Cancer Hallmark Analytics / OMICS Data Pathway Studio Review

2019 John A. Catanzaro

https://www.linkedin.com/pulse/cancer-hallmark-analytics-omics-data-pathway-studio-review-catanzaro/

Cancer Hallmark Features (I am proposing 12)

Initially, the Cancer Hallmark model began with eight key elements and then expanded to ten elements. I am proposing that there are two additional key elements that deserve, in their own right, coverage as there are differing signaling factors in both that can be profiled and targeted. The two additional categories added are Mitochondria Metabolism Instability, as it has its own dynamic features that differ from general cancer metabolism. The other is Cancer Cell / Tumor Microenvironment Physics Adaptation, as it has its own governing dynamics in wave and transitional spatial relationships.

The expanded controls of each of the twelve categories is crucial in understanding how a cancer is behaving from the core DNA expression, integral genetic / epigenetics, cell cycling and programming, metabolism, inflammation signaling, activation and induction, immune defense and regulation, evasion, resistance and immortalization. Mutation, migration and proliferation are all influenced by a combination of these Hallmarks and can be cohesive to one another for the good or the bad responses seen in cancer progression or regression. I will be covering each of these elements in review article format.

1. Genome Instability and Mutation (DNA Repair, DNA Damage, Mutation, Strand Breaks, Adducts) https://www.linkedin.com/pulse/genomic-instability-mutations-john-catanzaro/?published=t
2. Metabolism / Oxidative Adaptation and Deregulating Cellular Energetics (Glycolysis / Warburg Effect Modification) https://www.linkedin.com/pulse/cancer-cell-tumor-metabolism-profiling-targeting-john-catanzaro/
3. Mitochondria Metabolism Instability / Sensitivity (Autophagy / Mitophagy) https://www.nature.com/articles/cr2017155
4. Evading Growth Suppressors (Cell Cycle, Deregulating Checkpoint, Evading Contact Inhibition)
5. Sustaining Proliferative Signaling (Cell Cycle, Growth Factors, Receptors, Downstream Signaling)
6. Inducing Angiogenesis (Angiogenic Factors, Deregulating angiogenesis)
7. Activating Invasion and Metastasis (Invasion and Metastasis Distribution Patterns)
8. Tumor Promoting Inflammation (Inflammation Immune Cell Stress Factors and Oxidative Triggers)
9. Immune Surveillance, Evasion / Escape (Avoiding Immune Destruction, Immune Response / Suppression)
10. Resisting Cell Death (Apoptosis, Autophagy and Necrosis)
11. Enabling Replicative Immortality (Immortalization and senescence)
12. Cancer Cell / Tumor Microenvironment Physics Adaptation (Waveform and Transitional Dynamics)

I am discussing the Cancer Hallmarks Analytics as an expandable model. Anton Yeryuv’s work is an example an expandable comprehensive biointelligence analytics with the sophistication that allows for an integration of patientcentricity in precision medicine treatment innovations, matching and architecture.

The Pathway Studio Evolution

https://clinmedjournals.org/articles/ijccr/international-journal-of-cancer-and-clinical-research-ijccr-3-043.pdf

Anton Yeryuv, Director of Professional Services Elsevier, received his Ph.D. at Johns Hopkins University where he discovered the proteins physically linking RNA polymerase II transcription and RNA processing in eukaryotic cells. During his postdoc at Novartis Pharmaceuticals he showed that mammalian protein kinase could be imported into mitochondria.

At the birth of Bioinformatics he started working as Senior Scientist at InforMax on sequence and genotyping analysis, continued at Orchid Bioscience as Senior Bioinformatics Analyst optimizing primer design, and then at Ariadne Genomics developing new approaches for pathway and network analysis. He has published over 50 scientific publications, edited three scientific books, and authored several algorithms for primer design and pathway analysis.

He is currently the Director of Professional services at Elsevier and focus on developing bioinformatics solutions to meet customer needs through consulting, custom software development and custom analytics. My current research focuses on studying topological and evolutionary properties of biological networks and developing algorithms and workflows for pathway reconstruction, analysis of molecular profiling data for drug discovery, disease modelling, and personalised medicine using pathway and network analytics and artificial intelligence methods.

Specialties: Bioinformatics, Molecular biology, Drug development, Pathway Analysis, Statistical algorithm development, Biological database development and management, C/C++, SQL-PLUS, artificial intelligence

A Sample of Eight Pathways Expandable Features

Anton, shared with me sample of his work which demonstrates the expand-ability and integration networking of bio-intelligence (AI) platform that profiles, matches and designs an application model. The figure below gives process flow of finding the right drug compatabile with the patient. In this expandable technology, other immunocentric innovations that are precision-based and patient-centered can also be developed.

Click one of 87 pathways names below to view pathway in your browser. Pathway Studio allows analysis of patient tumor molecular profiling OMICS data to find hallmark(s) driving tumor growth and metastasis.

Hallmarks of Cancer (1): Sustaining Proliferative Signaling

1. DNA Damage Checkpoint Impairment in Cancer
2. DREAM Complex and FOXM1/MYBL2 Promote Cell Cycle Progression in Cancer
3. EGFR Transactivation in Cancer and Non-Cancer Cellsromote Cell Cycle Progression in Cancer
4. G0/G1 Cell Cycle Phase Transition Activation in Cancer
5. Genes with Mutations in Cancer-Associated Sustaining of Proliferative Signaling
6. Hedgehog Signaling Activation in Cancer
7. Hippo/YAP1 Signaling Deregulation in Cancer
8. NFKB Canonical Signaling Activation in Cancer
9. NFKB Non-Canonical Signaling Activation in Cancer
10. NOTCH Signaling Deregulation in Cancer
11. PI3K/AKT/MTOR Signaling Activation in Cancer
12. Proteins with Altered Expression in Cancer-Associated Sustaining of Proliferative Signaling
13. RAS/RAF/MAPK Signaling Activation in Cancer
14. Receptors and Adaptor Proteins Activated in Cancer
15. TAM Receptors Signaling Activation in Cancer
16. WNT Canonical Signaling Activation in Cancer

Hallmarks of Cancer (2): Evading Growth Suppressors

1. Genes with Mutations in Cancer-Associated Evading of Growth Suppressors
2. Hedgehog Signaling Tumor Suppressors Inactivation in Cancer
3. NFKB Canonical Signaling Tumor Suppressors Inactivation in Cancer
4. PI3K/AKT/MTOR Signaling Tumor Suppressors Inactivation in Cancer
5. Proteins with Altered Expression in Cancer-Associated Evading of Growth Suppressors
6. RAS/RAF/MAPK Signaling Tumor Suppressors Inactivation
7. TGFB Signaling Tumor Suppressors Inactivation in Cancer
8. WNT Signaling Tumor Suppressors Inactivation in Cancer

Hallmarks of Cancer (3): Resisting Cell Death

1. Apoptosis Evading in Cancer: Overview
2. Apoptosis Inhibitors Supression in Cancer
3. BCL2 Family Proteins Impaired Apoptosis in Cancer Cell
4. Caspases Activity Reduction in Cancer
5. Death Receptor Signaling Impaires Apoptosis in Cancer
6. Genes with Mutations in Cancer-Associated Resisting to Cell Death
7. Hypoxia-Induced Cell Death Evading in Cancer
8. Proteins with Altered Expression in Cancer-Associated Resisting to Cell Death

Hallmarks of Cancer (4): Enabling Replicative Immortality

1. Genes with Mutations in Cancer-Associated Enabling of Replicative Immortality
2. Proteins with Altered Expression in Cancer-Associated Enabling of Replicative Immortality
3. Telomere Alternative Lengthening in Cancer
4. Telomeric Proteins/NFKB Activation in Cancer
5. TERT Activation in Cancer
6. TERT/WNT Activation in Cancer

Hallmarks of Cancer (5): Inducing Angiogenesis

1. ANGPT/TEK Stimulates Endothelial Cell Migration
2. FBLN5 Potentiates Migration and Proliferation of Endothelial Cells in Cancer
3. HIF1A in Vasculogenic Mimicry of Cancer Cells
4. Inhibition of THBS1 Activates Angiogenesis in Cancer
5. Oxidized LDL/OLR1 Activates Cell Proliferation in Cancer Cells
6. VEGF Independent Angiogenesis in Cancer
7. VEGFA Dependent Angiogenesis in Cancer
8. HIF1A as Master Regulator of Angiogenesis in Cancer

Hallmarks of Cancer (6): Activating Invasion and Metastasis

1. BSG (CD147) in Cancer Cells Motility, Invasion and Survival
2. CDH1 Downregulation Promotes Cancer Cell Migration and Metastases
3. CDH2 Upregulation Promotes Cancer Cell Migration and Survival
4. CFL1 in Cancer Cell Motility
5. EPCAM in Cancer Cells Motility and Proliferation
6. Genes with Mutations in Cancer Metastasis
7. Hyaluronic Acid, CD44 and HMMR in Cancer Cells Motility, Invasion, Proliferation and Survival
8. Integrins in Cancer Cells Motility, Invasion and Survival
9. Invadopodia Formation in Cancer Cells
10. MicroRNAs in Epithelial to Mesenchymal Transition in Cancer
11. MTOR/TORC in Cancer Cells Motility and Invasion
12. Proteins with Altered Expression in Cancer Metastases
13. Receptors Signalings in Epithelial to Mesenchymal Transition in Cancer
14. TGFB Family in Epithelial to Mesenchymal Transition in Cancer
15. WNT in Epithelial to Mesenchymal Transition in Cancer

Hallmarks of Cancer (7): Deregulated Metabolism

1. Cancer Cells Inhibit Adipocyte Differentiation
2. Genes with Mutations in Cancer Metabolic Reprogramming
3. Genes with Mutations in Cancer Metabolic Reprogramming (Krebs Cycle)
4. Genes with Mutations in Cancer Metabolic Reprogramming (Respiratory Chain)
5. Glutamine in Cancer Metabolism
6. Glycolysis Activation in Cancer (Warburg Effect)
7. Krebs Cycle Enzyme Mutations in Cancer
8. Lactate as a Signalling Molecule in Cancer Cells
9. Lipogenesis Activation in Cancer Cells and Lipolysis Activation in Cancer-Associated Adipocytes
10. Metabolic Effects of Oncogenes and Tumor Suppressor in Cancer Cells
11. Metabolic Reprogramming in Cancer: Overview
12. Proteins with Altered Expression in Cancer Metabolic Reprogramming
13. Proteins with Altered Expression in Cancer Metabolic Reprogramming (Krebs Cycle)
14. Respiratory Chain Impairment in Cancer
15. Skeletal Muscle Wasting in Cancer Cachexia

Hallmarks of Cancer (8): Evading Immune Destruction

1. Adenosine/cAMP Promote Immunosuppression by Treg Cell
2. Effector T-cells Inactivation in Cancer Immune Escape
3. Genes with Mutations in Cancer Immune Escape
4. IDO1 in Cancer Immune Escape
5. Kynurenine Metabolites Supress T-Cells in Cancer Immune Escape
6. MHC1 Causes Antigen Presentation Failure in Cancer Immune Escape
7. Myeloid Derived Suppressor Cell in Cancer Immune Escape
8. Proteins with Altered Expression in Cancer Immune Escape
9. Treg Cell Promote Immunosupression in Cancer Immune Escape
10. Tumor Infiltrating Macrophage in Cancer Progression and Immune Escape

Patient Centered Precision Medicine Model

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SNEA (Sub-Network Enrichment Analysis)

“Sub-Network Enrichment Analysis (aka SNEA, Causal Reasoning) Calculates regulator activity from the changes observed in the downstream targets (activity biomarkers). SNEA builds networks from all genes/proteins measured in the analysis using all relations in the database. SNEA can include indirect regulation i.e. expression regulatory cascades consisting of 2–3 steps. Significant network centers may be found that are not measured in the primary dataset. No prior curation of gene sets is required. Can work with partial information about regulators targets. Does not require knowledge about all targets. P-value is sensitive to the size of the chip.”

Construction of Pathways in Patient-Centered Precision Medicine

Keep in mind that the preferred nomenclature is Precision Medicine. However, there are patients that will require a compatabile personalized treatment innovation. There is a difference and distinction in the terminology, however, this application and platform can be used for both Precision Medicine and Personalized Medicine applications. https://www.hemoncnc.com/

https://pharma.elsevier.com/wp-content/uploads/2017/01/word-mark.png

https://image.slidesharecdn.com/31064846-cbc0-45f8-85e3-2097c02e86c1-141125145801-conversion-gate01/95/construction-of-cancer-pathways-for-personalized-medicine-1-638.jpg?cb=1416927564

Summary

In previous articles I have outlined the essential need to have a patientcentricity with an immunocentric based design platform for profiling, matching, designing and applying downstream innovations.

Focus of a Progressive System

The most essential movement in cancer / disease treatment as a means to innovate a progressive system, fast available innovations with clinical outcomes that improve quality of health and life must be patient-centered. We are teaming a progressive system, namely, Cancer Hallmark Analytics and Immunocentrics model to propose that there are seven proposed pillars, which are expandable is truly the center of all innovation:

Cancer Hallmark Analytics and Immunocentrics is a Biointelligence, Diagnostic and Therapeutic Applications platform / company. The implementation model and mission are architecting patient compatibility precision medicine-based treatment innovations and designs assisting existing pharmaceutical pipeline developers, clinicians, scientists, institutions and related innovation businesses. The platform core is built upon the following pillars:

1. Patientcentricity: What is unique and effective for the patient at any time in their cancer fight. Innovation, re-innovation, Immunoediting and revisions to break resistance and facilitate epigenetic and favorable evolution adaptation. This can be implemented in cancer and autoimmune disease with potential expansion to other diseases
2. Immunogenecity: What is unique and compatible with the individual patient’s genetics and immune compatibility.
3. Oncogenicity: What is unique to the cancer behavior in the patient in order to facilitate a molecular / immunocentric precision-based control.
4. Tumorcentricity: What is unique to the tumor microenvironment to profile hot / cold diffuse / proliferation, invasion, inflammation, provocation and resistance in the tumor bed. To determine what immortalization plays resistance to immune surveillance.
5. Immunocentricity: Unique and compatible to the patient’s immune defense, regulation and immunoediting amplification / augmentation.
6. Innovationcentricity: Unique patient-centered innovation, an adaptive model of defense / regulation mapping patient-precision treatment. Selection of the best delivery can be made by profiling in this way.
7. Epigenecity: Unique to the patient’s evolutionary adaptability and epigenetic controls.
8. Outcomecentricity: Unique patient-centric outcome metrics to evaluate efficacy, barriers and treatment challenges / changes / revisions.

Dr. Yeryuv’s model can integrate into this model in both patient-centered precision medicine and personalized medicine applications, evolving and expanding a technology utility accessible to patients, scientists, clinicians, pipeline developers, institutions and innovation businesses. Our team partnership in Cancer Hallmark Analytics and Immunocentrics is cohesively networking key scientists, clinicians, thought leaders, institutions, organizations and innovators that are creating a global partnership and design.

Fight Cancer Global

This is the organization mission and the model to move a global awakening for innovating and supporting the blend of patient-centered personalized / precision medicine and compassionate care. Fight Cancer Global is an example of embracing outreach and action in every sector.

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