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.

/pdf/signatures-of-mutational-processes-in-human-cancer-j3duru54oq.pdf

Signatures of mutational processes in human cancer

The present invention provides immortalized human bone marrow endothelial cells which are useful for the study of tumor metastasis. In particular, the human bone marrow endothelial cell lines provided by the invention provide an in vitro model system for screening compounds for the ability to reduce, prevent, or inhibit the metastasis of cancer cells to bone tissue.

Human bone marrow endothelial cell line and methods of use thereof

Prostate cancer is a leading cause of cancer-related deaths of men in the U.S., and in the past year, over 30,000 men died from this disease. While localized prostate cancer is highly treatable by surgical resection and radiation, cancer that has metastasized remains incurable. Alternatively activated macrophages, also known as M2-macrophages, primarily scavenge debris and in the process, promote angiogenesis and wound repair. M2-macrophages are phenotypically similar to M2-tumor associated macrophages (M2-TAMs) have been reported to associate with solid tumors such as prostate cancer to facilitate epithelial to mesenchymal transition (EMT), tumor invasiveness, metastasis, and resistance to therapy. As an invasive species within the tumor microenvironment, this makes M2-TAMs an ideal therapeutic target in prostate cancer. The purpose of this project is to develop novel therapeutics that will directly target M2-TAMs for destruction and subsequently attenuate prostate tumor growth and progression. The central hypothesis of this study is to determine if targeting of M2-TAMs using specific surface antigens will be an effective therapy for lethal prostate cancer and potentially, other cancers. Our study will first elucidate M2-TAM biology, including both a study of known M2-TAM cell surface antigen markers expressed in human prostate cancers as well as rigorous tests to target these markers for anti-tumor cell efficacy using relevant model systems. Functional experiments will then be done in vitro using primary antibodies that are bound by a saporin-conjugated secondary antibody and also, in vivo by utilizing human xenograft mouse models. We will also assess internalization of armed antibodies, M2-TAM cell death, M2-TAM functionality, and EMT status of PCa tumors after targeting. This will be followed by target validation using tissues from rapid autopsies and surgical specimens supplied by the Department of Urology at Johns Hopkins University School of Medicine. By targeting specific markers on M2-TAMs, we predict that this targeting will provide a better prognosis for patients who have been diagnosed with lethal prostate cancer. Modification of targeted ligands and drug combinations will produce a flexible platform for cancer therapy. The studies described here represent an underdeveloped frontier in cancer therapeutics and outline a method of altering the tumor immune microenvironment. If successful, antibodies used against specific M2-TAM surface markers will be constructed into antibody drug conjugates (ADCs). Citation Format: Jelani C. Zarif, James R. Hernandez, Kris F. Sachsenmeier, Robert E. Hollingsworth, Kenneth J. Pienta. Targeting M2-tumor associated macrophages (M2-TAMs) in prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2365. doi:10.1158/1538-7445.AM2015-2365

Abstract 2365: Targeting M2-tumor associated macrophages (M2-TAMs) in prostate cancer

Circulating tumor cells (CTCs) are a promising biomarker for cancer liquid biopsy. To evaluate the CTC capture bias and detection capability of the slit filter-based CTC isolation platform (CTC-FIND), we prospectively compared it head to head to a selection-free platform (AccuCyte®-CyteFinder® system). We used the two methods to determine the CTC counts, CTC positive rates, CTC size distributions, and CTC phenotypes in 36 patients with metastatic cancer. Between the two methods, the median CTC counts were not significantly different and the total counts were correlated (r = 0.63, p < 0.0001). The CTC positive rate by CTC-FIND was significantly higher than that by AccuCyte®-CyteFinder® system (91.7% vs. 66.7%, p < 0.05). The median diameter of CTCs collected by CTC-FIND was significantly larger (13.0 μm, range 5.2-52.0 vs. 10.4 μm, range 5.2-44.2, p < 0.0001). The distributions of CTC phenotypes (CK+EpCAM+, CK+EpCAM- or CK-EpCAM+) detected by both methods were similar. These results suggested that CTC-FIND can detect more CTC-positive cases but with a bias toward large size of CTCs.

https://www.mdpi.com/1422-0067/21/23/9031/pdf

Analysis of the Circulating Tumor Cell Capture Ability of a Slit Filter-Based Method in Comparison to a Selection-Free Method in Multiple Cancer Types

We define cell morphodynamics as the cell's time dependent morphology. It could be called the cell's shape shifting ability. To measure it we use a biomarker free, dynamic histology method, which is based on multiplexed Cell Magneto-Rotation and Machine Learning. We note that standard studies looking at cells immobilized on microscope slides cannot reveal their shape shifting, no more than pinned butterfly collections can reveal their flight patterns. Using cell magnetorotation, with the aid of cell embedded magnetic nanoparticles, our method allows each cell to move freely in 3 dimensions, with a rapid following of cell deformations in all 3-dimensions, so as to identify and classify a cell by its dynamic morphology. Using object recognition and machine learning algorithms, we continuously measure the real-time shape dynamics of each cell, where from we successfully resolve the inherent broad heterogeneity of the morphological phenotypes found in a given cancer cell population. In three illustrative experiments we have achieved clustering, differentiation, and identification of cells from (A) two distinct cell lines, (B) cells having gone through the epithelial-to-mesenchymal transition, and (C) cells differing only by their motility. This microfluidic method may enable a fast screening and identification of invasive cells, e.g., metastatic cancer cells, even in the absence of biomarkers, thus providing a rapid diagnostics and assessment protocol for effective personalized cancer therapy.

Kenneth J. Pienta

Papers

Human bone marrow endothelial cell line and methods of use thereof

Abstract 2365: Targeting M2-tumor associated macrophages (M2-TAMs) in prostate cancer

Analysis of the Circulating Tumor Cell Capture Ability of a Slit Filter-Based Method in Comparison to a Selection-Free Method in Multiple Cancer Types

Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning

Re: RANKL inhibition is an effective adjuvant for docetaxel in a prostate cancer bone metastasis model.