A radio-transcriptome signature for nodule-level prediction of response to atezolizumab plus bevacizumab in hepatocellular carcinoma

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.