Cachexia: Leveraging transcriptomics to identify potential therapeutics

Cachexia is a common cause of morbidity across multiple cancer types, yet remains largely under-studied especially in the realm of transcriptomics. An essential component of cancer cachexia is the induced transition of adipocytes from white to brown, which increases energetic inefficiency and contributes to systemic loss of adipose tissue. This is likely caused by the tumor secreting thermo-stimulatory factors into the extracellular space.

 

Here, we integrate publicly-available data to pinpoint actionable gene targets that enable cancer cells to induce fat “browning”. In particular, we identify genes differentially expressed between Lewis lung carcinoma subclones that can induce thermogenesis in primary adipocytes at high or low levels in vitro (GSE57797). We then evaluate whether the expression of these thermo-stimulatory genes predict patient survival in an independent gene expression dataset of primary tumors from advanced stage gastric cancer patients (GSE15459). Finally, we leverage drug gene expression data from CMAP and LINCS to identify molecules that reverse the thermo-stimulatory signature.

 

Our analysis reveals that the thermo-stimulatory signature predicts prognosis in patients with advanced gastric cancer (p=0.005), with the low thermo-stimulatory group exhibiting improved prognosis. The thermo-stimulatory signature is most strongly reversed by expression signatures from inhibitors of MEK (MAP2K1), including pd-0325901, AZD-8330 (ARRY-704), CI-1040, trametinib, and selumetinib. Interestingly, we observe a time-dependent effect of these MEK inhibitors, with shorter durations of exposure consistently reversing thermo-stimulatory signatures more effectively than longer durations of exposure.

 

Analysis of cachexia-related transcriptomic data reveals key marker genes and pathways which can be used to optimize MEK inhibitors for cachexia. Results from this study support the development of MEK inhibitors to treat cachexia, and suggest that careful optimization of pharmacokinetics will be important for success in the clinic.