Posts Tagged ‘Therapy’

Human personalized autologous cell therapy for Parkinson’s Disease

Posted in Autologous Cell Therapy, Biological Engineering, Biological Networks, Biomarkers & Medical Diagnostics, Biotechnology, Cell Biology, Cell Biology, Signaling & Cell Circuits, Cell Processing System in Cell Therapy Process Development, cell-based therapy, Cerebrovascular and Neurodegenerative Diseases, Competition in regenerative stem cell science, Genomic Endocrinology, hNPCs, Innovations in Neurophysiology & Neuropsychology, Neurodegenerative Diseases, Neurohumoral Transmission, Neurological Diseases, Neuronal Circuits, Neuroscience, Parkinson's disease dyskinesia levodopa, Patient-centered Medicine, Personal Health Applications: Tech Innovations serves HealhCare, Personalized and Precision Medicine & Genomic Research, Signaling & Cell Circuits, stem cell biology and patient-specific, Stem Cells for Regenerative Medicine, tagged brain, dopamine, hiPSC, Parkinsons disease, Therapy on December 2, 2019| Leave a Comment »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Parkinson’s Disease (PD), characterized by both motor and non-motor system pathology, is a common neurodegenerative disorder affecting about 1% of the population over age 60. Its prevalence presents an increasing social burden as the population ages. Since its introduction in the 1960’s, dopamine (DA)-replacement therapy (e.g., L-DOPA) has remained the gold standard treatment. While improving PD patients’ quality of life, the effects of treatment fade with disease progression and prolonged usage of these medications often (>80%) results in side effects including dyskinesias and motor fluctuations. Since the selective degeneration of A9 mDA neurons (mDANs) in the substantia nigra (SN) is a key pathological feature of the disease and is directly associated with the cardinal motor symptoms, dopaminergic cell transplantation has been proposed as a therapeutic strategy.

Researchers showed that mammalian fibroblasts can be converted into embryonic stem cell (ESC)-like induced pluripotent stem cells (iPSCs) by introducing four transcription factors i.e., Oct4, Sox2, Klf4, and c-Myc. This was then accomplished with human somatic cells, reprogramming them into human iPSCs (hiPSCs), offering the possibility of generating patient-specific stem cells. There are several major barriers to implementation of hiPSC-based cell therapy for PD. First, probably due to the limited understanding of the reprogramming process, wide variability exists between the differentiation potential of individual hiPSC lines. Second, the safety of hiPSC-based cell therapy has yet to be fully established. In particular, since any hiPSCs that remain undifferentiated or bear sub-clonal tumorigenic mutations have neoplastic potential, it is critical to eliminate completely such cells from a therapeutic product.

In the present study the researchers established human induced pluripotent stem cell (hiPSC)-based autologous cell therapy. Researchers reported a platform of core techniques for the production of mDA progenitors as a safe and effective therapeutic product. First, by combining metabolism-regulating microRNAs with reprogramming factors, a method was developed to more efficiently generate clinical grade iPSCs, as evidenced by genomic integrity and unbiased pluripotent potential. Second, a “spotting”-based in vitro differentiation methodology was established to generate functional and healthy mDA cells in a scalable manner. Third, a chemical method was developed that safely eliminates undifferentiated cells from the final product. Dopaminergic cells thus produced can express high levels of characteristic mDA markers, produce and secrete dopamine, and exhibit electrophysiological features typical of mDA cells. Transplantation of these cells into rodent models of PD robustly restored motor dysfunction and reinnervated host brain, while showing no evidence of tumor formation or redistribution of the implanted cells.

Together these results supported the promise of these techniques to provide clinically applicable personalized autologous cell therapy for PD. It was recognized by researchers that this methodology is likely to be more costly in dollars and manpower than techniques using off-the-shelf methods and allogenic cell lines. Nevertheless, the cost for autologous cell therapy may be expected to decrease steadily with technological refinement and automation. Given the significant advantages inherent in a cell source free of ethical concerns and with the potential to obviate the need for immunosuppression, with its attendant costs and dangers, it was proposed that this platform is suitable for the successful implementation of human personalized autologous cell therapy for PD.

References:

https://www.jci.org/articles/view/130767/pdf?elqTrackId=2fd7d0edee744f9cb6d70a686d7b273b

https://www.ncbi.nlm.nih.gov/pubmed/31714896

https://www.ncbi.nlm.nih.gov/pubmed/23666606

https://www.ncbi.nlm.nih.gov/pubmed/27343168

https://www.ncbi.nlm.nih.gov/pubmed/21495962

https://www.ncbi.nlm.nih.gov/pubmed/28083784

https://www.ncbi.nlm.nih.gov/pubmed/20336395

https://www.ncbi.nlm.nih.gov/pubmed/28585381

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An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression

Posted in Apoptosis, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Breast Cancer - impalpable breast lesions, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, cancer metabolism, Cancer Prevention: Research & Programs, Cancer Screening, Cancer Vaccines: Targeting Cancer Genes for Immunotherapy, Cancer-Immune Interactions, Cell Biology, Cell Biology, Signaling & Cell Circuits, Metabolic Immuno-Oncology, Monoclonal Immunotherapy, Oligonucleotide Therapeutics & Delivery, Precision Cancer Medicine, RNA Biology, Cancer and Therapeutics, Signaling & Cell Circuits, tagged Cancer, gene expression, HApt, HER2, Nanorobot, tFNA, Therapy on July 24, 2019| Leave a Comment »

An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

3.2.9

3.2.9 An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

HER2 is an important prognostic biomarker for 20–30% of breast cancers, which is the most common cancer in women. Overexpression of the HER2 receptor stimulates breast cells to proliferate and differentiate uncontrollably, thereby enhancing the malignancy of breast cancer and resulting in a poor prognosis for affected individuals. Current therapies to suppress the overexpression of HER2 in breast cancer mainly involve treatment with HER2-specific monoclonal antibodies. However, these monoclonal anti-HER2 antibodies have severe side effects in clinical trials, such as diarrhea, abnormal liver function, and drug resistance. Removing HER2 from the plasma membrane or inhibiting the gene expression of HER2 is a promising alternative that could limit the malignancy of HER2-positive cancer cells.

DNA origami is an emerging field of DNA-based nanotechnology and intelligent DNA nanorobots show great promise in working as a drug delivery system in healthcare. Different DNA-based nanorobots have been developed as affordable and facile therapeutic drugs. In particular, many studies reported that a tetrahedral framework nucleic acid (tFNA) could serve as a promising DNA nanocarrier for many antitumor drugs, owing to its high biocompatibility and biosecurity. For example, tFNA was reported to effectively deliver paclitaxel or doxorubicin to cancer cells for reversing drug resistance, small interfering RNAs (siRNAs) have been modified into tFNA for targeted drug delivery. Moreover, the production and storage of tFNA are not complicated, and they can be quickly degraded in lysosomes by cells. Since both free HApt and tFNA can be diverted into lysosomes, so, combining the HApt and tFNA as a novel DNA nanorobot (namely, HApt-tFNA) can be an effective strategy to improve its delivery and therapeutic efficacy in treating HER2-positive breast cancer.

Researchers reported that a DNA framework-based intelligent DNA nanorobot for selective lysosomal degradation of tumor-specific proteins on cancer cells. An anti-HER2 aptamer (HApt) was site-specifically anchored on a tetrahedral framework nucleic acid (tFNA). This DNA nanorobot (HApt-tFNA) could target HER2-positive breast cancer cells and specifically induce the lysosomal degradation of the membrane protein HER2. An injection of the DNA nanorobot into a mouse model revealed that the presence of tFNA enhanced the stability and prolonged the blood circulation time of HApt, and HApt-tFNA could therefore drive HER2 into lysosomal degradation with a higher efficiency. The formation of the HER2-HApt-tFNA complexes resulted in the HER2-mediated endocytosis and digestion in lysosomes, which effectively reduced the amount of HER2 on the cell surfaces. An increased HER2 digestion through HApt-tFNA further induced cell apoptosis and arrested cell growth. Hence, this novel DNA nanorobot sheds new light on targeted protein degradation for precision breast cancer therapy.

It was previously reported that tFNA was degraded by lysosomes and could enhance cell autophagy. Results indicated that free Cy5-HApt and Cy5-HApt-tFNA could enter the lysosomes; thus, tFNA can be regarded as an efficient nanocarrier to transmit HApt into the target organelle. The DNA nanorobot composed of HApt and tFNA showed a higher stability and a more effective performance than free HApt against HER2-positive breast cancer cells. The PI3K/AKT pathway was inhibited when membrane-bound HER2 decreased in SK-BR-3 cells under the action of HApt-tFNA. The research findings suggest that tFNA can enhance the anticancer effects of HApt on SK-BR-3 cells; while HApt-tFNA can bind to HER2 specifically, the compounded HER2-HApt-tFNA complexes can then be transferred and degraded in lysosomes. After these processes, the accumulation of HER2 in the plasma membrane would decrease, which could also influence the downstream PI3K/AKT signaling pathway that is associated with cell growth and death.

However, some limitations need to be noted when interpreting the findings: (i) the cytotoxicity of the nanorobot on HER2-positive cancer cells was weak, and the anticancer effects between conventional monoclonal antibodies and HApt-tFNA was not compared; (ii) the differences in delivery efficiency between tFNA and other nanocarriers need to be confirmed; and (iii) the confirmation of anticancer effects of HApt-tFNA on tumors within animals remains challenging. Despite these limitations, the present study provided novel evidence of the biological effects of tFNA when combined with HApt. Although the stability and the anticancer effects of HApt-tFNA may require further improvement before clinical application, this study initiates a promising step toward the development of nanomedicines with novel and intelligent DNA nanorobots for tumor treatment.

References:

https://pubs.acs.org/doi/10.1021/acs.nanolett.9b01320

https://www.ncbi.nlm.nih.gov/pubmed/27939064

https://www.ncbi.nlm.nih.gov/pubmed/11694782

https://www.ncbi.nlm.nih.gov/pubmed/27082923

https://www.ncbi.nlm.nih.gov/pubmed/25365825

https://www.ncbi.nlm.nih.gov/pubmed/26840503

https://www.ncbi.nlm.nih.gov/pubmed/29802035

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Extracellular RNA and their carriers in disease diagnosis and therapy

Posted in Advanced Drug Manufacturing Technology, Bioengineering & reverse engineering design, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Engineering, Biological Networks, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Biotechnology, Cancer and Current Therapeutics, cell-based therapy, Chemical Genetics, Clinical Diagnostics, Clinical Genomics, Disease Biology, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Exosomes: Natural Carriers for siRNA Delivery, Gene Therapy & Gene Editing Development, Genetics & Innovations in Treatment, Genomic Testing: Methodology for Diagnosis, Microvesicle, mRNA, mRNA platform in Drug DIscovery, mRNA Therapeutics, Population genetics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, RNA Biology, RNA Biology, Cancer and Therapeutics, RNA interference (RNAi) therapeutic, Transformative Technologies in Healthcare, tagged Diagnosis, extracellular RNA, new role of RNA, RNA Carrier, Therapy on May 4, 2019| Leave a Comment »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

RNA plays various roles in determining how the information in our genes drives cell behavior. One of its roles is to carry information encoded by our genes from the cell nucleus to the rest of the cell where it can be acted on by other cell components. Rresearchers have now defined how RNA also participates in transmitting information outside cells, known as extracellular RNA or exRNA. This new role of RNA in cell-to-cell communication has led to new discoveries of potential disease biomarkers and therapeutic targets. Cells using RNA to talk to each other is a significant shift in the general thought process about RNA biology.

Researchers explored basic exRNA biology, including how exRNA molecules and their transport packages (or carriers) were made, how they were expelled by producer cells and taken up by target cells, and what the exRNA molecules did when they got to their destination. They encountered surprising complexity both in the types of carriers that transport exRNA molecules between cells and in the different types of exRNA molecules associated with the carriers. The researchers had to be exceptionally creative in developing molecular and data-centric tools to begin making sense of the complexity, and found that the type of carrier affected how exRNA messages were sent and received.

As couriers of information between cells, exRNA molecules and their carriers give researchers an opportunity to intercept exRNA messages to see if they are associated with disease. If scientists could change or engineer designer exRNA messages, it may be a new way to treat disease. The researchers identified potential exRNA biomarkers for nearly 30 diseases including cardiovascular disease, diseases of the brain and central nervous system, pregnancy complications, glaucoma, diabetes, autoimmune diseases and multiple types of cancer.

As for example some researchers found that exRNA in urine showed promise as a biomarker of muscular dystrophy where current studies rely on markers obtained through painful muscle biopsies. Some other researchers laid the groundwork for exRNA as therapeutics with preliminary studies demonstrating how researchers might load exRNA molecules into suitable carriers and target carriers to intended recipient cells, and determining whether engineered carriers could have adverse side effects. Scientists engineered carriers with designer RNA messages to target lab-grown breast cancer cells displaying a certain protein on their surface. In an animal model of breast cancer with the cell surface protein, the researchers showed a reduction in tumor growth after engineered carriers deposited their RNA cargo.

Other than the above research work the scientists also created a catalog of exRNA molecules found in human biofluids like plasma, saliva and urine. They analyzed over 50,000 samples from over 2000 donors, generating exRNA profiles for 13 biofluids. This included over 1000 exRNA profiles from healthy volunteers. The researchers found that exRNA profiles varied greatly among healthy individuals depending on characteristics like age and environmental factors like exercise. This means that exRNA profiles can give important and detailed information about health and disease, but careful comparisons need to be made with exRNA data generated from people with similar characteristics.

Next the researchers will develop tools to efficiently and reproducibly isolate, identify and analyze different carrier types and their exRNA cargos and allow analysis of one carrier and its cargo at a time. These tools will be shared with the research community to fill gaps in knowledge generated till now and to continue to move this field forward.

References:

https://www.nih.gov/news-events/news-releases/scientists-explore-new-roles-rna

https://www.cell.com/consortium/exRNA

https://www.sciencedaily.com/releases/2016/06/160606120230.htm

https://www.pasteur.fr/en/multiple-roles-rnas

https://www.nature.com/scitable/topicpage/rna-functions-352

https://www.umassmed.edu/rti/biology/role-of-rna-in-biology/

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Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals

Posted in Artificial Heart, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial intelligence applications for cardiology, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Artificial Pancreas for Type1 Diabetes, Artificial Vascular Structures, Biological Engineering, Digital HealthCare – biotech & internet joint ventures, Disease Biology, Drug Carrier Design, Drug Delivery Platform Technology, Drug Development/Formulation using 3D Printing, Electronic Health Record, Health Economics and Outcomes Research, Health Law & Patient Safety, HealthCare IT, Healthcare Reform, Intelligent Information Systems, Mechanical Assist Devices: LVAD, RVAD, BiVAD, Artificial Heart, Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound, Mobile Healthcare, Organ-on-a-Chip, Personal Health Applications: Tech Innovations serves HealhCare, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Transformative Technologies in Healthcare, Voices of Patients and Healthcare Providers, Wearable Tech + Digital Health, tagged digital solution, digital therapeutics, Pharmaceuticals, Therapy, treatment on March 18, 2019| Leave a Comment »

Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

3.3.7

3.3.7 Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

Digital Therapeutics (DTx) have been defined by the Digital Therapeutics Alliance (DTA) as “delivering evidence based therapeutic interventions to patients, that are driven by software to prevent, manage or treat a medical disorder or disease”. They might come in the form of a smart phone or computer tablet app, or some form of a cloud-based service connected to a wearable device. DTx tend to fall into three groups. Firstly, developers and mental health researchers have built digital solutions which typically provide a form of software delivered Cognitive-Behaviour Therapies (CBT) that help patients change behaviours and develop coping strategies around their condition. Secondly there are the group of Digital Therapeutics which target lifestyle issues, such as diet, exercise and stress, that are associated with chronic conditions, and work by offering personalized support for goal setting and target achievement. Lastly, DTx can be designed to work in combination with existing medication or treatments, helping patients manage their therapies and focus on ensuring the therapy delivers the best outcomes possible.

Pharmaceutical companies are clearly trying to understand what DTx will mean for them. They want to analyze whether it will be a threat or opportunity to their business. For a long time, they have been providing additional support services to patients who take relatively expensive drugs for chronic conditions. A nurse-led service might provide visits and telephone support to diabetics for example who self-inject insulin therapies. But DTx will help broaden the scope of support services because they can be delivered cost-effectively, and importantly have the ability to capture real-world evidence on patient outcomes. They will no-longer be reserved for the most expensive drugs or therapies but could apply to a whole range of common treatments to boost their efficacy. Faced with the arrival of Digital Therapeutics either replacing drugs, or playing an important role alongside therapies, pharmaceutical firms have three options. They can either ignore DTx and focus on developing drug therapies as they have done; they can partner with a growing number of DTx companies to develop software and services complimenting their drugs; or they can start to build their own Digital Therapeutics to work with their products.

Digital Therapeutics will have knock-on effects in health industries, which may be as great as the introduction of therapeutic apps and services themselves. Together with connected health monitoring devices, DTx will offer a near constant stream of data about an individuals’ behavior, real world context around factors affecting their treatment in their everyday lives and emotional and physiological data such as blood pressure and blood sugar levels. Analysis of the resulting data will help create support services tailored to each patient. But who stores and analyses this data is an important question. Strong data governance will be paramount to maintaining trust, and the highly regulated pharmaceutical industry may not be best-placed to handle individual patient data. Meanwhile, the health sector (payers and healthcare providers) is becoming more focused on patient outcomes, and payment for value not volume. The future will say whether pharmaceutical firms enhance the effectiveness of drugs with DTx, or in some cases replace drugs with DTx.

Digital Therapeutics have the potential to change what the pharmaceutical industry sells: rather than a drug it will sell a package of drugs and digital services. But they will also alter who the industry sells to. Pharmaceutical firms have traditionally marketed drugs to doctors, pharmacists and other health professionals, based on the efficacy of a specific product. Soon it could be paid on the outcome of a bundle of digital therapies, medicines and services with a closer connection to both providers and patients. Apart from a notable few, most pharmaceutical firms have taken a cautious approach towards Digital Therapeutics. Now, it is to be observed that how the pharmaceutical companies use DTx to their benefit as well as for the benefit of the general population.

References:

https://eloqua.eyeforpharma.com/LP=23674?utm_campaign=EFP%2007MAR19%20EFP%20Database&utm_medium=email&utm_source=Eloqua&elqTrackId=73e21ae550de49ccabbf65fce72faea0&elq=818d76a54d894491b031fa8d1cc8d05c&elqaid=43259&elqat=1&elqCampaignId=24564

https://www.s3connectedhealth.com/resources/white-papers/digital-therapeutics-pharmas-threat-or-opportunity/

http://www.pharmatimes.com/web_exclusives/digital_therapeutics_will_transform_pharma_and_healthcare_industries_in_2019._heres_how._1273671

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/exploring-the-potential-of-digital-therapeutics

https://player.fm/series/digital-health-today-2404448/s9-081-scaling-digital-therapeutics-the-opportunities-and-challenges

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Immunotherapy may help in glioblastoma survival

Posted in Anticancer Resistance, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Breast Cancer - impalpable breast lesions, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Informatics, cancer metabolism, Cancer Prevention: Research & Programs, Cancer Screening, Cancer Vaccines: Targeting Cancer Genes for Immunotherapy, Cancer-Immune Interactions, Cell Biology, Childhood cancer, CNS-Immune Interface, combination immunotherapies., Efficacy of Anti-Tumor T Cells, Engineering Better T Cells, Glioblastoma, Imaging-based Cancer Patient Management, Immune Engineering, Immune Modulatory, Immuno-Oncology & Genomics, Immunodiagnostics, Immunology, Immunotherapy, Inflammasome, Innovation in Immunology Diagnostics, Integrin-targeted Combination Immunotherapy, Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood, Metabolic Immuno-Oncology, Metastasis Process, Modulating Macrophages in Cancer Immunotherapy, Monoclonal Immunotherapy, Neuronal Circuits, NK Cell-Based Cancer Immunotherapy, Pancreatic cancer, Population genetics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Precision Cancer Medicine, Prostate Cancer: Monitoring vs Treatment, RNA Biology, Cancer and Therapeutics, Synergistic Innate and Adaptive Immunotherapy, Synthetic Immunology: Hacking Immune Cells, Universal Immune Cell Therapies (uICT), tagged Cancer, Glioblastoma, Immunotherapy, pembrolizumab, survival, Therapy on March 16, 2019| Leave a Comment »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Glioblastoma is the most common primary malignant brain tumor in adults and is associated with poor survival. But, in a glimmer of hope, a recent study found that a drug designed to unleash the immune system helped some patients live longer. Glioblastoma powerfully suppresses the immune system, both at the site of the cancer and throughout the body, which has made it difficult to find effective treatments. Such tumors are complex and differ widely in their behavior and characteristics.

A small randomized, multi-institution clinical trial was conducted and led by researchers at the University of California at Los Angeles involved patients who had a recurrence of glioblastoma, the most common central nervous system cancer. The aim was to evaluate immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab (checkpoint inhibitor) in 35 patients with recurrent, surgically resectable glioblastoma. Patients who were randomized to receive neoadjuvant pembrolizumab, with continued adjuvant therapy following surgery, had significantly extended overall survival compared to patients that were randomized to receive adjuvant, post-surgical programmed cell death protein 1 (PD-1) blockade alone.

Neoadjuvant PD-1 blockade was associated with upregulation of T cell– and interferon-γ-related gene expression, but downregulation of cell-cycle-related gene expression within the tumor, which was not seen in patients that received adjuvant therapy alone. Focal induction of programmed death-ligand 1 in the tumor microenvironment, enhanced clonal expansion of T cells, decreased PD-1 expression on peripheral blood T cells and a decreasing monocytic population was observed more frequently in the neoadjuvant group than in patients treated only in the adjuvant setting. These findings suggest that the neoadjuvant administration of PD-1 blockade enhanced both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor.

Immunotherapy has not proved to be effective against glioblastoma. This small clinical trial explored the effect of PD-1 blockade on recurrent glioblastoma in relation to the timing of administration. A total of 35 patients undergoing resection of recurrent disease were randomized to either neoadjuvant or adjuvant pembrolizumab, and surgical specimens were compared between the two groups. Interestingly, the tumoral gene expression signature varied between the two groups, such that those who received neoadjuvant pembrolizumab displayed an INF-γ gene signature suggestive of T-cell activation as well as suppression of cell-cycle signaling, possibly consistent with growth arrest. Although the study was not powered for efficacy, the group found an increase in overall survival in patients receiving neoadjuvant pembrolizumab compared with adjuvant pembrolizumab of 13.7 months versus 7.5 months, respectively.

In this small pilot study, neoadjuvant PD-1 blockade followed by surgical resection was associated with intratumoral T-cell activation and inhibition of tumor growth as well as longer survival. How the drug works in glioblastoma has not been totally established. The researchers speculated that giving the drug before surgery prompted T-cells within the tumor, which had been impaired, to attack the cancer and extend lives. The drug didn’t spur such anti-cancer activity after the surgery because those T-cells were removed along with the tumor. The results are very important and very promising but would need to be validated in much larger trials.

References:

https://www.washingtonpost.com/health/2019/02/11/immunotherapy-may-help-patients-with-kind-cancer-that-killed-john-mccain/?noredirect=on&utm_term=.e1b2e6fffccc

https://www.ncbi.nlm.nih.gov/pubmed/30742122

https://www.practiceupdate.com/content/neoadjuvant-anti-pd-1-immunotherapy-promotes-immune-responses-in-recurrent-gbm/79742/37/12/1

https://www.esmo.org/Oncology-News/Neoadjuvant-PD-1-Blockade-in-Glioblastoma

https://neurosciencenews.com/immunotherapy-glioblastoma-cancer-10722/

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Tumor Organoids Used to Speed Cancer Treatment

Posted in 3D Printing for Medical Application, Cancer - General, tagged cancer cells, Drug discovery, Organoids, Therapy on January 26, 2019| Leave a Comment »

Tumor Organoids Used to Speed Cancer Treatment

Reporter: Irina Robu, PhD

Collecting cancer cells from patients and growing them into 3-D mini tumors could make it possible to quickly screen large numbers of potential drugs for ultra-rare cancers. Preliminary success with a new high-speed, high-volume approach is already guiding treatment decisions for some patients with recurring hard-to-treat cancers.

A London-based team labelled how a “tumor-in-a-dish” approach positively forecasted drug responses in cancer patients who previously took part in clinical trials. That study was a major development in a new research area focused on “organoids” — tiny 3-D versions of the brain, gut, lung and other organs grown in the lab to probe basic biology or test drugs.

UCLA cancer biologist Alice Soragni and her colleagues developed a high-volume, automated method to rapidly study drug responses in tumor organoids grown from patient cells. By studying mini tumors grown on a plate with 96 tiny test tubes, her team can screen hundreds of compounds at once and classify promising candidates within a time frame that is therapeutically actionable. According to Dr. Soragni, the method seemed to work for various kinds of ovarian cancer. It was shown that the lab-grown organoids mimicked how tumors in the body look and behave. And even in cases when mini tumors had a hard time growing in a dish, scientists still acknowledged potential drug candidates.
Up to now, the UCLA team has produced organoids from 35 to 40 people with various types of sarcoma which will allow them to classify tumors that won’t respond to conventional therapy. This proves useful for people with recurrent metastases, where it’s not clear if we’re doing anything for their overall survival or giving them more toxicity.

Source

https://www.sciencenews.org/article/tumor-organoids-may-speed-cancer-treatment

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Renal (Kidney) Cancer: Connections in Metabolism at Krebs cycle and Histone Modulation

Posted in Biomarkers & Medical Diagnostics, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Human Immune System in Health and in Disease, Imaging-based Cancer Patient Management, tagged biomarkers, Cancer - General, Demet Sag, diagnostics, GWAS, Immunotherapy, Kidney, reanl cancer, Targeted therapy, Therapy on October 14, 2015| Leave a Comment »

Renal (Kidney) Cancer: Connections in Metabolism at Krebs cycle and Histone Modulation

Curator: Demet Sag, PhD, CRA, GCP

Through Histone Modulation

Renal cell carcinoma accounts for only 3% of total human malignancies but it is still the most common type of urological cancer with a high prevalence in elderly men (>60 years of age).

ICD–10	C64
ICD–9-CM	189.0
ICD-O	M 8312/3
OMIM	144700 605074
DiseasesDB	11245
MedlinePlus	000516
eMedicine	med/2002

Most kidney cancers are renal cell carcinomas (RCC). RCC lacks early warning signs and 70 % of patients with RCC develop metastases. Among them, 50 % of patients having skeletal metastases developed a dismal survival of less than 10 % at 5 years.

There are three main histopathological entities:

Clear cell RCC (ccRCC), dominant in histology (65%)
Papillary (15-20%) and
Chromophobe RCC (5%).

There are very rare forms of RCC shown in collecting duct, mucinous tubular, spindle cell, renal medullary, and MiTF-TFE translocation carcinomas.

Subtypes of clear cell and papillary RCC, and a new subtype, clear cell papillary http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f6.jpg

Different subtypes of clear cell RCC can be defined by HIF patterns as well as by transcriptomic expression as defined by ccA and ccB subtypes. Papillary RCC also demonstrates distinct histological subtypes. A recently described variant denoted as clear cell papillary RCC is VHL wildtype (VHL WT), while other clear cell tumors are characterized by VHL mutation, loss, or inactivation (VHL MT).

KEY POINTS

Renal cell cancer is a disease in which malignant (cancer) cells form in tubules of the kidney.
Smoking and misuse of certain pain medicines can affect the risk of renal cell cancer.
Signs of renal cell cancer include

Blood in your urine, which may appear pink, red or cola colored
A lump in the abdomen.
Back pain just below the ribs that doesn’t go away
Weight loss
Fatigue
Intermittent fever

Factors that can increase the risk of kidney cancer include:

Older age.
High blood pressure (hypertension).
Treatment for kidney failure.(long-term dialysis to treat chronic kidney failure)
Certain inherited syndromes.
von Hippel-Lindau disease

Tests that examine the abdomen and kidneys are used to detect (find) and diagnose renal cell cancer.

The following tests and procedures may be used:

Physical exam and history
Ultrasound exam
Blood chemistry studies
Urinalysis
Liver function test
Intravenous pyelogram (IVP)
CT scan (CAT scan)
MRI (magnetic resonance imaging)
Biopsy

Ureteroscopy

There are 3 treatment approaches for Renal Cancer:

Wilms Tumor and Other Childhood Kidney Tumors Treatment

Stages of Renal Cancer:

Stage I	Tumour of a diameter of 7 cm (approx. 23⁄4 inches) or smaller, and limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage II	Tumour larger than 7.0 cm but still limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage III any of the following	Tumor of any size with involvement of a nearby lymph node but no metastases to distant organs. Tumour of this stage may be with or without spread to fatty tissue around the kidney, with or without spread into the large veins leading from the kidney to the heart.
Stage III any of the following	Tumour with spread to fatty tissue around the kidney and/or spread into the large veins leading from the kidney to the heart, but without spread to any lymph nodes or other organs.
Stage IV any of the following	Tumour that has spread directly through the fatty tissue and the fascia ligament-like tissue that surrounds the kidney.
	Involvement of more than one lymph node near the kidney
	Involvement of any lymph node not near the kidney
	Distant metastases, such as in the lungs, bone, or brain.

Grade Level	Nuclear Characteristics
Grade I	Nuclei appear round and uniform, 10 μm; nucleoli are inconspicuous or absent.
Grade II	Nuclei have an irregular appearance with signs of lobe formation, 15 μm; nucleoli are evident.
Grade III	Nuclei appear very irregular, 20 μm; nucleoli are large and prominent.
Grade IV	Nuclei appear bizarre and multilobated, 20 μm or more; nucleoli are prominent

GENETICS:

90% or more of kidney cancers are believed to be of epithelial cell origin, and are referred to as renal cell carcinoma (RCC), which are further subdivided based on histology into clear-cell RCC (75%), papillary RCC (15%),

chromophobe tumor (5%), and oncocytoma (5%).

Nephrectomy continues to be the cornerstone of treatment for localized renal cell carcinoma (RCC). Research is still underway to developed targeted agents against the vascular endothelial growth factor (VEGF) molecule and related pathways as well as inhibitors of the mammalian target of rapamycin (mTOR),

clear cell RCC (ccRCC) doesn’t respond well to radiation chemotherapy due to high radiation resistancy. The hallmark genetic features of solid tumors such as KRAS or TP53 mutations are also absent. However, there is a well-designed association presented between ccRCC and mutations in the VHL gene

Hereditary RCC, accounts for around 4% of cases, has been a relatively dominant area of RCC genetics.

Causative genes have been identified in several familial cancer syndromes that predispose to RCC including

VHLmutations in von Hippel-Lindau disease that predispose to ccRCC and VHL is somatically mutated in up to 80% of ccRCC
METmutations in familial papillary renal cancer,
dominantly activating kinase domainMET mutation reported in 4–10% of sporadic papillary RCC[2].
FH (fumarate hydratase) mutations in hereditary leiomyomatosis and renal cell cancer that predispose to papillary RCC
FLCN(folliculin) mutations in Birt-Hogg-Dubé syndrome that predispose to primarily chromophobe RCC.

In addition, there are germline mutations:

in theTSC1/2 genes predispose to tuberous sclerosis complex where approximately 3% of cases develop ccRCC,
in the SDHB(succinate dehydrogenase type B) in patients with paraganglioma syndrome shows elevated risk to develop multiple types of RCC.

GWAS in almost 6000 RCC cases demonstrated that loci on 2p21 and 11q13.3 play a role in RCC. Although EPAS1 gene encoding a transcription factor operative in hypoxia-regulated responses in 2p21 , 11q13.3 has no known coding genes.

There has been, however, comparatively less progress in the elaboration of the somatic genetics of sporadic RCC.

Absent mutations in sporadic RCC:

somaticFH mutations
somatic mutations ofTSC12 and SDHB

Present mutations in sporadic ccRCC (chromophobe RCC) are

TSC1mutations occur in 5% of ccRCCs and
somatic mutations inFLCN rare
may predict for extraordinary sensitivity to mTORC1 inhibitors clinically.

The COSMIC database reports somatic point mutations in TP53 in 10% of cases, KRAS/HRAS/NRAS combined ≤1%, CDKN2A 10%, PTEN 3%, RB1 3%, STK11/LKB1 ≤1%, PIK3Ca ≤1%, EGFR1% and BRAF ≤1% in all histological samples. Further information can be found at (http://www.sanger.ac.uk/ genetics/CGP/cosmic/) for the RCC somatic genetics.

HIF- and hypoxia-mediated epigenetic regulation work together due to histone modification because HIF activate several chromatin demethylases, including JMJD1A (KDM3A), JMJD2B (KDM4B), JMJD2C (KDM4C) and JARID1B (KDM5B), all of which are directly targeted by HIF.

Overview of Histone 3 modifications implicated in RCC genetics http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f1.jpg

A number of histone modifying genes are mutated in renal cell carcinoma. These include the H3K36 trimethylase SETD2, the H3K27 demethylase UTX/KDM6A, the H3K4 demethylase JARID1C/KDM5C and the SWI/SNF complex compenent PBRM1, shown in this cartoon to represent their relative activities on Histone H3.

Hyper-methylation is observed on RASSF1 highly (50% f RCC) yet less on VHL and CDKN2A, yet there is a methylation and silencing observed on TIMP3 and secreted frizzled-related protein 2.

RCC is ONE OF THE “CILIOPATHIES” among Polycystic Kidney Disease (PKD), Tuberous Sclerosis Complex (TSC) and VHL Syndrome. The main display of cysts is dysfunctional primary cilia.

Mol Cancer Res. Author manuscript; available in PMC 2013 Jan 1.

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi: 10.1158/1541-7786.MCR-12-0117

pVHL mutants are categorized as Class A, B and C depending on the affected step in pVHL protein quality control http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f2.jpg

VHL proteostasis involves the chaperone mediated translocation of nascent VHL peptide from the ribosome to the TRiC/CCT chaperonin, where folding occurs in an ATP dependent process. The VBC complex is formed while VHL is bound to TRiC, and the mature complex is then released. Three different classes of mutation exist: Class A mutations prevent binding of VHL to TRiC, and abrogate folding into a mature complex. Class B mutations prevent association of Elongins C and B to VHL. Class C mutations inhibit interaction between VHL and HIF1 a.

# 193300. VON HIPPEL-LINDAU SYNDROME; VHL	ICD+, Links
	VON HIPPEL-LINDAU SYNDROME, MODIFIERS OF, INCLUDED Cytogenetic locations: 3p25.3 , 11q13.3 Matching terms: lindau, disease, von, hippellindau, hippel

Birt-Hogg-Dube syndrome,

# 135150. BIRT-HOGG-DUBE SYNDROME; BHD	ICD+, Links
	Cytogenetic location: 17p11.2 Matching terms: birthoggdube, syndrome, birt, hogg, dube

tuberous sclerosis

# 191100. TUBEROUS SCLEROSIS 1; TSC1	ICD+, Links
	Cytogenetic location: 9q34.13 Matching terms: tuber, sclerosi, tuberous

familial papillary renal cell carcinoma.

# 144700. RENAL CELL CARCINOMA, NONPAPILLARY; RCC	ICD+, Links
	NONPAPILLARY RENAL CARCINOMA 1 LOCUS, INCLUDED Cytogenetic locations: 3p25.3 , 3p25.3 , 3q21.1 , 8q24.13 , 12q24.31 , 17p11.2 , 17q12 Matching terms: renal, familial, papillary, carcinoma, cell

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358399/bin/467fig3.jpg

Model for the control of the fate of nephron progenitor cells. Eya1 lies genetically upstream of Six2. Six2 labels the nephron progenitor cells, which can either maintain a progenitor state and self-renew or differentiate via the Wnt4-mediated MET. Wnt4 expression is under the direct control of Wt1. β-Catenin is involved in both progenitor cell fates through activation of different transcriptional programs. Active nuclear phosphorylated Yap/Taz shifts the progenitor balance toward the self-renewal fate. Eya1 and Six2 interact directly with Mycn, leading to dephosphorylation of Mycn pT58, stabilization of the protein, increased proliferation, and potentially a shift of the nephron progenitor toward self-renewal. Genes activated in Wilms’ tumors are depicted in green, and inactivated genes are in blue. Deregulation of Yap/Taz in Wilms’ tumors results in phosphorylated Yap not being retained in the cytoplasm as it should, but it translocates to the nucleus and thus shifts the progenitor cell balance toward self-renewal. This model is likely a simplification, as it presumes that all Wilms’ tumors, regardless of causative mutation, are caused by the same mechanism.

Epigenetic aberrations associated with Wilms’ tumor

Chinese Case Study: PMCID: PMC4471788

They u8ndertook this study based on association of low circulating adiponectin concentrations with a higher risk of several cancers, including renal cell carcinoma. Thus they demonstrated that by case–control study that ADIPOQ rs182052 is significantly associated with ccRCC risk.

They investigated the frequency of three single nucleotide polymorphisms (SNPs), rs182052G>A, rs266729C>G, rs3774262G>A, in the adiponectin gene (ADIPOQ). 1004 registered patients with clear cell renal cell carcinoma (ccRCC) compared with 1108 healthy subjects (n = 1108).

The first table presents the characteristics of 1004 patients with clear cell renal cell carcinoma and 1108 cancer-free controls from a Chinese Han population. The Second and third table shows the SNP results.

Table 1: The characteristics of the examined population.

Variable	Cases, n (%)	Controls, n (%)	P-value^†
Variable	1004 (100)	1108 (100)	P-value^†
Age, years
≤44	195 (19.4)	230 (20.8)	0.559
45–64	580 (57.8)	644 (58.1)
≥65	229 (22.8)	234 (21.1)
Sex
Male	711 (70.8)	815 (73.6)	0.160
Female	293 (29.2)	293 (26.4)	0.160
BMI, kg/m²
<25	480 (47.8)	589 (53.2)	0.014
≥25	524 (52.2)	519 (46.8)	0.014
Smoking status
Never	455 (45.3)	529 (47.7)	0.265
Ever/current	549 (54.7)	579 (52.3)	0.265
Hypertension
No	639 (63.6)	780 (70.4)	0.001
Yes	365 (36.4)	328 (29.6)	0.001
Fuhrman grade
I	40 (4.0)
II	380 (37.8)
III	347 (34.6)
IV	175 (17.4)
Missing	62 (6.2)
Stage at diagnosis
I	738 (73.5)
II	71 (7.1)
III	19 (1.9)
IV	176 (17.5)

^†Pearson’s χ²-test.

Table 2:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP	HWE	Cases, n(%)	Controls, n(%)	Crude OR (95% CI)	P-value	Adjusted OR (95% CI)	P-value^†
rs182052
GG	0.636	249 (24.8)	315 (28.4)	1.00		1.00
AG		485 (48.3)	544 (49.1)	1.13 (0.92–1.39)	0.253	1.11 (0.90–1.37)	0.331
AA		270 (26.9)	249 (22.5)	1.37 (1.08–1.75)	0.010	1.36 (1.07–1.74)	0.013
AG/AA versusGG				1.20 (0.99–1.46)	0.060	1.19 (0.98–1.45)	0.086
AA versusGG/AG				1.28 (1.04–1.57)	0.019	1.27 (1.04–1.56)	0.019
rs266729
CC	0.143	502 (50.0)	572 (51.6)	1.00		1.00
CG		398 (39.6)	434 (39.2)	1.05 (0.88–1.25)	0.635	1.05 (0.87–1.26)	0.633
GG		104 (10.4)	102 (9.2)	1.16 (0.86–1.57)	0.324	1.17 (0.86–1.58)	0.307
CG/GG versusCC				1.07 (0.91–1.29)	0.456	1.07 (0.90–1.27)	0.445
GG versus CC/CG				1.19 (0.83–1.59)	0.377	1.15 (0.86–1.54)	0.353
rs3774262
GG	0.106	482 (48.0)	523 (47.2)	1.00		1.00
AG		420 (41.8)	459 (41.4)	0.99 (0.83–1.20)	0.938	0.99 (0.82–1.19)	0.905
AA		102 (10.2)	126 (11.4)	0.88 (0.66–1.17)	0.381	0.90 (0.67–1.20)	0.463
AG/AA versusGG				0.98 (0.80–1.16)	0.711	0.97 (0.82–1.15)	0.722
AA versusGG/AG				0.88 (0.67–1.18)	0.372	0.90 (0.68–1.19)	0.465

Bold values indicate significance.

^†Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Table 3:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP	HWE	Cases, n(%)	Controls, n(%)	Crude OR (95% CI)	P-value	Adjusted OR (95% CI)	P-value^†
rs182052
GG	0.636	249 (24.8)	315 (28.4)	1.00		1.00
AG		485 (48.3)	544 (49.1)	1.13 (0.92–1.39)	0.253	1.11 (0.90–1.37)	0.331
AA		270 (26.9)	249 (22.5)	1.37 (1.08–1.75)	0.010	1.36 (1.07–1.74)	0.013
AG/AA versusGG				1.20 (0.99–1.46)	0.060	1.19 (0.98–1.45)	0.086
AA versusGG/AG				1.28 (1.04–1.57)	0.019	1.27 (1.04–1.56)	0.019
rs266729
CC	0.143	502 (50.0)	572 (51.6)	1.00		1.00
CG		398 (39.6)	434 (39.2)	1.05 (0.88–1.25)	0.635	1.05 (0.87–1.26)	0.633
GG		104 (10.4)	102 (9.2)	1.16 (0.86–1.57)	0.324	1.17 (0.86–1.58)	0.307
CG/GG versusCC				1.07 (0.91–1.29)	0.456	1.07 (0.90–1.27)	0.445
GG versus CC/CG				1.19 (0.83–1.59)	0.377	1.15 (0.86–1.54)	0.353
rs3774262
GG	0.106	482 (48.0)	523 (47.2)	1.00		1.00
AG		420 (41.8)	459 (41.4)	0.99 (0.83–1.20)	0.938	0.99 (0.82–1.19)	0.905
AA		102 (10.2)	126 (11.4)	0.88 (0.66–1.17)	0.381	0.90 (0.67–1.20)	0.463
AG/AA versusGG				0.98 (0.80–1.16)	0.711	0.97 (0.82–1.15)	0.722
AA versusGG/AG				0.88 (0.67–1.18)	0.372	0.90 (0.68–1.19)	0.465

Bold values indicate significance.

^†Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Molecular Genetics Level for Physiology (Function):

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503866/bin/10585_2015_9731_Fig6_HTML.jpg

a The protein–protein interaction for the identified 8 proteins in STRING (10 necessary proteins/genes were added into the network so as to find the potential strong connection among them. The red dotted lines circled three main pathways. b The ingenuity pathway analysis (IPA) for all these 18 genes showing that oxidative phosphorylation, mitochondria dysfunction and granzyme A are the significantly activated pathways (fold change over 1.5, P < 0.05). c The possible mechanism related mitochondria functions: unspecific condition like inflammation, carcinogens, radiation (ionizing or ultraviolet), intermittent hypoxia, viral infections which is carcinogenesis in our study that damages a cell’s oxidative phosphorylation. Any of these conditions can damage the structure and function of mitochondria thus activating a respiratory chain changes (Complex I, II, III, IV) and also cytochrome c release. When the mitochondrial dysfunction persists, it produces genome instability (mtDNA mutation), and further lead to malignant transformation (metastasis) via increased ROS and apoptotic resistance. (Color figure online)

RENAL CELL CARCINOMA AND METABOLISM goes hand to hand in genes encoding enzymes of the Krebs cycle suppress tumor formation in kidney cells. This includes Succinate dehydrogenase (SDH), Fumarate hydratase (FH). As a result of accumulation of succinate or fumarate causes the inhibition of a family of 2-oxoglutarate-dependent dioxygeneases.

**The FH and SDH genes function as two-hit tumor suppressor genes.**

SDH has a complex of 4 different polypeptides (SDHA-D) function in electron transfer, catalyzes the conversion of succinate to fumarate. Furthermore, heterozygous germline mutations in SDHsubunits predispose to pheochromocytoma/paraganglioma. FH function to convert fumarate to malate. When its mutations presented as heterozygous germline, it predisposes hereditary leiomyomatosis and renal cell cancer (HLRCC). Among them about 20–50% of HLRCC families are typically papillary-type 2 (pRCC-2) and overwhelmingly aggressive.RCC is increasingly being recognized as a metabolic disease, and key lesions in nutrient sensing and processing have been detected.

Regulation of Prolyl Hydroxylases and Keap1 by Krebs cycle http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f4.jpg

Regulation of Prolyl Hydroxylases by Tricarboxylic Acid (TCA) Cycle Intermediates. Prolyl hydroxylases use TCA cycle intermediates to help catalyze the oxygen, iron and ascorbate dependent- addition of a hydroxyl side chain to a Pro⁴⁰² and Pro⁵⁶⁴ of HIF alpha subunits, leading to VHL binding and degradation. Defects in either fumarate hydratase or succinate dehydrogenase will drive up levels of fumarate and succinate, which competitively bind prolyl hydroxylases, and prevent HIF prolyl hydroxylation. This results in higher intracellular HIF levels.

Regulation of mTORC1 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f5.jpg

HIF regulation and mTOR pathway connections. Hypoxia blocks HIF expression in a TSC1/2 and REDD dependent pathway [155]. HIF1α appears to be both TORC1 and TORC2 dependent, whereas HIF2α is only TORC2 dependent [275]. Signaling via TORC2 appears to upregulate HIF2α in an AKT dependent manner [69].

TREATMENT:

Based on the types of renal cancers the treatment method may vary but the general scheme is:

Drugs Approved for Kidney (Renal Cell) Cancer

Food and Drug Administration (FDA) approved drugs for kidney (renal cell) cancer. Some of the drug names link to NCI’s Cancer Drug Information summaries.

T cell regulation in RCC http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f7.jpg

Immune regulation of renal tumor cells. A: When an antigen presenting cell (APC) engages a T-cell via a cognate T-cell receptor (TCR) and CD28, T-cell cell activation occurs. B: Early and late T-cell inhibitory signals are mediated via CTLA-4 and PD-1 receptors, and this occurs via engagement of the APC via B7 and PD-L1, respectively. C: Inhibitory antibodies against CTLA-4 and PD-1 can overcome T-cell downregulation and once again allow cytokine production.

Phase III Trials of Targeted Therapy in Metastatic Renal Cell Carcinoma

Trial	Number of patients	Clinical setting	RR (%)	PFS (months)	OS (months)
*VEGF-Targeted Therapy*
*AVOREN Bevacizumab + IFNa vs.IFNa[270]	649	First-line	31 vs. 12	10.2 vs. 5.5 (p<0.001)	23.3 vs. 21.3 (p=0.129)
*CALBG 90206 Bevacizumab + IFNa vs.IFNa[271]	732	First-line	25.5 vs. 13	8.4 vs. 4.9 (p<0.001)	18.3 vs. 17.4 (p=0.069)
Sunitinib vs. IFNa[248]	750	First-line	47 vs. 12	11 vs. 5 (p=0.0001)	26.4 vs. 21.8 (p=0.051)
*TARGET Sorafenib vs. Placebo[272]	903	Second-line (post-cytokine)	10 vs. 2	5.5 vs. 2.8 (p<0.01)	17.8vs.15.2 (p=0.88)
Pazopanib vs. placebo[273]	435	First line/second line (post-cytokine)	30 vs. 3	9.2 vs. 4.2 (p<0.0001)	22.9 vs. 20.5 (p=0.224)
*AXIS Axitinib vs. sorafenib [269]	723	Second line (post-sunitinib, cytokine, bevacizumab or temsirolimus)	19 vs. 9 (p=0.0001)	6.7 vs. 4.7 (p<0.0001)	Not reported
*mTOR-Targeted Therapy*
*ARCC Temsirolimus vs. Tem + IFNa vs. IFNa[249]	624	First line, ≥ 3 poor risk features^a	9 vs. 5	3.8 vs. 1.9 for IFNa monotherapy (p=0.0001)	10.9 vs. 7.3 for IFNa(p=0.008)
*RECORD-1 Everolimus vs. placebo [274]	410	Second line (post sunitinib and/or sorafenib)	2 vs. 0	4.9 vs. 1.9 (p<0.0001)	14.8 vs. 14.5

RCC renal cell carcinoma, RR response rate, OS overall survival, PFS progression free survival, VEGFvascular endothelial growth factor, IFNa interferon alpha, mTOR mammalian target of rapamycin. AVORENAVastin fOr RENal cell cancer, CALBG Cancer and Leukemia Group B. TARGET Treatment Approaches in Renal Cancer Global Evaluation Trial. AXIS Axitinib in Second Line. ARCC Advanced Renal-Cell Carcinoma. RECORD-1 REnal Cell cancer treatment withOral RAD001 given Daily.

^aIncluding serum lactate dehydrogenase level of more than 1.5 times the upper limit of the normal range, a hemoglobin level below the lower limit of the normal range; a corrected serum calcium level of more than 10 mg per deciliter (2.5 mmol per liter), a time from initial diagnosis of renal-cell carcinoma to randomization of less than 1 year, a Karnofsky performance score of 60 or 70, or metastases in multiple organs.

PMC full text:

Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi: 10.2147/RRU.S7242

Table: RCC-Associated Antigens (RCCAA) Recognized by T Cells.

Antigen	Antigen Category	Frequency of Expression Among RCC Tumors (%)	CD8+ T cell recognition: Patients with HLA Class I Allele(s)	CD4+ T cell recognition: Patients with HLA Class II Allele(s)	References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
Survivin^a	ML	100	Multiple	Multiple	114
OFA-iLR	OF	100	A2	NR	115, 116
IGFBP3^a, ^b	ML	97	NR	Multiple	117, 118
EphA2^a	ML	> 90	A2	DR4	17, 44, 119
RU2AS	Antisense transcript	> 90	B7	NR	120
G250 (CA-IX) ^a, ^b	RCC	90	A2, A24	Multiple	47, 51
EGFR^a, ^b	ML	85	A2	NR	121, 122
HIFPH3^a	ML	85	A24	NR	123
c-Met^a	ML	> 80	A2	NR	124
WT-1^a	ML	80	A2, A24	NR	125–¹²⁸
MUC1^a, ^b	ML	76	A2	DR3	46, 129, 130
5T4	ML	75	A2, Cw7	DR4	54, 131–133
iCE	aORF	75	B7	NR	134
MMP7^a	ML	75	A3	Multiple	117, 135, 136
Cyclin D1^a	ML	75	A2	Multiple	117, 137, 138
HAGE ^b	CT	75	A2	DR4	139
hTERT ^a, ^b	ML	> 70	Mutliple	Multiple	140–¹⁴²
FGF-5	Protein splice variant	> 60	A3	NR	143
mutVHL^a, ^b	ML	> 60	NR	NR	144
MAGE-A3 ^b	CT	60	Multiple	Multiple	145
SART-3	ML	57	Mulitple	NR	146–¹⁴⁹
SART-2	ML	56	A24	NR	150
PRAME ^b	CT	40	Multiple	NR	151–¹⁵⁴
p53^a, ^b	Mutant/WT ML	32	Mutliple	Multiple	155, 156
MAGE-A9^b	CT	>30	A2	NR	157
MAGE-A6^b	CT	30	Mutliple	DR4	18, 158
MAGE-D4^b	CT	30	A25	NR	159
Her2/neu^a	ML	10–³⁰	Multiple	Multiple	45, 160–164
SART-1^a	ML	25	Multiple	NR	165–¹⁶⁷
RAGE-1	CT (ORF2/5)	21	Mutliple	Multiple	151, 157, 168, 169
TRP-1/ gp75	ML	11	A31	DR4	151, 170–172

A summary is provided for RCCAA that have been defined at the molecular level. RCCAA are characterized with regard to their antigen category, their prevalence of (over)expression among total RCC specimens evaluated, whether RCCAA expression is modulated by hypoxia or tumor DNA methylation status, and which HLA class I and class II alleles have been reported to serve as presenting molecules for T cell recognition of peptides derived from a given RCCAA.

Abbreviations: CT = Cancer-Testis Antigens; ML = Multi-lineage Antigens; NR = Not Reported; OF = Oncofetal Antigen; aORF = altered open reading frame; ORF = open reading frame; RCC = Renal cell carcinoma; WT = Wild-Type;

^aHypoxia-Induced;

^bHypomethylation-Induced.

PMC full text:

Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi: 10.2147/RRU.S7242

Expected Impact on Teff versus Suppressor Cells
Co-Therapeutic Agent	Teff priming	Teff function	Teff survival	Teff (TME)	Treg/ MDSC	References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
Cytokines
IL-2	↑	↑	+/−	↑	↑ (Treg)	173–¹⁷⁵
IL-7	↑	↑	↑	↑	↑ (Treg)	176–¹⁷⁸
IL-12	↑	↑	↑	↑	– (Treg), ↓ (MDSC)	179–¹⁸¹
IL-15	↑	↑	↑	↑	↑ (Treg)*	182, 183
IL-18	↑	↑	↑	↑	↓ (Treg)	184–¹⁸⁶
IL-21	↑	↑	↑	?	+/− (Treg)	187–¹⁹⁰
IFN-α	↑	↑	↑	↑	+/− (Treg)	175, 191–194
IFN-γ	↑	↑	-?	? ↑	↑ (Treg); ↑ ?(MDSC)	195–¹⁹⁷
GM-CSF	↑	↑	?	↑	↑ (Treg); ↑(MDSC)	198–²⁰²
Coinhibitory Antagonist
CTLA-4	↑	↑	?	↑	↓ (Treg)	203, 204
PD1/PD1L	↑	↑	↑	↑	↓ (Treg)	205–²⁰⁷
Costimulatory Agonist
CD40/CD40L	↑	↑	↑	↑	↑ (Treg); ↑(MDSC)	208–²¹¹
GITR/GITRL	↑	↑	↑	↑	↓ (Treg); ↓ (MDSC)	212, 213
OX40/OX86	↑	↑	↑	↑	↑↓ (Treg); ↓ (MDSC)	214–²¹⁹
4-1BB/4-1BBL	↑	↑	↑	↑	↑ (Treg)	220–²²⁴
TLR Agonists
Imiquimod (TLR7)	↑	↑	↑	↑	?	225–²²⁷
Resiquimod (TLR8)	↑	↑	↑	?	?	228, 229
CpG (TLR9)	↑	↑	↑	↑	↓ (Treg)	230–²³²
Anti-Angiogenic
VEGF-Trap	–	–	?	?	–	233
Sunitinib	↑	↑	?	↑	↓ (Treg/MDSC)	98, 100, 234
Sorafenib	↓	↓	↓	?	↓ (MDSC)	235
Bevacizumab	↑	↑	?	?	↓ (MDSC)	236, 237
Gefitinib (IRESSA)	?	?	?	?	?	238, 239
Cetuximab	?	↑	?	?	?	240
mTOR Inhibitors
Temsirolimus/Everolimus	↓	↓	↓	?	↓ (Treg)	241
Treg/MDSC Inhibitors
Iplimumab (CTLA-4)	↑	↑	?	↑	↓ (Treg)	242, 243
ONTAK (CD25)	+/−	+/−	?	?	↓ (Treg)	244
Anti-TGFβ/TGFβR	↑	↑	↑	↑	↓ (Treg)	245–²⁴⁷
Anti-IL10/IL10R	↑	↑	↑	+/−	↓ (Treg)	248, 249
Anti-IL35/IL35R	↑?	↑?	↑?	↑?	↓ (Treg)	250
1-methyl trytophan	↑	↑	?	?	↓ (MDSC)	251
ATRA	↑	↑	?	?	↑ (Treg), ↓ (MDSC)	90–⁹³

Agents that are currently or soon-to-be in clinical trials are summarized with regard to their anticipated impact(s) on Type-1 anti-tumor T cell (T_e) activation, function, survival and recruitment into the TME. Additional anticipated effects of drugs on suppressor cells (Treg and MDSC) are also summarized. Key: ↑, agent is expected to increase parameter; ↓, agent is expected to inhibit parameter; +/−, minimal increase or decrease is expected in parameter as a consequence of treatment with agent; ?, unknown effect of agent on parameter.

Abbreviations: ATRA, all-trans retinoic acid; CTLA-4, cytotoxic T Lymphocyte antigen 4; GITR(L), glucocorticoid-induced TNF receptor (ligand); GM-CSF, granulocyte-macrophage colony stimulating factor; IFN, interferon; IL, interleukin; MDSC, myeloid-derived suppressor cell; PD1/PD1L, programmed cell death 1 (ligand); TGF-β(R), tumor necrosis factor-β(receptor); TLR, Toll-like receptor; TME, tumor microenvironment; Treg, regulatory T cell; VEGF, vascular endothelial growth factor.

Alternative and Complementary Therapies for Cancer:

Art therapy
Dance or movement therapy
Exercise
Meditation
Music therapy
Relaxation exercises

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi: 10.1158/1541-7786.MCR-12-0117 PMCID: PMC3399969 NIHMSID: NIHMS380694

State-of-the-science: An update on renal cell carcinoma

Eric Jonasch,¹ Andrew Futreal,¹ Ian Davis,² Sean Bailey,² William Y. Kim,² James Brugarolas,³ Amato Giaccia,⁴ Ghada Kurban,⁵ Armin Pause,⁶ Judith Frydman,⁴ Amado Zurita,¹ Brian I. Rini,⁷ Pam Sharma,⁸Michael Atkins,⁹ Cheryl Walker,^8,* and W. Kimryn Rathmell^2,*

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Imaging-guided cancer treatment

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, CANCER BIOLOGY & Innovations in Cancer Therapy, CE Mark & Global Regulatory Affairs: process management and strategic planning - GCP, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Coagulation Therapy and Internal Bleeding, Components and IRB related issues, Computational Biology/Systems and Bioinformatics, CT, Disease Biology, Drug Delivery Platform Technology, Ecosystems & Industrial Concentration in the Medical Device Sector, FDA, FDA Regulatory Affairs, Gene Regulation and Evolution, Genetics & Pharmaceutical, GLP, Health Economics and Outcomes Research, Healthcare costs and reimbursement, Human Immune System in Health and in Disease, Image Processing/Computing, Imaging-based Cancer Patient Management, Innovation in Immunology Diagnostics, ISO 14155, Medical and Population Genetics, Medical Devices R&D and Inventions, Medical Devices R&D Investment, Medical Imaging Technology, Methods, Molecular Genetics & Pharmaceutical, MRI, Nanotechnology for Drug Delivery, Nuclear Medicine, Personalized and Precision Medicine & Genomic Research, Population Health Management, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Reproductive Biology & Bio Instrumentation, Resident-cell-based, Small Molecules in Development of Therapeutic Drugs, Technology Advance Assessment of, Technology Transfer: Biotech and Pharmaceutical, Translational Research, Translational Science, Ultra Sound, tagged Biochemistry and Molecular Biology, biomarkers, breast cancer, Cancer - General, Cancer research, Cell Biology, Chemotherapy, clinical imaging, Clinical trial, Clinical Trials, Conditions and Diseases, DNA, DNA Sequencing, gene, gene expression, genetics, health, imaging guided therapy, Lung cancer, Magnetic resonance imaging, mammography, medicine, Melanoma, Metabolism, Metabolomics, MRI, nanotechnology, ovarian cancer, Personalized medicine, Prostate cancer, Prostate-specific antigen, research, science, surgery, Therapy on September 30, 2014| Leave a Comment »

Imaging-guided cancer treatment

Writer & reporter: Dror Nir, PhD

It is estimated that the medical imaging market will exceed $30 billion in 2014 (FierceMedicalImaging). To put this amount in perspective; the global pharmaceutical market size for the same year is expected to be ~$1 trillion (IMS) while the global health care spending as a percentage of Gross Domestic Product (GDP) will average 10.5% globally in 2014 (Deloitte); it will reach ~$3 trillion in the USA.

Recent technology-advances, mainly miniaturization and improvement in electronic-processing components is driving increased introduction of innovative medical-imaging devices into critical nodes of major-diseases’ management pathways. Consequently, in contrast to it’s very small contribution to global health costs, medical imaging bears outstanding potential to reduce the future growth in spending on major segments in this market mainly: Drugs development and regulation (e.g. companion diagnostics and imaging surrogate markers); Disease management (e.g. non-invasive diagnosis, guided treatment and non-invasive follow-ups); and Monitoring aging-population (e.g. Imaging-based domestic sensors).

In; The Role of Medical Imaging in Personalized Medicine I discussed in length the role medical imaging assumes in drugs development. Integrating imaging into drug development processes, specifically at the early stages of drug discovery, as well as for monitoring drug delivery and the response of targeted processes to the therapy is a growing trend. A nice (and short) review highlighting the processes, opportunities, and challenges of medical imaging in new drug development is: Medical imaging in new drug clinical development.

The following is dedicated to the role of imaging in guiding treatment.

Precise treatment is a major pillar of modern medicine. An important aspect to enable accurate administration of treatment is complementing the accurate identification of the organ location that needs to be treated with a system and methods that ensure application of treatment only, or mainly to, that location. Imaging is off-course, a major component in such composite systems. Amongst the available solution, functional-imaging modalities are gaining traction. Specifically, molecular imaging (e.g. PET, MRS) allows the visual representation, characterization, and quantification of biological processes at the cellular and subcellular levels within intact living organisms. In oncology, it can be used to depict the abnormal molecules as well as the aberrant interactions of altered molecules on which cancers depend. Being able to detect such fundamental finger-prints of cancer is key to improved matching between drugs-based treatment and disease. Moreover, imaging-based quantified monitoring of changes in tumor metabolism and its microenvironment could provide real-time non-invasive tool to predict the evolution and progression of primary tumors, as well as the development of tumor metastases.

A recent review-paper: Image-guided interventional therapy for cancer with radiotherapeutic nanoparticles nicely illustrates the role of imaging in treatment guidance through a comprehensive discussion of; Image-guided radiotherapeutic using intravenous nanoparticles for the delivery of localized radiation to solid cancer tumors.

Abstract

One of the major limitations of current cancer therapy is the inability to deliver tumoricidal agents throughout the entire tumor mass using traditional intravenous administration. Nanoparticles carrying beta-emitting therapeutic radionuclides [DN: radioactive isotops that emits electrons as part of the decay process a list of β-emitting radionuclides used in radiotherapeutic nanoparticle preparation is given in table1 of this paper.) that are delivered using advanced image-guidance have signiﬁcant potential to improve solid tumor therapy. The use of image-guidance in combination with nanoparticle carriers can improve the delivery of localized radiation to tumors. Nanoparticles labeled with certain beta-emitting radionuclides are intrinsically theranostic agents that can provide information regarding distribution and regional dosimetry within the tumor and the body. Image-guided thermal therapy results in increased uptake of intravenous nanoparticles within tumors, improving therapy. In addition, nanoparticles are ideal carriers for direct intratumoral infusion of beta-emitting radionuclides by convection enhanced delivery, permitting the delivery of localized therapeutic radiation without the requirement of the radionuclide exiting from the nanoparticle. With this approach, very high doses of radiation can be delivered to solid tumors while sparing normal organs. Recent technological developments in image-guidance, convection enhanced delivery and newly developed nanoparticles carrying beta-emitting radionuclides will be reviewed. Examples will be shown describing how this new approach has promise for the treatment of brain, head and neck, and other types of solid tumors.

The challenges this review discusses

intravenously administered drugs are inhibited in their intratumoral penetration by high interstitial pressures which prevent diffusion of drugs from the blood circulation into the tumor tissue [1–5].
relatively rapid clearance of intravenously administered drugs from the blood circulation by kidneys and liver.
drugs that do reach the solid tumor by diffusion are inhomogeneously distributed at the micro-scale – This cannot be overcome by simply administering larger systemic doses as toxicity to normal organs is generally the dose limiting factor.
even nanoparticulate drugs have poor penetration from the vascular compartment into the tumor and the nanoparticles that do penetrate are most often heterogeneously distributed

How imaging could mitigate the above mentioned challenges

The inclusion of an imaging probe during drug development can aid in determining the clearance kinetics and tissue distribution of the drug non-invasively. Such probe can also be used to determine the likelihood of the drug reaching the tumor and to what extent.

Note: Drugs that have increased accumulation within the targeted site are likely to be more effective as compared with others. In that respect, Nanoparticle-based drugs have an additional advantage over free drugs with their potential to be multifunctional carriers capable of carrying both therapeutic and diagnostic imaging probes (theranostic) in the same nanocarrier. These multifunctional nanoparticles can serve as theranostic agents and facilitate personalized treatment planning.

Imaging can also be used for localization of the tumor to improve the placement of a catheter or external device within tumors to cause cell death through thermal ablation or oxidative stress secondary to reactive oxygen species.

See the example of Vintfolide in The Role of Medical Imaging in Personalized Medicine

Note: Image guided thermal ablation methods include radiofrequency (RF) ablation, microwave ablation or high intensity focused ultrasound (HIFU). Photodynamic therapy methods using external light devices to activate photosensitizing agents can also be used to treat superﬁcial tumors or deeper tumors when used with endoscopic catheters.

Quality control during and post treatment

For example: The use of high intensity focused ultrasound (HIFU) combined with nanoparticle therapeutics: HIFU is applied to improve drug delivery and to trigger drug release from nanoparticles. Gas-bubbles are playing the role of the drug’s nano-carrier. These are used both to increase the drug transport into the cell and as ultrasound-imaging contrast material. The ultrasound is also used for processes of drug-release and ablation.

Additional example; Multifunctional nanoparticles for tracking CED (convection enhanced delivery) distribution within tumors: Nanoparticle that could serve as a carrier not only for the therapeutic radionuclides but simultaneously also for a therapeutic drug and 4 different types of imaging contrast agents including an MRI contrast agent, PET and SPECT nuclear diagnostic imaging agents and optical contrast agents as shown below. The ability to perform multiple types of imaging on the same nanoparticles will allow studies investigating the distribution and retention of nanoparticles initially in vivo using non-invasive imaging and later at the histological level using optical imaging.

Conclusions

Image-guided radiotherapeutic nanoparticles have signiﬁcant potential for solid tumor cancer therapy. The current success of this therapy in animals is most likely due to the improved accumulation, retention and dispersion of nanoparticles within solid tumor following image-guided therapies as well as the micro-ﬁeld of the β-particle which reduces the requirement of perfectly homogeneous tumor coverage. It is also possible that the intratumoral distribution of nanoparticles may beneﬁt from their uptake by intratumoral macrophages although more research is required to determine the importance of this aspect of intratumoral radionuclide nanoparticle therapy. This new approach to cancer therapy is a fertile ground for many new technological developments as well as for new understandings in the basic biology of cancer therapy. The clinical success of this approach will depend on progress in many areas of interdisciplinary research including imaging technology, nanoparticle technology, computer and robot assisted image-guided application of therapies, radiation physics and oncology. Close collaboration of a wide variety of scientists and physicians including chemists, nanotechnologists, drug delivery experts, radiation physicists, robotics and software experts, toxicologists, surgeons, imaging physicians, and oncologists will best facilitate the implementation of this novel approach to the treatment of cancer in the clinical environment. Image-guided nanoparticle therapies including those with β-emission radionuclide nanoparticles have excellent promise to significantly impact clinical cancer therapy and advance the ﬁeld of drug delivery.

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What is the key method to harness Inflammation to close the doors for many complex diseases?

Posted in Biomarkers & Medical Diagnostics, Calcium Signaling, CANCER BIOLOGY & Innovations in Cancer Therapy, Genome Biology, Human Immune System in Health and in Disease, Human Sensation and Cellular Transduction: Physiology and Therapeutics, Immunodiagnostics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Pharmacogenomics, Systemic Inflammatory Response Related Disorders, Translational Research, Translational Science, tagged angiogenesis, Blood, Cancer - General, coagulationi, complement activation, Dee Sag, Demet Sag, diagnostics, endothelial cell activation, fibrinolysis, genomics screen, heart, immunomodulation, inflammation, inflammation disorders, inflammatory diseases, kinin system, liver, lymphatic system, Plasma, review, Therapy on March 21, 2014| Leave a Comment »

What is the key method to harness Inflammation to close the doors for many complex diseases?

Author and Curator: Larry H Bernstein, MD, FCAP

The main goal is to have a quality of a healthy life.

When we look at the picture 90% of main fluid of life, blood, carried by cardiovascular system with two main pumping mechanisms, lung with gas exchange and systemic with complex scavenger actions, collection of waste, distribution of nutrition and clean gases etc. Yet without lymphatic system body can’t make up the 100% fluid. Therefore, 10% balance is completed by lymphatic system as a counter clockwise direction so that not only the fluid balance but also mass balance is maintained. Finally, the immune system patches the remaining mechanism by providing cellular support to protect the body because it contains 99% of white cells to fight against any kinds of invasion, attack, trauma.

These three musketeers, ccardiovascular, lyphatic and immune systems, create the core mechanism of survival during human life.

However, there is a cellular balance between immune and cardiovascular system since blood that made up off 99% red cells and 1% white blood cells that are used to scavenger hunt circulating foreign materials. These three systems are acting with a harmony not only defend the body but provide basic needs of life. Thus, controlling angiogenesis and working mechanisms in blood not only helps to develop new diagnostic tools but more importantly establishes long lasting treatments that can harness Immunomodulation.

The word inflammation comes from the Latin “inflammo”, meaning “I set alight, I ignite”.

Medical Dictionary description is:

“A fundamental pathologic process consisting of a dynamic complex of histologically apparent cytologic changes, cellular infiltration, and mediator release that occurs in the affected blood vessels and adjacent tissues in response to an injury or abnormal stimulation caused by a physical, chemical, or biologic agent, including the local reactions and resulting morphologic changes; the destruction or removal of the injurious material; and the responses that lead to repair and healing.”

The five elements makes up the signature of inflammation: rubor, redness; calor, heat (or warmth); tumor swelling; and dolor, pain; a fifth sign, functio laesa, inhibited or lost function. However, these indications may not be present at once.

Please click on to the following link for genetic association of autoimmune diseases (Cho Et al selected major association signals in autoimmune diseases) from Cho JH, Gregersen PK. N Engl J Med 2011;365:1612-1623.

Inflammatory diseases grouped under two classification: the immune system related due to inflammatory disorders, such as both allergic reactions and some myopathies, with many immune system disorders. The examples of inflammatory disorders include Acne vulgaris, asthma, autoimmune disorders, celiac disease, chronic prostatitis, glomerulonepritis, hypersensitivities, inflammatory bowel diseases, pelvic inflammatory diseases, reperfusion diseases, rheumatoid arthritis, sarcoidosis, transplant rejection, vasculitis, interstitial cyctitis, The second kind of inflammation are related to non-immune diseases such as cancer, atherosclerosis, and ischaemic heart disease.

This seems simple yet at molecular physiology and gene activation levels this is a complex response as an innate immune response from body. There can be acute lasting few days after exposure to bacterial pathogens, injured tissues or chronic inflammation continuing few months to years after unresolved acute responses such as non-degradable pathogens, viral infection, antigens or any foreignmaterials, or autoimmune responses.

As the system responses arise from plasma fluid, blood vessels, blood plasma through vasciular changes, differentiation in plasma cascade systems like coagulation system, fibrinolysis, complement system and kinin system. Some of the various mediators include bradykinin produced by kinin system, C3, C5, membrane attack system (endothelial cell activation or endothelial coagulation activation mechanism) created by the complement system; factor XII that can activate kinin, fibrinolysys and coagulation systems at the same time produced in liver; plasmin from fibrinolysis system to inactivate factor Xii and C3 formation, and thrombin of coagulation system with a reaction through protein activated receptor 1 (PAR1), which is a seven spanning membrane protein-GPCR. This system is quite fragile and well regulated. For example activation of inactive Factor XII by collagen, platelets, trauma such as cut, wound, surgery that results in basement membrane changes since it usually circulate in inactive form in plasma automatically initiates and alerts kinin, fibrinolysis and coagulation systems.

Furthermore, the changes reflected through receptors and create gene activation by cellular mediators to establish system wide unified mechanisms. These factors (such as IFN-gamma, IL-1, IL-8, prostaglandins, leukotrene B4, nitric oxide, histamines,TNFa) target immune cells and redesign their responses, mast cells, macrophages, granulocytes, leukocytes, B cells, T cells) platelets, some neuron cells and endothelial cells. Therefore, immune system can react with non-specific or specific mechanisms either for a short or a long term.

As a result, controlling of mechanisms in blood and prevention of angiogenesis answer to cure/treat many diseases Description of angiogenesis is simply formation of new blood vessels without using or changing pre-existing capillaries. This involves serial numbers of events play a central role during physiologic and pathologic processes such as normal tissue growth, such as in embryonic development, wound healing, and the menstrual cycle. However this system requires three main elements: oxygen, nutrients and getting rid of waste or end products.

Genome Wide Gene Association Studies, Genomics and Metabolomics, on the other hand, development of new technologies for diagnostics and non-invasive technologies provided better targeting systems.

In this token recent genomewide association studies showed a clear view on a disease mechanism, or that suggest a new diagnostic or therapeutic approach particularly these disorders are related to genes within the major histocompatibility complex (MHC) that predisposes the most significant genetic effect. Presumably, these genes are reflecting the immunoregulatory effects of the HLA molecules themselves. As a result, the working mechanism of pathological conditions are revisited or created new assumptions to develop new targets for diagnosis and treatments.

Even though B and T cells are reactive to initiate responses there are several level of mechanisms control the cell differentiation for designing rules during health or diseases. These regulators are in check for both T and B cells. For example, during Type 1 diabetes there are presence of more limited defects in selection against reactivity with self-antigens like insulin, thus, T cell differentiation is in jeopardy. In addition, B cells have many active checkpoints to modulate the immune responses like pre-B cells in the bone marrow are highly autoreactive yet they prefer to stay in naïve-B cell forms in the periphery through tyrosine phosphatase nonreceptor type 22 (PTPN22) along with many genes play a role in autoimmunity. In a nut shell this is just peeling the first layer of the onion at the level of Mendelian Genetics.

There is a great work to be done but if one can harness the blood and immune responses many complex diseases patients may have a big relief and have a quality of life. When we look at the picture 90% of main fluid of life, blood, carried by cardiovascular system with two main pumping mechanisms, lung with gas exchange and systemic with complex scavenger actions, collection of waste, distribution of nutrition and clean gases. Yet, without lymphatic system body can’t make up the 100% fluid. Therefore, 10% balance is completed by lymphatic system as a counter clockwise direction so that not only the fluid balance but also mass balance is maintained. Finally, the immune system patches the remaining mechanism by providing cellular support to protect the body because it contains 99% of white cells to fight against any kinds of invasion, attack, trauma.

FURTHER READINGS AND REFERENCES:

Arap W, Pasqualini R, Ruoslahti E (1998) Cancer treatment by targeted drug delivery to tumor vasculature in a mouse model. Science (Wash DC)279:377–380.

Brouty BD, Zetter BR (1980) Inhibition of cell motility by interferon.Science (Wash DC) 208:516–518.

Ferrara N, Alitalo K (1999) Clinical Applications of angiogenic growth factors and their inhibitors. Nat Med 5:1359–1364.

Ferrara N (1999) Role of vascular endothelial growth factor in the regulation of angiogenesis. Kidney Int 56:794–814.

Ferrara N (1995) Leukocyte adhesion: Missing link in angiogenesis. Nature (Lond) 376:467.

Kohn EC, Alessandro R, Spoonster J, Wersto RP, Liotta LA (1995) Angiogenesis: Role of calcium-mediated signal transduction. Proc Natl Acad Sci U S A 92:1307–1311

Meijer DKF, Molema G (1995) Targeting of drugs to the liver. Semin Liver Dis 15:202–256.

Sidky YA, Borden EC (1987) Inhibition of angiogenesis by interferons: Effects on tumor- and lymphocyte-induced vascular responses. Cancer Res47:5155–5161.

Anonymous (1999a) Genentech takes VEGF back to lab. SCRIP 2493:24.

Ziche M, Morbidelli L, Choudhuri R, Zhang HT, Donnini S, Granger HJ,Bicknell R (1997) Nitric oxide synthase lies downstream from vascular endothelial growth factor-induced but not basic fibroblast growth factor-induced angiogenesis. J Clin Invest 99:2625–2634.

Yoshida S, Ono M, Shono T, Izumi H, Ishibashi T, Suzuki H, Kuwano M(1997) Involvement of interleukin-8, vascular endothelial growth factor, and basic fibroblast growth factor in tumor necrosis factor α-dependent angiogenesis. Mol Cell Biol 17:4015–4023.

Vittet D, Prandini MH, Berthier R, Schweitzer A, Martin SH, Uzan G,Dejana E (1996) Embryonic stem cells differentiate in vitro to endothelial cells through successive maturation steps. Blood 88:3424–3431.

Ruegg C, Yilmaz A, Bieler G, Bamat J, Chaubert P, Lejeune FJ (1998) Evidence for the involvement of endothelial cell integrin αvβ3 in the disruption of the tumor vasculature induced by TNF and IFN. Nat Med4:408–414

Patey N, Vazeux R, Canioni D, Potter T, Gallatin WM, Brousse N (1996) Intercellular adhesion molecule-3 on endothelial cells. Expression in tumors but not in inflammatory responses. Am J Pathol 148:465–472.

Oliver SJ, Banquerigo ML, Brahn E (1994) Supression of collagen-induced arthritis using an angiogenesis inhibitor AGM-1470 and microtubule stabilizer taxol. Cell Immunol 157:291–299

Molema G, Griffioen AW (1998) Rocking the foundations of solid tumor growth by attacking the tumor’s blood supply. Immunol Today 19:392–394.

Losordo DW, Vale PR, Symes JF, Dunnington CH, Esakof DD, Maysky M,Ashare AB, Lathi K, Isner JM (1998) Gene therapy for myocardial angiogenesis: Initial clinical results with direct myocardial injection of PhVEGF165 as sole therapy for myocardial ischemia. Circulation98:2800–2804.

Jain RK, Schlenger K, Hockel M, Yuan F (1997) Quantitative angiogenesis assays: Progress and problems. Nat Med 3:1203–1208.

Jain RK (1996) 1995 Whitaker Lecture: Delivery of molecules, particles and cells to solid tumors. Ann Biomed Eng 24:457–473.

Giraudo E, Primo L, Audero E, Gerber H, Koolwijk P, Soker S,Klagsbrun M, Ferrara N, Bussolino F (1998) Tumor necrosis factor-alpha regulates expression of vascular endothelial growth factor receptor-2 and of its co-receptor neuropilin-1 in human vascular endothelial cells. J Biol Chem273:22128–22135.

Inflammation Genomics

Kocarnik JM, Pendergrass SA, Carty CL, Pankow JS, Schumacher FR, Cheng I, Durda P, Ambite JL, Deelman E, Cook NR, Liu S, Wactawski-Wende J, Hutter C, Brown-Gentry K, Wilson S, Best LG, Pankratz N, Hong CP, Cole SA, Voruganti VS, Bůžkova P, Jorgensen NW, Jenny NS, Wilkens LR, Haiman CA, Kolonel LN, Lacroix A, North K, Jackson R, Le Marchand L, Hindorff LA, Crawford DC, Gross M, Peters U. Multi-Ancestral Analysis of Inflammation-Related Genetic Variants and C-Reactive Protein in the Population Architecture using Genomics and Epidemiology (PAGE) Study. Circ Cardiovasc Genet. 2014 Mar 12

Ellis J, Lange EM, Li J, Dupuis J, Baumert J, Walston JD, Keating BJ, Durda P, Fox ER, Palmer CD, Meng YA, Young T, Farlow DN, Schnabel RB, Marzi CS, Larkin E, Martin LW, Bis JC, Auer P, Ramachandran VS, Gabriel SB, Willis MS, Pankow JS, Papanicolaou GJ, Rotter JI, Ballantyne CM, Gross MD, Lettre G, Wilson JG, Peters U, Koenig W, Tracy RP, Redline S, Reiner AP, Benjamin EJ, Lange LA. Large multiethnic Candidate Gene Study for C-reactive protein levels: identification of a novelassociation at CD36 in African Americans. Hum Genet. 2014 Mar 19.

Ricaño-Ponce I, Wijmenga C. Mapping of immune-mediated disease genes. Annu Rev Genomics Hum Genet. 2013;14:325-53. doi: 10.1146/annurev-genom-091212-153450. Epub 2013 Jul 3. Review.

McKillop AM, Flatt PR. Emerging applications of metabolomic and genomic profiling in diabetic clinical medicine. Diabetes Care. 2011 Dec;34(12):2624-30. doi: 10.2337/dc11-0837. Review.

Ricaño-Ponce I, Wijmenga C. Mapping of immune-mediated disease genes. Annu Rev Genomics Hum Genet. 2013;14:325-53. doi: 10.1146/annurev-genom-091212-153450. Epub 2013 Jul 3.Review.

Chen YB, Cutler CS. Biomarkers for acute GVHD: can we predict the unpredictable? Bone Marrow Transplant. 2013 Jun;48(6):755-60. doi: 10.1038/bmt.2012.143. Epub 2012 Aug 6. Review.

Cho JH, Gregersen PK. Genomics and the multifactorial nature of human autoimmune disease. N Engl J Med. 2011 Oct 27;365(17):1612-23. doi: 10.1056/NEJMra1100030. Review.

Shikama N, Nusspaumer G, Hollander GA. Clearing the AIRE: on the pathophysiological basis of the autoimmune polyendocrinopathy syndrome type-1. Endocrinol Metab Clin North Am2009;38:273-288

Concannon P, Rich SS, Nepom GT. Genetics of type 1A diabetes. N Engl J Med 2009;360:1646-1654

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Interaction of enzymes and hormones

Posted in Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Metabolomics, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, tagged active compounds, enzyme, hormone, interaction, Metabolism, Therapy on July 1, 2013| 3 Comments »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

WordCloud by Zach Day; Article Title: Interaction of enzymes and hormones

The majority of living forms depend for their functioning upon two classes of biocatalysts, the enzymes and the hormones. These biocatalysts permit the diverse chemical reactions of the organism to proceed at 38°C with specificity and at rates frequently unattainable in vitro at elevated temperatures with similar reactants. The physiologic importance of enzymes and hormones is evident not only under normal circumstances, but is reflected clinically in the diverse descriptions of errors of metabolism, due to lack or deficiency of one or more enzymes, and the numerous hypo and hyper functioning states resulting from imbalance of hormonal supply.

In as much as both enzymes and hormones function, with rare exception, to accelerate the rates of processes in cells, investigators have sought possible interrelationships and interactions of enzymes and hormones, particularly as a basis for the mechanism of hormonal action. It has seemed logical to hypothesize that hormones, while not essential for reactions to proceed but never the less affecting the rates of reactions, may function by altering either the concentration or activity of the prime cellular catalysts, the enzymes. This proposed influence of hormones on enzymatic activity might be a primary, direct effect achieved by the hormone participating as an integral part of an enzyme system, or an indirect influence based upon the hormone altering the concentration of available enzyme and/or substrate utilized by a particular enzyme. Many publications have described alterations in the activity of enzymes in various tissues following administration in vivo of diverse hormonal preparations. However, it is not possible to judge, in the in vivo experiments, whether the reported effects are examples of direct enzyme-hormone interaction, or an indirect influence of the hormone mediated via one or more metabolic pathways, and therefore other enzyme systems whose activities are not being measured. Data from in-vivo studies of this type are thus not pertinent to a discussion of direct hormone-enzyme interaction.

Enzyme hormone interaction, as seen, for example, in the profound role of the enzymes of the liver in the metabolism of certain hormones, is of paramount importance in determining the effectiveness of these hormones. The ability of the organic chemist to prepare synthetic hormonal derivatives which are relatively resistant to enzymatic processes in the liver has been of outstanding value for approaches to oral hormonal therapy. Largely unexplored as yet is the possibility that enzyme-hormone interactions may lead to the production of physiologically more active substances from compounds normally synthesized and secreted by a particular endocrine gland. It may be said at the outset that in no instance has a hormone been demonstrated to influence the rate of a cellular reaction by functioning as a component of an enzyme system.

It is plausible that enzymes in a pathway might be structurally conserved because of their similar substrates and products for linked metabolic steps. However, this is not typically observed, and sequence analysis confirms the lack of convergent or divergent evolution. One might postulate that, if the folds or overall structures of the enzymes in a pathway are not conserved, then perhaps at least pathway-related active site similarities would exist. It is true that metal-binding sites and nucleotide-binding sites are structurally conserved. For example, cofactor-binding motifs for zinc, ATP, biopterin and NAD have been observed and biochemically similar reactions appear to maintain more structural similarity than pathway-related structural motifs. In general, ‘horizontal’ structural equivalency is prevalent in that chemistry-related structural similarities exist, but ‘vertical’ pathway-related structural similarities do not hold.

For metabolic pathways, protein fold comparisons and corresponding active site comparisons are sometimes possible if structural and functional homology exists. Unfortunately, with the current structural information available, the majority of active sites that can be structurally characterized are not similar within a metabolic pathway. Other examples exist of nearly completed pathways, for example, the tricarboxylic acid (TCA) cycle, and similar observations are observed. Situations in which different metals are incorporated in enzyme active sites lead to inherently different catalytic portions of the active sites. Slight differences in the ligand-binding portions of the respective active sites must lead to the observed differences in pathway-related enzyme specificities. These modifications in enzymatic activity are similar to what Koshland and co-workers previously observed. They showed that very minor active site perturbations to isocitrate dehydrogenase had drastic effects on catalysis.

Molecular level understanding of chemical and biological processes requires mechanistic details and active site information. The current knowledge regarding enzyme active sites is incomplete. Even in situations in which ATP-, ADP- or NAD(P)+-binding domains are observed or in situations in which similar folds are found (e.g. even for related kinases or for proteins involved in the immune system), structural comparisons do not yield specific details about active sites and it is not possible to predict where the substrate binds or to identify determinants of active site substrate specificity. Therefore, in this era of structural genomics, there should be major continued emphasis on completing structural information for important metabolic pathways. This will require improved efforts to obtain structures for enzyme complexes with appropriate cofactors, substrates or substrate analogs, as well as with inhibitors and regulators of activity. Then and only then will we have complete structural knowledge and facilitated structure-based drug design efforts. Structural genomics efforts promise to provide structural data in a high-throughput mode. However, we need to ensure that much of this focus is placed on completing the picture of metabolic pathways and enzyme active sites.

The availability of the human genomic sequence is changing the way in which biological questions are addressed. Based on the prediction of genes from nucleotide sequences, homologies among their encoded amino acids can be analyzed and used to place them in distinct families. This serves as a first step in building hypotheses for testing the structural and functional properties of previously uncharacterized paralogous genes. As genomic information from more organisms becomes available, these hypotheses can be refined through comparative genomics and phylogenetic studies. Instead of the traditional single-gene approach in endocrine research, we are beginning to gain an understanding of entire mammalian genomes, thus providing the basis to reveal subfamilies and pathways for genes involved in ligand signaling. The present review provides selective examples of postgenomic approaches in the analysis of novel genes involved in hormonal signaling and their chromosomal locations, polymorphisms, splicing variants, differential expression, and physiological function. In the postgenomic era, scientists will be able to move from a gene-by-gene approach to a reconstructionistic one by reading the encyclopedia of life from a global perspective. Eventually, a community-based approach will yield new insights into the complexity of intercellular communications, thereby offering us an understanding of hormonal physiology and pathophysiology. Many cellular signaling pathways ultimately control specific patterns of gene expression in the nucleus through a variety of signal-regulated transcription factors, including nuclear hormone receptors. The advent of genomic technologies for examining signal-regulated transcriptional responses and transcription factor binding on a genomic scale has dramatically increased our understanding of the cellular programs that control hormonal signaling and gene regulation. Studies of transcription factors, especially nuclear hormone receptors, using genomic approaches have revealed novel and unexpected features of hormone-regulated transcription, and a global view is beginning to emerge.

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