Abstract 4650: From bench to bedside: are cytokines still relevant biomarkers for staging cancer cachexia.
Bibliographic record
Abstract
Abstract Background: We have provided initial evidence1 on the clinical usefulness of the cancer cachexia stages (CCS) proposed by Fearon et al2. However it is still unclear3 if particular molecular phenotypes are also associated with these stages, as well with relevant clinical outcomes. Methods: A candidate list of cytokines (Activin A, Eotaxin, FGF, G-CSF, GDF15, GM-CSF, IFN-g, IL-10, IL-12_p70, IL-13, IL-15, IL-17, IL-1b, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IP-10, MCP-1_MCAF, MIP-1a, MIP-1b, PDGF-bb, RANTES, TNF-a, VEGF) was measured in 210 blood samples from patients with advanced lung and gastrointestinal cancers, via Luminex and ELISA methods. Non-parametric t-test, Kaplan-Meier and Kruskal-Wallis analyses were used to test the association between cytokines levels with CCS, Patient-Generated Subjective Global Assessment scores (PG-SGA) and survival. Results: Using non-cachectic patients as controls, Activin A and GDF15 were significantly up-regulated in pre-cachectic (p<0.01), cachectic (p<0.05) and refractory cachectic (p<0.001) patients. IL-6, IL-8 and VEGFa were significantly up-regulated only in refractory cachectic (p<0.001) patients. Activin A (p<0.001), GDF15 (p<0.001) and IL-8 (P<0.001) plasma levels correlated with PG-SGA. Quartiles of GDF15 plasma levels identified better survival curves as compared to Activin A, IL-6 and IL-8 quartiles. Conclusions: Activin A and GDF15 appear to be useful aids for the diagnosis of all cachexia stages in advanced cancer. Because of their correlation with nutritional and survival outcomes, these cytokines may also represent useful targets in the development of new compounds for the treatment of cancer cachexia. 1. Vigano A, Del Fabbro E, Bruera E, Borod M. The cachexia clinic: from staging to managing nutritional and functional problems in advancer cancer patients. Critical Reviews in Oncogenesis, 2012 17(3), 293-304 2. Fearon K,Strasser F,Anker SD,Bosaeus I,Bruera E,Fainsinger RL,Jatoi A, Loprinzi C, MacDonald N, Mantovani G, Davis M, Muscaritoli M, Ottery F, Radbruch L, Ravasco P, Walsh D, Wilcock A, Kaasa S, Baracos VE. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol. 2011; 12:489-495. 3. Scheede-Bergdahl, C., Watt, H.L., Trutschnigg, B., Kilgour, R.D., Haggarty, A., Lucar, E., Vigano, A. 2011. Is IL-6 the best pro-inflammatory biomarker of clinical outcomes of cancer cachexia? Clinical Nutrition 31: 85-8. Citation Format: Antonio Vigano, Lorena Lerner, Nianjun Tao, Brian Krieger, Bin Feng, Richard Nicoletti, Qing Liu, Ailin Bai, Zhigang Weng, Thierry Alcindor, Domenico Fuoco, Jeno Gyuris, Maria Isabel Chiu. From bench to bedside: are cytokines still relevant biomarkers for staging cancer cachexia. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4650. doi:10.1158/1538-7445.AM2013-4650
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".