A Novel Approach to Quantify Heterogeneity of Intrahepatic Cholangiocarcinoma: The Hidden-Genome Classifier.

PURPOSE: Intrahepatic cholangiocarcinoma (IHC) is a heterogeneous tumor. The hidden-genome classifier, a supervised machine learning-based algorithm, was used to quantify tumor heterogeneity and improve classification. EXPERIMENTAL DESIGN: A retrospective review of 1,370 patients with IHC, extrahepatic cholangiocarcinoma (EHC), gallbladder cancer (GBC), hepatocellular carcinoma (HCC), or biphenotypic tumors was conducted. A hidden-genome model classified 527 IHC based on genetic similarity to EHC/GBC or HCC. Genetic, histologic, and clinical data were correlated. RESULTS: In this study, 410 IHC (78%) had >50% genetic homology with EHC/GBC; 122 (23%) had >90% homology ("biliary class"), characterized by alterations of KRAS, SMAD4, and CDKN2A loss; 117 IHC (22%) had >50% genetic homology with HCC; and 30 (5.7%) had >90% homology ("HCC class"), characterized by TERT alterations. Patients with biliary- versus non-biliary-class IHC had median overall survival (OS) of 1 year (95% CI, 0.77, 1.5) versus 1.8 years (95% CI, 1.6, 2.0) for unresectable disease and 2.4 years (95% CI, 2.1, NR) versus 5.1 years (95% CI, 4.8, 6.9) for resectable disease. Large-duct IHC (n = 28) was more common in the biliary class (n = 27); the HCC class was composed mostly of small-duct IHC (64%, P = 0.02). The hidden genomic classifier predicted OS independent of FGFR2 and IDH1 alterations. By contrast, the histology subtype did not predict OS. CONCLUSIONS: IHC genetics form a spectrum with worse OS for tumors genetically aligned with EHC/GBC. The classifier proved superior to histologic subtypes for predicting OS independent of FGFR2 and IDH1 alterations. These results may explain the differential treatment responses seen in IHC and may direct therapy by helping stratify patients in future clinical trials.

Concordance in Oncogenic Alterations Between the Primary Tumor and Advanced Disease: Insights Into the Heterogeneity of Intrahepatic Cholangiocarcinoma.

PURPOSE: Intrahepatic cholangiocarcinoma (ICCA) is characterized by significant phenotypic and clinical heterogeneities and poor response to systemic therapy, potentially related to underlying heterogeneity in oncogenic alterations. We aimed to characterize the genomic heterogeneity between primary tumors and advanced disease in patients with ICCA. METHODS: Biopsy-proven CCA specimens (primary tumor and paired advanced disease [metastatic disease, progressive disease on systemic therapy, or postoperative recurrence]) from two institutions were subjected to targeted next-generation sequencing. Overall concordance (oncogenic driver mutations, copy number alterations, and fusion events) and mutational concordance (only oncogenic mutations) were compared across paired samples. A subgroup analysis was performed on the basis of exposure to systemic therapy. Patients with extrahepatic CCA (ECCA) were included as a comparison group. RESULTS: Sample pairs from 65 patients with ICCA (n = 54) and ECCA (n = 11) were analyzed. The median time between sample collection was 19.6 months (range, 2.7-122.9). For the entire cohort, the overall oncogenic concordance was 49% and the mutational concordance was 62% between primary and advanced disease samples. Subgroup analyses of ICCA and ECCA revealed overall/mutational concordance rates of 47%/58% and 60%/84%, respectively. Oncogenic concordance was similarly low for pairs exposed to systemic therapy between sample collections (n = 50, 53% overall, 68% mutational). In patients treated with targeted therapy for IDH1/2 alterations (n = 6) or FGFR2 fusions (n = 3), there was 100% concordance between the primary and advanced disease specimens. In two patients, FGFR2 (n = 1) and IDH1 (n = 1) alterations were detected de novo in the advanced disease specimens. CONCLUSION: The results reflect a high degree of heterogeneity in ICCA and argue for reassessment of the dominant driver mutations with change in disease status.

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.