Novel Molecular Targets of Azadirachta indica Associated with Inhibition of Tumor Growth in Prostate Cancer

Advanced prostate cancer has significant long-term morbidity, and there is a growing interest in alternative and complimentary forms of therapy that will improve the outcomes of patients. Azadirachta indica (common name: neem) contains multiple active compounds that have potent anti-inflammatory and anticancer properties. The present study investigates the novel targets of the anticancer activity of ethanol extract of neem leaves (EENL) in vitro and evaluates the in vivo efficacy in the prostate cancer models. Analysis of the components in the EENL by mass spectrometry suggests the presence of 2′,3′-dehydrosalannol, 6-desacetyl nimbinene, and nimolinone. Treatment of C4-2B and PC-3M-luc2 prostate cancer cells with EENL inhibited the cell proliferation. Genome-wide expression profiling, using oligonucleotide microarrays, revealed genes differentially expressed with EENL treatment in prostate cancer cells. Functional analysis unveiled that most of the up-regulated genes were associated with cell death, and drug metabolism, and the down-regulated genes were associated with cell cycle, DNA replication, recombination, and repair functions. Quantitative PCR confirmed significant up-regulation of 40 genes and immunoblotting revealed increase in the protein expression levels of HMOX1, AKR1C2, AKR1C3, and AKR1B10. EENL treatment inhibited the growth of C4-2B and PC-3M-luc2 prostate cancer xenografts in nude mice. The suppression of tumor growth is associated with the formation of hyalinized fibrous tumor tissue and the induction of cell death by apoptosis. These results suggest that EENL-containing natural bioactive compounds could have potent anticancer property and the regulation of multiple cellular pathways could exert pleiotrophic effects in prevention and treatment of prostate cancer

Clinical development of new drug-radiotherapy combinations.

In countries with the best cancer outcomes, approximately 60% of patients receive radiotherapy as part of their treatment, which is one of the most cost-effective cancer treatments. Notably, around 40% of cancer cures include the use of radiotherapy, either as a single modality or combined with other treatments. Radiotherapy can provide enormous benefit to patients with cancer. In the past decade, significant technical advances, such as image-guided radiotherapy, intensity-modulated radiotherapy, stereotactic radiotherapy, and proton therapy enable higher doses of radiotherapy to be delivered to the tumour with significantly lower doses to normal surrounding tissues. However, apart from the combination of traditional cytotoxic chemotherapy with radiotherapy, little progress has been made in identifying and defining optimal targeted therapy and radiotherapy combinations to improve the efficacy of cancer treatment. The National Cancer Research Institute Clinical and Translational Radiotherapy Research Working Group (CTRad) formed a Joint Working Group with representatives from academia, industry, patient groups and regulatory bodies to address this lack of progress and to publish recommendations for future clinical research. Herein, we highlight the Working Group's consensus recommendations to increase the number of novel drugs being successfully registered in combination with radiotherapy to improve clinical outcomes for patients with cancer.National Institute for Health ResearchThis is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/nrclinonc.2016.7

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

MAGIC and Fermi-LAT gamma-ray results on unassociated HAWC sources

The HAWC Collaboration released the 2HWC catalogue of TeV sources, in which 19 show no association with any known high-energy (HE; E greater than or similar to 10 GeV) or very-high-energy (VHE; E greater than or similar to 300 GeV) sources. This catalogue motivated follow-up studies by both the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) and Fermi-LAT (Large Area Telescope) observatories with the aim of investigating gamma-ray emission over a broad energy band. In this paper, we report the results from the first joint work between High Altitude Water Cherenkov (HAWC), MAGIC, and Fermi-LAT on three unassociated HAWC sources: 2HWC J2006+341, 2HWC J1907+084*, and 2HWC J1852+013*. Although no significant detection was found in the HE and VHE regimes, this investigation shows that a minimum 1 degrees extension (at 95 per cent confidence level) and harder spectrum in the GeV than the one extrapolated from HAWC results are required in the case of 2HWC J1852+013*, whilst a simply minimum extension of 0.16 degrees (at 95 per cent confidence level) can already explain the scenario proposed by HAWC for the remaining sources. Moreover, the hypothesis that these sources are pulsar wind nebulae is also investigated in detail

Finding faces in wavelet domain for content-based coding of color images

Human face images form the important database in police departments, banks, security kiosks, and they are also found in abundance in day-to-day life. In these databases the important content, of course, is the face region. We present a highly efficient system that detects the human faces in the wavelet transform for discriminative quantization to achieve high perceptual quality content-based image coding technique. The proposed method gives superior subjective performance over JPEG without sacrificing the performance in the rate-distortion spectrum.© IEE

3D object recognition using Bayesian geometric hashing and pose clustering

Geometric hashing (GH) and partial pose clustering are well-known algorithms for pattern recognition. However, the performance of both these algorithms degrades rapidly with an increase in scene clutter and the measurement uncertainty in the detected features. The primary contribution of this paper is the formulation of a framework that unifies the GH and the partial pose clustering paradigms for pattern recognition in cluttered scenes. The proposed scheme has a better discrimination capability as compared to the GA algorithm, thus improving recognition accuracy. The scheme is incorporated in a Bayesian MLE framework to make it robust to the presence of sensor noise. It is able to handle partial occlusions, is robust to measurement uncertainty in the data features and to the presence of spurious scene features (scene clutter). An efficient hash table representation of 3D features extracted from range images is also proposed. Simulations with real and synthetic 2D/3D objects show that the scheme performs better than the GH algorithm in scenes with a large amount of clutter. (C) 2002 on behalf of Pattern Recognition Society

Face recognition using a DCT-HMM approach

A transform domain approach coupled with Hidden Markov Model (HMM) for face recognition is presented. JPEG kind of strategy is employed to transform input subimage for training HMMs. DCT transformed vectors efface images are used to train ergodic HMM and later for recognition. ORL face database of 40 subjects with 10 images per subject is used to evaluate the performance of the proposed method. 5 images per subject are used for training and the rest 5 for recognition. This method has an accuracy of 99.5%. The results, to the best of knowledge of the authors, give the best recognition percentage as compared to any other method reported so far on ORL face database.© IEE