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Predictive Oncology



Four current Alternative Predictive Oncology approaches:
Click to Replace

1. DOE and NIH Partnerships in Predictive Oncology

 

 

 Link to PDF https://www.anl.gov/articles/cancer-s-big-data-problem

Transition cancer therapy away from a “one-size-fits-all” approach. Instead, the goal is to move toward individualized diagnosis and treatment that accommodates a patient’s unique body chemistry and genetics.

 

Quote from https://www.anl.gov/articles/cancer-s-big-data-problem



Link to PDF Joint Design of Advanced Computing Solutions for Cancer (JDACSC)





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2. Watson Brings the Promise of Precision Medicine to the Fight Against Cancer

 

 PDF link to Helping your care team make informed decisions


IBM and Quest Diagnostics Launch

Watson-Powered Genomic Sequencing

Service to Help Physicians Bring

Precision Cancer Treatments to Patients

Nationwide

Memorial Sloan Kettering Cancer Center to Provide Deep Knowledge

Base to Augment Watson’s Data Sources and Quest’s Medical

Reporting

New Service Extends Reach to Community Oncologists Who Provide

70 Percent of Cancer Care

 

Link to PDF: IBM and Quest Diagnostics Launch

Watson-Powered Genomic Sequencing

_________________________________________

https://www.ibm.com/blogs/think/2017/05/41023/

On Healthcare, Moonshots and Open Data

May 3, 2017 | Written by: Deborah DiSanzo

Categorized: Watson Health

For decades, scientists, researchers, hospitals and clinics have been amassing a stockpile of information on cancer. This data takes many forms: medical texts and journals, patient records, medical images, genetic profiles and more. And it lives in many places — from highly structured digital databases to forgotten filing cabinets, owned by government agencies, insurance companies, research scientists and healthcare providers.

In a sense, we’ve become data rich. But somehow, we remain insight poor.

 

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3.  More immediate alternatives

    The cBioPortal for Cancer Genomics provides visualization, analysis and download of large-scale cancer genomics data sets.

    cBioPortal, New York

    Link to cBioPortal:  http://www.cbioportal.org/

     

    Microsoft Analytics analysis method.

    Link to PDF: Microsoft Analytics Overview

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    4. A pathology atlas of the human cancer transcriptome

    Research Article

    A pathology atlas of the human cancer transcriptome

    1.    Mathias Uhlen1,2,3,*,

    2.    Cheng Zhang1,

    3.    Sunjae Lee1,

    4.    Evelina Sjöstedt1,4,

    5.    Linn Fagerberg1,

    6.    Gholamreza Bidkhori1,

    7.    Rui Benfeitas1,

    8.    Muhammad Arif1,

    9.    Zhengtao Liu1,

    10. Fredrik Edfors1,

    11. Kemal Sanli1,

    12. Kalle von Feilitzen1,

    13. Per Oksvold1,

    14. Emma Lundberg1,

    15. Sophia Hober3,

    16. Peter Nilsson1,

    17. Johanna Mattsson4,

    18. Jochen M. Schwenk1,

    19. Hans Brunnström5,

    20. Bengt Glimelius4,

    21. Tobias Sjöblom4,

    22. Per-Henrik Edqvist4,

    23. Dijana Djureinovic4,

    24. Patrick Micke4,

    25. Cecilia Lindskog4,

    26. Adil Mardinoglu1,3,6,,

    27. Fredrik Ponten4,

    1.    1Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

    2.    2Center for Biosustainability, Danish Technical University, Copenhagen, Denmark.

    3.    3School of Biotechnology, AlbaNova University Center, KTH–Royal Institute of Technology, Stockholm, Sweden.

    4.    4Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

    5.    5Division of Pathology, Lund University, Skåne University Hospital, Lund, Sweden.

    6.    6Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.

    1.    *Corresponding author. Email: mathias.uhlen@scilifelab.se

    1.    † These authors contributed equally to this work.

    + See all authors and affiliations

    Science  18 Aug 2017:
    Vol. 357, Issue 6352, eaan2507
    DOI: 10.1126/science.aan2507

    Mathias Uhlen

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.Center for Biosustainability, Danish Technical University, Copenhagen, Denmark.School of Biotechnology, AlbaNova University Center, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Cheng Zhang

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Sunjae Lee

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Evelina Sjöstedt

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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    Linn Fagerberg

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Gholamreza Bidkhori

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Rui Benfeitas

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Muhammad Arif

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Zhengtao Liu

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Fredrik Edfors

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Kemal Sanli

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Kalle von Feilitzen

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Per Oksvold

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    ·ORCID record for Per Oksvold

    Emma Lundberg

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Sophia Hober

    School of Biotechnology, AlbaNova University Center, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    Peter Nilsson

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    ·ORCID record for Peter Nilsson

    Johanna Mattsson

    Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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    Jochen M. Schwenk

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.

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    ·ORCID record for Jochen M. Schwenk

    Hans Brunnström

    Division of Pathology, Lund University, Skåne University Hospital, Lund, Sweden.

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    Bengt Glimelius

    Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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    ·ORCID record for Bengt Glimelius

    Tobias Sjöblom

    Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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    Per-Henrik Edqvist

    Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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    Dijana Djureinovic

    Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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    ·ORCID record for Dijana Djureinovic

    Patrick Micke

    Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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    Cecilia Lindskog

    Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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    ·ORCID record for Cecilia Lindskog

    Adil Mardinoglu

    Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden.School of Biotechnology, AlbaNova University Center, KTH–Royal Institute of Technology, Stockholm, Sweden.Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.

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    Fredrik Ponten

    Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden.

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    ·ORCID record for Fredrik Ponten

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    Modeling the cancer transcriptome

    Recent initiatives such as The Cancer Genome Atlas have mapped the genome-wide effect of individual genes on tumor growth. By unraveling genomic alterations in tumors, molecular subtypes of cancers have been identified, which is improving patient diagnostics and treatment. Uhlen et al. developed a computer-based modeling approach to examine different cancer types in nearly 8000 patients. They provide an open-access resource for exploring how the expression of specific genes influences patient survival in 17 different types of cancer. More than 900,000 patient survival profiles are available, including for tumors of colon, prostate, lung, and breast origin. This interactive data set can also be used to generate personalized patient models to predict how metabolic changes can influence tumor growth.

    Science, this issue p. eaan2507

    Structured Abstract

    INTRODUCTION

    Cancer is a leading cause of death worldwide, and there is great need to define the molecular mechanisms driving the development and progression of individual tumors. The Hallmarks of Cancer has provided a framework for a deeper molecular understanding of cancer, and the focus so far has been on the genetic alterations in individual cancers, including genome rearrangements, gene amplifications, and specific cancer-driving mutations. Using systems-level approaches, it is now also possible to define downstream effects of individual genetic alterations in a genome-wide manner.

    RATIONALE

    In our study, we used a systems-level approach to analyze the transcriptome of 17 major cancer types with respect to clinical outcome, based on a genome-wide transcriptomics analysis of ~8000 individual patients with clinical metadata. The study was made possible through the availability of large open-access knowledge-based efforts such as the Cancer Genome Atlas and the Human Protein Atlas. Here, we used the data to perform a systems-level analysis of 17 major human cancer types, describing both interindividual and intertumor variation patterns.

    RESULTS

    The analysis identified candidate prognostic genes associated with clinical outcome for each tumor type; the results show that a large fraction of cancer protein-coding genes are differentially expressed and, in many cases, have an impact on overall patient survival. Systems biology analyses revealed that gene expression of individual tumors within a particular cancer varied considerably and could exceed the variation observed between distinct cancer types. No general prognostic gene necessary for clinical outcome was applicable to all cancers. Shorter patient survival was generally associated with up-regulation of genes involved in mitosis and cell growth and down-regulation of genes involved in cellular differentiation. The data allowed us to generate personalized genome-scale metabolic models for cancer patients to identify key genes involved in tumor growth. In addition, we explored tissue-specific genes associated with the dedifferentiation of tumor cells and the role of specific cancer testis antigens on a genome-wide scale. For lung and colorectal cancer, a selection of prognostic genes identified by the systems biology effort were analyzed in independent, prospective cancer cohorts using immunohistochemistry to validate the gene expression patterns at the protein level.

    CONCLUSION

    A Human Pathology Atlas has been created as part of the Human Protein Atlas program to explore the prognostic role of each protein-coding gene in 17 different cancers. Our atlas uses transcriptomics and antibody-based profiling to provide a standalone resource for cancer precision medicine. The results demonstrate the power of large systems biology efforts that make use of publicly available resources. Using genome-scale metabolic models, cancer patients are shown to have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. With more than 900,000 Kaplan-Meier plots, this resource allows exploration of the specific genes influencing clinical outcome for major cancers, paving the way for further in-depth studies incorporating systems-level analyses of cancer. All data presented are available in an interactive open-access database (www.proteinatlas.org/pathology) to allow for genome-wide exploration of the impact of individual proteins on clinical outcome in major human cancers.

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    Schematic overview of the Human Pathology Atlas.

    A systems-level approach enables analysis of the protein-coding genes of 17 different cancer types from ~8000 patients. Results are available in an interactive open-access database.

    Abstract

    Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

    http://www.sciencemag.org/about/science-licenses-journal-article-reuse

    This is an article distributed under the terms of the Science Journals Default License.

     

    http://www.proteinatlas.org/images_static/logo_text_small.gifhttp://www.proteinatlas.org/images_static/logo_hex_anim_small.gif

    Link below to: prognostic:pancreatic cancer 

    AND sort_by:prognostic pancreatic cancer

    http://www.proteinatlas.org/search/prognostic:pancreatic+cancer+AND+sort_by:prognostic+pancreatic+cancer+AND+show_columns:prognostic

    Prognostic means serving to predict the likely outcome of a disease or ailment of or relating to a medical prognosis.

    Link below to The Human Protein Atlas Dictionary – Pathology Pancreatic cancer:

    http://www.proteinatlas.org/learn/dictionary/pathology/pancreatic+cancer

    Extract;

    Pancreatic cancer

    Pancreatic cancer is a relatively common form of human cancer and is associated with a poor prognosis. The risk of pancreatic cancer increases with age and the tumor is slightly more common in women than in men. Pancreatic cancer is often detected at such a late stage of the disease that curative surgery is not possible. The spread of pancreatic cancer at time for diagnosis is a major reason for the dismal prognosis, rendering pancreatic cancer a leading cause of cancer death. Signs and symptoms are diffuse and do usually not become apparent before the cancer has reached an advanced stage. Most patients are above 50 years of age and pain, jaundice and weight loss are the most common symptoms. The cause of pancreatic cancer is unknown. However, pancreatic cancer is more common in persons with diabetes and chronic pancreatitis (persistent inflammation in the pancreas) as well as in tobacco smokers.

    Most cancers of the pancreas are ductal adenocarcinomas, with two-thirds of tumors arising in the head of the pancreas. Ductal carcinomas grow rapidly and have often spread beyond the pancreas at time of diagnosis. Ductal carcinomas are poorly demarcated tumors that are characterized by atypical cells forming irregular, often complex and incomplete, tubular or glandular structures, embedded in a dense desmoplastic tumor stroma. Ductal carcinoma is often accompanied by chronic pancreatitis showing infiltrates of inflammatory cells, fibrosis and atrophy of normal exocrine pancreatic structures. Tumor cells appear pleomorphic with variation in shape and size of tumor cell nuclei, often with conspicuous nucleoli. The rate of proliferation is often high and mitotic figures are easy to find. Necrotic cellular debris can often be found in the lumina of cancerous glands and ducts. Invasion into tiny vessels or into perineural lymphatics is a common feature. Ductal carcinomas are classified as well, moderately or poorly differentiated dependent on morphology. The degree of differentiation may vary within a tumor so that occasional anaplastic foci of tumor can be present within an otherwise well-differentiated ductal carcinoma. The differentiation grade has not proven helpful for predicting prognosis and likewise has staging only limited value as the vast majority of patients have an advanced stage of the disease when diagnosed.

    Aside from local spread in the pancreas and to surrounding tissues, pancreatic cancer often spreads to regional lymph nodes and to the liver. In addition to the typical ductal carcinoma there are uncommon variants of pancreatic cancer, including adenosquamous carcinoma, mucinous carcinoma, papillary carcinoma and acinar cell carcinoma. Moreover, mucinous cystadenocarcinoma and tumors corresponding to the endocrine compartment of the pancreas also exist with various symptoms, microscopical features and often less severe prognosis.

     

    Link below to to Dictionary – Normal Tissue Histology Pancreas

    :

    http://www.proteinatlas.org/learn/dictionary/normal/pancreas

    Extract:

    Pancreas

    The normal adult pancreas has a dual function and composition - the exocrine component that produces enzymes necessary for digestion of food and the endocrine component, necessary for insulin production and regulation of blood glucose levels. The exocrine component is composed of lobular units of acini, that discharge their secretions into progressively larger ducts that finally merge into the main pancreatic duct - the duct of Wirsung and the accessory pancreatic duct of Santorini. The duct of Wirsung ends at the major duodenal papilla (papilla Vateri) and the duct of Santorini ends at a minor duodenal papilla.

    The pyramidal-shaped acinar cells are large with their apical areas filled by markedly eosinophilic zymogen granules and the basal cytoplasm is deeply basophilic. The acinar cells at the central portion of the acinus (centroacinar cells) fuse with the intercalated duct that fuses with the acinus. The intercalated ducts combine to form the intralobular ducts, which are lined by small cuboidal cells with pale cytoplasm. The intralobular ducts combine to form the interlobular ducts that are lined by mucin-secreting, tall columnar epithelium. The ducts of Wirsung and Santorini are lined by similar columnar epithelium with a greater proportion of goblet cells.

    The Langerhans islets, which constitute 1-2% of the cell mass in the adult pancreas, represent the endocrine component of the pancreas. These islets comprise a greater proportion of the pancreas at the time of birth. The islets are round, compact structures that are highly vascularised with sparse connective tissue. The diameter of islets is highly variable with an average of approximately 225µm. The main cell types in the islets are beta cells - responsible for insulin production, alpha cells - responsible for glucagon secretion, delta cells - responsible for somatostatin secretion and PP cells - the pancreatic polypeptide secreting cells.