Machine Learning Predictive Model to Guide Treatment Allocation for Recurrent Hepatocellular Carcinoma After Surgery.

Importance: Clear indications on how to select retreatments for recurrent hepatocellular carcinoma (HCC) are still lacking. Objective: To create a machine learning predictive model of survival after HCC recurrence to allocate patients to their best potential treatment. Design, Setting, and Participants: Real-life data were obtained from an Italian registry of hepatocellular carcinoma between January 2008 and December 2019 after a median (IQR) follow-up of 27 (12-51) months. External validation was made on data derived by another Italian cohort and a Japanese cohort. Patients who experienced a recurrent HCC after a first surgical approach were included. Patients were profiled, and factors predicting survival after recurrence under different treatments that acted also as treatment effect modifiers were assessed. The model was then fitted individually to identify the best potential treatment. Analysis took place between January and April 2021. Exposures: Patients were enrolled if treated by reoperative hepatectomy or thermoablation, chemoembolization, or sorafenib. Main Outcomes and Measures: Survival after recurrence was the end point. Results: A total of 701 patients with recurrent HCC were enrolled (mean [SD] age, 71 [9] years; 151 [21.5%] female). Of those, 293 patients (41.8%) received reoperative hepatectomy or thermoablation, 188 (26.8%) received sorafenib, and 220 (31.4%) received chemoembolization. Treatment, age, cirrhosis, number, size, and lobar localization of the recurrent nodules, extrahepatic spread, and time to recurrence were all treatment effect modifiers and survival after recurrence predictors. The area under the receiver operating characteristic curve of the predictive model was 78.5% (95% CI, 71.7%-85.3%) at 5 years after recurrence. According to the model, 611 patients (87.2%) would have benefited from reoperative hepatectomy or thermoablation, 37 (5.2%) from sorafenib, and 53 (7.6%) from chemoembolization in terms of potential survival after recurrence. Compared with patients for which the best potential treatment was reoperative hepatectomy or thermoablation, sorafenib and chemoembolization would be the best potential treatment for older patients (median [IQR] age, 78.5 [75.2-83.4] years, 77.02 [73.89-80.46] years, and 71.59 [64.76-76.06] years for sorafenib, chemoembolization, and reoperative hepatectomy or thermoablation, respectively), with a lower median (IQR) number of multiple recurrent nodules (1.00 [1.00-2.00] for sorafenib, 1.00 [1.00-2.00] for chemoembolization, and 2.00 [1.00-3.00] for reoperative hepatectomy or thermoablation). Extrahepatic recurrence was observed in 43.2% (n = 16) for sorafenib as the best potential treatment vs 14.6% (n = 89) for reoperative hepatectomy or thermoablation as the best potential treatment and 0% for chemoembolization as the best potential treatment. Those profiles were used to constitute a patient-tailored algorithm for the best potential treatment allocation. Conclusions and Relevance: The herein presented algorithm should help in allocating patients with recurrent HCC to the best potential treatment according to their specific characteristics in a treatment hierarchy fashion.

Targeting Stem Cells with Hyperthermia: Translational Relevance in Cancer Patients.

BACKGROUND: Tumor recurrences or metastases remain a major hurdle in improving overall cancer survival. In anticancer therapy, some patients inevitably develop chemo-/radiotherapy resistance at some point. Cancer stem cells are the driving force of tumorigenesis, recurrences, and metastases, contributing also to the failure of some cancer treatments. SUMMARY: Emergent evidence suggests that stem cell diseases are at the base of human cancers, and tumor progression and chemo-/radiotherapy resistance may be dependent on just a small subpopulation of cancer stem cells. Hyperthermia can be a strong cancer treatment, especially when combined with radio- or chemotherapy. It is a relatively safe therapy, may kill or weaken tumor cells, and significantly increases the effectiveness of other treatments. However, these mechanisms remain largely unknown. A literature search was performed using PubMed including cited English publications. The search was last conducted in December 2019. Search phrases included "stem cells," "hyperthermia," "cancer," and "therapy." Abstracts, letters, editorials, and expert opinions were not considered for the drafting of the study. Key Message: Our goal was to focus on and to summarize different biological features of cancer stem cells and new therapeutic approaches using hyperthermia and its potential translation to human clinical trials.

Emerging role of Immune Checkpoint Inhibitors in Hepatocellular Carcinoma.

Hepatocellular carcinoma is the most common primary liver cancer and the fourth leading cause of cancer death worldwide. A total of 70-80% of patients are diagnosed at an advanced stage with a dismal prognosis. Sorafenib had been the standardcare for almost a decade until 2018 when the Food and Drug Administration approved an alternative first-line agent namely lenvatinib. Cabozantinib, regorafenib, and ramucirumab also displayed promising results in second line settings. FOLFOX4, however, results inan alternative first-line treatment for the Chineseclinical oncology guidelines. Moreover,nivolumab and pembrolizumab,two therapeutics against the Programmed death (PD)-ligand 1 (PD-L1)/PD1 axis have been recently approvedfor subsequent-line therapy. However, similar to other solid tumors, the response rate of single agent targeting PD-L1/PD1 axis is low. Therefore, a lot of combinatory approaches are under investigation, including the combination of different immune checkpoint inhibitors (ICIs), the addition of ICIs after resection or during loco-regional therapy, ICIs in addition to kinase inhibitors, anti-angiogenic therapeutics, and others. This review focuses on the use of ICIs for the hepatocellular carcinoma with a careful assessmentof new ICIs-based combinatory approaches.