NEK5 activity regulates the mesenchymal and migratory phenotype in breast cancer cells.

PURPOSE: Breast cancer remains a prominent global disease affecting women worldwide despite the emergence of novel therapeutic regimens. Metastasis is responsible for most cancer-related deaths, and acquisition of a mesenchymal and migratory cancer cell phenotypes contributes to this devastating disease. The utilization of kinase targets in drug discovery have revolutionized the field of cancer research but despite impressive advancements in kinase-targeting drugs, a large portion of the human kinome remains understudied in cancer. NEK5, a member of the Never-in-mitosis kinase family, is an example of such an understudied kinase. Here, we characterized the function of NEK5 in breast cancer. METHODS: Stably overexpressing NEK5 cell lines (MCF7) and shRNA knockdown cell lines (MDA-MB-231, TU-BcX-4IC) were utilized. Cell morphology changes were evaluated using immunofluorescence and quantification of cytoskeletal components. Cell proliferation was assessed by Ki-67 staining and transwell migration assays tested cell migration capabilities. In vivo experiments with murine models were necessary to demonstrate NEK5 function in breast cancer tumor growth and metastasis. RESULTS: NEK5 activation altered breast cancer cell morphology and promoted cell migration independent of effects on cell proliferation. NEK5 overexpression or knockdown does not alter tumor growth kinetics but promotes or suppresses metastatic potential in a cell type-specific manner, respectively. CONCLUSION: While NEK5 activity modulated cytoskeletal changes and cell motility, NEK5 activity affected cell seeding capabilities but not metastatic colonization or proliferation in vivo. Here we characterized NEK5 function in breast cancer systems and we implicate NEK5 in regulating specific steps of metastatic progression.

A novel screening approach comparing kinase activity of small molecule inhibitors with similar molecular structures and distinct biologic effects in triple-negative breast cancer to identify targetable signaling pathways.

Breast cancer affects women globally; the majority of breast cancer-related mortalities are due to metastasis. Acquisition of a mesenchymal phenotype has been implicated in the progression of breast cancer cells to an invasive, metastatic state. Triple-negative breast cancer (TNBC) subtypes have high rates of metastases, recurrence, and have poorer prognoses compared to other breast cancer types, partially due to lack of commonly targeted receptors. Kinases have diverse and pivotal functions in metastasis in TNBC, and discovery of new kinase targets for TNBC is warranted. We previously used a screening approach to identify intermediate-synthesis nonpotent, nonselective small-molecule inhibitors from the Published Kinase Inhibitor Set that reversed the mesenchymal phenotype in TNBC cells. Two of these inhibitors (GSK346294A and GSK448459A) are structurally similar, but have unique kinase activity profiles and exhibited differential biologic effects on TNBC cells, specifically on epithelial-to-mesenchymal transition (EMT). Here, we further interrogate these effects and compare activity of these inhibitors on transwell migration, gene (qRT-PCR) and protein (western blot) expressions, and cancer stem cell-like behavior. We incorporated translational patient-derived xenograft models in these studies, and we focused on the lead inhibitor hit, GSK346294A, to demonstrate the utility of our comparative analysis as a screening modality to identify novel kinase targets and signaling pathways to pursue in TNBC. This study introduces a new method for discovering novel kinase targets that reverse the EMT phenotype; this screening approach can be applied to all cancer types and is not limited to breast cancer.

A novel approach correlating pathologic complete response with digital pathology and radiomics in triple-negative breast cancer.

This rapid communication highlights the correlations between digital pathology-whole slide imaging (WSI) and radiomics-magnetic resonance imaging (MRI) features in triple-negative breast cancer (TNBC) patients. The research collected 12 patients who had both core needle biopsy and MRI performed to evaluate pathologic complete response (pCR). The results showed that higher collagenous values in pathology data were correlated with more homogeneity, whereas higher tumor expression values in pathology data correlated with less homogeneity in the appearance of tumors on MRI by size zone non-uniformity normalized (SZNN). Higher myxoid values in pathology data are correlated with less similarity of gray-level non-uniformity (GLN) in tumor regions on MRIs, while higher immune values in WSIs correlated with the more joint distribution of smaller-size zones by small area low gray-level emphasis (SALGE) in the tumor regions on MRIs. Pathologic complete response (pCR) was associated with collagen, tumor, and myxoid expression in WSI and GLN and SZNN in radiomic features. The correlations of WSI and radiomic features may further our understanding of the TNBC tumoral microenvironment (TME) and could be used in the future to better tailor the use of neoadjuvant chemotherapy (NAC). This communication will focus on the post-NAC MRI features correlated with pCR and their association with WSI features from core needle biopsies.

Application of artificial intelligence in predicting lymph node metastasis in breast cancer.

Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.

Radiation hematologic toxicity prediction for locally advanced rectal cancer using dosimetric and radiomics features.

BACKGROUND: Hematologic toxicity (HT) is a common adverse tissue reaction during radiotherapy for rectal cancer patients, which may lead to various negative effects such as reduced therapeutic effect, prolonged treatment period and increased treatment cost. Therefore, predicting the occurrence of HT before radiotherapy is necessary but still challenging. PURPOSE: This study proposes a hybrid machine learning model to predict the symptomatic radiation HT in rectal cancer patients using the combined demographic, clinical, dosimetric, and Radiomics features, and ascertains the most effective regions of interest (ROI) in CT images and predictive feature sets. METHODS: A discovery dataset of 240 rectal cancer patients, including 145 patients with HT symptoms and a validation dataset of 96 patients (63 patients with HT) with different dose prescription were retrospectively enrolled. Eight ROIs were contoured on patient CT images to derive Radiomics features, which were then, respectively, combined with the demographic, clinical, and dosimetric features to classify patients with HT symptoms. Moreover, the survival analysis was performed on risky patients with HT in order to understand the HT progression. RESULTS: The classification models in ROIs of bone marrow and femoral head exhibited relatively high accuracies (accuracy = 0.765 and 0.725) in the discovery dataset as well as comparable performances in the validation dataset (accuracy = 0.758 and 0.714). When combining the two ROIs together, the model performance was the best in both discovery and validation datasets (accuracy = 0.843 and 0.802). In the survival analysis test, only the bone marrow ROI achieved statistically significant performance in accessing risky HT (C-index = 0.658, P = 0.03). Most of the discriminative features were Radiomics features, and only gender and the mean dose in Irradvolume was involved in HT. CONCLUSION: The results reflect that the Radiomics features of bone marrow are significantly correlated with HT occurrence and progression in rectal cancer. The proposed Radiomics-based model may help the early detection of radiotherapy induced HT in rectal cancer patients and thus improve the clinical outcome in future.

MAP3K Family Review and Correlations with Patient Survival Outcomes in Various Cancer Types.

The mitogen-activated protein kinase (MAPK) pathways are ubiquitous in cellular signaling and are essential for proper biological functions. Disruptions in this signaling axis can lead to diseases such as the development of cancer. In this review, we discuss members of the MAP3K family and correlate their mRNA expression levels to patient survival outcomes in different cancers. Furthermore, we highlight the importance of studying the MAP3K family due to their important roles in the larger, overall MAPK pathway, relationships with cancer progression, and the understudied status of these kinases.

A Role for Adipocytes and Adipose Stem Cells in the Breast Tumor Microenvironment and Regenerative Medicine.

Obesity rates are climbing, representing a confounding and contributing factor to many disease states, including cancer. With respect to breast cancer, obesity plays a prominent role in the etiology of this disease, with certain subtypes such as triple-negative breast cancer having a strong correlation between obesity and poor outcomes. Therefore, it is critical to examine the obesity-related alterations to the normal stroma and the tumor microenvironment (TME). Adipocytes and adipose stem cells (ASCs) are major components of breast tissue stroma that have essential functions in both physiological and pathological states, including energy storage and metabolic homeostasis, physical support of breast epithelial cells, and directing inflammatory and wound healing responses through secreted factors. However, these processes can become dysregulated in both metabolic disorders, such as obesity and also in the context of breast cancer. Given the well-established obesity-neoplasia axis, it is critical to understand how interactions between different cell types in the tumor microenvironment, including adipocytes and ASCs, govern carcinogenesis, tumorigenesis, and ultimately metastasis. ASCs and adipocytes have multifactorial roles in cancer progression; however, due to the plastic nature of these cells, they also have a role in regenerative medicine, making them promising tools for tissue engineering. At the physiological level, the interactions between obesity and breast cancer have been examined; here, we will delineate the mechanisms that regulate ASCs and adipocytes in these different contexts through interactions between cancer cells, immune cells, and other cell types present in the tumor microenvironment. We will define the current state of understanding of how adipocytes and ASCs contribute to tumor progression through their role in the tumor microenvironment and how this is altered in the context of obesity. We will also introduce recent developments in utilizing adipocytes and ASCs in novel approaches to breast reconstruction and regenerative medicine.