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

Most definitions of cancer broadly conform to the current National Cancer Institute definition: "Cancer is a disease in which some of the body's cells grow uncontrollably and spread to other parts of the body". These definitions tend to describe what cancer "looks like" or "does" but do not describe what cancer "is" or "has become." While reflecting past insights, current definitions have not kept pace with the understanding that the cancer cell is itself transformed and evolving. We propose a revised definition of cancer: Cancer is a disease of uncontrolled proliferation by transformed cells subject to evolution by natural selection. We believe this definition captures the essence of the majority of previous and current definitions. To the simplest definition of cancer as a disease of uncontrolled proliferation of cells, our definition adds in the adjective "transformed" to capture the many tumorigenic processes that cancer cells adopt to metastasize. To the concept of uncontrolled proliferation of transformed cells, our proposed definition then adds "subject to evolution by natural selection." The subject to evolution by natural selection modernizes the definition to include the genetic and epigenetic changes that accumulate within a population of cancer cells that lead to the lethal phenotype.

Updating the definition of cancer.


 We present a theoretical modeling framework, called the G function, to understand cancer speciation, diversification, and environmental adaptation. The G function integrates both the ecology and evolution of cancer and has been used in evolutionary ecology. However, the G function has not yet been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer is inherently an evolutionary game. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly online tool. We argue that G-functions are useful to understand the games cancer plays in a biologically mechanistic fashion.
 Citation Format: Anuraag Bukkuri, Kenneth J. Pienta, Ian Hockett, Robert H. Austin, Emma U. Hammarlund, Sarah R. Amend, Joel S. Brown. Modeling cancer’s ecological and evolutionary dynamics [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A001.

Abstract A001: Modeling cancer’s ecological and evolutionary dynamics

The 2022 Coffey−Holden Prostate Cancer Academy (CHPCA) Meeting, “Exploring New Frontiers in Prostate Cancer Research,” was held from June 23 to 26, 2022, at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA.

Exploring new frontiers in prostate cancer research: Report from the 2022 Coffey−Holden prostate cancer academy meeting

Retraction: Characterization of Phosphoglycerate Kinase-1 Expression of Stromal Cells Derived from Tumor Microenvironment in Prostate Cancer Progression.


 Metastatic cancer is responsible for 90% of cancer deaths and is incurable due to resistance to all systemic anticancer therapies. While the classic model of therapy resistance is described by the presence of pre-existing resistant clones from tumor cell heterogeneity, our lab proposes the polyaneuploid cancer cell (PACC) state as a survival mechanism that cancer cells access when exposed to environmental stress such as hypoxia and chemotherapy. Our preliminary data has shown that cells that enter the PACC state exhibit repeated whole genome doubling, an increase in cell size, and proliferative arrest. In addition, we observed that a subpopulation of PACCs can give rise to non-polyploid, proliferative progeny after release from chemotherapy, modeling cancer recurrence. We hypothesize that the PACC state drives therapeutic resistance and elimination of this adaptive state is key in tackling the incurability of cancer. Mitochondria are essential organelles that have multifaceted roles in the cellular stress response and drug resistance. They integrate metabolic pathways for ATP generation and macromolecule biosynthesis, regulate apoptotic cell death, and activate stress response mechanisms in presence of drug treatment. We found that cells in the PACC state increase mitochondrial biogenesis and live cell imaging with MitoTraker dyes indicate that they also have higher activity per mitochondria when compared to parental cells. Cells that accessed the PACC state also had higher levels of TCA cycle metabolites including citrate, isocitrate, alpha-ketoglutarate, succinate, fumarate, and malate, suggesting increased mitochondrial function. Future work includes analysis of mitochondrial morphology via live cell confocal microscopy and of mitochondrial function via metabolic flux analysis on glucose and glutamine metabolism of cells in the PACC state. Our eventual goal is to characterize how cells in the PACC state alter their mitochondrial structure and function to survive therapy. Identifying vulnerabilities of the PACC phenotype will enable new therapeutic approaches to overcome therapy resistance in cancer.
 Citation Format: Melvin Li, Laurie G. Kostecka, Sarah R. Amend, Kenneth J. Pienta. Stress-induced mitochondrial adaptations in the polyaneuploid cancer cell state. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4842.

Kenneth J. Pienta

Papers

Updating the definition of cancer.

Abstract A001: Modeling cancer’s ecological and evolutionary dynamics

Exploring new frontiers in prostate cancer research: Report from the 2022 Coffey−Holden prostate cancer academy meeting

Retraction: Characterization of Phosphoglycerate Kinase-1 Expression of Stromal Cells Derived from Tumor Microenvironment in Prostate Cancer Progression.

Abstract 4842: Stress-induced mitochondrial adaptations in the polyaneuploid cancer cell state