Hi-C profiling of cancer spheroids identifies 3D-growth-specific chromatin interactions in breast cancer endocrine resistance

ONECUT2 is a druggable driver of luminal to basal breast cancer plasticity

PURPOSE

Tumor heterogeneity complicates patient treatment and can be due to transitioning of cancer cells across phenotypic cell states. This process is associated with the acquisition of independence from an oncogenic driver, such as the estrogen receptor (ER) in breast cancer (BC), resulting in tumor progression, therapeutic failure and metastatic spread. The transcription factor ONECUT2 (OC2) has been shown to be a master regulator protein of metastatic castration-resistant prostate cancer (mCRPC) tumors that promotes lineage plasticity to a drug-resistant neuroendocrine (NEPC) phenotype. Here, we investigate the role of OC2 in the dynamic conversion between different molecular subtypes in BC.

METHODS

We analyze OC2 expression and clinical significance in BC using public databases and immunohistochemical staining. In vitro, we perform RNA-Seq, RT-qPCR and western-blot after OC2 enforced expression. We also assess cellular effects of OC2 silencing and inhibition with a drug-like small molecule in vitro and in vivo.

RESULTS

OC2 is highly expressed in a substantial subset of hormone receptor negative human BC tumors and tamoxifen-resistant models, and is associated with poor clinical outcome, lymph node metastasis and heightened clinical stage. OC2 inhibits ER expression and activity, suppresses a gene expression program associated with luminal differentiation and activates a basal-like state at the gene expression level. We also show that OC2 is required for cell growth and survival in metastatic BC models and that it can be targeted with a small molecule inhibitor providing a novel therapeutic strategy for patients with OC2 active tumors.

CONCLUSIONS

The transcription factor OC2 is a driver of BC heterogeneity and a potential drug target in distinct cell states within the breast tumors.

Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations

An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-context dependent cell subpopulations or topologically integrated subpopulations (TISPs). We demonstrate its algorithmic utility on the publicly available and newly generated scHi-C and scRNA-seq data. We then test and apply MUDI in a breast cancer cell model system to demonstrate its biological-context dependent utility. We found the newly defined topologically conserved associating domain (CAD) is the characteristic single-cell 3D chromatin structure and better characterizes chromatin domains in single-cell resolution. We further identify 20 TISPs uniquely characterizing 3D-regulated breast cancer cellular states. We reveal two of TISPs are remarkably resemble to high cycling breast cancer persister cells and chromatin modifying enzymes might be functional regulators to drive the alteration of the 3D chromatin structures. Our comprehensive integration of scHi-C and scRNA-seq data in cancer cells at single-cell resolution provides mechanistic insights into 3D-regulated heterogeneity of developing drug-tolerant cancer cells.

Combination of multiple omics techniques for a personalized therapy or treatment selection

Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its ex vivo characterization for susceptibility to different therapies.

Network characterization linc1393 in the maintenance of pluripotency provides the principles for lncRNA targets prediction

Long non-coding RNAs (lncRNAs) have been implicated in diverse biological processes. However, the functional mechanisms have not yet been fully explored. Characterizing the interactions of lncRNAs with chromatin is central to determining their functions but, due to precise and efficient approaches lacking, our understanding of their functional mechanisms has progressed slowly. In this study, we demonstrate that a nuclear lncRNA linc1393 maintains mouse ESC pluripotency by recruiting SET1A near its binding sites, to establish H3K4me3 status and activate the expression of specific pluripotency-related genes. Moreover, we characterized the principles of lncRNA-chromatin interaction and transcriptional regulation. Accordingly, we developed a computational framework based on the XGBoost model, LncTargeter, to predict the targets of a given lncRNA, and validated its reliability in various cellular contexts. Together, these findings elucidate the roles and mechanisms of lncRNA on pluripotency maintenance, and provide a promising tool for predicting the regulatory networks of lncRNAs.

In Vitro 3D Spheroid Model Preserves Tumor Microenvironment of Hot and Cold Breast Cancer Subtypes

Dynamic interaction of cancer, immune, and stromal cells with extracellular matrix components modulates and resists the response of standard care therapies. To mimic this, an in vitro 3D spheroid model is designed using liquid overlay method to simulate hot (MDA-MB-231) and cold (MCF-7) breast tumor microenvironment (TME). This study shows increased mesenchymal phenotype, stemness, and suppressive microenvironment in MDA-MB-231-spheroids upon exposure to doxorubicin. Intriguingly, the presence of human dermal fibroblasts enhances cancer-associated fibroblast phenotype in MDA-MB-231-spheroids through increased expression of CXCL12 and FSP-1, leading to higher infiltration of immune cells (THP-1 monocytes). However, a suppressive TME is observed in both subtypes, as seen by upregulation of M2-macrophage-specific CD68 and CD206 markers. Specifically, increased PDL-1 expressing tumor-associated macrophages along with FoxP3 expressing T regulatory cells are found in MDA-MB-231-spheroids when cultured with peripheral blood mononuclear cells. Further, it is found that the addition of 1-methyl-tryptophan, a potent indoleamine-2,3-dioxygenase-1 inhibitor, subsides the suppressive phenotype by decreasing the M2 polarization via downregulation of tryptophan metabolism and IL10 expression, particularly in MCF-7 triculture spheroids. Thus, the in vitro 3D spheroid model of TME can be utilized in therapeutics to validate immunomodulatory drugs for various breast cancer subtypes.

Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective

Breast cancer is well-known to be a highly heterogenous disease. This facet of cancer makes finding a research model that mirrors the disparate intrinsic features challenging. With advances in multi-omics technologies, establishing parallels between the various models and human tumors is increasingly intricate. Here we review the various model systems and their relation to primary breast tumors using available omics data platforms. Among the research models reviewed here, breast cancer cell lines have the least resemblance to human tumors since they have accumulated many mutations and copy number alterations during their long use. Moreover, individual proteomic and metabolomic profiles do not overlap with the molecular landscape of breast cancer. Interestingly, omics analysis revealed that the initial subtype classification of some breast cancer cell lines was inappropriate. In cell lines the major subtypes are all well represented and share some features with primary tumors. In contrast, patient-derived xenografts (PDX) and patient-derived organoids (PDO) are superior in mirroring human breast cancers at many levels, making them suitable models for drug screening and molecular analysis. While patient derived organoids are spread across luminal, basal- and normal-like subtypes, the PDX samples were initially largely basal but other subtypes have been increasingly described. Murine models offer heterogenous tumor landscapes, inter and intra-model heterogeneity, and give rise to tumors of different phenotypes and histology. Murine models have a reduced mutational burden compared to human breast cancer but share some transcriptomic resemblance, and representation of many breast cancer subtypes can be found among the variety subtypes. To date, while mammospheres and three- dimensional cultures lack comprehensive omics data, these are excellent models for the study of stem cells, cell fate decision and differentiation, and have also been used for drug screening. Therefore, this review explores the molecular landscapes and characterization of breast cancer research models by comparing recent published multi-omics data and analysis.

The Variety of 3D Breast Cancer Models for the Study of Tumor Physiology and Drug Screening

Breast cancer is the most common cancer in women and responsible for multiple deaths worldwide. 3D cancer models enable a better representation of tumor physiology than the conventional 2D cultures. This review summarizes the important components of physiologically relevant 3D models and describes the spectrum of 3D breast cancer models, e.g., spheroids, organoids, breast cancer on a chip and bioprinted tissues. The generation of spheroids is relatively standardized and easy to perform. Microfluidic systems allow control over the environment and the inclusion of sensors and can be combined with spheroids or bioprinted models. The strength of bioprinting relies on the spatial control of the cells and the modulation of the extracellular matrix. Except for the predominant use of breast cancer cell lines, the models differ in stromal cell composition, matrices and fluid flow. Organoids are most appropriate for personalized treatment, but all technologies can mimic most aspects of breast cancer physiology. Fetal bovine serum as a culture supplement and Matrigel as a scaffold limit the reproducibility and standardization of the listed 3D models. The integration of adipocytes is needed because they possess an important role in breast cancer.

Investigation of reversible histone acetylation and dynamics in gene expression regulation using 3D liver spheroid model

BACKGROUND

Three-dimensional (3D) cell culture has emerged as an alternative approach to 2D flat culture to model more accurately the phenotype of solid tissue in laboratories. Culturing cells in 3D more precisely recapitulates physiological conditions of tissues, as these cells reduce activities related to proliferation, focusing their energy consumption toward metabolism and homeostasis.

RESULTS

Here, we demonstrate that 3D liver spheroids are a suitable system to model chromatin dynamics and response to epigenetics inhibitors. To delay necrotic tissue formation despite proliferation arrest, we utilize rotating bioreactors that apply active media diffusion and low shearing forces. We demonstrate that the proteome and the metabolome of our model resemble typical liver functions. We prove that spheroids respond to sodium butyrate (NaBut) treatment, an inhibitor of histone deacetylases (HDACi), by upregulating histone acetylation and transcriptional activation. As expected, NaBut treatment impaired specific cellular functions, including the energy metabolism. More importantly, we demonstrate that spheroids reestablish their original proteome and transcriptome, including pre-treatment levels of histone acetylation, metabolism, and protein expression once the standard culture condition is restored after treatment. Given the slow replication rate (> 40 days) of cells in 3D spheroids, our model enables to monitor the recovery of approximately the same cells that underwent treatment, demonstrating that NaBut does not have long-lasting effects on histone acetylation and gene expression. These results suggest that our model system can be used to quantify molecular memory on chromatin.

CONCLUSION

Together, we established an innovative cell culture system that can be used to model anomalously decondensing chromatin in physiological cell growth and rule out epigenetics inheritance if cells recover the original phenotype after treatment. The transient epigenetics effects demonstrated here highlight the relevance of using a 3D culture model system that could be very useful in studies requiring long-term drug treatment conditions that would not be possible using a 2D cell monolayer system.

In vitro breast cancer models for studying mechanisms of resistance to endocrine therapy

The development of endocrine resistance is a common reason for the failure of endocrine therapies in hormone receptor-positive breast cancer. This review provides an overview of the different types of in vitro models that have been developed as tools for studying endocrine resistance. In vitro models include cell lines that have been rendered endocrine-resistant by ex vivo treatment; cell lines with de novo resistance mechanisms, including genetic alterations; three-dimensional (3D) spheroid, co-culture, and mammosphere techniques; and patient-derived organoid models. In each case, the key discoveries, different analysis strategies that are suitable, and strengths and weaknesses are discussed. Certain recently developed methodologies that can be used to further characterize the biological changes involved in endocrine resistance are then emphasized, along with a commentary on the types of research outcomes that using these techniques can support. Finally, a discussion anticipates how these recent developments will shape future trends in the field. We hope this overview will serve as a useful resource for investigators that are interested in understanding and testing hypotheses related to mechanisms of endocrine therapy resistance.