Biology subjects

Homicsko, K.

Publications and source records attributed to Homicsko, K..

9 recordsLinked to original sources

bioRxiv2026

Identification of non-covalent inhibitors for the atypical peroxiredoxin PRDX5 as a therapeutic strategy in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is an aggressive asbestos-linked cancer with limited therapeutic options and a dismal 5-year survival rate of [~]5%. While aberrant production of reactive oxygen and nitrogen species (ROS/RNS) is a hallmark of MPM, targeted approaches to exploit these redox vulnerabilities remain scarce. Here, using the MOSAIC multimodal cancer patient atlas, we identify Peroxiredoxin 5 (PRDX5) as being significantly upregulated in the epithelioid subtype of MPM. We show that MPM cells exhibit enhanced resistance to nitrosative and oxidative stress compared to healthy mesothelial cells, a phenotype correlated with basal PRDX5 expression. Next, utilising a machine learning guided discovery pipeline, we identified three putative allosteric pockets in PRDX5 and conducted a virtual screen of 3.6 million compounds. High-throughput biochemical validation of 452 candidates yielded 36 non-covalent hits, including sub-micromolar inhibitors. These findings establish PRDX5 as a novel, subtype specific therapeutic target in MPM and provide a chemical framework for the development of next-generation redox-modulating oncology treatments.

cancer biology↗

bioRxiv2026

Pan-cancer virtual spatial transcriptomics from routine histology with Phoenix

Spatial transcriptomics links gene expression to tissue architecture, providing a mechanistic view of cellular organization. Yet existing datasets cover few donors and miss the complexity of human disease. Experimental costs remain prohibitive, and large-scale profiling is impractically slow for population-level studies. Accurate computational methods are urgently needed. Predicting gene expression from standard histology, however, remains an open problem, as current approaches transfer poorly to unseen cohorts and diseases. Here, we present Phoenix, a (latent) flow matching generative model that infers pan-cancer spatially resolved single-cell gene expression with high accuracy. Phoenix analyzes treatment response in silico: Applied to 763 head and neck cancer patients, it identified three new spatial biomarkers that we validated across two cancers (breast cancer, n = 84; ovarian cancer, n = 157) and treatment regimens (platinum, trastuzumab). Phoenix generalizes beyond carcinomas: In a large sarcoma cohort (802 tissue microarray cores), it accurately predicted cell-type-specific signatures in held-out samples and captured chemotherapy-induced immune remodeling. Phoenix also extends across species: In a mouse model, it accurately predicted the expression of pancreatic cancer lineage markers and the mutant mKras^G12D allele in silico. In total, we evaluated Phoenix on over 10,000 patients. Our results establish virtual spatial transcriptomics as a scalable framework for studying tissue organization, therapeutic response, and disease mechanisms.

bioinformatics↗

bioRxiv2026

Deciphering spatial heterogeneity of patient-derived organoids of colorectal tumors for drug discovery

The spatial organization of tumors is critical for drug development, as spatial heterogeneity in cancer cells and the tumor microenvironment can present a significant barrier to effective therapies. Patient-derived tumor organoids (PDOs) offer promising platforms to study tumor biology and for drug development. Previous studies have demonstrated that PDOs can maintain the genomic/clonal heterogeneity of tumors, as well as expression heterogeneity. However, the spatial heterogeneity of PDOs and its correlation with the original tumors remains unclear. We propose an integrated pipeline combining spatial transcriptomics and phenotypic analyses of PDOs to capture and track spatial heterogeneity. This platform integrates multiplexed organoid cultures with imaging-based spatial transcriptomics and analysis at single organoid resolution. We validate the platform by comparing results with non-spatial single-cell transcriptomic data and spatial transcriptomic analysis of donor tumors. The combination of ex vivo PDOs and spatial transcriptomics could open novel avenues for developing drugs.

cancer biology↗

bioRxiv2025

Tissue specificity and chromosomal alterations shape divergent immune programs in HRD tumors

Homologous recombination deficiency (HRD) activates pro-inflammatory cGAS/STING signaling, positioning it as a biomarker for combining immune checkpoint blockade (ICB) and PARP inhibition (PARPi). However, the consequences of HRD on the immune landscape across cancers remain unclear. Here, we applied a pan-cancer HRD classifier to >10,000 tumors from The Cancer Genome Atlas and uncovered striking heterogeneity in immune activity. Compared to HR-proficient tumors, HRD tumors showed elevated inflammation in breast, ovarian, and endometrial cancers. These tumors exhibited robust activation of innate and adaptive immune pathways (IFN, NF-{kappa}B) and transcriptional hallmarks of senescence, angiogenesis, and adenosine signaling. In contrast, lung, head and neck, and melanoma HRD tumors displayed suppressed inflammation and evidence of immune escape through large-scale loss-of-heterozygosity (LOH) at IFNA/B, STING, and other loci. These tumors also frequently presented HLA LOH and oncogene amplifications, suggesting selection under immune pressure and replication stress. Together, our study resolves HRD tumors into two immune archetypes, immune-inflamed and immune-evasive, linked to chromosomal instability and lineage, informing biomarker-driven evaluation of immune checkpoint blockade/PARPi combinatorial therapies.

genomics↗

bioRxiv2025

From Transcripts to Cells: Dissecting Sensitivity, Signal Contamination, and Specificity in Xenium Spatial Transcriptomics

Spatial transcriptomics has transformed our ability to map gene expression within intact tissues at cellular and subcellular resolution. Among current platforms, Xenium is widely adopted for its reliability, accessibility, and high data quality. Yet, the properties and limitations of Xenium-derived data remain poorly characterized. Here, we present one of the most comprehensive Xenium datasets to date, encompassing over 40 breast and lung tumor sections profiled using a diverse set of gene panels. Leveraging this resource, we systematically dissect technical noise--including transcript diffusion--alongside assay specificity, panel performance, and segmentation strategies. Our comparison of targeted panels with the newer 5K panel reveals that although the latter captures more transcripts overall, it suffers from reduced per-gene sensitivity and persistent diffusion, even with enhanced chemistry. We demonstrate that single-nucleus RNA-seq (snRNA-seq) markedly improves cell type annotation and enables more precise quantification of diffusion. Building on this, we introduce SPLIT (Spatial Purification of Layered Intracellular Transcripts), a novel method that integrates snRNA-seq with RCTD deconvolution to enhance signal purity. SPLIT effectively resolves mixed transcriptomic signals, improving background correction and cell-type resolution. Together, our findings provide a critical benchmark for Xenium performance and introduce a scalable strategy for signal refinement--advancing the accuracy and utility of spatial transcriptomics.

cancer biology↗

bioRxiv2024

Transcriptome Analysis of Archived Tumor Tissues by Visium, GeoMx DSP, and Chromium Methods Reveals Inter- and Intra-Patient Heterogeneity

Recent advancements in probe-based, full-transcriptome, high-resolution technologies for Formalin-Fixed Paraffin-Embedded (FFPE) tissues, such as Visium CytAssist, Chromium Flex (10X Genomics), and GeoMx DSP (Nanostring), have opened new opportunities for studying decades-old archival samples in biobanks, facilitating the generation of data from extensive cohorts. However, the experimental protocols can be labor-intensive and costly; therefore, it is thus essential for researchers to carefully evaluate the strengths and limitations of each technology in relation to their specific research objectives. Here, we report the results of a comparative analysis of the three methods mentioned above on FFPE archival tumor samples from four non-small cell lung cancer, four breast cancer and six diffuse large B-cell lymphoma. We highlight some relative advantages and disadvantages of each method in the context of operational challenges, bioinformatic analysis and biological discovery. Our results show that: 1) all three methods yielded good-quality, highly reproducible transcriptomic data from serial sections of the same FFPE block; 2) GeoMx data contained mixtures of cell types, even when pre-selecting areas with cell type-specific markers; 3) high-throughput spot-level (Visium) or cell-level (Chromium) data enabled the identification of tumor heterogeneity within and between patients, which could be used to identify targeted therapies. Our data support the use of Visium and Chromium for high-throughput and discovery-driven projects, while the GeoMx platform could be suited for addressing specialized questions on targeted regions. All data generated from this study, including GeoMx, Visium, Chromium, H&E, and expert annotations are publicly available.

bioinformatics↗

bioRxiv2023

Dissection and reconstruction of the colorectal cancer tumor microenvironment

Patient-derived organoids (PDOs) are the reference in vitro human disease models. However, the utility of colorectal cancer (CRC) PDOs is hindered by the lack of a tumor microenvironment (TME). To address this limitation, we built a living biobank of CRC PDOs with autologous stromal and immune TME. We characterized the original tumors and traditional monocultures using single-cell RNA-seq (scRNA-seq) and whole exome sequencing (WES) to obtain insights into cell type selection and phenotypic drift in culture. Subsequently, we developed culture conditions supporting all cell types to recapitulate the CRC-TME around PDOs. From the transcriptomes of >180k cells obtained from 260 such co-cultures, we illuminated the mutual influence of cells within CRC tumors. Based on original tumor data, atlases of predicted interactions and transcriptional networks elucidated why monocultures were altered and suggested that TME reconstruction more accurately reflected original tumor behavior. We found that inflammatory signals were absent in vitro and recovered upon co-culture with tumor-infiltrating lymphocytes (TILs). We also functionally confirmed that stromal, not cancer cells, mediated immune evasion. Additionally, stroma induced an invasive phenotype in cancer cells. From this deep dive into CRC-TME interactions, we built the human CRC-TME atlas (https://crc-tme.com/), an online portal for interactive exploration of gene expression data, prediction of cell-cell interactions at the pathway and receptor/ligand levels, transcriptional networks, and more. We anticipate PDO cultures with reconstructed TMEs will be valuable for discovery efforts, preclinical studies, and personalized medicine, with the atlas as a framework and inspiration for future CRC-TME studies.

cancer biology↗

bioRxiv2023

Tumor-reactive clonotype dynamics underlying clinical response to TIL therapy in melanoma

The profiles, specificity and dynamics of tumor-specific clonotypes that are associated with clinical response to adoptive cell therapy (ACT) using tumor-infiltrating lymphocytes (TILs) remain unclear. Using single-cell RNA/TCR-sequencing, we tracked TIL clonotypes from baseline tumors to ACT products and post-ACT blood and tumor samples in melanoma patients treated with TIL-ACT. Patients with clinical responses had baseline tumors enriched in tumor-reactive TILs, which were more effectively mobilized upon in vitro expansion, yielding products with higher numbers of tumor-specific CD8+ cells, which also preferentially infiltrated tumors post-ACT. Conversely, lack of clinical responses was associated with tumors devoid of tumor-reactive resident clonotypes, and with cell products mostly composed of blood-borne clonotypes mainly persisting in blood but not in tumors post-ACT. Upon expansion, tumor-specific TILs lost the specific signatures of states originally exhibited in tumors, including exhaustion, and in responders acquired an intermediate exhausted effector state after tumor engraftment, revealing important functional cell reinvigoration.

cancer biology↗

bioRxiv2021

Probing the killing potency of tumor-infiltrating lymphocytes on microarrayed autologous tumoroids

Immunotherapy has shown promise as an approach to fight cancer by harnessing the immune system. However, due to the lack of biomarkers to guide treatment regimens and predict response rates, there is an unmet need for more robust ex vivo and in vitro systems that recapitulate patient-specific tumor biology and enable response prediction for immune therapies in an autologous setting. To address this issue, we developed a high-content screening-compatible assay based on microcavity arrays to study tumor-infiltrating lymphocyte (TIL) functionality on 3D tumoroid models. We validated our system using the pmel-1 activated T cell mouse model to assess both cancer immunogenicity and T cell functionality. To demonstrate the translational potential of the platform, we used it to evaluate the response of patient-derived TILs to autologous human colorectal cancer (CRC) tumoroids. Using a combination of imaging and flow cytometry, we determined several features of the antitumor activity of TILs, including the extent of tumoroid killing and secretion of cytokines. We then used the approach to identify responders to immunotherapy, such as the immune checkpoint blockade (ICB) agent Nivolumab (PD-1 inhibitor) and Ipilimumab (CTLA-4 inhibitor). Our system allows not only the identification of immunogenic tumors, but also the testing of patients for response to immunomodulators, enabling personalized immuno-oncology.

cancer biology↗