Exploring heterogeneity: a dive into preclinical models of cancer cachexia.
Francielly MorenaAna Regina CabreraNicholas P GreenePublished in: American journal of physiology. Cell physiology (2024)
Cancer cachexia (CC) is a multifactorial and complex syndrome experienced by up to 80% of patients with cancer and implicated in ∼40% of cancer-related deaths. Given its significant impact on patients' quality of life and prognosis, there has been a growing emphasis on elucidating the underlying mechanisms of CC using preclinical models. However, the mechanisms of cachexia appear to differ across several variables including tumor type and model and biologic variables such as sex. These differences may be exacerbated by variance in experimental approaches and data reporting. This review examines literature spanning from 2011 to March 2024, focusing on common preclinical models of CC, including Lewis Lung Carcinoma, pancreatic KPC, and colorectal colon-26 and Apc min/+ models. Our analysis reveals considerable heterogeneity in phenotypic outcomes, and investigated mechanisms within each model, with particular attention to sex differences that may be exacerbated through methodological differences. Although searching for unified mechanisms is critical, we posit that effective treatment approaches are likely to leverage the heterogeneity presented by the tumor and pertinent biological variables to direct specific interventions. In exploring this heterogeneity, it becomes critical to consider methodological and data reporting approaches to best inform further research.
Keyphrases
- single cell
- papillary thyroid
- end stage renal disease
- squamous cell
- systematic review
- electronic health record
- cell therapy
- chronic kidney disease
- ejection fraction
- big data
- newly diagnosed
- physical activity
- type diabetes
- working memory
- adverse drug
- peritoneal dialysis
- lymph node metastasis
- escherichia coli
- klebsiella pneumoniae
- mesenchymal stem cells
- bone marrow
- mass spectrometry
- data analysis
- case report
- deep learning
- skeletal muscle
- combination therapy