Triple-negative breast cancer (TNBC) is notoriously aggressive with high metastatic potential, which has recently been linked to high rates of fatty acid oxidation (FAO). Here we report the mechanism of lipid metabolism dysregulation in TNBC through the prometastatic protein, CUB-domain containing protein 1 (CDCP1). We show that a "low-lipid" phenotype is characteristic of breast cancer cells compared with normal breast epithelial cells and negatively correlates with invasiveness in 3D culture. Using coherent anti-Stokes Raman scattering and two-photon excited fluorescence microscopy, we show that CDCP1 depletes lipids from cytoplasmic lipid droplets (LDs) through reduced acyl-CoA production and increased lipid utilization in the mitochondria through FAO, fueling oxidative phosphorylation. These findings are supported by CDCP1's interaction with and inhibition of acyl CoA-synthetase ligase (ACSL) activity. Importantly, CDCP1 knockdown increases LD abundance and reduces TNBC 2D migration in vitro, which can be partially rescued by the ACSL inhibitor, Triacsin C. Furthermore, CDCP1 knockdown reduced 3D invasion, which can be rescued by ACSL3 co-knockdown. In vivo, inhibiting CDCP1 activity with an engineered blocking fragment (extracellular portion of cleaved CDCP1) lead to increased LD abundance in primary tumors, decreased metastasis, and increased ACSL activity in two animal models of TNBC. Finally, TNBC lung metastases have lower LD abundance than their corresponding primary tumors, indicating that LD abundance in primary tumor might serve as a prognostic marker for metastatic potential. Our studies have important implications for the development of TNBC therapeutics to specifically block CDCP1-driven FAO and oxidative phosphorylation, which contribute to TNBC migration and metastasis.he transmembrane glycoprotein, CUB-domain containing protein 1 (CDCP1), is a driver of migration and invasion in multiple forms of carcinoma, including renal (1, 2), ovarian (3, 4), prostate (5), pancreatic (6, 7), colon (8-12), and triple-negative breast (TNBC) (13, 14) carcinomas, among others. Furthermore, CDCP1's role in tumor metastasis was confirmed in vivo in lung (15, 16), ovarian (17), prostate (5), and colon (9) cancers. Although CDCP1's role in TNBC metastasis has not been established to date, high CDCP1 expression has been validated as a prognostic marker of poor survival in TNBC when combined with positive node status (18).Our understanding of CDCP1's upstream regulators, its mechanism of activation, and downstream signaling continues to expand (recently reviewed in ref. 19). Studies by others (5) and our group (14) have demonstrated that CDCP1 cleavage is necessary for its activation, and recently, we have further shown that cleavage stimulates CDCP1 homodimerization (14). Homodimeric CDCP1 stimulates phosphorylation of protein kinase C δ (PKCδ) by Src kinase, leading to migration and invasion of TNBC cells in vitro (14). Adding to our knowledge of CDCP1's downstream signaling, we report that CDCP1 regulates lipid m...

Cancers are a complex conglomerate of heterogeneous cell populations with varying genotypes and phenotypes. The intercellular heterogeneity within the same tumor and intratumor heterogeneity within various tumors are the leading causes of resistance to cancer therapies and varied outcomes in different patients. Therefore, performing single‐cell analysis is essential to identify and classify cancer cell types and study cellular heterogeneity. Here, the development of a machine learning‐assisted nanoparticle‐printed biochip for single‐cell analysis is reported. The biochip is integrated by combining powerful machine learning techniques with easily accessible inkjet printing and microfluidics technology. The biochip is easily prototype‐able, miniaturized, and cost‐effective, potentially capable of differentiating a variety of cell types in a label‐free manner. n‐feature classifiers are established and their performance metrics are evaluated. The biochip's utility to discriminate noncancerous cells from cancerous cells at the single‐cell level is demonstrated. The biochip's utility in classifying cancer sub‐type cells is also demonstrated. It is envisioned that such a chip has potential applications in single‐cell studies, tumor heterogeneity studies, and perhaps in point‐of‐care cancer diagnostics—especially in developing countries where the cost, limited infrastructures, and limited access to medical technologies are of the utmost importance.

Precision mapping of landslide inventory is crucial for hazard mitigation. Most landslides generally co-exist with other confusing geological features, and the presence of such areas can only be inferred unambiguously at a large scale. In addition, local information is also important for the preservation of object boundaries. Aiming to solve this problem, this paper proposes an effective approach to fuse both local and non-local features to surmount the contextual problem. Built upon the U-Net architecture that is widely adopted in the remote sensing community, we utilize two additional modules. The first one uses dilated convolution and the corresponding atrous spatial pyramid pooling, which enlarged the receptive field without sacrificing spatial resolution or increasing memory usage. The second uses a scale attention mechanism to guide the up-sampling of features from the coarse level by a learned weight map. In implementation, the computational overhead against the original U-Net was only a few convolutional layers. Experimental evaluations revealed that the proposed method outperformed state-of-theart general-purpose semantic segmentation approaches. Furthermore, ablation studies have shown that the two models afforded extensive enhancements in landslide-recognition performance.

In article number 2000160, Rahim Esfandyarpour and co‐workers present a machine‐learning‐assisted‐nanoparticle‐printed biochip for single‐cell analysis. It integrates powerful machine‐learning techniques, microfluidics, and easily accessible inkjet‐printing. The biochip is envisioned to have potential applications in single‐cell studies, tumor heterogeneity studies, and perhaps in point‐of‐care diagnostics‐ especially in developing countries where the cost, limited infrastructures, and limited access to medical technologies are of utmost importance.

Clear cell renal cell carcinoma (CC-RCC) remains one of the most deadly forms of kidney cancer despite recent advancements in targeted therapeutics, including tyrosine kinase and immune checkpoint inhibitors. Unfortunately, these therapies have not been able to show better than a 16% complete response rate. In this study we evaluated a cyclin-dependent kinase inhibitor, Dinaciclib, as a potential new targeted therapeutic for CC-RCC. In vitro , Dinaciclib showed anti-proliferative and pro-apoptotic effects on CC-RCC cell lines in Cell Titer Glo, Crystal Violet, FACS-based cell cycle analysis, and TUNEL assays. Additionally, these responses were accompanied by a reduction in phospho-Rb and pro-survival MCL-1 cell signaling responses, as well as the induction of caspase 3 and PARP cleavage. In vivo , Dinaciclib efficiently inhibited primary tumor growth in an orthotopic, patient-derived xenograft-based CC-RCC mouse model. Importantly, Dinaciclib targeted both CD105 + cancer stem cells (CSCs) and CD105 − non-CSCs in vivo . Moreover, normal cell lines, as well as a CC-RCC cell line with re-expressed von-Hippel Lindau ( VHL ) tumor suppressor gene, were protected from Dinaciclib-induced cytotoxicity when not actively dividing, indicating an effective therapeutic window due to synthetic lethality of Dinaciclib treatment with VHL loss. Thus, Dinaciclib represents a novel potential therapeutic for CC-RCC.