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Improving diagnostic yield in pediatric cancer precision medicine
Elaine R Mardis
Advent of genomics have revolutionized how we diagnose and treat lung cancer
We are currently needing to understand the driver mutations and variants where we can personalize therapy
PD-L1 and other checkpoint therapy have not really been used in pediatric cancers even though CAR-T have been successful
The incidence rates and mortality rates of pediatric cancers are rising
Large scale study of over 700 pediatric cancers show cancers driven by epigenetic drivers or fusion proteins. Need for transcriptomics. Also study demonstrated that we have underestimated germ line mutations and hereditary factors.
They put together a database to nominate patients on their IGM Cancer protocol. Involves genetic counseling and obtaining germ line samples to determine hereditary factors. RNA and protein are evaluated as well as exome sequencing. RNASeq and Archer Dx test to identify driver fusions
PECAN curated database from St. Jude used to determine driver mutations. They use multiple databases and overlap within these databases and knowledge base to determine or weed out false positives
They have used these studies to understand the immune infiltrate into recurrent cancers (CytoCure)
They found 40 germline cancer predisposition genes, 47 driver somatic fusion proteins, 81 potential actionable targets, 106 CNV, 196 meaningful somatic driver mutations
They are functioning well at NCI with respect to grant reviews, research, and general functions in spite of the COVID pandemic and the massive demonstrations on also focusing on the disparities which occur in cancer research field and cancer care
There are ongoing efforts at NCI to make a positive difference in racial injustice, diversity in the cancer workforce, and for patients as well
Need a diverse workforce across the cancer research and care spectrum
Data show that areas where the clinicians are successful in putting African Americans on clinical trials are areas (geographic and site specific) where health disparities are narrowing
Grants through NCI new SeroNet for COVID-19 serologic testing funded by two RFAs through NIAD (RFA-CA-30-038 and RFA-CA-20-039) and will close on July 22, 2020
Tuesday, June 23
12:45 PM – 1:46 PM EDT
Virtual Educational Session
Immunology, Tumor Biology, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics
This educational session will update cancer researchers and clinicians about the latest developments in the detailed understanding of the types and roles of immune cells in tumors. It will summarize current knowledge about the types of T cells, natural killer cells, B cells, and myeloid cells in tumors and discuss current knowledge about the roles these cells play in the antitumor immune response. The session will feature some of the most promising up-and-coming cancer immunologists who will inform about their latest strategies to harness the immune system to promote more effective therapies.
Judith A Varner, Yuliya Pylayeva-Gupta
Introduction
Judith A Varner
New techniques reveal critical roles of myeloid cells in tumor development and progression
Different type of cells are becoming targets for immune checkpoint like myeloid cells
In T cell excluded or desert tumors T cells are held at periphery so myeloid cells can infiltrate though so macrophages might be effective in these immune t cell naïve tumors, macrophages are most abundant types of immune cells in tumors
CXCLs are potential targets
PI3K delta inhibitors,
Reduce the infiltrate of myeloid tumor suppressor cells like macrophages
When should we give myeloid or T cell therapy is the issue
Judith A Varner
Novel strategies to harness T-cell biology for cancer therapy
Positive and negative roles of B cells in cancer
Yuliya Pylayeva-Gupta
New approaches in cancer immunotherapy: Programming bacteria to induce systemic antitumor immunity
There are numerous examples of highly successful covalent drugs such as aspirin and penicillin that have been in use for a long period of time. Despite historical success, there was a period of reluctance among many to purse covalent drugs based on concerns about toxicity. With advances in understanding features of a well-designed covalent drug, new techniques to discover and characterize covalent inhibitors, and clinical success of new covalent cancer drugs in recent years, there is renewed interest in covalent compounds. This session will provide a broad look at covalent probe compounds and drug development, including a historical perspective, examination of warheads and electrophilic amino acids, the role of chemoproteomics, and case studies.
Benjamin F Cravatt, Richard A. Ward, Sara J Buhrlage
Discovering and optimizing covalent small-molecule ligands by chemical proteomics
Benjamin F Cravatt
Multiple approaches are being investigated to find new covalent inhibitors such as: 1) cysteine reactivity mapping, 2) mapping cysteine ligandability, 3) and functional screening in phenotypic assays for electrophilic compounds
Using fluorescent activity probes in proteomic screens; have broad useability in the proteome but can be specific
They screened quiescent versus stimulated T cells to determine reactive cysteines in a phenotypic screen and analyzed by MS proteomics (cysteine reactivity profiling); can quantitate 15000 to 20,000 reactive cysteines
Isocitrate dehydrogenase 1 and adapter protein LCP-1 are two examples of changes in reactive cysteines they have seen using this method
They use scout molecules to target ligands or proteins with reactive cysteines
For phenotypic screens they first use a cytotoxic assay to screen out toxic compounds which just kill cells without causing T cell activation (like IL10 secretion)
INTERESTINGLY coupling these MS reactive cysteine screens with phenotypic screens you can find NONCANONICAL mechanisms of many of these target proteins (many of the compounds found targets which were not predicted or known)
Electrophilic warheads and nucleophilic amino acids: A chemical and computational perspective on covalent modifier
The covalent targeting of cysteine residues in drug discovery and its application to the discovery of Osimertinib
Richard A. Ward
Cysteine activation: thiolate form of cysteine is a strong nucleophile
Thiolate form preferred in polar environment
Activation can be assisted by neighboring residues; pKA will have an effect on deprotonation
pKas of cysteine vary in EGFR
cysteine that are too reactive give toxicity while not reactive enough are ineffective
Accelerating drug discovery with lysine-targeted covalent probes
This Educational Session aims to guide discussion on the heterogeneous cells and metabolism in the tumor microenvironment. It is now clear that the diversity of cells in tumors each require distinct metabolic programs to survive and proliferate. Tumors, however, are genetically programmed for high rates of metabolism and can present a metabolically hostile environment in which nutrient competition and hypoxia can limit antitumor immunity.
Jeffrey C Rathmell, Lydia Lynch, Mara H Sherman, Greg M Delgoffe
T-cell metabolism and metabolic reprogramming antitumor immunity
Jeffrey C Rathmell
Introduction
Jeffrey C Rathmell
Metabolic functions of cancer-associated fibroblasts
Mara H Sherman
Tumor microenvironment metabolism and its effects on antitumor immunity and immunotherapeutic response
Greg M Delgoffe
Multiple metabolites, reactive oxygen species within the tumor microenvironment; is there heterogeneity within the TME metabolome which can predict their ability to be immunosensitive
Took melanoma cells and looked at metabolism using Seahorse (glycolysis): and there was vast heterogeneity in melanoma tumor cells; some just do oxphos and no glycolytic metabolism (inverse Warburg)
As they profiled whole tumors they could separate out the metabolism of each cell type within the tumor and could look at T cells versus stromal CAFs or tumor cells and characterized cells as indolent or metabolic
T cells from hyerglycolytic tumors were fine but from high glycolysis the T cells were more indolent
When knock down glucose transporter the cells become more glycolytic
If patient had high oxidative metabolism had low PDL1 sensitivity
Showed this result in head and neck cancer as well
Metformin a complex 1 inhibitor which is not as toxic as most mito oxphos inhibitors the T cells have less hypoxia and can remodel the TME and stimulate the immune response
Metformin now in clinical trials
T cells though seem metabolically restricted; T cells that infiltrate tumors are low mitochondrial phosph cells
T cells from tumors have defective mitochondria or little respiratory capacity
They have some preliminary findings that metabolic inhibitors may help with CAR-T therapy
Obesity, lipids and suppression of anti-tumor immunity
Lydia Lynch
Hypothesis: obesity causes issues with anti tumor immunity
Less NK cells in obese people; also produce less IFN gamma
RNASeq on NOD mice; granzymes and perforins at top of list of obese downregulated
Upregulated genes that were upregulated involved in lipid metabolism
All were PPAR target genes
NK cells from obese patients takes up palmitate and this reduces their glycolysis but OXPHOS also reduced; they think increased FFA basically overloads mitochondria
Long recognized for their role in cancer diagnosis and prognostication, pathologists are beginning to leverage a variety of digital imaging technologies and computational tools to improve both clinical practice and cancer research. Remarkably, the emergence of artificial intelligence (AI) and machine learning algorithms for analyzing pathology specimens is poised to not only augment the resolution and accuracy of clinical diagnosis, but also fundamentally transform the role of the pathologist in cancer science and precision oncology. This session will discuss what pathologists are currently able to achieve with these new technologies, present their challenges and barriers, and overview their future possibilities in cancer diagnosis and research. The session will also include discussions of what is practical and doable in the clinic for diagnostic and clinical oncology in comparison to technologies and approaches primarily utilized to accelerate cancer research.
Jorge S Reis-Filho, Thomas J Fuchs, David L Rimm, Jayanta Debnath
Using old methods and new methods; so cell counting you use to find the cells then phenotype; with quantification like with Aqua use densitometry of positive signal to determine a threshold to determine presence of a cell for counting
Hiplex versus multiplex imaging where you have ten channels to measure by cycling of flour on antibody (can get up to 20plex)
Hiplex can be coupled with Mass spectrometry (Imaging Mass spectrometry, based on heavy metal tags on mAbs)
However it will still take a trained pathologist to define regions of interest or field of desired view
Introduction
Jayanta Debnath
Challenges and barriers of implementing AI tools for cancer diagnostics
Jorge S Reis-Filho
Implementing robust digital pathology workflows into clinical practice and cancer research
Jayanta Debnath
Invited Speaker
Thomas J Fuchs
Founder of spinout of Memorial Sloan Kettering
Separates AI from computational algothimic
Dealing with not just machines but integrating human intelligence
Making decision for the patients must involve human decision making as well
How do we get experts to do these decisions faster
AI in pathology: what is difficult? =è sandbox scenarios where machines are great,; curated datasets; human decision support systems or maps; or try to predict nature
1) learn rules made by humans; human to human scenario 2)constrained nature 3)unconstrained nature like images and or behavior 4) predict nature response to nature response to itself
In sandbox scenario the rules are set in stone and machines are great like chess playing
In second scenario can train computer to predict what a human would predict
So third scenario is like driving cars
System on constrained nature or constrained dataset will take a long time for commuter to get to decision
Fourth category is long term data collection project
He is finding it is still finding it is still is difficult to predict nature so going from clinical finding to prognosis still does not have good predictability with AI alone; need for human involvement
End to end partnering (EPL) is a new way where humans can get more involved with the algorithm and assist with the problem of constrained data
An example of a workflow for pathology would be as follows from Campanella et al 2019 Nature Medicine: obtain digital images (they digitized a million slides), train a massive data set with highthroughput computing (needed a lot of time and big software developing effort), and then train it using input be the best expert pathologists (nature to human and unconstrained because no data curation done)
Led to first clinically grade machine learning system (Camelyon16 was the challenge for detecting metastatic cells in lymph tissue; tested on 12,000 patients from 45 countries)
The first big hurdle was moving from manually annotated slides (which was a big bottleneck) to automatically extracted data from path reports).
Now problem is in prediction: How can we bridge the gap from predicting humans to predicting nature?
With an AI system pathologist drastically improved the ability to detect very small lesions
Incidence rates of several cancers (e.g., colorectal, pancreatic, and breast cancers) are rising in younger populations, which contrasts with either declining or more slowly rising incidence in older populations. Early-onset cancers are also more aggressive and have different tumor characteristics than those in older populations. Evidence on risk factors and contributors to early-onset cancers is emerging. In this Educational Session, the trends and burden, potential causes, risk factors, and tumor characteristics of early-onset cancers will be covered. Presenters will focus on colorectal and breast cancer, which are among the most common causes of cancer deaths in younger people. Potential mechanisms of early-onset cancers and racial/ethnic differences will also be discussed.
Stacey A. Fedewa, Xavier Llor, Pepper Jo Schedin, Yin Cao
Cancers that are and are not increasing in younger populations
Stacey A. Fedewa
Early onset cancers, pediatric cancers and colon cancers are increasing in younger adults
Younger people are more likely to be uninsured and these are there most productive years so it is a horrible life event for a young adult to be diagnosed with cancer. They will have more financial hardship and most (70%) of the young adults with cancer have had financial difficulties. It is very hard for women as they are on their childbearing years so additional stress
Types of early onset cancer varies by age as well as geographic locations. For example in 20s thyroid cancer is more common but in 30s it is breast cancer. Colorectal and testicular most common in US.
SCC is decreasing by adenocarcinoma of the cervix is increasing in women’s 40s, potentially due to changing sexual behaviors
Breast cancer is increasing in younger women: maybe etiologic distinct like triple negative and larger racial disparities in younger African American women
Increased obesity among younger people is becoming a factor in this increasing incidence of early onset cancers
Other Articles on this Open Access Online Journal on Cancer Conferences and Conference Coverage in Real Time Include
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 Cells10) 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.
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)
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
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2 Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature551, 115-118, doi:10.1038/nature24057 (2017).
3 Mashimo, T. et al. Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell159, 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. Nature540, 150, doi:10.1038/nature19851 (2016).
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6 Commisso, C. et al. Macropinocytosis of protein is an amino acid supply route in Ras-transformed cells. Nature497, 633-637, doi:10.1038/nature12138 (2013).
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8 Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell144, 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. Science358, 941-946, doi:10.1126/science.aam9305 (2017).
10 Dang, C. V. Feeding frenzy for cancer cells. Science358, 862-863, doi:10.1126/science.aaq1070 (2017).
11 Smith, T. J. & Stanley, C. A. Untangling the glutamate dehydrogenase allosteric nightmare. Trends in biochemical sciences33, 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. Nature481, 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 & metabolism6, 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 reports19, 1858-1873, doi:10.1016/j.celrep.2017.05.014 (2017).
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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 medicine207, 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 cell17, 225-234, doi:10.1016/j.ccr.2010.01.020 (2010).
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Other articles on this Open Access Journal on Cancer Metabolism Include:
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
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 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.
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.
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 [208–210], 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.
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 ROSlevels from getting dangerously high,” Dr. DeBerardinis remarked.
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 …
Changes in cell metabolism are increasingly recognized as an important way tumors develop and progress, yet these changes are hard to measure and interpret. A new tool designed by MSK scientists allows users to identify metabolic changes in kidney cancer tumors that may one day be targets for therapy.
Much of what we know about cancer comes from studying genes. By sequencing genes in tumors, for example, scientists have learned what mutations are typically found in different cancer types. Genetic methods can also be used to survey which proteins are made in tumors.
Yet this information provides only an indirect measure of how cancer cells operate. To really capture that, you need to know about the dynamic chemical changes occurring in these cells; you need to know about cancer metabolism.
Tracing the products of cell metabolism, known as metabolites, is not easy to do. “Looking at metabolites in cancer has been very difficult because the technology was not available,” says James Hsieh, a physician-scientist at Memorial Sloan Kettering and an expert in kidney cancer. “Until recently, we didn’t have the capacity to look at hundreds, even thousands, of different metabolites inside of cells.”
But with advanced biochemical methods, these myriad metabolites are finally coming into focus. Dr. Hsieh’s team has used such methods to profile metabolic changes in hundreds of kidney cancer tumor samples. What’s more, they’ve developed a new online tool that will help researchers make sense of this vast data pool, highlighting previously unknown connections between metabolism and clear cell renal cell carcinoma — the most common, lethal form of the disease.
Metabolism Explained
Think of a cell as a factory. If genes provide the floor plan for the factory, and proteins make up the built environment, then metabolism is the movement of materials through the factory to make products.
For many years, investigators wanting to understand cancer metabolism looked at enzyme levels — proteins that catalyze chemical reactions. Publically accessible databases, such as those maintained by The Cancer Genome Atlas (TCGA), provide this information. The problem is that enzyme levels don’t necessarily tell you whether, and at what rate, metabolites are actually being made.
“There’s no good way to infer how changes in metabolite levels are connected to enzyme levels,” says Ed Reznik, a postdoctoral fellow in computational biology at the Sloan Kettering Institute who is a co-first author on the study. “You really have to go after the metabolites directly.” (To continue the factory analogy, just because a forklift is present on the shop floor doesn’t mean it’s being used.)
If genes provide the floor plan for the factory, and proteins make up the built environment, then metabolism is the movement of materials through the factory to make products.
To track metabolites, the team obtained samples of tumor tissue and normal tissue from 138 clear cell kidney cancer patients treated at MSK. A surgeon on the team and the paper’s other co-first author, Ari Hakimi, performed these operations.
The researchers then used mass spectrometry and liquid and gas chromatography to analyze the levels of more than 800 different metabolites in these samples. By comparing the levels of metabolites in tumors with those in normal tissues, they were able to chart the rise and fall of these chemicals.
There’s an App for That
Making sense of the metabolic data was challenging at first, since there was so much of it. “If you look at human metabolism, there are upward of 5,000 distinct biochemical reactions,” says Dr. Reznik. “It’s really hard to make sense of that in a way that humans can parse.”
So the team decided to build a tool that would help them visualize what was going on. Working with a team of programmers, Dr. Reznik developed what he calls a “metabologram,” which allows users to review the metabolite data for any number of different metabolic pathways, one pathway at a time. Users can compare metabolites between tumor samples and normal samples, or between lower-stage tumors and higher-stage tumors. They can also see how the metabolic data line up against the gene expression data obtained from TCGA.
With the help of their new tool, the team made some startling discoveries. They found that the genetic data from TCGA were not always reflective of what was happening to metabolites in kidney cancer cells, and that the metabolic data help to make better sense of the clinical behavior of kidney cancer tumors.
“Our data are actually much more consistent with the human data obtained from pathology,” Dr. Hsieh says.
Charting Aggressiveness
Taking a bird’s-eye view of the metabolic data, the team found four distinct groupings, or clusters, of tumor samples that they could distinguish based on levels of metabolites. The clusters differed in their level of tumor aggressiveness and highlighted who the high-risk patients were.
“You can use the metabologram to get a sense of what’s driving the aggressive tumors from a metabolic standpoint,” Dr. Hakimi says. Once you have that, you can then think about ways to target that altered metabolism.
The team hopes that the new tool, which is being made freely available online, will help researchers generate novel hypotheses about metabolism and kidney cancer, and even encourage other teams to create metabolograms for other cancer types.
“The goal is ultimately to use this information to improve clinical prediction for kidney cancer and to understand how best to treat it,” Dr. Hsieh says.
Background Tumoral heterogeneity is a major determinant of resistance in solid tumors. FDG-PET/CT can identify early during chemotherapy non-responsive lesions within the whole body tumor load. This prospective multicentric proof-of-concept study explores intra-individual metabolic response (mR) heterogeneity as a treatment efficacy biomarker in chemorefractory metastatic colorectal cancer (mCRC).
Methods Standardized FDG-PET/CT was performed at baseline and after the first cycle of combined sorafenib (600mg/day for 21 days, then 800mg/day) and capecitabine (1700 mg/m²/day administered D1-14 every 21 days). MR assessment was categorized according to the proportion of metabolically non-responding (non-mR) lesions (stable FDG uptake with SUV-max decrease <15%) among all measurable lesions.
Results Ninety-two patients were included. The median overall survival(OS) and progression-free survival (PFS) were 8.2months (95%CI:6.8–10.5) and 4.2months (95%CI:3.4–4.8) respectively. In the 79 assessable patients, early PET-CT showed no metabolically refractory lesion in 47%, a heterogeneous mR with at least one non-mR lesion in 32%, and a consistent non-mR or early disease progression in 21%. On exploratory analysis, patients without any non-mR lesion showed a significantly longer PFS (HR 0.34; 95% CI: 0.21–0.56, P-value 0.02) compared to the other patients. The proportion of non-mR lesions within the tumor load did not impact PFS/OS.
Conclusion The presence of at least one metabolically refractory lesion is associated with a poorer outcome in advanced mCRC patients treated with combined sorafenib-capecitabine. Early detection of treatment-induced mR heterogeneity may represent an important predictive efficacy biomarker in mCRC.
Trial Registration ClinicalTrials.gov NCT01290926
Background
Tumoral heterogeneity is a major determinant of resistance in solid tumors. FDG-PET/CT can identify early during chemotherapy non-responsive lesions within the whole body tumor load. This prospective multicentric proof-of-concept study explores intra-individual metabolic response (mR) heterogeneity as a treatment efficacy biomarker in chemorefractory metastatic colorectal cancer (mCRC).
Methods
Standardized FDG-PET/CT was performed at baseline and after the first cycle of combined sorafenib (600mg/day for 21 days, then 800mg/day) and capecitabine (1700 mg/m²/day administered D1-14 every 21 days). MR assessment was categorized according to the proportion of metabolically non-responding (non-mR) lesions (stable FDG uptake with SUVmax decrease <15%) among all measurable lesions.
Results
Ninety-two patients were included. The median overall survival (OS) and progression-free survival (PFS) were 8.2 months (95% CI: 6.8–10.5) and 4.2 months (95% CI: 3.4–4.8) respectively. In the 79 assessable patients, early PET-CT showed no metabolically refractory lesion in 47%, a heterogeneous mR with at least one non-mR lesion in 32%, and a consistent non-mR or early disease progression in 21%. On exploratory analysis, patients without any non-mR lesion showed a significantly longer PFS (HR 0.34; 95% CI: 0.21–0.56, P-value <0.001) and OS (HR 0.58; 95% CI: 0.36–0.92, P-value 0.02) compared to the other patients. The proportion of non-mR lesions within the tumor load did not impact PFS/OS.
Conclusion
The presence of at least one metabolically refractory lesion is associated with a poorer outcome in advanced mCRC patients treated with combined sorafenib-capecitabine. Early detection of treatment-induced mR heterogeneity may represent an important predictive efficacy biomarker in mCRC.
The development of new therapeutics for solid tumors is currently strained by increasing regulatory demands to better define subpopulations bearing resistant diseases in order to spare patients from useless toxicities and the society from unaffordable costs in case of ineffective treatments.
Tumor heterogeneity through the existence of resistant subclones (genetic drift) or local environmental factors is nowadays accepted as a major determinant of treatment resistance. However, sensitive biomarkers of tumoral heterogeneity are lacking.[1–3] Current response assessment methods using morphology (RECIST using MRI/CT) or metabolism (PERCIST using FGD-PET/CT) do not allow the description of tumor heterogeneity because dichotomization of response (versus non-response) requires summing of measurements or the selection of the one single most representative lesion.[4] Moreover most of the new biological therapies render response evaluation even more challenging by the infrequency of tumor shrinkage.[5–8]
Imaging tumour metabolism using 18F-Fluorodeoxyglucose positron emission tomography coupled with computed tomography (FDG-PET/CT) allows rapid identification of treatment-refractory lesions with a high negative predictive value (NPV).[9–14] FDG-PET is currently central in the international recommendations for response assessment for Hodgkin’s disease and aggressive non-Hodgkin’s lymphoma, in which medical conditions it is used commonly as a basis for therapeutic decisions. [14–17] In contrast, solid tumors are frequently more refractory to treatment and reveal smaller and slower changes in FDG uptake under therapy leading to the existence of different criteria for metabolic response assessment at the lesion as well as at the patient level.[18,19] This ongoing discussion explain why metabolic imaging has still not acquired a biomarker status in solid tumors.
Metabolic imaging provides a whole-body quantitative assessment of treatment-induced changes in tumoral glycolysis early after treatment initiation, before any morphological changes are observed. It has therefore the potential to detect tumoral heterogeneity by revealing how distinct tumor sites behave in response to treatment.
Several trials suggest meaningful clinical activity of combined sorafenib-capecitabine in metastatic breast and colorectal cancer. However the significant toxicity of the combination renders its use practically incompatible with a palliative setting, further underscoring the need to identify a sensitive biomarker for patient selection.[20,21] Preliminary reports in lung and renal cancer suggest that FDG-PET-based metabolic response assessment could be used as a predictive biomarker of sorafenib.[22,23]
The trial is a proof-of-concept study designed to explore intra-individual mR heterogeneity as a prognostic biomarker for this combination of a biological and a cytotoxic agent in mCRC.
Material and Methods
Belgian competent authorities and ethical committees of the 6 participating centres approved the study (EudraCT 2010-023695-91, clinicaltrials.gov NCT01290926), designed as a prospective multicentric single-arm phase II, with one-stage accrual.
Patients with histologically proven unresectable metastatic CRC failing all standard treatments but not necessarily bevacizumab were eligible. Exclusion criteria were contraindications for capecitabine and sorafenib, ECOG performance status (PS) > 1, age < 18 years, and cerebral metastasis. Normal organ and bone marrow function, a life expectancy >12 weeks, and a signed informed consent were required.
Both drugs were given orally on an outpatient basis: sorafenib 200mg in the morning and 400 mg in the evening every day for the first cycle, then 400 mg twice a day every day; capecitabine 850 mg/m2 twice a day on days 1 to 14, every 21 days. One cycle was defined as a 21-day period. Adverse events were reported according to the National Cancer Institute Criteria, version 3.0 (http://ctep.cancer.gov/protocolDevelopment/electronic_applications/docs/ctcaev3.pdf). Study medications were to be stopped at disease progression or when unacceptable toxicity occurred. RECIST 1.1-radiological response was assessed locally every two cycles (6weeks). Patients were followed until objective disease progression and every 3 months thereafter for survival assessment.
FDG-PET/CT Imaging
For the FDG-PET/CT, patients were referred to one of the 5 participating PET/CT centres, previously approved for participation based on FDG-PET phantom imaging study for quality’s central assessment [24]. An independent academic molecular imaging core laboratory (OriLab) centralized all FDG-PET/CT images through anonymized CD-Rom transfers, checked image’s quality, DICOM headers, compliance to the Standard Procedures Imaging Manual and imaging case report forms.
Baseline FDG-PET/CT was performed within 7 days preceding chemotherapy initiation and repeated under the same technical and patient conditions on day (D)21 (range D19-D23), with D1 as the first day of chemotherapy administration. Prior to FDG injection, fasting ≥ 6 hours and glycemia levels <120 mg/dL for non-diabetic patients, and <180 mg/dL for diabetic patients were required. Short-acting insulin use on the day of FDG-PET/CT was not allowed.
The PET/CT was initiated 60 to 90 minutes after intravenous injection of 3.7 to 7.4 MBq/kg FDG, optimized for body weight. Similar FDG activity (+/-15%) and time window (+/- 15 min) were used for the second PET/CT.
Whole body scanning with a low dose CT (without intravenous or oral contrast, from proximal femur to skull) was performed, immediately followed by the PET acquisition. Imaging acquisition and reconstruction remained stable over the whole study period. The second FDG-PET/CT was strictly blinded to the investigators, and was not added to the patient’s (electronic) medical records.
The standard uptake value (SUV) of FDG used was the lean body mass-based maximal SUV value within the lesion (SUVmax, g/ml).
All FDG-PET/CT images were analysed in batches using the same software (PETVcar version 4.6, General Electric, USA) and display techniques. Two senior nuclear medicine physicians (PF, CG) performed independent mR analyses using a predefined 3-step methodology.[13] First, on the baseline PET/CT, target lesions were identified according to the following criteria: transaxial diameter (measured on the CT of the PET/CT) > 15 mm, intense FDG uptake (> 2 x normal liver parenchym uptake) and with an unequivocally neoplastic basis. Each target lesion was then classified as non-responding (decrease of SUVmax on second PET-CT<15%) or responding. Second, the patients were classified according to the lesional distribution of mR; class I: absence of any metabolically non-responding lesion, class II: a minor part of whole body tumour load shows a non-response, class III: major part of whole body target tumour load does not respond, and, class IV: all target lesions are non-responding, or presence of a progressive lesion (progression defined as >25% increase of SUVMax, or appearance of a new lesion). (Fig 1) Finally, different methods of patient response dichotomization (metabolic responders versus non-responders) were explored.
Classes of metabolic responses. Class1: no metabolic unresponsive lesion; Class2: minority of unresponsive lesions among whole body target tumour load; Class3: majority of whole body target tumour load does not respond; Class4: all target lesions are non-responding,or, presence of progressive lesions [progression defined as > 25% increase of FDG up take on second PET or appearance of a new lesion] http://dx.doi.org:/10.1371/journal.pone.0138341.g001
Classes of metabolic responses.
Statistical considerations
A first co-primary objective defined the minimal clinical activity necessary to explore the negative predictive value of metabolic response imaging on OS as a survival rate at 6 months > 30% according to the existing literature on chemorefractory CRC. To reject the null hypothesis that the 6 month-OS rate would be <30% using a binomial distribution, a 1-sided test with α = 0.025 and a power of 90% in case of a true 6 months-OS ≥ 50% was used, requiring a sample size of 66 eligible patients followed for at least 6 months. An intent-to-treat (ITT) approach was used.
The second co-primary objective was the prognostic value of mR classification. Based on a previous study,[13] and anticipating a 95% eligibility rate, a 50% early PET/CT non-responders rate, and a hazard ratio (HR) around 0.385 for comparison between the survival distributions, 54 events were needed for a 90% power and a two-sided logrank test at the 2.5% level.
Because the mR rate monitored during the study was higher than expected, the number of events to be observed was increased to 62. This decision was taken without changing the HR to be detected and without estimating this HR during study conduct.
Secondary objectives were to describe PFS, objective response rate and toxicity and to determine the predictive value of early MR on PFS.
For the first co-primary objective, the 6 month-OS, median (m)OS and mPFS were calculated from the patient’s inclusion. For the second co-primary objective, the predictive value assessment of mR on OS and PFS was done from the time of the second FDG-PET/CT on patients having undergone the second FDG-PET/CT in order to control for guarantee-time bias.[25] PFS was calculated up to the time of disease progression or death, whichever occurred first. Kaplan-Meier estimates were used to characterise PFS and OS, and the log-rank test to investigate comparisons between survival curves. Cox’s proportional hazards model was used to calculate HR and their 95% CI
The multivariate analysis was performed using Cox’s proportional hazard model. Variables with a univariate P-value < 0.20 were considered as possible predictors in the multivariate model. We performed stepwise forward selection of variables, i.e. forward selection but at each step variables already in the model could be dropped if their associated p-value became >0.05. To verify the final model, also backward selection of variables was performed on all variables with univariate p-value<0.20, resulting in the same set of variables.[26]
All statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA) and GraphPad Prism 6 software.
Patients found with an early metabolic progressive disease (class IV) were not excluded from the statistical analyses as the objectives of the paper were to show the predictive value of early metabolic response on OS and PFS, which implies the necessity of an intent-to-treat analysis. The event “progression” in the definition of PFS is moreover a radiological progression. Patients belonging to class IV do not meet this definition of radiological progression, which remains an event to be predicted.
Results
Between February and October 2011, 97 consecutive patients were enrolled in 6 clinical centres. The CONSORT diagram details the reasons for considering 5 patients as ineligible, excluding them from all analysis (Fig 2). The eligible patients (N = 92), median age 63 (range 28–83), male/female ratio of 54/46, PS 0 (55%) or 1(45%) received a median of 5 (range 0–44+) cycles of sorafenib-capecitabine after an history of a median of 3 (range 1–6) prior therapeutic lines including bevacizumab in 55% of patients. Codons 12–13 KRAS mutations were present in 52%.
Patients presented a median of 7 (Q1 = 4, Q3 = 9) different adverse reactions during therapy. All but one patient experienced at least one toxicity of any grade, of whom 61.4% with at least one grade III-IV. Grade III-IV side effects were mainly fatigue (21.6%), hand-foot skin reactions (HFSR) (15.9%), and diarrhoea (12.5%). No toxic death was observed. Toxicity led to dose modifications in 63.6% and therapy discontinuation in 5.7% of cases.
Survival data and radiological response
The mOS and mPFS were 8.2 months (95% CI: 6.8–10.5) and 4.2 months (95% CI: 3.4–4.8) respectively. The OS rate at 6 months was 71% (65/92) (95% CI: 61%-79%), significantly higher than the 30% minimal efficiency level predefined in the statistical plan (p-value <0.001), meeting the clinical co-primary endpoint.
According to RECIST, partial response was observed in 7/92 patients (7.6%, 95%CI 2.2–13.0). In the 79 assessable patients, disease control at first evaluation (partial responses and stable diseases according to RECIST) was noted in 32/37 (80%) of the patients with consistent mR versus 24/42 (57%) in other patients (p-value 0.006) (Table 2).
MR data were available for 79 patients: 37 (46.8%) were classified as class I; 14 (17.7%) as class II; 11 (13.9%) as class III; and 17 (21.5%) as class IV. Within Class IV, 8 patients (10%) showed early metabolic disease progression.
Patients without any metabolically non-responding lesions (Class I) performed better than patients with heterogeneous responses (Class II and III) or with a consistent non-response or progressive disease (Class IV). The difference between the four classes is statistically significant for mPFS (p-value <0.001) but not for mOS (p-value = 0.13). (Fig 3A and 3B)
PFS* (A) and OS* (B) distribution according to the 4 classes of metabolic response. Class1: no metabolic unresponsive lesion; Class2: minority of unresponsive lesions among whole body target tumour load; Class3: majority of whole body target tumour load does not respond; Class 4: all target lesions are nonresponding, or, presence of progressive lesions [progression defined as >25% increase of FDG uptake on second PET, or appearance of a new lesion].*from date of the second FDG PET-CT.
Two classifications were considered for reporting response in a dichotomized way according to mR heterogeneity among lesions: classes (I and II) versus classes (III and IV),[13] and classes (I) versus classes (II+III+IV). The first compares outcome according to the dominance of non-mR lesions within the tumor load, the second according to the consistence of mR (Table 3, Fig 4). “Using the “dominance” classification to define early metabolic non response, the second co-primary objective, which was to identify a prognostic value on survival for early metabolic assessment, was not met while it was successful to discriminate patients according to their outcome using the exploratory “consistence” classification.“Five of the 42 patients (12%) with at least one non-responding lesion remained free of disease progression at 6 months, versus 15 of the 37 class I patients (41%) (p-value 0.005).
Multivariate analysis after stepwise variable selection of age, PS, number of previous chemotherapy lines, bevacizumab pretreatment, sex, Body Mass Index (BMI), HFSR occurrence and mR retained the absence of metabolically resistant lesion (class I) as the only variable significantly correlated with both mOS and mPFS (Table 4).
Tumoral heterogeneity, described as the coexistence of genomically different subclones within a patient tumor load or to local environmental aspects, is recognized as a major determinant of resistance to treatment in solid tumors.[1–3] However, interlesional tumor heterogeneity in metastatic setting is not covered by current response assessment methods because of the analysis’ methodology performing averaging of responses among lesions. This prospective multicentric proof-of-concept study explored interlesional mR heterogeneity as a biomarker of treatment resistance in advanced solid tumors.
As previously reported in several solid tumors, FDG-PET/CT response assessment after one therapy cycle allows a rapid identification of non-responding lesions/patients, fulfilling the necessary conditions to become potentially a good predictive biomarker, which is crucial to avoid useless toxicity.[4,9–12,22,27] Moreover, significant progresses and implementation of standardized methodology for FDG-PET/CT imaging, including homogenization of imaging procedures and patient’s preparation, quality control and independent central analysis, now allows its use in multicentric trials.[24,27,28]
Studying tumoral heterogeneity requires assessing the response of the whole baseline metastatic tumor load without restriction in number nor site. However, existing morphological (WHO, RECIST) and metabolic (EORTC, PERCIST) response assessment methods do not take into account this response heterogeneity because they only consider a limited number of operator-selected target lesions and/or perform summing or averaging of response variables.[4,19,29,30] Moreover, being classically performed late during treatment, these assessment criteria measure response, while from a clinical point of view, it is the presence of non-response that triggers the need for treatment adaptation. For this, based on prior research, in order to optimize the negative predictive value (NPV) of mR assessment, a 15% cut-off value of SUVmax decrease instead of the standard 25–30% response cut-off value was chosen.[18,31] Such low cut-off value maximally avoids unjustified denial to a potentially active treatment regimen.
With regard to the characterization of response heterogeneity among lesions, this study adopted a multistep descriptive procedure. First, a lesion-by-lesion response analysis of all measurable lesions on baseline FDG-PET/CT without restriction of their number was performed applying the 15% cut-off for non-response. Then, a patient-based 4-class classification was applied, describing the presence and proportion of metabolically non-responding lesions among the whole-body tumor load.[13]
Using such methodology, 22% of the patients showed overall treatment resistance of whom 10% showed early metabolic disease progression at 3 weeks. This observation indicates the importance of performing the baseline FDG-PET/CT as close as possible before the start of the tested drug administration, because rapid disease progression during this timeframe could lead to false negative mR assessment.
On the other hand, after one treatment cycle, 32% of the patients showed heterogeneous metabolic responses combining resistant with potentially responding lesions (Class II and III). Of these, 18% showed non-mR in the minor, while 14% showed a non-mR in the major part of the tumor load. The proportion of heterogeneous response observed in this study is considerable, confirming earlier observation in an independent mCRC patient group treated with chemotherapy, where heterogeneity of mR was described in 67% of patients.[13] Other comparisons are impossible because information about heterogeneity is lacking in most available literature, which apply dichotomization to response assessment.[32–34]
Indeed, for clinical decision-making, the response assessment is generally reported dichotomously, because clinicians have to decide whether to continue or adapt the initiated treatment. Such information-reducing response reporting may only be adequate in case of homogeneous mR, but blurs useful information in case of response heterogeneity.
Outcome analysis in this study indicated that mPFS and mOS are comparable in patients bearing one or more metabolically resistant lesion. Only patients without any resistant lesion (class I) seemed to have a better outcome (mPFS and mOS) compared to all others. Therefore it seems that the presence but not the number/proportion of non-responding lesions is the most important prognostic determinant. Moreover, its value is reinforced by a multivariate analysis showing absence of any metabolically treatment resistant lesion as an independent prognostic factor for both PFS and OS.
A valid assessment of a predictive biomarker requires a significant level of activity of the regimen under study. This was achieved, as 71% of the included patients were still alive at 6 months, which was significantly higher than the minimal activity predefined in the study design. ITT analysis of the 92 eligible patients showed a mPFS of 4.2 months and a mOS of 8.2 months respectively, suggesting an overall beneficial effect for this drug combination compared to recent historical data with 2 months mPFS and 4–6 months mOS in the same clinical setting.[6,31,35–37]
Moreover, this study confirms the need for an effective predictive response biomarker for a sorafenib-containing regimen, because of the high toxicity profile together with the poor sensitivity of morphology-based imaging (CT/MRI) for detecting responses (only 8% of partial response according to RECIST) during treatment.[7,8,38]
A major application of standardized metabolic imaging is expected in early drug development (phase I-II) for two reasons: (i) as FDG-PET response analysis seems to be correlated with prognosis, it provides a rapid appraisal of the new drug activity even in small patient populations, and (ii) image-guided biopsies of resistant lesions could identify the molecular basis of treatment resistance by demonstrating genomic or epigenomic heterogeneity.
In this study for instance, half (47%) of the patients didn’t demonstrate any resistant lesion, indicating a remarkable activity level for such a heavily pre-treated patients population, unsuspected by classical morphological imaging.
Furthermore, in the metastatic setting, FDG-PET/CT may provide a tool for the identification of patients with one or very few metastatic sites resisting to treatment for whom the continuation of unchanged therapy carries a grim prognosis. This raises the potential of adding locoregional ablative treatments guided by the imaging of metabolic response, in order to achieve homogeneity of disease control and restore prognosis. If the current observation is confirmed by an ongoing multicentric trial, (clinicaltrials.gov NCT01929616), randomized prospective trials using early FDG-PET/CT response assessment as an interventional tool for targeting locoregional therapy (eg. surgery, radioembolization, radiofrequency ablation) will be justified.
Finally, in the absence of randomized data based on PET response, it remains to be proven whether the presence of metabolically non-responding lesions is a biomarker identifying more heterogeneous diseases with intrinsically a worse prognosis, or a genuine therapeutic predictive tool for a given treatment.
Conclusions
Metabolic response assessment allows the early identification of treatment-resistant tumor sites. The presence of metabolically refractory lesions seems to negatively impact overall treatment outcome whatever their number, adding to the mounting evidence that tumour heterogeneity is a key element in cancer management.
Early assessment of mR heterogeneity is a potentially powerful predictive biomarker enabling the personalization of anticancer treatments by increasing their cost-effectiveness and sparing useless toxicities.
Aparicio S, Caldas C (2013) The implications of clonal genome evolution for cancer medicine. N Engl J Med 368: 842–851. doi: 10.1056/NEJMra1204892[PubMed]
Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. (2009) NSew response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45: 228–247.[PubMed]
Grothey A, Hedrick EE, Mass RD, Sarkar S, Suzuki S, Ramanathan RK, et al. (2008) Response-independent survival benefit in metastatic colorectal cancer: a comparative analysis of N9741 and AVF2107.J Clin Oncol 26: 183–189. doi: 10.1200/JCO.2007.13.8099[PubMed]
Grothey A, Van Cutsem E, Sobrero A, Siena S, Falcone A, Ychou M, et al. (2012) Regorafenib monotherapy for previously treated metastatic colorectal cancer (CORRECT): an international, multicentre, randomised, placebo-controlled, phase 3 trial. Lancet 381: 303–312. doi: 10.1016/S0140-6736(12)61900-X[PubMed]
Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc JF, et al. (2008) Sorafenib in advanced hepatocellular carcinoma. N Engl J Med 359: 378–390. doi: 10.1056/NEJMoa0708857[PubMed]
Awada A, Gil T, Whenham N, Van Hamme J, Besse-Hammer T, Brendel E, et al. (2011) Safety and pharmacokinetics of sorafenib combined with capecitabine in patients with advanced solid tumors: results of a phase 1 trial. J Clin Pharmacol 51: 1674–1684. doi: 10.1177/0091270010386226[PubMed]
Ott K, Weber WA, Lordick F, Becker K, Busch R, Herrmann K, et al. (2006) Metabolic imaging predicts response, survival, and recurrence in adenocarcinomas of the esophagogastric junction. J Clin Oncol 24: 4692–4698. [PubMed]
Hoekstra CJ, Stroobants SG, Smit EF, Vansteenkiste J, van Tinteren H, Postmus PE, et al. (2005)Prognostic relevance of response evaluation using [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography in patients with locally advanced non-small-cell lung cancer. J Clin Oncol 23: 8362–8370. [PubMed]
Rousseau C, Devillers A, Sagan C, Ferrer L, Bridji B, Campion L, et al. (2006) Monitoring of early response to neoadjuvant chemotherapy in stage II and III breast cancer by [18F]fluorodeoxyglucose positron emission tomography. J Clin Oncol 24: 5366–5372. [PubMed]
de Geus-Oei LF, van Laarhoven HW, Visser EP, Hermsen R, van Hoorn BA, Kamm YJ, et al. (2008)Chemotherapy response evaluation with FDG-PET in patients with colorectal cancer. Ann Oncol 19: 348–352. [PubMed]
Hendlisz A, Golfinopoulos V, Garcia C, Covas A, Emonts P, Ameye L, et al. (2012) Serial FDG-PET/CT for early outcome prediction in patients with metastatic colorectal cancer undergoing chemotherapy.Ann Oncol 23: 1687–1693. doi: 10.1093/annonc/mdr554[PubMed]
Barrington SF, Mikhaeel NG, Kostakoglu L, Meignan M, Hutchings M, Mueller SP, et al. (2014) Role of Imaging in the Staging and Response Assessment of Lymphoma: Consensus of the International Conference on Malignant Lymphomas Imaging Working Group. J Clin Oncol. [PubMed]
As the molecular processes that control mRNA translation and ribosome biogenesis in the eukaryotic cell are extremely complex and multilayered, their deregulation can in principle occur at multiple levels, leading to both disease and cancer pathogenesis. For a long time, it was speculated that disruption of these processes may participate in tumorigenesis, but this notion was, until recently, solely supported by correlative studies. Strong genetic support is now being accrued, while new molecular links between tumor-suppressive and oncogenic pathways and the control of protein synthetic machinery are being unraveled. The importance of aberrant protein synthesis in tumorigenesis is further underscored by the discovery that compounds such as Rapamycin, known to modulate signaling pathways regulatory of this process, are effective anticancer drugs. A number of fundamental questions remain to be addressed and a number of novel ones emerge as this exciting field evolves.
mRNA Translation and Energy Metabolism in Cancer
I. Topisirovic and N. Sonenberg
Cold Spring Harbor Symposia on Quantitative Biology, Volume LXXVI http://dx.doi.org:/10.1101/sqb.2011.76.010785
A prominent feature of cancer cells is the use of aerobic glycolysis under conditions in which oxygen levels are sufficient to support energy production in the mitochondria (Jones and Thompson 2009; Cairns et al. 2010). This phenomenon, named the “Warburg effect,” after its discoverer Otto Warburg, is thought to fuel the biosynthetic requirements of the neoplastic growth (Warburg 1956; Koppenol et al. 2011) and has recently been acknowledged as one of the hallmarks of cancer (Hanahan and Weinberg 2011). mRNA translation is the most energy-demanding process in the cell (Buttgereit and Brand 1995).In mammalian cells it consumes >20% of cellular ATP, not considering the energy that is required for the biosynthesis of the components of the translational machinery (e.g., ribosome biogenesis; Buttgereit and Brand 1995). Control of mRNA translation plays a pivotal role in the regulation of gene expression (Sonenberg and Hinnebusch 2009). In fact, a recent study demonstrated that mammalian proteome is mostly governed at the mRNA translation level (Schwanhausser et al. 2011). Malfunction of mRNA translation critically contributes to human disease, including diabetes, heart disease, blood disorders, and, most notably, cancer (Fig. 1; Crozier et al. 2006; Narla and Ebert 2010; Silvera et al. 2010; Spriggs et al. 2010). The first account of changes in the translational apparatus in cancer dates back to 1896, showing enlarged and irregularly shaped nucleoli that are the site of ribosome biogenesis (Pianese 1896). Rapidly proliferating cancer cells have more ribosomes than normal cells.
Figure 1. Dysregulated mRNA translation plays a pivotal role in cancer. Malignant cells are characterized by enlarged nucleoli and a larger number of ribosomes than their normal counterparts. Mutations and/or altered expression of ribosomal proteins (e.g., RPS19, RPS 24), rRNA-modifying enzymes (e.g., dyskerin), translation initiation factors (e.g., eIF4E), or the initiator tRNA (tRNAiMet) result in malignant transformation. Signaling pathways whose dysfunction is frequent in cancer (e.g., MAPK, PI3K/AKT) affect mRNA translation. Perturbations in the translatome result in aberrant cellular growth, proliferation, and survival characteristic of tumorigenesis.
In stark contrast to normal cells, in cancer cells ribosomal biogenesis is uncoupled from cell proliferation (Stanners et al. 1979). Accordingly, cancer cells exhibit abnormally high rates of protein synthesis (Silvera et al. 2010). That ribosomal dysfunction plays a central role in cancer is further corroborated by the findings that genetic alterations, which encompass the components of the ribosome machinery (i.e., “ribosomopathies”), are characterized by elevated cancer risk (Narla and Ebert 2010).
mRNA translation is the most energy-consuming process in the cell and strongly correlates with cellular metabolic activity. Translation and energy metabolism play important roles in homeostatic cell growth and proliferation, and when dysregulated lead to cancer. eIF4E is a key regulator of translation, which promotes oncogenesis by selectively enhancing translation of a subset of tumor-promoting mRNAs (e.g., cyclins and c-myc). PI3K/AKT and mitogen-activated protein kinase (MAPK) pathways, which are strongly implicated in cancer etiology, exert a number of their biological effects by modulating translation. The PI3K/AKT pathway regulates eIF4E function by inactivating the inhibitory 4E-BPs via mTORC1, whereas MAPKs activate MAP kinase signal-integrating kinases 1 and 2, which phosphorylate eIF4E. In addition, AMP-activated protein kinase, which is a central sensor of the cellular energy balance, impairs translation by inhibiting mTORC1. Thus, eIF4E plays a major role in mediating the effects of PI3K/AKT, MAPK, and cellular energetics on mRNA translation.Figure 2. eIF4E is regulated by multiple mechanisms. The expression of eIF4E is regulated by several transcription factors (e.g., c-myc, hnRNPK, p53) and adenine-uracil-rich element binding proteins (i.e., HuR and AUF1). eIF4E is suppressed by 4E-BPs, which are regulated by mTORC1. MAP kinase signal integrating kinases 1 and 2 (MNKs) phosphorylate eIF4E.
Figure 3. Ras/MAPK and PI3K/AKT/mTORC1 regulate the activity of eIF4E. Various stimuli activate phosphoinositide-3-kinase (PI3K) through the receptor tyrosine kinases (RTKs). Upon activation, PI3K converts phosphatidylinositol 4,5-bisphosphate (PIP2) into phosphatidylinositol-3,4,5-triphosphate (PIP3). This reaction is reversed by PTEN. Phosphoinositide-dependent protein kinase 1 (PDK1) and AKT bind to PIP3 via their pleckstrin homology domains, which allows for the phosphorylation and activation of AKT by PDK1. In addition, the mammalian target of rapamycin complex 2 (mTORC2) modulates the activity of AKT by phosphorylating its hydrophobic motif. AKT phosphorylates tuberous sclerosis complex 2 (TSC2) at multiple sites, which results in its inhibition and consequent activation of Ras homolog enriched in brain (Rheb), which is a small GTPase that activates mTORC1. mTORC1 phosphorylates 4E-BPs leading to their dissociation from eIF4E. In addition to the PI3K/AKT pathway, the activity of mTORC1 is regulated by the serine/threonine kinase 11/LKB1/AMP-kinase (LKB1/AMPK) pathway, regulated in development and DNA damage response 1 (REDD1) and Rag GTPases in response to the changes in cellular energy balance, oxygen and amino acid availability, respectively. Ras and the MAPK pathways are activated by various stimuli through receptor tyrosine kinases (RTKs). In addition the MAPK pathway isactivatedthrough theGprotein–coupled receptors(GPCRs) and byproteinkinaseC (PKC;notshown).TheMAPK pathways encompass an initial GTPase-regulated kinase (MAPKKK), which activates an effector kinase (MAPK) via an intermediate kinase (MAPKK). In response to stimuli such as growth factors, hormones, and phorbol-esters, Ras GTPase stimulates Raf kinase (MAPKKK), which activates extracellular signal-regulated kinases 1 and 2 (ERK 1 and 2) via extracellular signal-regulated kinase activator kinases MEK1 and 2 (MAPKK). Cellular stresses, including osmotic shock, inflammatory cytokines, and UV light, activate p38 MAPKs via multiple mechanisms including Rac kinase (MAPKKK) and MKK3 and 6 (MAPKK). p38 MAPK and ERK activate the MAPK signal–integrating kinases 1 and 2 (MNK1/2), which phosphorylate eIF4E. Additional abbreviations are provided in the text.
Breast cancer cells secrete exosomes with specific capacity for cell-independent miRNA biogenesis, while normal cellderivedexosomes lack thisability. Exosomes derivedfrom cancer cellsand serum frompatients withbreast cancer contain the RISC loading complex proteins, Dicer, TRBP, and AGO2, which process pre-miRNAs into mature miRNAs. Cancer exosomes alter the transcriptome of target cells in a Dicer-dependent manner, which stimulate nontumorigenic epithelial cells to form tumors.This study identifies a mechanism whereby cancer cells impart an oncogenic field effect by manipulating the surrounding cells via exosomes. Presence of Dicer in exosomes may serve as biomarker for detection of cancer.
Dicers at RISC. The Mechanism of RNAi
Marcel Tijsterman and Ronald H.A. Plasterk
Cell, Apr 2014; 117:1–4
Figure 1. Model for RNA Silencing in Drosophila In an ordered biochemical pathway, miRNAs (left panel) and siRNAs (right panel) are processed from double-stranded precursor molecules by Dcr-1and Dcr-2, respectively, and stay attached to Dicer-containing complexes, which assemble into RISC. The degree of complementarity between the RNA silencing molecule (in red) and its cognate target determines the fate of the mRNA: blocked translation or immediate destruction.
Fig. 1. Domain organization of RNaseIII gene family. Three classes of RNaseIII genes are shown. The PAZ domain in Dm-Dicer-2 contains mutations in several residues required for RNA binding and may not be functional.
Fig. 2. Model for Dicer catalysis. The PAZ domain binds the 2 nt 30 overhang of a dsRNA terminus. The RNaseIII domains form a pseudo-dimer. Each domain hydrolyzes one strand of the substrate. The binding site of the dsRBD is not defined. The function of the helicase domain is not known.
Fig. 3. Biogenesis pathway of microRNAs. MicroRNA genes are transcribed by RNA polymerase II. The primary transcript is referred to as ‘‘primicroRNA’’. Drosha processing occurs in the nucleus. The resulting precursor, ‘‘pre-microRNA’’, is exported to the cytoplasm for Dicer processing. In a coordinated manner, the mature microRNA is transferred to RISC and unwound by a helicase. mRNA targets that duplex in the Slicer scissile site are cleaved and degraded, if the microRNA is loaded into an Ago2 RISC. Mismatched targets are translationally suppressed. All Ago family members are believed to function in translational suppression.
Fig. 4. Model for Slicer catalysis. The siRNA guide strand is bound at the 50 end by the PIWI domain and at the 30 end by the PAZ domain. The 50 phosphate is coordinated by conserved basic residues. mRNA targets are initially bound by the seed region of the siRNA and pairing is extended to the 30 end. The RNaseH fold hydrolyzes the target in a cation dependent manner. Slicer cleavage is measured from the 50 end of the siRNA. Product is released by an unknown mechanism and the enzyme recycles.
RNA interference (RNAi) is a biological process in which RNA molecules inhibit gene expression, typically by causing the destruction of specific mRNA molecules. Historically, it was known by other names, including co-suppression, post transcriptional gene silencing (PTGS), and quelling. Only after these apparently unrelated processes were fully understood did it become clear that they all described the RNAi phenomenon. Andrew Fire and Craig C. Mello shared the 2006 Nobel Prize in Physiology or Medicine for their work on RNA interference in the nematode worm Caenorhabditis elegans, which they published in 1998.
Two types of small ribonucleic acid (RNA) molecules – microRNA (miRNA) and small interfering RNA (siRNA) – are central to RNA interference. RNAs are the direct products of genes, and these small RNAs can bind to other specific messenger RNA (mRNA) molecules and either increase or decrease their activity, for example by preventing an mRNA from producing a protein. RNA interference has an important role in defending cells against parasitic nucleotide sequences – viruses and transposons. It also influences development.
The RNAi pathway is found in many eukaryotes, including animals, and is initiated by the enzyme Dicer, which cleaves long double-stranded RNA (dsRNA) molecules into short double stranded fragments of ~20 nucleotide siRNAs. Each siRNA is unwound into two single-stranded RNAs (ssRNAs), the passenger strand and the guide strand. The passenger strand is degraded and the guide strand is incorporated into the RNA-induced silencing complex (RISC). The most well-studied outcome is post-transcriptional gene silencing, which occurs when the guide strand pairs with a complementary sequence in a messenger RNA molecule and induces cleavage by Argonaute, the catalytic component of the RISC complex. In some organisms, this process spreads systemically, despite the initially limited molar concentrations of siRNA. http://en.wikipedia.org/wiki/RNA_interference
The enzyme dicer trims double stranded RNA, to form small interfering RNA or microRNA. These processed RNAs are incorporated into the RNA-induced silencing.
MiRNA biogenesis and function. (A) The canonical miRNA biogenesis pathway is Drosha- and Dicer-dependent. It begins with RNA Pol II-mediated transcription..
Dicer Promotes Transcription Termination
Dicer Promotes Transcription Termination at Sites of Replication Stress to Maintain Genome Stability
Cell Oct 2014; 159(3): 572–583 http://dx.doi.org/10.1016/j.cell.2014.09.031
18-13 miRNA- protein complex (a) Primary miRNA transcript Translation blocked Hydrogen bond (b) Generation and function of miRNAs Hairpin miRNA miRNA Dicer …
Fig. 1. Small RNA cloning procedure. Outline of the small RNA cloning procedure. RNA is dephosphorylated (step 1) for joining the 30 adapter by T4 RNA ligase 1 in the presence of ATP (step 2). The use of a chemically adenylated adapter and truncated form of T4 RNA ligase 2 (Rnl2) allows eliminating the dephosphorylation step (step 4). If the RNA was dephosphorylated, it is re-phosphorylated (step 3) prior to 50 adapter ligation with T4 RNA ligase 1 and ATP (step 5). After 50 adapter ligation, a standard reverse transcription is performed (step 6). Alternatively, after 30 adapter ligation, the RNA is used directly for reverse transcription simultaneously with 50 adaptor joining (step 7). In this case, the property of reverse transcriptase to add non-templated cytidine residues at the 50 end of synthesized DNA is used to facilitate template switch of the reverse transcriptase to the 30 guanosine residues of the 50 adapter (SMART technology, Invitrogen). Abbreviations: P and OH indicate phosphate and hydroxyl ends of the RNA; App indicates 50 chemically adenylated adapter; L, 30 blocking group; CIP, calf alkaline phosphatase and PNK, polynucleotide kinase.
Fig. 1. Schematic representation of gene silencing by an shRNA-expression vector. The shRNA is processed by Dicer. The processed siRNA enters the RNA-induced silencing complex (RISC), where it targets mRNA for degradation.
Fig. 2. Schematic representation of a transcription system for production of siRNA
Fig. 3. (A) Schematic representation of the proposed siRNA-expression system. Three or four C to U or A to G mutations are introduced into the sense strand. (B) Schematic representation of the discovery of a novel gene using an siRNA library.
Imperfect centered miRNA binding sites are common and can mediate repression of target mRNAs
Martin et al. Genome Biology 2014, 15:R51 http://genomebiology.com/2014/15/3/R51
Table 1 Number of inferred targets for each miRNA tested
miRNA
Probes
Transcripts
Genes
miR-10a
2,206
5,963
1,887
miR-10a-iso
1,648
1,468
4,211
miR-10b
1,588
3,940
1,365
miR-10b-iso
963
2,235
889
miR-17-5p
1,223
2,862
1,137
miR-17-5p-iso
1,656
3,731
1,461
miR-182
2,261
6,423
2,008
miR-182-iso
1,569
4,316
1,444
miR-23b
2,248
5,383
1,990
miR-27a
2,334
5,310
2,069
Probes: number of probes significantly enriched in pull-downs compared to controls (5% FDR). Transcripts: number of transcripts to which those probes map exactly. Genes: number of genes from which those transcripts originate
Figure 2 Biotin pull-downs identify bone fide miRNA targets. (A) Volcano plot showing the significance of the difference in expression between the miR-17-5p pull-down and the mock-transfected control, for all transcripts expressed in HEK293T cells. Both targets predicted by TargetScan or validated previously via luciferase assay were significantly enriched in the pull-down compared to the controls. (B) Results from luciferase assays on previously untested targets predicted using TargetScan and uncovered using the biotin pull-down. The plot indicates mean luciferase activity from either the empty plasmid or from pMIR containing a miRNA binding site in the 3′ UTR, relative to a negative control. Asterisks indicate a significant reduction in luciferase activity (one-sided t-test; P<0.05) and error bars the standard error of the mean over three replicates. (C-E) Targets identified through PAR-CLIP or through miRNA over-expression studies show greater enrichment in the pull-down. Cumulative distribution of log fold-change in the pull-down for transcripts identified as targets by the indicated miRNA over-expression study or not. Red, canonical transcripts found to be miR-17-5p targets in the indicated study (Table S5 in Additional file 1); black, all other canonical transcripts; p, one-sided P-value from Kolmogorov-Smirnov test for a difference in distributions. (F) To confirm that our results were dependent on RISC association, cells were transfected with either single or double-stranded synthetic miRNAs, then subjected to AGO2 immunoprecipitation. The biotin pull-down was performed in the AGO2-enriched and AGO2-depleted fractions. (G-H) Quantitative RT-PCR revealed that, with double-stranded (ds) miRNA (G), four out of five known targets were enriched relative to input mRNA (*P≤0.05, **P<0.01, ***P<0.001) in the AGO2-enriched but not in the AGO2-depleted fractions, but this enrichment was not seen for the cells transfected with a single-stranded (ss) miRNA (H). The numbers on the x-axis correspond to those in Figure 2F. Error bars represent the standard error of mean (sem).
Figure 5 IsomiRs and canonical miRNAs target many of the same transcripts.
Figure 1. Features of hammerhead ribozymes. A generic diagram of a hammerhead ribozyme bound to its target substrate: NUH is the cleavage triplet on target sequence, stems I and III are sites of the specific interactions between ribozyme and target, stem II is the structural element connecting separate parts of the catalytic core. Arrows represent the cleavage site, numbering system according to Hertel et al. [60].
Figure 1 Schematic (A) and ribbon (B) diagrams depicting the crystal structure of the full-length hammerhead ribozyme. The sequence and secondary structure
TABLE 1 Typical examples of successful applications of hammerhead ribozymes. Most of the data are derived from [10] and [11], the others are expressly specified.
Growth factors, receptors, transduction elements
Oncogenes, protoncogenes, fusion genes
Apoptosis, survival factors, drug resistance
Transcription factors
Extracellular matrix, matrix modulating factors
Circulating factors
Viral genome, viral genes
Figure 2.Target–ribozyme interactions. (a) As cheme of ribozyme binding to full substrate. The calculated energy of this binding ensures the formation of a stable complex. At the denaturating temperature, Tm, will allow this complex to survive to biological conditions. Conversely, after cleavage, binding energies calculated on single, (b) and (c), ribozyme arms are very low and no longer stable. These properties will ensure both the efficient release of cleavage fragments and the prevention of binding to unrelated targets. RNAs complementary to one binding arm only will not be bound or cleaved by the hammerhead catalytic sequence.
Figure 3. ‘Chemical omics’ approach. According to this target discovery strategy: (1) a first round of ‘omic’ study (proteomic, genomic, metabolomic, …) will enable the discovery of a set of (2) putative markers. A series of hammerhead ribozymes will then be prepared in order to target each marker. (4) A second ‘omic’ study round will be performed on (3) knocked down samples obtained after ribozymes administration. (5) A new series of markers will then be produced. An expanding analytical process of this type may be further repeated. Finally, a robust bioinformatic algorithm will make it possible to connect the different markers and draw new hypothetical links and pathways.
miRNA
ADAR Enzyme and miRNA Story Sara Tomaselli, Barbara Bonamassa, Anna Alisi, et al.
Int. J. Mol. Sci. 2013, 14, 22796-22816; http://dx.doi.org:/10.3390/ijms141122796
Adenosine deaminase acting on RNA (ADAR) enzymes convert adenosine (A) to inosine (I) in double-stranded (ds) RNAs. Since Inosine is read as Guanosine, the biological consequence of ADAR enzyme activity is an A/G conversion within RNA molecules. A-to-I editing events can occur on both coding and non-coding RNAs, including microRNAs (miRNAs), which are small regulatory RNAs of ~20–23 nucleotides that regulate several cell processes by annealing to target mRNAs and inhibiting their translation. Both miRNA precursors and mature miRNAs undergo A-to-I RNA editing, affecting the miRNA maturation process and activity. ADARs can also edit 3′ UTR of mRNAs, further increasing the interplay between mRNA targets and miRNAs. In this review, we provide a general overview of the ADAR enzymes and their mechanisms of action as well as miRNA processing and function. We then review the more recent findings about the impact of ADAR-mediated activity on the miRNA pathway in terms of biogenesis, target recognition, and gene expression regulation.
Figure 1. Structure of ADAR family proteins: ADAR1, ADAR2, and ADAR3. The ADAR enzymes contain a C-terminal conserved catalytic deaminase domain (DM), two or three dsRBDs in the N-terminal portion. ADAR1 full-length protein also contains a N-terminal Zα domain with a nuclear export signal (NES) and a Zβ domain, while ADAR3 has a R-domain. A nuclear localization signal is also indicated.
Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
Doron Betel, Anjali Koppal, Phaedra Agius, Chris Sander, Christina Leslie
Genome Biology 2010, 11:R90 http://genomebiology.com/2010/11/8/R90
microRNAs are a class of small regulatory RNAs that are involved in post-transcriptional gene silencing. These small (approximately 22 nucleotide) single-strand RNAs guide a gene silencing complex to an mRNA by complementary base pairing, mostly at the 3′ untranslated region (3′ UTR). The association of the RNAinduced silencing complex (RISC) to the conjugate mRNA results in silencing the gene either by translational repression or by degradation of the mRNA. Reliable microRNA target prediction is an important and still unsolved computational challenge, hampered both by insufficient knowledge of microRNA biology as well as the limited number of experimentally validated targets.
mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites. Human RISC – MicroRNA Biogenesis and Posttranscriptional Gene Silencing
Cell 2005; 123:631-640 http://dx.doi.org:/10.1016/j.cell.2005.10.022 Development of microRNA therapeutics
Eva van Rooij & Sakari Kauppinen
EMBO Mol Med (2014) 6: 851–864 http://dx.doi.org:/10.15252/emmm.20110089
MicroRNAs (miRNAs) play key regulatory roles in diverse biological processes and are frequently dysregulated in human diseases. Thus, miRNAs have emerged as a class of promising targets for therapeutic intervention. Here, we describe the current strategies for therapeutic modulation of miRNAs and provide an update on the development of miRNA-based therapeutics for the treatment of cancer, cardiovascular disease and hepatitis C virus (HCV) infection.
Figure 1. miRNA biogenesis and modulation of miRNA activity by miRNA mimics and antimiR oligonucleotides. MiRNA genes are transcribed by RNA polymerase II from intergenic, intronic or polycistronic loci to long primary miRNA transcripts (pri-miRNAs) and processed in the nucleus by the Drosha–DGCR8 complex to approximately 70 nt pre-miRNA hairpin structures. The most common alternative miRNA biogenesis pathway involves short intronic hairpins, termed mirtrons, that are spliced and debranched to form pre-miRNA hairpins. Pre-miRNAs are exported into the cytoplasm and then cleaved by the Dicer–TRBP complex to imperfect miRNA: miRNA* duplexes about 22 nucleotides in length. In the cytoplasm, miRNA duplexes are incorporated into Argonaute-containing miRNA induced silencing complex (miRISC), followed by unwinding of the duplex and retention of the mature miRNA strand in miRISC, while the complementary strand is released and degraded. The mature miRNA functions as a guide molecule for miRISC by directing it to partially complementary sites in the target mRNAs, resulting in translational repression and/or mRNA degradation. Currently, two strategies are employed to modulate miRNA activity: restoring the function of a miRNA using double-stranded miRNA mimics, and inhibition of miRNA function using single-stranded anti-miR oligonucleotides.
Figure 2. Design of chemically modified miRNA modulators. (A) Structures of chemical modifications used in miRNA modulators. A number of different sugar modifications are used to increase the duplex melting temperature (Tm) of anti-miR oligonucleotides. The20-O-methyl(20-O-Me), 20-O-methoxyethyl(20-MOE )and 20-fluoro(20-F) nucleotides are modified at the 20 position of the sugar moiety, whereas locked nucleic acid (LNA) is a bicyclic RNA analogue in which the ribose is locked in a C30-endo conformation by introduction of a 20-O,40-C methylene bridge. To increase nuclease resistance and enhance the pharmacokinetic properties, most anti-miR oligonucleotides harbor phosphorothioate (PS) backbone linkages, in which sulfur replaces one of the non-bridging oxygen atoms in the phosphate group. In morpholino oligomers, a six-membered morpholine ring replaces the sugar moiety. Morpholinos are uncharged and exhibit a slight increase in binding affinity to their cognate miRNAs. PNA oligomers are uncharged oligonucleotide analogues, in which the sugar–phosphate backbone has been replaced by a peptide-like backbone consisting of N-(2-aminoethyl)-glycine units. (B) An example of a synthetic double-stranded miRNA mimic described in this review. One way to therapeutically mimic a miRNA is by using synthetic RNA duplexes that harbor chemical modifications for improved stability and cellular uptake. In such constructs, the antisense (guide) strand is identical to the miRNA of interest, while the sense (passenger) strand is modified and can be linked to a molecule, such as cholesterol, for enhanced cellular uptake. The sense strand contains chemical modifications to prevent mi-RISC loading. Several mismatches can be introduced to prevent this strand from functioning as an anti-miR, while it is further left unmodified to ensure rapid degradation.The20-F modification helps to protect the antisense strand against exonucleases, hence making the guide strand more stable, while it does not interfere with mi-RISC loading. (C) Design of chemically modified anti-miR oligonucleotides described in this review. Antagomirs are30 cholesterol-conjugated,20-O-Me oligonucleotides fully complementary to the mature miRNA sequence with several PS moieties to increase their in vivo stability. The use of unconjugated 20-F/MOE-, 20-MOE- or LNA-modified anti-miR oligonucleotides harboring a complete PS backbone represents another approach for inhibition of miRNA function in vivo. The high duplex melting temperature of LNA-modified oligonucleotides allows efficient miRNA inhibition using truncated, high-affinity 15–16-nucleotide LNA/DNA anti-miR oligonucleotides targeting the 50 region of the mature miRNA. Furthermore, the high binding affinity of fully LNA-modified 8-mer PS oligonucleotides, designated as tiny LNAs, facilitates simultaneous inhibition of entire miRNA seed families by targeting the shared seed sequence.
Human MicroRNA Targets
Bino John, Anton J. Enright, Alexei Aravin, Thomas Tuschl,.., Debora S. Mark
PLoS Biol 2004; 2(11): e363 http://www.plosbiology.org
More than ten years after the discovery of the first miRNA gene, lin-4 (Chalfie et al. 1981; Lee et al. 1993), we know that miRNA genes constitute about 1%–2% of the known genes in eukaryotes. Investigation of miRNA expression combined with genetic and molecular studies in Caenorhabditis elegans, Drosophila melanogaster, and Arabidopsis thaliana have identified the biological functions of several miRNAs (recent review, Bartel 2004). In C. elegans, lin-4 and let-7 were first discovered as key regulators of developmental timing in early larval developmental transitions (Ambros 2000; Abrahante et al. 2003; Lin et al. 2003; Vella et al. 2004). More recently lsy-6 was shown to determine the left–right asymmetry of chemoreceptor expression (Johnston and Hobert 2003). In D. melanogaster, miR-14 has a role in apoptosis and fat metabolism (Xu et al. 2003) and the bantam miRNA targets the gene hid involved in apoptosis and growth control (Brennecke et al. 2003).
MicroRNAs (miRNAs) interact with target mRNAs at specific sites to induce cleavage of the message or inhibit translation. The specific function of most mammalian miRNAs is unknown. We have predicted target sites on the 39 untranslated regions of human gene transcripts for all currently known 218 mammalian miRNAs to facilitate focused experiments. We report about 2,000 human genes with miRNA target sites conserved in mammals and about 250 human genes conserved as targets between mammals and fish. The prediction algorithm optimizes sequence complementarity using position-specific rules and relies on strict requirements of interspecies conservation. Experimental support for the validity of the method comes from known targets and from strong enrichment of predicted targets in mRNAs associated with the fragile X mental retardation protein in mammals. This is consistent with the hypothesis that miRNAs act as sequence-specific adaptors in the interaction of ribonuclear particles with translationally regulated messages. Overrepresented groups of targets include mRNAs coding for transcription factors, components of the miRNA machinery, and other proteins involved in translational regulation, as well as components of the ubiquitin machinery, representing novel feedback loops in gene regulation. Detailed information about target genes, target processes, and open-source software for target prediction (miRanda) is available at http://www.microrna.org. Our analysis suggests that miRNA genes, which are about 1% of all human genes, regulate protein production for 10% or more of all human genes.
Figure 1. Target Prediction Pipeline for miRNA Targets in Vertebrates The mammalian (human, mouse, and rat) and fish (zebra and fugu) 39 UTRs were first scanned for miRNA target sites using position specific rules of sequence complementarity. Next, aligned UTRs of orthologous genes were used to check for conservation of miRNA– target relationships (‘‘target conservation’’) between mammalian genomes and, separately, between fish genomes. The main results (bottom) are the conserved mammalian and conserved fish targets, for each miRNA,as well as a smaller set of super-conserved vertebrate targets. http://dx.doi.org:/10.1371/journal.pbio.0020363.g00
Figure 2. Distribution of Transcripts with Cooperativity of Target Sites and Estimated Number of False Positives Each bar reflects the number of human transcripts with a given number of target sites on their UTR. Estimated rate of false positives(e.g., 39%for2 targets) is given by the number of target sites predicted using shuffled miRNAs processed in a way identical to real miRNAs, including the use of interspecies conservation filter. http://dx.doi.org:/10.1371/journal.pbio.0020363.g002
Conserved Seed Pairing, Often improved an-Flanked by Adenosines, Indicates Thousands of Human Genes are MicroRNA Targets Cell, Jan 2005; 120: 15–20 http://dx.doi.org:/10.1016/j.cell.2004.12.035
Integrated analysis of microRNA and mRNA expression. adding biological significance to microRNA target predictions.
Maarten van Iterson, Sander Bervoets, Emile J. de Meijer, et al.
Nucleic Acids Research, 2013; 41(15), e146 http://dx.doi.org:/10.1093/nar/gkt525
Current microRNA target predictions are based on sequence information and empirically derived rules but do not make use of the expression of microRNAs and their targets. This study aimed to improve microRNA target predictions in a given biological context, using in silico predictions, microRNA and mRNA expression. We used target prediction tools to produce lists of predicted targets and used a gene set test designed to detect consistent effects of microRNAs on the joint expression of multiple targets. In a single test, association between microRNA expression and target gene set expression as well as the contribution of the individual target genes on the association are determined. The strongest negatively associated mRNAs as measured by the test were prioritized. We applied our integration method to a well-defined muscle differentiation model. Validation of our predictions in C2C12 cells confirmed predicted targets of known as well as novel muscle-related microRNAs. We further studied associations between microRNA–mRNA pairs in human prostate cancer, finding some pairs that have been recently experimentally validated by others. Using the same study, we showed the advantages of the global test over Pearson correlation and lasso. We conclude that our integrated approach successfully identifies regulated microRNAs and their targets.
Fig. 1. A ‘‘Domain-centric’’ view of RNAi. (A) The conserved pathways of RNA silencing. The domain structure of each protein in (hypothetical) interaction with its RNA is shown. For clarity, the second column lists domains in order N- to C-terminal. Figures are not to scale. In brief, Drosha, an RNase III enzyme, and its obligate binding partner, Pasha recognize pri-mRNA loops, and cut these into 70 nt hairpin pre-miRNAs. Dicer utilizes a PAZ domain to sense the 30 2-nt overhang created, and further processes these, and dsRNAs into miRNAs and siRNAs. Argonaute binds the 50 end of guide RNAs via its PIWI domain, and the 30 end via a PAZ domain, yielding RISCs that effect RNA silencing through several mechanisms. A Viral protein, VP19 can suppress RNA silencing by sequestering siRNAs. (B) A summary of known siRNA structural biology. Listed by domain are solved structures, their protein/organism of origin, and ligands, where applicable. Also shown are PDB codes.
Fig. 2. Novel modes of RNA recognition. (A) A typical dsRBD: Xenopus binding protein A (1DI2). A RNA helix is modeled pink, and the protein is rendered in transparent electrostatic contours (blue is basic, red acidic). Note the interaction of helices along the major groove, and the position of helix 1. A second dsRBD protein is visible, in the lower right. (B) A dsRBD, Saccharomyces Rnt1P (1T4L), recognizes hairpin loops. A novel third helix (top) pushes helix one into the loop of a hairpin RNA. (C) 30-OH recognition by PAZ. Human Eif2c1 (1SI3) bound to RNA (pink) is shown. PAZ is green, with transparent electrostatic surface plot. The OB-fold (nucleotide binding fold) and the insertion domain are labeled. Note the glove-and-thumb like cleft they form, that the 30-OH is inserted into. A basic groove (blue) the RNA binds along outside the cleft is visible. (D) A close-up view of PAZ, as in C (surface not-transparent, slightly rotated). See white arrows for orientation, and location of 30-OH binding site. RNA is shown red in sticks. The terminal –OH is barely visible, buried in a cleft. It and the carbon it bonds have been colored yellow for clarity. (E) The PIWI domain (2BGG). Note the insertion of the 50P red (labeled) into the binding site. Its complimentary strand (pink) is not annealed to it, and the 30 overhang and first complimentary bases sit on the protein surface. (F) An enlarged view of (E), with protein in slate and RNA modeled as red sticks. The coordinated magnesium is a grey sphere, which is coordinated by the terminal carboxylate of the protein, protein side chains, and RNA phosphate oxygens. The 50 base stacks against a conserved Tyr. Several other sidechain contacts are shown.
Fig. 3. Argonaute/RISC. (A) P. furiosus Argonaute (PDB 1Z26). A color-guided key to the domains is presented. PAZ sits over the PIWI/N/MID bowl and active site. The liganding atoms for the catalytic metal are depicted as yellow balls for clarity. The tungstate binding site (50P surrogate) is shown as tan spheres. (B) A guide strand channel. Looking down from the PAZ domain towards the active site, Z-sections are clipped off. Colors of domains are as in the key in (A). Wrapping down along a basic cleft from the PAZ 30OH binding site (approximate position labeled), a RNA binding groove passes the active site (yellow), and runs down to the 50P binding site (tan balls). A second cleft running perpendicular to this one at its entry may accommodate target strand RNA. For more detail, and models of siRNA placed into the grooves, see [27,29].
Fig. 4. VP19 sequestration of siRNA. (A) CIRV VP19 (1RPU, RNA removed). Two monomers (blue and cyan) form an 8 strand, concave b-sheet with bracketing helices at the ends. (B) Tombus viral VP19 bound to siRNA (1 monomer shown). RNA strands are modeled as sticks, with one strand pink and one red. The bracketing helix places two tryptophans in position to stack over the terminal RNA bases. On the b-sheet surface, and Arg and a Lys interact with the phosphate backbone, and at the center of the RNA binding surface, a number of Ser and Thr mediate an extensive hydrogen bond network. Both the Trp brackets and RNA binding by an extended b-sheet are unique.
Figure 1. Noncoding RNAs Function in Diverse Contexts Noncoding RNAs function in all domains of life, regulating gene expression from transcription to splicing to translation and contributing to genome organization and stability. Self-splicing RNAs, ribosomes, and riboswitches function in both eukaryotes and bacteria. Archaea (not shown) also utilize ncRNA systems including ribosomes, riboswitches, snoRNPs, and CRISPR. Orange strands, ncRNA performing the action indicated; red strands, the RNA acted upon by the ncRNA. Blue strands, DNA. Triangle, small-molecule metabolite bound by a riboswitch. Ovals indicate protein components of an RNP, such as the spliceosome (white oval), ribosome (two purple subunits), or other RNPs (yellow ovals). Because of the importance of RNA structure in these ncRNAs, some structures are shown but they are not meant to be realistic.
miRNAs and cancer targeting
Table 1 of targets
miRNA
Cancer type
reference
NA
GI cancer
Current status of miRNA-targeting therapeutics and preclinical studies against gastroenterological carcinoma
NA
Renal cell
Differential expression profiling of microRNAs and their potential involvement in renal cell carcinoma pathogenesis
NA
urothelial
cancer
A microRNA expression ratio defining the invasive phenotype in bladder tumors
miR-31
breast
A Pleiotropically Acting MicroRNA, miR-31, inhibits breast cancer growth
miR-512-3p
NSCLC
Inhibition of RAC1-GEF DOCK3 by miR-512-3p contributes to suppression of metastasis in non-small cell lung cancer
miR-495
gastric
Methylation-associated silencing of miR-495 inhibit the migration and invasion of human gastric cancer cells
microRNA-218
prostate
microRNA-218 inhibits prostate cancer cell growth and promotes apoptosis by repressing TPD52 expression
MicroRNA-373
cervical cancer
MicroRNA-373 functions as an oncogene and targets YOD1 gene in cervical cancer
miR-92a. upregulated in cervical cancer & promotes cell proliferation and invasion by targeting FBXW7
MiR-153
NSCLC
MiR-153 inhibits migration and invasion of human non-small-cell lung cancer by targeting ADAM19
miR-203
melanoma
miR-203 inhibits melanoma invasive and proliferative abilities by targeting the polycomb group gene BMI1
miR-204-5p
Papillary thyroid
miR-204-5p suppresses cell proliferation by inhibiting IGFBP5 in papillary thyroid carcinoma
miR-342-3p
Hepato-cellular
miR-342-3p affects hepatocellular carcinoma cell proliferation via regulating NF-κB pathway
miR-1271
NSCLC
miR-1271 promotes non-small-cell lung cancer cell proliferation and invasion via targeting HOXA5
miR-203
pancreas
Pancreatic cancer derived exosomes regulate the expression of TLR4 in dendritic cells via miR-203
miR-203
metastatic SCC
Rewiring of an Epithelial Differentiation Factor, miR-203, to Inhibit Human SCC Metastasis
miR-204
RCC
TRPM3 and miR-204 Establish a Regulatory Circuit that Controls Oncogenic Autophagy in Clear Cell Renal Cell Carcinoma
NA
urologic
MicroRNAs and cancer. Current and future perspectives in urologic oncology
NA
RCC
MicroRNAs and their target gene networks in renal cell carcinoma
NA
osteoSA
MicroRNAs in osteosarcoma
NA
urologic
MicroRNA in Prostate, Bladder, and Kidney Cancer
NA
urologic
Micro-RNA profiling in kidney and bladder cancers
Current status of miRNA-targeting therapeutics and preclinical studies against gastroenterological carcinoma
Shibata et al. Molecular and Cellular Therapies 2013, 1:5 http://www.molcelltherapies.com/content/1/1/5
Differential expression profiling of microRNAs and their potential involvement in renal cell carcinoma pathogenesis
Clinical Biochemistry 43 (2010) 150–158 http://dx.doi.org:/10.1016/j.clinbiochem.2009.07.020
A microRNA expression ratio defining the invasive phenotype in bladder tumors
Urologic Oncology: Seminars and Original Investigations 28 (2010) 39–48 http://dx.doi.org:/10.1016/j.urolonc.2008.06.006
Inhibition of RAC1-GEF DOCK3 by miR-512-3p contributes to suppression of metastasis in non-small cell lung cancer
Intl JBiochem & Cell Biol 2015; 61:103-114 http://dx.doi.org/10.1016/j.biocel.2015.02.005
Methylation-associated silencing of miR-495 inhibit the migration and invasion of human gastric cancer cells by directly targeting PRL-3
Biochem Biochem Res Commun 2014; 456:344-350 http://dx.doi.org/10.1016/j.bbrc.2014.11.083
microRNA-218 inhibits prostate cancer cell growth and promotes apoptosis by repressing TPD52 expression
Biochem Biophys Res Commun 2015; 456:804-809 http://dx.doi.org/10.1016/j.bbrc.2014.12.026
Rewiring of an Epithelial Differentiation Factor, miR-203, to Inhibit Human Squamous Cell Carcinoma Metastasis
Cell Reports 2014; 9:104-117 http://dx.doi.org/10.1016/j.celrep.2014.08.062
TRPM3 and miR-204 Establish a Regulatory Circuit that Controls Oncogenic Autophagy in Clear Cell Renal Cell Carcinoma
Cancer Cell Nov 10, 2014; 26: 738–753 http://dx.doi.org/10.1016/j.ccell.2014.09.015
MicroRNAs and cancer. Current and future perspectives in urologic oncology
Urologic Oncology: Seminars and Original Investigations 2010; 28:4–13 http://dx.doi.org:/10.1016/j.urolonc.2008.10.021
miRNA and mRNA cancer signatures determined by analysis of expression levels in large cohorts of patients
| PNAS | Nov 19, 2013; 110(47): 19160–19165 http://www.pnas.org/cgi/doi/10.1073/pnas.1316991110The study of mRNA and microRNA (miRNA) expression profiles of cells and tissue has become a major tool for therapeutic development. The results of such experiments are expected to change the methods used in the diagnosis and prognosis of disease. We introduce surprisal analysis, an information-theoretic approach grounded in thermodynamics, to compactly transform the information acquired from microarray studies into applicable knowledge about the cancer phenotypic state. The analysis of mRNA and miRNA expression data from ovarian serous carcinoma, prostate adenocarcinoma, breast invasive carcinoma, and lung adenocarcinoma cancer patients and organ specific control patients identifies cancer-specific signatures. We experimentally examine these signatures and their respective networks as possible therapeutic targets for cancer in single cell experiments.
RNA editing is vital to provide the RNA and protein complexity to regulate the gene expression. Correct RNA editing maintains the cell function and organism development. Imbalance of the RNA editing machinery may lead to diseases and cancers. Recently,RNA editing has been recognized as a target for drug discovery although few studies targeting RNA editing for disease and cancer therapy were reported in the field of natural products. Therefore, RNA editing may be a potential target for therapeutic natural products
Aberrant microRNA (miRNA) expression is implicated in tumorigenesis. The underlying mechanisms are unclear because the regulations of each miRNA on potentially hundreds of mRNAs are sample specific.
We describe a novel approach to infer Probabilistic Mi RNA–mRNA Interaction Signature (‘ProMISe’) from a single pair of miRNA–mRNA expression profile. Our model considers mRNA and miRNA competition as a probabilistic function of the expressed seeds (matches). To demonstrate ProMISe, we extensively exploited The Cancer Genome Atlas data. As a target predictor, ProMISe identifies more confidence/validated targets than other methods. Importantly, ProMISe confers higher cancer diagnostic power than using expression profiles alone.
Gene set enrichment analysis on averaged ProMISe uniquely revealed respective target enrichments of oncomirs miR-21 and 145 in glioblastoma and ovarian cancers. Moreover, comparing matched breast (BRCA) and thyroid (THCA) tumor/normal samples uncovered thousands of tumor-related interactions. For example, ProMISe– BRCA network involves miR-155/183/21, which exhibits higher ProMISe coupled with coherently higher miRNA expression and lower target expression; oncomirs miR-221/222 in the ProMISe–THCA network engage with many downregulated target genes. Together, our probabilistic approach of integrating expression and sequence scores establishes a functional link between the aberrant miRNA and mRNA expression, which was previously under-appreciated due to the methodological differences.
Delineation of metabolic pathways are leading to novel insights into the way cancer cells behave. A recent study demonstrated the metabolic programs that drive glioblastoma, most aggressive form of glioma. Dr. Prakash Chinnaiyan’s group from Moffitt Cancer Center identified metabolic signatures that may pave a way for personalized therapy in glioma. They presented their findings in Cancer Research, the premier journal of American Association of Cancer Research.
This study used metabolomics, which is the global quantitative assessment of metabolites within a biological system, to identify some of the central metabolic pathways that allow for these tumors to grow. These findings provide a unique insight into the underlying biology of glioma and appear to have prognostic significance.
These studies conducted global metabolomic profiling on patient-derived glioma specimens and identified specific metabolic programs differentiating low- and high-grade tumors, with the metabolic signature of glioblastoma reflecting accelerated anabolic metabolism. When coupled with transcriptional profiles, Dr. Chinnaiyan’s group identified the metabolic phenotype of the mesenchymal subtype to consist of accumulation of the glycolytic intermediate phosphoenolpyruvate and decreased pyruvate kinase activity. Unbiased hierarchical clustering of metabolomic profiles identified three subclasses, which they termed energetic, anabolic, and phospholipid catabolism with prognostic relevance. These studies represent the first global metabolomic profiling of glioma, offering a previously undescribed window into their metabolic heterogeneity, and provide the requisite framework for strategies designed to target metabolism in this rapidly fatal malignancy.
Ref:
1. H. Lee Moffitt Cancer Center & Research Institute. “Novel metabolic programs found driving aggressive brain tumors.” ScienceDaily, 9 Nov. 2012. Web. 11 Nov. 2012.
2. P. Chinnaiyan, E. Kensicki, G. Bloom, A. Prabhu, B. Sarcar, S. Kahali, S. Eschrich, X. Qu, P. Forsyth, R. Gillies. The Metabolomic Signature of Malignant Glioma Reflects Accelerated Anabolic Metabolism. Cancer Research, 2012; DOI: 10.1158/0008-5472.CAN-12-1572-T
2012pharmaceutical
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Alan F. Kaul, PharmD., MS, MBA, FCCP
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David Orchard-Webb, PhD
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