Summary The ATM kinase plays a critical role in the maintenance of genetic stability. ATM is activated in response to DNA damage and is essential for cell cycle checkpoints. Here, we report that ATM is activated in mitosis in the absence of DNA damage. We demonstrate that mitotic ATM activation is dependent on the Aurora-B kinase and that Aurora-B phosphorylates ATM on serine 1403. This phosphorylation event is required for mitotic ATM activation. Further, we show that loss of ATM function results in shortened mitotic timing and a defective spindle checkpoint, and that abrogation of ATM Ser1403 phosphorylation leads to this spindle checkpoint defect. We also demonstrate that mitotically-activated ATM phosphorylates Bub1, a critical kinetochore protein, on Ser314. ATM-mediated Bub1 Ser314 phosphorylation is required for Bub1 activity and is essential for the activation of the spindle checkpoint. Collectively, our data highlight mechanisms of a critical function of ATM in mitosis.

Little research has been done to address the huge opportunities that may exist to reposition existing approved or generic drugs for alternate uses in cancer therapy. Additionally, there has been little work on strategies to reposition experimental cancer agents for testing in alternate settings that could shorten their clinical development time. Progress in each area has lagged in part due to the lack of systematic methods to define drug off-target effects (OTEs) that might affect important cancer cell signaling pathways. In this study, we addressed this critical gap by developing an OTE-based method to repurpose drugs for cancer therapeutics, based on transcriptional responses made in cells before and after drug treatment. Specifically, we defined a new network component called cancer-signaling bridges (CSBs) and integrated it with Bayesian Factor Regression Model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof of concept studies were performed in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to >90% of FDA-approved drugs and >75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for several high-ranking drug-dose pairs suggested repositioning opportunities for cancer therapy, based on the ability to enforce Rb-dependent repression of important E2F-dependent cell cycle genes. Together, our findings establish new methods to identify opportunities for drug repositioning or to elucidate the mechanisms of action of repositioned drugs.

Triple-negative breast cancer (TNBC) exhibits more traits possessed by cancer stem cells (CSC) than other breast cancer subtypes and is more likely to develop brain metastases. TNBC patients usually have shorter survival time after diagnosis of brain metastasis, suggesting an innate ability of TNBC tumor cells in adapting to the brain. In this study, we establish novel animal models to investigate early tumor adaptation in brain metastases by introducing both patient-derived and cell line-derived CSC-enriched brain metastasis tumorsphere cells into mice. We discovered astrocyte-involved tumor activation of protocadherin 7 (PCDH7)-PLCβ-Ca2+-CaMKII/S100A4 signaling as a mediator of brain metastatic tumor outgrowth. We further identified and evaluated the efficacy of a known drug, the selective PLC inhibitor edelfosine, in suppressing the PCDH7 signaling pathway to prohibit brain metastases in the animal models. The results of this study reveal a novel signaling pathway for brain metastases in TNBC and indicate a promising strategy of metastatic breast cancer prevention and treatment by targeting organ-adaptive cancer stem cells. These findings identify a compound to block adaptive signaling between cancer stem cells and brain astrocytes. .

The activation of the epithelial-to-mesenchymal transition (EMT) program is a critical step in cancer progression and metastasis, but visualization of this process at the single cell level, especially in vivo, remains challenging. We established an in vivo approach to track the fate of tumor cells based on a novel EMT-driven fluorescent color switching breast cancer mouse model and intravital two-photon laser scanning microscopy. Specifically, the MMTV-PyMT, Rosa26-RFP-GFP, and Fsp1-Cre triple transgenic mouse model was used to monitor the conversion of RFP-positive epithelial cells to GFP-positive mesenchymal cells in mammary tumors under the control of the Fsp1 (ATL1) promoter, a gate-keeper of EMT initiation. RFP-positive cells were isolated from the tumors, sorted, and transplanted into mammary fat pads of SCID mice to monitor EMT during breast tumor formation. We found that the conversion from RFP to GFP-positive and spindle-shaped cells was a gradual process, and that GFP-positive cells preferentially localized close to blood vessels, independent of tumor size. Furthermore, cells undergoing EMT expressed high levels of the HGF receptor, c-Met, and treatment of RFP-positive cells with the c-Met inhibitor, cabozantinib, suppressed the RFP-to-GFP conversion in vitro. Moreover, administration of cabozantinib to mice with palpable RFP-positive tumors resulted in a silent EMT phenotype whereby GFP-positive cells exhibited reduced motility, leading to suppressed tumor growth. In conclusion, our imaging technique provides a novel opportunity for visualizing tumor EMT at the single cell level and may help to reveal the intricacies underlying tumor dynamics and treatment responses.

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.

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Medulloblastoma (MB) is the most common malignant brain tumor of childhood. Although outcomes have improved in recent decades, new treatments are still needed to improve survival and reduce treatment-related complications. The MB subtypes groups 3 and 4 represent a particular challenge due to their intragroup heterogeneity, which limits the options for “rational” targeted therapies. Here, we report a systems biology approach to drug repositioning that integrates a nonparametric, bootstrapping-based simulated annealing algorithm and a 3D drug functional network to characterize dysregulated driver signaling networks, thereby identifying potential drug candidates. From more than 1300 drug candidates studied, we identified five members of the cardiac glycoside family as potentially inhibiting the growth of groups 3 and 4 MB and subsequently confirmed this in vitro. Systemic in vivo treatment of orthotopic patient-derived xenograft (PDX) models of groups 3 and 4 MB with digoxin, a member of the cardiac glycoside family approved for the treatment of heart failure, prolonged animal survival at plasma concentrations known to be tolerated in humans. These results demonstrate the power of a systematic drug repositioning method in identifying a potential treatment for MB. Our strategy could potentially be used to accelerate the repositioning of treatments for other human cancers that lack clearly defined rational targets.

Convincing epidemiological data suggest an inverse association between cancer and neurodegeneration, including Alzheimer's disease (AD). Since both AD and cancer are characterized by abnormal, but opposing cellular behavior, i.e., increased cell death in AD while excessive cell growth occurs in cancer, this motivates us to initiate the study into unraveling the shared genes and cell signaling pathways linking AD and glioblastoma multiform (GBM). In this study, a comprehensive bioinformatics analysis on clinical microarray datasets of 1,091 GBM and 524 AD cohorts was performed. Significant genes and pathways were identified from the bioinformatics analyses – in particular ERK/MAPK signaling, up-regulated in GBM and Angiopoietin Signaling pathway, reciprocally up-regulated in AD – connecting GBM and AD (P < 0.001), were investigated in details for their roles in GBM growth in an AD environment. Our results showed that suppression of GBM growth in an AD background was mediated by the ERK-AKT-p21-cell cycle pathway and anti-angiogenesis pathway.