Krt6a-positive mammary epithelial progenitors are not at increased vulnerability to tumorigenesis initiated by ErbB2

Advances in Immunocompetent Mouse and Rat Models

Rodent models of breast cancer have played critical roles in our understanding of breast cancer development and progression as well as preclinical testing of cancer prevention and therapeutics. In this article, we first review the values and challenges of conventional genetically engineered mouse (GEM) models and newer iterations of these models, especially those with inducible or conditional regulation of oncogenes and tumor suppressors. Then, we discuss nongermline (somatic) GEM models of breast cancer with temporospatial control, made possible by intraductal injection of viral vectors to deliver oncogenes or to manipulate the genome of mammary epithelial cells. Next, we introduce the latest development in precision editing of endogenous genes using in vivo CRISPR-Cas9 technology. We conclude with the recent development in generating somatic rat models for modeling estrogen receptor-positive breast cancer, something that has been difficult to accomplish in mice.

In Vivo Gene Delivery into Mouse Mammary Epithelial Cells Through Mammary Intraductal Injection

Mouse mammary glands comprise ductal trees, which are lined by epithelial cells and have one opening at the tip of each nipple. The epithelial cells play a major role in mammary gland function and are the origin of most mammary tumors. Introducing genes of interest into mouse mammary epithelial cells is a critical step in evaluating gene function in epithelial cells and generating mouse mammary tumor models. This goal can be accomplished through the intraductal injection of a viral vector carrying the genes of interest into the mouse mammary ductal tree. The injected virus subsequently infects mammary epithelial cells, bringing in the genes of interest. The viral vector can be lentiviral, retroviral, adenoviral, or adenovirus-associated viral (AAV). This study demonstrates how a gene of interest is delivered into mammary epithelial cells through mouse mammary intraductal injection of a viral vector. A lentivirus carrying GFP is used to show stable expression of a delivered gene, and a retrovirus carrying Erbb2 (HER2/Neu) is used to demonstrate oncogene-induced atypical hyperplastic lesions and mammary tumors.

Intraductal Injection of Lentivirus Vectors for Stably Introducing Genes into Rat Mammary Epithelial Cells in Vivo

Various retroviral and lentiviral vectors have been used for up-the-teat intraductal injection to deliver markers, oncogenes, and other genes into mammary epithelial cells in mice. These methods along with the large number of genetically engineered mouse lines have greatly helped us learn normal breast development and tumorigenesis. Rats are also valuable models for studying human breast development and cancer. However, genetically engineered rats are still uncommon, and previous reports of intraductal injection of retroviral vectors into rats appear to be inefficient in generating mammary tumors. Here, we report, and describe the method for, stably introducing marker genes and oncogenes into mammary glands in rats using intraductal injection of commonly used lentiviral vectors. This method can infect mammary epithelial cells efficiently, and the infected cells can initiate tumorigenesis, including estrogen receptor-positive and hormone-dependent tumors, which are the most common subtype of human breast cancer but are yet still difficult to model in mice. This technique provides another tool for studying formation, prevention, and treatment of breast cancer, especially estrogen receptor-positive breast cancer.

Alteration of tumor-associated macrophage subtypes mediated by KRT6A in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is severely affecting the health and lives of patients. Clarifying the composition and regulatory factors of tumor immune microenvironment (TIME) is helpful for the treatment of PDAC. We analyzed the unique TIMEs and gene expression patterns between PDAC and adjacent normal tissue (ANT) using Gene Expression Omnibus (GEO) to find new immunotherapy targets. The Cancer Genome Atlas (TCGA) datasets were used to elucidate the possible mechanism of which tumor-associated macrophages (TAMs) changed in PDAC. We found that the composition of TAMs subtypes, including M0, M1, and M2, was different between PDAC and ANT, which was validated in recently published single-cell RNA-seq data. Many immune cells interacted with each other to affect the TIME. There were many DEGs enriched in some pathways that could potentially change the immune cell composition. KRT6A was found to be a DEG between PDAC and ANT that overlapped with DEGs between the M0-high group and the M0-low group in TCGA datasets, and it might alter and regulate TAMs via a collection of genes including COL5A2, COL1A2, MIR3606, SPARC, and COL6A3. TAMs, which could be a target of immunotherapy, might be influenced by genes through KRT6A and indicate an undesirable prognosis in PDAC.

Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication

Lung cancer is the most occurring cancer type, and its mortality rate is also the highest, among them lung adenocarcinoma (LUAD) accounts for about 40 % of lung cancer. There is an urgent need to develop a prognosis prediction model for lung adenocarcinoma. Previous LUAD prognosis studies only took single-omics data, such as mRNA or miRNA, into consideration. To this end, we proposed a deep learning-based autoencoding approach for combination of four-omics data, mRNA, miRNA, DNA methylation and copy number variations, to construct an autoencoder model, which learned representative features to differentiate the two optimal patient subgroups with a significant difference in survival (P = 4.08e-09) and good consistency index (C-index = 0.65). The multi-omics model was validated though four independent datasets, i.e. GSE81089 for mRNA (n = 198, P = 0.0083), GSE63805 for miRNA (n = 32, P = 0.018), GSE63384 for DNA methylation (n = 35, P = 0.009), and TCGA independent samples for copy number variations (n = 94, P = 0.0052). Finally, a functional analysis was performed on two survival subgroups to discover genes involved in biological processes and pathways. This is the first study incorporating deep autoencoding and four-omics data to construct a robust survival prediction model, and results show the approach is useful at predicting LUAD prognostication.

Mammary Precancerous Stem and Non-Stem Cells Evolve into Cancers of Distinct Subtypes

There are distinct cell subpopulations in normal epithelial tissue, including stem cells, progenitor cells, and more differentiated cells, all of which have been extensively studied for their susceptibility to tumorigenesis. However, normal cells usually have to progress through a precancerous lesion state before becoming a full-blown tumor. Precancerous early lesions are heterogeneous, and the cell subset that is the primary source of the eventual tumor remains largely unknown. By using mouse models that are tailored to address this question, we identified a keratin 6a-expressing precancerous stem cell (PcSC) subset and a more differentiated whey acidic protein-positive (WAP+) cell subset in mammary precancerous lesions initiated by the Wnt1 oncogene. Both cell subsets rapidly progressed to cancer upon introduction of constitutively active versions of either HRAS or BRAF. However, the resulting tumors were dramatically different in protein profiles and histopathology: keratin 6a+ precancerous cells gave rise to adenocarcinoma, whereas WAP+ cells yielded metaplastic carcinoma with severe squamous differentiation and more robust activation of MEK/ERK signaling. Therefore, both stem and non-stem cells in mammary precancerous lesions can contribute to the eventual cancers, but their differentiation status determines the resulting cancer phenotype. This work identifies a previously unknown player in cancer heterogeneity and suggests that cancer prevention should target precancerous cells broadly and not be limited to PcSC. SIGNIFICANCE: This work uses a novel mouse mammary gland cancer model to show that tumors initiated from different precancerous mammary epithelial cells are distinct.

Pancreatic cancer survival analysis defines a signature that predicts outcome

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the US. Despite multiple large-scale genetic sequencing studies, identification of predictors of patient survival remains challenging. We performed a comprehensive assessment and integrative analysis of large-scale gene expression datasets, across multiple platforms, to enable discovery of a prognostic gene signature for patient survival in pancreatic cancer. PDAC RNA-Sequencing data from The Cancer Genome Atlas was stratified into Survival+ (>2-year survival) and Survival-(<1-year survival) cohorts (n = 47). Comparisons of RNA expression profiles between survival groups and normal pancreatic tissue expression data from the Gene Expression Omnibus generated an initial PDAC specific prognostic differential expression gene list. The candidate prognostic gene list was then trained on the Australian pancreatic cancer dataset from the ICGC database (n = 103), using iterative sampling based algorithms, to derive a gene signature predictive of patient survival. The gene signature was validated in 2 independent patient cohorts and against existing PDAC subtype classifications. We identified 707 candidate prognostic genes exhibiting differential expression in tumor versus normal tissue. A substantial fraction of these genes was also found to be differentially methylated between survival groups. From the candidate gene list, a 5-gene signature (ADM, ASPM, DCBLD2, E2F7, and KRT6A) was identified. Our signature demonstrated significant power to predict patient survival in two distinct patient cohorts and was independent of AJCC TNM staging. Cross-validation of our gene signature reported a better ROC AUC (≥ 0.8) when compared to existing PDAC survival signatures. Furthermore, validation of our signature through immunohistochemical analysis of patient tumor tissue and existing gene expression subtyping data in PDAC, demonstrated a correlation to the presence of vascular invasion and the aggressive squamous tumor subtype. Assessment of these genes in patient biopsies could help further inform risk-stratification and treatment decisions in pancreatic cancer.

Hyperprolactinemia-inducing antipsychotics increase breast cancer risk by activating JAK-STAT5 in precancerous lesions

BACKGROUND

Psychiatric medications are widely prescribed in the USA. Many antipsychotics cause serum hyperprolactinemia as an adverse side effect; prolactin-Janus kinase 2 (JAK2)-signal transducer and activator of transcription 5 (STAT5) signaling both induces cell differentiation and suppresses apoptosis. It is controversial whether these antipsychotics increase breast cancer risk.

METHODS

We investigated the impact of several antipsychotics on mammary tumorigenesis initiated by retrovirus-mediated delivery of either ErbB2 or HRas or by transgenic expression of Wnt-1.

RESULTS

We found that the two hyperprolactinemia-inducing antipsychotics, risperidone and pimozide, prompted precancerous lesions to progress to cancer while aripiprazole, which did not cause hyperprolactinemia, did not. We observed that risperidone and pimozide (but not aripiprazole) caused precancerous cells to activate STAT5 and suppress apoptosis while exerting no impact on proliferation. Importantly, we demonstrated that these effects of antipsychotics on early lesions required the STAT5 gene function. Furthermore, we showed that only two-week treatment of mice with ruxolitinib, a JAK1/2 inhibitor, blocked STAT5 activation, restored apoptosis, and prevented early lesion progression.

CONCLUSIONS

Hyperprolactinemia-inducing antipsychotics instigate precancerous cells to progress to cancer via JAK/STAT5 to suppress the apoptosis anticancer barrier, and these cancer-promoting effects can be prevented by prophylactic anti-JAK/STAT5 treatment. This preclinical work exposes a potential breast cancer risk from hyperprolactinemia-inducing antipsychotics in certain patients and suggests a chemoprevention regime that is relatively easy to implement compared to the standard 5-year anti-estrogenic treatment in women who have or likely have already developed precancerous lesions while also requiring hyperprolactinemia-inducing antipsychotics.