Cancer Cachexia: Emerging pre-clinical evidence and the pathway forward to clinical trials
Notice bibliographique
Résumé
Cancer cachexia is a multifactorial syndrome characterized by ongoing loss of skeletal muscle (with or without loss of fat) leading to functional decline (1). This loss is driven by variable combinations of reduced food intake and abnormal metabolism (1). Cachexia affects 60% to 80% of patients with advanced cancer and results in reduced tolerance to cancer therapies, quality of life, and survival (1,2). Cachexia is an unmet medical need in oncology (2) because of its devastating effects on patients, for which there is no approved therapy. Cachexia treatment is complex because it cannot be reversed by nutritional support alone. Cachexia therapy targets have included stimulation of appetite, regulation of catabolic pathways, and protein synthesis (3). Interventions to prevent, treat, or support patients with cancer cachexia have been tested in trials with limited success. Literature reviews suggest trial design may be partly to blame (4,5). The design of past and current trials are variable (eg, patient selection criteria, end points defining effectiveness) and controversial. Fearon et al. (1) introduced the concept of refractory cachexia, which adds to our understanding about the abilities of patients to respond to treatment. The intense catabolism associated with advanced, chemoresistant disease may be refractory to cachexia therapies, which must then be deployed earlier in the disease trajectory, at which time the potential to stimulate anabolism clearly exists (6). Recognition that anabolism is possible earlier in the disease trajectory, in addition to other important developments, has renewed interest toward developing more effective cachexia therapies. Cachexia research is building a solid foundation with support from an international society, international conferences, and an increasingly impactful scientific journal (JCSM). We have an international consortium of researchers who have: 1) produced a definition and consensus framework for assessing cancer cachexia (1) and 2) contributed data to define and develop diagnostic criteria for cancer cachexia (7). In addition, there have been funding initiatives from national agencies (eg, the National Cancer Institute) to support cachexia research. These efforts have set the stage for the emergence of a new set of mechanisms including growth differentiation factor-15 (GDF-15), macrophage inhibitory cytokine-1 (MIC-1) (8,9), leukemia inhibitory factor (LIF) (10), myostatin, activin type-2 receptor (ActRIIB) (11), Fn14 (12), signal transducer and activator of transcription 3 (STAT3) (13,14), and parathyroid hormone-related protein (PTHrP) (14). In this issue of the Journal, Tseng et al. (15) report on a novel approach to treat cancer cachexia, the use of histone deacetylase (HDAC) inhibitors. This was a well-designed and comprehensive study that compared and contrasted effects of AR-42 with other HDAC inhibitors such as vorinostat and romidepsin in two murine models: C-26 colon adenocarcinoma in male CD2F1 mice and the Lewis lung carcinoma (LLC) in male C57BL/6 mice. In C-26 mouse models, AR-42 protected against weight loss with AR-42–treated mice at day 15. These mice experienced a 6% weight loss compared with control mice with greater than 20% weight loss. In addition, despite AR-42 having no effects on tumor growth, AR-42–treated mice had increased survival compared with controls. This is a provocative finding; treating cachexia alone resulted in prolonged survival of tumor-bearing mice in the absence of direct effects of the cachexia treatment on the tumor. This is not the first time that this has been observed. Zhou et al. (11) showed inhibition of ActRIIB with a decoy receptor sActRIIB led to prolonged mouse survival in the absence of any effect on tumor growth. In both C26 and LLC mouse models, AR-42 protected against muscle wasting whereas other HDAC inhibitors romidepsin and vorinostat did not. These findings suggest not all HDAC inhibitors are created equal in terms of their ability to treat cancer cachexia. Exact mechanisms of AR-42 effects are not entirely clear. HDAC inhibitors are likely to have pleiotropic effects on many genes as well as their expression. The authors clearly showed AR-42 affected known mediators of cancer cachexia including interleukin-6 (IL-6) and LIF. Novel to this study was the examination of AR-42 on muscle metabolism using metabolomics. Comparing AR-42–treated tumor mice to control tumor mice and mice without tumors, investigators showed AR-42 had significant effects on glycolysis and amino acid metabolism in the muscle, the result being the muscle of AR-42–treated mice resembled control normal mice more than tumor-bearing control mice. This suggests AR-42 may preserve muscle metabolism. We commend the Tseng et al. for assembling a strong preclinical case for moving AR-42 into trials in cancer patients. Many agents have had similarly strong preclinical arguments but have not lived up to their initial promise in the clinic. Reasons for these failures may not have been because of agents lacking an intrinsic ability to ameliorate or treat cachexia, but perhaps they were the right drugs at the wrong time in the disease trajectory of cancer patients. It is now recognized that cachexia is a continuum with three stages of clinical relevance (eg, precachexia, cachexia, refractory cachexia) (1). In the short term, we need to understand how these three stages are represented in animal models used to study cachexia, and to define which mechanisms result in the best control of cachexia. In addition, for those undergoing active treatment, these agents should not interfere with anticancer therapy and as a palliative intervention require a limited side effect profile. In the near future, based on the preclinical case built by Tseng et al., we foresee a clinical trial where benefits of AR-42 can be tested in first-line patients likely to be receiving effective anticancer treatment rather than end-stage patients. A major challenge in moving drugs such as AR-42 to the clinic is identification of functional and other outcomes that are meaningful to both patients and drug regulators. Functional outcomes measured in current phase III trials of cachexia therapy are hand grip strength and stair climb tests. Generally, regulators have required improvements in lean body mass and functional outcomes as coprimary endpoints for approval. An additional measure that has not been often been considered might be survival, especially if these drugs are moved earlier in the patient’s disease trajectory. The authors have no conflicts of interest to disclose.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,034 | 0,044 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,007 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».