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Tumor Ammonia Recycling: How Cancer Cells Use Glutamate Dehydrogenase to Recycle Tumor Microenvironment Waste Products for Biosynthesis

Posted in Breast Cancer - impalpable breast lesions, Cancer - General, CANCER BIOLOGY & Innovations in Cancer Therapy, cancer metabolism, Cancer-Immune Interactions, Glioblastoma, Immunology, Inflammasome, Metabolic Immuno-Oncology, Oxidative phosphorylation, tumor microenvironment, Warburg effect, tagged alpha ketoglutarate, ammonia metabolism, biomass, Biosynthesis, breast cancer, cancer metabolic pathways, cancer metabolism, ER positive breast cancer, estrogen receptor positive cancer, glutaminolysis, IDH1 and IDH2, metabolic reprogramming, tumor microenvironment, Warburg Effect on February 13, 2019| Leave a Comment »

Tumor Ammonia Recycling: How Cancer Cells Use Glutamate Dehydrogenase to Recycle Tumor Microenvironment Waste Products for Biosynthesis

Reporter: Stephen J. Williams, PhD

A feature of the tumorigenic process is the rewiring of the metabolic processes that provides a tumor cell the ability to grow and thrive in conditions of limiting nutrients as well as the ability to utilize waste products in salvage pathways for production of new biomass (amino acids, nucleic acids etc.) required for cellular growth and division ^1-8. A Science article from Spinelli et al. ⁹ (and corresponding Perspective article in the same issue by Dr. Chi V. Dang entitled Feeding Frenzy for Cancer Cells ¹⁰) describes the mechanism by which estrogen-receptor positive (ER+) breast cancer cells convert glutamine to glutamate, release ammonia into the tumor microenvironment, diffuses into tumor cells and eventually recycle this ammonia by reductive amination of a-ketoglutarate by glutamate dehydrogenase (GDH) to produce glutamic acid and subsequent other amino acids needed for biomass production. Ammonia can accumulate in the tumor microenvironment in poorly vascularized tumor. Thus ammonia becomes an important nitrogen source for tumor cells.

Mammalian cells have a variety of mechanisms to metabolize ammonia including

Glutamate synthetase (GS) in the liver can incorporate ammonia into glutamate to form glutamine
glutamate dehydrogenase (GDH) converts glutamate to a-ketoglutarate and ammonia under allosteric regulation (discussed in a post on this site by Dr. Larry H. Berstein; subsection Drugging Glutaminolysis)
the reverse reaction of GDH, which was found to occur in ER+ breast cancer cells, a reductive amination of a-ketoglutarate to glutamate¹¹, is similar to the reductive carboxylation of a-ketoglutarate to citrate by isocitrate dehydrogenase (IDH) for fatty acid synthesis (IDH is overexpressed in many tumor types including cancer stem cells ^12-15), and involved in immune response and has been developed as a therapeutic target for various cancers. IDH mutations were shown to possess the neomorphic activity to generate the oncometabolite, 2-hydroxyglutarate (2HG) ^16-18. With a single codon substitution, the kinetic properties of the mutant IDH isozyme are signiﬁcantly altered, resulting in an obligatory sequential ordered reaction in the reverse direction ¹⁹.

In the Science paper, Spinelli et al. report that ER+ breast cancer cells have the ability to utilize ammonia sources from their surroundings in order to produce amino acids and biomass as these ER+ breast cancer cells have elevated levels of GS and GDH with respect to other breast cancer histotypes.

GDH was elevated in ER+ luminal cancer cells and the quiescent epithelial cells in organoid culture

However proliferative cells were dependent on transaminases, which transfers nitrogen from glutamate to pyruvate or oxaloacetate to form a-ketoglutarate and alanine or aspartate. a-ketoglutarate is further metabolized in the citric acid cycle.

Figure 1. Reductive amination and transamination reactions of glutamic acid. Source http://www.biologydiscussion.com/organism/metabolism-organism/incorporation-of-ammonia-into-organic-compounds/50870

Spinelli et al. showed GDH is necessary for ammonia reductive incorporation into a-ketoglutarate and also required for ER+ breast cancer cell growth in immunocompromised mice.

In addition, as commented by Dr. Dang in his associated Perspectives article, (quotes indent)

The metabolic tumor microenvironment produced by resident cells, such as fibroblasts and macrophages, can create an immunosuppressive environment ²⁰. Hence, it will be of great interest to further understand whether products such as ammonia could affect tumor immunity or induce autophagy (end quote indent)

Figure 2. Tumor ammonia recycling. Source: From Chi V. Dang Feeding Frenzy for cancer cells. Rights from RightsLink (copyright.com)

Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass

Jessica B. Spinelli¹,², Haejin Yoon¹, Alison E. Ringel¹, Sarah Jeanfavre², Clary B. Clish², Marcia C. Haigis¹ *

1. ¹Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA. 2. ²Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

* ↵*Corresponding author. Email: marcia_haigis@hms.harvard.edu

Science 17 Nov 2017:Vol. 358, Issue 6365, pp. 941-946 DOI: 10.1126/science.aam9305

Abstract

Ammonia is a ubiquitous by-product of cellular metabolism; however, the biological consequences of ammonia production are not fully understood, especially in cancer. We found that ammonia is not merely a toxic waste product but is recycled into central amino acid metabolism to maximize nitrogen utilization. In our experiments, human breast cancer cells primarily assimilated ammonia through reductive amination catalyzed by glutamate dehydrogenase (GDH); secondary reactions enabled other amino acids, such as proline and aspartate, to directly acquire this nitrogen. Metabolic recycling of ammonia accelerated proliferation of breast cancer. In mice, ammonia accumulated in the tumor microenvironment and was used directly to generate amino acids through GDH activity. These data show that ammonia is not only a secreted waste product but also a fundamental nitrogen source that can support tumor biomass.

References

1 Strickaert, A. et al. Cancer heterogeneity is not compatible with one unique cancer cell metabolic map. Oncogene 36, 2637-2642, doi:10.1038/onc.2016.411 (2017).

2 Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115-118, doi:10.1038/nature24057 (2017).

3 Mashimo, T. et al. Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell 159, 1603-1614, doi:10.1016/j.cell.2014.11.025 (2014).

4 Sousa, C. M. et al. Erratum: Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature 540, 150, doi:10.1038/nature19851 (2016).

5 Sousa, C. M. et al. Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature 536, 479-483, doi:10.1038/nature19084 (2016).

6 Commisso, C. et al. Macropinocytosis of protein is an amino acid supply route in Ras-transformed cells. Nature 497, 633-637, doi:10.1038/nature12138 (2013).

7 Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57-70 (2000).

8 Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646-674, doi:10.1016/j.cell.2011.02.013 (2011).

9 Spinelli, J. B. et al. Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass. Science 358, 941-946, doi:10.1126/science.aam9305 (2017).

10 Dang, C. V. Feeding frenzy for cancer cells. Science 358, 862-863, doi:10.1126/science.aaq1070 (2017).

11 Smith, T. J. & Stanley, C. A. Untangling the glutamate dehydrogenase allosteric nightmare. Trends in biochemical sciences 33, 557-564, doi:10.1016/j.tibs.2008.07.007 (2008).

12 Metallo, C. M. et al. Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. Nature 481, 380-384, doi:10.1038/nature10602 (2011).

13 Garrett, M. et al. Metabolic characterization of isocitrate dehydrogenase (IDH) mutant and IDH wildtype gliomaspheres uncovers cell type-specific vulnerabilities. Cancer & metabolism 6, 4, doi:10.1186/s40170-018-0177-4 (2018).

14 Calvert, A. E. et al. Cancer-Associated IDH1 Promotes Growth and Resistance to Targeted Therapies in the Absence of Mutation. Cell reports 19, 1858-1873, doi:10.1016/j.celrep.2017.05.014 (2017).

15 Sciacovelli, M. & Frezza, C. Metabolic reprogramming and epithelial-to-mesenchymal transition in cancer. The FEBS journal 284, 3132-3144, doi:10.1111/febs.14090 (2017).

16 Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739-744, doi:10.1038/nature08617 (2009).

17 Gross, S. et al. Cancer-associated metabolite 2-hydroxyglutarate accumulates in acute myelogenous leukemia with isocitrate dehydrogenase 1 and 2 mutations. The Journal of experimental medicine 207, 339-344, doi:10.1084/jem.20092506 (2010).

18 Ward, P. S. et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer cell 17, 225-234, doi:10.1016/j.ccr.2010.01.020 (2010).

19 Rendina, A. R. et al. Mutant IDH1 enhances the production of 2-hydroxyglutarate due to its kinetic mechanism. Biochemistry 52, 4563-4577, doi:10.1021/bi400514k (2013).

20 Zhang, X. et al. IDH mutant gliomas escape natural killer cell immune surveillance by downregulation of NKG2D ligand expression. Neuro-oncology 18, 1402-1412, doi:10.1093/neuonc/now061 (2016).

LIVE – OCTOBER 16 – DAY 1- Koch Institute Immune Engineering Symposium 2017, MIT, Kresge Auditorium

Posted in Adaptive Immune Response to Biomaterials and Tissue Repair, Biological Engineering, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer-Immune Interactions, combination immunotherapies., Conference Coverage with Social Media, Drug Carrier Design, Efficacy of Anti-Tumor T Cells, Engineering Better T Cells, Engineering Enhanced Cancer Vaccines, Human Antibody Response, Human Naive B Cell Repertoire, Inflammatory Pathophysiology, Synthetic Immunology: Hacking Immune Cells, Systemic Inflammatory Response Related Disorders, Tissue Microenvironment, tumor microenvironment on October 14, 2017| Leave a Comment »

LIVE – OCTOBER 16 – DAY 1- Koch Institute Immune Engineering Symposium 2017, MIT, Kresge Auditorium

Reporter: Aviva Lev-Ari, PhD, RN

Image Source:Koch Institute

Koch Institute

The Immune System, Stress Signaling, Infectious Diseases and Therapeutic Implications: VOLUME 2: Infectious Diseases and Therapeutics and VOLUME 3: The Immune System and Therapeutics (Series D: BioMedicine & Immunology) Kindle Edition – on Amazon.com since September 4, 2017

https://www.amazon.com/dp/B075CXHY1B

SYMPOSIUM SCHEDULE

OCTOBER 16 – DAY 1

7:00 – 8:15 Registration

8:15 – 8:30Introductory Remarks
Darrell Irvine | MIT, Koch Institute; HHMI

Stimulating the Immune system not only sustaining it for therapies

K. Dane Wittrup | MIT, Koch Institute

8:30 – 9:45Session I
Moderator: Douglas Lauffenburger | MIT, Biological Engineering and Koch Institute

Garry P. Nolan – Stanford University School of Medicine
Pathology from the Molecular Scale on Up

Intracellular molecules,
how molecules are organized to create tissue
Meaning from data Heterogeneity is an illusion: Order in Data ?? Cancer is heterogeneous, Cells in suspension – number of molecules
System-wide changes during Immune Response (IR)
Untreated, Ineffective therapy, effective therapy
Days 3-8 Tumor, Lymph node…
Variation is a Feature – not a bug: Effective therapy vs Ineffective – intercellular modules – virtual neighborhoods
ordered by connectivity: very high – CD4 T-cells, CD8 T-cels, moderate, not connected
Landmark nodes, Increase in responders
CODEX: Multiples epitome detection
Adaptable to proteins & mRNA
Rendering antibody staining via removal to neighborhood mapping
Human tonsil – 42 parameters: CD7, CD45, CD86,
Automated Annotations of tissues: F, P, V,
Normal BALBs
Marker expression defined by the niche: B220 vs CD79
Marker expression defines the niche
Learn neighborhoods and Trees
Improving Tissue Classification and staining – Ce3D – Tissue and Immune Cells in 3D
Molecular level cancer imaging
Proteomic Profiles: multi slice combine
Theory is formed to explain 3D nuclear images of cells – Composite Ion Image, DNA replication
Replication loci visualization on DNA backbone – nascent transcriptome – bar code of isotopes – 3D 600 slices
use CRISPR Cas9 for Epigenetics

Susan Napier Thomas – Georgia Institute of Technology
Transport Barriers in the Tumor Microenvironment: Drug Carrier Design for Therapeutic Delivery to Sentinel Lymph Nodes

Lymph Nodes important therapeutics target tissue
Lymphatic flow support passive and active antigen transport to lymph nodes
clearance of biomolecules and drug formulations: Interstitial transport barriers influence clearance: Arteriole to Venule –
Molecular tracers to analyze in vivo clearance mechanisms and vascular transport function
quantifying molecular clearance and biodistribution
Lymphatic transport increases tracer concentrations within dLN by orders of magnitude
Melanoma growth results in remodeled tumor vasculature
passive transport via lymphatic to dLN sustained in advanced tumors despite abrogated cell trafficking
Engineered biomaterial drug carriers to enhance sentinel lymph node-drug delivery: facilitated by exploiting lymphatic transport
TLR9 ligand therapeutic tumor in situ vaccination – Lymphatic-draining CpG-NP enhanced
Sturcutral and Cellular barriers: transport of particles is restriced by
Current drug delivery technology: lymph-node are undrugable
Multistage delivery platform to overcome barriers to lymphatic uptake and LN targeting
nano particles – OND – Oxanorbornade OND Time sensitive Linker synthesized large cargo – NP improve payload
OND release rate from nanoparticles changes retention in lymph nodes – Axilliary-Brachial delivery
Two-stage OND-NP delivery and release system dramatically – OND acumulate in lymphocyte
delivers payload to previously undraggable lymphe tissue
improved drug bioactivity – OND-NP eliminate LN LYMPHOMAS
Engineered Biomaterials

Douglas Lauffenburger – MIT, Biological Engineering and Koch Institute
Integrative Multi-Omic Analysis of Tissue Microenvironment in Inflammatory Pathophysiology

How to intervene, in predictive manner, in immunesystem-associated complex diseases
Understand cell communication beteen immune cells and other cells, i.e., tumor cells
Multi-Variate in Vivo – System Approach: Integrative Experiment & COmputational Analysis
Cell COmmunication & Signaling in CHronic inflammation – T-cell transfer model for colitis
COmparison of diffrential Regulation (Tcell transfer-elicited vs control) anong data types – relying solely on mRNA can be misleading
Diparities in differential responses to T cell transfer across data types yield insights concerning broader multi-organ interactions
T cell transfer can be ascertained and validated by successful experimental test
Cell COmmunication in Tumor MIcro-Environment — integration of single-cell transcriptomic data and protein interaction
Standard Cluster Elucidation – Classification of cell population on Full gene expression Profiles using Training sets: Decision Tree for Cell Classification
Wuantification of Pairwise Cell-Cell Receptor/Ligand Interactions: Cell type Pairs vs Receptor/Ligand Interaction
Pairwise Cell-Cell Receptor/Ligand Interactions
Calculate strength of interaction and its statistical significance
How the interaction is related to Phenotypic Behaviors – tumor growth rate, MDSC levels,
Correlated the Interactions translated to Phynotypic behavior for Therapeutic interventions (AXL via macrophage and fibroblasts)
Mouth model translation to Humans – New machine learning approach
Pathways, false negative, tumor negative expression
Molecular vs Phynotypical expression
Categories of inter-species translation
Semi-supervised Learning ALgorithms on Transcriptomic Data can ascertain Key Pathways/Processes in Human IBD from mapping mouse IBD

9:45 – 10:15 Break

10:15 – 11:30Session II
Moderator: Tyler Jacks | MIT, Koch Institute; HHMI

Tyler Jacks – MIT, Koch Institute; HHMI
Using Genetically Engineered Mouse Models to Probe Cancer-Immune Interactions

Utility of genetically-engineered mouse models of Cancer:

Immune Response (IR),
Tumor0immune microenvironment

Lung adenocarcinoma – KRAS mutation: Genetically-engineered model, applications: CRISPR, genetic interactions
Minimal Immune response to KP lung tumors: H&E, T cells (CD3), Bcells (B220) for Lenti-x 8 weeks
Exosome sequencing : Modeling loss-and gain-of-function mutations in Lung Cancer by CRISPR-Cas9 – germline – tolerance in mice, In vivo CRISPR-induced knockout of Msh2
Signatures of MMR deficient
Mutation burden and response to Immunotherapy (IT)
Programmed neoantigen expression – robust infiltration of T cells (evidence of IR)
Immunosuppression – T cell rendered ineffective
Lymphoid infiltration: Acute Treg depletion results in T cell infiltration — this depletion causes autoimmune response
Lung Treg from KP tumor-bearing mice have a distinct transcriptional heterogeneity through single cell mRNA sequencing
KP, FOXP3+, CD4
Treg from no existent to existance, Treg cells increase 20 fold =>>> Treg activation and effectiveness
Single cells cluster by tissue and cell type: Treg, CD4+, CD8+, Tetramer-CD4+
ILrl1/II-33r unregulated in Treg at late time point
Treg-specific deletion of IL-33r results in fewer effector Tregs in Tumor-bearing lungs
CD8+ T cell infiltration
Tetramer-positive T cells cluster according to time point: All Lung CD8+ T cells
IR is not uniform functional differences – Clones show distinct transcriptional profiles
Different phynotypes Exhaustive signature
CRISPR-mediated modulation of CD8 T cell regulatory genes
Genetic dissection of the tumor-immune microenvironment
Single cell analysis, CRISPR – CRISPRa,i, – Drug development

Wendell Lim – University of California, San Francisco

Synthetic Immunology: Hacking Immune Cells

Precision Cell therapies – engineered by synthetic biology
Anti CD19 – drug approved
CAR-T cells still face major problems

success limited to B cells cancers = blood vs solid tumors
adverse effects
OFF-TUMOR effects

Cell engineering for Cancer Therapy: User remote control (drug) – user control safety
Cell Engineering for TX

new sensors – decision making for
tumor recognition – safety,
Cancer is a recognition issue

How do we avoid cross-reaction with bystader tissue (OFF TISSUE effect)
Tumor recognition: More receptors & integration
User Control
synthetic NOTCH receptors (different flavors of synNotch) – New Universal platform for cell-to -cell recognition: Target molecule: Extracellular antigen –>> transciptional instruction to cell
nextgen T cell: Engineer T cell recognition circuit that integrates multiple inputs: Two receptors – two antigen priming circuit
UNARMED: If antigen A THEN receptor A activates CAR
“Bystander” cell single antigen vs “tumor” drug antigen
Selective clearance of combinatorial tumor – Boulian formulation, canonical response
Cell response: Priming –>> Killing: Spatial & Temporal choreographed cell
CAR expression while removed from primed cells deminished
Solid Tumor: suppress cell microenvironment: Selected response vs non-natural response
Immune stimulator IR IL2, IL12, flagellin in the payload — Ourcome: Immune enhancement “vaccination”
Immune suppression – block
Envision ideal situation: Unarmed cells
FUTURE: identify disease signatures and vulnerabilities – Precision Medicine using Synthetic Biology

Darrell Irvine – MIT, Koch Institute; HHMI
Engineering Enhanced Cancer Vaccines to Drive Combination Immunotherapies

Vaccine to drive IT
Intervening in the cancer-immunity cycle – Peptide Vaccines
poor physiology of solute transport to tissue
endogenous albumin affinity – Lymphe Node dying
Designing Albumin-hitchhiking vaccines
Amphiphile-vaccine enhance uptake in lymph nodes in small and large animal models
soluble vaccine vs Amphiphile-vaccine
DIRECTING Vaccines to the Lymph nodes
amph-peptide antigen: Prime, booster, tetramer
albimin-mediated LN-targeting of both antigen and adjuvant maximizes IR
Immuno-supressed microenvironment will not be overcome by vaccines
Replacing adoptive T cell transfer with potent vaccine
exploiting albumin biology for mucosal vaccine delivery by amph-vaccines
Amph-peptides and -adjuvants show enhanced uptake/retention in lung tissue
Enhancing adoptive T cell therapy: loss of T cell functionality, expand in vivo
boost in vivo enhanced adoptive T cell therapy
CAR-T cells: Enable T cells to target any cell surface protein
“Adaptor”-targeting CAR-T cells to deal with tumor cell heterogeneity
Lymph node-targeting Amph as CAR T booster vaccine: prining, production of cytokines
Boosting CAR T with amph-caccines: anti FITC CAR-T by DSPE=PEG-FITC coated
Targeting FITC to lymph node antigen presenting cells
Modulatory Macrophages
Amph-FITC expands FITC-CAR T cells in vivo – Adjuvant is needed
Hijacking albumin’s natural trafficking pathway

11:30 – 1:00 Lunch Break

1:00 – 2:15Session III
Moderator: Darrell Irvine | MIT, Koch Institute; HHMI

Nicholas P. Restifo – National Cancer Institute
Extracellular Potassium Regulates Epigenetics and Efficacy of Anti-Tumor T Cells

Why T cell do not kill Cancer cells?

co-inhibition
hostile tumor microenvironment

CAR T – does not treat solid tumors

Somatic mutation

resistence of T cell based IT due to loss of function mutations
Can other genes be lost?

CRISPR Cas9 – used to identify agents – GeCKOv2 Human library

Two cell-type (2CT) CRISPR assay system for genome-wide mutagenesis

work flow for genome-scale SRISPR mutagenesis profiling of genes essential for T cell mediate cytosis
sgRNA enrichment at the individual gene level by multiple methods:

subunits of the MHC Class I complex
CRISPR mutagenesis cut germline

Measutring the generalizability of resistance mechanism and mice in vivo validation
Validation of top gene candidates using libraries: MART-1
Checkpoint blockade: cells LOF causes tumor growth and immune escape
Weird genesL Large Ribisomal Subunit Proteins are nor all essential for cell survival
Bias in enrichment of 60S vs 40S
Novel elements of MHC class I antigen processing and presentation
Association of top CRISPR hits with response rates to IT – antiCTLA-4
CRISPR help identify novel regulators of T cells
Analyzed sgRNA – second rarest sgRNA for gene BIRC2 – encoded the Baculoviral Inhibitor
Drugs that inhibit BIRC2
How T cells can kill tumor cells more efficiently
p38kiaseas target for adoptive immunotherapy
FACS-based – Mapk14
Potent targets p38 – Blockade PD-1 or p38 ??
p38 signaling: Inhibition augments expansion and memory-marked human PBMC and TIL cells, N. P. Restifo
Tumor killing capacity of human CD19-specific, gene engineered T cells

Jennifer Elisseeff – Johns Hopkins University
The Adaptive Immune Response to Biomaterials and Tissue Repair

design scafolds, tissue-specific microenvironment
clinical translation of biosynthetic implants for soft tissue reconstruction
Local environment affects biomaterials: Epidermis, dermis
CD4+ T cells
Immune system – first reponders to materials: Natural or Synthetic
Biological (ECM) scaffolds to repair muscle injury
Which immune cells enter the WOUND?
ECM alters Macrophages: CD86, CD206
Adaptive system impact on Macrophages: CD86
mTOR signaling pathway M2 depend on Th2 Cells in regeneration of cell healing of surgical wounds
Systemic Immunological changes
Is the response antigen specific? – IL-4 expression in ILN,
Tissue reconstruction Clinical Trial: FDA ask to look at what cells infiltrate the scaffold
Trauma/biomaterial response – Injury induction of Senescence, anti apoptosis
Injury to skin or muscle
Is pro-regenerative environment (Th2/M2) pro-tumorigenic?
SYNTHETIC Materials for scafolds
Biomaterials and Immunology

Immune response to bioscafolds
environment modulate the immune system

Regenerative Immunetherapy

Marcela Maus – Massachusetts General Hospital

Engineering Better T Cells

Comparing CD19 CARs for Leukemia – anti-CD19- directed CAR T cells with r/r B-cell ALL – age 3-25 – FDA approved Novartis tisagenlecleucel – for pediatric r/r/ ALL
Phase II in diffuse large B cell lymphoma. Using T cells – increases prospects for cure
Vector retroviral – 30 day expression
measuring cytokines release syndrome: Common toxicity with CAR 19
neurological toxicity, B-cell aplagia
CART issues with heme malignancies

decrease cytokine release
avoid neurological toxicity – homing
new targets address antigene escape variants – Resistance, CD19 is shaded, another target needed
B Cell Maturation Antigen (BCMA) Target
Bluebird Bio: Response duratio up to 54 weeks – Active dose cohort
natural ligand CAR based on April
activated in response to TACI+ target cells – APRIL-based CARs but not BCMA-CAR is able to kill TACI+ target cells

Hurdles for Solid Tumors

Specific antigen targets
tumor heterogeneity
inhibitory microenvironment

CART in Glioblastoma

rationale for EGFRvIII as therapeutic target
Preclinical Studies & Phase 1: CAR t engraft, not as highly as CD19
Upregulation of immunosuppression and Treg infiltrate in CART EGFRvIII as therapeutic target, Marcela Maus

What to do differently?

2:15 – 2:45 Break

2:45 – 4:00 Session IV
Moderator: Arup K. Chakraborty | MIT, IMES

Laura Walker – Adimab, LLC
Molecular Dissection of the Human Antibody Response to Respiratory Syncytial Virus

prophylactic antibody is available
Barriers for development of Vaccine
Prefusion and Postfusion RSV structures
Six major antigenic sites on RSV F
Blood samples Infants less 6 month of age and over 6 month: High abundance RSV F -specific memory B Cells are group less 6 month

Arup K. Chakraborty – MIT, Institute for Medical Engineering & Science
How to Hit HIV Where it Hurts

antibody – Model IN SILICO
Check affinity of each Ab for the Seaman panel of strain
Breadth of coverage
immmunize with cocktail of variant antigens
Mutations on Affinity Maturation: Molecular dynamics
bnAb eveolution: Hypothesis – mutations evolution make the antigen binding region more flexible,
Tested hypothesisi: carrying out affinity maturation – LOW GERMLINE AFFINITY TO CONSERVE RESIDUES IN 10,000 trials, acquire the mutation (generation 300)

William Schief – The Scripps Research Institute
HIV Vaccine Design Targeting the Human Naive B Cell Repertoire

HIV Envelope Trimer Glycan): the Target of neutralizing Antibodies (bnAbs)
Proof of principle for germline-targeting: VRC)!-class bnAbs
design of a nanoparticle
can germline -targeting innumogens prime low frequency precursors?
Day 14 day 42 vaccinate
Precursor frequency and affinity are limiting for germline center (GC) entry at day 8
Germline-targeting immunogens can elicit robust, high quality SHM under physiological conditions of precursor frequency and affinity at day 8, 16, 36
Germline-targeting immunogens can lead to production of memory B cells

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All Cancer Cells Are Not Created Equal: Some Cell Types Control Continued Tumor Growth, Others Prepare the Way for Metastasis

Posted in Cancer - General, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, cancer metabolism, Circulating Tumor Cells (CTC), tumor microenvironment, tagged American Cancer Society, Cancer - General, Harvard Medical School, Langenau, Massachusetts General Hospital, National Institute of Health, RMS on May 17, 2012| 1 Comment »

Reporter: Prabodh Kandala, PhD

Screen Shot 2021-07-19 at 6.21.05 PM

Word Cloud By Danielle Smolyar

A study from Massachusetts General Hospital (MGH) researchers suggests that specific populations of tumor cells have different roles in the process by which tumors make new copies of themselves and grow. In their report in the May 15 issue of Cancer Cell, researchers identify a tumor-propagating cell required for the growth of a pediatric muscle tumor in a zebrafish model and also show that another, more-differentiated tumor cell must first travel to sites of new tumor growth to prepare an environment that supports metastatic growth.

“Most investigators have thought that tumor-propagating cells — what are sometimes called cancer stem cells — must be the first colonizing cells that travel from the primary tumor to start the process of local invasion and metastasis, but in this model, this is simply not the case,” says David Langenau, PhD, of the MGH Department of Pathology and Center for Cancer Research, who led the study. “Instead, the colonizing cells lack the ability to divide and instead prime newly infiltrated regions for the eventual recruitment of slow-moving cancer stem cells. It will be important to test how broadly this phenomenon is found in a diversity of animal and human cancers.”

Langenau’s team has long been using zebrafish to study rhabdomyosarcoma (RMS), an aggressive pediatric cancer. In embryonic zebrafish, RMS can develop within 10 days, and since the tiny fish are transparent at that stage, fluorescent markers attached to particular cellular proteins can easily be imaged. The current study used these properties to monitor how specific populations of tumor cells develop and their role in initiating new tumor growth.

Previous research from the MGH team had discovered that RMS cells expressing marker proteins also seen on muscle progenitor cells had significantly more tumor-propagating potential than did other tumor cells. Fluorescently labeling proteins associated with different stages of cellular differentiation revealed distinct populations of RMS cells in the zebrafish model. Cells expressing the progenitor cell marker myf5, were labeled green, and those expressing myogenin, a marker of mature muscle cells, were labeled red.

In a series of experiments, the research team confirmed that myf5-expressing RMS cells had powerful tumor-propagating potential, but the ability to visualize how tumor cells move in living fish produced a surprising observation. While myf5-expressing cells largely remained within the primary tumor itself, myogenin-expressing RMS cells easily moved out from the tumor, entering the vascular system and passing through usually impenetrable layers of collagen. Only after the more-differentiated but non-proliferative myogenin-expressing cells had colonized an area did the myf5-expressing tumor-propagating cells appear and start the growth a new tumor. Imaging the labeled tumor cells also revealed that different cellular populations tended to cluster in different areas of later-stage tumors.

“Our direct in-vivo imaging studies are the first to suggest such diverse cellular functions in solid tumors, based on differentiation and the propensity for self-renewal,” says Myron Ignatius, PhD, of MGH Pathology and Center for Cancer Research, the study’s first author. “I think we will find that this kind of division of labor is a common theme in cancer, especially given that the vast majority of cells within a tumor are not tumor-propagating cells. We suspect there will be molecularly defined populations that make niches for tumor-propagating cells, secrete factors to recruit vasculature and create boundaries to suppress immune cell invasion.”

Langenau adds, “Division of labor is a new and emerging concept in cancer research that we hope will lead to new targets for rationally designed therapies. In rhabdomyosarcoma it will be important to target both the tumor-propagating cells and the highly migratory colonizing cells for destruction — a major focus of ongoing studies in our group.” Langenau is an assistant professor of Genetics at Harvard Medical School and a principal faculty member at the Harvard Stem Cell Institute.

Additional co-authors author of the Cancer Cell article are Eleanor Chen, Adam Fuller, Ines Tenente Rayn Clagg, Sali Liu, Jessica Blackburn, MGH Pathology and Center for Cancer Research; Andrew Rosenberg, and Petur Neilsen, MGH Pathology; Natalie Elpek and Thorsten Mempel, MGH Center for Immunology and Inflammatory Diseases; and Corinne Linardic, Duke University Medical Center. The study was supported by grants from the National Institute of Health, the Alex’s Lemonade Stand Foundation, the Sarcoma Foundation of America, the American Cancer Society and the Harvard Stem Cell Institute.

http://www.sciencedaily.com/releases/2012/05/120515131756.htm