Prognostic role of body composition parameters in gastric/gastroesophageal junction cancer patients from the EXPAND trial.


Journal

Journal of cachexia, sarcopenia and muscle
ISSN: 2190-6009
Titre abrégé: J Cachexia Sarcopenia Muscle
Pays: Germany
ID NLM: 101552883

Informations de publication

Date de publication:
02 2020
Historique:
received: 22 05 2019
revised: 29 06 2019
accepted: 08 07 2019
pubmed: 30 8 2019
medline: 29 5 2021
entrez: 30 8 2019
Statut: ppublish

Résumé

Body fat and/or muscle composition influences prognosis in several cancer types. For advanced gastric and gastroesophageal junction cancer, we investigated which body composition parameters carry prognostic information beyond well-established clinical parameters using robust model selection strategy such that parameters identified can be expected to generalize and to be reproducible beyond our particular data set. Then we modelled how differences in these parameters translate into survival outcomes. Fat and muscle parameters were measured on baseline computed tomography scans in 761 patients with advanced gastric or gastroesophageal junction cancer from the phase III EXPAND trial, undergoing first-line chemotherapy. Cox regression analysis for overall survival (OS) and progression-free survival (PFS) included body composition parameters and clinical prognostic factors. All continuous variables were entered linearly into the model as there was no evidence of non-linear prognostic impact. For transferability, the final model included only parameters that were picked by Bayesian information criterion model selection followed by bootstrap analysis to identify the most robust model. Muscle and fat parameters formed correlation clusters without relevant between-cluster correlation. Mean muscle attenuation (MA) clusters with the fat parameters. In multivariate analysis, MA was prognostic for OS (P < 0.0001) but not for PFS, while skeletal muscle index was prognostic for PFS (P = 0.02) but not for OS. Worse performance status Eastern Cooperative Oncology Group (ECOG 1/0), younger age (on a linear scale), and the number of metastatic sites were strong negative clinical prognostic factors for both OS and PFS. MA remained in the model for OS (P = 0.0001) following Bayesian information criterion model selection in contrast to skeletal muscle index that remained prognostic for PFS (P = 0.009). Applying stricter criteria for transferability, MA represented the only prognostic body composition parameter for OS, selected in >80% of bootstrap replicates. Finally, Cox model-derived survival curves indicated that large differences in MA translate into only moderate differences in expected OS in this cohort. Among body composition parameters, only MA has robust prognostic impact for OS. Data suggest that treatment approaches targeting muscle quality are unlikely to prolong OS noticeably on their own in advanced gastric cancer patients, indicating that multimodal approaches should be pursued in the future.

Sections du résumé

BACKGROUND
Body fat and/or muscle composition influences prognosis in several cancer types. For advanced gastric and gastroesophageal junction cancer, we investigated which body composition parameters carry prognostic information beyond well-established clinical parameters using robust model selection strategy such that parameters identified can be expected to generalize and to be reproducible beyond our particular data set. Then we modelled how differences in these parameters translate into survival outcomes.
METHODS
Fat and muscle parameters were measured on baseline computed tomography scans in 761 patients with advanced gastric or gastroesophageal junction cancer from the phase III EXPAND trial, undergoing first-line chemotherapy. Cox regression analysis for overall survival (OS) and progression-free survival (PFS) included body composition parameters and clinical prognostic factors. All continuous variables were entered linearly into the model as there was no evidence of non-linear prognostic impact. For transferability, the final model included only parameters that were picked by Bayesian information criterion model selection followed by bootstrap analysis to identify the most robust model.
RESULTS
Muscle and fat parameters formed correlation clusters without relevant between-cluster correlation. Mean muscle attenuation (MA) clusters with the fat parameters. In multivariate analysis, MA was prognostic for OS (P < 0.0001) but not for PFS, while skeletal muscle index was prognostic for PFS (P = 0.02) but not for OS. Worse performance status Eastern Cooperative Oncology Group (ECOG 1/0), younger age (on a linear scale), and the number of metastatic sites were strong negative clinical prognostic factors for both OS and PFS. MA remained in the model for OS (P = 0.0001) following Bayesian information criterion model selection in contrast to skeletal muscle index that remained prognostic for PFS (P = 0.009). Applying stricter criteria for transferability, MA represented the only prognostic body composition parameter for OS, selected in >80% of bootstrap replicates. Finally, Cox model-derived survival curves indicated that large differences in MA translate into only moderate differences in expected OS in this cohort.
CONCLUSIONS
Among body composition parameters, only MA has robust prognostic impact for OS. Data suggest that treatment approaches targeting muscle quality are unlikely to prolong OS noticeably on their own in advanced gastric cancer patients, indicating that multimodal approaches should be pursued in the future.

Identifiants

pubmed: 31464089
doi: 10.1002/jcsm.12484
pmc: PMC7015239
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

135-144

Informations de copyright

© 2019 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.

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Auteurs

Ulrich T Hacker (UT)

1st Medical Department, University Cancer Center Leipzig (UCCL), University Leipzig Medical Center, Leipzig, Germany.

Dirk Hasenclever (D)

Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Medical Faculty of the University Leipzig, Leipzig, Germany.

Nicolas Linder (N)

Department of Radiology, University Leipzig Medical Center, Leipzig, Germany.

Gertraud Stocker (G)

1st Medical Department, University Cancer Center Leipzig (UCCL), University Leipzig Medical Center, Leipzig, Germany.

Hyun-Cheol Chung (HC)

Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.

Yoon-Koo Kang (YK)

Division Oncology Department, Medical Center, Seoul, South Korea.

Markus Moehler (M)

First Department of Internal Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.

Harald Busse (H)

Department of Radiology, University Leipzig Medical Center, Leipzig, Germany.

Florian Lordick (F)

1st Medical Department, University Cancer Center Leipzig (UCCL), University Leipzig Medical Center, Leipzig, Germany.

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Classifications MeSH