RNA TargetsUrothelial OncologyResearch Use Only
Bladder Cancer Gene Expression
Reference Targets
Reference Targets
A biologically curated RNA target reference for bladder cancer spanning NMIBC/MIBC molecular subtypes, FGFR3/PIK3CA driver pathways, immune checkpoint biology, and the full luminal/basal/squamous histotype axis — enabling researchers to configure custom Tapestri assays for precision IO and targeted therapy studies.
256
Total Genes
7
Functional Categories
4
MIBC Subtypes Covered
6+
Curation Sources
1
Panel Power Scorecard & Functional Categories
● Panel Power Scorecard
Panel Score: 72 / 100
88%
Landmark
Biomarker
Coverage
Biomarker
Coverage
83%
COSMIC
Tier-1
Coverage
Tier-1
Coverage
9 genes
FDA
Biomarker
Genes
Biomarker
Genes
14 genes
Clinical Trial
Biomarkers
Biomarkers
7 states
Cell States
Resolvable
Resolvable
256 genes
Total Panel
Genes
Genes
Published precedent — targeted panels are sufficient
Robertson et al. 2017 Cell (TCGA BLCA 4-subtype classification)
Loriot et al. 2019 NEJM (erdafitinib FGFR3 trial BLC2001)
48
Driver / FGFR3
52
Lineage Subtypes
52
Immune TME
32
DDR / Chemo
38
Stroma / EMT
24
Cell Cycle
19
Metabolism
2
Background & Curation Principles
Commercial Assays
- FoundationOne CDx (urothelial panel)
- Tempus xT (solid tumor RNA)
- QIAGEN QIAseq Bladder Tumor Panel
- Illumina TruSight Oncology 500
Public Databases
- TCGA BLCA dataset (408 tumors)
- COSMIC (urothelial mutations)
- MSigDB hallmark signatures
- Human Cell Atlas (bladder)
- GEO bladder scRNA atlases
Peer-Reviewed Literature
- TCGA BLCA subtypes (Robertson 2017 Cell)
- MIBC luminal/basal classification (Damrauer 2014)
- FGFR3 as NMIBC biomarker (Loriot 2019 NEJM)
- Anti-PD-L1 (atezolizumab) response biomarkers
Why Single-Cell RNA for Bladder Cancer?
Bladder tumors are among the most immune-infiltrated solid tumors — TIL density is prognostic and predictive of ICI response. Bulk RNA averages over luminal tumor cells, basal/squamous cells, fibroblasts, and immune infiltrates. Tapestri co-detects FGFR3 or ERCC2 mutation status alongside luminal/basal transcriptional state per cell, enabling identification of the 15–20% of NMIBC tumors that transition from luminal to basal subtype under therapy — a transition invisible to bulk sequencing.
FGFR3 — The NMIBC Therapeutic Target
FGFR3 alterations (mutations + fusions) occur in ~75% of NMIBC and ~15% of MIBC. Erdafitinib (FDA-approved, 2019) targets FGFR3 in locally advanced/metastatic disease. FGFR3 mut/fused tumors have lower TIL infiltration — creating an exclusion paradox for ICI combinations. Panel enables per-cell co-detection of FGFR3 status + immune infiltrate composition.
3
Target Reference Structure — Gene Table
1 · Driver / FGFR3 / PIK3CA2 · Lineage / MIBC Subtypes3 · Immune TME4 · DDR / Cisplatin Sensitivity5 · Stroma / EMT6 · Cell Cycle7 · Metabolism
| Category | Representative Genes (n) | Biological Function | Disease Relevance | scD+R Use Case |
|---|---|---|---|---|
| 1 · Driver / FGFR3 / PIK3CA Signaling · 48 genes | ||||
| Driver | FGFR3, FGFR1, FGFR2, TACC3, PIK3CA, PTEN, AKT1, MTOR, KRAS, HRAS, NRAS, TP53, RB1, CDKN2A, MDM2, CCND1, CDK4, CDK6, TSC1, TSC2 (20) + 28 accessory | RTK/RAS signaling; tumor suppressor loss; cell cycle | FGFR3 = erdafitinib target (FDA 2019); PIK3CA = luminal tumors; TSC1 = mTOR activation; HRAS = papillary NMIBC | Co-detect FGFR3 mutation + luminal transcriptional state per cell |
| 2 · Lineage / MIBC Molecular Subtypes · 52 genes | ||||
| Lineage | GATA3, FOXA1, PPARG, KRT20, KRT5, KRT6A, KRT14, TP63, CDH1, CDH2, VIM, SNAI2, ZEB1, ZEB2, TWIST1, EGFR, CCND1, SOX2, FGFR3, UPK1A, UPK2, UPK3A (22) + 30 accessory | Luminal papillary / luminal unstable / basal-squamous / stroma-rich subtype markers | GATA3/KRT20 = luminal; KRT5/TP63 = basal/squamous; FOXA1 = luminal stable; subtype determines therapy choice | Resolve MIBC subtype composition; detect subtype switching under neoadjuvant chemotherapy |
| 3 · Immune TME / Checkpoint · 52 genes | ||||
| Immune | CD3E, CD8A, CD4, GZMB, PRF1, IFNG, TOX, PDCD1, LAG3, HAVCR2, TIGIT, CD274, CTLA4, FOXP3, IL2RA, TCF7, CXCL13, TREM2, SPP1, B2M, HLA-A, IDO1, ARG1, CD68, CD163, MS4A1 (26) + 26 accessory | TIL characterization; checkpoint expression; myeloid TME | CD274 = atezolizumab/pembrolizumab biomarker; B2M/HLA-A = MHC-I loss (resistance); high TIL = ICI response | Identify TIL-high vs TIL-excluded tumors; map checkpoint expression per immune cell type |
| 4 · DDR / Cisplatin Sensitivity · 32 genes | ||||
| DDR | ERCC2, ERCC1, BRCA1, BRCA2, ATM, CHEK1, CHEK2, TP53, RB1, MLH1, MSH2, POLE, PARP1, RAD51, FANCD2, MDM2 (16) + 16 accessory | DNA damage repair; neoadjuvant chemotherapy sensitivity | ERCC2 mut = cisplatin sensitivity (NAC response, TCGA); BRCA1/2 = HRD/olaparib; POLE = high TMB; DDR mut = ICI response | Identify DDR-deficient subclones that drive cisplatin sensitivity in neoadjuvant setting |
| 5 · Stroma / EMT / Invasion · 38 genes | ||||
| Stromal | ACTA2, FAP, PDGFRA, PDGFRB, CXCL12, VEGFA, KDR, COL1A1, MMP2, MMP9, FN1, SPARC, TGFB1, POSTN, LRRC15, CDH11, TWIST1, VIM (18) + 20 accessory | CAF subtypes; angiogenesis; EMT stroma-rich subtype | Stroma-rich MIBC subtype = poor prognosis; VEGFA = bevacizumab target; TGFβ1 = T cell exclusion | Resolve stroma-rich immunosuppressive TME; identify TGFβ-mediated T cell exclusion |
| 6 · Cell Cycle / Proliferation · 24 genes | ||||
| Cell Cycle | MKI67, TOP2A, AURKA, CCNB1, CDK2, E2F1, FOXM1, MCM2, PLK1, BUB1, CDC20, UBE2C (12) + 12 accessory | WHO grade / proliferative index | MKI67 = grade marker; FOXM1 = cisplatin resistance; AURKA = alisertib target | Score proliferating tumor cells; link cell cycle state to therapy resistance |
| 7 · Metabolism / Hypoxia · 19 genes | ||||
| Metabolism | SLC2A1, LDHA, HK2, CA9, HIF1A, VEGFA, FASN, ACACA, IDH1, PRKAA1, CPT1A (11) + 8 accessory | Aerobic glycolysis; hypoxia adaptation | HIF1A = hypoxic TME activation; CA9 = hypoxia marker; LDHA = Warburg effect | Map metabolic heterogeneity between tumor and stromal cells |
Total: 256 genesCat 1: 48 · Cat 2: 52 · Cat 3: 52 · Cat 4: 32 · Cat 5: 38 · Cat 6: 24 · Cat 7: 19
ⓘ 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.