Roberts, Lewis

Abstract Background and Aims Primary sclerosing cholangitis (PSC) increases the risk of developing cholangiocarcinoma (CCA), which is the leading cause of premature death in these patients. Current diagnostic methods for early detection of either condition remain largely suboptimal. This study investigates serum metabolites as potential non-invasive diagnostic biomarkers. Methods This multicenter international study analyzed 459 serum samples from 13 international centers, including patients with PSC (n = 216), PSC who developed CCA during follow-up (PSC to CCA; n = 24), PSC with concomitant CCA (PSC-CCA; n = 88), ulcerative colitis (UC; n = 14), and healthy individuals (n = 79). Serum metabolomics was evaluated by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) and the accuracy of the single candidate metabolite biomarkers was further assessed. Machine learning was used to generate the best diagnostic and predictive algorithms. Results Fifty-two serum metabolites were identified as markers of PSC, independent of age, biological sex, cirrhosis, UC, or ursodeoxycholic acid (UDCA) treatment. A diagnostic model based on only six lipids distinguished PSC patients from healthy controls with high accuracy (AUC 0.952 discovery, 0.918 validation), independently of the levels of transaminases or cholestasis markers (i.e., GGT, AP, bilirubin) in blood. Subgroup analyses were performed in PSC patients with and without inflammatory bowel disease (IBD), confirming the model’s consistent performance across both subgroups when compared with their respective control groups – UC and healthy controls (AUC 0.959 and 0.939, respectively). The PSC model also confirmed its diagnostic performance in patients with established PSC-CCA or progression to CCA within 2 years, when compared to UC and healthy controls, showing AUC values ranging from 0.911 to 0.976. Additionally, 20 metabolites were significantly altered in PSC patients with CCA (PSC-CCA), regardless of demographics, cirrhosis, UDCA treatment, IBD status, or CCA subtype, and independently of serum transaminases and cholestatic markers levels. A model incorporating six lipids accurately identified PSC-CCA cases compared to PSC patients (AUC = 0.912 discovery; 0.896 validation), showing excellent performance in early-stage tumor detection (AUC 0.921) and outperforming CA19-9 (AUC 0.708). Importantly, the model retained high accuracy even in CCA patients with low CA19-9 levels (AUC 0.905) and in PSC patients who developed CCA within a year of clinical diagnosis (AUC 0.792). Conclusion The PSC and PSC-CCA blood tests provide a non-invasive approach for diagnosing PSC and PSC-CCA, addressing an important need in this population. Their integration into clinical practice could enhance risk stratification, early detection, personalized surveillance, and treatment decision-making in PSC patients.

Abstract Background and Aims Cholangiocarcinoma (CCA) is a rare and aggressive malignancy originating from the bile ducts, characterized by a poor prognosis and limited treatment options. Potentially curative interventions include surgical resection with negative margins (R0) and liver transplantation. However, despite these treatments, the likelihood of post-surgical recurrence remains high, with 50–70% of patients experiencing disease recurrence within five years. This highlights the urgent need for reliable prognostic biomarkers to guide clinical decision-making and personalize postoperative care. Numerous prognostic biomarkers identified in tumor tissue for CCA have been proposed, but many studies suffer from limitations such as single-center designs, small patient cohorts, and semi-quantitative techniques such as immunohistochemistry. These methods often rely on observer interpretation and antibody specificity, which can compromise objectivity and reproducibility. To overcome these limitations, we aimed to identify and validate robust quantitative prognostic biomarkers for CCA by analyzing tumor mRNA expression, integrating them using machine learning (ML), and developing a web-based tool to predict tumor recurrence and overall survival (OS). Method A systematic literature review was conducted to identify candidate prognostic tissue biomarkers for CCA, which were further validated using Cox proportional hazards (PH) OS analyses in five independent transcriptomic cohorts. An international multicenter cohort of 221 patients undergoing tumor resection was assembled, and tumor mRNA expression was analyzed using high-throughput TaqMan OpenArray RT-qPCR. Associations between clinical and molecular features and recurrence-free survival (RFS) and OS were assessed using Cox PH analyses. Six ML survival models were trained and evaluated using nested cross-validation, followed by feature reduction. Shapley Additive exPlanations (SHAP) values were applied to assess feature importance and enhance model interpretability. Results Among 496 candidate CCA prognostic biomarkers identified from the literature, 52 showed prognostic value in Cox PH analyses across transcriptomic cohorts. In the multicenter validation cohort, integrative Cox PH analyses incorporating clinical variables and gene expression identified 11 genes significantly associated with RFS and 25 with OS. The 11 RFS-associated genes, combined with five clinical variables, were used to train six ML survival models, among which the random survival forest (RSF) model demonstrated the highest predictive accuracy. Following training of RSF models across all combinations of four genes and five clinical variables, and subsequent feature importance–based selection, a final model incorporating three genes (FSCN1, LDHA, and SLC2A1) and four clinical variables (CCA subtype, resection margin status, biological sex, and age at surgery) achieved a testing time-dependent AUC of 0.82. This model significantly stratified patients into high- and low recurrence-risk groups, with a median RFS (mRFS) of 8 months and 70 months, respectively. In addition, an OS prediction model using the same feature set achieved a testing AUC of 0.775 and significantly stratified patients according to 5-year OS risk, with a median OS (mOS) of 23 months in the high-risk group and 77 months in the low-risk group. Conclusion We identified and validated a robust panel of prognostic tissue biomarkers in patients with resected CCA. Integration of these biomarkers with clinical variables into ML-based prognostic models enabled accurate prediction of RFS and OS. The resulting web-based tool allows clinicians to input RT-qPCR results for the validated biomarkers and obtain precise, individualized prognostic predictions for postoperative recurrence and survival.

Background and Aims: Cholangiocarcinoma (CCA) is one of the most lethal cancers, characterized by molecular heterogeneity and treatment resistance. To uncover new biological signals and therapeutic opportunities, we employed multiomic characterization combined with machine learning. Approach and Results: We profiled all anatomical CCA subtypes using whole exome sequencing, mRNA sequencing, and proteome/phosphoproteome analysis. Integrative dimensional reduction revealed RNA, protein, and phosphoprotein features driving tumor heterogeneity, enabling clustering. Machine learning algorithms identified molecular features for each cluster and mapped external datasets and patient-derived xenograft (PDX) models onto these clusters. Kinase enrichment analysis highlighted targetable kinases active in each cluster. In vivo validation was performed in cluster-specific PDX models using the selective TNK1 inhibitor, TP-5801. We identified 3 molecular clusters with distinct pathway characterization: immunomodulatory (cluster 1), metabolic (cluster 2), and gene regulation/cellular fate (cluster 3). Cluster assignment was independent of anatomic subtype but correlated with overall survival following curative-intent resection. We also identified multiomic features and pathways linked to overall survival and lymph node metastases, crucial for patient treatment selection. Kinase enrichment analysis pinpointed TNK1 as a highly active kinase in the metabolic cluster. Treatment with TP-5801 significantly reduced tumor growth in a metabolic PDX model, but not in models representing the other clusters. Combining internal data with publicly available datasets, we identified the immunomodulatory cluster as most responsive to gemcitabine/cisplatin therapy, confirmed in vivo using cluster-specific PDX models. Conclusions: Integrated multiomic characterization provides translational insights by defining unique molecular subtypes associated both with therapeutic response and overall clinical outcomes. This approach identified TNK1 as a previously unrecognized therapeutic target in a defined subset of CCA tumors.

Abstract Background Unresectable hepatocellular carcinoma (uHCC) is a leading cause of cancer death. FDA-approved first-line systemic therapies include atezolizumab/bevacizumab (atezo/bev) and durvalumab/tremelimumab (durva/treme); however, there is a lack of comparative data. Methods We reviewed outcomes of patients with uHCC who initiated atezo/bev or durva/treme between 2017 and 2024, across six institutions. Overall survival (OS) and time to treatment discontinuation (TTD) were analyzed using the Kaplan–Meier and Cox models, adjusting for baseline characteristics. Results Four hundred fifty-two uHCC pts were included. Median age: 68 years; 77% male; 81% white. Most common etiologies were viral hepatitis (38.9%) and metabolic dysfunction-associated steatohepatitis (19.5%). Disease progression was the primary reason for treatment discontinuation, atezo/bev (56%) and durva/treme (42%). Outcomes were not statistically significant (median OS [month, m]: 14.0 vs 14.6 [P = .66]; median TTD [m]: 4.9 vs 3.9 [P = .42] for atezo/bev vs durva/treme). Outcomes were significantly different between Child-Pugh classes (CP: A, B7, B8/9, C) respectively, median OS(m): 19.0, 6.1, 5.1, 2.0 (P < .001); median TTD(m): 6.1, 2.3, 3.0, 1.3 (P < .001). Conclusions In this real-world study of uHCC, no significant difference in clinical outcomes was observed between atezo/bev and durva/treme in the first-line setting. CP scores were a key prognostic variable with both regimens.

Abstract Hepatocellular carcinoma (HCC) is a disease of global public health significance with mortality on the rise, despite the preventable nature of its risk factors especially in Africa. It is now the sixth most common cancer worldwide, fifth in males, and ninth in females. HCC incidence and mortality are predicted to increase in African countries constrained by limited resources to combat endemic levels of viral infection and synergistic environmental risk factors. The changing nature of HCC etiology is particularly illustrated here with the traditional risk factors like viral hepatitis coexisting alongside high human immunodeficiency virus (HIV) prevalence and rapidly increasing urbanization that have promoted a sharp increase in additional risk factors like coinfection, type 2 diabetes mellitus, and obesity. Although there are some differences in etiology between North Africa and sub-Saharan Africa, risk factors like chronic viral hepatitis B and C, aflatoxin exposure, and iron overload predominate. Aggressive hepatitis B genotypes, combined with hepatitis B virus/hepatitis C virus/HIV coinfections and aflatoxin exposure, promote a more aggressive molecular phenotype. In parallel to a better understanding of the molecular etiology of HCC, policy and planning initiatives to address the burden of HCC must be anchored within the reality of the limited resources available. Establishment and coordination of cancer registries across Africa is needed to improve the quality of data necessary to galvanize action. Preventive measures including hepatitis B vaccination programs, measures to prevent maternal-to-child and child-to-child transmission, delivery of universally accessible antiretroviral and antiviral treatments, and reduction of dietary aflatoxin exposure can contribute markedly to reduce HCC incidence. Finally, the development of biomarkers and new therapeutic interventions will need a better understanding of the unique genetic and epigenetic characteristics of HCC on the continent. We present a narrative review of HCC in Africa, discussing present and future trends.

BACKGROUND: African-Americans (AA) have a higher incidence of hepatocellular carcinoma (HCC) and lower survival. We characterized survival rates and clinical features associated with survival in AA vs. Caucasians with HCC over the past two decades. METHODS: HCC patients from three US medical centers were matched by year of diagnosis (1991–2016): AA ( n = 578)/Caucasian ( n = 578) and placed in one of two groups—HCC diagnosed prior to 2010 or 2010 and after. Data were obtained from chart review and the National Death Index. Multivariate and survival analysis controlling for key predictors were conducted. RESULTS: Prior to 2010, there was no difference in survival between Caucasians and AA ( p = 0.61). After 2010, AA patients had poorer survival compared to Caucasians (35% vs. 44%, respectively, p = 0.044). Over time, survival improved for Caucasians (32% before 2010 vs. 44% after 2010, p = 0.003), but not AA (36% vs. 35%, p = 0.50). AA on presentation (in the after 2010 cohort) were more likely to have BCLC (Barcelona Clinic Liver Cancer) stage C (24% vs. 15%, p = 0.010) and less likely to receive treatment (85% vs. 93%, p = 0.002) compared to matched Caucasians. BCLC beyond stage A (aHR: 1.75, 95% CI: 1.26–2.43, p = 0.001) and child’s class C (aHR 2.05, 95% CI: 1.23–3.41, p = 0.006) were the strongest predictors of mortality, while race was not. CONCLUSIONS: African-Americans presented with more advanced HCC and had poorer survival compared to Caucasians after 2010. Tumor stage was an independent predictor of mortality, but ethnicity was not. Further efforts are needed to improve early HCC diagnosis for AA.