p27 transcriptionally coregulates cJun to drive programs of tumor progression.

p27 shifts from CDK inhibitor to oncogene when phosphorylated by PI3K effector kinases. Here, we show that p27 is a cJun coregulator, whose assembly and chromatin association is governed by p27 phosphorylation. In breast and bladder cancer cells with high p27pT157pT198 or expressing a CDK-binding defective p27pT157pT198 phosphomimetic (p27CK-DD), cJun is activated and interacts with p27, and p27/cJun complexes localize to the nucleus. p27/cJun up-regulates TGFB2 to drive metastasis in vivo. Global analysis of p27 and cJun chromatin binding and gene expression shows that cJun recruitment to many target genes is p27 dependent, increased by p27 phosphorylation, and activates programs of epithelial-mesenchymal transformation and metastasis. Finally, human breast cancers with high p27pT157 differentially express p27/cJun-regulated genes of prognostic relevance, supporting the biological significance of the work.

Erratum to: PI3K/mTOR inhibition can impair tumor invasion and metastasis in vivo despite a lack of antiproliferative action in vitro: implications for targeted therapy.

Erratum to: Breast Cancer Res Treat (2013),138:369­381,DOI 10.1007/s10549-012-2389-6. In the original publication of the article, the Fig. 4c and d were published erroneously. The revised Fig. 4 is given in this erratum.

PI3K/mTOR inhibition can impair tumor invasion and metastasis in vivo despite a lack of antiproliferative action in vitro: implications for targeted therapy.

Oncogenic PI3K/mTOR activation is frequently observed in human cancers and activates cell motility via p27 phosphorylations at T157 and T198. Here we explored the potential for a novel PI3K/mTOR inhibitor to inhibit tumor invasion and metastasis. An MDA-MB-231 breast cancer line variant, MDA-MB-231-1833, with high metastatic bone tropism, was treated with a novel catalytic PI3K/mTOR inhibitor, PF-04691502, at nM doses that did not impair proliferation. Effects on tumor cell motility, invasion, p27 phosphorylation, localization, and bone metastatic outgrowth were assayed. MDA-MB-231-1833 showed increased PI3K/mTOR activation, high levels of cytoplasmic p27pT157pT198 and increased cell motility and invasion in vitro versus parental. PF-04691502 treatment, at a dose that did not affect proliferation, reduced total and cytoplasmic p27, decreased p27pT157pT198 and restored cell motility and invasion to levels seen in MDA-MB-231. p27 knockdown in MDA-MB-231-1833 phenocopied PI3K/mTOR inhibition, whilst overexpression of the phosphomimetic mutant p27T157DT198D caused resistance to the anti-invasive effects of PF-04691502. Pre-treatment of MDA-MB-231-1833 with PF-04691502 significantly impaired metastatic tumor formation in vivo, despite lack of antiproliferative effects in culture and little effect on primary orthotopic tumor growth. A further link between cytoplasmic p27 and metastasis was provided by a study of primary human breast cancers which showed cytoplasmic p27 is associated with increased lymph nodal metastasis and reduced survival. Novel PI3K/mTOR inhibitors may oppose tumor metastasis independent of their growth inhibitory effects, providing a rationale for clinical investigation of PI3K/mTOR inhibitors in settings to prevent micrometastasis. In primary human breast cancers, cytoplasmic p27 is associated with worse outcomes and increased nodal metastasis, and may prove useful as a marker of both PI3K/mTOR activation and PI3K/mTOR inhibitor efficacy.

Tri-Compartmental Restriction Spectrum Imaging Breast Model Distinguishes Malignant Lesions from Benign Lesions and Healthy Tissue on Diffusion-Weighted Imaging.

Diffusion-weighted MRI (DW-MRI) offers a potential adjunct to dynamic contrast-enhanced MRI to discriminate benign from malignant breast lesions by yielding quantitative information about tissue microstructure. Multi-component modeling of the DW-MRI signal over an extended b-value range (up to 3000 s/mm2) theoretically isolates the slowly diffusing (restricted) water component in tissues. Previously, a three-component restriction spectrum imaging (RSI) model demonstrated the ability to distinguish malignant lesions from healthy breast tissue. We further evaluated the utility of this three-component model to differentiate malignant from benign lesions and healthy tissue in 12 patients with known malignancy and synchronous pathology-proven benign lesions. The signal contributions from three distinct diffusion compartments were measured to generate parametric maps corresponding to diffusivity on a voxel-wise basis. The three-component model discriminated malignant from benign and healthy tissue, particularly using the restricted diffusion C1 compartment and product of the restricted and intermediate diffusion compartments (C1 and C2). However, benign lesions and healthy tissue did not significantly differ in diffusion characteristics. Quantitative discrimination of these three tissue types (malignant, benign, and healthy) in non-pre-defined lesions may enhance the clinical utility of DW-MRI in reducing excessive biopsies and aiding in surveillance and surgical evaluation without repeated exposure to gadolinium contrast.

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging.

Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promise to transform the practice of medicine, and, in particular, the practice of diagnostic radiology. These technologies are evolving at a rapid pace due to innovations in computational hardware and novel neural network architectures. Several cutting-edge postprocessing analysis applications are actively being developed in the fields of thoracic and cardiovascular imaging, including applications for lesion detection and characterization, lung parenchymal characterization, coronary artery assessment, cardiac volumetry and function, and anatomic localization. Cardiothoracic and cardiovascular imaging lies at the technological forefront of radiology due to a confluence of technical advances. Enhanced equipment has enabled computed tomography and magnetic resonance imaging scanners that can safely capture images that freeze the motion of the heart to exquisitely delineate fine anatomic structures. Computing hardware developments have enabled an explosion in computational capabilities and in data storage. Progress in software and fluid mechanical models is enabling complex 3D and 4D reconstructions to not only visualize and assess the dynamic motion of the heart, but also quantify its blood flow and hemodynamics. And now, innovations in machine learning, particularly in the form of deep neural networks, are enabling us to leverage the increasingly massive data repositories that are prevalent in the field. Here, we discuss developments in machine learning techniques and deep neural networks to highlight their likely role in future radiologic practice, both in and outside of image interpretation and analysis. We discuss the concepts of validation, generalizability, and clinical utility, as they pertain to this and other new technologies, and we reflect upon the opportunities and challenges of bringing these into daily use.

MAPK Activation Predicts Poor Outcome and the MEK Inhibitor, Selumetinib, Reverses Antiestrogen Resistance in ER-Positive High-Grade Serous Ovarian Cancer.

PURPOSE: Although 67% of high-grade serous ovarian cancers (HGSOC) express the estrogen receptor (ER), most fail antiestrogen therapy. Because MAPK activation is frequent in ovarian cancer, we investigated if estrogen regulates MAPK and if MEK inhibition (MEKi) reverses antiestrogen resistance. EXPERIMENTAL DESIGN: Effects of MEKi (selumetinib), antiestrogen (fulvestrant), or both were assayed in ER-positive HGSOC in vitro and in xenografts. Response biomarkers were investigated by gene expression microarray and reverse phase protein array (RPPA). Genes differentially expressed in two independent primary HGSOC datasets with high versus low pMAPK by RPPA were used to generate a "MAPK-activated gene signature." Gene signature components that were reversed by MEKi were then identified. RESULTS: High intratumor pMAPK independently predicts decreased survival (HR, 1.7; CI > 95%,1.3-2.2; P = 0.0009) in 408 HGSOC from The Cancer Genome Atlas. A differentially expressed "MAPK-activated" gene subset was also prognostic. "MAPK-activated genes" in HGSOC differ from those in breast cancer. Combined MEK and ER blockade showed greater antitumor effects in xenografts than monotherapy. Gene set enrichment analysis and RPPA showed that dual therapy downregulated DNA replication and cell-cycle drivers, and upregulated lysosomal gene sets. Selumetinib reversed expression of a subset of "MAPK-activated genes" in vitro and/or in xenografts. Three of these genes were prognostic for poor survival (P = 0.000265) and warrant testing as a signature predictive of MEKi response. CONCLUSIONS: High pMAPK is independently prognostic and may underlie antiestrogen failure. Data support further evaluation of fulvestrant and selumetinib in ER-positive HGSOC. The MAPK-activated HGSOC signature may help identify MEK inhibitor responsive tumors.

Interactions between Adipocytes and Breast Cancer Cells Stimulate Cytokine Production and Drive Src/Sox2/miR-302b-Mediated Malignant Progression.

Consequences of the obesity epidemic on cancer morbidity and mortality are not fully appreciated. Obesity is a risk factor for many cancers, but the mechanisms by which it contributes to cancer development and patient outcome have yet to be fully elucidated. Here, we examined the effects of coculturing human-derived adipocytes with established and primary breast cancer cells on tumorigenic potential. We found that the interaction between adipocytes and cancer cells increased the secretion of proinflammatory cytokines. Prolonged culture of cancer cells with adipocytes or cytokines increased the proportion of mammosphere-forming cells and of cells expressing stem-like markers in vitro. Furthermore, contact with immature adipocytes increased the abundance of cancer cells with tumor-forming and metastatic potential in vivo. Mechanistic investigations demonstrated that cancer cells cultured with immature adipocytes or cytokines activated Src, thus promoting Sox2, c-Myc, and Nanog upregulation. Moreover, Sox2-dependent induction of miR-302b further stimulated cMYC and SOX2 expression and potentiated the cytokine-induced cancer stem cell-like properties. Finally, we found that Src inhibitors decreased cytokine production after coculture, indicating that Src is not only activated by adipocyte or cytokine exposures, but is also required to sustain cytokine induction. These data support a model in which cancer cell invasion into local fat would establish feed-forward loops to activate Src, maintain proinflammatory cytokine production, and increase tumor-initiating cell abundance and metastatic progression. Collectively, our findings reveal new insights underlying increased breast cancer mortality in obese individuals and provide a novel preclinical rationale to test the efficacy of Src inhibitors for breast cancer treatment.