Reprogramming neuroblastoma by diet-enhanced polyamine depletion.

Neuroblastoma is a highly lethal childhood tumor derived from differentiation-arrested neural crest cells1,2. Like all cancers, its growth is fueled by metabolites obtained from either circulation or local biosynthesis3,4. Neuroblastomas depend on local polyamine biosynthesis, with the inhibitor difluoromethylornithine showing clinical activity5. Here we show that such inhibition can be augmented by dietary restriction of upstream amino acid substrates, leading to disruption of oncogenic protein translation, tumor differentiation, and profound survival gains in the TH-MYCN mouse model. Specifically, an arginine/proline-free diet decreases the polyamine precursor ornithine and augments tumor polyamine depletion by difluoromethylornithine. This polyamine depletion causes ribosome stalling, unexpectedly specifically at adenosine-ending codons. Such codons are selectively enriched in cell cycle genes and low in neuronal differentiation genes. Thus, impaired translation of these codons, induced by the diet-drug combination, favors a pro-differentiation proteome. These results suggest that the genes of specific cellular programs have evolved hallmark codon usage preferences that enable coherent translational rewiring in response to metabolic stresses, and that this process can be targeted to activate differentiation of pediatric cancers.

Phase 1 study of high-dose DFMO, celecoxib, cyclophosphamide and topotecan for patients with relapsed neuroblastoma: a New Approaches to Neuroblastoma Therapy trial.

BACKGROUND: MYC genes regulate ornithine decarboxylase (Odc) to increase intratumoral polyamines. We conducted a Phase I trial [NCT02030964] to determine the maximum tolerated dose (MTD) of DFMO, an Odc inhibitor, with celecoxib, cyclophosphamide and topotecan. METHODS: Patients 2-30 years of age with relapsed/refractory high-risk neuroblastoma received oral DFMO at doses up to 9000 mg/m2/day, with celecoxib (500 mg/m2 daily), cyclophosphamide (250 mg/m2/day) and topotecan (0.75 mg/m2/day) IV for 5 days, for up to one year with G-CSF support. RESULTS: Twenty-four patients (median age, 6.8 years) received 136 courses. Slow platelet recovery with 21-day courses (dose-levels 1 and 2) led to subsequent dose-levels using 28-day courses (dose-levels 2a-4a). There were three course-1 dose-limiting toxicities (DLTs; hematologic; anorexia; transaminases), and 23 serious adverse events (78% fever-related). Five patients (21%) completed 1-year of therapy. Nine stopped for PD, 2 for DLT, 8 by choice. Best overall response included two PR and four MR. Median time-to-progression was 19.8 months, and 3 patients remained progression-free at >4 years without receiving additional therapy. The MTD of DFMO with this regimen was 6750 mg/m2/day. CONCLUSION: High-dose DFMO is tolerable when added to chemotherapy in heavily pre-treated patients. A randomized Phase 2 trial of DFMO added to chemoimmunotherapy is ongoing [NCT03794349].

Research on Mechanical and Carbonization Properties of Hybrid Fiber Iron Tailings Concrete Based on Deep Learning.

Iron tailings sand is a kind of mineral waste, and open-air storage is a common treatment method for iron tailings, which not only has a huge impact on the ecological environment but also occupies a lot of land resources. Therefore, the preparation of high-ductility fiber reinforced iron tailings concrete and its application in practical engineering structures have good application prospects. This paper is based on the deep learning research on the mechanical and carbonization properties of hybrid fiber iron tailings concrete. Therefore, tailings sands with different substitution rates, single-mixed steel fiber, and mixed steel-PVA fiber concrete were prepared in this paper. Its compressive strength, split tensile strength, axial compressive strength, elastic modulus, strain, and carbonization depth were tested. Through the existing concrete compressive stress-strain curve equations, the nonlinear calculation of each group of concrete compressive stress-strain curve equations in this paper is carried out, some parameters are determined, and the carbonation depth equation is established. The results show that, with the increase of tailings content, the properties of concrete increase first and then decrease and the addition of fibers can effectively improve the strength, elastic modulus, peak strain, and carbonization depth of concrete. However, with the increase of PVA fiber content, its performance enhancement effect decreased.

Reduced ER-mitochondria connectivity promotes neuroblastoma multidrug resistance.

Most cancer deaths result from progression of therapy resistant disease, yet our understanding of this phenotype is limited. Cancer therapies generate stress signals that act upon mitochondria to initiate apoptosis. Mitochondria isolated from neuroblastoma cells were exposed to tBid or Bim, death effectors activated by therapeutic stress. Multidrug-resistant tumor cells obtained from children at relapse had markedly attenuated Bak and Bax oligomerization and cytochrome c release (surrogates for apoptotic commitment) in comparison with patient-matched tumor cells obtained at diagnosis. Electron microscopy identified reduced ER-mitochondria-associated membranes (MAMs; ER-mitochondria contacts, ERMCs) in therapy-resistant cells, and genetically or biochemically reducing MAMs in therapy-sensitive tumors phenocopied resistance. MAMs serve as platforms to transfer Ca2+ and bioactive lipids to mitochondria. Reduced Ca2+ transfer was found in some but not all resistant cells, and inhibiting transfer did not attenuate apoptotic signaling. In contrast, reduced ceramide synthesis and transfer was common to resistant cells and its inhibition induced stress resistance. We identify ER-mitochondria-associated membranes as physiologic regulators of apoptosis via ceramide transfer and uncover a previously unrecognized mechanism for cancer multidrug resistance.

Anlotinib, a novel TKI, as a third-line or further-line treatment in patients with advanced non-small cell lung cancer in China: A systemic review and meta-analysis of its efficacy and safety.

PURPOSE: In this meta-analysis and systemic review, we focused on the effectiveness and safety of anlotinib in patients with advanced non-small cell lung cancer(NSCLC). METHODS: The databases of PubMed, EMBASE, Cochrane Library, CNKI, Wanfang, and CBM were searched by 2 investigators up to April 2020. Titles and abstracts of all records were screened and eligible publications were retrieved in full. Review Manager (version 5.2, Cochrane Library) was used for data analysis. The outcomes of interest were disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and treatment-related adverse event (TRAE). Data was pooled for quantitative analysis and the effect size was reported as hazard ratio for survival outcomes and odds ratio (OR) for safety outcomes, both with a random-effects model. RESULTS: A sum of 1480 patients were included in 11 trials ranging from 2018 to 2020. Substantial improvements of PFS, OS, and DCR were observed in patients treated with anlotinib alone or in combination with other conventional treatment. Accompanied TRAE included statistically significant higher risk for hypertension (OR = 11.05, 95% confidence interval [CI] = 7.85-15.55, P < .001), hepatic dysfunction (OR = 1.96, 95% CI = 1.29-2.68, P < .001), diarrhea (OR = 2.20, 95% CI = 1.17-4.16, P < .05), and hemoptysis (OR = 2.59, 95% CI = 1.71-3.93, P < .01). CONCLUSIONS: Our study suggested that anlotinib as maintenance therapy for advanced NSCLC patients is associated with prolonged PFS and OS as well as DCR improvement, but it was accompanied by increased risk of TRAE, such as hypertension, hepatic dysfunction, diarrhea and hemoptysis. Although much effort has been made to clinical trials of anlotinib, further studies are warranted to provide more convincing evidence.

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis.

In the last few years, deep learning classifiers have shown promising results in image-based medical diagnosis. However, interpreting the outputs of these models remains a challenge. In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i.e. the location of a lesion. Alternatively, segmentation or detection models can be trained with pixel-wise annotations indicating the locations of malignant lesions. Unfortunately, acquiring such labels is labor-intensive and requires medical expertise. To overcome this difficulty, weakly-supervised localization can be utilized. These methods allow neural network classifiers to output saliency maps highlighting the regions of the input most relevant to the classification task (e.g. malignant lesions in mammograms) using only image-level labels (e.g. whether the patient has cancer or not) during training. When applied to high-resolution images, existing methods produce low-resolution saliency maps. This is problematic in applications in which suspicious lesions are small in relation to the image size. In this work, we introduce a novel neural network architecture to perform weakly-supervised segmentation of high-resolution images. The proposed model selects regions of interest via coarse-level localization, and then performs fine-grained segmentation of those regions. We apply this model to breast cancer diagnosis with screening mammography, and validate it on a large clinically-realistic dataset. Measured by Dice similarity score, our approach outperforms existing methods by a large margin in terms of localization performance of benign and malignant lesions, relatively improving the performance by 39.6% and 20.0%, respectively. Code and the weights of some of the models are available at https://github.com/nyukat/GLAM.

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.

Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analysis. In this work, we propose a novel neural network model to address these unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a prediction. While existing methods often require lesion segmentation during training, our model is trained with only image-level labels and can generate pixel-level saliency maps indicating possible malignant findings. We apply the model to screening mammography interpretation: predicting the presence or absence of benign and malignant lesions. On the NYU Breast Cancer Screening Dataset, our model outperforms (AUC = 0.93) ResNet-34 and Faster R-CNN in classifying breasts with malignant findings. On the CBIS-DDSM dataset, our model achieves performance (AUC = 0.858) on par with state-of-the-art approaches. Compared to ResNet-34, our model is 4.1x faster for inference while using 78.4% less GPU memory. Furthermore, we demonstrate, in a reader study, that our model surpasses radiologist-level AUC by a margin of 0.11.

Polyamine Antagonist Therapies Inhibit Neuroblastoma Initiation and Progression.

PURPOSE: Deregulated MYC drives oncogenesis in many tissues yet direct pharmacologic inhibition has proven difficult. MYC coordinately regulates polyamine homeostasis as these essential cations support MYC functions, and drugs that antagonize polyamine sufficiency have synthetic-lethal interactions with MYC Neuroblastoma is a lethal tumor in which the MYC homologue MYCN, and ODC1, the rate-limiting enzyme in polyamine synthesis, are frequently deregulated so we tested optimized polyamine depletion regimens for activity against neuroblastoma. EXPERIMENTAL DESIGN: We used complementary transgenic and xenograft-bearing neuroblastoma models to assess polyamine antagonists. We investigated difluoromethylornithine (DFMO; an inhibitor of Odc, the rate-limiting enzyme in polyamine synthesis), SAM486 (an inhibitor of Amd1, the second rate-limiting enzyme), and celecoxib (an inducer of Sat1 and polyamine catabolism) in both the preemptive setting and in the treatment of established tumors. In vitro assays were performed to identify mechanisms of activity. RESULTS: An optimized polyamine antagonist regimen using DFMO and SAM486 to inhibit both rate-limiting enzymes in polyamine synthesis potently blocked neuroblastoma initiation in transgenic mice, underscoring the requirement for polyamines in MYC-driven oncogenesis. Furthermore, the combination of DFMO with celecoxib was found to be highly active, alone, and combined with numerous chemotherapy regimens, in regressing established tumors in both models, including tumors harboring highest risk genetic lesions such as MYCN amplification, ALK mutation, and TP53 mutation with multidrug resistance. CONCLUSIONS: Given the broad preclinical activity demonstrated by polyamine antagonist regimens across diverse in vivo models, clinical investigation of such approaches in neuroblastoma and potentially other MYC-driven tumors is warranted. Clin Cancer Res; 22(17); 4391-404. ©2016 AACR.

Characterization of Abeta11-40/42 peptide deposition in Alzheimer's disease and young Down's syndrome brains: implication of N-terminally truncated Abeta species in the pathogenesis of Alzheimer's disease.

Senile plaques (SPs), one of two defining lesions of Alzheimer's disease (AD), are composed of a mixture of full-length Abeta1-40/42, and N- or C-terminally truncated Abeta peptides, including Abeta11-40/42. Sequential proteolysis of amyloid precursor protein (APP) by beta- and gamma-secretases produces Abeta1-40/42, but beta-site APP-cleaving enzyme 1 (BACE1), the major beta-secretase, also generates Abeta11-40/42, and BACE1 overexpression in cultured cells results primarily in secretion of Abeta11-40/42. The ratio of Abeta11-40/42 to Abeta1-40/42 depends on the ratio of BACE1 to APP, and Abeta11-40/42 can be generated from both full-length APP and its carboxy-terminal fragment (C99). Here, we investigated the role of Abeta11-40/42 in the pathogenesis of AD and Down's syndrome (DS) brains. We demonstrated significant amount of Abeta11-42 in DS brains by Western blots. While pyroAbeta11-42-modified Abeta species existed predominantly in mature SP cores in AD brain sections, both unmodified free Abeta11-40 and pyro-modified Abeta11-40 are detected in vascular amyloid deposits by immunohistochemistry. Using novel ELISAs for quantifying free Abeta11-40/42 and pyroAbeta11-40/42, we showed that insoluble Abeta11-42 predominated in extracts of AD and DS brains. This is the first systematic study of Abeta11-40/42 in neurodegenerative Abeta amyloidosis implicating Abeta11-40/42 in SP formation of AD and DS brains. The detection of Abeta11-42 in young DS brain suggests an early role for this N-terminally truncated Abeta peptide in the pathogenesis of SPs in AD and DS.