Oncometabolism: Difference between revisions
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'''Oncometabolism''' is a new field of study that focuses on the metabolic changes that occur in cells that make up the [[Tumor microenvironment|tumor microenvironment (TME)]] and accompany oncogenesis and tumor progression toward a neoplastic state<ref name=":0">{{ |
'''Oncometabolism''' is a new field of study that focuses on the metabolic changes that occur in cells that make up the [[Tumor microenvironment|tumor microenvironment (TME)]] and accompany oncogenesis and tumor progression toward a neoplastic state<ref name=":0">{{cite journal | vauthors = Urbano AM | title = Otto Warburg: The journey towards the seminal discovery of tumor cell bioenergetic reprogramming | journal = Biochimica et Biophysica Acta. Molecular Basis of Disease | volume = 1867 | issue = 1 | pages = 165965 | date = January 2021 | pmid = 32949769 | doi = 10.1016/j.bbadis.2020.165965 }}</ref>. |
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Oncometabolism is a term used to describe how cells with increased growth and survivability differ from non-tumorigenic cells in terms of [[metabolism]]<ref name=":8" />. This is explained by the [[Warburg effect (oncology)|Warburg Effect]], which describes how cancer cells change their metabolism to become more oncogenic in order to proliferate and eventually invade other tissues in a process known as [[metastasis]]<ref name=":0" /> |
Oncometabolism is a term used to describe how cells with increased growth and survivability differ from non-tumorigenic cells in terms of [[metabolism]]<ref name=":8" />. This is explained by the [[Warburg effect (oncology)|Warburg Effect]], which describes how cancer cells change their metabolism to become more oncogenic in order to proliferate and eventually invade other tissues in a process known as [[metastasis]].<ref name=":0" /> |
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The chemical reactions associated with oncometabolism are triggered by the alteration of [[Oncogene|oncogenes]], which are genes that have the potential to cause [[cancer]]<ref>{{ |
The chemical reactions associated with oncometabolism are triggered by the alteration of [[Oncogene|oncogenes]], which are genes that have the potential to cause [[cancer]].<ref>{{cite book | vauthors = Cooper CS | chapter = The Role of Oncogene Activation in Chemical Carcinogenesis |date=1990| doi = 10.1007/978-3-642-74778-6_12| title =Handbook of Experimental Pharmacology|pages=319–352|place=Berlin, Heidelberg|publisher=Springer Berlin Heidelberg }}</ref> These genes can be functional and active during physiological conditions, producing normal amounts of metabolites. However, their upregulation as a result of DNA damage can result in an overabundance of these metabolites, which can lead to tumorigenesis. These metabolites are known as oncometabolites, and they are thought to be very useful in the early stages of cancer diagnosis and prevention because they can act as biomarkers.<ref name=":7">{{cite journal | vauthors = Dando I, Pozza ED, Ambrosini G, Torrens-Mas M, Butera G, Mullappilly N, Pacchiana R, Palmieri M, Donadelli M | display-authors = 6 | title = Oncometabolites in cancer aggressiveness and tumour repopulation | journal = Biological Reviews of the Cambridge Philosophical Society | volume = 94 | issue = 4 | pages = 1530–1546 | date = August 2019 | pmid = 30972955 | doi = 10.1111/brv.12513 }}</ref> |
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[[File:Otto Warburg.jpg|thumb|[[Otto Heinrich Warburg]]. Considered the "Father of Oncometabolism" for his early discoveries in the field. ]] |
[[File:Otto Warburg.jpg|thumb|[[Otto Heinrich Warburg]]. Considered the "Father of Oncometabolism" for his early discoveries in the field. ]] |
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== History == |
== History == |
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In the 1920s, [[Otto Heinrich Warburg]] discovered an intriguing bioenergetic phenotype shared by most tumor cells: a higher-than-normal reliance on lactic acid fermentation for energy generation. He is known as the "Father of Oncometabolism" ''<ref name=":0" /><ref name=":8">{{ |
In the 1920s, [[Otto Heinrich Warburg]] discovered an intriguing bioenergetic phenotype shared by most tumor cells: a higher-than-normal reliance on lactic acid fermentation for energy generation. He is known as the "Father of Oncometabolism" ''<ref name=":0" /><ref name=":8">{{cite journal | vauthors = Oliveira PJ, Urbano AM | title = "Oncometabolism: The switchboard of cancer - An editorial" | journal = Biochimica et Biophysica Acta. Molecular Basis of Disease | volume = 1867 | issue = 2 | pages = 166031 | date = February 2021 | pmid = 33310398 | doi = 10.1016/j.bbadis.2020.166031 }}</ref>.'' Although the roots of this research field trace back to the 1920s, it was only recently recognized<ref name=":0" />. Over the last decade, research on cancer progression has focused on the role of shifting [[Metabolic pathway|metabolic pathways]] for both the cancer and immune cells, leading to an increase interest in characterizing the metabolic alterations that cells undergo in the [[Tumor microenvironment|TME]]<ref>{{cite journal | vauthors = Biswas SK | title = Metabolic Reprogramming of Immune Cells in Cancer Progression | journal = Immunity | volume = 43 | issue = 3 | pages = 435–449 | date = September 2015 | pmid = 26377897 | doi = 10.1016/j.immuni.2015.09.001 }}</ref>. |
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== Warburg Effect == |
== Warburg Effect == |
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In the absence of hypoxic conditions (i.e. physiological levels of oxygen), [[Cancer cell|cancer cells]] preferentially convert [[glucose]] to lactate, according to Otto H. Warburg, who believed that aerobic glycolysis was the key metabolic change in cancer cell malignancy. The "Warburg effect" was later coined to describe this metabolic shift<ref name=":1">{{ |
In the absence of hypoxic conditions (i.e. physiological levels of oxygen), [[Cancer cell|cancer cells]] preferentially convert [[glucose]] to lactate, according to Otto H. Warburg, who believed that aerobic glycolysis was the key metabolic change in cancer cell malignancy. The "Warburg effect" was later coined to describe this metabolic shift<ref name=":1">{{cite journal | vauthors = Vaupel P, Schmidberger H, Mayer A | title = The Warburg effect: essential part of metabolic reprogramming and central contributor to cancer progression | journal = International Journal of Radiation Biology | volume = 95 | issue = 7 | pages = 912–919 | date = July 2019 | pmid = 30822194 | doi = 10.1080/09553002.2019.1589653 }}</ref>. Warburg thought this change in metabolism was due to [[Mitochondrion|mitochondrial]] "respiration injury", but this interpretation was questioned by other researchers in 1956 showing that intact and functional cytochromes detected in most tumor cells clearly speak against a general mitochondrial dysfunction<ref>{{cite journal | vauthors = Pascale RM, Calvisi DF, Simile MM, Feo CF, Feo F | title = The Warburg Effect 97 Years after Its Discovery | journal = Cancers | volume = 12 | issue = 10 | pages = 2819 | date = September 2020 | pmid = 33008042 | doi = 10.3390/cancers12102819 }}</ref>. Furthermore, Potter et al. and several other authors provided significant evidence that [[oxidative phosphorylation]] and a normal [[Citric acid cycle|Krebs cycle]] persist in the vast majority malignant tumors, adding to the growing body of evidence that most cancers exhibit the Warburg effect while maintaining a proper mitochondrial respiration<ref name=":1" /><ref>{{cite journal | vauthors = Potter M, Newport E, Morten KJ | title = The Warburg effect: 80 years on | journal = Biochemical Society Transactions | volume = 44 | issue = 5 | pages = 1499–1505 | date = October 2016 | pmid = 27911732 | doi = 10.1042/bst20160094 }}</ref>. Dang et al<ref>Dang, et al. The interplay between MYC and HIF in cancer. ''Nature Reviews Cancer'' volume 8, pages51–56 (2008).</ref> in 2008 provided evidence that the tumor tissue sections used in Warburg's experiments should have been thinner for the oxygen diffusion constants employed, implying that the tissue slices studied were partially hypoxic and the calculated critical diffusion distance was of 470 micrometers<ref name=":1" />. As a result, endless debates and discussions about Warburg's discovery took place and have piqued the interest of scientists all over the world, which has helped bring attention to cell metabolism in cancer and immune cells and the use of modern technology to discover what these pathways are and how they are modified as well as potential therapeutic targets. |
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== Metabolic reprogramming == |
== Metabolic reprogramming == |
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[[File:Warburg's effect.png|thumb|350x350px|Simplified view of the aerobic glycolysis (Warburg's effect).]] |
[[File:Warburg's effect.png|thumb|350x350px|Simplified view of the aerobic glycolysis (Warburg's effect).]] |
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Carcinogenic cells undergo a metabolic rewiring during oncogenesis, and oncometabolites play an important role. In cancer, there are several reprogrammed metabolic pathways that help cells survive when nutrients are scarce: [[Aerobic glycolysis]], an increase in glycolytic flux, also known as the Warburg effect, allows glycolytic intermediates to supply subsidiary pathways to meet the metabolic demands of proliferating tumorigenic cells<ref>{{ |
Carcinogenic cells undergo a metabolic rewiring during oncogenesis, and oncometabolites play an important role. In cancer, there are several reprogrammed metabolic pathways that help cells survive when nutrients are scarce: [[Aerobic glycolysis]], an increase in glycolytic flux, also known as the Warburg effect, allows glycolytic intermediates to supply subsidiary pathways to meet the metabolic demands of proliferating tumorigenic cells<ref>{{cite journal | vauthors = Lunt SY, Vander Heiden MG | title = Aerobic glycolysis: meeting the metabolic requirements of cell proliferation | journal = Annual Review of Cell and Developmental Biology | volume = 27 | issue = 1 | pages = 441–464 | date = 2011-11-10 | pmid = 21985671 | doi = 10.1146/annurev-cellbio-092910-154237 }}</ref>. Another studied reprogrammed pathway is gain of function of the [[oncogene]] [[MYC|MYC.]] This gene encodes a transcription factor that boosts the expression of a number of genes involved in anabolic growth via mitochondrial metabolism<ref>{{Cite journal| vauthors = Van Dang C |date= October 2015 |title=Abstract IA05: Targeting MYC-mediated cancer metabolism |journal=Myc and Metabolism - Metabolomics|publisher=American Association for Cancer Research|doi=10.1158/1557-3125.myc15-ia05}}</ref>. Oncometabolite production is another example of metabolic deregulation<ref>{{cite journal | vauthors = DeBerardinis RJ, Chandel NS | title = Fundamentals of cancer metabolism | journal = Science Advances | volume = 2 | issue = 5 | pages = e1600200 | date = May 2016 | pmid = 27386546 | pmc = 4928883 | doi = 10.1126/sciadv.1600200 }}</ref>. |
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== Oncometabolites == |
== Oncometabolites == |
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Oncometabolites are metabolites whose abundance increases markedly in cancer cells through loss-of-function or gain-of-function mutations in specific enzymes involved in their production, the accumulation of these endogenous metabolites initiates or sustains tumor growth and metastasis<ref name=":6">{{ |
Oncometabolites are metabolites whose abundance increases markedly in cancer cells through loss-of-function or gain-of-function mutations in specific enzymes involved in their production, the accumulation of these endogenous metabolites initiates or sustains tumor growth and metastasis<ref name=":6">{{cite journal | vauthors = Wishart DS | title = Emerging applications of metabolomics in drug discovery and precision medicine | journal = Nature Reviews. Drug Discovery | volume = 15 | issue = 7 | pages = 473–484 | date = July 2016 | pmid = 26965202 | doi = 10.1038/nrd.2016.32 }}</ref>. Cancer cells rely on aerobic glycolysis, which is reached through defects in [[Enzyme|enzymes]] involved in normal cell metabolism, this allows the cancer cells to meet their energy needs and divert [[Acetyl-CoA|acetyl-coA]] from the [[Citric acid cycle|TCA cycle]] to build essential [[Biomolecule|biomolecules]] such as [[Amino acid|amino acids]] and [[Lipid|lipids]]<ref name=":5">{{cite journal | vauthors = Gupta S, Chawla K | title = Oncometabolomics in cancer research | journal = Expert Review of Proteomics | volume = 10 | issue = 4 | pages = 325–336 | date = August 2013 | pmid = 23992416 | doi = 10.1586/14789450.2013.828947 }}</ref>. These defects cause an overabundance of endogenous [[Metabolite|metabolites]], which are frequently involved in critical [[Epigenetics|epigenetic]] changes and signaling pathways that have a direct impact on [[cancer cell]] metabolism<ref name=":2">{{cite journal | vauthors = Collins RR, Patel K, Putnam WC, Kapur P, Rakheja D | title = Oncometabolites: A New Paradigm for Oncology, Metabolism, and the Clinical Laboratory | journal = Clinical Chemistry | volume = 63 | issue = 12 | pages = 1812–1820 | date = December 2017 | pmid = 29038145 | doi = 10.1373/clinchem.2016.267666 }}</ref>. |
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{| class="wikitable" |
{| class="wikitable" |
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|[[Isocitrate dehydrogenase]] |
|[[Isocitrate dehydrogenase]] |
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|Brain cancer, Leukemia |
|Brain cancer, Leukemia |
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|<ref name=":2" /><ref name=":3">{{ |
|<ref name=":2" /><ref name=":3">{{cite journal | vauthors = Yang M, Soga T, Pollard PJ, Adam J | title = The emerging role of fumarate as an oncometabolite | journal = Frontiers in Oncology | volume = 2 | pages = 85 | date = 2012 | pmid = 22866264 | doi = 10.3389/fonc.2012.00085 }}</ref><ref>{{cite journal | vauthors = Garber K | title = Oncometabolite? IDH1 discoveries raise possibility of new metabolism targets in brain cancers and leukemia | journal = Journal of the National Cancer Institute | volume = 102 | issue = 13 | pages = 926–928 | date = July 2010 | pmid = 20576929 | doi = 10.1093/jnci/djq262 }}</ref><ref name=":6" /> |
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|[[Succinic acid|Succinate]] |
|[[Succinic acid|Succinate]] |
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|[[Glutamine]]* |
|[[Glutamine]]* |
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| colspan="4" |''*(Primary carbon-source for the biosynthesis of the oncometabolite 2-hydroxyglutarate)'' |
| colspan="4" |''*(Primary carbon-source for the biosynthesis of the oncometabolite 2-hydroxyglutarate)'' |
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|<ref name=":4">{{ |
|<ref name=":4">{{cite journal | vauthors = Khatami F, Aghamir SM, Tavangar SM | title = Oncometabolites: A new insight for oncology | journal = Molecular Genetics & Genomic Medicine | volume = 7 | issue = 9 | pages = e873 | date = September 2019 | pmid = 31321921 | doi = 10.1002/mgg3.873 }}</ref><ref>{{cite journal | vauthors = Salamanca-Cardona L, Shah H, Poot AJ, Correa FM, Di Gialleonardo V, Lui H, Miloushev VZ, Granlund KL, Tee SS, Cross JR, Thompson CB, Keshari KR | display-authors = 6 | title = In Vivo Imaging of Glutamine Metabolism to the Oncometabolite 2-Hydroxyglutarate in IDH1/2 Mutant Tumors | journal = Cell Metabolism | volume = 26 | issue = 6 | pages = 830–841.e3 | date = December 2017 | pmid = 29056515 | doi = 10.1016/j.cmet.2017.10.001 }}</ref> |
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|[[Sarcosine]] |
|[[Sarcosine]] |
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|[[Glycine N-methyltransferase|Glycine-N-methyltransferase]] |
|[[Glycine N-methyltransferase|Glycine-N-methyltransferase]] |
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|[[Pancreatic cancer]],[[Hepatocellular carcinoma]] |
|[[Pancreatic cancer]],[[Hepatocellular carcinoma]] |
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|<ref name=":4" /><ref>Amjad P Khan, et al. The role of sarcosine metabolism in prostate cancer progression. Neoplasia. 2013 May;15(5):491-501.doi: 10.1593/neo.13314.</ref><ref>{{ |
|<ref name=":4" /><ref>Amjad P Khan, et al. The role of sarcosine metabolism in prostate cancer progression. Neoplasia. 2013 May;15(5):491-501.doi: 10.1593/neo.13314.</ref><ref>{{cite journal | vauthors = Rodrigo MA, Strmiska V, Horackova E, Buchtelova H, Michalek P, Stiborova M, Eckschlager T, Adam V, Heger Z | display-authors = 6 | title = Sarcosine influences apoptosis and growth of prostate cells via cell-type specific regulation of distinct sets of genes | journal = The Prostate | volume = 78 | issue = 2 | pages = 104–112 | date = February 2018 | pmid = 29105933 | doi = 10.1002/pros.23450 }}</ref><ref name=":6" /><ref name=":12">{{cite journal | vauthors = Chen M, Yang MH, Chang MM, Tyan YC, Chen YA | title = Tumor suppressor gene glycine N-methyltransferase and its potential in liver disorders and hepatocellular carcinoma | journal = Toxicology and Applied Pharmacology | volume = 378 | pages = 114607 | date = September 2019 | pmid = 31170416 | doi = 10.1016/j.taap.2019.114607 }}</ref><ref name=":13">{{cite journal | vauthors = Heinzman Z, Schmidt C, Sliwinski MK, Goonesekere NC | title = The Case for GNMT as a Biomarker and a Therapeutic Target in Pancreatic Cancer | journal = Pharmaceuticals | volume = 14 | issue = 3 | pages = 209 | date = March 2021 | pmid = 33802396 | doi = 10.3390/ph14030209 }}</ref> |
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|[[Asparagine]] |
|[[Asparagine]] |
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|[[Asparagine synthetase]] |
|[[Asparagine synthetase]] |
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|Acute Lymphoblastic Leukemia |
|Acute Lymphoblastic Leukemia |
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|<ref name=":4" /><ref name=":6" /><ref name=":9">{{ |
|<ref name=":4" /><ref name=":6" /><ref name=":9">{{cite journal | vauthors = Chiu M, Taurino G, Bianchi MG, Kilberg MS, Bussolati O | title = Asparagine Synthetase in Cancer: Beyond Acute Lymphoblastic Leukemia | journal = Frontiers in Oncology | volume = 9 | pages = 1480 | date = 2020-01-09 | pmid = 31998641 | doi = 10.3389/fonc.2019.01480 }}</ref> |
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|[[Choline]] |
|[[Choline]] |
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|LDHA |
|LDHA |
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|[[Lactate dehydrogenase A]] |
|[[Lactate dehydrogenase A]] |
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|Various types of cancer<ref>{{ |
|Various types of cancer<ref>{{cite journal | vauthors = Feng Y, Xiong Y, Qiao T, Li X, Jia L, Han Y | title = Lactate dehydrogenase A: A key player in carcinogenesis and potential target in cancer therapy | journal = Cancer Medicine | volume = 7 | issue = 12 | pages = 6124–6136 | date = December 2018 | pmid = 30403008 | doi = 10.1002/cam4.1820 }}</ref> |
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|<ref name=":6" /><ref>{{ |
|<ref name=":6" /><ref>{{cite journal | vauthors = Valvona CJ, Fillmore HL, Nunn PB, Pilkington GJ | title = The Regulation and Function of Lactate Dehydrogenase A: Therapeutic Potential in Brain Tumor | journal = Brain Pathology | volume = 26 | issue = 1 | pages = 3–17 | date = January 2016 | pmid = 26269128 | doi = 10.1111/bpa.12299 }}</ref> |
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== Epigenetics == |
== Epigenetics == |
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Oncometabolite dysregulation and cancer progression are linked to [[Epigenetics|epigenetic]] changes in cancer cells. Several mechanisms have been linked to D-2-hydroxyglutarate, succinate, and fumarate with the inhibition of α-KG–dependent dioxygenases, this causes epigenetic changes that affect the expression of genes involved in cell differentiation and the development of malignant characteristics.<ref name=":10">{{ |
Oncometabolite dysregulation and cancer progression are linked to [[Epigenetics|epigenetic]] changes in cancer cells. Several mechanisms have been linked to D-2-hydroxyglutarate, succinate, and fumarate with the inhibition of α-KG–dependent dioxygenases, this causes epigenetic changes that affect the expression of genes involved in cell differentiation and the development of malignant characteristics.<ref name=":10">{{cite journal | vauthors = Yang M, Soga T, Pollard PJ | title = Oncometabolites: linking altered metabolism with cancer | journal = The Journal of Clinical Investigation | volume = 123 | issue = 9 | pages = 3652–3658 | date = September 2013 | pmid = 23999438 | doi = 10.1172/jci67228 }}</ref>. The group of Timothy A. Chan<ref>{{cite journal | vauthors = Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, Yilmaz E, Campos C, Fabius AW, Lu C, Ward PS, Thompson CB, Kaufman A, Guryanova O, Levine R, Heguy A, Viale A, Morris LG, Huse JT, Mellinghoff IK, Chan TA | display-authors = 6 | title = IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype | journal = Nature | volume = 483 | issue = 7390 | pages = 479–483 | date = February 2012 | pmid = 22343889 | pmc = 3351699 | doi = 10.1038/nature10866 }}</ref> described a mechanism by which abnormal accumulation of the oncometabolite D-2-hydroxyglutarate in brain tumor samples increased DNA methylation, a process that has been shown to play a key role in oncogenesis<ref>{{cite journal | vauthors = Jones PA, Baylin SB | title = The epigenomics of cancer | journal = Cell | volume = 128 | issue = 4 | pages = 683–692 | date = February 2007 | pmid = 17320506 | pmc = 3894624 | doi = 10.1016/j.cell.2007.01.029 }}</ref>. On the other hand in paraganglioma cells, succinate and fumarate were found to methylate histones, effectively silencing the genes PNMT and KRT19, which are involved in neuroendocrine differentiation and epithelial-mesenchymal transition, respectively<ref name=":11">{{cite journal | vauthors = Letouzé E, Martinelli C, Loriot C, Burnichon N, Abermil N, Ottolenghi C, Janin M, Menara M, Nguyen AT, Benit P, Buffet A, Marcaillou C, Bertherat J, Amar L, Rustin P, De Reyniès A, Gimenez-Roqueplo AP, Favier J | display-authors = 6 | title = SDH mutations establish a hypermethylator phenotype in paraganglioma | journal = Cancer Cell | volume = 23 | issue = 6 | pages = 739–752 | date = June 2013 | pmid = 23707781 | doi = 10.1016/j.ccr.2013.04.018 }}</ref>. |
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== Biomarkers for cancer detection == |
== Biomarkers for cancer detection == |
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The discovery of oncometabolites has ushered in a new era in cancer biology, one that has the potential to improve patient care. The discovery of new therapeutic and reliable markers that exploit vulnerabilities of cancer cells, are being used to targeting either upstream or downstream effectors of these pathways<ref>{{ |
The discovery of oncometabolites has ushered in a new era in cancer biology, one that has the potential to improve patient care. The discovery of new therapeutic and reliable markers that exploit vulnerabilities of cancer cells, are being used to targeting either upstream or downstream effectors of these pathways<ref>{{cite journal | vauthors = Collins RR, Patel K, Putnam WC, Kapur P, Rakheja D | title = Oncometabolites: A New Paradigm for Oncology, Metabolism, and the Clinical Laboratory | journal = Clinical Chemistry | volume = 63 | issue = 12 | pages = 1812–1820 | date = December 2017 | pmid = 29038145 | doi = 10.1373/clinchem.2016.267666 }}</ref>. Oncometabolites can be used as diagnostic biomarkers and may be able to assist oncologists in making more precise decisions in early stages of tumorigenesis, particularly in predicting more aggressive tumor behavior<ref name=":7" />. |
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== Isocitrate dehydrogenase == |
== Isocitrate dehydrogenase == |
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[[File:Isocitrate dehydrogenase.png|thumb|Crystallographic structure of protein isocitrate dehydrogenase.|256x256px]] |
[[File:Isocitrate dehydrogenase.png|thumb|Crystallographic structure of protein isocitrate dehydrogenase.|256x256px]] |
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The detection of D-2-hydroxyglutarate in glioma patients using proton magnetic resonance spectroscopy (MRS) has been shown to be a noninvasive procedure. The presence of IDH1 or IDH2 mutations was linked to the detection of this oncometabolite 100 percent of the time<ref>{{ |
The detection of D-2-hydroxyglutarate in glioma patients using proton magnetic resonance spectroscopy (MRS) has been shown to be a noninvasive procedure. The presence of IDH1 or IDH2 mutations was linked to the detection of this oncometabolite 100 percent of the time<ref>{{cite journal | vauthors = Choi C, Ganji SK, DeBerardinis RJ, Hatanpaa KJ, Rakheja D, Kovacs Z, Yang XL, Mashimo T, Raisanen JM, Marin-Valencia I, Pascual JM, Madden CJ, Mickey BE, Malloy CR, Bachoo RM, Maher EA | display-authors = 6 | title = 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas | journal = Nature Medicine | volume = 18 | issue = 4 | pages = 624–629 | date = January 2012 | pmid = 22281806 | doi = 10.1038/nm.2682 }}</ref><ref name=":10" />. IDH2/R140Q is a specific mutation that has shown promising results after its inhibition by the small molecule AGI-6780.<ref>{{cite journal | vauthors = Wang F, Travins J, DeLaBarre B, Penard-Lacronique V, Schalm S, Hansen E, Straley K, Kernytsky A, Liu W, Gliser C, Yang H, Gross S, Artin E, Saada V, Mylonas E, Quivoron C, Popovici-Muller J, Saunders JO, Salituro FG, Yan S, Murray S, Wei W, Gao Y, Dang L, Dorsch M, Agresta S, Schenkein DP, Biller SA, Su SM, de Botton S, Yen KE | display-authors = 6 | title = Targeted inhibition of mutant IDH2 in leukemia cells induces cellular differentiation | journal = Science | volume = 340 | issue = 6132 | pages = 622–626 | date = May 2013 | pmid = 23558173 | doi = 10.1126/science.1234769 }}</ref> Therefore, limiting the supply of D-2-hydroxyglutarate by inhibiting the detected mutant IDH enzymes could be a good therapeutical approach to IDH-mutant cancers.<ref>{{cite journal | vauthors = Ye D, Guan KL, Xiong Y | title = Metabolism, Activity, and Targeting of D- and L-2-Hydroxyglutarates | journal = Trends in Cancer | volume = 4 | issue = 2 | pages = 151–165 | date = February 2018 | pmid = 29458964 | pmc = 5884165 | doi = 10.1016/j.trecan.2017.12.005 }}</ref> |
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== Succinate Dehydrogenase == |
== Succinate Dehydrogenase == |
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[[Immunohistochemistry|IHC]] staining has been shown to be a useful diagnostic tool for prioritizing patients for SDH mutation testing in early stages of cancer. The absence of SDHB in IHC staining would be linked to the presence of SDH oncogene mutations |
[[Immunohistochemistry|IHC]] staining has been shown to be a useful diagnostic tool for prioritizing patients for SDH mutation testing in early stages of cancer. The absence of SDHB in IHC staining would be linked to the presence of SDH oncogene mutations.<ref>{{cite journal | vauthors = van Nederveen FH, Gaal J, Favier J, Korpershoek E, Oldenburg RA, de Bruyn EM, Sleddens HF, Derkx P, Rivière J, Dannenberg H, Petri BJ, Komminoth P, Pacak K, Hop WC, Pollard PJ, Mannelli M, Bayley JP, Perren A, Niemann S, Verhofstad AA, de Bruïne AP, Maher ER, Tissier F, Méatchi T, Badoual C, Bertherat J, Amar L, Alataki D, Van Marck E, Ferrau F, François J, de Herder WW, Peeters MP, van Linge A, Lenders JW, Gimenez-Roqueplo AP, de Krijger RR, Dinjens WN | display-authors = 6 | title = An immunohistochemical procedure to detect patients with paraganglioma and phaeochromocytoma with germline SDHB, SDHC, or SDHD gene mutations: a retrospective and prospective analysis | journal = The Lancet. Oncology | volume = 10 | issue = 8 | pages = 764–771 | date = August 2009 | pmid = 19576851 | doi = 10.1016/S1470-2045(09)70164-0 }}</ref> The already commercialized drug [[decitabine]] (''Dacogen®'') could be an effective therapy to repress the migration capacities of SDHB-mutant cells,<ref name=":11" /> |
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== Fumarate Hydratase == |
== Fumarate Hydratase == |
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IHC staining for FH is used to detect lack of this protein in patients with papillary renal cell carcinoma type 2<ref>{{ |
IHC staining for FH is used to detect lack of this protein in patients with papillary renal cell carcinoma type 2<ref>{{cite journal | vauthors = Trpkov K, Hes O, Agaimy A, Bonert M, Martinek P, Magi-Galluzzi C, Kristiansen G, Lüders C, Nesi G, Compérat E, Sibony M, Berney DM, Mehra R, Brimo F, Hartmann A, Husain A, Frizzell N, Hills K, Maclean F, Srinivasan B, Gill AJ | display-authors = 6 | title = Fumarate Hydratase-deficient Renal Cell Carcinoma Is Strongly Correlated With Fumarate Hydratase Mutation and Hereditary Leiomyomatosis and Renal Cell Carcinoma Syndrome | journal = The American Journal of Surgical Pathology | volume = 40 | issue = 7 | pages = 865–875 | date = July 2016 | pmid = 26900816 | doi = 10.1097/pas.0000000000000617 }}</ref>. The lack of FH in renal carcinoma cells induces pro-survival metabolic adaptations where several cascades are affected<ref>{{cite journal | vauthors = Schmidt C, Sciacovelli M, Frezza C | title = Fumarate hydratase in cancer: A multifaceted tumour suppressor | journal = Seminars in Cell & Developmental Biology | volume = 98 | pages = 15–25 | date = February 2020 | pmid = 31085323 | doi = 10.1016/j.semcdb.2019.05.002 }}</ref>. |
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== Glycine-N-methyltransferase == |
== Glycine-N-methyltransferase == |
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== Oncometabolomics == |
== Oncometabolomics == |
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[[Metabolomics]] can be applied to oncometabolism, since the changes in cancer's genomic, transcriptomic, and proteomic profiles can result in changes in downstream metabolic pathways. With this information we can elucidate the responsible pathways and oncometabolites for various diseases. Actually, through the use of this technique, the dysregulation of the pyruvate kinase enzyme in glucose metabolism was discovered in cancer cells. Another common used technique is glucose or glutamine labeled with 13C to show that the [[Citric acid cycle|TCA cycle]] is used to generate large amounts of fatty acids (phospholipids) and to replenish the [[Citric acid cycle|TCA cycle]] intermediates<ref>{{ |
[[Metabolomics]] can be applied to oncometabolism, since the changes in cancer's genomic, transcriptomic, and proteomic profiles can result in changes in downstream metabolic pathways. With this information we can elucidate the responsible pathways and oncometabolites for various diseases. Actually, through the use of this technique, the dysregulation of the pyruvate kinase enzyme in glucose metabolism was discovered in cancer cells. Another common used technique is glucose or glutamine labeled with 13C to show that the [[Citric acid cycle|TCA cycle]] is used to generate large amounts of fatty acids (phospholipids) and to replenish the [[Citric acid cycle|TCA cycle]] intermediates<ref>{{cite journal | vauthors = Benjamin DI, Cravatt BF, Nomura DK | title = Global profiling strategies for mapping dysregulated metabolic pathways in cancer | journal = Cell Metabolism | volume = 16 | issue = 5 | pages = 565–577 | date = November 2012 | pmid = 23063552 | doi = 10.1016/j.cmet.2012.09.013 }}</ref>. But oncometabolomics does not necessarily need to be used on cancer cells, but on cells immediately surrounding them in the [[Tumor microenvironment|TME]]<ref>{{cite journal | vauthors = Chaudhri VK, Salzler GG, Dick SA, Buckman MS, Sordella R, Karoly ED, Mohney R, Stiles BM, Elemento O, Altorki NK, McGraw TE | display-authors = 6 | title = Metabolic alterations in lung cancer-associated fibroblasts correlated with increased glycolytic metabolism of the tumor | journal = Molecular Cancer Research | volume = 11 | issue = 6 | pages = 579–592 | date = June 2013 | pmid = 23475953 | doi = 10.1158/1541-7786.mcr-12-0437-t }}</ref>. |
||
Metabolomics applied to cancer has the potential to significantly improve current oncological treatments and has a great diagnostic value, since metabolic changes are the prequel of phenotypic changes in cells (thus tissues and organs) making it suitable for early detection of difficult-to-detect cancers.<ref name=":5" /> This also leads to a more personalized medicine and customize an individual's cancer treatment according to their specific oncometabolite profiles, which would allow for better cancer therapy customization or informed adjustments<ref name=":6" /><ref>{{ |
Metabolomics applied to cancer has the potential to significantly improve current oncological treatments and has a great diagnostic value, since metabolic changes are the prequel of phenotypic changes in cells (thus tissues and organs) making it suitable for early detection of difficult-to-detect cancers.<ref name=":5" /> This also leads to a more personalized medicine and customize an individual's cancer treatment according to their specific oncometabolite profiles, which would allow for better cancer therapy customization or informed adjustments<ref name=":6" /><ref>{{cite journal | vauthors = Wishart DS | title = Is Cancer a Genetic Disease or a Metabolic Disease? | journal = EBioMedicine | volume = 2 | issue = 6 | pages = 478–479 | date = June 2015 | pmid = 26288805 | doi = 10.1016/j.ebiom.2015.05.022 }}</ref>. |
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== Software and libraries == |
== Software and libraries == |
||
=== Ingenuity Pathway Analysis (IPA) === |
=== Ingenuity Pathway Analysis (IPA) === |
||
[https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/ Ingenuity Pathway Analysis (IPA)] is a metabolic pathway analysis software package that helps researchers model, analyze, and comprehend complex biological systems by associating specific metabolites with potential metabolic pathways for data analysis<ref>{{ |
[https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/ Ingenuity Pathway Analysis (IPA)] is a metabolic pathway analysis software package that helps researchers model, analyze, and comprehend complex biological systems by associating specific metabolites with potential metabolic pathways for data analysis.<ref>{{cite journal | vauthors = Koo I, Wei X, Zhang X | title = Analysis of metabolomic profiling data acquired on GC-MS | journal = Methods in Enzymology | volume = 543 | pages = 315–324 | date = 2014 | pmid = 24924140 | doi = 10.1016/B978-0-12-801329-8.00016-7 | publisher = Elsevier }}</ref> This software has been used by researchers to elucidate regulatory networks on oncometabolites like hydroxyglutarate.<ref>{{cite journal | vauthors = Liu L, Hu K, Feng J, Wang H, Fu S, Wang B, Wang L, Xu Y, Yu X, Huang H | display-authors = 6 | title = The oncometabolite R-2-hydroxyglutarate dysregulates the differentiation of human mesenchymal stromal cells via inducing DNA hypermethylation | journal = BMC Cancer | volume = 21 | issue = 1 | pages = 36 | date = January 2021 | pmid = 33413208 | doi = 10.1186/s12885-020-07744-x }}</ref> |
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=== Metabolights === |
=== Metabolights === |
||
[[MetaboLights|Metabolights]] is an open-access database for metabolomics research that collects all experimental data from leading journals' metabolic experiments<ref>{{Cite web|title=MetaboLights - Metabolomics experiments and derived information|url=https://www.ebi.ac.uk/metabolights/|access-date=2021-11-08|website=www.ebi.ac.uk}}</ref>. Since its initial release in 2012, the MetaboLights repository has seen consistent year-on-year growth. It is a resource that surged in response to the needs of the scientific community to easy access to metabolite data<ref>{{ |
[[MetaboLights|Metabolights]] is an open-access database for metabolomics research that collects all experimental data from leading journals' metabolic experiments<ref>{{Cite web|title=MetaboLights - Metabolomics experiments and derived information|url=https://www.ebi.ac.uk/metabolights/|access-date=2021-11-08|website=www.ebi.ac.uk}}</ref>. Since its initial release in 2012, the MetaboLights repository has seen consistent year-on-year growth. It is a resource that surged in response to the needs of the scientific community to easy access to metabolite data<ref>{{cite journal | vauthors = Haug K, Cochrane K, Nainala VC, Williams M, Chang J, Jayaseelan KV, O'Donovan C | title = MetaboLights: a resource evolving in response to the needs of its scientific community | journal = Nucleic Acids Research | volume = 48 | issue = D1 | pages = D440-D444 | date = January 2020 | pmid = 31691833 | doi = 10.1093/nar/gkz1019 }}</ref><ref>{{cite journal | vauthors = Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M, Mahendraker T, Williams M, Neumann S, Rocca-Serra P, Maguire E, González-Beltrán A, Sansone SA, Griffin JL, Steinbeck C | display-authors = 6 | title = MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data | journal = Nucleic Acids Research | volume = 41 | issue = Database issue | pages = D781-D786 | date = January 2013 | pmid = 23109552 | doi = 10.1093/nar/gks1004 }}</ref>. |
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== Research == |
== Research == |
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[[File:Exosomas vesículas Cardiomiocito.png|thumb|296x296px|Transmission electron microscopy of purified exosomes.]] |
[[File:Exosomas vesículas Cardiomiocito.png|thumb|296x296px|Transmission electron microscopy of purified exosomes.]] |
||
Cancer research has been ongoing for centuries, trying to elucidate the origin of its cause<ref>{{Cite web|title=Understanding Cancer Causes: Ancient Times to Present|url=https://www.cancer.org/cancer/cancer-basics/history-of-cancer/modern-knowledge-and-cancer-causes.html|access-date=2021-12-06|website=www.cancer.org|language=en}}</ref>. As cancer research evolves with time, the scientific community tends to pay more attention to cell metabolism and how to target these metabolic needs and changes that cells undergo during carcinogenesis<ref>{{ |
Cancer research has been ongoing for centuries, trying to elucidate the origin of its cause<ref>{{Cite web|title=Understanding Cancer Causes: Ancient Times to Present|url=https://www.cancer.org/cancer/cancer-basics/history-of-cancer/modern-knowledge-and-cancer-causes.html|access-date=2021-12-06|website=www.cancer.org|language=en}}</ref>. As cancer research evolves with time, the scientific community tends to pay more attention to cell metabolism and how to target these metabolic needs and changes that cells undergo during carcinogenesis<ref>{{cite journal | vauthors = Park JH, Pyun WY, Park HW | title = Cancer Metabolism: Phenotype, Signaling and Therapeutic Targets | journal = Cells | volume = 9 | issue = 10 | pages = 2308 | date = October 2020 | pmid = 33081387 | doi = 10.3390/cells9102308 }}</ref>. There is growing evidence that metabolic dependencies in cancer are influenced by tissue environment, being this important to consider the [[Tumor microenvironment|TME]] for different in vitro and in vivo models to study oncometabolism in different cancer scenarios<ref>{{cite journal | vauthors = Luengo A, Gui DY, Vander Heiden MG | title = Targeting Metabolism for Cancer Therapy | journal = Cell Chemical Biology | volume = 24 | issue = 9 | pages = 1161–1180 | date = September 2017 | pmid = 28938091 | pmc = 5744685 | doi = 10.1016/j.chembiol.2017.08.028 }}</ref>. |
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There is extensive research on the modulation of [[Bromodomain|BET proteins]] in cancer models of breast. These proteins appear to be involved in oncometabolism and targeting and uncoupling [[BRD4]] actions in carcinogenic cells, as well as stopping pro-migratory signals and changing [[cytokine]] metabolism, particularly [[Interleukin 6|IL-6]] metabolism.<ref>{{ |
There is extensive research on the modulation of [[Bromodomain|BET proteins]] in cancer models of breast. These proteins appear to be involved in oncometabolism and targeting and uncoupling [[BRD4]] actions in carcinogenic cells, as well as stopping pro-migratory signals and changing [[cytokine]] metabolism, particularly [[Interleukin 6|IL-6]] metabolism.<ref>{{cite journal | vauthors = Andrieu G, Tran AH, Strissel KJ, Denis GV | title = BRD4 Regulates Breast Cancer Dissemination through Jagged1/Notch1 Signaling | journal = Cancer Research | volume = 76 | issue = 22 | pages = 6555–6567 | date = November 2016 | pmid = 27651315 | pmc = 5290198 | doi = 10.1158/0008-5472.CAN-16-0559 }}</ref>. The same group has reported on the importance of [[Exosome (vesicle)|exosomes]] in the [[Tumor microenvironment|TME]] and how these vesicles, shed by [[Adipocyte|adipocytes]], can carry a specific molecular cargo that causes metabolic changes in the cell, leading to pro-metastatic changes in the recipient [[breast cancer]] cells.<ref>{{cite journal | vauthors = Jafari N, Kolla M, Meshulam T, Shafran JS, Qiu Y, Casey AN, Pompa IR, Ennis CS, Mazzeo CS, Rabhi N, Farmer SR, Denis GV | display-authors = 6 | title = Adipocyte-derived exosomes may promote breast cancer progression in type 2 diabetes | journal = Science Signaling | volume = 14 | issue = 710 | pages = eabj2807 | date = November 2021 | pmid = 34813359 | doi = 10.1126/scisignal.abj2807 }}</ref>. |
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== References == |
== References == |
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Revision as of 09:22, 11 December 2021
Oncometabolism is a new field of study that focuses on the metabolic changes that occur in cells that make up the tumor microenvironment (TME) and accompany oncogenesis and tumor progression toward a neoplastic state[1].
Oncometabolism is a term used to describe how cells with increased growth and survivability differ from non-tumorigenic cells in terms of metabolism[2]. This is explained by the Warburg Effect, which describes how cancer cells change their metabolism to become more oncogenic in order to proliferate and eventually invade other tissues in a process known as metastasis.[1]
The chemical reactions associated with oncometabolism are triggered by the alteration of oncogenes, which are genes that have the potential to cause cancer.[3] These genes can be functional and active during physiological conditions, producing normal amounts of metabolites. However, their upregulation as a result of DNA damage can result in an overabundance of these metabolites, which can lead to tumorigenesis. These metabolites are known as oncometabolites, and they are thought to be very useful in the early stages of cancer diagnosis and prevention because they can act as biomarkers.[4]
History
In the 1920s, Otto Heinrich Warburg discovered an intriguing bioenergetic phenotype shared by most tumor cells: a higher-than-normal reliance on lactic acid fermentation for energy generation. He is known as the "Father of Oncometabolism" [1][2]. Although the roots of this research field trace back to the 1920s, it was only recently recognized[1]. Over the last decade, research on cancer progression has focused on the role of shifting metabolic pathways for both the cancer and immune cells, leading to an increase interest in characterizing the metabolic alterations that cells undergo in the TME[5].
Warburg Effect
In the absence of hypoxic conditions (i.e. physiological levels of oxygen), cancer cells preferentially convert glucose to lactate, according to Otto H. Warburg, who believed that aerobic glycolysis was the key metabolic change in cancer cell malignancy. The "Warburg effect" was later coined to describe this metabolic shift[6]. Warburg thought this change in metabolism was due to mitochondrial "respiration injury", but this interpretation was questioned by other researchers in 1956 showing that intact and functional cytochromes detected in most tumor cells clearly speak against a general mitochondrial dysfunction[7]. Furthermore, Potter et al. and several other authors provided significant evidence that oxidative phosphorylation and a normal Krebs cycle persist in the vast majority malignant tumors, adding to the growing body of evidence that most cancers exhibit the Warburg effect while maintaining a proper mitochondrial respiration[6][8]. Dang et al[9] in 2008 provided evidence that the tumor tissue sections used in Warburg's experiments should have been thinner for the oxygen diffusion constants employed, implying that the tissue slices studied were partially hypoxic and the calculated critical diffusion distance was of 470 micrometers[6]. As a result, endless debates and discussions about Warburg's discovery took place and have piqued the interest of scientists all over the world, which has helped bring attention to cell metabolism in cancer and immune cells and the use of modern technology to discover what these pathways are and how they are modified as well as potential therapeutic targets.
Metabolic reprogramming
Carcinogenic cells undergo a metabolic rewiring during oncogenesis, and oncometabolites play an important role. In cancer, there are several reprogrammed metabolic pathways that help cells survive when nutrients are scarce: Aerobic glycolysis, an increase in glycolytic flux, also known as the Warburg effect, allows glycolytic intermediates to supply subsidiary pathways to meet the metabolic demands of proliferating tumorigenic cells[10]. Another studied reprogrammed pathway is gain of function of the oncogene MYC. This gene encodes a transcription factor that boosts the expression of a number of genes involved in anabolic growth via mitochondrial metabolism[11]. Oncometabolite production is another example of metabolic deregulation[12].
Oncometabolites
Oncometabolites are metabolites whose abundance increases markedly in cancer cells through loss-of-function or gain-of-function mutations in specific enzymes involved in their production, the accumulation of these endogenous metabolites initiates or sustains tumor growth and metastasis[13]. Cancer cells rely on aerobic glycolysis, which is reached through defects in enzymes involved in normal cell metabolism, this allows the cancer cells to meet their energy needs and divert acetyl-coA from the TCA cycle to build essential biomolecules such as amino acids and lipids[14]. These defects cause an overabundance of endogenous metabolites, which are frequently involved in critical epigenetic changes and signaling pathways that have a direct impact on cancer cell metabolism[15].
| Oncometabolite | Role | Oncogenes | Enzyme affected | Associated malignancies | References |
|---|---|---|---|---|---|
| D-2-hydroxyglutarate | Inhibits ATP synthase and mTOR signalling | IDH1
IDH2 |
Isocitrate dehydrogenase | Brain cancer, Leukemia | [15][16][17][13] |
| Succinate | Inhibits 2-oxoglutarate-dependent oxygenase | SDHA,SDHB,SDHC,SDHD,SDHAF1,SDHAF2 | succinate dehydrogenase | Renal and Thyroid tumors | [15][13] |
| Fumarate | Inhibits 2-oxoglutarate-dependent oxygenase | FH | Fumarate hydratase | Leiomyomata, Renal cysts | [15][16][13] |
| Glutamine* | *(Primary carbon-source for the biosynthesis of the oncometabolite 2-hydroxyglutarate) | [18][19] | |||
| Sarcosine | Activates mTOR signalling pathway | GNMT | Glycine-N-methyltransferase | Pancreatic cancer,Hepatocellular carcinoma | [18][20][21][13][22][23] |
| Asparagine | Anti-apoptotic agent | ASNS | Asparagine synthetase | Acute Lymphoblastic Leukemia | [18][13][24] |
| Choline | Methyl donor for DNA methylation which disrupts DNA repair | PCYT1A | phosphate cytidylyltransferase 1 choline-α | Breast, Brain and Prostate cancer | [18][13][24] |
| Lactate | Induces local immunosuppression | LDHA | Lactate dehydrogenase A | Various types of cancer[25] | [13][26] |
Epigenetics
Oncometabolite dysregulation and cancer progression are linked to epigenetic changes in cancer cells. Several mechanisms have been linked to D-2-hydroxyglutarate, succinate, and fumarate with the inhibition of α-KG–dependent dioxygenases, this causes epigenetic changes that affect the expression of genes involved in cell differentiation and the development of malignant characteristics.[27]. The group of Timothy A. Chan[28] described a mechanism by which abnormal accumulation of the oncometabolite D-2-hydroxyglutarate in brain tumor samples increased DNA methylation, a process that has been shown to play a key role in oncogenesis[29]. On the other hand in paraganglioma cells, succinate and fumarate were found to methylate histones, effectively silencing the genes PNMT and KRT19, which are involved in neuroendocrine differentiation and epithelial-mesenchymal transition, respectively[30].
Biomarkers for cancer detection
The discovery of oncometabolites has ushered in a new era in cancer biology, one that has the potential to improve patient care. The discovery of new therapeutic and reliable markers that exploit vulnerabilities of cancer cells, are being used to targeting either upstream or downstream effectors of these pathways[31]. Oncometabolites can be used as diagnostic biomarkers and may be able to assist oncologists in making more precise decisions in early stages of tumorigenesis, particularly in predicting more aggressive tumor behavior[4].
Isocitrate dehydrogenase
The detection of D-2-hydroxyglutarate in glioma patients using proton magnetic resonance spectroscopy (MRS) has been shown to be a noninvasive procedure. The presence of IDH1 or IDH2 mutations was linked to the detection of this oncometabolite 100 percent of the time[32][27]. IDH2/R140Q is a specific mutation that has shown promising results after its inhibition by the small molecule AGI-6780.[33] Therefore, limiting the supply of D-2-hydroxyglutarate by inhibiting the detected mutant IDH enzymes could be a good therapeutical approach to IDH-mutant cancers.[34]
Succinate Dehydrogenase
IHC staining has been shown to be a useful diagnostic tool for prioritizing patients for SDH mutation testing in early stages of cancer. The absence of SDHB in IHC staining would be linked to the presence of SDH oncogene mutations.[35] The already commercialized drug decitabine (Dacogen®) could be an effective therapy to repress the migration capacities of SDHB-mutant cells,[30]
Fumarate Hydratase
IHC staining for FH is used to detect lack of this protein in patients with papillary renal cell carcinoma type 2[36]. The lack of FH in renal carcinoma cells induces pro-survival metabolic adaptations where several cascades are affected[37].
Glycine-N-methyltransferase
Downregulation of glycine-N-methyltransferase has been linked to hepatocellular carcinoma and pancreatic cancer. Serving this as a reliable marker for oncogenesis [22]. When compared to patients with deletions in GNMT, patients with no deletions early-stage pancreatic cancer had twice the median months overall survival[23].
Applications
Oncometabolomics
Metabolomics can be applied to oncometabolism, since the changes in cancer's genomic, transcriptomic, and proteomic profiles can result in changes in downstream metabolic pathways. With this information we can elucidate the responsible pathways and oncometabolites for various diseases. Actually, through the use of this technique, the dysregulation of the pyruvate kinase enzyme in glucose metabolism was discovered in cancer cells. Another common used technique is glucose or glutamine labeled with 13C to show that the TCA cycle is used to generate large amounts of fatty acids (phospholipids) and to replenish the TCA cycle intermediates[38]. But oncometabolomics does not necessarily need to be used on cancer cells, but on cells immediately surrounding them in the TME[39].
Metabolomics applied to cancer has the potential to significantly improve current oncological treatments and has a great diagnostic value, since metabolic changes are the prequel of phenotypic changes in cells (thus tissues and organs) making it suitable for early detection of difficult-to-detect cancers.[14] This also leads to a more personalized medicine and customize an individual's cancer treatment according to their specific oncometabolite profiles, which would allow for better cancer therapy customization or informed adjustments[13][40].
Software and libraries
Ingenuity Pathway Analysis (IPA)
Ingenuity Pathway Analysis (IPA) is a metabolic pathway analysis software package that helps researchers model, analyze, and comprehend complex biological systems by associating specific metabolites with potential metabolic pathways for data analysis.[41] This software has been used by researchers to elucidate regulatory networks on oncometabolites like hydroxyglutarate.[42]
Metabolights
Metabolights is an open-access database for metabolomics research that collects all experimental data from leading journals' metabolic experiments[43]. Since its initial release in 2012, the MetaboLights repository has seen consistent year-on-year growth. It is a resource that surged in response to the needs of the scientific community to easy access to metabolite data[44][45].
Research
Cancer research has been ongoing for centuries, trying to elucidate the origin of its cause[46]. As cancer research evolves with time, the scientific community tends to pay more attention to cell metabolism and how to target these metabolic needs and changes that cells undergo during carcinogenesis[47]. There is growing evidence that metabolic dependencies in cancer are influenced by tissue environment, being this important to consider the TME for different in vitro and in vivo models to study oncometabolism in different cancer scenarios[48].
There is extensive research on the modulation of BET proteins in cancer models of breast. These proteins appear to be involved in oncometabolism and targeting and uncoupling BRD4 actions in carcinogenic cells, as well as stopping pro-migratory signals and changing cytokine metabolism, particularly IL-6 metabolism.[49]. The same group has reported on the importance of exosomes in the TME and how these vesicles, shed by adipocytes, can carry a specific molecular cargo that causes metabolic changes in the cell, leading to pro-metastatic changes in the recipient breast cancer cells.[50].
References
- ^ a b c d Urbano AM (January 2021). "Otto Warburg: The journey towards the seminal discovery of tumor cell bioenergetic reprogramming". Biochimica et Biophysica Acta. Molecular Basis of Disease. 1867 (1): 165965. doi:10.1016/j.bbadis.2020.165965. PMID 32949769.
- ^ a b Oliveira PJ, Urbano AM (February 2021). ""Oncometabolism: The switchboard of cancer - An editorial"". Biochimica et Biophysica Acta. Molecular Basis of Disease. 1867 (2): 166031. doi:10.1016/j.bbadis.2020.166031. PMID 33310398.
- ^ Cooper CS (1990). "The Role of Oncogene Activation in Chemical Carcinogenesis". Handbook of Experimental Pharmacology. Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 319–352. doi:10.1007/978-3-642-74778-6_12.
- ^ a b Dando I, Pozza ED, Ambrosini G, Torrens-Mas M, Butera G, Mullappilly N, et al. (August 2019). "Oncometabolites in cancer aggressiveness and tumour repopulation". Biological Reviews of the Cambridge Philosophical Society. 94 (4): 1530–1546. doi:10.1111/brv.12513. PMID 30972955.
- ^ Biswas SK (September 2015). "Metabolic Reprogramming of Immune Cells in Cancer Progression". Immunity. 43 (3): 435–449. doi:10.1016/j.immuni.2015.09.001. PMID 26377897.
- ^ a b c Vaupel P, Schmidberger H, Mayer A (July 2019). "The Warburg effect: essential part of metabolic reprogramming and central contributor to cancer progression". International Journal of Radiation Biology. 95 (7): 912–919. doi:10.1080/09553002.2019.1589653. PMID 30822194.
- ^ Pascale RM, Calvisi DF, Simile MM, Feo CF, Feo F (September 2020). "The Warburg Effect 97 Years after Its Discovery". Cancers. 12 (10): 2819. doi:10.3390/cancers12102819. PMID 33008042.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Potter M, Newport E, Morten KJ (October 2016). "The Warburg effect: 80 years on". Biochemical Society Transactions. 44 (5): 1499–1505. doi:10.1042/bst20160094. PMID 27911732.
- ^ Dang, et al. The interplay between MYC and HIF in cancer. Nature Reviews Cancer volume 8, pages51–56 (2008).
- ^ Lunt SY, Vander Heiden MG (2011-11-10). "Aerobic glycolysis: meeting the metabolic requirements of cell proliferation". Annual Review of Cell and Developmental Biology. 27 (1): 441–464. doi:10.1146/annurev-cellbio-092910-154237. PMID 21985671.
- ^ Van Dang C (October 2015). "Abstract IA05: Targeting MYC-mediated cancer metabolism". Myc and Metabolism - Metabolomics. American Association for Cancer Research. doi:10.1158/1557-3125.myc15-ia05.
- ^ DeBerardinis RJ, Chandel NS (May 2016). "Fundamentals of cancer metabolism". Science Advances. 2 (5): e1600200. doi:10.1126/sciadv.1600200. PMC 4928883. PMID 27386546.
- ^ a b c d e f g h i Wishart DS (July 2016). "Emerging applications of metabolomics in drug discovery and precision medicine". Nature Reviews. Drug Discovery. 15 (7): 473–484. doi:10.1038/nrd.2016.32. PMID 26965202.
- ^ a b Gupta S, Chawla K (August 2013). "Oncometabolomics in cancer research". Expert Review of Proteomics. 10 (4): 325–336. doi:10.1586/14789450.2013.828947. PMID 23992416.
- ^ a b c d Collins RR, Patel K, Putnam WC, Kapur P, Rakheja D (December 2017). "Oncometabolites: A New Paradigm for Oncology, Metabolism, and the Clinical Laboratory". Clinical Chemistry. 63 (12): 1812–1820. doi:10.1373/clinchem.2016.267666. PMID 29038145.
- ^ a b Yang M, Soga T, Pollard PJ, Adam J (2012). "The emerging role of fumarate as an oncometabolite". Frontiers in Oncology. 2: 85. doi:10.3389/fonc.2012.00085. PMID 22866264.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Garber K (July 2010). "Oncometabolite? IDH1 discoveries raise possibility of new metabolism targets in brain cancers and leukemia". Journal of the National Cancer Institute. 102 (13): 926–928. doi:10.1093/jnci/djq262. PMID 20576929.
- ^ a b c d Khatami F, Aghamir SM, Tavangar SM (September 2019). "Oncometabolites: A new insight for oncology". Molecular Genetics & Genomic Medicine. 7 (9): e873. doi:10.1002/mgg3.873. PMID 31321921.
- ^ Salamanca-Cardona L, Shah H, Poot AJ, Correa FM, Di Gialleonardo V, Lui H, et al. (December 2017). "In Vivo Imaging of Glutamine Metabolism to the Oncometabolite 2-Hydroxyglutarate in IDH1/2 Mutant Tumors". Cell Metabolism. 26 (6): 830–841.e3. doi:10.1016/j.cmet.2017.10.001. PMID 29056515.
- ^ Amjad P Khan, et al. The role of sarcosine metabolism in prostate cancer progression. Neoplasia. 2013 May;15(5):491-501.doi: 10.1593/neo.13314.
- ^ Rodrigo MA, Strmiska V, Horackova E, Buchtelova H, Michalek P, Stiborova M, et al. (February 2018). "Sarcosine influences apoptosis and growth of prostate cells via cell-type specific regulation of distinct sets of genes". The Prostate. 78 (2): 104–112. doi:10.1002/pros.23450. PMID 29105933.
- ^ a b Chen M, Yang MH, Chang MM, Tyan YC, Chen YA (September 2019). "Tumor suppressor gene glycine N-methyltransferase and its potential in liver disorders and hepatocellular carcinoma". Toxicology and Applied Pharmacology. 378: 114607. doi:10.1016/j.taap.2019.114607. PMID 31170416.
- ^ a b Heinzman Z, Schmidt C, Sliwinski MK, Goonesekere NC (March 2021). "The Case for GNMT as a Biomarker and a Therapeutic Target in Pancreatic Cancer". Pharmaceuticals. 14 (3): 209. doi:10.3390/ph14030209. PMID 33802396.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ a b Chiu M, Taurino G, Bianchi MG, Kilberg MS, Bussolati O (2020-01-09). "Asparagine Synthetase in Cancer: Beyond Acute Lymphoblastic Leukemia". Frontiers in Oncology. 9: 1480. doi:10.3389/fonc.2019.01480. PMID 31998641.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Feng Y, Xiong Y, Qiao T, Li X, Jia L, Han Y (December 2018). "Lactate dehydrogenase A: A key player in carcinogenesis and potential target in cancer therapy". Cancer Medicine. 7 (12): 6124–6136. doi:10.1002/cam4.1820. PMID 30403008.
- ^ Valvona CJ, Fillmore HL, Nunn PB, Pilkington GJ (January 2016). "The Regulation and Function of Lactate Dehydrogenase A: Therapeutic Potential in Brain Tumor". Brain Pathology. 26 (1): 3–17. doi:10.1111/bpa.12299. PMID 26269128.
- ^ a b Yang M, Soga T, Pollard PJ (September 2013). "Oncometabolites: linking altered metabolism with cancer". The Journal of Clinical Investigation. 123 (9): 3652–3658. doi:10.1172/jci67228. PMID 23999438.
- ^ Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, Yilmaz E, et al. (February 2012). "IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype". Nature. 483 (7390): 479–483. doi:10.1038/nature10866. PMC 3351699. PMID 22343889.
- ^ Jones PA, Baylin SB (February 2007). "The epigenomics of cancer". Cell. 128 (4): 683–692. doi:10.1016/j.cell.2007.01.029. PMC 3894624. PMID 17320506.
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