Characterization of the cachexia pathway in pancreatic ductal adenocarcinoma.
Authors: Ashouri Karam , Baca Yasmine , Xiu Joanne , Soni Shivani , Algaze Sandra , Jayachandran Priya , Wong Alexandra , Yang Yan , Torres-Gonzalez Lesly , Lo Jae Ho , Zhang Wu , Khushman Moh'd M. , Weinberg Benjamin Adam , Goel Sanjay , Lou Emil , Battaglin Francesca , Lenz Heinz-Josef 
Published: January 2024

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Background: Cancer cachexia is characterized by progressive weight loss and skeletal muscle degradation, contributing to 33% of pancreatic ductal adenocarcinoma (PDAC) deaths. Targeting myostatin in the myostatin-activin pathway has been shown to reverse cachexia. Here, we present comprehensive clinical and molecular characterization of the myostatin-activin pathway in PDAC. Methods: 9,607 samples of PDAC tested at Caris Life Sciences (Phoenix, AZ) with WTS (Illumina NovaSeq) and NextGen DNA sequencing (NextSeq, 592 Genes and NovaSEQ, WES) were analyzed. Cachexia gene scores (GS) were calculated by averaging the positive z scores of activators and negative z scores of repressors in the myostatin-activin pathway. Activators were ACVR1B, ACVR1C, ACVR2A, ACVR2B, SMAD2, SMAD3, SMAD4, and TGFBR1, while repressors were SMAD1, SMAD5, SMAD6, SMAD7, SMURF1, and SMURF2. The top quartile (Q4) and bottom quartile (Q1) of GS were compared using chi-squared and Fisher-Exact tests. RNA deconvolution analysis with QuantiSEQ estimated cell infiltration in the tumor microenvironment (TME). Differences in overall survival (OS) were analyzed from insurance claims data and calculated from time of tissue collection using Kaplan-Meier estimates. Statistical significance was determined as a P-value adjusted for multiple comparisons ( q<0.05). Results: GS were higher in primary tumors compared to metastases (median: -0.71 vs -0.86, q<0.05). GS was associated with increased PD-L1 IHC expression (Q1 21.2% vs Q4 10.3%, q<0.001), but not TMB-high (1.9% vs 2.1%, q=1) or MSI-H status (1.1% vs 1.3%, q=1). GS correlated with increased expression of immune related genes ( CD274, CD80, IDO1, CD86, PDCD1, LAG3, CTLA4, HAVCR2, and IFNG, q<0.05). However, TME immune cell infiltration did not vary based on cachexia GS. Mutation rates of TP53 (Q1 79.4% vs Q4 73.7%), ARID1A (Q1 11.8% vs Q4 6.9%) and KRAS (Q1 92.7% vs Q4 86.4%) were associated with Q1-GS (all q<0.01), while STK11 mutations (1.1% vs 3.0%, q=0.001) were associated with Q4-GS. Spearman correlation linked cachexia GS with the lipid metabolizing genes UCP2 (rho=0.49) and UCP3 (rho=0.30), as well as the inflammatory markers CCL2/MCP-1(rho=0.34) and IL1B (rho=0.33). Decreased OS was seen with higher tumor expression of the myostatin-activin pathway activator, SMAD3 (6.6 vs 8.5 mo, HR=1.22, CI 1.12-1.33, P<0.001), and lower expression of the repressor SMAD7 (6.9 vs 8.9 mo, HR=0.91, CI 0.79-0.90, P=0.034) in metastatic PDAC. Conclusions: This is the largest molecular and clinical characterization of the myostatin-activin cachexia pathway in PDAC. Our data show that increased activation of the myostatin-activin pathway is associated with immune mediators, lipid metabolism, and inflammatory gene activation. Activators and repressors are significant predictors of survival in PDAC, suggesting possible novel therapeutic targets.