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However, despite this therapeutic diversification, first\u2010line treatment selection remains largely empirical, as few biomarkers are available at diagnosis to inform therapeutic choice. This review provides a practice\u2010oriented synthesis of predictive biomarkers evaluated in advanced HCC, distinguishing those that are clinically transposable from those that remain exploratory. Among currently available tools, only a limited set of biomarkers demonstrates sufficient robustness and feasibility for real\u2010world use, and most provide information after treatment initiation rather than guiding upfront selection. Liver function assessment (ALBI score), dynamic Alpha\u2010fetoprotein (AFP) kinetics and anti\u2010drug antibodies (ADA) emerge as the most actionable parameters. ALBI retains predominantly prognostic value, whereas early AFP decline and ADA formation offer treatment\u2010specific, on\u2010treatment insights that may support clinical decision\u2010making. Imaging biomarkers such as mRECIST remain essential for early efficacy assessment but lack predictive value at baseline. Routine histological markers\u2014including PD\u2010L1, mismatch repair proteins and \u03b2\u2010catenin immunostaining\u2014do not reliably predict response prior to therapy initiation. In contrast, a broad range of circulating, tissue\u2010based and imaging\u2010derived biomarkers\u2014including liquid biopsy analytes (cytokines, CTCs, ctDNA, miRNAs), transcriptomic immune signatures, artificial intelligence AI\u2010assisted histopathology, radiomics and multi\u2010omic tumour profiling\u2014provide substantial mechanistic insight but remain investigational. Their limited clinical applicability reflects methodological heterogeneity, insufficient standardisation and the absence of prospective validation in biomarker\u2010driven trials. Among emerging approaches, proteomics stands out as a particularly promising strategy. By directly capturing the protein expression landscape of the tumour and its microenvironment, proteomics may overcome the inferential limitations of genomic and transcriptomic biomarkers and contribute to the development of biologically grounded, pre\u2010treatment stratification tools in advanced HCC. Ultimately, this review underscores a critical unmet need: the development of integrated, multi\u2010omic predictive strategies that combine baseline tissue characteristics with dynamic liquid biopsy markers and advanced imaging. Such approaches, strengthened by AI and prospective biomarker\u2010driven trials, are essential to move beyond empirical therapy selection and toward true precision medicine in advanced HCC.<\/jats:p>","DOI":"10.1111\/liv.70684","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T11:24:01Z","timestamp":1778671441000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["From Theory to Practice: Which Biomarkers Are Ready for Predicting Response in Advanced\n                    <scp>HCC<\/scp>\n                    ?"],"prefix":"10.1111","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3304-2106","authenticated-orcid":false,"given":"Marie","family":"Decraecker","sequence":"first","affiliation":[{"name":"Univ. 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