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Enregistrement W2939038933 · doi:10.1097/01.cot.0000557846.91598.e8

Metabolism

2019· article· en· W2939038933 sur OpenAlexaboutno aff
Rebecca Hepp

Notice bibliographique

RevueOncology Times · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChemistry

Résumé

récupéré en direct d'OpenAlex

hematologic malignancies; metabolism: hematologic malignancies; metabolismModern research methods are providing an unprecedented look into the microenvironment of hematologic malignancies—revealing possible therapy targets in the process. Blood malignancies have been at the forefront of such research (Nat Rev Cancer 2015; doi: 10.1038/nrc3907), and several teams are currently tackling yet another promising avenue: the complex interplay between metabolic alterations and tumorigenesis. “There is a growing understanding from very exciting, active research that metabolic pathways are commonly dysregulated in hematologic malignancies, including leukemias, lymphomas, and other related diseases,” said Ravi Majeti, MD, PhD, Professor of Medicine and Chief of the Division of Hematology at Stanford University and co-chair of the American Society of Hematology's (ASH) 2018 Metabolism and Hematologic Malignancies Scientific Program. ASH's program provided a snapshot of current and ongoing research on how specific metabolic inhibitors might one day revolutionize treatments for acute myeloid leukemia (AML), angioimmunoblastic T-cell lymphoma (AITL), multiple myeloma (MM), and Myc-driven B-cell lymphomas, to name a few. Exploring Metabolic Alterations In studying cancer on a molecular level, researchers now know genetic alterations are key players in the pathogenesis of hematologic malignancies. The literature also suggests “metabolic rewiring” is another vital component (Free Radic Biol Med 2016; doi:10.1016/j.freeradbiomed.2016.04.025). According to Tak Wah Mak, PhD, of the Princess Margaret Cancer Center in Toronto, tumor cells require different metabolic phenotypes to generate the increased energy necessary for unrestricted growth (ASH 2018; Metabolism and Hematologic Malignancies Scientific Program). These altered metabolic phenotypes provide biosynthetic precursor molecules and help to maintain both the balance of redox/oxidative stress and intracellular biochemical homeostasis for the tumor cells' growth and survival (Blood 2018;132:SCI-9; doi:https://doi.org/10.1182/blood-2018-99-109468). All of these disrupted metabolic pathways may predispose premalignant cells to genomic instability, ultimately leading to malignancy. Inhibiting such metabolic dysfunction is a goal for many early research efforts. The following four are showing significant promise 1. Gene Mutations. Mak and his team are taking a hard look at several gene mutations that may play a role in metabolic alterations in AML and AITL. Using mouse models and single-cell RNA sequencing, he has discovered that mutations of isocitrate dehydrogenase (IDH) 1 and 2, as well as the methyl dioxygenase ten-eleven translocation-2 (TET2), interfere with cellular differentiation and thus drive tumorigenesis. The discovery may have clinical relevance far sooner than some expect, considering specific inhibitors of IDH1 and IDH2 mutants were approved by the FDA in the last 2 years, according to Majeti. “You have gone from a field where there wasn't much known about metabolism in both disease pathogenesis and therapy to the point of making the discovery, showing how it works and getting approved drugs, all in a short amount on time,” he noted. 2. Amino Acids. When tumor cells disrupt the normal metabolic pathways to meet their high proliferation demands, they often develop an inability to synthesize the specific amino acids (AAs) needed for protein biosynthesis, according to Marina Y. Konopleva, MD, PhD, of the Department of Leukemia at the University of Texas MD Anderson Cancer Center (Blood 2018;132:SCI-10; doi:https://doi.org/10.1182/blood-2018-99-109469). This provides a novel therapeutic avenue through various AA deprivation strategies. Glutaminase. Depletion of asparagine (ASNase) can thwart hematologic malignancies, and Konopleva and others have discovered that the glutaminase activity of ASNase is key for anticancer activity against ASNS-positive leukemia cell types in vitro (Blood 2014; doi:10.1182/blood-2013-10-535112). While studies still show leukemia recurrence in vivo with a glutaminase-deficient mutant of ASNase, researchers continue to explore various enzymes with reduced glutaminase coactivity. Arginine. Although arginine (ARG) depletion, which uses two critical enzymes of the ARG metabolic pathway to reduce the leukemia tumor burden in AML models, shows some promise, phase I/II trials have provided only minimal efficacy in relapsed/refractory AML and solid tumors. Cysteine. This amino acid helps to synthesize glutathione for antioxidant cellular defense and may be another deprivation target in multiple malignancy subtypes. AML, acute lymphocytic leukemia, poor-risk chronic lymphocytic leukemia, and MM are all sensitive to cysteine and cystine degradation (Blood 2012; doi:10.1038/ncb2432). Konopleva warned that the tumor microenvironment may play a crucial role in resistance, as many environments import or secrete AAs such as cysteine, asparagine, and glutamine and thwart current therapies. In addition, many of these AAs are important for immune cell proliferation, and excessive use by tumor cells and deprivation strategies both starve immune cells of the nutrients they need. Recent research has shifted its focus toward therapies that increase these AAs' availability for immune cells, rather than simple deprivation. 3. Mitochondrial Dysfunction. AML cells rely on mitochondrial metabolic pathways for proliferation and are sensitive to even slight reductions in the rate of oxidative metabolism. Aaron Schimmer, MD, PhD, and his team at the Princess Margaret Cancer Center have discovered that caseinolytic protease P (ClpP), a serine protease located in the mitochondrial matrix, is present in nearly half of AML patients (Blood 2018;132:SCI-11; doi:https://doi.org/10.1182/blood-2018-99-109470). They speculate that increased ClpP may signal increased mitochondrial stress in some AML patients and cause multiple genetic mutations further along the pathway. Inhibiting this protease preferentially kills AML cells, as well as any stem cells with high ClpP expression. Another mitochondrial protease, neurolysin (NLN), is also up-regulated in certain AML cells and helps with respiratory chain supercomplex proliferation. NLN maintains efficient oxidative metabolism and the integrity of the mitochondria. Schimmer's unpublished data suggests inhibiting NLN stymies respiratory chain supercomplex formation and slows AML cell growth. 4. Myc. Despite years of research and reams of data on the Myc oncogene and the B-cell antigen receptor (BCR), researchers are still unsure of how the BCR influences malignant B-cell behavior. Stefano Casola, MD, PhD, and his colleagues at the FIRC Institute of Molecular Oncology Foundation in Milan, Italy, recently set out to solve the problem using mouse models of Myc-driven B-cell lymphomas (Blood 2018;132:SCI-12; doi:https://doi.org/10.1182/blood-2018-99-109471). They discovered that inactivating BCRs didn't necessarily prevent growth of receptor-less Myc lymphoma cells in vitro and in vivo. However, it weakened the malignant B cells, which helped to eliminate the BCR-less tumor cells when their BCR-expressing counterparts were present (Nature 2017; doi:10.1038/nature22353). This has led Casola to speculate that malignant B cells without BCR are spontaneously generated during tumor growth in some B-cell lymphoproliferative disorders—a possible stumbling block for anti-BCR therapies. Casola and his team are now working on better understanding the gene networks and metabolic pathways that maintain the competitive fitness of Myc-transformed lymphoma B cells—all of which are influenced by BCR expression. Metabolism in the Crosshairs This area of research has been expanding since the 1920s when Otto Warburg first discovered that cancers are often dominated by glycolysis rather than oxidative phosphorylation, even in the presence of oxygen (Cell 2008; doi:10.1016/j.cell.2008.08.021). This Warburg effect stands as the basis of most people's understanding of aberrant metabolism in cancer, according to Majeti. But these research efforts prove it's just the tip of the iceberg. “Metabolism is diverse; it is dysregulated in cancer development for a lot of hematologic malignancies and it is targetable with drugs both in development and approved,” he added. “There will be practice-changing drugs and treatment strategies as a result of studying metabolism, and that's what clinicians should be thinking about.” Rebecca Hepp is a contributing writer.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,241
Score d'incertitude au seuil0,991

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0100,013

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,334
Écart entre enseignants0,316 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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