Cancer Cachexia: Emerging pre-clinical evidence and the pathway forward to clinical trials
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.034 | 0.044 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".