Biology subjects

Grondal, S. M.

Publications and source records attributed to Grondal, S. M..

4 recordsLinked to original sources

bioRxiv2025

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells

BackgroundExisting breast cancer risk models inadequately identify individuals at latent risk, particularly among women without known genetic mutations or family history. Risk is often underestimated or overestimated due to reliance on population-level data and neglect of cellular aging and mechanobiological alterations. MethodsWe profiled primary human mammary epithelial cells (HMECs) from women of varying ages and risk backgrounds using mechano-node-pore sensing (mechano-NPS), a high-throughput microfluidic platform that captures single-cell mechanical properties. Using machine learning, we developed a classifier, MechanoAge, to predict age-related mechanical phenotypes and introduce a novel index, mechano-RISQ, to quantify deviations linked to breast cancer risk. We further assessed the cytoskeletal protein keratin 14 (KRT14) as a molecular mediator of these mechanical states through overexpression and knockdown experiments. FindingsCells from younger women carrying BRCA1/2 mutations or with a family history of breast cancer exhibited accelerated mechanical aging compared to age-matched controls. Elevated mechano-RISQ scores reflected an increased proportion of cells with "older" mechanical profiles. KRT14 overexpression induced an aged mechanical phenotype in younger cells, while knockdown partially reversed this state in older cells. CyTOF profiling and modeling showed KRT14 modulation impacted protein expression signatures associated with aging and risk, particularly in luminal cells. InterpretationMechanical properties of breast epithelial cells reflect biologic aging and cancer susceptibility. Mechano-RISQ offers a new approach for identifying individuals at elevated risk, especially among average-risk populations, and may complement existing risk models by incorporating biophysical measures of epithelial aging.

cancer biology↗

bioRxiv2024

Interactions between neutrophils and macrophages harboring gram-negative bacteria promote obesity-associated breast cancer

Obesity promotes a more aggressive breast cancer phenotype. Through spatial and single-cell-based analysis of hormone receptor-negative breast cancers, we identify a subset of tumor-associated neutrophils (TANs) positive for granzyme B (GZMB) enriched in the tumor microenvironment of obese patients. In breast tumors evolved in obese environments, TANs are in proximity of M2 polarized macrophages containing lipopolysaccharides (LPS) from gram-negative bacteria. Pyroptosis of macrophages releases bacterial LPS, activating local GZMB+ TANs. This induces release of the S100 family member S100A8 that promotes tumor progression. In sum, we describe an obesity associated cellular network of cancer cells, neutrophils and M2 polarized macrophages that promotes tumor growth.

cancer biology↗

bioRxiv2020

High dimensional immunotyping of the obese tumor microenvironment reveals model specific adaptation

Obesity is a disease characterized by chronic low-grade systemic inflammation and has been causally linked to the development of 13 cancer types. Several studies have been undertaken to determine if tumors evolving in obese environments adapt differential interactions with immune cells and if this can be connected to disease outcome. Most of these studies have been limited to single cell lines and tumor models and analysis of limited immune cell populations. Given the multicellular complexity of the immune system and its dysregulation in obesity, we applied high-dimensional suspension mass cytometry to investigate how obesity affects tumor immunity. We used a 36-marker immune-focused mass cytometry panel to interrogate the immune landscape of orthotopic syngeneic mouse models of pancreatic and breast cancer. Unanchored batch correction was implemented to enable simultaneous analysis of tumor cohorts to uncover the immunotypes of each cancer model and reveal remarkably model-specific immune regulation. In the E0771 breast cancer model, we demonstrate an important link to obesity with an increase in two T cell suppressive cell types and a decrease in CD8 T-cells.

cancer biology↗

bioRxiv2020

C/EBPB-dependent Adaptation to Palmitic Acid Promotes Stemness in Hormone Receptor Negative Breast Cancer

Epidemiological studies have established a positive association between obesity and the incidence of postmenopausal breast cancer. Moreover, it is known that obesity promotes stem cell-like properties of breast cancer cells. However, the cancer cell-autonomous mechanisms underlying this correlation are not well defined. Here we demonstrate that obesity-associated tumor formation is driven by cellular adaptation rather than expansion of pre-existing clones within the cancer cell population. While there is no correlation with specific mutations, cellular adaptation to obesity is governed by palmitic acid (PA) and leads to enhanced tumor formation capacity of breast cancer cells. This process is governed epigenetically through increased chromatin occupancy of the transcription factor CCAAT/enhancer-binding protein beta (C/EBPB). Obesity-induced epigenetic activation of C/EBPB regulates cancer stem-like properties by modulating the expression of key downstream regulators including CLDN1 and LCN2. Collectively, our findings demonstrate that obesity drives cellular adaptation to PA drives tumor initiation in the obese setting through activation of a C/EBPB dependent transcriptional network. SignificanceCellular adaptation to obesity-induced palmitic acid drives tumor initiation through activation of a C/EBPB-dependent transcriptional network. This highlights a cell-autonomous mechanistic connection between obesity and postmenopausal hormone receptor negative breast cancer.

cancer biology↗