Biology subjects

Andrechek, E.

Publications and source records attributed to Andrechek, E..

7 recordsLinked to original sources

bioRxiv2026

Tumor-derived FXII engages the intrinsic coagulation cascade to support breast cancer liver metastasis.

Metastasis is the leading cause of death in breast cancer, yet the mechanisms controlling organotropism are not well defined. Coagulation has emerged as a biologically relevant contributor to metastatic progression, but mechanisms linking pro-thrombotic phenotypes to organ-specific metastasis remain unresolved, significantly hindering the development of novel treatments. Here, a serial transplantation approach was used to enrich for liver organotropism from a spontaneous mouse mammary tumor model with occasional liver and lymph node metastasis. Comparative transcriptomics between the enriched liver and lymph node metastases revealed strong upregulation of coagulation in liver metastases, due in part to loss of repression of FXII with knockout of the E2F5 transcription factor. In vitro clotting assays demonstrated that tumor-derived FXII was sufficient to induce fibrin(ogen) clot formation. Moreover, liver metastatic cells exhibit elevated lipid peroxide levels and impaired lipid droplet formation associated with a pro-coagulant phenotype. Inhibition of coagulation with low molecular weight heparin reduced the presence of circulating tumor cells and suppressed liver metastasis in the mouse model. Human electronic health record data supported the translational relevance of these findings. Together, these data reveal a new mouse model where loss of E2F5 has resulted in tumors with elevated expression of FXII that have a propensity for liver metastasis and illustrates that anti-coagulation dramatically reduces the liver-specific metastasis in breast cancer. HighlightsE2F5 conditional knockout model develops breast tumors with liver tropism Liver metastasis hijacks the intrinsic coagulation cascade mediated by tumor-derived FXII Liver metastatic cells displayed lipid metabolic alterations that contributed to a pro-coagulant phenotype Low molecular weight heparin blocks liver metastasis and significantly reduces circulating tumor cells

cancer biology↗

bioRxiv2025

Neuronal innervation of breast cancer promotes metastatic dissemination

Metastasis is a leading cause of mortality in breast cancer patients, yet the signaling promoting metastatic dissemination is not completely understood. Prior literature implicates neuronal innervation in tumor progression, including recent studies in breast cancer progression with the 4T1 and PyMT cell line orthotopic injection models. Our experiments address the immune limitations of these studies with an alternative model to elucidate neuronal control of metastatic breast cancer by using a MMTV-PyMT transplant model and resiniferatoxin (RTX) for denervation. To this end, we generated a robust array of spontaneous MMTV-PyMT tumors with various histological subtypes. These tumors were transplanted into RTX challenged MMTV-Cre mice. In contrast to previous literature, denervation did not impact survival or tumor growth. Interestingly, we noticed a slight reduction in the percent of the solid poorly differentiated tumors with a corresponding increase in tumors that contained a mixed pathology in RTX challenged mice. Strikingly, and consistent with prior work, we noted a reduction in metastasis with denervation. Together, these data suggest neuronal innervation promotes metastasis without impacting tumor growth.

cancer biology↗

bioRxiv2024

Transcriptional Regulation of Mammary Alveolar Proliferation and Differentiation during Early Pregnancy

The E2F transcription factors are well-established cell cycle regulators, but their roles in coordinating proliferation and differentiation remain poorly understood. Here, we investigated the function of E2F5 during mammary gland development using a mammary epithelial-specific conditional knockout model. We found that E2F5 expression and chromatin binding increase during early pregnancy, coinciding with the critical window of alveolar development. Loss of E2F5 resulted in delayed alveolar expansion during early pregnancy, characterized by reduced side branching and smaller alveolar structures during early pregnancy. Mechanistically, E2F5 deletion led to reduced expression of canonical E2F target genes involved in cell cycle progression. Surprisingly, E2F5 loss also caused enrichment of luminal progenitor populations at the expense of differentiated alveolar cells, with chromatin profiling revealing substantial depletion of the repressive H3K27me3 mark at luminal progenitor-associated genes. These findings suggest that E2F5 promotes differentiation of luminal progenitors into proliferative alveolar precursors during early pregnancy. We propose that E2F5 coordinates both proliferation and differentiation by driving progenitor cells to differentiate into mature luminal cells. The dual function of E2F5 in mammary development distinguishes it from classical cell cycle regulators and positions it as a critical coordinator of the developmental transitions required for lactation.

developmental biology↗

bioRxiv2023

Integrative multi-omic sequencing reveals the MMTV-Myc mouse model mimics human breast cancer heterogeneity

BackgroundBreast cancer is a complex and heterogeneous disease with distinct subtypes and molecular profiles corresponding to different clinical outcomes. Mouse models of breast cancer are widely used, but their relevance in capturing the heterogeneity of human disease is unclear. Previous studies have shown the heterogeneity at the gene expression level for the MMTV-Myc model, but have only speculated on the underlying genetics. ResultsHerein, we examine three common histological subtypes of the MMTV-Myc model through whole genome sequencing and have integrated these results with gene expression data. Significantly, key genomic alterations driving cell signaling pathways were well conserved within histological subtypes. Genomic changes included frequent, co-occurring mutations in KIT and RARA in the microacinar histological subtype as well as SCRIB mutations in the EMT subtype. EMT tumors additionally displayed strong KRAS activation signatures downstream of genetic activating events primarily ascribed to KRAS activating mutations, but also FGFR2 amplification. Analogous genetic events in human breast cancer showed stark decreases in overall survival. In further analyzing transcriptional heterogeneity of the MMTV-Myc model, we report a supervised machine learning model that classifies MMTV-Myc histological subtypes and other mouse models as being representative of different human intrinsic breast cancer subtypes. ConclusionsWe conclude the well-established MMTV-Myc mouse model presents further opportunities for investigation of human breast cancer heterogeneity.

cancer biology↗

bioRxiv2019

Metastasis is altered through multiple processes regulated by the E2F1 transcription factor

The E2F family of transcription factors is important for many cellular processes, from their canonical role in cell cycle regulation to other roles in angiogenesis and metastasis. Alteration of the Rb/E2F pathway occurs in various forms of cancer, including breast cancer. E2F1 ablation has been shown to decrease metastasis in MMTV-Neu and MMTV-PyMT transgenic mouse models of breast cancer. Here we take a bioinformatic approach to determine the E2F1 regulated genomic alterations involved in the metastatic cascade, in both Neu and PyMT models. Through gene expression analysis, we reveal few transcriptome changes in non-metastatic E2F1-/- tumors relative to transgenic tumor controls. However investigation of these models through whole genome sequencing found numerous differences between the models, including differences in the proposed tumor etiology between E2F1-/- and E2F1+/+ tumors induced by Neu or PyMT. Investigating mutated genes through gene set analysis also found a significant number of genes mutated in the cell adhesion pathway in E2F1-/- tumors, indicating this may be a route for disruption of metastasis in E2F1-/- tumors. Overall, these findings illustrate the complicated nature of uncovering drivers of the metastatic process.

cancer biology↗

bioRxiv2018

Integrated sequence and gene expression analysis of mouse models of breast cancer reveals critical events with human parallels

Mouse models have an essential role in cancer research, yet little is known about how various models resemble human cancer at a genomic level. However, the shared genomic alterations in each model and corresponding human cancer are critical for translating findings in mice to the clinic. We have completed whole genome sequencing and transcriptome profiling of two widely used mouse models of breast cancer, MMTV-Neu and MMTV-PyMT. This genomic information was integrated with phenotypic data and CRISPR/Cas9 studies to understand the impact of key events on tumor biology. Despite the engineered initiating transgenic event in these mouse models, they contain similar copy number alterations, single nucleotide variants, and translocation events as human breast cancer. Through integrative in vitro and in vivo studies, we identified copy number alterations in key extracellular matrix proteins including Collagen 1 Type 1 alpha 1 (Col1a1) and Chondroadherin (CHAD) that drive metastasis in these mouse models. Importantly this amplification is also found in 25% of HER2+ human breast cancer and is associated with increased metastasis. In addition to copy number alterations, we observed a propensity of the tumors to modulate tyrosine kinase mediated signaling through mutation of phosphatases. Specifically, we found that 81% of MMTV-PyMT tumors have a mutation in the EGFR regulatory phosphatase, PTPRH. Mutation in PTPRH led to increased phospho-EGFR levels and decreased latency. Moreover, PTPRH mutations increased response to EGFR kinase inhibitors. Analogous PTPRH mutations are present in lung cancer patients and together this data suggests that a previously unidentified population of human lung cancer patients may respond to EGFR targeted therapy. These findings underscore the importance of understanding the complete genomic landscape of a mouse model and illustrate the utility this has in understanding human cancers.

cancer biology↗

bioRxiv2018

Evaluating cancer cell lines as models for metastatic breast cancer

Metastasis is the most common cause of cancer-related death and, as such, there is an urgent need to discover new therapies to treat metastasized cancers. Cancer cell lines are widely-used models to study cancer biology and test drug candidates. However, it is still unknown to what extent they adequately resemble the disease in patients. The recent accumulation of large-scale genomic data in cell lines, mouse models, and patient tissue samples provides an unprecedented opportunity to evaluate the suitability of cell lines for metastatic cancer research. In this work, we used breast cancer as a case study. The comprehensive comparison of the genetic profiles of 57 breast cancer cell lines with those of metastatic breast cancer samples revealed substantial genetic differences. In addition, we identified cell lines that more closely resemble different subtypes of metastatic breast cancer. Surprisingly, a combined analysis of mutation, copy number variation and gene expression data suggested that MDA-MB-231, the most commonly used triple negative cell line for metastatic breast cancer research, had little genomic similarity with Basal-like metastatic breast cancer samples. We further compared cell lines with organoids, a new type of preclinical model which are becoming more popular in recent years. We found that organoids outperformed cell lines in resembling the transcriptome of metastatic breast cancer samples. However, additional differential expression analysis suggested that both types of models could not mimic the effects of tumor microenvironment and meanwhile had their own bias towards modeling specific biological processes. Our work provides a guide of cell line selection in metastasis-related study and sheds light on the potential of organoids in translational research.

bioinformatics↗