AI and Cancer Research ── Liver ── 2026-10-30
Liver cancer: treatment, trials and AI
- Abstract
Background & Aims: Clinical responses to atezolizumab plus bevacizumab (AB) therapy can vary across hepatocellular carcinoma (HCC) nodules among individual patients. This study aimed to derive and externally validate a non-invasive radiomics signature (ABRad) translated from a transcriptome signature for nodule-level prediction of AB response (ABS).
Methods: We originally derived and validated ABS using 209 and 44 pre-treatment HCC samples from the IMbrave150 and GO30140 trials, respectively. ABS was then translated into ABRad using a machine-learning approach on 59 patients with paired genome-wide transcriptome and pre-resection computed tomography imaging from surgically resected HCC. Subsequently, ABRad was externally validated using 178 HCC nodules from 55 patients treated with AB therapy.
Results: The 148-gene ABS predicted therapeutic response and progression-free survival (PFS) in the clinical trial cohorts. The final ABRad model, constructed using a random forest algorithm, was heavily weighted toward contrast-enhanced features. In the external validation cohort, ABRad demonstrated a significant positive association with the degree of therapeutic response (p <0.001). The areas under receiver operating characteristics curve for predicting complete response (CR), objective response (OR), and disease control (DC) were 0.71 (95% confidence interval [CI], 0.61-0.80), 0.65 (95% CI, 0.56-0.73), and 0.69 (95% CI, 0.59-0.78), respectively. Nodule-level ABRad was independently associated with therapeutic response with adjusted odds ratios of 3.39 (95% CI, 1.40-8.21) for CR, 2.55 (95% CI, 1.19-5.67) for OR, and 2.60 (95% CI, 1.03-6.56) for DC. Patient-level ABRad was significantly associated with prolonged PFS (p = 0.005).
Conclusions: ABRad successfully predicted nodule-level response to AB therapy in HCC.
Journal IF-equivalent: 5.3 (OpenAlex 2-year mean citedness, value as of 2026-10-24, retrieved 2026-10-26; not the official Clarivate IF)Reference: Tanaka T, Fujiwara N, Fujii T, Kimura G, Izuoka K, Tempaku M, et al. A radio-transcriptome signature for nodule-level prediction of response to atezolizumab plus bevacizumab in hepatocellular carcinoma. Liver Cancer. 2026 Oct 27 [Epub ahead of print]. doi:10.1159/lic/admag026.Checked: Abstract only - Abstract
Background: Radioresistance limits durable local control in hepatocellular carcinoma (HCC), but the relationship between cuproptosis-related metabolism and radiation response remains incompletely characterized.
Methods: We integrated 26 cuproptosis- and radiation-related genes across LIRI-JP ( n = 231), GSE14520 ( n = 225), and TCGA-LIHC ( n = 325). Consensus clustering and 127 machine-learning configurations were used to develop and validate a prognostic score. GSE149614 single-cell data, scTenifoldKnk virtual knockout, an irradiated MHCC97H model, and postoperative immunohistochemistry were used for biological evaluation.
Results: A gradient boosting machine score consistently stratified overall survival across the three cohorts. In bulk cohorts, ENTPD5 expression was lower in the high-risk group and inversely correlated with the risk score. By contrast, ENTPD5 showed a weak positive association with the score in 6,033 malignant cells and was upregulated in radiation-selected MHCC97H cells ( p = 0.036; log2 fold change = 0.316). Virtual knockout predicted attenuation of unfolded-protein-response and radioresistance-associated programs. Among 42 patients with evaluable clinical outcomes, recurrence occurred in 11/39 ENTPD5-positive and 3/3 ENTPD5-negative cases (28.2% vs. 100%; Fisher’s exact p = 0.032). ENTPD5-positive cases had longer progression-free survival (log-rank p = 0.019; univariable Cox hazard ratio = 0.22, 95% confidence interval 0.05–0.89), although the ENTPD5-negative group was small.
Conclusions: The integrated score is prognostic in retrospective HCC cohorts, and ENTPD5 shows context-dependent associations across bulk tumors, malignant-cell states, radiation-selected cells, and postoperative tissues. These findings support ENTPD5 as a candidate marker for further study rather than establish a causal or treatment-predictive role.
Journal IF-equivalent: 3.9 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Wu QQ, Zhang XF, Zhang Y, Wang HY, Wang LF, Xu Y, et al. Deciphering ENTPD5-driven radioresistance in hepatocellular carcinoma via multi-algorithm machine learning and single-cell virtual knockout. BMC Cancer. 2026 Oct 26 [Epub ahead of print]. doi:10.1186/s12885-026-17093-w.Checked: Abstract only