Prior Visual-guided Self-supervised Learning Enables Color Vignetting Correction for High-throughput Microscopic Imaging.

Vignetting constitutes a prevalent optical degradation that significantly compromises the quality of biomedical microscopic imaging. However, a robust and efficient vignetting correction methodology in multi-channel microscopic images remains absent at present. In this paper, we take advantage of a prior knowledge about the homogeneity of microscopic images and radial attenuation property of vignetting to develop a self-supervised deep learning algorithm that achieves complex vignetting removal in color microscopic images. Our proposed method, vignetting correction lookup table (VCLUT), is trainable on both single and multiple images, which employs adversarial learning to effectively transfer good imaging conditions from the user visually defined central region of its own light field to the entire image. To illustrate its effectiveness, we performed individual correction experiments on data from five distinct biological specimens. The results demonstrate that VCLUT exhibits enhanced performance compared to classical methods. We further examined its performance as a multi-image-based approach on a pathological dataset, revealing its advantage over other stateof-the-art approaches in both qualitative and quantitative measurements. Moreover, it uniquely possesses the capacity for generalization across various levels of vignetting intensity and an ultra-fast model computation capability, rendering it well-suited for integration into high-throughput imaging pipelines of digital microscopy.

Filamentous Actin in the Nucleus in Triple-Negative Breast Cancer Stem Cells: A Key to Drug-Induced Nucleolar Stress and Stemness Inhibition?

Actin, primarily a cytoplasmic cytoskeleton protein, is transported in and out of the nucleus with the help of actin-binding proteins (ABPs). Actin exists in two forms, i.e., monomeric globular (G-actin) and polymerized filamentous (F-actin). While G-actin promotes gene transcription by associating with RNA polymerases, F-actin can inhibit this effect in the nucleus. Unexpectedly, we found that lovastatin, an FDA-approved lipid-lowering drug, induces actin redistribution and its translocation into the nucleus in triple-negative breast cancer (TNBC) cancer stem cells. Lovastatin treatment also decreased levels of rRNAs and stemness markers, which are transcription products of RNA Pol I and Pol II, respectively. Bioinformatics analysis showed that actin genes were positively correlated with ABP genes involved in the translocation/polymerization and transcriptional regulation of nuclear actin in breast cancer. Similar correlations were found between actin genes and RNA Pol I genes and stemness-related genes. We propose a model to explain the roles of lovastatin in inducing nucleolar stress and inhibiting stemness in TNBC cancer stem cells. In our model, lovastatin induces translocation/accumulation of F-actin in the nucleus/nucleolus, which, in turn, induces nucleolar stress and stemness inhibition by suppressing the synthesis of rRNAs and decreasing the expression of stemness-related genes. Our model has opened up a new field of research on the roles of nuclear actin in cancer biology, offering potential therapeutic targets for the treatment of TNBC.

Cardinality-constrained plan-quality and delivery-time optimization method for proton therapy.

BACKGROUND: While minimizing plan delivery time is beneficial for proton therapy in terms of motion management, patient comfort, and treatment throughput, it often poses a tradeoff with optimizing plan quality. A key component of plan delivery time is the energy switching time, which is approximately proportional to the number of energy layers, that is, the cardinality. PURPOSE: This work aims to develop a novel optimization method that can efficiently compute the pareto surface between plan quality and energy layer cardinality, for the planner to navigate through this quality-and-efficiency tradeoff and select the appropriate plan of a balanced tradeoff. METHODS: A new IMPT method CARD is proposed that (1) explicitly incorporates the minimization of energy layer cardinality as an optimization objective, and (2) automatically generates a set of plans sequentially with a descending order in number of energy layers. The energy layer cardinality is penalized through the l1,0-norm regularization with an upper bound, and the upper bound is monotonically decreased to compute a series of treatment plans with gradually decreased energy layer cardinality on the quality-and-efficiency pareto surface. For any given treatment plan, the plan optimality is enforced using dose-volume planning objectives and the plan deliverability is imposed through minimum-monitor-unit (MMU) constraints, with optimization solution algorithm based on iterative convex relaxation. RESULTS: The new method CARD was validated in comparison with the benchmark plan of all energy layers (P0), and a state-of-the-art method called MMSEL, using prostate, head-and-neck (HN), lung, pancreas, liver and brain cases. While labor-intensive and time-consuming manual parameter tuning was needed for MMSEL to generate plans of predefined energy layer cardinality, CARD automatically and efficiently computed all plans with sequentially decreasing predefined energy layer cardinality all at once. With the acceptable plan quality (i.e., no more than 110% of total optimization objective value from P0), CARD achieved the reduction of number of energy layers to 52% (from 77 to 40), 48% (from 135 to 65), 59% (from 85 to 50), 67% (from 52 to 35), 80% (from 50 to 40), and 30% (from 66 to 20), for prostate, HN, lung, pancreas, liver, and brain cases, respectively, compared to P0, with overall better plan quality than MMSEL. Moreover, due to the nonconvexity of the MMU constraint, CARD provided the similar or even smaller optimization objective than P0, at the same time with fewer number of energy layers, that is, 55 versus 77, 85 versus 135, 45 versus 52, and 25 versus 66 for prostate, HN, pancreas, and brain cases, respectively. CONCLUSIONS: We have developed a novel optimization algorithm CARD that can efficiently and automatically compute a series of treatment plans of any given energy layer sequentially, which allows the planner to navigate through the plan-quality and energy-layer-cardinality tradeoff and select the appropriate plan of a balanced tradeoff.

Neutrophil Extracellular Traps in Breast Cancer: Roles in Metastasis and Beyond.

Despite advances in the treatment of breast cancer, the disease continues to exhibit high global morbidity and mortality. The importance of neutrophils in cancer development has been increasingly recognized. Neutrophil extracellular traps (NETs) are web-like structures released into the extracellular space by activated neutrophils, serving as a potential antimicrobial mechanism for capturing and eliminating microorganisms. The roles played by NETs in cancer development have been a subject of intense research in the last decade. In breast cancer, current evidence suggests that NETs are involved in various stages of cancer development, particularly during metastasis. In this review, we try to provide an updated overview of the roles played by NETs in breast cancer metastasis. These include: 1) facilitating systemic dissemination of cancer cells; 2) promoting cancer-associated inflammation; 3) facilitating cancer-associated thrombosis; 4) facilitating pre-metastatic niche formation; and 5) awakening dormant cancer cells. The translational implications of NETs in breast cancer treatment are also discussed. Understanding the relationship between NETs and breast cancer metastasis is expected to provide important insights for developing new therapeutic strategies for breast cancer patients.

Dysregulated Ribosome Biogenesis Is a Targetable Vulnerability in Triple-Negative Breast Cancer: MRPS27 as a Key Mediator of the Stemness-inhibitory Effect of Lovastatin.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with limited effective therapeutic options readily available. We have previously demonstrated that lovastatin, an FDA-approved lipid-lowering drug, selectively inhibits the stemness properties of TNBC. However, the intracellular targets of lovastatin in TNBC remain largely unknown. Here, we unexpectedly uncovered ribosome biogenesis as the predominant pathway targeted by lovastatin in TNBC. Lovastatin induced the translocation of ribosome biogenesis-related proteins including nucleophosmin (NPM), nucleolar and coiled-body phosphoprotein 1 (NOLC1), and the ribosomal protein RPL3. Lovastatin also suppressed the transcript levels of rRNAs and increased the nuclear protein level and transcriptional activity of p53, a master mediator of nucleolar stress. A prognostic model generated from 10 ribosome biogenesis-related genes showed outstanding performance in predicting the survival of TNBC patients. Mitochondrial ribosomal protein S27 (MRPS27), the top-ranked risky model gene, was highly expressed and correlated with tumor stage and lymph node involvement in TNBC. Mechanistically, MRPS27 knockdown inhibited the stemness properties and the malignant phenotypes of TNBC. Overexpression of MRPS27 attenuated the stemness-inhibitory effect of lovastatin in TNBC cells. Our findings reveal that dysregulated ribosome biogenesis is a targetable vulnerability and targeting MRPS27 could be a novel therapeutic strategy for TNBC patients.

Disulfidptosis: Six Riddles Necessitating Solutions.

Disulfidptosis occurs as a result of the accumulation of intracellular cystine followed by disulfide stress in actin cytoskeleton proteins due to a reduction of NADPH produced through the pentose phosphate pathway in cells with high expression of SLC7A11. It is a cell death caused by the redox imbalance resulting from the disruption of amino acid metabolism and glucose metabolism. The discovery of disulfidptosis has sparked immense enthusiasm, but there are numerous unresolved issues that need to be addressed. Solutions to these riddles will provide insights into the detailed mechanisms and the pathophysiological relevance of disulfidptosis and utilizing disulfidptosis as an actionable therapeutic target.

A three-path network with multi-scale selective feature fusion, edge-inspiring and edge-guiding for liver tumor segmentation.

Automatic liver tumor segmentation is one of the most important tasks in computer-aided diagnosis and treatment. Deep learning techniques have gained increasing popularity for medical image segmentation in recent years. However, due to the various shapes, sizes, and obscure boundaries of tumors, it is still difficult to automatically extract tumor regions from CT images. Based on the complementarity of edge detection and region segmentation, a three-path structure with multi-scale selective feature fusion (MSFF) module, multi-channel feature fusion (MFF) module, edge-inspiring (EI) module, and edge-guiding (EG) module is proposed in this paper. The MSFF module includes the process of generation, fusion, and selection of multi-scale features, which can adaptively correct the response weights in multiple branches to filter redundant information. The MFF module integrates richer hierarchical features to capture targets at different scales. The EI module aggregates high-level semantic information at different levels to obtain fine edge semantics, which is injected into the EG module for representation learning of segmentation features. Experiments on the LiTs2017 dataset show that our proposed method achieves a Dice index of 85.55% and a Jaccard index of 81.11%, which are higher than what can be obtained by the current state-of-the-art methods. Cross-dataset validation experiments conducted on 3Dircadb and Clinical datasets show the generalization and robustness of the proposed method by achieving dice indices of 80.14% and 81.68%, respectively.

Amino acids and their roles in tumor immunotherapy of breast cancer.

Breast cancer is the most commonly diagnosed cancer among women. The primary treatment options include surgery, radiotherapy, chemotherapy, targeted therapy and hormone therapy. The effectiveness of breast cancer therapy varies depending on the stage and aggressiveness of the cancer, as well as individual factors. Advances in early detection and improved treatments have significantly increased survival rates for breast cancer patients. Nevertheless, specific subtypes of breast cancer, particularly triple-negative breast cancer, still lack effective treatment strategies. Thus, novel and effective therapeutic targets for breast cancer need to be explored. As substrates of protein synthesis, amino acids are important sources of energy and nutrition, only secondly to glucose. The rich supply of amino acids enables the tumor to maintain its proliferative competence through participation in energy generation, nucleoside synthesis and maintenance of cellular redox balance. Amino acids also play an important role in immune-suppressive microenvironment formation. Thus, the biological effects of amino acids may change unexpectedly in tumor-specific or oncogene-dependent manners. In recent years, there has been significant progress in the study of amino acid metabolism, particularly in their potential application as therapeutic targets in breast cancer. In this review, we provide an update on amino acid metabolism and discuss the therapeutic implications of amino acids in breast cancer.

Molecular Characterization and Establishment of a Prognostic Model Based on Primary Immunodeficiency Features in Association with RNA Modifications in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Although immunotherapy is effective for some patients, most find it difficult to benefit from it. This study aims to explore the impact of specific immune pathways and their regulated molecular mechanisms in TNBC. The gene expression data of breast cancer patients were obtained from the TCGA and METABRIC databases. Gene set variation analysis (GSVA) revealed specific upregulation or abnormal expression of immunodeficiency pathways in TNBC patients. Multi-omics data showed significant differential expression of Primary Immunodeficiency Genes (PIDGs) in TNBC patients, who are prone to genomic-level variations. Consensus clustering was used in two datasets to classify patients into two distinct molecular subtypes based on PIDGs expression patterns, with each displaying different biological features and immune landscapes. To further explore the prognostic characteristics of PIDGs-regulated molecules, we constructed a four-gene prognostic PIDG score model and a nomogram using least absolute shrinkage and selection operator (LASSO) regression analysis in combination with clinicopathological parameters. The PIDG score was closely associated with the immune therapy and drug sensitivity of TNBC patients, providing potential guidance for clinical treatment. Particularly noteworthy is the close association of this scoring with RNA modifications; patients with different scores also exhibited different mutation landscapes. This study offers new insights for the clinical treatment of TNBC and for identifying novel prognostic markers and therapeutic targets in TNBC.

Context fusion network with multi-scale-aware skip connection and twin-split attention for liver tumor segmentation.

Manually annotating liver tumor contours is a time-consuming and labor-intensive task for clinicians. Therefore, automated segmentation is urgently needed in clinical diagnosis. However, automatic segmentation methods face certain challenges due to heterogeneity, fuzzy boundaries, and irregularity of tumor tissue. In this paper, a novel deep learning-based approach with multi-scale-aware (MSA) module and twin-split attention (TSA) module is proposed for tumor segmentation. The MSA module can bridge the semantic gap and reduce the loss of detailed information. The TSA module can recalibrate the channel response of the feature map. Eventually, we can count tumors based on the segmentation results from a 3D perspective for cancer grading. Extensive experiments conducted on the LiTS2017 dataset show the effectiveness of the proposed method by achieving a Dice index of 85.97% and a Jaccard index of 81.56% over the state of the art. In addition, the proposed method also achieved a Dice index of 83.67% and a Jaccard index of 80.11% in 3Dircadb dataset verification, which further reflects its robustness and generalization ability.

Neutrophils in triple-negative breast cancer: an underestimated player with increasingly recognized importance.

Triple-negative breast cancer (TNBC) is the most lethal subtype of breast cancer, with limited therapeutic options readily available. Immunotherapy such as immune checkpoint inhibition has been investigated in TNBC but still encounters low overall response. Neutrophils, the most abundant leukocytes in the body, are increasingly recognized as an active cancer-modulating entity. In the bloodstream, neutrophils escort circulating tumor cells to promote their survival and stimulate their proliferation and metastasis. In the tumor microenvironment, neutrophils modulate the immune milieu through polarization between the anti-tumor and the pro-tumor phenotypes. Through a comprehensive review of recently published literature, it is evident that neutrophils are an important player in TNBC immunobiology and can be used as an important prognostic marker of TNBC. Particularly, in their pro-tumor form, neutrophils facilitate TNBC metastasis through formation of neutrophil extracellular traps and the pre-metastatic niche. These findings will help advance the potential utilization of neutrophils as a therapeutic target in TNBC.

RFPNet: Reorganizing feature pyramid networks for medical image segmentation.

Medical image segmentation is a crucial step in clinical treatment planning. However, automatic and accurate medical image segmentation remains a challenging task, owing to the difficulty in data acquisition, the heterogeneity and large variation of the lesion tissue. In order to explore image segmentation tasks in different scenarios, we propose a novel network, called Reorganization Feature Pyramid Network (RFPNet), which uses alternately cascaded Thinned Encoder-Decoder Modules (TEDMs) to construct semantic features in various scales at different levels. The proposed RFPNet is composed of base feature construction module, feature pyramid reorganization module and multi-branch feature decoder module. The first module constructs the multi-scale input features. The second module first reorganizes the multi-level features and then recalibrates the responses between integrated feature channels. The third module weights the results obtained from different decoder branches. Extensive experiments conducted on ISIC2018, LUNA2016, RIM-ONE-r1 and CHAOS datasets show that RFPNet achieves Dice scores of 90.47%, 98.31%, 96.88%, 92.05% (Average between classes) and Jaccard scores of 83.95%, 97.05%, 94.04%, 88.78% (Average between classes). In quantitative analysis, RFPNet outperforms some classical methods as well as state-of-the-art methods. Meanwhile, the visual segmentation results demonstrate that RFPNet can excellently segment target areas from clinical datasets.

Hexokinase 2 Is a Pivot for Lovastatin-induced Glycolysis-to-Autophagy Reprogramming in Triple-Negative Breast Cancer Cells.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with limited therapeutic options available. We have recently demonstrated that lovastatin, a 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitor, suppresses TNBC cell proliferation and stemness properties in vitro and in vivo. However, the mechanisms through which lovastatin inhibits TNBC cells are not fully understood. Here, we used 1H NMR-based metabolomic profiling to investigate lovastatin-induced metabolic changes in TNBC cell line MDA-MB-231. Among the 46 metabolites identified, lactate demonstrated the highest variable importance in projection (VIP) score. Glycolysis stress test revealed that lovastatin significantly decreased the extracellular acidification rate (ECAR) in MDA-MB-231 cells. Furthermore, lovastatin treatment down-regulated the levels of glycolysis-related proteins including GLUT1, PFK1, and PKM2 in MDA-MB-231 but not non-TNBC MDA-MB-453 cells. In addition, lovastatin induced autophagy as evidenced by increased LC3 puncta formation and LC3-II/I ratio, increased AMPK phosphorylation, and decreased Akt phosphorylation. We also revealed the interaction between the glycolytic enzyme hexokinase 2 (HK2) and the mitochondrial membrane protein voltage-dependent anion channel 1 (VDAC1), an important regulator of autophagy. Further bioinformatics analysis revealed that VDAC1 was expressed at a higher level in breast cancer than normal tissues and higher level of VDAC1 predicted poorer survival outcomes in breast cancer patients. The present study suggests that lovastatin might exert anti-tumor activity by reprogramming glycolysis toward autophagy in TNBC cells through HK2-VDAC1 interaction.

Automatic segmentation of hyperreflective dots via focal priors and visual saliency.

PURPOSE: Hyperreflective dots (HRDs) can be observed in spectral domain optical coherence tomography (SD-OCT), which can provide a sensitive marker in the treatment decision process. Quantitative analyses of HRDs are the key to make appropriate decisions on observation, treatment, and retreatment. The purpose of this study is to automatically and accurately segment HRDs in SD-OCT B-scans with diabetic retinopathy (DR). METHODS: The authors propose an automatic segmentation algorithm of HRDs via focal priors and visual saliency. The algorithm is divided into three stages: segmentation of retinal layers, calculation of the multiscale local contrast saliency map, and adaptive threshold segmentation. First, a method based on improved graph search is used to segment retinal layers to obtain the region of interest (ROI) and the reflectivity estimation of the retinal pigment epithelium (RPE) layer; then, the multiscale local contrast saliency map is obtained by using a local contrast measure, which measures the dissimilarity between the current pixels and corresponding neighborhoods; finally, an adaptive threshold is applied to segment HRDs. RESULTS: Experimental results on 20 SD-OCT B-scans demonstrate that our method is effective for HRDs segmentation. The average dice similarity coefficient (DSC) and detection accuracy are 71.12% and 85.07%, respectively. CONCLUSIONS: The proposed method can accurately segment HRDs in SD-OCT B-scans with DR and outperforms current state-of-the-art methods. Our method can provide reliable HRDs segmentation to assist ophthalmologists in clinical diagnosis, treatment, disease monitoring, and progression.

Co-Expression and Combined Prognostic Value of CSPG4 and PDL1 in TP53-Aberrant Triple-Negative Breast Cancer.

Background: In triple-negative breast cancer (TNBC), PDL1/PD1-directed immunotherapy is effective in less than 20% of patients. In our preliminary study, we have found CSPG4 to be highly expressed together with PDL1 in TNBCs, particularly those harboring TP53 aberrations. However, the clinical implications of co-expressed CSPG4 and PDL1 in TNBCs remain elusive. Methods: A total of 85 advanced TNBC patients treated in the Hunan Cancer Hospital between January 2017 and August 2019 were recruited. The expressions of CSPG4 and PDL1 in TNBC tissues were investigated using immunohistochemistry (IHC). The RNA-seq dataset from the TCGA-BRCA project was further used to analyze the mRNA expression of CSPG4 and PDL1 in TP53-aberrant TNBCs. Cox proportional hazards model and Kaplan-Meier curves with Logrank test was used to analyze the effects of CSPG4 and PDL1 on survival. TNBC cell lines were further used to investigate the molecular mechanism that were involved. Results: TP53 aberrations occurred in more than 50% of metastatic TNBCs and were related to higher tumor mutation burden (TMB). In TCGA-BRCA RNA-seq dataset analysis, both CSPG4 and PDL1 levels were high in TNBCs, especially in TP53-aberrant TNBCs. IHC assay showed nearly 60% of advanced TNBCs to be CSPG4-positive and about 25% to be both CSPG4-positive and PDL1-positive. The levels of CSPG4 and PDL1 were high in TNBC cell lines as revealed by flow cytometry and immunoblotting compared with non-TNBC cells. Univariate Cox regression analysis indicated that CSPG4 positivity was a significant risk factor for progression-free survival in metastatic TNBCs, with a hazard ratio (HR) of 2.26 (P = 0.05). KM curves with Logrank test also identified high level of CSPG4 as a significant risk factor for overall survival in advanced breast cancers in TCGA-BRCA samples (P = 0.02). The immunoblotting assays showed that EMT-related pathways were involved in CSPG4-mediated invasion. Conclusions: CSPG4 expression level is associated with PDL1 positivity in TP53-aberrant TNBC cells. Patients with CSPG4 expression have poor treatment response and poor overall survival. Co-expressed CSPG4 and PDL1 may have an important prognostic value and provide new therapeutic targets in TNBC patients. CSPG4 might mediate tumor invasion and PDL1 overexpression through EMT-related pathway.

Nucleolar and Coiled-Body Phosphoprotein 1 Is Associated With Stemness and Represents a Potential Therapeutic Target in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and lacks approved specific targeted therapies. One of the major reasons why TNBC is difficult to treat is the high proportion of cancer stem cells within the tumor tissue. Nucleolus is the location of ribosome biogenesis which is frequently overactivated in cancer cells and overactivation of ribosome biogenesis frequently drives the malignant transformation of cancer. Nucleolar and coiled-body phosphoprotein 1 (NOLC1) is a nucleolar protein responsible for nucleolus organization and rRNA synthesis and plays an important role in ribosome biogenesis. However, the correlation of NOLC1 expression with patient prognosis and its value as a therapeutic target have not been evaluated in TNBC. In the current study, based on bioinformatics analysis of the online databases, we found that the expression of NOLC1 was higher in breast cancer tissues than normal tissues, and NOLC1 was expressed at a higher level in TNBC than other subtypes of breast cancer. GSEA analysis revealed that stemness-related pathways were significantly enriched in breast cancer with high NOLC1 gene expression. Further analyses using gene expression profiling interactive analysis 2 (GEPIA2), tumor immune estimation resource (TIMER) and search tool for retrieval of interacting genes/proteins (STRING) demonstrated that NOLC1 was significantly associated with stemness in both all breast cancer and basal-like breast cancer/TNBC patients at both gene and protein levels. Knockdown of NOLC1 by siRNA decreased the protein level of the key stemness regulators MYC and ALDH and inhibited the sphere-forming capacity in TNBC cell line MDA-MB-231. Univariate and multivariate Cox regression analyses demonstrated that NOLC1 was an independent risk factor for overall survival in breast cancer. PrognoScan and Kaplan-Meier plotter analyses revealed that high expression of NOLC1 was associated with poor prognosis in both all breast cancer and TNBC patients. Further immunohistochemical analysis of breast cancer patient samples revealed that TNBC cells had a lower level of NOLC1 in the nucleus compared with non-TNBC cells. These findings suggest that NOLC1 is closely associated with the stemness properties of TNBC and represents a potential therapeutic target for TNBC.

Lovastatin enhances chemosensitivity of paclitaxel-resistant prostate cancer cells through inhibition of CYP2C8.

Chemotherapy is the mainstay of treatment for prostate cancer, with paclitaxel being commonly used for hormone-resistant prostate cancer. However, drug resistance often develops and leads to treatment failure in a variety of prostate cancer patients. Therefore, it is necessary to enhance the sensitivity of prostate cancer to chemotherapy. Lovastatin (LV) is a natural compound extracted from Monascus-fermented foods and is an inhibitor of HMG-CoA reductase (HMGCR), which has been approved by the FDA for hyperlipidemia treatment. We have previously found that LV could inhibit the proliferation of refractory cancer cells. Up to now, the effect of LV on chemosensitization and the mechanisms involved have not been evaluated in drug-resistant prostate cancer. In this study, we used prostate cancer cell line PC3 and its paclitaxel-resistant counterpart PC3-TxR as the cell model. Alamar Blue cell viability assay showed that LV and paclitaxel each conferred concentration-dependent inhibition of PC3-TxR cells. When paclitaxel was combined with LV, the proliferation of PC3-TxR cells was synergistically inhibited, as demonstrated by combination index <1. Moreover, colony formation decreased while apoptosis increased in paclitaxel plus LV group compared with paclitaxel alone group. Quantitative RT-PCR showed that the combination of paclitaxel and LV could significantly reduce the expression of CYP2C8, an important drug-metabolizing enzyme. Bioinformatics analysis from the TCGA database showed that CYP2C8 expression was negatively correlated with progression-free survival (PFS) in prostate cancer patients. Our results suggest that LV might increase the sensitivity of resistant prostate cancer cells to paclitaxel through inhibition of CYP2C8 and could be utilized as a chemosensitizer for paclitaxel-resistant prostate cancer cells.

Low Expression of ECT2 Confers Radiation Therapy Resistance Through Transcription Coupled Nucleolar DNA Damage Repair.

PURPOSE: Radioresistance contributes to poor clinical therapeutic efficacy in most cancers. Emerging evidence shows that aberrant DNA damage repair is involved in radioresistance. This study aimed to elucidate the mechanism for radioresistance and explore the precise treatment to sensitize the radioresistant tumors. METHODS AND MATERIALS: Real-time polymerase chain reaction and Western blot were used to confirm the differential expression of epithelial cell transforming 2 (ECT2) in irradiation-resistant and sensitive cell lines. Laser microirradiation was used to examine the ribosome DNA (rDNA) damage response of ECT2. Biotin-identification, in vivo, in vitro binding assay, and dot blotting were used to confirm the interaction of ECT2 and PARP1. The xenograft mouse model and cell survival assay were used to assess the irradiation sensitivity with or without PARP1 inhibitor. RESULTS: We found the expression of ECT2 correlates with sensitivity to radiation therapy in both lung cancer and nasopharyngeal carcinoma. We demonstrated that low expression of ECT2 causes radioresistance, mainly by protecting rDNA in nucleoli from persistent irradiation exposure through transcriptional recovery prevention. ECT2 is recruited to the rDNA damage site in an ataxia-telangiectasia-mutated RNA polymerase I dependent manner. The recruited ECT2 interacts with PARP1 and facilitates the disassociation of PARP1 from rDNA in nucleoli. Thus, ECT2 deficiency results in sustained activation of PARP1, which subsequently inhibits nucleolar transcription and results in a low frequency of rDNA exposure under DNA damage. PARP inhibition synergized with irradiation can sensitize radioresistant tumors with low ECT2 expression. CONCLUSIONS: Our study provides a potential perspective for the application of PARP inhibitor to sensitize low-ECT2 expressing tumors to radiation therapy.

Natural products inducing nucleolar stress: implications in cancer therapy.

The nucleolus is the site of ribosome biogenesis and is found to play an important role in stress sensing. For over 100 years, the increase in the size and number of nucleoli has been considered as a marker of aggressive tumors. Despite this, the contribution of the nucleolus and the biologic processes mediated by it to cancer pathogenesis has been largely overlooked. This state has been changed over the recent decades with the demonstration that the nucleolus controls numerous cellular functions associated with cancer development. Induction of nucleolar stress has recently been regarded as being superior to conventional cytotoxic/cytostatic strategy in that it is more selective to neoplastic cells while sparing normal cells. Natural products represent an excellent source of bioactive molecules and some of them have been found to be able to induce nucleolar stress. The demonstration of these nucleolar stress-inducing natural products has paved the way for a new therapeutic approach to more delicate tumor cell-killing. This review provides a contemporary summary of the role of the nucleolus as a novel promising target for cancer therapy, with particular emphasis on natural products as an exciting new class of anti-cancer drugs with nucleolar stress-inducing properties.

An adaptive spot placement method on Cartesian grid for pencil beam scanning proton therapy.

Pencil beam scanning proton radiotherapy (RT) offers flexible proton spot placement near treatment targets for delivering tumoricidal radiation dose to tumor targets while sparing organs-at-risk. Currently the spot placement is mostly based on a non-adaptive sampling (NS) strategy on a Cartesian grid. However, the spot density or spacing during NS is a constant for the Cartesian grid that is independent of the geometry of tumor targets, and thus can be suboptimal in terms of plan quality (e.g. target dose conformality) and delivery efficiency (e.g. number of spots). This work develops an adaptive sampling (AS) spot placement method on the Cartesian grid that fully accounts for the geometry of tumor targets. Compared with NS, AS places (1) a relatively fine grid of spots at the boundary of tumor targets to account for the geometry of tumor targets and treatment uncertainties (setup and range uncertainty) for improving dose conformality, and (2) a relatively coarse grid of spots in the interior of tumor targets to reduce the number of spots for improving delivery efficiency and robustness to the minimum-minitor-unit (MMU) constraint. The results demonstrate that (1) AS achieved comparable plan quality with NS for regular MMU and substantially improved plan quality from NS for large MMU, using merely about 10% of spots from NS, where AS was derived from the same Cartesian grid as NS; (2) on the other hand, with similar number of spots, AS had better plan quality than NS consistently for regular and large MMU.