Systemic inflammation is a key risk factor for hepatocellular carcinoma (HCC) progression and poor outcomes. Inflammatory markers such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) may have prognostic value in HCC treated with standard of care atezolizumab plus bevacizumab (Atezo-Bev). We conducted a multicenter, international retrospective cohort study of patients with unresectable HCC treated with Atezo-Bev to assess the association of NLR and PLR with overall survival (OS), progression-free survival (PFS), and objective response rates. Patients with NLR ≥ 5 had a significantly shorter OS (9.38 vs. 16.79 months, p < 0.001) and PFS (4.90 vs. 7.58 months, p = 0.03) compared to patients with NLR < 5. NLR ≥ 5 was an independent prognosticator of worse OS (HR 2.01, 95% CI 1.22–3.56, p = 0.007) but not PFS. PLR ≥ 300 was also significantly associated with decreased OS (9.38 vs. 15.72 months, p = 0.007) and PFS (3.45 vs. 7.11 months, p = 0.04) compared to PLR < 300, but it was not an independent prognosticator of OS or PFS. NLR and PLR were not associated with objective response or disease control rates. NLR ≥ 5 independently prognosticated worse survival outcomes and is worthy of further study and validation.

Atezolizumab/bevacizumab (A/B) is now a standard first-line treatment for advanced hepatocellular carcinoma (HCC), but the optimal second-line regimen is not known. We evaluated real-world treatment patterns and outcomes to investigate factors associated with post-progression survival (PPS). In this multicenter, international, retrospective study, we examined clinical characteristics and outcomes of patients with advanced HCC who progressed on first-line A/B. The primary outcome of PPS was defined as time from first radiographic progression on A/B to death. A total of 406 patients alive after progression on first-line A/B were included in the final analysis, of whom 45.3% (n = 184) received best supportive treatment (BST) and 54.7% (n = 222) continued active systemic treatment. In the second line, 155 patients were treated with tyrosine kinase inhibitors (TKIs), 45 with immune checkpoint inhibitor (IO)-based regimens, and 3 had missing data. Median PPS of the whole cohort (mPPS) was 6.0 months (95% CI 5.2-7.2). On multivariate Cox regression analysis, absence of portal vein tumor thrombus, ECOG <2, and continued active treatment were predictors of better PPS. mPPS was significantly longer for patients who continued active treatment vs. BST (9.7 vs. 2.6 months; HR 0.41, p <0.001). In the second-line setting, patients treated with TKIs had a numerically shorter mPPS compared to those treated with IO (8.4 vs. 14.9 months; HR 1.37, p = 0.256). Continuation of active therapy after A/B progression was independently associated with better survival even after adjusting for baseline disease characteristics. mPPS with IO-based therapy exceeded a year, suggesting that IO continuation post-progression may retain benefit. The precise sequencing of TKI and IO regimens warrants further investigation. There is currently a lack of level 1 data on second-line treatment options for patients with advanced hepatocellular carcinoma who progress after frontline atezolizumab plus bevacizumab, as all second-line approvals were established during the frontline sorafenib era. Our study aims to fill in some of the knowledge gap by investigating real-world patient outcomes in the second-line treatment setting. Findings from this study show that patients who continued active treatment had improved post-progression survival compared to those who received best supportive care, and medication regimens incorporating tyrosine kinase inhibitors as well as immunotherapy agents were active. These results can help inform clinicians of possible treatment options for patients who progress after frontline atezolizumab plus bevacizumab while we await maturing data from randomized-controlled trials.

Background: Since the introduction of the combination treatment of anti-programmed death-ligand 1 antibody atezolizumab and anti-VEGF antibody bevacizumab (AB), median overall survival in HCC has drastically improved. However, evidence on the efficacy and safety of the novel treatment standard in patients with prior exposure to systemic treatment is scarce. The aim of this global, multicenter, observational study was to evaluate the efficacy and safety of AB in patients after previous systemic therapy. Methods: We screened our global, multicenter, prospectively maintained registry database for patients who received any systemic therapy before AB. The primary end point was overall survival; secondary end points were time-to-progression, progression-free survival, objective response rate, and safety (rate and severity of adverse events). Results: Among 493 patients who received AB for unresectable HCC, 61 patients received prior systemic therapy and were included in this analysis. The median age of the study population was 66 years, with 91.8% males. Predominant risk factors for HCC were viral hepatitis (59%) and alcohol (23%). Overall survival for AB was 16.2 (95% CI, 14.5–17.9) months, time-to-progression and progression-free survival were 4.1 (95% CI, 1.5–6.6) and 3.1 (95% CI, 1.1–5.1) months, respectively. The objective response rate was 38.2% (7.3% with complete and 30.9% with partial response). Overall survival was not influenced by treatment line (2nd vs. >2nd) or previous systemic treatment modality (tyrosine kinase inhibitors vs. immune checkpoint inhibitors). Treatment-related adverse events of all grades according to Common Terminology Criteria for Adverse Events were documented in 42.6% of patients, with only 13.1% of grade ≥3, including one death. Conclusion: In this observational study, AB emerges as a safe and efficacious treatment option in patients with HCC previously treated with other systemic therapy. Export

Combination immune checkpoint inhibitors are recommended as first-line therapy for advanced hepatocellular carcinoma. However, only a third of patients respond to treatment, and improved approaches to predict response are required. Using baseline clinical data, we aimed to use advanced machine learning models to predict overall survival and progression-free survival in patients with advanced hepatocellular carcinoma receiving atezolizumab plus bevacizumab. This retrospective cohort study was conducted at 24 centres across eight countries. Patients aged 18 years and older with a histological or radiological diagnosis of advanced hepatocellular carcinoma were included; those who had received previous systemic therapy for hepatocellular carcinoma were excluded. All patients received intravenous atezolizumab 1200 mg plus bevacizumab 15 mg/kg once every 3 weeks until disease progression. Seven supervised machine learning models, in combination with 13 feature selection techniques, were trained on 44 baseline clinical variables for the prediction of overall survival and progression-free survival. The three best-performing models, combined with their optimum feature selection techniques, were used to develop ensemble machine learning models for the prediction of overall survival and progression-free survival. The primary outcomes of the study were the predictions of overall survival, progression-free survival, and immunotherapy response using advanced machine learning. k-means clustering was used to stratify patients into two groups: those at low risk and those at high risk of either death (in the overall survival model) or disease progression (in the progression-free survival model). 934 patients who received immunotherapy from May 1, 2018 and were followed up until Oct 1, 2023 were screened, of whom 160 were excluded and 774 were included in the final study. Patients were divided into training (n=339), internal validation (n=146) and external validation (n=289) cohorts. Support vector machine, neural network, and naive Bayes algorithms had the best performance in the prediction of overall survival; for progression-free survival, the highest-performing algorithms were ridge regression, naive Bayes, and logistic regression. In the external validation cohort, the ensemble model for the prediction of overall survival (area under the receiver operating characteristic curve 0·75 [95% CI 0·69-0·81]) significantly outperformed all eight of the tested clinical benchmark variables: Barcelona Clinic Liver Cancer (BCLC) stage (0·54 [0·48-0·61]; p<0·0001), α-fetoprotein (AFP) concentration (0·60 [0·54-0·67]; p=0·0007), albumin-bilirubin (ALBI) grade (0·64 [0·58-0·71]; p=0·0003), neutrophil-to-lymphocyte ratio (0·56 [0·49-0·62]; p<0·0001), platelet-to-lymphocyte ratio (0·51 [0·44-0·58]; p<0·0001), combined ALBI grade and BCLC stage (0·67 [0·60-0·73]; p=0·0074), and two BCLC subclassifications (0·62 [0·55-0·69]; p=0·0007 and 0·61 [0·55-0·68]; p=0·0018). The ensemble model for the prediction of progression-free survival (0·64 [0·59-0·70]) outperformed five of the eight clinical predictors: BCLC stage (0·52 [0·46-0·58]; p<0·0001), neutrophil-to-lymphocyte ratio (0·53 [0·47-0·59]; p=0·0069), platelet-to-lymphocyte ratio (0·54 [0·48-0·60]; p=0·016), and two BCLC subclassifications (0·57 [0·50-0·64]; p=0·020 and 0·55 [0·49-0·62]; p=0·0091); the model did not outperform AFP concentration (0·59 [0·53-0·64]; p=0·14), ALBI grade (0·62 [0·56-0·67]; p=0·44), or combined ALBI grade and BCLC stage (0·59 [0·53-0·66]; p=0·12). For the overall survival model, patients stratified into the low-risk group had significantly longer median overall survival (16·4 months [95% CI 14·2-21·6]) than those in the high-risk group (4·8 months [3·0-6·9]; p<0·0001); similarly, patients stratified by the progression-free survival model into the low-risk group had significantly longer median progression-free survival (8·9 months [7·3-11·1]) than those in the high-risk group (3·7 months [2·9-5·6]; p=0·0021). Our advanced machine learning models, which use routinely collected baseline clinical variables, are robust and externally validated and outperform established clinical biomarkers for predicting clinical outcomes with atezolizumab plus bevacizumab. These data-driven models could be used to stratify patients with hepatocellular carcinoma for personalised treatment strategies. None.

PURPOSE: Combined mitogen-activated protein kinase kinase (MEK) and autophagy inhibition has shown antitumor efficacy in preclinical pancreatic ductal adenocarcinoma (PDAC) models. We evaluated this therapeutic strategy in the autochthonous K-ras ^LSL.G12D/+ ; Trp53 ^R172H/+ ; Pdx-1-Cre (KPC) mouse model and in the MEKiAUTO phase I trial of patients with metastatic KRAS -mutant PDAC, with or without anti–PD-L1 antibody atezolizumab. Methods: Short-term intervention studies in KPC mice and a phase I clinical trial based on a time-to-event continual reassessment method design were performed. Tumor response was assessed through multiplex immunofluorescence, reverse-phase protein array, and single-nucleus RNA sequencing of serial tumor specimens. RESULTS: MEK and autophagy inhibition suppressed tumor growth in KPC mice. In the clinical trial, 14 patients were treated and four experienced a dose-limiting toxicity. The median progression-free survival (PFS) and overall survival were 7.7 weeks (95% CI, 6 to 15.6) and 20.7 weeks (95% CI, 15.6 to 46.1), respectively. snRNA-seq on paired tumor biopsies identified substantial malignant cell heterogeneity, including an epithelial subtype enriched for autophagy and mitogen-activated protein kinase (MAPK) signaling that decreased after treatment. Higher baseline abundance of this subtype correlated with longer PFS but was rare in TCGA PDAC tumors. By contrast, KPC tumors and PDAC cell lines exhibited reduced heterogeneity and uniform enrichment of this MAPK/autophagy-high state, consistent with their stronger preclinical responses. CONCLUSION: Combined MEK and autophagy inhibition showed limited tolerability in human PDAC. Divergent efficacy between preclinical and clinical settings likely reflects differences in tumor cell state heterogeneity between models. Integration of diverse, representative preclinical models is critical to guide development of effective therapies in PDAC.