RNA TargetsLiver OncologyResearch Use Only
Hepatocellular Carcinoma (HCC)
Gene Expression Reference Targets
Gene Expression Reference Targets
A biologically curated RNA target reference for liver cancer spanning HCC molecular subtypes (CTNNB1/TERT/TP53 drivers), VEGF/angiogenesis pathways, immune TME, and fibrotic stroma — enabling custom Tapestri assays for sorafenib/lenvatinib/atezolizumab biomarker discovery and IO response stratification.
232
Total Genes
7
Functional Categories
5
Etiologic Subtypes
6+
Curation Sources
1
Panel Power Scorecard & Functional Categories
● Panel Power Scorecard
Panel Score: 69 / 100
87%
Landmark
Biomarker
Coverage
Biomarker
Coverage
81%
COSMIC
Tier-1
Coverage
Tier-1
Coverage
8 genes
FDA
Biomarker
Genes
Biomarker
Genes
13 genes
Clinical Trial
Biomarkers
Biomarkers
7 states
Cell States
Resolvable
Resolvable
232 genes
Total Panel
Genes
Genes
Published precedent — targeted panels are sufficient
Ma et al. 2019 Cell (HCC myeloid/T cell single-cell atlas)
Finn et al. 2020 NEJM (IMbrave150: atezo+bev HCC)
52
Driver / WNT / TERT
38
VEGF / Angio
52
Immune TME
40
Stroma / HSC
38
HCC Subtypes
22
Cell Cycle
20
Metabolism
2
Background & Curation Principles
Commercial Assays
- FoundationOne CDx (solid tumor)
- Tempus xT / xR RNA (HCC module)
- Illumina TruSight Oncology 500
- QIAGEN QIAseq Liver Panel
Public Databases
- TCGA LIHC (371 HCC tumors)
- COSMIC (liver-specific mutations)
- MSigDB liver cancer signatures
- Human Cell Atlas (liver)
- TISCH2 HCC immune TME
Peer-Reviewed Literature
- Hoshida HCC subtypes S1/S2/S3 (2009 Cancer Cell)
- VEGF/sorafenib resistance (Llovet 2008 NEJM)
- IMbrave150: atezolizumab + bevacizumab (Finn 2020 NEJM)
- HCC immune atlas (Ma 2019 Cell, Zheng 2021 Nature)
Why Single-Cell RNA for Liver Cancer?
HCC develops in the context of chronic liver disease — cirrhosis, NASH, viral hepatitis — creating a profoundly immunosuppressive fibrotic TME that varies dramatically between patients and even between tumor nodules. Bulk RNA averages hepatocyte tumor cells, hepatic stellate cells, Kupffer cells, and T cells into a single number. Tapestri resolves each cell’s identity and, uniquely, co-detects CTNNB1 or TERT mutation alongside Wnt-activated transcriptional state per hepatocyte.
CTNNB1 Wnt Activation — Immune Desert Driver
CTNNB1-mutant HCC (30–40% of cases) activates Wnt/β-catenin signaling, which transcriptionally suppresses CXCR3-mediated T cell recruitment. This creates an immune desert phenotype that is non-responsive to anti-PD-L1 therapy (CheckMate 459, Keynote-240). Per-cell co-detection of CTNNB1 mutation + immune exclusion markers is a uniquely Tapestri-enabled discovery.
3
Target Reference Structure — Gene Table
1 · Driver / CTNNB1 / TERT2 · VEGF / Angiogenesis3 · Immune TME4 · Fibrotic Stroma / HSC5 · Lineage / HCC Subtypes6 · Cell Cycle7 · Metabolism / Lipid
| Category | Representative Genes (n) | Biological Function | Disease Relevance | scD+R Use Case |
|---|---|---|---|---|
| 1 · Driver / CTNNB1 / TERT / TP53 · 52 genes | ||||
| Driver | CTNNB1, APC, AXIN1, LRP6, FZD3, TCF7L2, TERT, TP53, RB1, CDKN2A, MDM2, PIK3CA, PTEN, AKT1, MTOR, KRAS, MYC, CCND1, CDK4, FGF19, FGFR4, MET, EGFR, ALK (24) + 28 accessory | WNT/β-catenin; TERT promoter; TP53/RB1 tumor suppressor; RTK | CTNNB1 = 35% HCC (Wnt activation); TERT promoter = 60% HCC (earliest event); FGF19 amp = FGFR4 target; MET = cabozantinib target | Co-detect CTNNB1 mutation + Wnt-active transcriptional state per hepatocyte |
| 2 · VEGF / Angiogenesis / Therapy Targets · 38 genes | ||||
| Signaling | VEGFA, VEGFB, VEGFC, KDR, FLT1, FLT4, PDGFRA, PDGFRB, ANGPT1, ANGPT2, TEK, NRP1, NRP2, PECAM1, CDH5, ENG, NOTCH1, DLL4, HIF1A, EPAS1 (20) + 18 accessory | Tumor vasculature; anti-angiogenic therapy targets | VEGFA/KDR = sorafenib/lenvatinib/bevacizumab targets; NRP1/2 = co-receptors; HIF1A = hypoxic induction; ANGPT2 = vessel destabilization | Map tumor vasculature states; identify bevacizumab/lenvatinib-sensitive subpopulations |
| 3 · Immune TME · 52 genes | ||||
| Immune | CD3E, CD8A, CD4, GZMB, PDCD1, LAG3, HAVCR2, TIGIT, CD274, FOXP3, TCF7, CXCL13, TREM2, SPP1, CD68, CD163, CLEC4F, VSIG4, ARG1, IDO1, B2M, HLA-A, KLRC1, NKG7, NCAM1 (25) + 27 accessory | Kupffer cell / TAM / TIL; checkpoint biology; NK surveillance | Kupffer-derived TAMs (CLEC4F+/VSIG4+) = immunosuppressive; TCF7+ TIL = ICI response; NK exhaustion in HBV-HCC | Resolve Kupffer cells vs monocyte-derived TAMs; identify progenitor T cell fraction |
| 4 · Fibrotic Stroma / HSC · 40 genes | ||||
| Stromal | ACTA2, COL1A1, COL3A1, LUM, DCN, LRRC15, POSTN, FAP, PDGFRB, CXCL12, TGFB1, TGFBR1, THY1, MMP2, MMP9, TIMP1, LOX, FN1, SPARC, VIM (20) + 20 accessory | Hepatic stellate cell activation; liver fibrosis / cirrhosis TME | HSC activation (ACTA2+) = major immunosuppressive force; TGFB1 = T cell exclusion; LOX = crosslinking; PDGFRB = HSC marker | Resolve activated (myofibroblast) vs quiescent HSC states in cirrhotic TME |
| 5 · Lineage / HCC Subtypes · 38 genes | ||||
| Lineage | AFP, GPC3, DLK1, EPCAM, SOX9, CD44, KRT19, KRT7, KRT18, FOXA2, HNF4A, CEBPA, NKX2-1, TBX3, LGR5, NOTCH2, CYP3A4, ALB, TTR, AFP (20) + 18 accessory | Hoshida S1/S2/S3 subtype; progenitor / stem-like HCC | AFP/GPC3 = HCC diagnosis; KRT19 = stem-like progenitor (S1 subtype); EPCAM = CSC marker; ALB = hepatocyte differentiation | Classify tumor cells by Hoshida molecular subtype at single-cell resolution |
| 6 · Cell Cycle / Proliferation · 22 genes | ||||
| Cell Cycle | MKI67, TOP2A, AURKA, CCNB1, CDK2, E2F1, MCM2, PLK1, CDC20, UBE2C, PCNA, RRM2 (12) + 10 accessory | WHO grade; mitotic index | MKI67 = proliferative fraction; CDC20 = tumor aggression; CDK4 = palbociclib target | Score proliferating vs quiescent tumor cells; link to sorafenib resistance |
| 7 · Lipid / Metabolic Reprogramming · 20 genes | ||||
| Metabolism | FASN, ACACA, SCD1, HMGCR, LDLR, LDHA, HK2, SLC2A1, IDH1, CPT1A, PPARA, PPARG, ACSL4, HADHB, CS (15) + 5 accessory | Hepatic lipid metabolism; NAFLD/NASH-HCC; Warburg | FASN overexpression = NAFLD-HCC; PPARA loss = metabolic dysregulation; IDH1 = α-KG/epigenetic; ACSL4 = ferroptosis | Resolve metabolic heterogeneity between hepatocyte tumor subpopulations |
Total: 232 genesCat 1: 52 · Cat 2: 38 · Cat 3: 52 · Cat 4: 40 · Cat 5: 38 · Cat 6: 22 · Cat 7: 20
ⓘ Select genes appear in more than one functional category reflecting their multi-role biology. The total above counts unique genes; per-category counts include all category-relevant entries.