Biology subjects

Ramaswamy, B.

Publications and source records attributed to Ramaswamy, B..

6 recordsLinked to original sources

bioRxiv2026

Multidimensional Cellular Micro-Compartments to Model Invasive Lobular Carcinoma Dormancy

Invasive lobular carcinoma (ILC) accounts for 10-15% of breast cancers. Despite favorable responses to anti-estrogen therapy, the dissemination of cancer cells and resistance to therapies are significant risks for patients with ILC. Late recurrences are prevalent in ILC, suggesting that disseminated tumor cell dormancy may be a mechanism preceding their late overt growth into metastatic lesions. Herein, we investigated the relationship between anti-estrogen resistance and dormancy through multidimensional in vitro models. The bioengineered platforms recapitulated the morphological characteristics of ILC and highlighted its distinction from invasive ductal carcinoma. Inducing a dormant phenotype revealed epigenetic changes and enhanced chemical and mechanical sensing of anti-estrogen-resistant ILC cells to the substrate surface, with p27Kip1 signaling playing a central role. We propose this platform as a high-throughput method to investigate the propensity of dormancy and its manifestation via a simplified and expedited approach.

bioengineering↗

bioRxiv2025

Estrogen Signaling During Abrupt Involution Leads to Long-Term Metabolic Dysfunction Similar to Estrogen Receptor Negative Breast Cancer

Epidemiological data links lack of breastfeeding with increased risk of breast cancer. Breast tissue undergoes remodeling to pre-pregnancy state after birth through involution. Long-term breastfeeding leads to gradual involution (GI). Lack of breastfeeding leads to abrupt involution (AI). While estrogen impacts repopulation of adipocytes, AI causes several precancerous changes in the mouse mammary gland. The impact of AI on adipocyte repopulation and metabolism is yet to be elucidated. ObjectivesTo investigate effects of AI on mammary gland metabolism and its potential link to breast cancer. MethodsAt partum (day 0), FVB/n dams were randomized to AI or GI and standardized to 6 pups. AI mice had pups removed on day 7 postpartum to mimic short-term breastfeeding. GI mice had 3 pups each were removed on day 28 and 31 postpartum to mimic gradual weaning. Mammary glands were harvested on day 28, 56, and 120 postpartum. Subset of AI mice had long-term sustained release tamoxifen placed subscapular on day 8 postpartum. Metabolic changes were assessed using: 1) transcriptional; 2) functional; 3) oxidative stress; and 4) metabolites analysis. ResultsDay 28 GI glands sustains/continues milk synthesis pathways impacting metabolic comparison with day28 AI glands. Day 28 AI when compared to day 56 GI showed upregulation of estrogen signaling, neutrophil degranulation, glucose metabolism, RNA synthesis, and down regulation of adipogenesis and glycolysis. At day 120, AI glands had downregulation of oxidative phosphorylation and upregulation of mitochondria dysfunction similar to pregnancy associated estrogen receptor negative breast cancer. Tamoxifen treatment of AI dams showed metabolic pathways and estrogen signaling similar to that of GI glands on day 28. ConclusionEarly metabolic phenotypes in AI and GI glands may be caused by differences in adipocyte repopulation related to estrogen signaling. Long-term metabolic effects of AI lead to similar metabolic effects found in breast cancer.

cancer biology↗

bioRxiv2025

Invasive lobular carcinoma integrated multi-omics analysis reveals silencing of Arginosuccinate synthase and upregulation of nucleotide biosynthesis in tamoxifen resistance

Invasive Lobular Carcinoma (ILC), a distinct subtype of breast cancer is hallmarked by E-Cadherin loss, slow proliferation, and strong hormone receptor positivity. ILC faces significant challenges in clinical management due to advanced stage at diagnosis, late recurrence, and development of resistance to endocrine therapy - a cornerstone of ILC treatment. To elucidate the mechanisms underlying endocrine resistance in ILC, ILC cell lines (MDA-MB-134-VI, SUM44PE) were generated to be resistant to tamoxifen, a selective estrogen receptor modulator. The tamoxifen-resistant (TAMR) cells exhibit a 2-fold increase tamoxifen IC50 relative to parental cells. Metabolomics and RNA-sequencing revealed deregulation of alanine, aspartate, and glutamate metabolism, purine metabolism, and arginine and proline metabolism in TAMR cells. Among the fifteen commonly dysregulated genes in these pathways, low ASS1 expression was identified in the TAMR cells and was significantly correlated with poor outcome in ILC patients, specifically in the context of endocrine therapy. Our study reveals methylation mediated silencing of ASS1 in TAMR cells as a likely mechanism of downregulation. Demethylation restored ASS1 expression and correspondingly reduced tamoxifen IC50 toward parental levels. Nucleic acid biosynthesis is augmented in TAMR cells, evidenced by increase in nucleotide intermediates. Both TAMR cell lines demonstrated increased expression of several nucleic acid biosynthesis enzymes, including PAICS, PRPS1, ADSS2, CAD, and DHODH. Furthermore, CAD, the key multifunctional protein of de novo pyrimidine biosynthesis pathway is differentially activated in TAMR cells. Treating TAMR cell with Decitabine, a demethylating agent, or Farudodstat, a pyrimidine biosynthesis inhibitor, markedly augmented efficacy of tamoxifen. Collectively, our study unveils ASS1 downregulation as a novel mechanism underlying acquired tamoxifen resistance in ILC and establishes a metabolic link between ASS1 and nucleic acid biosynthesis. Restoring ASS1 expression or inhibiting pyrimidine biosynthesis restored tamoxifen sensitivity. ASS1 could be a potential biomarker and therapeutic target in tamoxifen resistant ILC patients, warranting further investigation.

cancer biology↗

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↗

bioRxiv2022

Augmentation of extracellular ATP synergizes with chemotherapy in triple negative breast cancer

IntroductionBreast cancer affects two million women worldwide every year and is the most common cause of cancer-related death among women. The triple-negative breast cancer (TNBC) sub-type is associated with an especially poor prognosis because currently available therapies, fail to induce long-lasting responses. Therefore, there is an urgent need to develop novel therapies that result in durable responses. One universal characteristic of the tumor microenvironment is a markedly elevated concentration of extracellular adenosine triphosphate (eATP). Chemotherapy exposure results in further increases in eATP through its release into the extracellular space of cancer cells via P2RX channels. eATP levels are reduced by eATPases. Given that high concentrations of eATP are cytotoxic, we hypothesized that augmenting the release of eATP through P2RX channels and inhibiting extracellular ATPases would sensitize TNBC cells to chemotherapy. MethodsTNBC cell lines MDA-MB 231, Hs 578t and MDA-MB 468 and non-tumorigenic immortalized mammary epithelial MCF-10A cells were treated with increasing concentrations the chemotherapeutic agent paclitaxel in the presence of eATPase inhibitors, specific agonists or antagonists of P2RXs with cell viability and eATP content being measured. Additionally, the mRNA, protein and cell surface expressions of the purinergic receptors P2RX4 and P2RX7 were evaluated in all examined cell lines via qRT-PCR, western blot, and flow cytometry analyses, respectively. ResultsIn the present study, we observed dose-dependent declines in cell viability and increases in eATP in paclitaxel-treated TNBC cell lines in the presence of inhibitors of eATPases. These effects were reversed by specific antagonists of P2RXs. Similar results were observed with P2RX activators. All examined cell lines expressed both P2RX4 and P2RX7 at the mRNA, protein and cell surface levels. ConclusionThese results reveal that eATP modulates the chemotherapeutic response in TNBC cell lines which could be exploited to enhance the efficacy of chemotherapy regimens for TNBC.

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↗