Metastasis, a fatal complication of breast cancer, does not fully benefit from available therapies. In this study, we investigated whether ATIP3, the major product of 8p22 MTUS1 gene, may be a novel biomarker and therapeutic target for metastatic breast tumors. We show that ATIP3 is a prognostic marker for overall survival among patients with breast cancer. Notably, among metastatic tumors, low ATIP3 levels associate with decreased survival of the patients. By using a well-defined experimental mouse model of cancer metastasis, we show that ATIP3 expression delays the time-course of metastatic progression and limits the number and size of metastases in vivo. In functional studies, ATIP3 silencing increases breast cancer cell migration, whereas ATIP3 expression significantly reduces cell motility and directionality. We report here that ATIP3 is a potent microtubule-stabilizing protein whose depletion increases microtubule dynamics. Our data support the notion that by decreasing microtubule dynamics, ATIP3 controls the ability of microtubule tips to reach the cell cortex during migration, a mechanism that may account for reduced cancer cell motility and metastasis. Of interest, we identify a functional ATIP3 domain that associates with microtubules and recapitulates the effects of ATIP3 on microtubule dynamics, cell proliferation, and migration. Our study is a major step toward the development of new personalized treatments against metastatic breast tumors that have lost ATIP3 expression. Cancer Res; 73(9); 2905-15. Ó2013 AACR.

The regulation of microtubule dynamics is critical to ensure essential cell functions. End binding protein 1 (EB1) is a master regulator of microtubule dynamics that autonomously binds an extended GTP/GDP-Pi structure at growing microtubule ends and recruits regulatory proteins at this location. However, negative regulation of EB1 association with growing microtubule ends remains poorly understood. We show here that microtubule-associated tumor suppressor ATIP3 interacts with EB1 through direct binding of a non-canonical proline-rich motif. Results indicate that ATIP3 does not localize at growing microtubule ends and that in situ ATIP3-EB1 molecular complexes are mostly detected in the cytosol. We present evidence that a minimal EB1-interacting sequence of ATIP3 is both necessary and sufficient to prevent EB1 accumulation at growing microtubule ends in living cells and that EB1-interaction is involved in reducing cell polarity. By fluorescence recovery of EB1-GFP after photobleaching, we show that ATIP3 silencing accelerates EB1 turnover at microtubule ends with no modification of EB1 diffusion in the cytosol. We propose a novel mechanism by which ATIP3-EB1 interaction indirectly reduces the kinetics of EB1 exchange on its recognition site, thereby accounting for negative regulation of microtubule dynamic instability. Our findings provide a unique example of decreased EB1 turnover at growing microtubule ends by cytosolic interaction with a tumor suppressor.

Cancer cell heterogeneity is well-documented. Therefore, techniques to monitor single cell heterogeneous responses to treatment are needed. We developed a highly translational and quantitative bioluminescence microscopy method to measure single cell androgen receptor (AR) activity modulation by antiandrogens from fluid biopsies. We showed that this assay can detect heterogeneous cellular response to drug treatment and that the sum of single cell AR activity can mirror the response in the whole cell population. This method may thus be used to monitor heterogeneous dynamic treatment responses in cancer cells.

A precisely controlled network of protein-protein interactions constitutes the basis for functional signaling pathways. This equilibrium is more often than not disrupted in cancer cells, by the aberrant expression or activation of oncogenic proteins. Therefore, the analysis of protein interaction networks in cancer cells has become crucial to expand our comprehension of the molecular underpinnings of tumor formation and progression. This protocol describes a sample preparation method for the analysis of signaling complexes by mass spectrometry (MS), following the affinity purification of a protein of interest from a cancer cell line or a solid tumor. In particular, we provide a spin tip-based protease digestion procedure that offers a more rapid and controlled alternative to other gel-based and gel-free methods. This sample preparation protocol represents a useful strategy to identify protein interactions and to gain insight into the molecular mechanisms that contribute to a given cancer phenotype.

Prostate cancer (PCa) is the most frequently diagnosed cancer in men and the third cause of cancer mortality. PCa initiation and growth are driven by the androgen receptor (AR). The AR is activated by androgens such as testosterone and controls prostatic cell proliferation and survival. Here, we report an AR signaling network generated using BioID proximity labeling proteomics in androgen-dependent LAPC4 cells. We identified 31 AR-associated proteins in nonstimulated cells. Strikingly, the AR signaling network increased to 182 and 200 proteins, upon 24 h or 72 h of androgenic stimulation, respectively, for a total of 267 nonredundant AR-associated candidates. Among the latter group, we identified 213 proteins that were not previously reported in databases. Many of these new AR-associated proteins are involved in DNA metabolism, RNA processing, and RNA polymerase II transcription. Moreover, we identified 44 transcription factors, including the Kru¨ppel-like factor 4 (KLF4), which were found interacting in androgen-stimulated cells. Interestingly, KLF4 repressed the well-characterized AR-dependent transcription of the KLK3 ( PSA ) gene; AR and KLF4 also colocalized genome-wide. Taken together, our data report an expanded high-confidence proximity network for AR, which will be instrumental to further dissect the molecular mechanisms underlying androgen signaling in PCa cells.

No abstract

When several life-prolonging drugs are indicated for cancer treatment, predictive drug-response tumor biomarkers are essential to guide management. Most conventional biomarkers are based on bulk tissue analysis, which cannot address the complexity of single-cell heterogeneity responsible for drug resistance. Therefore, there is a need to develop alternative drug response predictive biomarker approaches that could directly interrogate single-cell and whole population cancer cell drug sensitivity. In this study, we report a novel method exploiting bioluminescence microscopy to detect single prostate cancer (PCa) cell response to androgen receptor (AR)-axis-targeted therapies (ARAT) and predict cell population sensitivity. Methods: We have generated a new adenovirus-delivered biosensor, PCA3-Cre-PSEBC-ITSTA, which combines an integrated two-step transcriptional amplification system (ITSTA) and the activities of the prostate cancer antigen 3 (PCA3) and modified prostate-specific antigen (PSEBC) gene promoters as a single output driving the firefly luciferase reporter gene. This system was tested on PCa cell lines and on primary PCa cells. Single cells, exposed or not to ARAT, were dynamically imaged by bioluminescence microscopy. A linear discriminant analysis (LDA)-based method was used to determine cell population sensitivities to ARAT. Results: We show that the PCA3-Cre-PSEBC-ITSTA biosensor is PCa-specific and can dynamically monitor single-cell AR transcriptional activity before and after ARAT by bioluminescence microscopy. After biosensor transduction and bioluminescence microscopy single-cell luminescence dynamic quantification, LDA analysis could discriminate the cell populations overall ARAT sensitivity despite heterogeneous single-cell responses. Indeed, the biosensor could detect a significant decrease in AR activity following exposure to conventional ARAT in hormone-naive primary PCa cells, while in castration-resistant PCa patients, treatment response correlated with the observed clinical ARAT resistance. Conclusion:The exploitation of bioluminescence microscopy and multi-promoter transcriptionally-regulated biosensors can aptly define the overall treatment response of patients by monitoring live single cell drug response from primary cancer tissue. This approach can be used to develop predictive biomarkers for drug response in order to help clinicians select the best drug combinations or sequences for each patient.

<p>Movie 7 AVI file - 629K, Time-lapse videomicroscopy of wound healing assay in GFP-D2-transiently transfected D3H2LN cells</p>