Biology subjects

Haugen, M. H.

Publications and source records attributed to Haugen, M. H..

3 recordsLinked to original sources

bioRxiv2025

Spatial organization of the tumor-immune microenvironment in ER-positive breast cancer: remodeling during treatment and associations with clinical response

BackgroundThe tumor microenvironment influences treatment response in ER-positive breast cancer, but what distinguishes responders from non-responders and how it changes during treatment is poorly understood. MethodsER-positive breast tumors treated with neoadjuvant chemotherapy with or without bevacizumab were profiled with bulk proteomics pre-(n = 95), on-(n = 84) and post-treatment (n = 100). A subset of tumors was profiled with spatial single-cell proteomics pre-(n = 13) and on-treatment (n = 11). Cell phenotypes, spatial location and activation states were determined, and cellular colocalization assessed with spatial metrics. Bulk and spatial features were evaluated against treatment response defined by residual cancer burden. ResultsTreatment with bevacizumab amplified chemotherapy effects on proteomic signaling. The immune contexture shifted from suppressive to supportive during treatment through decreased macrophage, regulatory and anergic T-cell density and increased colocalization between epithelial cells and CD8+, CD4+ T-cells and dendritic cells. At baseline, responders had high density of effector memory T-cells, while non-responders had more naive T-cells. In addition, responders had increased colocalization of epithelial cells with macrophages, and effector memory T-cells with M1-like macrophages compared to non-responders. ConclusionsSpatially distinct tumor-immune microenvironments influence response to neoadjuvant treatment, offering valuable insights for guiding treatment decisions.

cancer biology↗

bioRxiv2024

Integrated multiomics analysis unveils how macrophages drive immune suppression in breast tumors and affect clinical outcomes

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression and patient prognosis are still poorly understood. Unraveling these effects is vital for identifying potential targets to improve treatment outcomes. In this study, we devised an explainable machine learning (XML) pipeline to scrutinize the associations between TME cellular constituents and relapse-free survival (RFS). By applying our pipeline to estimated cell fractions in the METABRIC and TCGA datasets and comparing these results with associations to pathological complete response (pCR) after neoadjuvant chemotherapy (NAC), we created a comprehensive catalog of the TMEs role based on 5000 patient samples. Our findings reveal an unexpected dichotomy in which macrophages correlate positively with pCR but negatively with RFS, particularly within estrogen receptor-positive (ER+) and Luminal A and B (LumA/B) cancer subtypes. We show that this pattern is driven by heterogeneity in breast tumors characterized by increasing levels of macrophage infiltration. Through imaging mass cytometry (IMC) analysis, we discovered that macrophages tend to accumulate in the vicinity of HLA-ABChi epithelial cells as their frequency increases in tumor tissues and also express elevated levels of HLA-ABC protein. Combining IMC with single-cell RNA sequencing (scRNA-seq) data, we uncovered a significant association between these HLA-ABChi macrophages and regulatory and exhausted T cells (TReg and TEx), suggesting their involvement in immune suppression, likely by creating a chronically activated immunosuppressive TME. Subsequent cell-cell communication analysis predicted interactions between HLA-ABChi macrophages and TEx cells via the ligands SIGLEC9, ALCAM, and CSF1, and with TReg cells through APP, ANGPTL4, and SIGLEC9 signaling. Considering the clinical relevance of macrophages in ER+ (LumA/B) subtypes, our research enhances the characterization of macrophage-driven immune suppression in these tumors and identifies potential targets for immunomodulatory strategies.

bioinformatics↗

bioRxiv2022

An integrated 'omics approach highlights the role of epigenetic events to explain and predict response to neoadjuvant chemotherapy and bevacizumab

Here we present an integrated omics approach for DNA methylation profiling using copy number alteration, gene expression and proteomic data to predict response to therapy and to pinpoint response-related epigenetic events. Fresh frozen tumor biopsies taken before, during and after treatment from patients receiving neoadjuvant chemotherapy with or without the anti-angiogenic drug bevacizumab were subjected to molecular profiling. Our previous studies have shown that administration of bevacizumab in addition to chemotherapy (combination treatment) may confer improved response for patients; here we report that DNA methylation at enhancer CpGs related to cell cycle regulation can predict response to chemotherapy and bevacizumab for ER positive patients with high fidelity (AUC=0.874), and we validate this observation in an independent patient cohort with similar treatment regimen (AUC=0.762). When combining the DNA methylation score with a previously reported proteomic score (ViRP), the prediction accuracy further improved in the validation cohort (AUC=0.784). We also show that tumors receiving the combination treatment underwent more extensive epigenetic alterations than tumors receiving only chemotherapy. Finally, we performed an integrative emQTL analysis on alterations in DNA methylation and gene expression levels, showing that the epigenetic alterations that occur during treatment are different between responders and non-responders and that these differences may be explained by the proliferation-EMT axis through the activity of the transcription factor GRHL2. Taken together, these results illustrate the clinical benefit of the addition of bevacizumab to chemotherapy if administered to the correct patients.

cancer biology↗