Biology subjects

Lustberg, M.

Publications and source records attributed to Lustberg, M..

3 recordsLinked to original sources

bioRxiv2022

Aromatase Inhibitor Induced Musculoskeletal Inflammation is Observed Independent of Oophorectomy in a Novel Mouse Model

BackgroundAromatase Inhibitors (AIs) block physiological estrogen production in peripheral tissues and are used clinically to reduce disease recurrences and improve overall survival rates in hormone receptor-positive breast cancer patients. However, half of patients taking these drugs develop aromatase inhibitor induced arthralgia (AIIA), which is characterized by severe pain and inflammation in various joints and the surrounding musculoskeletal tissue. While the pathophysiology is not currently understood, it has been proposed to be associated with systemic estrogen deficiency resulting from AI treatment. Since AIIA leads to suspension of therapy in 20-30% of patients, reducing AIIA incidence may provide sustained AI treatment and enhance long-term survival. ObjectiveIn order to establish a better understanding of disease pathology and to create a platform that can be used to explore future interventional strategies, our objective in this study was to design a novel animal model of AIIA. MethodsFemale BALB/C-Tg(NF{kappa}B-RE-luc)-Xen mice, which have a firefly luciferase cDNA reporter transgene under the regulation of NF{kappa}B binding sites, were oophorectomized and treated with AI (letrozole) by daily subcutaneous injections for 5 weeks. Control groups included oophorectomized mice receiving vehicle injections and non-oophorectomized mice treated with AI. Knee joints and surrounding muscle tissue were imaged on the BioSpec 94/30 micro-MRI. The primary weight-bearing joint (hind limb) was examined histopathologically and NF{kappa}B activity was measured by bioluminescent imaging. Serum was collected for cytokine analysis. Additionally, healthy human PBMCs were treated with letrozole, estrogen, or both, and RNA sequencing was performed at 36 hrs. ResultsBioluminescent imaging showed significantly enhanced NF{kappa}B activation with AI treatment in the hind limbs compared to controls receiving vehicle treatment. Moreover, analysis of knee joints and legs by MRI showed enhanced signal detection in the joint space and surrounding tissue following daily AI injections. Surprisingly, the enhanced MRI detection and NF{kappa}B activation was observed with AI treatment independent of the oophorectomy procedure. This indicates that the induction of musculoskeletal-directed inflammation by AI is not mediated by changes in physiological estrogen levels, which is contrary to proposed mechanisms of disease pathogenesis. Similarly, histopathological analysis showed tenosynovitis and musculoskeletal infiltrates in all mice receiving AI with or without oophorectomy. IHC analysis of the infiltrates demonstrated a predominantly macrophage-mediated inflammatory response with scattered CD4+ T cells. Additionally, serum cytokine levels of IL-2, IL-4, IL-6, and CXCL1 were significantly elevated in mice with AI treatment. RNA sequencing of human PBMCs after in vitro AI stimulation did not demonstrate an AI-specific gene expression pattern associated with immune system activation directly, suggesting that the pathogenesis of AIIA may be mediated through cells in other tissues in vivo. ConclusionsCollectively, these data establish a novel mouse model of AIIA and identify an estrogen-independent stimulation of disease pathology via AI-mediated induction. This suggests that the pathogenesis of AIIA may not be mediated by estrogen deficiency, as previously hypothesized, and indicates that AI-induced inflammation may not be regulated directly through a pathogenic mechanism initially derived from circulating mononuclear cells. Future studies aim to characterize this inflammatory mechanism in vivo with a focus on other cells, including macrophages, synovial cells and chondrocytes, to provide insight into putative therapeutic strategies directed at mitigating disease pathology.

immunology↗

bioRxiv2022

Activity of Estrogen Receptor β Agonists in Therapy-Resistant Estrogen Receptor-Positive Breast Cancer

BackgroundAmong women, breast cancer is the leading cause of cancer-related death worldwide. Estrogen receptor positive (ER+) breast cancer accounts for 70% of all breast cancer subtypes. Although ER+ breast cancer initially responds to estrogen deprivation or blockade, resistance emergence compelling the use of more aggressive therapies. While ER is a driver in ER+ breast cancer, ER{beta} plays an inhibitory role in several different cancer types. To date, the lack of highly selective ER{beta} agonists without ER activity has limited the exploration of ER{beta} activation as a strategy for ER+ breast cancer. MethodsWe measured expression levels of ESR1 and ESR2 genes in immortalized mammary epithelial cells and different breast cancer cell lines. The viability of ER+ breast cancer cell lines upon treatments with specific ER{beta} agonists, including OSU-ERb-12 and LY500307 was assessed. The specificity of the ER{beta} agonists, OSU-ERb-12 and LY500307, was confirmed by reporter assays. The effects of the agonists on cell proliferation, cell cycle, apoptosis, colony formation, cell migration, and expression of tumor suppressor proteins were analyzed. The expression of ESR2 and genes containing ERE-AP1 composite response elements was examined in ER+ human breast cancer samples to determine the correlation between ESR2 expression and overall survival and that of putative ESR2 regulated genes. ResultsIn this study, we demonstrate the efficacy of highly selective ER{beta} agonists in ER+ breast cancer cell lines and drug-resistant derivatives. ER{beta} agonists blocked cell proliferation, migration and colony formation; and induced apoptosis and S and/or G2/M cell cycle arrest of ER+ breast cancer cell lines. Also, increases in the expression of the key tumor suppressors FOXO1 and FOXO3a were noted. Importantly, the strong synergy between ER{beta} agonists and ER antagonists suggested that the efficacy of ER{beta} agonists is maximized by combination with ER blockade. Lastly, ESR2 (ER{beta} gene) expression was negatively correlated with ESR1 (ER gene) and CCND1 RNA expression in human metastatic ER+/HER2-breast cancer samples. ConclusionOur results demonstrate that highly selective ER{beta} agonists attenuate the viability of ER+ breast cancer cell lines in vitro and suggest that this therapeutic strategy merits further evaluation for ER+ breast cancer.

cancer biology↗

bioRxiv2019

Machine learning predicts rapid relapse of triple negative breast cancer

PurposeMetastatic relapse of triple-negative breast cancer (TNBC) within 2 years of diagnosis is associated with particularly aggressive disease and a distinct clinical course relative to TNBCs that relapse beyond 2 years. We hypothesized that rapid relapse TNBCs (rrTNBC; metastatic relapse or death <2 years) reflect unique genomic features relative to late relapse (lrTNBC; >2 years).\n\nPatients and MethodsWe identified 453 primary TNBCs from three publicly-available datasets and characterized each as rrTNBc, lrTNBC, or no relapse (nrTNBC: no relapse/death with at least 5 years follow-up). We compiled primary tumor clinical and multi-omic data, including transcriptome (n=453), copy number alterations (CNAs; n=317), and mutations in 171 cancer-related genes (n=317), then calculated published gene expression and immune signatures.\n\nResultsPatients with rrTNBC were higher stage at diagnosis (Chi-square p<0.0001) while lrTNBC were more likely to be non-basal PAM50 subtype (Chi-square p=0.03). Among 125 expression signatures, five immune signatures were significantly higher in nrTNBCs while lrTNBC were enriched for eight estrogen/luminal signatures (all FDR p<0.05). There was no significant difference in tumor mutation burden or percent genome altered across the groups. Among mutations, only TP53 mutations were significantly more frequent in rrTNBC compared to lrTNBC (Fisher exact FDR p=0.009). To develop an optimal classifier, we used 77 significant clinical and omic features to evaluate six modeling approaches encompassing simple, machine learning, and artificial neural network (ANN). Support vector machine outperformed other models with average receiver-operator characteristic area under curve >0.75.\n\nConclusionsWe provide a new approach to define TNBCs based on timing of relapse. We identify distinct clinical and genomic features that can be incorporated into machine learning models to predict rapid relapse of TNBC.

cancer biology↗