Epigenetic alterations to Polycomb targets precede malignant transition in a mouse model of breast cancer

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.

Tailoring therapies to counter the divergent immune landscapes of breast cancer

Breast cancer remains a significant clinical concern affecting millions of women worldwide. Immunotherapy is a rapidly growing drug class that has revolutionized cancer treatment but remains marginally successful in breast cancer. The success of immunotherapy is dependent on the baseline immune responses as well as removing the brakes off pre-existing anti-tumor immunity. In this review, we summarize the different types of immune microenvironment observed in breast cancer as well as provide approaches to target these different immune subtypes. Such approaches have demonstrated pre-clinical success and are currently under clinical evaluation. The impact of combination of these approaches with already approved chemotherapies and immunotherapies may improve patient outcome and survival.

The Role of H3K4 Trimethylation in CpG Islands Hypermethylation in Cancer

CpG methylation in transposons, exons, introns and intergenic regions is important for long-term silencing, silencing of parasitic sequences and alternative promoters, regulating imprinted gene expression and determining X chromosome inactivation. Promoter CpG islands, although rich in CpG dinucleotides, are unmethylated and remain so during all phases of mammalian embryogenesis and development, except in specific cases. The biological mechanisms that contribute to the maintenance of the unmethylated state of CpG islands remain elusive, but the modification of established DNA methylation patterns is a common feature in all types of tumors and is considered as an event that intrinsically, or in association with genetic lesions, feeds carcinogenesis. In this review, we focus on the latest results describing the role that the levels of H3K4 trimethylation may have in determining the aberrant hypermethylation of CpG islands in tumors.

Insights from transgenic mouse models of PyMT-induced breast cancer: recapitulating human breast cancer progression in vivo

Breast cancer is associated with the second highest cancer-associated deaths worldwide. Therefore, understanding the key events that determine breast cancer progression, modulation of the tumor-microenvironment and metastasis, which is the main cause of cancer-associated death, are of great importance. The mammary specific polyomavirus middle T antigen overexpression mouse model (MMTV-PyMT), first published in 1992, is the most commonly used genetically engineered mouse model (GEMM) for cancer research. Mammary lesions arising in MMTV-PyMT mice follow similar molecular and histological progression as human breast tumors, making it an invaluable tool for cancer researchers and instrumental in understanding tumor biology. In this review, we will highlight key studies that demonstrate the utility of PyMT derived GEMMs in understanding the molecular basis of breast cancer progression, metastasis and highlight its use as a pre-clinical tool for therapeutic discovery.

Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz

DNA methylation profiling offers unique insights into human development and diseases. Often the analysis of complex tissues and cell mixtures is the only feasible option to study methylation changes across large patient cohorts. Since DNA methylomes are highly cell type specific, deconvolution methods can be used to recover cell type-specific information in the form of latent methylation components (LMCs) from such 'bulk' samples. Reference-free deconvolution methods retrieve these components without the need for DNA methylation profiles of purified cell types. Currently no integrated and guided procedure is available for data preparation and subsequent interpretation of deconvolution results. Here, we describe a three-stage protocol for reference-free deconvolution of DNA methylation data comprising: (i) data preprocessing, confounder adjustment using independent component analysis (ICA) and feature selection using DecompPipeline, (ii) deconvolution with multiple parameters using MeDeCom, RefFreeCellMix or EDec and (iii) guided biological inference and validation of deconvolution results with the R/Shiny graphical user interface FactorViz. Our protocol simplifies the analysis and guides the initial interpretation of DNA methylation data derived from complex samples. The harmonized approach is particularly useful to dissect and evaluate cell heterogeneity in complex systems such as tumors. We apply the protocol to lung cancer methylomes from The Cancer Genome Atlas (TCGA) and show that our approach identifies the proportions of stromal cells and tumor-infiltrating immune cells, as well as associations of the detected components with clinical parameters. The protocol takes slightly >3 d to complete and requires basic R skills.

Modifications of H3K4 methylation levels are associated with DNA hypermethylation in acute myeloid leukemia

The 'instructive model' of aberrant DNA methylation in human tumors is based on the observation that CpG islands prone to hypermethylation in cancers are embedded in chromatin enriched in H3K27me3 in human embryonic stem cells (hESC). Recent studies also link methylation of CpG islands to the methylation status of H3K4, where H3K4me3 is inversely correlated with DNA methylation. To provide insight into these conflicting findings, we generated DNA methylation profiles for acute myeloid leukemia samples from patients and leukemic cell lines and integrated them with publicly available ChIp-seq data, containing H3K4me3 and H3K27me3 CpG island occupation in hESC, or hematopoietic stem or progenitor cells (hHSC/MPP). Hypermethylated CpG islands in AML samples displayed H3K27me3 enrichments in hESC and hHSC/MPP; however, ChIp analysis of specific hypermethylated CpG islands revealed a significant reduction in H3K4me3 signal with a concomitant increase in H3K4me0 levels as opposed to a nonsignificant increase in H3K27me3 marks. The integration of AML DNA methylation profiles with the ChIp-seq data in hESC and hHSC/MPP also led to the identification of Iroquois homeobox 2 (IRX2) as a previously unknown factor promoting differentiation of leukemic cells. Our results indicate that in contrast to the 'instructive model', H3K4me3 levels are strongly associated with DNA methylation patterns in AML and have a role in the regulation of critical genes, such as the putative tumor suppressor IRX2.

Polycomb repressive 2 complex-Molecular mechanisms of function

Numerous molecular processes conduct epigenetic regulation of protein transcription to maintain cell specification. In this review, we discuss molecular mechanisms of the Polycomb group of proteins and its enzymatic role in epigenetics. More specifically, we focus on the Polycomb repressive complex 2 (PRC2) and the effects of its repressive marker. We have compiled information regarding the biological structure and how that impacts the stability of the complex. In addition, we examined functions of the individual core proteins of PRC2 in relation to the accessory proteins that interact with the complex. Lastly, we discuss the implications of unregulated and downregulated PRC2 activity in Alzheimer's disease and cancer and possible methods of treatment related to PRC2 regulation.

Beyond DNA: the Role of Epigenetics in the Premalignant Progression of Breast Cancer

Ductal Carcinoma in Situ (DCIS) is an early breast cancer lesion that is considered a nonobligate precursor to development of invasive ductal carcinoma (IDC). Although only a small subset of DCIS lesions are predicted to progress into a breast cancer, distinguishing innocuous from minacious DCIS lesions remains a clinical challenge. Thus, patients diagnosed with DCIS will undergo surgery with the potential for radiation and hormone therapy. This has led to a current state of overdiagnosis and overtreatment. Interrogating the transcriptome alone has yet to define clear functional determinants of progression from DCIS to IDC. Epigenetic changes, critical for imprinting and tissue specific development, in the incorrect context can lead to global signaling rewiring driving pathological phenotypes. Epigenetic signaling pathways, and the molecular players that interpret and sustain their signals, are critical to understanding the underlying pathology of breast cancer progression. The types of epigenetic changes, as well as the molecular players, are expanding. In addition to DNA methylation, histone modifications, and chromatin remodeling, we must also consider enhancers as well as the growing field of noncoding RNAs. Herein we will review the epigenetic interactions that have been uncovered in early stage lesions that impact breast cancer progression, and how these players may be utilized as biomarkers to mitigate overdiagnosis and overtreatment.