Multi-parametric atlas of the pre-metastatic liver for prediction of metastatic outcome in early-stage pancreatic cancer.

Metastasis occurs frequently after resection of pancreatic cancer (PaC). In this study, we hypothesized that multi-parametric analysis of pre-metastatic liver biopsies would classify patients according to their metastatic risk, timing and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PaC and 19 control patients with non-cancerous pancreatic lesions were analyzed, combining metabolomic, tissue and single-cell transcriptomics and multiplex imaging approaches. Patients were followed prospectively (median 3 years) and classified into four recurrence groups; early (<6 months after resection) or late (>6 months after resection) liver metastasis (LiM); extrahepatic metastasis (EHM); and disease-free survivors (no evidence of disease (NED)). Overall, PaC livers exhibited signs of augmented inflammation compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LiM were characterized by scant T cell lobular infiltration, less steatosis and higher levels of citrullinated H3 compared to patients who developed EHM, who had overexpression of interferon target genes (MX1 and NR1D1) and an increase of CD11B+ natural killer (NK) cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+ NK cells, differentiated patients with early-onset LiM from those with late-onset LiM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine-learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multi-parametric profiling of liver biopsies at the time of PaC diagnosis may determine metastatic risk and organotropism and guide clinical stratification for optimal treatment selection.

Genome wide association (GWA) predictors of anti-TNFalpha therapeutic responsiveness in pediatric inflammatory bowel disease.

BACKGROUND: Interindividual variation in response to anti-TNFalpha therapy may be explained by genetic variability in disease pathogenesis or mechanism of action. Recent genome-wide association studies (GWAS) in inflammatory bowel disease (IBD) have increased our understanding of the genetic susceptibility to IBD. The aim was to test associations of known IBD susceptibility loci and novel "pharmacogenetic" GWAS identified loci with primary nonresponse to anti-TNFalpha in pediatric IBD patients and develop a predictive model of primary nonresponse. METHODS: Primary nonresponse was defined using the Harvey Bradshaw Index (HBI) for Crohn's disease (CD) and partial Mayo score for ulcerative colitis (UC). Genotyping was performed using the Illumina Infinium platform. Chi-square analysis tested associations of phenotype and genotype with primary nonresponse. Genetic associations were identified by testing known IBD susceptibility loci and by performing a GWAS for primary nonresponse. Stepwise multiple logistic regression was performed to build predictive models. RESULTS: Nonresponse occurred in 22 of 94 subjects. Six known susceptibility loci were associated with primary nonresponse (P < 0.05). Only the 21q22.2/BRWDI loci remained significant in the predictive model. The most predictive model included 3 novel "pharmacogenetic" GWAS loci, the previously identified BRWD1, pANCA, and a UC diagnosis (R(2) = 0.82 and area under the curve [AUC] = 0.98%). The relative risk of nonresponse increased 15-fold when number of risk factors increased from 0-2 to > or =3. CONCLUSIONS: The combination of phenotype and genotype is most predictive of primary nonresponse to anti-TNFalpha in pediatric IBD. Defining predictors of response to anti-TNFalpha may allow the identification of patients who will not benefit from this class of therapy.