Machine learning models in the prediction of 1-year mortality in patients with advanced hepatocellular cancer on immunotherapy: a proof-of-concept study

A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma

PURPOSE

Given its heterogeneity and diverse clinical outcomes, precise subclassification of Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment.

EXPERIMENTAL DESIGN

We recruited 2,626 patients with BCLC-C HCC from multiple centers, comprising training/test (n = 1,693) and validation cohorts (n = 933). The XGBoost model was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-, intermediate-, high-, and very high-risk subgroups based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C).

RESULTS

The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-, 12-, and 24-month survival of patients with BCLC-C were 0.800, 0.831, and 0.715, respectively-significantly higher than those of the conventional models, which were consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKI) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab-bevacizumab as the best therapy, particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high- to very high-risk subgroup.

CONCLUSIONS

ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.

Liver Injury and Its Impact on Prognosis in Patients with HBV-Related Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization Combined with Tyrosine Kinase Inhibitors Plus Immune Checkpoint Inhibitors

Purpose

Recently, the triple therapy of transarterial chemoembolization (TACE) combined with tyrosine kinase inhibitors (TKIs) plus immune checkpoint inhibitors (ICIs) has become a new treatment option for advanced or unresectable hepatocellular carcinoma (HCC) patients. We aimed to explore the liver injury and its effect on overall survival (OS) in patients treated with this combination therapy.

Patients and Methods

Patients with HBV-related HCC who were treated with TACE-TKIs-ICIs from January 2020 to December 2021 were enrolled. Liver injury and survival time were the main endpoints of the study. Logistic regression analysis was used to analyze the factors associated with liver injury. Cox regression and Kaplan-Meier analysis were used to determine prognostic factors for OS.

Results

As of March 2022, 52 of the 119 enrolled patients developed any grade hepatotoxicity: 15 cases with grade 1, 19 cases with grade 2, 16 cases with grade 3 and 2 cases with grade 4. Our analysis indicated that lack of antiviral prevention was a risk factor for liver injury (OR = 0.149; 95% CI: 0.050-0.442; P = 0.001). The findings suggested that liver injury events (HR = 1.912; 95% CI: 1.031-3.546; P = 0.040) was associated with patient death. The median OS of patients without liver injury, grade 1-2 and grade 3-4 liver injury were undefined, 13.7 months and 11.1 months, respectively (log-rank P = 0.034).

Conclusion

Liver injury adverse events are common in HBV-related HCC patients treated with TACE-TKIs-ICIs. Patients who developed liver injury had a poor prognosis. For HBV-related HCC patients, effective prophylactic antiviral therapy and regular liver function testing are required before and during this triple therapy.

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Prognostic significance of the CRAFITY score in hepatocellular carcinoma treated with immunotherapy: a systematic review and meta-analysis

BACKGROUND

This meta-analysis aimed to assess the performance of the CRAFITY (CRP and AFP in immunotherapy) score as a prognostic factor in hepatocellular carcinoma (HCC) treated with immunotherapy.

METHODS

The PubMed, Cochrane Library, and Web of Science databases were searched for published studies. Hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) and progression-free survival (PFS) outcomes were pooled using fixed- and random-effects models. Odds ratios (ORs) with 95% CI were used to measure the association of individual CRAFITY scores with the disease control rate (DCR).

RESULTS

Four eligible studies comprising 786 patients were included. The results indicate that a lower CRAFITY score is a significant predictor of better OS (HR = 0.22, 95% CI: 0.10-0.50) and PFS (HR = 0.36, 95% CI: 0.23-0.55) outcomes. In addition, the DCR was significantly higher in patients with lower CRAFITY scores (OR = 3.16, 95% CI: 2.00-4.99). A significant positive association between low CRAFITY scores and favorable prognoses was also observed in Barcelona Clinic Liver Cancer stage B/C/D patients.

CONCLUSION

In this study, a low CRAFITY score was associated with better overall outcomes in HCC patients treated with immunotherapy. However, this finding requires further investigation.

Real-world efficacy and prognostic factors of lenvatinib plus PD-1 inhibitors in 378 unresectable hepatocellular carcinoma patients

INTRODUCTION

Combining lenvatinib with a programmed cell death protein-1 (PD-1) inhibitor has been explored for the treatment of un-resectable hepatocellular carcinoma (uHCC). This study aimed to investigate the real-world efficacy of and prognostic factors for survival associated with lenvatinib plus PD-1 inhibitor treatment in a large cohort of Asian uHCC patients even the global LEAP-002 study failed to achieve the primary endpoints.

METHODS

Patients with uHCC treated with lenvatinib and PD-1 inhibitors were included. The primary endpoints were overall survival (OS) and progression-free survival (PFS), and the secondary endpoints were the objective response rate (ORR) and adverse events (AEs). Prognostic factors for survival were also analyzed.

RESULTS

A total of 378 uHCC patients from two medical centers in China were assessed retrospectively. The median patient age was 55 years, and 86.5% of patients were male. Hepatitis B virus (HBV) infection (89.9%) was the dominant etiology of uHCC. The median OS was 17.8 (95% confidence interval (CI) 14.0-21.6) months. The median PFS was 6.9 (95% CI 6.0-7.9) months. The best ORR and disease control rate (DCR) were 19.6% and 73.5%, respectively. In multivariate analysis, Child‒Pugh grade, Barcelona Clinic Liver Cancer stage, Eastern Cooperative Oncology Group performance status score, involved organs, tumor burden score, and combination with local therapy were independent prognostic factors for OS. A total of 100% and 57.9% of patients experienced all-grade and grade 3/4 treatment-emergent AEs, respectively.

CONCLUSION

This real-world study of lenvatinib plus PD-1 inhibitor treatment demonstrated long survival and considerable ORRs and DCRs in uHCC patients in China. The tolerability of combination therapy was acceptable but must be monitored closely.