Biology subjects

Matusiak, M.

Publications and source records attributed to Matusiak, M..

8 recordsLinked to original sources

bioRxiv2026

Resolving the immune response across clonal cancer evolution in situ with whole transcriptome profiling

Single-cell spatial transcriptomics is now central to studying tumors in their native tissue context. Here we present the first comprehensive, independent evaluation of Atera, a new spatial whole-transcriptome platform, compared with Xenium in human ductal carcinoma in situ (DCIS). We show that Atera enables granular cell-state annotation and resolves rare cell populations, experimentally validated by multiplex immunofluorescence (IF). We further show that its transcriptome-wide coverage enables inference of copy-number alterations at single-cell resolution, allowing us to reconstruct the clonal evolution of DCIS. We orthogonally confirm the inferred copy-number alterations by whole-genome sequencing of 16 microdissected tumor regions from consecutive tissue sections. Finally, by mapping the immune microenvironment onto this clonal architecture, we demonstrate the feasibility of tracking the changes in immune response along the clonal tumor evolution in situ. Together, our results establish Atera as a validated platform for tracking clonal evolution and immune adaptation in clinical samples.

bioinformatics↗

bioRxiv2026

Machine Learning Analysis to Define Cell Lineage in Leiomyosarcoma

IntroductionCellular differentiation and lineage commitment are known to be associated with differences in DNA methylation. Leiomyosarcoma (LMS) is a tumor thought to originate from smooth muscle cells in the walls of vessels in the soft tissue (STLMS) or from the uterine myometrium (ULMS). Here, we identify the methylation signatures of normal smooth muscle cells from blood vessels and the uterine wall and compare these with those found in STLMS and ULMS. We hypothesized that these methylation signatures could be used to assign a smooth muscle subtype of origin to individual leiomyosarcomas, and that tumors of different origin would show biological differences with potential therapeutic relevance. MethodsTo define methylation profiles for smooth muscle from vessel walls versus those found in myometrium, EPIC methylation profiling was performed on DNA from 49 formalin-fixed paraffin-embedded (FFPE) normal smooth muscle samples. A supervised machine learning algorithm (Random Forest) was used to distinguish the methylation patterns of normal smooth muscle cells in vessel walls from those in the myometrium. The resulting classifier was applied to methylation data on 67 cases of LMS with corresponding bulk RNAseq data to identify which tumors showed a methylation signature most consistent with either blood vessel wall (LMSvessel) or myometrial smooth muscle (LMSwall). A custom signature matrix derived from scRNAseq data from 6 samples of LMS was used in CIBERSORTx analysis to compare the cellular composition of LMS cases with a vessel or uterine wall methylation signature. ResultsA high degree of correlation was found between the known site of origin for LMS (STLMS vs ULMS) and the methylation signature derived from different types of normal smooth muscle. LMSwall tumors compared to LMSvessel tumors had significantly higher activation of the PD-1 checkpoint pathway in RNAseq analysis. Digital flow cytometry by CIBERSORTx analysis showed an increased expression of transcriptomic signatures of several immune cell subtypes in LMSvessel tumors. ConclusionUsing a supervised machine learning approach we classified LMS samples as either showing a high similarity in methylation patterns to normal smooth muscle cells of either the vessel wall or the myometrium. We found a correlation between LMS showing either a "vessel" or "muscle wall" methylation signature and their site of origin, but notably we also identified some exceptions. When classified based on their methylation signature LMSwall and LMSvessel differed in their PD-1 pathway activation and in their predicted immune cell populations, suggesting potential implications for immunotherapeutic approaches.

cancer biology↗

bioRxiv2026

Identification and Characterization of Neoplastic Cells in Keratin-Positive Giant Cell-Rich Tumors

Keratin-positive giant cell-rich tumor (KPGCT) is a newly described bone and soft tissue tumor. The tumor is characterized by scattered keratin-positive cells and the presence of HMGA2::NCOR2 fusions. It is not known if the HMGA2::NCOR2 fusion is located in the keratin-positive cells, and little is known about how KPGCT develops. KPGCT shares some histologic features with tenosynovial giant cell tumor (TGCT), a soft tissue tumor with CSF1 rearrangements. Single-nuclei RNA sequencing (snRNA-seq) and Xenium spatial transcriptomics were used to elucidate the mechanisms driving KPGCT and compare KPGCT to TGCT. We show that the neoplastic cells in KPGCT constitute only a minority of cells in the tumor, and that they co-express keratin, HMGA2 and CSF1. The neoplastic cells in KPGCT express no synovial markers, confirming KPGCT as a distinct entity, separate from TGCT. The bulk of the tumor consists of CSF1R-expressing macrophages and osteoclast-like giant cells, suggesting an important role for CSF1-CSF1R signaling. In addition, we find that the cells with the HMGA2 translocation show activation of the hippo signaling pathway, which is known to regulate CSF1 expression. We show that the CSF1-CSF1R axis, possibly regulated through the hippo signaling pathway, plays an important role in KPGCT. This axis likely stimulates the migration and proliferation of macrophages, which form the majority of cells in the tumor, as well as their differentiation into osteoclasts-like giant cells. These results provide a rationale for the use of CSF1R inhibitors, which have already shown efficacy in TGCT, as a therapy for KPGCT. SignificanceKeratin-positive giant cell-rich tumor (KPGCT) is a rare, newly described soft tissue and bone tumor. By examining this tumour on a single-cell level, we confirm the identity of the neoplastic cells on a molecular level, showing these form a minority of cells in the tumor. We show that activation of the hippo pathway in the neoplastic cells is a likely driver of tumorigenesis. Additionally, we show the neoplastic cells produce large amounts of CSF1, attracting the macrophages that form the majority of cells in the tumor. This finding gives supporting evidence for anecdotal reports of response to CSF1 inhibitor therapy. Finally, we identify key differences between KPGCT and tenosynovial giant cell tumor, a tumor that shares histological features with KPGCT.

cancer biology↗

bioRxiv2025

Spatially organized inflammatory myeloid-CD8+ T cell aggregates linked to Merkel-cell Polyomavirus driven Reorganization of the Tumor Microenvironment

Merkel cell carcinoma (MCC) is an aggressive skin cancer with high propensity for metastasis, caused by Merkel-cell-polyomavirus (MCPyV), or chronic UV-light-exposure. How MCPyV spatially modulates immune responses within the tumor microenvironment and how such are linked to patient outcomes remains unknown. We interrogated the cellular and transcriptional landscapes of 60 MCC-patients using a combination of multiplex proteomics, in-situ RNA-hybridization, and spatially oriented transcriptomics. We identified a spatial co-enrichment of activated CD8+ T-cells and CXCL9+PD-L1+ macrophages at the invasive front of virus-positive MCC. This spatial immune response pattern was conserved in another virus-positive tumor, HPV+ head-and-neck cancer. Importantly, we show that virus-negativity correlated with high risk of metastasis through low CD8+ T-cell infiltration and the enrichment of cancer-associated-fibroblasts at the tumor boundary. By contrast, responses to immune-checkpoint blockade (ICB) were independent of viral-status but correlated with the presence of a B-cell-enriched spatial contexts. Our work is the first to reveal distinct immune-response patterns between virus-positive and virus-negative MCC and their impact on metastasis and ICB-response.

immunology↗

bioRxiv2024

TissueMap: A Web-Based Multiplexed Image Viewer

Datasets generated by spatial biology techniques such as multiplex immunofluorescence staining or spatial transcriptomics profiling of histologic sections carry a tremendous wealth of information. Several commercial platforms exist that can simultaneously acquire 1-1000 distinct marker signals (e.g., MIBI, CODEX, Orion, Nanostring CosMX SMI, Vizgen). However, due to the large size of these datasets, their viewing and sharing are slow, laborious, and require extensive computational resources. To overcome this, we developed TissueViewer, a web-based viewer designed to deliver high-resolution images over the internet with low bandwidth requirements and at high speed.

bioinformatics↗

bioRxiv2022

A spatial map of human macrophage niches links tissue location with function

Macrophages are the most abundant immune cell type in the tumor microenvironment (TME). Yet the spatial distribution and cell interactions that shape macrophage function are incompletely understood. Here we use single-cell RNA sequencing data and multiplex imaging to discriminate and spatially resolve macrophage niches within benign and malignant breast and colon tissue. We discover four distinct tissue-resident macrophage (TRM) layers within benign bowel, two TRM niches within benign breast, and three tumor-associated macrophage (TAM) populations within breast and colon cancer. We demonstrate that IL4I1 marks phagocytosing macrophages, SPP1 TAMs are enriched in hypoxic and necrotic tumor regions, and a novel subset of FOLR2 TRMs localizes within the plasma cell niche. Furthermore, NLRP3 TAMs that colocalize with neutrophils activate an inflammasome in the TME and in Crohns disease and are associated with poor outcomes in breast cancer patients. This work suggests novel macrophage therapy targets and provides a framework to study human macrophage function in clinical samples.

cancer biology↗

bioRxiv2022

Interactions in CSF1-driven Tenosynovial Giant Cell Tumors

The majority of cells in Tenosynovial Giant Cell Tumor (TGCT) are macrophages responding to CSF1 that is overproduced by a small number of neoplastic cells with a chromosomal translocation involving the CSF1 gene. Treatment with inhibitors of the CSF1 pathway has been clinically effective. An autocrine loop was postulated where the neoplastic cells are stimulated through the CSF1 receptor (CSF1R) expressed on their surface. Here we show that the neoplastic cells themselves do not express CSF1R and therefore may be unaffected by current therapies. We identified a new marker for synoviocytes, GFPT2, that highlights the tumor cells in TCGT and is associated with activation of the YAP1/TAZ pathway. The neoplastic cells in TGCT are highly similar non-neoplastic synoviocytes. Finally, we provide molecular support for the osteoclast-like features of the giant cells in TGCT that correlate with the destructive effects of TGCT on bone.

cancer biology↗

bioRxiv2021

The Human Tumor Atlas Network (HTAN) Breast PreCancer Atlas: A multi-omic integrative analysis of ductal carcinoma in situ with clinical outcomes

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We identified 812 genes associated with ipsilateral recurrence within 5 years from treatment and developed a classifier that was predictive of DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions were identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome. HIGHLIGHTS Development of a new classifier for DCIS recurrence or progression Outcome associated pathways identified across multiple data types and compartments Four stroma-specific signatures identified CNAs characterize DCIS subgroups associated with high risk invasive cancers

cancer biology↗