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Posts Tagged ‘tumor microenvironment’

Yet another Success Story: Machine Learning to predict immunotherapy response

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Immune-checkpoint blockers (ICBs) immunotherapy appears promising for various cancer types, offering a durable therapeutic advantage. Only a number of cases with cancer respond to this therapy. Biomarkers are required to adequately predict the responses of patients. This article evaluates this issue utilizing a system method to characterize the immune response of the anti-tumor based on the entire tumor environment. Researchers build mechanical biomarkers and cancer-specific response models using interpretable machine learning that predict the response of patients to ICB.

The lymphatic and immunological systems help the body defend itself by combating. The immune system functions as the body’s own personal police force, hunting down and eliminating pathogenic baddies.

According to Federica Eduati, Department of Biomedical Engineering at TU/e, “The immune system of the body is quite adept at detecting abnormally behaving cells. Cells that potentially grow into tumors or cancer in the future are included in this category. Once identified, the immune system attacks and destroys the cells.”

Immunotherapy and machine learning are combining to assist the immune system solve one of its most vexing problems: detecting hidden tumorous cells in the human body.

It is the fundamental responsibility of our immune system to identify and remove alien invaders like bacteria or viruses, but also to identify risks within the body, such as cancer. However, cancer cells have sophisticated ways of escaping death by shutting off immune cells. Immunotherapy can reverse the process, but not for all patients and types of cancer. To unravel the mystery, Eindhoven University of Technology researchers used machine learning. They developed a model to predict whether immunotherapy will be effective for a patient using a simple trick. Even better, the model outperforms conventional clinical approaches.

The outcomes of this research are published on 30th June, 2021 in the journal Patterns in an article entitled “Interpretable systems biomarkers predict response to immune-checkpoint inhibitors”.

The Study

  • Characterization of the tumor microenvironment from RNAseq and prior knowledge
  • Multi-task machine-learning models for predicting antitumor immune responses
  • Identification of cancer-type-specific, interpretable biomarkers of immune responses
  • EaSIeR is a tool to predict biomarker-based immunotherapy response from RNA-seq

“Tumor also contains multiple types of immune and fibroblast cells which can play a role in favor of or anti-tumor, and communicates among themselves,” said Oscar Lapuente-Santana, a researcher doctoral student in the computational biology group. “We had to learn how complicated regulatory mechanisms in the micro-environment of the tumor affect the ICB response. We have used RNA sequencing datasets to depict numerous components of the Tumor Microenvironment (TME) in a high-level illustration.”

Using computational algorithms and datasets from previous clinical patient care, the researchers investigated the TME.

Eduati explained

While RNA-sequencing databases are publically available, information on which patients responded to ICB therapy is only available for a limited group of patients and cancer types. So, to tackle the data problem, we used a trick.

All 100 models learned in the randomized cross-validation were included in the EaSIeR tool. For each validation dataset, we used the corresponding cancer-type-specific model: SKCM for the melanoma Gide, Auslander, Riaz, and Liu cohorts; STAD for the gastric cancer Kim cohort; BLCA for the bladder cancer Mariathasan cohort; and GBM for the glioblastoma Cloughesy cohort. To make predictions for each job, the average of the 100 cancer-type-specific models was employed. The predictions of each dataset’s cancer-type-specific models were also compared to models generated for the remaining 17 cancer types.

From the same datasets, the researchers selected several surrogate immunological responses to be used as a measure of ICB effectiveness.

Lapuente-Santana stated

One of the most difficult aspects of our job was properly training the machine learning models. We were able to fix this by looking at alternative immune responses during the training process.

Some of the researchers employed the machine learning approach given in the paper to participate in the “Anti-PD1 Response Prediction DREAM Challenge.”

DREAM is an organization that carries out crowd-based tasks with biomedical algorithms. “We were the first to compete in one of the sub-challenges under the name cSysImmunoOnco team,” Eduati remarks.

The researchers noted,

We applied machine learning to seek for connections between the obtained system-based attributes and the immune response, estimated using 14 predictors (proxies) derived from previous publications. We treated these proxies as individual tasks to be predicted by our machine learning models, and we employed multi-task learning algorithms to jointly learn all tasks.

The researchers discovered that their machine learning model surpasses biomarkers that are already utilized in clinical settings to evaluate ICB therapies.

But why are Eduati, Lapuente-Santana, and their colleagues using mathematical models to tackle a medical treatment problem? Is this going to take the place of the doctor?

Eduati explains

Mathematical models can provide an overview of the interconnection between individual molecules and cells and at the same time predicting a particular patient’s tumor behavior. This implies that immunotherapy with ICB can be personalized in a patient’s clinical setting. The models can aid physicians with their decisions about optimum therapy, it is vital to note that they will not replace them.

Furthermore, the model aids in determining which biological mechanisms are relevant for the biological response.

The researchers noted

Another advantage of our concept is that it does not need a dataset with known patient responses to immunotherapy for model training.

Further testing is required before these findings may be implemented in clinical settings.

Main Source:

Lapuente-Santana, Ó., van Genderen, M., Hilbers, P. A., Finotello, F., & Eduati, F. (2021). Interpretable systems biomarkers predict response to immune-checkpoint inhibitorsPatterns, 100293. https://www.cell.com/patterns/pdfExtended/S2666-3899(21)00126-4

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis

Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/02/20/inhibitory-cd161-receptor-identified-in-glioma-infiltrating-t-cells-by-single-cell-analysis-2/

Immunotherapy may help in glioblastoma survival

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

https://pharmaceuticalintelligence.com/2019/03/16/immunotherapy-may-help-in-glioblastoma-survival/

Deep Learning for In-silico Drug Discovery and Drug Repurposing: Artificial Intelligence to search for molecules boosting response rates in Cancer Immunotherapy: Insilico Medicine @John Hopkins University

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/07/17/deep-learning-for-in-silico-drug-discovery-and-drug-repurposing-artificial-intelligence-to-search-for-molecules-boosting-response-rates-in-cancer-immunotherapy-insilico-medicine-john-hopkins-univer/

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/05/04/machine-learning-ml-in-cancer-prognosis-prediction-helps-the-researcher-to-identify-multiple-known-as-well-as-candidate-cancer-diver-genes/

AI System Used to Detect Lung Cancer

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

Cancer detection and therapeutics

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/05/02/cancer-detection-and-therapeutics/

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Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

Register for FREE at https://www.aacr.org/

uesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Bioinformatics and Systems Biology

The Clinical Proteomic Tumor Analysis Consortium: Resources and Data Dissemination

This session will provide information regarding methodologic and computational aspects of proteogenomic analysis of tumor samples, particularly in the context of clinical trials. Availability of comprehensive proteomic and matching genomic data for tumor samples characterized by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) program will be described, including data access procedures and informatic tools under development. Recent advances on mass spectrometry-based targeted assays for inclusion in clinical trials will also be discussed.

Amanda G Paulovich, Shankha Satpathy, Meenakshi Anurag, Bing Zhang, Steven A Carr

Methods and tools for comprehensive proteogenomic characterization of bulk tumor to needle core biopsies

Shankha Satpathy
  • TCGA has 11,000 cancers with >20,000 somatic alterations but only 128 proteins as proteomics was still young field
  • CPTAC is NCI proteomic effort
  • Chemical labeling approach now method of choice for quantitative proteomics
  • Looked at ovarian and breast cancers: to measure PTM like phosphorylated the sample preparation is critical

 

Data access and informatics tools for proteogenomics analysis

Bing Zhang
  • Raw and processed data (raw MS data) with linked clinical data can be extracted in CPTAC
  • Python scripts are available for bioinformatic programming

 

Pathways to clinical translation of mass spectrometry-based assays

Meenakshi Anurag

·         Using kinase inhibitor pulldown (KIP) assay to identify unique kinome profiles

·         Found single strand break repair defects in endometrial luminal cases, especially with immune checkpoint prognostic tumors

·         Paper: JNCI 2019 analyzed 20,000 genes correlated with ET resistant in luminal B cases (selected for a list of 30 genes)

·         Validated in METABRIC dataset

·         KIP assay uses magnetic beads to pull out kinases to determine druggable kinases

·         Looked in xenografts and was able to pull out differential kinomes

·         Matched with PDX data so good clinical correlation

·         Were able to detect ESR1 fusion correlated with ER+ tumors

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Survivorship

Artificial Intelligence and Machine Learning from Research to the Cancer Clinic

The adoption of omic technologies in the cancer clinic is giving rise to an increasing number of large-scale high-dimensional datasets recording multiple aspects of the disease. This creates the need for frameworks for translatable discovery and learning from such data. Like artificial intelligence (AI) and machine learning (ML) for the cancer lab, methods for the clinic need to (i) compare and integrate different data types; (ii) scale with data sizes; (iii) prove interpretable in terms of the known biology and batch effects underlying the data; and (iv) predict previously unknown experimentally verifiable mechanisms. Methods for the clinic, beyond the lab, also need to (v) produce accurate actionable recommendations; (vi) prove relevant to patient populations based upon small cohorts; and (vii) be validated in clinical trials. In this educational session we will present recent studies that demonstrate AI and ML translated to the cancer clinic, from prognosis and diagnosis to therapy.
NOTE: Dr. Fish’s talk is not eligible for CME credit to permit the free flow of information of the commercial interest employee participating.

Ron C. Anafi, Rick L. Stevens, Orly Alter, Guy Fish

Overview of AI approaches in cancer research and patient care

Rick L. Stevens
  • Deep learning is less likely to saturate as data increases
  • Deep learning attempts to learn multiple layers of information
  • The ultimate goal is prediction but this will be the greatest challenge for ML
  • ML models can integrate data validation and cross database validation
  • What limits the performance of cross validation is the internal noise of data (reproducibility)
  • Learning curves: not the more data but more reproducible data is important
  • Neural networks can outperform classical methods
  • Important to measure validation accuracy in training set. Class weighting can assist in development of data set for training set especially for unbalanced data sets

Discovering genome-scale predictors of survival and response to treatment with multi-tensor decompositions

Orly Alter
  • Finding patterns using SVD component analysis. Gene and SVD patterns match 1:1
  • Comparative spectral decompositions can be used for global datasets
  • Validation of CNV data using this strategy
  • Found Ras, Shh and Notch pathways with altered CNV in glioblastoma which correlated with prognosis
  • These predictors was significantly better than independent prognostic indicator like age of diagnosis

 

Identifying targets for cancer chronotherapy with unsupervised machine learning

Ron C. Anafi
  • Many clinicians have noticed that some patients do better when chemo is given at certain times of the day and felt there may be a circadian rhythm or chronotherapeutic effect with respect to side effects or with outcomes
  • ML used to determine if there is indeed this chronotherapy effect or can we use unstructured data to determine molecular rhythms?
  • Found a circadian transcription in human lung
  • Most dataset in cancer from one clinical trial so there might need to be more trials conducted to take into consideration circadian rhythms

Stratifying patients by live-cell biomarkers with random-forest decision trees

Stratifying patients by live-cell biomarkers with random-forest decision trees

Guy Fish CEO Cellanyx Diagnostics

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Molecular and Cellular Biology/Genetics, Bioinformatics and Systems Biology, Prevention Research

The Wound Healing that Never Heals: The Tumor Microenvironment (TME) in Cancer Progression

This educational session focuses on the chronic wound healing, fibrosis, and cancer “triad.” It emphasizes the similarities and differences seen in these conditions and attempts to clarify why sustained fibrosis commonly supports tumorigenesis. Importance will be placed on cancer-associated fibroblasts (CAFs), vascularity, extracellular matrix (ECM), and chronic conditions like aging. Dr. Dvorak will provide an historical insight into the triad field focusing on the importance of vascular permeability. Dr. Stewart will explain how chronic inflammatory conditions, such as the aging tumor microenvironment (TME), drive cancer progression. The session will close with a review by Dr. Cukierman of the roles that CAFs and self-produced ECMs play in enabling the signaling reciprocity observed between fibrosis and cancer in solid epithelial cancers, such as pancreatic ductal adenocarcinoma.

Harold F Dvorak, Sheila A Stewart, Edna Cukierman

 

The importance of vascular permeability in tumor stroma generation and wound healing

Harold F Dvorak

Aging in the driver’s seat: Tumor progression and beyond

Sheila A Stewart

Why won’t CAFs stay normal?

Edna Cukierman

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

 

 

 

 

 

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

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Engineered Bacteria used as Trojan Horse for Cancer Immunotherapy

Reporter: Irina Robu, PhD

Researchers are using synthetic biology— design and construction of new biological entities such as enzymes, genetic circuits, and cells or the redesign of existing biological systems—is changing medicine leading to innovative solution in molecular-based therapeutics. To address the issue of designing therapies that can induce a potent, anti-tumor immune response researchers at Columbia Engineering and Columbia Irving Medical Center engineered a strain of non-pathogenic bacteria that can colonize tumors in mice. The non-pathogenic bacteria act as Trojan Horse that can lead to complete tumor regression in a mouse model of lymphoma. Their results are currently published in Nature Medicine.

The scientists led by Nicholas Arpaia, used their expertise in synthetic biology and immunology to engineer a strain of bacteria able to grow and multiply in the necrotic core of tumors. The non-pathogenic E. coli are programmed to self-destruct when the bacteria numbers reach a critical threshold, allowing for actual release of therapeutics and averting them from causing havoc somewhere else in the body. Afterward, a small portion of bacteria survive lysis and repopulate the population which allows repeated rounds of drug delivery inside treated tumors.

In the present study, the scientists release a nanobody that targets CD47 protein, which defends cancer cells from being eaten by distinctive immune cells. The mutual effects of bacteria, induced local inflammation within the tumor and the blockage of the CD47 leads to better ingestion and activation of T-cells within the treated tumors. The team deduced that the treatment with their engineered bacteria not only cleared the treated tumors but also reduced the incidence of tumor metastasis.

Before moving to clinical trials, the team is performing proof-of-concept tests, safety and toxicology studies of their immunotherapeutic bacteria in a rand of advanced solid tumor settings in mouse models. They have currently collaborated with Gary Schwartz, deputy director of the Herbert Irving Comprehensive Cancer and have underway a company to translate their promising technology to patients.

SOURCE

Sreyan Chowdhury, Samuel Castro, Courtney Coker, Taylor E. Hinchliffe, Nicholas Arpaia, Tal Danino. Programmable bacteria induce durable tumor regression and systemic antitumor immunity. Nature Medicine, 2019

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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).

 

Other articles on this Open Access Journal on Cancer Metabolism Include:

 

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

 

Accumulation of 2-hydroxyglutarate is not a biomarker for malignant progression of IDH-mutated low grade gliomas

 

 

Protein-binding, Protein-Protein interactions & Therapeutic Implications [7.3]

Is the Warburg effect an effect of deregulated space occupancy of methylome?

Therapeutic Implications for Targeted Therapy from the Resurgence of Warburg ‘Hypothesis’

New Insights on the Warburg Effect [2.2]

The Inaugural Judith Ann Lippard Memorial Lecture in Cancer Research: PI 3 Kinase & Cancer Metabolism

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

Warburg Effect and Mitochondrial Regulation- 2.1.3

Refined Warburg Hypothesis -2.1.2

 

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Metabolic insight into cancer cell survival

Larry H. Bernstein, MD, FCAP, Curator

LPBI

UPDATED 9/26/2021

Periprostatic Adipose Tissue Favors Prostate Cancer Cell Invasion in an Obesity-Dependent Manner: Role of Oxidative Stress

Victor LaurentAurélie TouletCamille AttanéDelphine MilhasStéphanie DauvillierFalek ZaidiEmily ClementMathieu CinatoSophie Le GonidecAdrien GuérardCamille LehuédéDavid GarandeauLaurence NietoEdith Renaud-GabardosAnne-Catherine PratsPhilippe ValetBernard Malavaud and Catherine Muller

Abstract

Prostate gland is surrounded by periprostatic adipose tissue (PPAT), which is increasingly believed to play a paracrine role in prostate cancer progression. Our previous work demonstrates that adipocytes promote homing of prostate cancer cells to PPAT and that this effect is upregulated by obesity. Here, we show that once tumor cells have invaded PPAT (mimicked by an in vitro model of coculture), they establish a bidirectional crosstalk with adipocytes, which promotes tumor cell invasion. Indeed, tumor cells induce adipocyte lipolysis and the free fatty acids (FFA) released are taken up and stored by tumor cells. Incubation with exogenous lipids also stimulates tumor cell invasion, underlining the importance of lipid transfer in prostate cancer aggressiveness. Transferred FFAs (after coculture or exogenous lipid treatment) stimulate the expression of one isoform of the pro-oxidant enzyme NADPH oxidase, NOX5. NOX5 increases intracellular reactive oxygen species (ROS) that, in turn, activate a HIF1/MMP14 pathway, which is responsible for the increased tumor cell invasion. In obesity, tumor-surrounding adipocytes are more prone to activate the depicted signaling pathway and to induce tumor invasion. Finally, the expression of NOX5 and MMP14 is upregulated at the invasive front of human tumors where cancer cells are in close proximity to adipocytes and this process is amplified in obese patients, underlining the clinical relevance of our results.

Implications: Our work emphasizes the key role of adjacent PPAT in prostate cancer dissemination and proposes new molecular targets for the treatment of obese patients exhibiting aggressive diseases.

Introduction

Malignant evolution of solid cancers relies on complex cell-to-cell interactions sustained by a broad network of physical and chemical mediators that constitute the tumor microenvironment (1). Adipose tissue, and its main cellular component, adipocytes, have recently emerged as key actors in solid tumor progression (2). Upon proximal adipose tissue infiltration, a bidirectional crosstalk takes place between invasive tumor cells and adipocytes. Initially described in breast cancer, tumor-surrounding adipocytes exhibit extensive phenotypical changes defined by a decrease in lipid content, a decreased expression of adipocyte markers, and an activated state demonstrated predominantly by the overexpression of proinflammatory cytokines and ECM (extracellular matrix)-related molecules (3). We named these cells cancer-associated adipocytes (CAA; ref. 3). Occurrence of CAAs is not restricted to breast cancer and has been described in a wide range of solid tumors including metastatic ovarian cancer, renal and colon cancers, as well as melanoma (2). In turn, CAAs promote tumor aggressiveness by secreting soluble factors, ECM proteins, and ECM-remodeling enzymes and by modulating tumor cell metabolism (2). The decrease in size and lipid content of tumor-surrounding adipocytes results from a “dedifferentiation” process depending on the reactivation of the Wnt/β-catenin pathway in response to Wnt3a secreted by tumor cells (4) and from induction of lipolysis, the latter process resulting in release of free fatty acids (FFA; refs. 5–7). These FFAs are then taken up, stored in lipid droplets, and used by tumor cells, where they have been described to contribute to tumor progression mainly through modulation of tumor cell metabolism toward fatty acid oxidation (FAO; refs. 5–7). This metabolic symbiosis instilled between cancer cells and tumor-surrounding adipocytes is only beginning to be explored but could provide new therapeutic targets as we recently reviewed in ref. 8. In addition to FAO, FFAs acquired from adipocytes could be used as membrane building blocks and/or signaling lipids and the fate of these lipids may depend on the tumor cell type.

Among the different types of tumors whose close interaction with adipose tissue could influence tumor progression is prostate cancer, the most common malignancy in men in Western countries. The prostate is surrounded by periprostatic adipose tissue (PPAT) and extraprostatic extension into PPAT is a widely acknowledged adverse prognostic factor in prostate cancer and an important determinant of prostate cancer recurrence after treatment (9). The positive association between obesity and aggressive prostate cancer, defined by an increase in local and distant dissemination, is also in favor of a role for adipose tissue in tumor progression (10). We have recently demonstrated that adipocytes from PPAT favor the initial step of PPAT infiltration by secreting the CCL7 chemokine that attracts CCR3-expressing cancer cells, and this process is amplified in obesity (11). Prostate-confined cancer cell migration and invasion may also be promoted at distance by inflammatory cytokines and metalloproteases secreted by PPAT, as well as by adipocyte-derived exosomes (12–14). In contrast to other cancer types such as breast or ovarian cancer (2), the effect of the invaded cancer cells on adipocytes within PPAT has been poorly described. Secretions from PPAT are modified by tumor-conditioned medium with upregulation of osteopontin, TNFα, and IL6, highlighting that, like other adipose depots, it is not inert to tumors (15). Coculture of prostate cancer cells with rat epididymal adipocytes increases growth and changes the morphology of prostate cancer cells (16, 17), but phenotypical changes of adipocytes have not been investigated in these studies. In addition, the mechanisms that govern increased prostate cancer cell aggressiveness in the presence of adipocytes are poorly described and have been mainly attributed to soluble factors (such as IL6; for review, see ref. 18). Finally, lipid transfer has not been demonstrated between periprostatic adipocytes and tumor cells, but only with bone marrow–derived adipocytes present at prostate cancer’s most frequent metastatic site (19, 20). Here, we demonstrate that prostate cancer cells are able to induce a CAA phenotype in vitro and in vivo, and that CAAs, in turn, promote tumor invasion. Lipid transfer between tumor-surrounding adipocytes and cancer cells promotes tumor aggressiveness by inducing oxidative stress in a NADPH oxidase–dependent manner, activating a proinvasive signaling pathway. The overall pathway is amplified in obesity and has been validated in human tumors. This study highlights the importance of lipid transfer in the tumor-promoting effect of adipocytes but also underlines that the consequence of this process is not univocal among all tumor types.

Revised 4/20/2016

AACR 2016: Novel Epigenetic Drug Therapeutics Revealed

http://www.genengnews.com/gen-news-highlights/aacr-2016-epigenetic-drug-therapeutics/81252634/

As the 2016 American Association for Cancer Research meeting begins to downshift toward a close, the presentation sessions certainly did not suffer from a lack of enthusiasm from attendees or high-quality research from presenters. Of particular note was a major symposium that discussed next-generation epigenetic therapeutics.

In the past several years, there have been a variety of epigenetic targets exploited by newly developed drug compounds, many of which have already progressed into clinical trials. Often these compounds will target specific classes of epigenetic regulators like acetylases and histone demethylases, for instance, the small-molecule inhibitors of protein interacting bromodomains—implicated in a diverse range of cancers—and methyltransferase inhibitors, such as lysyl demethylases (KDM).

However, for all of their recently achieved success, researchers are continually searching for increasingly rapid methods to validate epigenetic drug targets. Session Chairperson Udo Oppermann, Ph.D., principal investigator at the University of Oxford, stressed that open access research and continued investigator cooperation were key factors for driving rapid development of novel therapeutics in the field. Anecdotally, Dr. Oppermann noted that if biologists were a bit more like the international cooperative teams of physicists that discovered the Higgs boson or gravitational waves, many biological endeavors would advance rather quickly.

After providing the audience with a brief introduction to the symposium’s topic, Dr. Oppermann described his current research on histone demethylase inhibitors in multiple myeloma and the connection to metabolic pathways. He surmised that tricarboxylic acid (TCA) cycle-derived metabolites can link cellular metabolism to cancer—impacting epigenetic landscapes. Specifically, the TCA intermediates are inhibitors of KDMs, ultimately controlling epigenetic and metabolic regulation.

Furthermore, Dr. Oppermann’s group was able to show that treatment of myeloma cell lines with the potent and specific histone demethylase inhibitor GSK-J4 was able to reverse the Myc-driven metabolic dependency, forcing a selected amino acid depletion. This deficiency led to the integrated stress response and the activation of proapoptotic genes. This work helps to solidify further the potent nature of GSK-J4 in cancer while simultaneously uncovering the metabolic mechanisms that cancer cells employ to keep their overproliferative phenotypes progressing forward.

Next, Tomasz Cierpicki, Ph.D., assistant professor at the University of Michigan, described his work on targeting leukemic stem cells with small-molecule inhibitors of the protein regulator of cytokinesis 1 (PRC1). Dr. Cierpicki took the audience through his research design, which was to target BMI1, an oncogene that determines the proliferative capacity and self-renewal potential of normal and leukemic stem cells. BMI1 has been implicated in a variety of tumors and is essential for the Polycomb Repressive Complex 1 (PRC1). Moreover, BMI1 interacts with the RING1B protein to form an active E3 ubiquitin ligase that targets histone H2A, modifying epigenetic regulation mechanisms.

Dr. Cierpicki’s laboratory looked at inhibitors of the RING1B–BMI1 E3 ligase complex as potential therapeutic agents targeting cancer stem cells. Using an array of techniques from fragment screening to medicinal chemistry, the researchers were able to identify potent compounds that bound to RING1B–BMI1 and inhibit its E3 ubiquitin ligase activity with low micromolar affinities. When testing in vitro, the inhibitors revealed robust downregulation of H2A ubiquitination. Dr. Cierpicki and his colleagues found that the RING1–BMI1 inhibitor blocked the self-renewal capacity of the stem cells and induced cellular differentiation—validating RING ligases as a novel epigenetic drug target.

Finishing up the session was William Sellers, M.D., vice president and global head of oncology for the Novartis Institutes for BioMedical Research. Dr. Sellers’ research is focused on what genes are necessary for epigenetic regulation of cancer and how they are linked to essential metabolic processes. He and his colleagues accomplished their studies through the use of large-scale shRNA screening across a diverse set of 390 cancer cell lines.

Utilizing deep small hairpin RNA (shRNA) screening libraries, at 20 shRNAs per genome, provided the investigators with highly robust gene-level data, which resulted in the emergence of several distinct classes of cancer-dependent genes. For example, Dr. Sellers’ group found that several known oncogenes fell into the genetic dependence class, whereas other genes were sorted into lineage, paralog, and collateral synthetic lethality dependent classes.

An interesting example from Dr. Sellers’ work was the link his laboratory discovered between polyamine metabolism and salvage and the protein arginine methyltransferase 5 (PRMT5). In particular, the loss of methylthioadenosine phosphorylase (MTAP)—which has been observed in many solid tumors and hematologic malignancies—resulted in the accumulation of S-methyl-5′-thioadenosine (MTA), which specifically inhibited the epigenetic mechanisms of PRMT5. The culmination Dr. Sellers’ analysis led to the finding that PRMT5 is a novel target for therapeutic development in MTAP deleted cancers.

These three presentations represented some of the excellent, cutting-edge research that is not only looking to develop novel drug therapeutics but also trying to uncover the underlying molecular mechanisms of epigenetic regulation and cancer.

A Metabolic Twist that Drives Cancer Survival

http://www.technologynetworks.com/Metabolomics/news.aspx?ID=190262

A novel metabolic pathway that helps cancer cells thrive in conditions that are lethal to normal cells has been identified.

“It’s long been thought that if we could target tumor-specific metabolic pathways, it could lead to effective ways to treat cancer,” said senior author Dr. Ralph DeBerardinis, Associate Professor of CRI and Pediatrics, Director of CRI’s Genetic and Metabolic Disease Program, and Chief of the Division of Pediatric Genetics and Metabolism at UT Southwestern. “This study finds that two very different metabolic processes are linked in a way that is specifically required for cells to adapt to the stress associated with cancer progression.”

The study, available online today in the journal Nature, reveals that cancer cells use an alternate version of two well-known metabolic pathways called the pentose phosphate pathway (PPP) and the Krebs cycle to defend against toxins. The toxins are reactive oxygen species (ROS) that kill cells via oxidative stress.

This work builds on earlier studies by Dr. DeBerardinis’ laboratory that found the Krebs cycle, a series of chemical reactions that cells use to generate energy, could reverse itself under certain conditions to nourish cancer cells.

Dr. DeBerardinis said most normal cells and tumor cells grow by attaching to nutrient-rich tissue called a matrix. “They are dependent on matrix attachment to receive growth-promoting signals and to regulate their metabolism in a way that supports cell growth, proliferation, and survival,” he said.

Detachment from the matrix results in a sudden increase in ROS that is lethal to normal cells, he added. Cancer cells seem to have a workaround.

The destruction of healthy cells when detached from the matrix was reported in a landmark 2009 Nature study by Harvard Medical School cell biologist Dr. Joan Brugge. Intriguingly, that same study found that inserting an oncogene – a gene with the potential to cause cancer – into a normal cell caused it to behave like a cancer cell and survive detachment, said Dr. DeBerardinis, who also is affiliated with the Eugene McDermott Center for Human Growth & Development, holds the Joel B. Steinberg, M.D. Chair in Pediatrics, and is a Sowell Family Scholar in Medical Research at UT Southwestern.

“Another Nature study, this one from CRI Director Dr. Sean Morrison’s laboratory in November 2015, found that the rare skin cancer cells that were able to detach from the primary tumor and successfully metastasize to other parts of the body had the ability to keep ROS levels from getting dangerously high,” Dr. DeBerardinis said. Dr. Morrison, also a CPRIT Scholar in Cancer Research and a Howard Hughes Medical Institute Investigator, holds the Mary McDermott Cook Chair in Pediatrics Genetics at UT Southwestern.

Working under the premise that the two findings were pieces of the same puzzle, a crucial part of the picture seemed to be missing, he said.

It was known for decades that the PPP was a major source of NADPH, which provides a source of reducing equivalents (that is, electrons) to scavenge ROS; however, the PPP produces NADPH in a part of the cell called the cytosol, whereas the reactive oxygen species are generated primarily in another subcellular structure called the mitochondria.

“If you think of ROS as fire, then NADPH is like the water used by cancer cells to douse the flames,” Dr. DeBerardinis said. But how could NADPH from the PPP help deal with the stress of ROS produced in a completely different part of the cell? “What we did was to discover how this happens,” Dr. DeBerardinis said.

The current study in Nature demonstrates that cancer cells use a “piggybacking” system to carry reducing equivalents from the PPP into the mitochondria. This movement involves an unusual reaction in the cytosol that transfers reducing equivalents from NADPH to a molecule called citrate, similar to a reversed reaction of the Krebs cycle, he said. The citrate then enters the mitochondria and stimulates another pathway that results in the release of reducing equivalents to produce NADPH right at the location of ROS creation, allowing the cancer cells to survive and grow without the benefit of matrix attachment.

“We knew that both the PPP and Krebs cycle provide metabolic benefits to cancer cells.  But we had no idea that they were linked in this unusual fashion,” he said. “Strikingly, normal cells were unable to transport NADPH by this mechanism, and died as a result of the high ROS levels.”

Dr. DeBerardinis stressed that the findings were based on cultured cell models, and more research will be necessary to test the role of the pathway in living organisms. “We are particularly excited to test whether this pathway is required for metastasis, because cancer cells need to survive in a matrix-detached state in the circulation in order to metastasize,” he said.

CRI scientists find novel metabolic twist that drives cancer survival
http://www.utsouthwestern.edu/newsroom/news-releases/year-2016/april/cancer-metabolism.html

https://pharmaceuticalintelligence.com/2016/04/09/programmed-cell-death-and-cancer-therapy/5 days ago Cancer Cell Survival Driven by Novel Metabolic Pathway … This new study describes an alternate version of two wellknown metabolic pathways, the pentose phosphate pathway (PPP) and the Krebs cycle,…

http://www.nature.com/nature/journal/v481/n7381/abs/nature10642.html Jan 19, 2012 Nature | Letter …. DeBerardinis, R. J. et al. Beyond aerobic …. Andrew R. Mullen,; Pei-Hsuan Chen,; Tzuling Cheng &; Ralph J. DeBerardinis ..   

Haematopoietic stem cells require a highly regulated protein – Nature
http://www.nature.com/nature/journal/v509/n7498/abs/nature13035.html May 1, 2014 Nature | Article. Print; Share/ ….. synthesis. Nature Methods 6, 275–277 (2009) …. Robert A. J. Signer,; Jeffrey A. Magee &; Sean J. Morrison …       
http://www.nature.com/nature/journal/v527/n7577/abs/nature15726.html Nov 12, 2015 Nature | Article ….. Multistep nature of metastatic inefficiency: dormancy of solitary cells after successful extravasation and ….. Sean J. Morrison …     
Deep imaging of bone marrow shows non-dividing stem Nature
http://www.nature.com/nature/journal/v526/n7571/abs/nature15250.html   Oct 1, 2015 Nature | Letter. Print; Share/ …… Morrison, S. J. & Scadden, D. T. The bone marrow niche for ….. Kiranmai S. Kocherlakota &; Sean J. Morrison …
https://pharmaceuticalintelligence.com/category/cancer-biology-innovations-in-cancer-therapy/genomic-expression/Glutamine and cancer: cell biology, physiology, and clinical opportunities …. Metabolism of glutamine-derived α-ketoglutarate in the TCA cycle serves … known as hexosamines, that are used to glycosylate growth factor receptors and …… of two wellknown metabolic pathways, the pentose phosphate pathway (PPP )
The Mitochondrial Warburg Effect: A Cancer Enigma – IBC Journal
http://www.ibc7.org/article/file_down.php?pid=48&mode=article This feature of cancer cells is known as the Warburg effect, named … new paradigm of collaboration and a well-designed systemic approach will supply … Krebs cycle. … The pentose phosphate pathway uses glucose to produce ribose, which is used … glucose is taken up into cells, it is used in two main metabolic pathways

New paper offers intriguing insights into tumor metabolism     William G. Gilroy    August 19, 2009

Posted In: Research

A paper appearing in this week’s edition of the journal Nature by a team of researchers that includes University of Notre Dame biologist Zachary T. Schafer has important new implications for understanding the metabolism of tumors.

Schafer, an assistant professor of biological sciences and Coleman Junior Chair of Cancer Biology, points out that in the early stages of tumor formation some cells become detached from their normal cellular matrix. These “homeless” cells tend to develop certain defects that stop them from becoming cancerous. In a process known as apoptosis, these precancerous cells essentially kill themselves, allowing them to be destroyed by immune system cells.

The prevailing wisdom among researchers has been that apoptosis was the only way that cells could die.

In studies conducted prior to the research described in the Nature paper, it was found that even when apoptosis was inhibited in detached, precancerous cells, they still eventually died. Intrigued by these results, a team of researchers led by Joan S. Brugge, Louise Foote Pfieffer Professor of Cell Biology at Harvard Medical School, and Schafer decided to take a closer look.

They report in this week’s Nature paper that they found that even when apoptosis was inhibited in detached cells endowed with a cancer-causing gene, they still were incapable of absorbing glucose, their primary energy source. Additionally, the cells displayed signs of oxidative stress, which is a harmful accumulation of oxygen-derived molecules called reactive oxygen species (ROS). The research also revealed decreased ATP production, a key factor in energy transport in the cells.

Schafer notes that this combination of loss of glucose transport, decreased ATP production and heightened oxidative stress reveal a manner of cell death that hadn’t been previously demonstrated to play a role in this context.

In the next phase of the study, Schafer engineered the cells to express a high level of HER2, a gene known to be hyperactive in many breast cancer tumors. He also treated the cells with antioxidants to relieve oxidative stress.

Both approaches helped the cells survive. The HER2-treated cells regained glucose transport, avoided oxidative stress and recovered ATP levels.
Most surprisingly, the antioxidants restored metabolic activity in the cells by allowing fatty acids to be effectively used instead of glucose as an energy source, providing them with a chance to survive.

“Our results raise the possibility that antioxidant activity might allow early stage tumor cells to survive where they would otherwise die from these metabolic defects,” Schafer said.

He also cautions that while the antioxidant findings were surprising, their research was done solely in cell cultures and more research needs to be done before there are clear implications for individuals and their diets.

The paper does, however, offer important new clues about the metabolism of tumor cells and important information that may lead to drugs that can developed to target them.

Antioxidant and Oncogene Rescue of Metabolic Defects Caused by
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2931797/Aug 19, 2009
Nature. Author manuscript; available in PMC 2010 Sep 2. Published in … Y. Irie,1 Sizhen Gao,1 Pere Puigserver,1,2 and Joan S. Brugge1,*.

http://www.nature.com/cdd/journal/v10/n8/full/4401251a.html
Proteasome inhibitors have been shown to be effective in cancer treatment, an ability … a specific inhibitor of 26S proteasome, also reduced cell viability ( 80% with 10 mu …
be a consequence of the increased generation of ROS caused by MG132. …. vectors endowed with the wild type forms of RB or p53 genes (Figure 1f).

The Metastasis-Promoting Roles of Tumor-Associated Immune Cells

Tumor metastasis is driven not only by the accumulation of intrinsic alterations in malignant cells, but also by the interactions of cancer cells with various stromal cell components of the tumor microenvironment. In particular, inflammation and infiltration of the tumor tissue by host immune cells, such as tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells have been shown to support tumor growth in addition to invasion and metastasis. Each step of tumor development, from initiation through metastatic spread, is promoted by communication between tumor and immune cells via the secretion of cytokines, growth factors and proteases that remodel the tumor microenvironment. Invasion and metastasis requires neovascularization, breakdown of the basement membrane, and remodeling of the extracellular matrix for tumor cell invasion and extravasation into the blood and lymphatic vessels. The subsequent dissemination of tumor cells to distant organ sites necessitates a treacherous journey through the vasculature, which is fostered by close association with platelets and macrophages. Additionally, the establishment of the pre-metastatic niche and specific metastasis organ tropism is fostered by neutrophils and bone marrow-derived hematopoietic immune progenitor cells and other inflammatory cytokines derived from tumor and immune cells, which alter the local environment of the tissue to promote adhesion of circulating tumor cells. This review focuses on the interactions between tumor cells and immune cells recruited to the tumor microenvironment, and examines the factors allowing these cells to promote each stage of metastasis.

Once established, tumors are quite adept at preventing anti-tumor immune responses, and several defense mechanisms to circumvent immune detection have been described including antigen loss, down-regulation of major histocompatibility molecules (MHC), deregulation or loss of components of the endogenous antigen presentation pathway, and tumor-induced immune suppression mediated through cytokine secretion or direct interactions between tumor ligands and immune cell receptors [2]. These mechanisms contribute to the process of immunoediting in which tumor cell subpopulations susceptible to immune recognition are lysed and eliminated, while resistant tumor cells proliferate and increase their frequency in the developing neoplasia [3]. However, tumors not only effectively escape immune recognition, they also actively subvert the normal anti-tumor activity of immune cells to promote further tumor growth and metastasis.

During early stages of cancer development, infiltrating immune cell populations are primarily tumor suppressive, but depending on the presence of accessory stromal cells, the local cytokine milieu, and tumor-specific interactions, these immune cells can undergo phenotypic changes to enhance tumor cell dissemination and metastasis. For instance, CD4+ T cells, macrophages, and neutrophils have all been shown to possess opposing properties depending on the inflammatory state of the tumor environment, the tissue context, and other cellular stimuli intrinsic to the altered tumor cells [4, 5]. These features are dependent upon the inherent plasticity of immune cells in response to stimulatory or suppressive cytokines [6]. Notably, the switch from a Th1 tumor-suppressive phenotype such as CD4+ “helper” T cells, which aid cytotoxic CD8+ T cells in tumor rejection, to a Th2 tumor-promoting “regulatory” phenotype, which blocks CD8+ T-cell activity, is a characteristic outcome in the inflammatory, immune-suppressive tumor microenvironment [5, 7]. Likewise, M1 macrophages and N1 neutrophils are known to have pronounced anti-tumor activity; however, these immune cells are often subverted to a tumor-promoting M2 and N2 phenotype, respectively, in response to immune-suppressive cytokines secreted by tumor tissue [8].

The crosstalk that occurs between tumor and immune cells within the tumor microenvironment, the circulation, or at distant metastatic sites has been clearly shown to foster metastatic dissemination. Immune cells as well as the suppressive factors that they secrete represent potential targets for therapeutic intervention. Regardless of their source, cytokines, chemokines, proteases, and growth factors are some of the main factors contributing to immunosuppression and immune-mediated tumor progression. These molecules can be produced by immune, stromal, or malignant cells and can act in paracrine and autocrine fashion to promote each stage of tumor cell invasion and metastasis by enhancing inflammation, angiogenesis, tumor proliferation, and recruitment of additional immunosuppressive and tumor-promoting immune cells. These secreted factors provide the malignant cells with an abundant source of growth and survival signals that perpetuate a supportive microenvironment for tumor metastasis and represent some of the most attractive targets for directed anti-tumor therapy. Immune pathways provide numerous soluble targets for cancer treatment, and indeed, many drugs to target immune-suppressive molecules are moving forward in clinical trials. For instance, the anti-RANKL (Denosumab) antibody has been shown to effectively inhibit bone metastasis in prostate cancer patients [201], while a variety of neutralizing antibodies to IL-1β and IL-1 receptor have been shown to have efficacy in treating metastasis in pre-clinical animal models [202]. Several agents that target IL-1 or other immune-suppressive cytokines are already approved for the treatment of some inflammatory diseases and are prime candidates for human trails [202]. Additionally, other proteins involved in tumor progression that are induced directly or indirectly by immune cell populations, such as EMT-associated transcription factors, adhesion molecules, and tumor receptors and ligands which mediate immune suppression, could also be targeted with small molecules or blocking antibodies. Antibodies against two surface molecules expressed by suppressive lymphoid cells, anti-CTLA-4 (ipiliumimab) [203, 204] and anti-PD-1 have been recently gaining increasing support from clinical trials for their effective treatment for many forms of cancer including advanced melanoma and prostate cancer [205, 206]. Specifically, anti-CTLA-4 has been shown to be particularly efficacious in metastatic melanoma, while anti-PD-1 has only just begun a comprehensive evaluation in clinical trials [204, 207]. Likewise, non-steroidal anti-inflammatory drugs (NSAIDS) to prevent or treat chronic inflammation and lymphangiogenesis [208210], and anti-coagulants to prevent platelet aggregation on circulating tumor cells [211] are just two examples of a multitude of therapeutic agents that could be utilized to prevent immune-mediated tumor progression at unique stages of metastasis. Of course, new methods or biomarkers for the detection of patients at risk of tumor progression or metastasis are also desperately needed to tailor personalized therapy for patients to obtain the best possible clinical outcome.

  1. https://pharmaceuticalintelligence.com/category/cancer-and-therapeutics/Mar 26, 2016 This turns your immune systems ability to attack and kill cancer cells back on” …. the rare skin cancer cells that were able to detach from theprimary tumor and successfully metastasize to other parts of the body had the ability to keep ROS levels from getting dangerously high,” Dr. DeBerardinis remarked.

  2. https://pharmaceuticalintelligence.com/tag/histone-deacetylase-inhibitors-hdac/The HDAC-inhibiting agent romidepsin significantly increased T-cell tumor … skin cancer cells that were able to detach from the primary tumor and successfully … of the body had the ability to keep ROS levels from getting dangerously high,” Dr. …. Sensitivity for EGFR or KRAS was higher in patients with multiplemetastatic …

  3. https://pharmaceuticalintelligence.com/category/cancer-biology-innovations-in-cancer-therapy/genomic-expression/In a study involving 320 patients, the researchers were able to infer cell death in …. Glutamine and cancer: cell biology, physiology, and clinical opportunities … On the other hand, GLS2 expression is enhanced in some neuroblastomas, …… of the body had the ability to keep ROS levels from getting dangerously high,” Dr.

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Tumor Associated Macrophages: The Double-Edged Sword Resolved?

Writer/Curator: Stephen J. Williams, Ph.D.

UPDATED 10/04/2021 TAMs Enhance Tumor Hypoxia and Aerobic Glycolysis

Cell-based immunity is vital for our defense against pathologic insult but recent evidence has shown the role of cell-based immunity, especially macrophages to play an important role in both the development and hindrance of tumor growth, including role in ovarian, hematologic cancers, melanoma, and breast cancer.  In the past half century, new immunological concepts of cancer initiation and progression have emerged, including the importance of the harnessing the immune system as a potential anti-cancer strategy. However, as our knowledge of the immune system and tumor biology has grown, the field has realized an immunological conundrum: how can an immune system act to both prevent tumor growth and promote the tumor’s growth?

As discussed in the lower section of this post, authors of a paper in the journal Science show how different populations of tumor-associated macrophages (TAMs) may exert both positive and negative effects on tumor cells, producing a sort of ying-yang war between the tumor and the immune system.

The Immune System: Brief Overview and Role in Cancer

celllineageimmunesystem

Figure. Cell lineage of the immune system. A description of the different cell types can be found here.

Histologic evaluation of multiple tumor types, especially solid tumors, reveal the infiltration of diverse immunological cell types, including myeloid and lymphoid cell lineages, such as macrophages and NK, T cell and B cells respectively.

The immunological conundrum

immuncecancerconundrum

Figure. Potential inflammatory signaling pathways in breast cancer stem cells.
Breast cancer stem cells may be regulated by chemokine- and/or cytokine-mediated inflammatory signaling in an autocrine or paracrine manner. (from University of Tokyo at http://www.ims.u-tokyo.ac.jp/system-seimei/en/research2_e.htm)

Role of Tumor Associated Macrophages

There are conflicting reports as to the functional consequence of these infiltrating tumor-associated macrophages (TAMs). TAMs have been shown to secrete mediators such as interleukins and cytokines in a paracrine manner such as CCL2, IL10 and TGFβ. In certain instances these cytokines and mediators actually promote the growth of the surrounding tumors.

J Leukoc Biol 2009 Nov 86(5) 1065-73, Figure 1

Figure.  TAMs can be divided into subpopulations with distinctive functions and secretogogues.

For Further Reference

Tumor-associated macrophages and the profile of inflammatory cytokines in oral squamous cell carcinoma. http://www.ncbi.nlm.nih.gov/pubmed/23089461 anti-inflamm IL10 and TGFB

Tumor-associated macrophage-derived IL-6 and IL-8 enhance invasive activity of LoVo cells induced by PRL-3 in a KCNN4 channel-dependent manner

TAMscytokines

Figure 2: TAM functions in tumor progression. Tumor cells and stromal cells, which produce a series of chemokines and growth factors, induce monocytes to differentiate into macrophages. In the tumor, most macrophages are M2-like, and they express some cytokines, chemokines, and proteases, which promote tumor angiogenesis, metastasis, and immunosuppression. From Macrophages in Tumor Microenvironments and the Progression of Tumors

ICB-14-NC-BRONTE-V2

Macrophages integrate metabolic and environmental signals to promote tumor growth. Area within dotted rectangle indicates proposed mechanisms of action. ARG, arginase; HIF, hypoxia-inducible factor; MCT, monocarboxylate transporter; NADH, nicotine adenine dinucleotide, reduced; PKM2, M2 isoform of pyruvate kinase; VEGF, vascular endothelial growth factor from Tumor cells hijack macrophages via lactic acid adapted from Colegio OR, Chu N-Q, Szabo AL, Chu T, Rhebergen AM, Jairam V et al. Functional polarization of tumour-associated macrophages by tumour-derived lactic acid. Nature (e-pub ahead of print 13 July 2014; doi:10.1038/nature13490). | Article |

Depletion of M2-Like Tumor-Associated Macrophages Delays Cutaneous T-Cell Lymphoma Development In Vivo

Targeting tumor-associated macrophages in an orthotopic murine model of diffuse malignant mesothelioma

Crosstalk between colon cancer cells and macrophages via inflammatory mediators and CD47 promotes tumour cell migration

Tumor-Associated Macrophages Regulate Murine Breast Cancer Stem Cells Through a Novel Paracrine EGFR/Stat3/Sox-2 Signaling Pathway

Science Paper: Different Populations of TAMS Have Different Tumor Effects

The cellular and molecular origin of tumor-associated macrophages Eric G. Pamer1 Ruth A. Franklin1,2, Will Liao3, Abira Sarkar1, Myoungjoo V. Kim1,2, Michael R. Bivona1, Kang Liu4, Ming O. Li1, Science 23 May 2014: Vol. 344 no. 6186 pp. 921-925

A recent Science paper from Cornel has investigated the origin, function, and characterization of TAMs on breast cancer growth. In summary, their efforts and research suggest different populations of TAMs with varied tumorigenic effects, a finding which may help explain the immunologic conundrum with respect to solid tumors.

The authors characterized the infiltrating immune cell types in a MMTV-PyMt model of breast cancer.

The MMTV-PyMt mouse breast cancer model:

is a transgenic model where mammary gland expression of the polyoma middle T antigen (PyMT) is driven by the Mouse Mammary Tumor Virus promoter (MMTV).

Microbiol. Mol. Biol. Rev. 2009 Sep 73(3) 542-63, FIG. 5

For a review of mouse models of breast cancer please see

Mouse models of breast cancer metastasis. Anna Fantozzi1 and Gerhard Christofori. Breast Cancer Res. 2006; 8(4): 212.

Results

1.     Macrophages constitute the predominate myeloid cell population in MMTV-PyMT mammary tumors

Tumor infiltrating immune cells included

  • Myeloid cells comprised 50% of CD45+ infiltrating leukocytes.
  • The CD45 antigen, also known as Protein tyrosine phosphatase, receptor type, C (PTPRC) is an enzyme that, in humans, is encoded by the PTPRC gene, and acts as a regulator of B and T-lymphocytes.
  • Authors noted three types of cells classified as Type I, II, and III based on
  1. Cell morphology
  2. Major histocompatibility complex
  • Infiltrating monocytes and neutrophils
  • Cells with dendritic and macrophage markers

2. TAMS differentiate from CCR2+ inflammatory monocytes

  • To determine whether Ly6C+CCR2+ inflammatory monocytes contributed to TAMs and MTMs, authors crossed PyMT mice to Ccr2−/− mice and found MTMs (mammary tumor macrophages) were significantly reduced in Ccr2−/− PyMT mice, implying that MTMs are constitutively repopulated by inflammatory monocytes
  • To determine whether inflammatory monocytes were required for TAM maintenance, we generated CCR2DTR PyMT mice expressing diphtheria toxin receptor (DTR) under control of the Ccr2 locus DT treatment resulted in 96% depletion of tumor-associated monocytes compared to 80% depletion in Ccr2−/− mice
  • To investigate whether monocytes could differentiate into TAMs in vivo, we transferred CCR2+ bone marrow cells isolated from CCR2GFP reporter mice into congenically marked CCR2DTR PyMT mice depleted of endogenous monocytes, we observed transferred cells in developing tumors demonstrate that tumor growth induces the differentiation of CCR2+ monocytes into TAMs.

3.     TAMs are phenotypically distinct from AAMs (M2 or alternatively activated macrophages)

  • Gene-expression profiling revealed the integrin CD11b (Itgam) was expressed at lower levels in TAMs than in MTMs while several other integrins and the integrin receptor Vcam1 were up-regulated in TAMs
  • AM population did not express AAM markers such as Ym1, Fizz1, and Mrc1; instead, MTMs more closely resembled AAMs. The authors detected Vcam1 up-regulation on TAMs as a late differentiation event

4.    RBPJ-dependent TAMs modulate the adaptive immune response

  • In DCs, canonical Notch signaling mediated by the key transcriptional regulator RBPJ controls lineage commitment and terminal differentiation. To explore whether Notch signaling played a role in TAM differentiation, authors used CD11ccre mice that efficiently deleted floxed DNA sequences to a greater extent in TAMs than MTMs, but not in monocytes or neutrophils (fig. S14). CD11ccreRbpjfl/fl PyMT mice exhibited a selective loss of MHCIIhiCD11blo TAMs ( 4A). However, a MHCIIhiCD11bhi population still remained
  • Transcriptional profiling comparing this population to WT TAMs confirmed a loss of the Notch-dependent program in RBPJ-deficient cells revealing that in the absence of RBPJ, inflammatory monocytes are unable to terminally differentiate into TAMs.

UPDATED 10/04/2021 TAMs Enhance Tumor Hypoxia and Aerobic Glycolysis

Tumor-Associated Macrophages Enhance Tumor Hypoxia and Aerobic Glycolysis

From:

Tumor-Associated Macrophages Enhance Tumor Hypoxia and Aerobic Glycolysis
Hoibin JeongSehui KimBeom-Ju HongChan-Ju LeeYoung-Eun KimSeoyeon BokJung-Min OhSeung-Hee GwakMin Young YooMin Sun LeeSeock-Jin ChungJoan DefrênePhilippe TessierMartin PelletierHyeongrin JeonTae-Young RohBumju KimKi Hean KimJi Hyeon JuSungjee KimYoon-Jin LeeDong-Wan KimIl Han KimHak Jae KimJong-Wan ParkYun-Sang LeeJae Sung LeeGi Jeong CheonIrving L. WeissmanDoo Hyun ChungYoon Kyung Jeon and G-One Ahn

Abstract

Tumor hypoxia and aerobic glycolysis are well-known resistance factors for anticancer therapies. Here, we demonstrate that tumor-associated macrophages (TAM) enhance tumor hypoxia and aerobic glycolysis in mice subcutaneous tumors and in patients with non–small cell lung cancer (NSCLC). We found a strong correlation between CD68 TAM immunostaining and PET 18fluoro-deoxyglucose (FDG) uptake in 98 matched tumors of patients with NSCLC. We also observed a significant correlation between CD68 and glycolytic gene signatures in 513 patients with NSCLC from The Cancer Genome Atlas database. TAM secreted TNFα to promote tumor cell glycolysis, whereas increased AMP-activated protein kinase and peroxisome proliferator-activated receptor gamma coactivator 1-alpha in TAM facilitated tumor hypoxia. Depletion of TAM by clodronate was sufficient to abrogate aerobic glycolysis and tumor hypoxia, thereby improving tumor response to anticancer therapies. TAM depletion led to a significant increase in programmed death-ligand 1 (PD-L1) expression in aerobic cancer cells as well as T-cell infiltration in tumors, resulting in antitumor efficacy by PD-L1 antibodies, which were otherwise completely ineffective. These data suggest that TAM can significantly alter tumor metabolism, further complicating tumor response to anticancer therapies, including immunotherapy.

Significance: These findings show that tumor-associated macrophages can significantly modulate tumor metabolism, hindering the efficacy of anticancer therapies, including anti-PD-L1 immunotherapy.

Introduction

Tumor hypoxia and glycolysis have long been recognized as major resistance factors contributing to failures of chemo- and radiotherapy (1, 2). Traditionally, tumor hypoxia is known to occur by two mechanisms: chronic or acute hypoxia (2). Chronic hypoxia occurs as a result of rapid proliferation of cancer cells and hence being constantly forced away from blood vessels beyond the oxygen diffusion distance of approximately 150 μm (2). Acute hypoxia on the other hand occurs by a temporary cessation of the blood flow due to highly disorganized tumor vasculature (2). Regardless of the mechanism, tumor hypoxia has been extensively documented for their contribution to resistance to all anticancer therapies including chemotherapy (2), surgery (3), radiotherapy (2), and recently immunotherapy (4).

Aerobic glycolysis, also known as Warburg effect, is a phenomenon whereby many types of tumors exhibit a preference of glucose over the oxygen for their energy substrate (5), and this has allowed us to track solid tumors in patients with PET using 18fluoro-deoxyglucose (FDG) radioactive tracer (6). Although several mechanisms for Warburg effect have been suggested including mitochondrial defects, adaptation to hypoxia [hence activation of hypoxia-inducible factor (HIF)], and oncogenic signals such as MYC and RAS (7), the exact mechanism is still controversial. Tumor glycolysis has also been reported to influence the therapy outcome (8). Preclinical studies have suggested that glycolysis can increase DNA repair enzyme expressions including Rad51 and Ku70, which can facilitate radiation-induced DNA double-strand break repair (9). Lactate, a major byproduct of glycolysis, has recently been shown to be utilized as a fuel source for oxidative phosphorylation in nearby cancer cells (10), which can promote the tumor recurrence following anticancer therapies.

Tumor-associated macrophages (TAM) are bone marrow–derived immune cells recruited to tumors and have been extensively reported for their protumoral role (11). Recruited to tumors by various tumor-secreting factors including stromal cell–derived factor-1 (SDF1; ref. 12), VEGF (13), semaphorin 3A (14), and colony-stimulating growth factor-1 (CSF1; ref. 15), TAMs have been shown to produce various growth factors and proteases necessary for tumor survival (11) or immunosuppressive cytokines inhibiting antitumor immune responses (16). Macrophages in general are known to be polarized to either classically activated M1 macrophages or alternatively activated M2 phenotype depending on the cytokine milieu in which they are exposed (17). Bacterial-derived products such as lipopolysaccharide have been shown to polarize macrophages toward M1 phenotype (17), while parasite-associated signals such as IL4 and IL13 can lead to M2-polarized macrophages with increased tissue repair abilities (17). It has been suggested that TAMs are M2-like, although various subpopulations of TAM have been also identified including TIE2-positive macrophages (18), programmed cell death protein-1 (PD-1)–expressing TAM (19), and C-C chemokine receptor type-2 (CCR2)–expressing TAM (20).

In this study, we demonstrate clinically and preclinically that TAMs are a novel contributor to tumor hypoxia and aerobic glycolysis by competing oxygen and glucose with cancer cells. We further observed that TAM can significantly interfere with T-cell infiltration thereby masking programmed death-ligand 1 (PD-L1) expression in the tumors. We believe that our results have an important clinical implication such that patients with high infiltration of TAM in their tumors may poorly respond to all anticancer therapies, including the latest immunotherapy.

Figure 1.

Strong correlations between TAM infiltration and glycolysis in patients with NSCLC. A, Representative PET/CT images for FDG uptake (top) and immunostaining of CD68 (bottom) from paired tumors of patients with NSCLC. Top, yellow circles, location of tumors. Bottom, red arrowheads, CD68-positive TAM. Scale bar, 100 μm. B, Correlation between glycolysis and CD68-positive TAM in 98 patients with NSCLC paired results as in A. Glycolysis was analyzed as FDG maximal standardized uptake value (FDG SUVmax; left) or 40% total lesion glycolysis (TLG; right). C, FDG SUVmax values for CD68low (n = 49) or CD68Hi (n = 49) NSCLC tumors. D, Subgroup analyses of FDG uptake in adenocarcinomas (n = 48; left) or squamous cell carcinomas (n = 50; right) of NSCLC. *, P < 0.05; ***, P < 0.001 in C and D as determined by the Student t test. Data are the mean ± SEM. E, TCGA analysis between CD68 and SLC2A1 (left) or HK2 (right) in 513 patients with adenocarcinoma NSCLC. P values are indicated in each plot.

TAMs make tumors more glycolytic

Figure 2.

TAMs make tumors more glycolytic. A, Left, PET/MRI images for FDG uptake in LLC tumors in mice before (D0, top) and after (D2, bottom) Veh or Clod treatment. Yellow circles, tumors. Right, T2-weighted MR images of LLC tumors treated with Veh or Clod pre (top)- or post (bottom)- contrast. Red arrowheads in Veh tumor, ferumoxytol-labeled TAM. B, FDG uptake SUVmax in A. **, P < 0.01, determined by two-way ANOVA. C, FACS plot indicating HoechstbrightKeratin+ (red boxes) population of cells sorted as aerobic cancer cells. D, Fold changes in gene expression in FACS-sorted aerobic cancer cells from LLC tumors treated with Clod or Veh. E, Glucose uptake (left) and lactate production (right) from the sorted aerobic cancer cells as in C. Data in D and E are the mean ± SEM from at least triplicate samples. *, P < 0.05; ***, P < 0.001 by the Student t test. F, Western blot of FACS-sorted aerobic cancer cells in C for GLUT1. β-Actin was used as the loading control. G, Oxygen consumption kinetics in FACS-sorted aerobic cancer cells as described in CH, LLC tumor growth in mice treated with Veh, Clod, Veh + metformin (Veh + Met), or Clod + metformin (Clod + Met). *, P < 0.05; **, P < 0.01; ***, P < 0.001, determined by two-way ANOVA. I, LLC tumor growth in mice treated with Veh, Clod, Veh + 2-DG, or Clod + 2-DG. Data in H and I are the mean ± SEM, with number of animals indicated in the graphs.

TAMs secrete TNFα to promote tumor glycolysis

Figure 3.

Macrophages secrete TNFα to facilitate glycolysis in cancer cells. A, Gene expression changes in LLC cocultured with (LLC+BMDM) or without (LLC) BMDM. Data are the mean ± SEM from at least triplicate determinations. B, Glucose uptake (left) and lactate production (right) in LLC cocultured with or without BMDM. Data are the mean ± SEM for triplicate samples per group. **, P < 0.01 by Student t test. C, Glucose uptake in LLC cultured alone, cocultured with BMDM, or cocultured with BMDM with glucose added back to the LLC compartment of the coculture system. Data are the mean ± SEM for n = 4 replicates per group. *, P < 0.05; **, P < 0.01 by one-way ANOVA. D, Antibody cytokine arrays in the supernatant obtained from BMDM culture with (BMDM+LLC) or without (BMDM) LLC. Red boxes indicate those cytokines whose expressions were increased in BMDM cocultured with LLC compared with BMDM alone. Blue box, CXCL1, a cytokine produced by LLC cancer cells themselves (Supplementary Fig. S2D). E, Luminex cytokine assays for TNFα in the supernatant from culture media, LLC alone, BMDM alone, or BMDM cocultured with LLC. Data are the mean ± SEM for n = 3 replicates per group. ***, P < 0.001 by one-way ANOVA. F, Glucose uptake in LLC alone (none), LLC cocultured with BMDM (+BMDM), or LLC treated with TNFα (+TNFα) or with IFNγ (+IFNγ). Data are the mean ± SEM from n = 3 samples per group. **, P < 0.01; ***, P < 0.001 by one-way ANOVA. G, Western blot for LLC cells treated with increasing concentrations of recombinant TNFα protein for GLUT1, HK2, or PGC-1α. β-Actin was used as the loading control. H, TNFα concentrations in the supernatant from LLC cultured with (+BMDM) or without (alone) BMDM, or in BMDM cultured with (+LLC) or without (alone) LLC, measured by ELISA. BD, below the detection limit. **, P < 0.01 by Student t test. I, Immunostaining of TNFα (red) and CD68 (green) in LLC tumors grown in mice. Nuclei are shown in blue with DAPI counterstaining. The inset shows magnified regions where indicated with the asterisk (*). White arrowheads, CD68-positive TAM-expressing TNFα. Scale bar, 100 μm. J, TNFα concentrations measured by ELISA in the supernatant from CD11b and F4/80 double-positive TAM sorted by FACS. Data are the mean ± SEM from triplicate determinations. ***, P < 0.001, determined by one-way ANOVA. K, TCGA analysis of clinical correlations between CD68 and TNF (left) or between TNF and HK2 (right) in 513 patients with adenocarcinoma NSCLC. P values are indicated in each plot.

TAMs exacerbate tumor hypoxia

Figure 4.

TAMs directly contribute to tumor hypoxia. A, Immunostaining of LLC tumors grown in mice for TAM by using S100A8 (red) and hypoxia by using pimonidazole (PIMO; green) antibodies. Nuclei are shown in blue with DAPI counterstaining. B, FACS analysis demonstrating that CD11b and F4/80 double-positive TAMs are pimonidazole-positive. C, Gene expression in CD11b and F4/80 double-positive TAM isolated from LLC tumors compared with those in cultured BMDM. Data are the mean ± SEM from triplicate determinations. D, Two-photon microscopy images of the dorsal window chamber whereby 5 × HRE-GFP–expressing LLC tumors had been implanted. Images were taken at 24 hours after a single intratumoral injection of PBS (+PBS) or PBS containing FACS-sorted TAM (+TAM). Scale bars in A and D, 100 μm. E, Representative FACS plots demonstrating HoechstbrightKeratin+ as aerobic tumor cells (red boxes) and HoechstdimKeratin+ as hypoxic tumor cells in LLC tumors grown in mice treated with Veh or Clod. F, Quantification of aerobic or hypoxic tumor cells in E. Data are the mean ± SEM for n = 6 mice per group. *, P < 0.05 by Student t test. G, TCGA analysis between CD68 and HIF1A in 513 patients with adenocarcinoma NSCLC. P value is indicated in the graph. H, Growth of LLC tumor treated with Veh or Clod immediately prior to a single dose of 20 Gy ionizing irradiation. *, P < 0.05 by two-way ANOVA.

 

Other posts on this site on Immunology and Cancer include

The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology

Innovations in Tumor Immunology

T cell-mediated immune responses & signaling pathways activated by TLRs

Vaccines, Small Peptides, aptamers and Immunotherapy [9]

Report on Cancer Immunotherapy Market & Clinical Pipeline Insight

Molecular Profiling in Cancer Immunotherapy: Debraj GuhaThakurta, PhD

Immunotherapy in Cancer: A Series of Twelve Articles in the Frontier of Oncology by Larry H Bernstein, MD, FCAP

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Cancer Metastasis

Author: Tilda Barliya PhD

Metastasis, a complex process that involves the spread of tumor cells, accounts for more than 90%of cancer-related mortality (1,2). A metastatic tumor cell has a treacherous journey to go through:

  • local invasion and intravasation
  • survival in the circulation
  • homing and extravasation into the parenchyma of distant organs
  • adaptation to the new environment
  • outgrowth of secondary lesions

Although tumor cells that are shed from the primary tumor disseminate throughout the body, they tend to colonize select organs, with characteristically different periods of latency and efficiency depending on tumor type or subtype (2).

Steven Paget’s century-old ‘seed and soil’ hypothesis (2, 9) likened tumor cells to ‘seeds’ that are systemically distributed, but that only inhabit particular environments, or ‘soils’, which are supportive to their sustained growth. Understanding the molecular complexity of this process is difficult and we’ll try to unravel some of the pathogenesis and cellular basis that support the metastatic process.

Progression models:

There are two major tumor progression models (2) :

  • Linear –  primary tumour cells undergo successive rounds of mutation and selection35, giving rise to a biologically heterogeneous cellular population in which a subset of malignant clones have accumulated genetic alterations, necessary for metastasis.
  • Parallel –  tumor cells may disseminate very early in malignant progression, colonize multiple secondary sites at different times and ultimately accumulate genetic changes independently from those incurred by the primary tumor.

While both theories are possible, the linear model is validated by both clinical evidence and animal models, the parallel model is mainly based on animal models and still under investigation for clinical clues.

Meera Saxena. Molecular Oncology
Volume 7, Issue 2 , Pages 283-296, April 2013

Drivers of metastasis

During the past few years several methods and studies have been used to find and correlate between a specific gene and it’s homing target.

These genes, which were found using next-generation sequencing and their equivalents, were also validated their actual functional consequences.

Figure 1 (Meera Saxena et al) represent some of the genes that were associated with organ-specific translocation (additional genes were recently identified and included in table 1 – Sethi N et all). Herein, we generally show the gene to organ-specific homing, yet we will not discuss each and every one of them.  An example of specific gene to organ will be further discussed in detail in follow up article.

Signalling pathways in cancer metastasis have been extensively studied at the level of individual proteins or as a linear cascade of proteins but they have been less frequently evaluated through a network approach (2). Understanding the different variables in the gene-metastasis network may be crucial for drug development.

For example;  the drug–gene–phenotype Connectivity Map approach was successfully used to identify the mTOR inhibitor rapamycin as an effective agent for overcoming dexamethasone resistance in acute lymphoblastic leukaemia (2, 4).

Microenvironment

“Non-neoplastic stromal cells have a function in the development of tumor metastasis. Stromal cells as important regulators of metastasis through their ability to influence cancer cell functions such as chemotaxis and invasion, as well as microenvironment properties. It should not come as a surprise that tumor angiogenesis was among the initial findings that supported a role for stromal cells in cancer metastasis; the poor vascular integrity of newly synthesized blood vessels within the tumour allows for the escape of malignant cells with the potential of distant spread” (2). Such cells include:

  • Tumour-associated macrophages,
  • Leukocytes and other immune cells,
  • Mesenchymal cells that reside in breast tissue
  • Mesenchymal cells and neuroendocrine cells

Although some of the molecular pathway was discovered, the molecular components that facilitate communication between tumour cells and individual stromal cells of the primary tumor have yet to be fully understood.

Circulating Tumor cells (CTC)

“Essential to cancer metastasis is the ability of primary tumor cells to enter the vasculature and to use these fluid ‘highways’ as a means to reach distant organs”. Tight vascular wall barriers, unfavorable conditions for survival in distant organs, and a rate-limiting acquisition of organ colonization functions are just some of the impediments to the formation of distant metastasis (2,5,6 ).

Despite their clear prognostic importance, the diagnostic value of CTCs is largely unknown and fairly unexplored. Research challenges both in detection and interpretation render their ability to  be clinically accepted. Additional research is needed to fully explore CTCs’ potential in to predict clinical response to therapy would also help to guide disease management.

Colonization

The colonization and outgrowth of tumor cells in a secondary organ is often considered the rate-limiting, as well as the most poorly delineated, step in the metastatic cascade. Understanding the functional involvement of the tumor stromal cells of the secondary site may be crucial to understanding their ability to colonize.

The pre-metastatic niche model shows that, preceding the arrival of  disseminated tumour cells (DTCs), bone marrow-derived haematopoietic stem cells are mobilized by tumour-derived factors and are recruited to the secondary site where they negotiate a more hospitable microenvironment to foster the survival and expansion of metastatic lesions. Inflammatory cytokines have emerged as crucial mediators of the pre-metastatic niche and self-seeding and include IL-6, SRC and NF-kB.

After surviving the adjustment to the secondary site, tumor cells must sustain their growth to develop overt metastases. Developmental pathways have emerged as important players in tumor progression and metastasis. These include: transforming growth factor-β (TGFβ), bone morphogenetic protein (BMP), WNT and Hedgehog.  These genes will trigger additional genes that will affect downstream steps of the colonization process.

Clinical Aspect

“As most metastatic cancers are inoperable, systemic treatments using chemotherapeutic or targeted therapy is often the only option to slow tumor growth or to relieve metastasis-associated morbidity”.  Genes and pathways that have crucial roles in primary tumour growth and metastasis are ideal targets for therapeutic inventions. One example is the oncogenic BRAF:  potent inhibitors of mutant BRAF, had initial clinical results which suggest dramatic efficacy in the treatment of metastatic malignant melanoma.  It is important to keep in mind that many cancers develop resistance to BRAF inhibitor and require used of next-generation drugs. More so,  the mechanism of resistance will be discussed elsewhere.

A sound framework of normal homeostatic mechanisms can improve our ability to understand and target tumor–stromal interactions in metastasis.

Summary:

“Despite recognizing the devastating consequences of metastasis, we are not yet able to effectively treat cancer that has spread to vital organs” .  Despite our increasing knowledge about metastatic colonization, we still hold little understanding of how metastatic tumour cells behave as solitary disseminated entities. Understanding the genomics of metastatic cancer cells and the complexity of the metastasis process will enable us to develop a better target-therapeutic drugs.

 

References:

1. Naure Review: Cancer: focus on metastasis. http://www.nature.com/nrc/focus/metastasis/index.html

2. Nilay Sethi and Yibin Kang. Unravelling the complexity of metastasis — molecular understanding and targeted therapies. Nature Reviews Cancer 2011; 11:732- 748. http://www.nature.com/nrc/journal/v11/n10/abs/nrc3125.html

3. Meera Saxena and Gerhard Christophor. Rebuilding cancer metastasis in the mouse. Molecular Oncology 2013, 7(2):283-296. http://www.moloncol.org/article/S1574-7891(13)00033-1/abstract

4. Lamb, J. et al. The Connectivity Map: using geneexpression signatures to connect small molecules, genes, and disease. Science 2006 313, 1929–1935. http://www.sciencemag.org/content/313/5795/1929.short

5. Chiang AC and Massagué J. Molecular basis of metastasis. N Engl J Med. 2008 Dec 25;359(26):2814 23 ;http://www.ncbi.nlm.nih.gov/pubmed/19109576

6. By: Ritu Saxena PhD. In focus: Circulating Tumor Cells. http://pharmaceuticalintelligence.com/2013/06/24/in-focus-circulating-tumor-cells/

7.   Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 2002, 417, 949–954. http://www.nature.com/nature/journal/v417/n6892/full/nature00766.html

8. Arozarena, I. et al. Oncogenic BRAF induces melanoma cell invasion by downregulating the cGMP specific phosphodiesterase PDE5A. Cancer Cell 19, 45–57 (2011). http://www.ncbi.nlm.nih.gov/pubmed/21215707

9. Isaiah J. Fidler. The pathogenesis of cancer metastasis: the ‘seed and soil’ hypothesis revisited. Nature Review Cancer. 2003 June. 3(6):453-8. http://www.ncbi.nlm.nih.gov/pubmed/12778135

10. Christoph A. Klein. Parallel progression of primary tumours and metastases.   Nat Rev Cancer. 2009 Apr;9(4):302-12  http://www.ncbi.nlm.nih.gov/pubmed/19308069 http://prometheus.fmrp.usp.br/biocelmolcancer/Klein.pdf

 

Other related articles published on this Open Access Scientific Journal, include the following:

I. By: Ritu Saxena PhD. In focus: Circulating Tumor Cells. http://pharmaceuticalintelligence.com/2013/06/24/in-focus-circulating-tumor-cells/

II. By: Ritu Saxena PhD. Scientists use natural agents for prostate cancer bone metastasis treatment. http://pharmaceuticalintelligence.com/2012/09/17/natural-agents-for-prostate-cancer-bone-metastasis-treatment/

III. By: Prabodh Kandala, PhD. All Cancer Cells Are Not Created Equal: Some Cell Types Control Continued Tumor Growth, Others Prepare the Way for Metastasis. http://pharmaceuticalintelligence.com/2012/05/17/all-cancer-cells-are-not-created-equal-some-cell-types-control-continued-tumor-growth-others-prepare-the-way-for-metastasis/

IV. By: Aviva Lev-Ari PhD RN. MIT Scientists Identified Gene that Controls Aggressiveness in Breast Cancer Cells. http://pharmaceuticalintelligence.com/2013/07/03/mit-scientists-identified-gene-that-controls-aggressiveness-in-breast-cancer-cells/

V. By: Demet Sag PhD CRA, GCP.  The Magic of the Pandora’s Box : Epigenetics and Stemness with Long non-coding RNAs (lincRNA). http://pharmaceuticalintelligence.com/2013/06/30/the-magic-of-the-pandoras-box-epigenetics-and-stemmness-with-long-non-coding-rnas-lincrna/

 

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Author/Curator: Ritu Saxena, PhD

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Word Cloud By Danielle Smolyar

For several decades, research efforts have focused on targeting progression of cancer cells in primary tumors. Primary tumor cell targeting strategies include standard chemotherapy and immunotherapy and modulation of host microenvironment including tumor vasculature. However, cancer progression is comprised of both primary tumor growth and secondary metastasis (Langley RR and Fidler IJ. Tumor cell-organ microenvironment interactions in the pathogenesis of cancer metastasis. Endocr Rev. 2007 May;28(3):297-321; http://www.ncbi.nlm.nih.gov/pubmed/17409287). Owing to the property of unilimited cell division, cells in primary tumor increase rapidly in number and density and are able to favorably influence their microenvironment. Metastasis, on the other hand, depends on the ability of cancer cells to disseminate, circulate, adapt to the harsh environment and seed in different organs to establish secondary tumors. Although tumor cells are shed into the circulation in large numbers since early stages of tumor formation, few tumor cells can survive and proceed to overt metastasis. (Husemann Y et al. Systemic spread is an early step in breast cancer. Cancer Cell. 2008 Jan;13(1):58-68; http://www.ncbi.nlm.nih.gov/pubmed/18167340). Tight vascular wall barriers, unfavorable conditions for survival in distant organs, and a rate-limiting acquisition of organ colonization functions are just some of the impediments to the formation of distant metastasis (Chiang AC and Massagué J. Molecular basis of metastasis. N Engl J Med. 2008 Dec 25;359(26):2814-23; http://www.ncbi.nlm.nih.gov/pubmed/19109576).

It has been hypothesized that metastasis is initiated by a subpopulation of circulating tumor cells (CTC) found in the blood of patients. Therefore, understanding the function of CTC and targeting the CTC is gaining attention as a possible therapeutic avenue in carcinoma treatment.

CTCs

Figure: Circulating tumor cells in the metastatic cascade

(Image source: Chaffer CL and Weinberg RA. Science 2011,331, pp. 1559-1564; http://www.ncbi.nlm.nih.gov/pubmed/21436443)

Isolation of CTC

Initial methods relied on the difference in physical properties of cells. When spun in a centrifuge, different cells in the blood sample settle in separate layers based on their byoyancy, and CTC are found in the white blood cell fraction. Because CTC are generally larger than white blood cells, a size-based filter could be used to separate the cell types (Vona G, et al, Isolation by size of epithelial tumor cells : a new method for the immunomorphological and molecular characterization of circulating tumor cells. Am J Pathol, 2000 Jan;156(1):57-63; http://www.ncbi.nlm.nih.gov/pubmed/10623654).

Herbert A Fritsche, PhD, Professor and Chief, Clinical Chemistry, Department of Laboratory Medicine, The University of Texas, MD Anderson Cancer Center, demonstrated that the CTC can be captured using antibody labeled magnetic beads, either in positive or negative selection schema. After the circulating tumor cells are isolated, they may be characterized by immunohistochemistry and counted.  Alternatively, these cells may be characterized by gene expression analysis using RT-PCR. One of the CTC detection methods, Veridex Inc, Cell Search Assay, has been cleared by the US FDA for use as a prognostic test in patients with metastatic cancers of the breast, prostate and colon. This technology relies on the expression of epithelial cellular adhesion molecular (EpCAM) by epithelial cells and the isolation of these cells by immunomagnetic capture using anti-EpCAM antibodies.  Enriched CTC are identified by immunofluorescence. Martin Fleisher, PhD, Chair, Department of Clinical Laboratories, Memorial Sloan-Kettering Cancer Center discussed in a webinar at the biomarker symposia, Cambridge Healthtech Institute, that every new technology has shortcomings, and the reliance on cancer cells to express sufficient EpCAM to enable capture may affect the role of this technology in future clinical use. Heterogeneous downregulation of epithelial surface antigen in invasive tumor cells has been reported. Thus, alternative methods to detect CTC are being developed. These new methods include-

  1. Flow cytometry that sorts cells by size and surface antigen expression.
  2. CTC microchips that are designed to capture CTC as whole blood flows past EpCAM-coated mirco-posts.
  3. Enrichment by filtration using filters with a pore size of 7-8 µm, that permits smaller red blood cell, leukocytes, and platelets to pass, but captures CTC that have diameters of about 12-15 µm.

Better identification of CTC

Baccelli et al (2013) developed a xenograft assay and demonstrated that the primary human luminal breast cancer CTC contain metastasis-initiated cells (MICs) that give rise to bone, lung and liver metastases in mice. These MIC-containing CTC populations expressed EPCAM, CD44, CD47 and MET. It was observed that in a small cohort of patients with metastases, the number of CTC expressing markers EPCAM,CD44, CD47 and MET, but not of bulk EPCAM+ CTC, correlated with lower overall survival and increased number of metastasic sites. These data describe functional circulating MICs and associated markers, which may aid the design of better tools to diagnose and treat metastatic breast cancer. The findings were published in the Nature Biotechnology journal recently (Baccelli I, et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nature Biotechnology 2013 31, 539–544; http://www.ncbi.nlm.nih.gov/pubmed/23609047).

CTC as prognostic and predictive factor for cancer progression

Martin Fleisher, PhD states “detecting CTC in peripheral blood of patients with cancer has become a clinically relevant and important prognostic biomarker and has been shown to be a predictive biomarker post-therapy. But, key to the use of CTC as a biomarker is the technology designed to enrich cancer cells from peripheral blood.”

Since CTC isolation methods started being established, correlation studies between the cells and a patient’s disease emerged. In 2004, investigators at the Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center (Houston, TX) discovered that the CTC were associated with disease progression and survival in metastatic breast cancer. The clinical trial recruited 177 patients with measurable metastatic breast cancer for levels of CTC both before the patients were to start a new line of treatment and at the first follow-up visit. The progression of the disease or the response to treatment was determined with the use of standard imaging studies at the participating centers. Patients in a training set with levels of CTC equal to or higher than 5 per 7.5 ml of whole blood, as compared with the group with fewer than 5 CTC per 7.5 ml, had a shorter median progression-free survival (2.7 months vs. 7.0 months, P<0.001) and shorter overall survival (10.1 months vs. >18 months, P<0.001). At the first follow-up visit after the initiation of therapy, this difference between the groups persisted (progression-free survival, 2.1 months vs. 7.0 months; P<0.001; overall survival, 8.2 months vs. >18 months; P<0.001), and the reduced proportion of patients (from 49 percent to 30 percent) in the group with an unfavorable prognosis suggested that there was a benefit from therapy.  Thus, the number of CTC was found to be an independent predictor of progression-free survival and overall survival in patients with metastatic breast cancer (Cristofanilli M, et al, Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004 Aug 19;351(8):781-91; http://www.ncbi.nlm.nih.gov/pubmed/15317891).

Similar results have been observed in other cancer types, including prostate and colorectal cancer. The Cell Search System developed by Veridex LLC (Huntingdon Valley, PA) enumerated CTC from 7.5 mL of venous blood and was used to compare the outcomes from three prospective multicenter studies investigating the use of CTC to monitor patients undergoing treatment for metastatic breast, colorectal, or prostate cancer. Evaluation of CTC at anytime during the course of disease allowed assessment of patient prognosis and is predictive of overall survival (Miller MC, et al. Significance of Circulating Tumor Cells Detected by the CellSearch System in Patients with Metastatic Breast Colorectal and Prostate Cancer. J Oncol. 2010; http://www.ncbi.nlm.nih.gov/pubmed/20016752). In addition, the CTC test may permit the oncologist to make an early decision to discontinue first line therapy for metastatic breast cancer and pursue more aggressive alternative treatments.

Genetic analysis of CTC

Additional studies have analyzed the genetic mutations that the cells carry, comparing the mutations to those in a primary tumor or correlating the findings to a patient’s disease severity or spread. In one study, lung cancer patients whose CTC carried a mutation known to cause drug resistance had faster disease progression than those whose CTC lacked the mutation. The investigators analyzed the evolutionary aspect of cancer progression and studied the precursor cells of metastases directly for the identification of prognostic and therapeutic markers. Single disseminated cancer cells isolated from lymph nodes and bone marrow of 107 consecutive esophageal cancer patients were analyzed by whole-genome screening which revealed that primary tumors and lymphatically and hematogenously disseminated cancer cells diverged for most genetic aberrations. Chromosome 17q12-21, the region comprising HER2, was identified as the most frequent gain in disseminated tumor cells that were isolated from both ectopic sites. Furthermore, survival analysis demonstrated that HER2 gain in a single disseminated tumor cell but not in primary tumors conferred high risk for early death (Stoecklein NH, et al. Direct genetic analysis of single disseminated cancer cells for prediction of outcome and therapy selection in esophageal cancer. Cancer Cell. 2008 May;13(5):441-53; http://www.ncbi.nlm.nih.gov/pubmed/18455127).

The abovementioned studies indicate that CTC blood tests have been successfully used to track the severity of a cancer or efficacy of a treatment. In conclusion, the evolution of the CTC technology will be critical in the emerging area of targeted therapy.  With the development and use of new technologies, the links between the genomic information and CTC could be explored and established for targeted therapy.

Challenges in CTC research

  1. Potential clinical significance of CTC has been demonstrated as early detection, diagnostic, prognostic, predictive, surrogate, stratification, and pharmacodynamic biomarkers. Hong B and Zu Y (2013) discuss that “the role of CTC as a disease marker may be unique in different clinical conditions and should be carefully interpreted. A good example is the comparison between the prognostic and predictive biomarkers. Both biomarkers employ progression-free survival and overall survival for data interpretation; however, the prognostic biomarker is independent of specific drug treatment or therapy, and used for the determination of outcomes before treatment, while the predictive biomarker is related to a particular treatment to predict the response. Furthermore, inconsistent results are increasingly reported among the various CTC assay methods, specifically pertaining to results for the CTC detection rate, patient positivity rate, and the correlation between the presence of CTC and survival rate (Hong B and Zu Y. Detecting circulating tumor cells: current challenges and new trends. Source. Theranostics. 2013 Apr 23;3(6):377-94; http://www.ncbi.nlm.nih.gov/pubmed/23781285).
  2. Heterogeneity in CTC along with several other technical factors contribute to discordance, including the changes in methodology, lack of reference standard, spectrum and selection bias, operator variability and bias, sample size, blurred clinical impact with known clinical/pathologic data, use of diverse capture antibodies from different sources, lack of awareness of the pre-analytical phase, oversimplification of the cytopathology process, use of dichotomous decision criteria, etc (Sturgeon C. Limitations of assay techniques for tumor markers. In: (ed.) Diamandis EP, Fritsche HA, Lilja H, Chan DW, Schwartz MK. Tumor markers: physiology, pathobiology, technology, and clinical applications. Washington, DC: AACC Press. 2002:65-82; Gion M and Daidone MG. Circulating biomarkers from tumour bulk to tumour machinery: promises and pitfalls. Eur J Cancer. 2004;40(17):2613-2622; http://www.ncbi.nlm.nih.gov/pubmed/15541962). Therefore, employing a standard protocol is essential in order to minimize a lot of inconsistencies and technical errors.
  3. CTC in a small amount of blood sample might not represent the actual CTC count in the whole blood. In fact, it has been reported that the Cell Search system might undercount the number of CTC. Nagrath et al (2007) have demonstrated that the average CTC number per mL of whole blood is approximately 79-155 in various cancers (Nagrath S, et al. Isolation of rare circulating tumous cells in cancer patients by microchip technology. Nature. 2007;450(7173):1235-1239; http://www.ncbi.nlm.nih.gov/pubmed/18097410). In addition, an investigative CellSearch Profile approach (for research use only) detected an approximately 30-fold higher number of the median CTC in the same paired blood samples (Flores LM, et al. Improving the yield of circulating tumour cells facilitates molecular characterisation and recognition of discordant HER2 amplification in breast cancer. Br J Cancer. 2010;102(10):1495-502; http://www.ncbi.nlm.nih.gov/pubmed/20461092). Such measurement discrepancies indicate that the actual CTC numbers in the blood of patients could be at least 30-100 fold higher than that currently reported by the only FDA-cleared CellSearch system.

Thus, although promising, the CTC technology faces several challenges both in detection and interpretation, which has resulted in its limited clinical acceptance and use. In order to prepare the CTC technology for future widespread clinical acceptance, a comprehensive guideline for all phases of CTC technology development was published by the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium. The guidelines describe methods for interactive comparisons of proprietary new technologies, clinical trial designs, a clinical validation qualification strategy, and an approach for effectively carrying out this work through a public-private partnership that includes test developers, drug developers, clinical trialists, the FDA and the National Cancer Institute (NCI) (Parkinson DR, et al. Considerations in the development of circulating tumor cell technology for clinical use. J Transl Med. 2012;10(1):138; http://www.ncbi.nlm.nih.gov/pubmed/22747748).

Reference:

  1. Langley RR and Fidler IJ. Tumor cell-organ microenvironment interactions in the pathogenesis of cancer metastasis. Endocr Rev. 2007 May;28(3):297-321; http://www.ncbi.nlm.nih.gov/pubmed/17409287
  2. Husemann Y et al. Systemic spread is an early step in breast cancer. Cancer Cell. 2008 Jan;13(1):58-68; http://www.ncbi.nlm.nih.gov/pubmed/18167340
  3. Chiang AC and Massagué J. Molecular basis of metastasis. N Engl J Med. 2008 Dec 25;359(26):2814-23; http://www.ncbi.nlm.nih.gov/pubmed/19109576
  4. Vona G, et al, Isolation by size of epithelial tumor cells : a new method for the immunomorphological and molecular characterization of circulating tumor cells. Am J Pathol, 2000 Jan;156(1):57-63; http://www.ncbi.nlm.nih.gov/pubmed/10623654
  5. Baccelli I, et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nature Biotechnology 2013 31, 539–544; http://www.ncbi.nlm.nih.gov/pubmed/23609047
  6. Cristofanilli M, et al, Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004 Aug 19;351(8):781-91; http://www.ncbi.nlm.nih.gov/pubmed/15317891
  7. Miller MC, et al. Significance of Circulating Tumor Cells Detected by the CellSearch System in Patients with Metastatic Breast Colorectal and Prostate Cancer. J Oncol. 2010; http://www.ncbi.nlm.nih.gov/pubmed/20016752
  8. Stoecklein NH, et al. Direct genetic analysis of single disseminated cancer cells for prediction of outcome and therapy selection in esophageal cancer. Cancer Cell. 2008 May;13(5):441-53; http://www.ncbi.nlm.nih.gov/pubmed/18455127
  9. Hong B and Zu Y. Detecting circulating tumor cells: current challenges and new trends. Source. Theranostics. 2013 Apr 23;3(6):377-94; http://www.ncbi.nlm.nih.gov/pubmed/23781285
  10. 10. Sturgeon C. Limitations of assay techniques for tumor markers. In: (ed.) Diamandis EP, Fritsche HA, Lilja H, Chan DW, Schwartz MK. Tumor markers: physiology, pathobiology, technology, and clinical applications. Washington, DC: AACC Press. 2002:65-82
  11. Gion M and Daidone MG. Circulating biomarkers from tumour bulk to tumour machinery: promises and pitfalls. Eur J Cancer. 2004;40(17):2613-2622; http://www.ncbi.nlm.nih.gov/pubmed/15541962
  12. Nagrath S, et al. Isolation of rare circulating tumous cells in cancer patients by microchip technology. Nature. 2007;450(7173):1235-1239; http://www.ncbi.nlm.nih.gov/pubmed/18097410
  13. Flores LM, et al. Improving the yield of circulating tumour cells facilitates molecular characterisation and recognition of discordant HER2 amplification in breast cancer. Br J Cancer. 2010;102(10):1495-502; http://www.ncbi.nlm.nih.gov/pubmed/20461092
  14. Chaffer CL and Weinberg RA. Science 2011,331, pp. 1559-1564; http://www.ncbi.nlm.nih.gov/pubmed/21436443

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