divergent thyroid nodules. Here we show the delineation of subtype-specific and malignancy-dependent molecular characteristics through integrative proteomic and phosphoproteomic analysis of 53 human thyroid tissues, encompassing five frequent benign and malignant tumors. We found that the (phospho)-proteomic profiles enable a clear stratification of malignant and benign thyroid tissues. The method also performs well in delineating follicular adenoma (FA) and follicular thyroid carcinoma (FTC) samples. Beside the dysregulation of cell cycle control, apoptosis, and metabolic reprogramming associated with tumor development and malignancy, we further report increased alterations within the well-established oncogenic RAS/BRAF/MAPK and AKT/MTOR signaling pathways, which, contrary to the prevailing paradigm, did not clearly differentiate between FTC and papillary thyroid carcinoma (PTC). In addition, activities of ATM, PLK2-3, and GRK5-6 kinases were predicted to be strongly upregulated in malignant subtypes. Together, this study provides an in-depth insight into molecular changes in different thyroid tumor subtypes. These findings highlight the potential of integrated proteomic approaches to refine our understanding of complex diseases like cancer. As such, they offer a pathway to more precise diagnostic and personalized treatment strategies.

bioinformatics↗

bioRxiv2024

Delineation of signaling routes that underlie differences in macrophage phenotypic states

Macrophages represent a major immune cell type in tumor microenvironments, they exist in multiple functional states and are of a strong interest for therapeutic reprogramming. While signaling cascades defining pro-inflammatory macrophages are better characterized, pathways that drive polarization in immunosuppressive macrophages are incompletely mapped. Here, we performed an in-depth characterization of signaling events in primary human macrophages in different functional states using mass spectrometry-based proteomic and phosphoproteomic profiling. Analysis of direct and indirect footprints of kinase activities has suggested PAK2 and PKC kinases as important regulators of in vitro immunosuppressive macrophages (IL-4/IL-13 or IL-10 stimulated). Network integration of these data with the corresesponding transcriptome profiles has further highlighted FOS and NCOR2 as central transcription regulators in immunosuppressive states. Furthermore, we retrieved single cell sequencing datasets for tumors from cancer patients and found that the unbiased signatures identified here through proteomic analysis were able to successfully separate pro-inflammatory macrophage populations in a clinical setting and could thus be used to expand state-specific markers. This study contributes to in-depth multi-omics characterizations of macrophage phenotypic landscapes, which could be valuable for assisting future interventions that therapeutically alter immune cell compartments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/574349v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1234713org.highwire.dtl.DTLVardef@10f4999org.highwire.dtl.DTLVardef@a8dd60org.highwire.dtl.DTLVardef@5dfb33_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIGlobal proteomic characterization of primary human macrophages in different states C_LIO_LIMapping of main signaling events through in-depth data analysis C_LIO_LIPKC and PAK2 kinases are important regulators of immunosuppressive macrophages C_LIO_LIProteomic signatures enable accurate detection of pro-inflammatory macrophages in patient tumors C_LI

bioinformatics↗

bioRxiv2023

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

cancer biology↗

bioRxiv2021

Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework

It is a general assumption of molecular biology that the ensemble of expressed molecules, their activities and interactions determine biological processes, cellular states and phenotypes. Quantitative abundance of transcripts, proteins and metabolites are now routinely measured with considerable depth via an array of "OMICS" technologies, and recently a number of methods have also been introduced for the parallel analysis of the abundance, subunit composition and cell state specific changes of protein complexes. In comparison to the measurement of the molecular entities in a cell, the determination of their function remains experimentally challenging and labor-intensive. This holds particularly true for determining the function of protein complexes, which constitute the core functional assemblies of the cell. Therefore, the tremendous progress in multi-layer molecular profiling has been slow to translate into increased functional understanding of biological processes, cellular states and phenotypes. In this study we describe PCfun, a computational framework for the systematic annotation of protein complex function using Gene Ontology (GO) terms. This work is built upon the use of word embedding-- natural language text embedded into continuous vector space that preserves semantic relationships-- generated from the machine reading of 1 million open access PubMed Central articles. PCfun leverages the embedding for rapid annotation of protein complex function by integrating two approaches: (1) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector, and (2) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing the statistical test for the enrichment of the top NN GO terms within the child terms of the predicted GO terms by RF models. Thus, PCfun amalgamates information learned from the gold-standard protein-complex database, CORUM, with the unbiased predictions obtained directly from the word embedding, thereby enabling PCfun to identify the potential functions of putative protein complexes. The documentation and examples of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

bioinformatics↗