Biology subjects

Tomsic, J.

Publications and source records attributed to Tomsic, J..

3 recordsLinked to original sources

bioRxiv2026

Spatial Landscape of Pregnancy-Associated Triple Negative Breast Cancer and Mammary Gland Involution

Pregnancy-associated triple negative breast cancer (PA-TNBC) is one of the highest-risk breast cancers, marked by an aggressive phenotype that lacks targeted treatment options. Studies have shown that post-lactational mammary gland involution plays a role in this increased risk. To delineate the underlying mechanisms, our study characterized the transcriptional state of the epithelia and surrounding microenvironment in women with PA-TNBC, comparing those diagnosed pre-involution (PRE) and post-involution (POST, <3 years after delivery). Spatial transcriptomics using the GeoMx Digital Spatial Profiler was performed on treatment-naive PA-TNBC tissues from 33 women (10 PRE, 23 POST). Regions of interest were segmented with pan-cytokeratin staining. We found that the most prominent transcriptional differences between PRE and POST epithelia occurred in the adjacent non-invasive regions and during the transition into invasive TNBC. POST non-invasive epithelia uniquely showed inflammatory and developmental pathway activation, while the transition into TNBC involved increased chromatin remodeling and cell migration pathways. Further, the tumor microenvironment (TME) in POST showed the highest proportion of immune cells and the highest prevalence of tumor- and immune exhaustion-associated cell states. Finally, a pseudotime analysis of POST transcriptional dynamics found that women diagnosed 1-2 years after delivery exhibited the strongest evidence for inflammatory signaling across the tissue. Our results highlight biological mechanisms distinguishing PRE and POST PA-TNBC across tissue regions and cell types. We emphasize the importance of early detection of malignant molecular signatures in morphologically normal epithelium in post-involution women and suggest that targeting the TME may improve treatment efficacy in post-involution PA-TNBC.

cancer biology↗

bioRxiv2025

A novel subtyping method for TNBC with implications for prognosis and therapy

The biological heterogeneity of triple-negative breast cancer (TNBC) poses significant challenges for diagnosis, prognosis, and treatment. While prior TNBC subtype classifications exist, they are not widely used clinically. Here, we aimed to subtype TNBC based on transcriptomic profiles using cell type and state heterogeneity in tumor tissue from 250 pre-treatment women (127 African-American and 123 European-American). We identified three major subtypes and three distinct groups exhibiting unique cell-type composition and mechanisms: Subtype-1 immune signaling/T-cell response; Subtype-2 pro-fibrotic and immune desert; Subtype-3 fatty acid and nuclear receptor signaling. Subtype-1 showed potential responsiveness to immunotherapy, while Subtypes-2 and 3 suggested alternative therapeutic targets. In Subtype-3, which contained a patient group with high ESR1, (but not high ER protein expression) we identified putative mutations in the gene that are unique to these patients. This framework provides a path toward personalized TNBC treatment and is accessible through a user-friendly RShiny application for clinical use.

bioinformatics↗

bioRxiv2025

Spatially distinct cellular and molecular landscapes define prognosis in triple negative breast cancer

BackgroundTriple-negative breast cancer is a prevalent breast cancer subtype with the lowest 5-year survival. Several factors contribute to its treatment response, but the inherent molecular and cellular tumor heterogeneity are increasingly acknowledged as crucial determinants. MethodsSpatial transcriptomic profiling was performed on FFPE tissues from a retrospective, treatment-naive group of women with differential prognoses (17 with >15 years survival-good prognosis (GPx) and 15 with <3 years survival-poor prognosis (PPx)) using GeoMX(R) Digital Spatial Profiler. Regions of interest were segmented on pan-cytokeratin and analyzed for tumor and stromal components, probed using GeoMx human whole transcriptome atlas (WTA) panel. Data quality control, normalization, and differential analysis was performed in R using GeomxTools and linear mixed models. Additional analyses including cell-type deconvolution, spatial entropy, functional enrichment, TF-target / ligand-receptor analysis and convolution neural networks were employed to identify significant gene signatures contributing to differential prognosis. ResultsHere we report on the spatial and molecular heterogeneity underlying differential prognosis. We observe that the state of the epithelia and its microenvironment (TME) are transcriptionally distinct between the two groups. Invasive epithelia in GPx show a significant increase in immune transcripts with the TME exhibiting increased immune cell presence (via IF), while in PPx they are more metabolically and translationally active, with the TME being more mesenchymal/fibrotic. Specifically, pre-cancerous epithelia in PPx display a prescience of aggressiveness as evidenced by increased EMT-signaling. We identify distinct epithelial gene signatures for PPx and GPx, that can, with high accuracy, classify samples at the time of diagnosis and likely inform therapy. ConclusionsTo the best of our knowledge, this is the first study to leverage spatial transcriptomics for an in-depth delineation of the cellular and molecular underpinnings of differential prognosis in TNBC. Our study highlights the potential of spatial transcriptomics to not only uncover the molecular drivers of differential prognosis in TNBC but also to pave the way for precision diagnostics and tailored therapeutic strategies, transforming the clinical landscape for this aggressive breast cancer subtype.

cancer biology↗