This prospective study evaluated the liver pre-metastatic niche from liver biopsies taken at the time of pancreatectomy in patients with localized pancreatic ductal adenocarcinoma (PDAC). The liver biopsies were analyzed using metabolomic, transcriptomic, and multiplex imaging approaches. The authors found that liver biopsies from patients with liver metastasis had less T cell lobular infiltration, less steatosis, and higher levels of citrullinated H3, while livers of patients who did not develop liver metastasis were similar to control liver biopsies taken from patients without PDAC. The authors were able to use the liver profiles and a machine-learning-based model to predict the metastatic outcome at the time of surgery with 78% accuracy.
Summary:
PDAC is a deadly disease with a dismal 5-year overall survival rate of 12% that has not significantly changed in 60 years. The only potential cure for PDAC is surgical resection, but this is only available to roughly 25% of cases due to the aggressive and metastatic nature of PDAC. Despite surgical resection, liver metastasis will develop in 40% of patients within the first 3 years after surgery and is almost uniformly fatal. The inability to predict the risk of subsequent metastases and effectively treat it represents a major challenge in the management of PDAC.
In this prospective study, the investigators enrolled 49 patients with resectable PDAC who had not received neoadjuvant therapy and 19 non-PDAC controls (IPMN, NET, and benign) to have a liver biopsy at the time of pancreatectomy. They then followed the 49 patients with PDAC and classified them into early (<6 months), late (>6 months) liver metastasis, extrahepatic metastasis, and disease-free survivors. First, the authors compared RNA transcriptomic profiles from liver biopsies of patients with PDAC to controls. They found that gene signatures associated with inflammation, such as IFNa response, allograft rejection, and monocyte chemotaxis, were higher in patients with PDAC compared to controls. This was supported with immunohistochemistry staining and single-cell RNA sequencing, which showed increased portal/lobular inflammation, T cell abundance, and NETs.
Next, the authors investigated differences between patients who developed liver metastasis versus those that did not (disease-free survivors and extrahepatic metastasis). At the transcriptomic level, patients who developed liver metastasis within 6 months of resection had a high expression of SORT1, which regulates cytokine secretion in myeloid and T cells. SORT1 was associated with a three-fold increased risk of early liver metastasis (HR 2.69, P=0.029). Liver metastasis patients were also less likely to have T cell lobular infiltration and higher levels of neutrophil extracellular traps. Both factors also correlated with time to liver metastasis. At a metabolic level, 15 metabolites distinguished most of the patients who subsequently developed liver metastasis. Of these, low hepatic creatine levels (P=0.04) and high citrullinated H3 (P=0.009) were associated with shorter time to liver metastasis. The authors concluded that patients who develop liver metastasis have a specific immunometabolic profile at the time of surgery.
Finally, the authors used all of the above data to generate a predictive model for metastatic outcome. They generated four models that would predict each of the four metastatic outcomes in a binary fashion. The AUC of the four models ranged from 0.83 to 0.89. The model predicting early recurrence did the best with a 90% sensitivity and 87% specificity. When the models were combined, the overall accuracy of predicting which patient will have which type of recurrence was 78%. The authors ultimately concluded that immunometabolic characterization of peri-operative liver biopsies could predict liver metastasis.
This study's limitations include a small sample size and potential biases from patient selection and pre-operative treatments. Additionally, the predictive models would benefit from further validation in larger cohorts. Lastly, technical variability could affect the reproducibility and reliability of the results.
Bottom Line:
Currently, there is no reliable method to predict metastasis in PDAC, but the authors of this study showed that immunometabolic profiling of peri-operative liver biopsies demonstrated a distinct pre-metastatic niche that could be used to predict liver metastasis.