The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M-mutant; RELA fusion-positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma-a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.

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The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

Tissue-based map of the human proteome

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.

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Signatures of mutational processes in human cancer

Regarding the Congruence between 2 Circulating Tumor DNA Sequencing Assays - Reply

Background: Many chemotherapeutic agents are believed to kill cancer cells by inflicting cellular damage which triggers the cell to enter apoptosis (programmed cell death). We investigated the means by which carboplatin induces cell death in three model cancer systems: the human prostate carcinoma cell lines PC-3 and LNCaP and the human cervical carcinoma cell line HeLa. Materials and Methods: Drug cytotoxicity, cell cycle effects, bcl-2 deactivation, and multiple markers for apoptosis were utilized to examine carboplatin activity within these cell lines. Results: In HeLa cells, carboplatin appears to induce an S-phase block followed by apoptosis. In contrast, PC-3 and LNCaP cells show no cell cycle phase block and die from necrosis rather than apoptosis. The effects of carboplatin contrast sharply with the effects of paclitaxel, which induces an M-phase block and apoptosis in all three cell lines. Conclusions: These results show that PC-3 and LNCaP cells are relatively resistant to carboplatin and suggest two causes of resistance: bypassing the cell cycle checkpoints which serve as points of entry into apoptosis, and incomplete execution of the effector mechanisms of apoptosis. Carboplatin resistance in the prostate cancer cell lines fits into the developing scheme of apoptosis-necrosis and raises valuable questions about the root causes of cancer resistance to chemotherapeutic agents.

Carboplatin-induced cell death in model prostate cancer systems.

In the field of oncology, positron emission tomography (PET) is most commonly performed using 2-deoxy-2-[F]fluoro-dglucose (F-FDG), a radiofluorinated glucose analogue that accumulates in cells undergoing aerobic glycolysis. Unfortunately, because of the low glycolytic activity of hormone-na€ıve prostate cancer cells, F-FDG PET has been of little value in imaging men with this malignancy [1]. Instead, clinicians have been left to rely mostly on Tc-methylene diphosphonate bone scan, CT, and MRI to stage and follow patients. Recently, however, the development of multiple ureabased small molecules targeting the type II transmembrane glycoprotein prostate-specific membrane antigen (PSMA) has allowed for the highly sensitive and specific detection of prostate cancer using PET imaging [2]. To date, the majority of clinical data with PSMA-targeted PET have been generated with the Ga-PSMA-11 radiotracer (also known as Ga-PSMA-HBEDCC). Notably, studies evaluating PSMA-targeted PET have mostly focused on establishing the diagnostic performance characteristics of the various radiotracers (e.g. sensitivity and specificity), with relatively few reports exploring the clinical impact or utility of this form of molecular imaging.

Defining the clinical utility of PSMA-targeted PET imaging of prostate cancer.

Purpose of review This review aims to highlight recent advances in prostate cancer tumor-immune microenvironment research and summarize the state-of-the-art knowledge of immune checkpoint inhibitors in prostate cancer. Recent findings Immune checkpoint inhibitors are the cornerstone of modern immunotherapy which have shown encouraging results across a spectrum of cancers. However, only limited survival benefit has been seen in patients with prostate cancer. Prostate cancer progression and its response to immunotherapies are strongly influenced by the tumor-immune microenvironment, whose feature can be summarized as low amounts of tumor-specific antigens, low frequency of tumor-infiltrating lymphocytes and high frequency of tumor-associated macrophages. To improve the therapeutic effect of immunotherapies, in recent years, many strategies have been applied, of which the most promising ones include the combination of multiple immunotherapeutic agents, the combination of an immunotherapeutic agent with other modalities in parallel or in sequential, and the development of biomarkers to find a subgroup of patients who may benefit the most from immunotherapeutic agents. Summary The impact of immune content and specific immune cell types on prostate cancer biology is highly complex. Recent clinical trials have shed light on the optimal use of immunotherapies for prostate cancer.

Understanding the tumor-immune microenvironment in prostate cancer.

Background: The etiology of prostate cancer is currently a mystery. Several epidemiological studies suggest a link between dietary fat and prostate cancer. In vitro and in vivo studies support this evidence. Using the Dunning model of rat prostate cancer we hypothesized that a high-fat diet (20%) would increase the growth of the R3327-H tumor. Materials and Methods: R3327-H tumors were implanted subcutaneously into male Copenhagen rats which were fed diets with 5 or 20% total fat. Tumors were allowed to grow for 16 weeks; they were then excised and weighed. The initial and final weights of the rats were also recorded. Results: Statistical analysis revealed the level of dietary fat was a positive predictor of weight gain (p < 0.01). No effect on tumor growth was seen when compared to dietary fat, fiber type, or the interaction of fat and fiber. Discussion: Growth of the R3327-H tumor, when implanted subcutaneously, is not affected by the level of dietary fat.

Kenneth J. Pienta

Papers

Regarding the Congruence between 2 Circulating Tumor DNA Sequencing Assays - Reply

Carboplatin-induced cell death in model prostate cancer systems.

Defining the clinical utility of PSMA-targeted PET imaging of prostate cancer.

Understanding the tumor-immune microenvironment in prostate cancer.

A high-fat diet does not influence the growth of the Dunning R3327-H transplantable prostate adenocarcinoma.