Biology subjects

Garred, O.

Publications and source records attributed to Garred, O..

3 recordsLinked to original sources

bioRxiv2026

Single-cell immune profiling of regional lymph nodes during early-stage breast cancer progression

INTRODUCIONTumor cell infiltration in regional lymph nodes is a strong prognostic marker, guiding treatment decisions in breast cancer. While the immune cell composition in primary tumors has been more widely explored in later years, the immune cell composition of the sentinel node (SN) and axillary lymph nodes (ALN) remains understudied. A better understanding of how primary tumor and metastatic tumor cells alter the nodal immune microenvironment can shed light on metastasis and cancer progression to unveil new treatment strategies. MATERIALS AND METHODSFrom a prospective clinical cohort of 458 treatment-naive patients with primary operable breast cancer, we performed comprehensive immunophenotypic analysis using mass cytometry analysis of non-metastatic (SN-) and metastatic (SN+) and ALN (ALN+) lymph nodes. RESULTSAs expected, patients with ALN+ cases had a shorter time to distant metastases than SN+ and SN- cases. We identified an exhausted T-cell phenotype and an increase in Germinal Center B (GC B) cells and plasma cells in ALN+ samples compared to SN- samples, both in the whole cohort as well as when investigating estrogen-receptor positive (ER+) patients only. There were no differences in immune cell composition across breast cancer (BC) subtypes within SN-samples. SN+ samples from triple negative BC (TNBC) showed a trend towards increased abundance of GC B and plasma cells, similar to more advanced ALN+, suggesting that smaller TN metastases may trigger an immune activation at an early stage of dissemination. Further analysis of SN- samples from ER+ patients revealed a subset of patients where the immune response had a more exhausted T-cell phenotype. This group was enriched for lymph nodes that were deemed negative by ordinary pathology examination (microscopy) but had detectable tumor cells by CyTOF analysis. CONCLUSIONThe immune profiles of SN and ALN samples from breast cancer patients are highly diverse, showing limited associations to BC subtype, clinical parameters or patient outcome. Metastatic tumor cells play a significant role in driving T-cell exhaustion and immunosuppression. Notably, in approximately 50% of the ER+ samples, T-cell exhaustion was detectable. This coincides with the presence of tumor cells identified by CyTOF, which were likely missed by conventional pathological examination. These findings suggest that small tumor deposits alter the immune composition, and the immune profile reveals the presence of tumor cells.

cancer biology↗

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↗

bioRxiv2018

Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data

Mathematical modeling and simulation have emerged as a potentially powerful, time and cost effective approach to personalized cancer treatment. The usefulness of mechanistic models to disentangle complex multi-scale cancer processes such as treatment response has been widely acknowledged. However, a major barrier for multi-scale models to predict the outcomes of therapeutic regimens in a particular patient lies in their initialization and parameterization which need to reflect individual cancer characteristics accurately. In this study we use multi-type routinely acquired measurements on a single breast tumor, including histopathology, magnetic resonance imaging, and molecular profiling to personalize parts of a complex multi-scale model of breast cancer treated with chemotherapeutic and anti-angiogenic agents. We model the dynamics of drugs in tissue (pharmacokinetics) and the corresponding effects on their targets (pharmacodynamics). We developed a open-source computer program that simulates cross-sections of tumors under 12-week therapy regimes and use it to individually reproduce and elucidate treatment outcomes of four patients. For two of the tumors that did not respond to therapy, we used model simulations to suggest alternative regimes, depending on their individual characteristics, with improved outcomes. We found that more frequent doses of chemothereapy reduce tumor burden in a low proliferative tumor while lower doses of anti-angiogenic agents improve drug penetration in a poorly perfused tumor. In addition to bridge multi-type clinical data to shed light on individual treatment outcomes, our approach identified a few tumor-related aspects that need to be clinically portraited better to allow for future model-driven personalized cancer therapy.

cancer biology↗