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Simple Summary: Hepatocellular carcinoma (HCC) is the most common type of liver cancer, accounting for approximately 85–90% of all cases of liver cancer worldwide. The gut microbiome can serve as a potential non-invasive biomarker for early HCC detection and may also impact the effectiveness of immunotherapy in cancer treatment. This review examines the gut microbiome's role as a predictive and diagnostic marker for HCC and explores its potential as a novel therapeutic approach, particularly in the context of immunotherapy. The microbiome is pivotal in maintaining health and influencing disease by modulating essential inflammatory and immune responses. Hepatocellular carcinoma (HCC), ranking as the third most common cause of cancer-related fatalities globally, is influenced by the gut microbiome through bidirectional interactions between the gut and liver, as evidenced in both mouse models and human studies. Consequently, biomarkers based on gut microbiota represent promising non-invasive tools for the early detection of HCC. There is a growing body of evidence suggesting that the composition of the gut microbiota may play a role in the efficacy of immunotherapy in different types of cancer; thus, it could be used as a predictive biomarker. In this review, we will dissect the gut microbiome's role as a potential predictive and diagnostic marker in HCC and evaluate the latest progress in leveraging the gut microbiome as a novel therapeutic avenue for HCC patients, with a special emphasis on immunotherapy. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide with slow progress in the development of effective therapies. The breakthrough IMbrave150 trial established atezolizumab plus bevacizumab as the standard of care first-line therapy for patients with unresectable/advanced HCC, demonstrating improved overall survival. Radiation therapy (RT) is a locoregional treatment that may prevent morbidity from tumor-related vascular compromise and associated liver failure. There is no documented evidence yet describing the safety and outcomes of combination RT and atezolizumab/bevacizumab. In the retrospective review of our experience, we identified 21 patients with advanced HCC who underwent liver-directed RT with atezolizumab/bevacizumab. From our findings, the treatment is well-tolerated, safe, and does not potentiate liver failure. There were uncommon autoimmune or GI bleeding events in some patients, mostly unrelated to RT. Post-RT absolute lymphocyte counts (ALC) improved more quickly with atezolizumab/bevacizumab than they did without, which may be a favorable treatment prognosticator. Atezolizumab plus bevacizumab has become frontline therapy for unresectable HCC. The compatibility of atezolizumab/bevacizumab with liver-directed RT has not been reported. Methods: HCC patients treated with liver-directed RT and atezolizumab/bevacizumab between 1/2020–11/2021 were included. Toxicity and outcomes were retrospectively recorded. For ALCs, we matched the analysis to a previously cohort of RT-treated HCC patients who did not receive atezolizumab/bevacizumab. Survival and time-to-liver-failure were analyzed using Kaplan–Meier. Results: Of 21 patients, with a median follow-up of 9.5 months, the median OS was 16.1 months. Post-RT, all patients had reduced tumors or treatment response. There were no ≥Grade 3 RT-related toxicities. Autoimmune complications occurred in two patients (9.5%), and GI bleeding in three patients (14.3%). Liver function remained stable post-RT. There was a marked decrease in ALCs immediately post-RT (post-RT/pre-RT ratio 47.3%, p < 0.0001), restored by 1 month to pre-treatment baseline (1-month post-RT/pre-RT ratio 95.1%, n.s.). Compared to HCC patients treated with RT alone, post-RT ALC recovery was faster with atezolizumab/bevacizumab (p = 0.009). Conclusion: In this first reported experience of RT with modern systemic therapy for HCC, combination therapy is safe and well-tolerated. As a favorable prognosticator, there appears to be faster recovery of ALC among patients who received RT with atezolizumab/bevacizumab. [ABSTRACT FROM AUTHOR]

Simple Summary: Liver cancer is one of the most common causes of cancer worldwide, but unfortunately, current technology has a limited ability to detect it early in high-risk patients. This study investigates a machine learning algorithm based on protein levels in the blood that can be used to help with diagnosis. The test shows promising results, especially in patients with smaller tumors and compared to current blood detection tests. This research suggests an important role in the future for machine learning algorithm-based blood detection tests. Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat® classification algorithm. Machine learning techniques then classified each sample as "Cancer" or "No Cancer". Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted. [ABSTRACT FROM AUTHOR]