Transforming Liver Cancer Therapy: Integrating Molecular Profiling with Precision and Transplant-Based Care
Simple Summary
Abstract
1. Introduction
2. Molecular Taxonomy of HCC and Clinical Correlates
2.1. Canonical Gene-Expression Taxonomies (Hoshida, Boyault, Chiang)
2.2. A Clinically Translatable, Integrative View
2.3. Proteogenomics: Transcript–Protein Discordance and Pathway-Level Rewiring
3. Tumour Microenvironment (TME) and Immune Modulation
3.1. Single-Cell Cartography and Immunosuppressive Niches in HCC
3.2. WNT/CTNNB1 Activation, Immune Exclusion, and ICI Resistance
3.3. Emerging Targets and Combination Strategies
3.3.1. Adenosine–CD73–A2A/A2B Axis
3.3.2. LAG-3 and TIGIT Checkpoints
3.3.3. Novel TAM Subsets (Early Signals)
4. Precision Systemic Therapy Landscape (2022–2025)
5. Adjuvant and Neoadjuvant Therapy
5.1. Where the Field Stands in 2025: Adjuvant Immunotherapy After Resection/Ablation
5.2. Signals from Neoadjuvant/Perioperative Immunotherapy (Hypothesis-Generating)
5.3. Ongoing and Recently Reported (Neo)Adjuvant Trials (Overview)
5.4. Practical Interpretation for Clinicians
6. Liquid Biopsy and Multi-Omics for Surveillance and MRD
6.1. Rationale and Clinical Use-Cases
6.2. Assay Modalities and Readouts
6.3. Evidence Summary by Setting
6.4. Practical Interpretation and Integration
6.5. Limitations and Pitfalls
7. Transplant-Based Care: Selection, Downstaging, and Allocation
7.1. BCLC 2022: Where Transplant Fits
7.2. Policy and Allocation (OPTN/UNOS): Standardised Exception, Downstaging, and Li-Rads Harmonisation
7.3. Indications and Outcomes: Deceased-Vs Living-Donor; Downstaging Performance
7.4. Biomarker-Integrated Selection Models (Schematic Summary)
7.5. Imaging, Radiomics, and FDG-PET for Microvascular Invasion (MVI) and Recurrence Risk
8. Integrating Biomarkers into Transplant Decisions
8.1. Pre-LT “Risk Biology” to Steer Selection, Waiting, and Downstaging
| Tool | Variables/Cut-offs | Output/Use | Notes | Refs. |
|---|---|---|---|---|
| AFP score (French model) | No. tumours; largest diameter; AFP (0–9 points) | Recurrence risk; allocation in France | Validated; improves over Milan | [100,101] |
| Metroticket 2.0 | Size; number; AFP | 5-year survival/HCC death (competing risks) | mRECIST response further improves discrimination | [98,102] |
| Kyoto (LDLT) | ≤10 nodules, each ≤5 cm; DCP ≤ 400 mAU/mL | Expanded LDLT gate with low recurrence | Biology-aware expansion | [96,97] |
| Up-to-Seven | Largest tumour (cm) + number ≤ 7 | Expanded morphologic gate | Outcomes depend on biology/response | [103,104] |
| RETREAT | AFP at LT + MVI + viable burden (diameter + number) | 5-year recurrence tiers (<3% → >75%) | Externally and prospectively validated | [111,112,113] |
| mRETREAT | RETREAT + AFP-L3, DCP | Improved AUC/calibration | Early data; thresholds †: AFP-L3 ≥ 15%, DCP ≥ 7.5 ng/mL | [114,115] |
8.2. Post-LT Risk Stratification and Surveillance
8.3. Risk-Adapted Immunosuppression
8.4. A Transplant-Aware Biomarker Workflow (Proposal)
9. Immunotherapy Around Transplant: Benefits and Risks
9.1. Pre-LT ICIs: Opportunity with a Time-Dependent Rejection Hazard
9.2. Post-LT ICIs for Recurrence: Selective Use with Strict Monitoring
9.3. Representative Cases and Key Immunologic Issues
9.4. Practice Points (For Multidisciplinary Tumour Boards)
10. Special Topics
10.1. ABO-Incompatible (ABOi) LDLT: Outcomes in the Rituximab Era
10.2. Aetiology-Specific Biology (HBV/HCV/NASH): Molecular and Immune Heterogeneity with Therapeutic and Transplant Implications
11. Proposed Practice-Oriented Integrated Algorithm
12. Conclusions and Key Messages
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
References
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| Framework | Biology (Signals) | Key Drivers | Clinical Correlates | Refs. |
|---|---|---|---|---|
| Hoshida S1–S3 | S1: WNT/TGF-β; S2: proliferation (MYC/AKT); S3: hepatocytic differentiation | CTNNB1 (S3 low), MYC/AKT (S2) | S2 larger tumours, higher AFP; S3 lower AFP, better differentiation; S1 early recurrence | [23] |
| Boyault G1–G6 | HBV/AKT-PI3K (G1/2); cell-cycle/TP53 (G3); TCF1 (G4); β-catenin (G5/6) | TP53, PIK3CA, TCF1, CTNNB1 | G6 E-cadherin loss, satellites; genotype–phenotype mapping | [24] |
| Chiang (5 classes) | CTNNB1; proliferation; IFN-related; chr7 polysomy; unannotated | CTNNB1, IGF/AKT-mTOR | Expression–copy-number integration | [25] |
| Integrative clinical view | Non-proliferation (often CTNNB1) vs. Proliferation (TERT-p, TP53, FGF19/FGFR4, CIN) | TERT-p, TP53, FGF19 amp/FGFR4 | Bedside-usable dichotomy guiding therapy hypotheses | [27,28,29,30,31] |
| Regimen/Trial | Comparator | Median OS (Months) | HR (OS) | Median PFS (Months) | Key Toxicities | References |
|---|---|---|---|---|---|---|
| Atezolizumab + Bevacizumab (IMbrave150) | Sorafenib | 19.2 vs. 13.4 | 0.66 | 6.9 vs. 4.3 | Hypertension, bleeding risk | [4,7] |
| Tremelimumab + Durvalumab (STRIDE/HIMALAYA) | Sorafenib | 16.4 vs. 13.8 | ≈0.78 | 3.8 vs. 4.1 | Immune-related AEs (CTLA-4 priming) | [5,52] |
| Nivolumab + Ipilimumab (CheckMate 9DW) | Lenvatinib/Sorafenib | 23.7 vs. 20.6 | 0.79 | (OS primary) | Immune-related AEs | [53] |
| Regorafenib (RESORCE) | Placebo (after Sorafenib) | 10.6 vs. 7.8 | 0.63 | 3.1 vs. 1.5 | Hand–foot reaction, fatigue | [54] |
| Cabozantinib (CELESTIAL) | Placebo (after Sorafenib) | 10.2 vs. 8.0 | 0.76 | 5.2 vs. 1.9 | Diarrhoea, hypertension, fatigue | [55] |
| Ramucirumab (REACH-2) | Placebo (AFP ≥ 400 ng/mL) | 8.5 vs. 7.3 | ~0.71 | 2.8 vs. 1.6 | Hypertension, proteinuria | [56] |
| Durvalumab + Bevacizumab + TACE (EMERALD-1) | TACE + Placebo | (OS immature) | — | 15.0 vs. 8.2 | Hypertension, elevated ALT | [57] |
| Lenvatinib + Pembrolizumab + TACE (LEAP-012) | TACE + Placebo | (OS immature) | — | 14.6 vs. 10.0 | Hypertension, immune-related AEs | [58] |
| Trial (Phase) | Setting/Regimen | Key Population and Timing | Primary End Point/Status (as of Sept 2025) | Notes |
|---|---|---|---|---|
| IMbrave050 (III; NCT04102098) | Adjuvant atezolizumab + bevacizumab vs. active surveillance | High-risk after R0 resection/ablation; start ≤ 12 weeks | Updated: RFS not sustained (HR 0.90); OS immature | Not recommended for universal use per AASLD 2025 [8,66]. |
| EMERALD-2 (III; NCT03847428) | Adjuvant durvalumab ± bevacizumab vs. placebo | High-risk after resection/ablation | Ongoing; results pending | Trial design available; no peer-reviewed outcomes yet. |
| CheckMate-9DX (III; NCT03383458) | Adjuvant nivolumab vs. placebo | High-risk after resection/ablation | Ongoing; results pending | Global, double-blind study. |
| KEYNOTE-937 (III; NCT03867084) | Adjuvant pembrolizumab vs. placebo | CR after resection/ablation | Ongoing; results pending | Tissue-agnostic approvals do not extrapolate to adjuvant HCC. |
| SHR-1210-III-325 (III; NCT04639180) | Adjuvant camrelizumab + rivoceranib (apatinib) vs. placebo | High-risk after resection/ablation | Ongoing (China); status “unknown” on registries | Non-global sponsor; watch for regional readouts. |
| PRIME-HCC/peri-op nivolumab ± ipilimumab (Ib/II) | Neoadjuvant/peri-op | Resectable HCC | Early-phase feasibility signals | Hypothesis-generating only [69]. |
| Cabozantinib + nivolumab (Ib; NCT03299946) | Neoadjuvant combo | Borderline-resectable | Early-phase conversion to resectability | Supports further testing [70]. |
| Domain | Policy Elements (Abridged) | Practical Notes | Refs. |
|---|---|---|---|
| Standardised exception (T2) | 1 lesion 2–5 cm or 2/3 lesions 1–3 cm; AFP ≤ 1000 ng/mL | Dynamic CT/MRI using LI-RADS; CEUS accepted | [89,90] |
| UNOS-DS (pre-LRT) | 1 lesion 5–8 cm; or 2/3 lesions (≤5 cm, sum ≤ 8 cm, ≥1 > 3 cm); or 4/5 lesions (< 3 cm, sum ≤ 8 cm). After LRT, must meet T2 for exception | [89] | |
| AFP rule | If AFP > 1000, treat; exception requires AFP < 500 and sustained | Safeguard against aggressive biology | [12,89] |
| Allocation lexicon | LI-RADS alignment (since 13 July 2023) | Harmonises radiology reporting across centres | [90] |
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Kim, S.H. Transforming Liver Cancer Therapy: Integrating Molecular Profiling with Precision and Transplant-Based Care. Cancers 2025, 17, 3671. https://doi.org/10.3390/cancers17223671
Kim SH. Transforming Liver Cancer Therapy: Integrating Molecular Profiling with Precision and Transplant-Based Care. Cancers. 2025; 17(22):3671. https://doi.org/10.3390/cancers17223671
Chicago/Turabian StyleKim, Seoung Hoon. 2025. "Transforming Liver Cancer Therapy: Integrating Molecular Profiling with Precision and Transplant-Based Care" Cancers 17, no. 22: 3671. https://doi.org/10.3390/cancers17223671
APA StyleKim, S. H. (2025). Transforming Liver Cancer Therapy: Integrating Molecular Profiling with Precision and Transplant-Based Care. Cancers, 17(22), 3671. https://doi.org/10.3390/cancers17223671

