RESUMEN
BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) is a highly fatal cancer characterized by high intra-tumor heterogeneity (ITH). A panoramic understanding of its tumor evolution, in relation to its clinical trajectory, may provide novel prognostic and treatment strategies. METHODS: Through the Asia-Pacific Hepatocellular Carcinoma trials group (NCT03267641), we recruited one of the largest prospective cohorts of patients with HCC, with over 600 whole genome and transcriptome samples from 123 treatment-naïve patients. RESULTS: Using a multi-region sampling approach, we revealed seven convergent genetic evolutionary paths governed by the early driver mutations, late copy number variations and viral integrations, which stratify patient clinical trajectories after surgical resection. Furthermore, such evolutionary paths shaped the molecular profiles, leading to distinct transcriptomic subtypes. Most significantly, although we found the coexistence of multiple transcriptomic subtypes within certain tumors, patient prognosis was best predicted by the most aggressive cell fraction of the tumor, rather than by overall degree of transcriptomic ITH level - a phenomenon we termed the 'bad apple' effect. Finally, we found that characteristics throughout early and late tumor evolution provide significant and complementary prognostic power in predicting patient survival. CONCLUSIONS: Taken together, our study generated a comprehensive landscape of evolutionary history for HCC and provides a rich multi-omics resource for understanding tumor heterogeneity and clinical trajectories. IMPACT AND IMPLICATIONS: This prospective study, utilizing comprehensive multi-sector, multi-omics sequencing and clinical data from surgically resected hepatocellular carcinoma (HCC), reveals critical insights into the role of tumor evolution and intra-tumor heterogeneity (ITH) in determining the prognosis of HCC. These findings are invaluable for oncology researchers and clinicians, as they underscore the influence of distinct evolutionary paths and the 'bad apple' effect, where the most aggressive tumor fraction dictates disease progression. These insights not only enhance prognostic accuracy post-surgical resection but also pave the way for personalized treatment strategies tailored to specific tumor evolutionary and transcriptomic profiles. The coexistence of multiple subtypes within the same tumor prompts a re-appraisal of the utilities of depending on single samples to represent the entire tumor and suggests the need for clinical molecular imaging. This research thus marks a significant step forward in the clinical understanding and management of HCC, underscoring the importance of integrating tumor evolutionary dynamics and multi-omics biomarkers into therapeutic decision-making. CLINICAL TRIAL NUMBER: NCT03267641 (Observational cohort).
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Nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) is a major cause of liver fibrosis/cirrhosis and liver-related mortality. Despite emergence of noninvasive tests, liver biopsy remains the mainstay for the diagnosis and assessment of disease severity and chronicity. Accurate detection and quantification of liver fibrosis with architectural localization are essential for assessing the severity of NAFLD and its response to antifibrotic therapy in clinical trials. Conventional histological scoring systems for liver fibrosis are semiquantitative. Collagen proportionate area is morphometric by measuring the percentage of fibrosis on a continuous scale but is limited by the absence of architectural input. Ultra-fast laser microscopy, e.g., second harmonic generation (SHG) imaging, has enabled in-depth analysis of fibrillary collagen based on intrinsic optical signals. Quantification and calculation of different detailed variables of collagen fibers can be used to establish algorithm-based quantitative fibrosis scores (e.g. qFibrosis, q-FPs) in NAFLD. Artificial intelligence is being explored to further develop quantitative fibrosis scoring methods. SHG microscopy should be considered the new gold standard for the quantitative assessment of liver fibrosis, reaffirming the pivotal role of the liver biopsy in NAFLD, at least for the near-future. The ability of SHG-derived algorithms to intuitively detect subtle nuances in liver fibrosis changes over a continuous scale should be employed to redress the efficacy endpoint for fibrosis in NASH clinical trials. The current decrease by 1-point or more in fibrosis stage may not be realistic for the evaluation of therapeutic response to antifibrotic drugs in relatively short-term trials.
