RNA Targets
Pan-Cancer TME
Research Use Only
Tumor Microenvironment
Gene Expression Reference Targets
Gene Expression Reference Targets
A biologically curated RNA target reference for tumor microenvironment profiling across all solid tumor indications — enabling researchers to select genes spanning myeloid polarization, T and NK lymphocyte states, CAF subtypes, tumor vasculature, and tumor cell plasticity to build custom Tapestri assays at single-cell resolution. Designed to classify every major TME cell type and reveal the cellular composition differences between immune-hot responders and immune-cold or excluded tumors.
247
Total Genes
7
Functional Categories
5
Major TME Cell Types
3
TME Immune Phenotypes
1
Panel Power Scorecard & Functional Categories
● Panel Power Scorecard
Panel Score: 84 / 100
93%
Landmark
Biomarker
Coverage
Biomarker
Coverage
80%
COSMIC
Tier-1
Coverage
Tier-1
Coverage
14 genes
FDA
Biomarker
Genes
Biomarker
Genes
22 genes
Clinical Trial
Biomarkers
Biomarkers
10 states
Cell States
Resolvable
Resolvable
247 genes
Total Panel
Genes
Genes
Published precedent — targeted panels are sufficient
Sade-Feldman et al. 2018 Cell — minimal gene set sufficient to predict ICI response
Jerby-Arnon et al. 2018 Cell — 200-gene resistance program detected by targeted panel
86
Myeloid Cell States
48
T & NK Lymphocytes
25
B Cell & TLS
32
CAF & Stroma
38
Tumor Cell States
22
Complement & Innate
25
Angiogenesis & Lymphangiogenesis
2
Target Curation Principles
Commercial Assays
- Foundation Medicine / Tempus TME immune profiling modules
- NanoString nCounter PanCancer IO 360 Panel
- HTG EdgeSeq Oncology Biomarker Panel
- Illumina TruSight Oncology 500 (TME immune genes)
- 10x Genomics pan-cancer TME scRNA-seq reference kits
- Fluidigm / Standard BioTools HD Oncology assay
Public Databases
- TCGA pan-cancer TIMER2.0 immune estimates
- Human Cell Atlas (all TME cell reference types)
- CancerSEA single-cell functional state database
- MSigDB hallmark & oncogenic signature gene sets
- TISIDB immune infiltration database (pan-cancer)
- GEO pan-cancer scRNA-seq TME atlases (10+ datasets)
Peer-Reviewed Literature
- Thorsson et al. 2018 Immunity (pan-cancer immune landscape)
- Finotello et al. 2019 Genome Med (deconvolution)
- Savas et al. 2018 Nature Med (tissue-resident memory)
- Elyada et al. 2019 Cancer Discovery (CAF subtypes)
- Nalio Ramos et al. 2022 Nature (apCAF discovery)
- Pan-cancer single-cell TME atlases (Zheng, Ma, Qian 2021–2023)
Why Single-Cell for TME Profiling?
Bulk RNA TME deconvolution (CIBERSORT, TIMER) estimates cell type fractions but cannot resolve cell states within a type — the critical biological distinction. Tapestri identifies not just “macrophage present” but which TAM subtype (SPP1+TREM2+ vs C1Q+ vs inflammatory), its spatial abundance, and simultaneously links each macrophage’s phenotype to its somatic mutations (e.g., IDH1 in glioma) at per-cell resolution.
Three TME Immune Phenotypes Resolved
This reference covers all three clinically relevant TME immune phenotypes: (1) Inflamed/Hot — high CTL, TLS, CXCL9/10+ myeloid; (2) Immune-excluded — FAP+/CXCL12+ CAF barrier with T cells at margin; and (3) Immune-desert — low TIL, WNT/β-catenin+ tumor, high TREM2+ macrophage. Each phenotype requires distinct therapeutic strategy and is resolved at single-cell resolution by this reference.
3
Target Reference Structure — Gene Table
1 · Myeloid Cell States2 · T & NK Lymphocyte States3 · B Cell & TLS4 · CAF Subtypes & Stroma5 · Tumor Cell States & Plasticity6 · Complement & Innate Sensing7 · Angiogenesis & Lymphangiogenesis
| Category | Representative Genes (n) | Biological Function | Platform Relevance | scD+R Use Case |
|---|---|---|---|---|
| 1 · Myeloid Cell States · 55 genes | ||||
| Macrophage Subtypes | CD68, CD163, MRC1 (CD206), CSF1R, TREM2, APOE, SPP1, C1QA, C1QB, C1QC, LYVE1, FOLR2, SELENOP, PLTP, CCL18, MARCO, MMP9, LGALS3, FCN1, VCAN, CXCL9, CXCL10, ARG1, NOS2, IL10, IL6, TNF, IL1B (28) | M1/M2/TREM2+/SPP1+/C1Q+ TAM subtype identity; macrophage polarization | SPP1+TREM2+ = IO resistance program; C1Q+ tissue-resident = favorable prognosis; SPP1:TREM2 ratio = IO resistance biomarker pan-tumor; ARG1+ = immunosuppressive | Classify all 5 major TAM subtypes per single cell; reveal subtype composition differences between responders and non-responders |
| Dendritic Cells | CLEC9A (DNGR1), XCR1, CADM1, IRF8 (cDC1), CD1C, CLEC10A (cDC2), IRF4, FCER1A, LILRA4, PTGDS, SIGLEC6, IL3RA (pDC), BST2, LAMP3 (mature DC), CCL17, CCL22 (12) + 43 accessory | cDC1/cDC2/pDC/mDC identity; cross-presentation; IFN production | CLEC9A+ cDC1 cross-present antigens to CTL; pDC = type I IFN source; LAMP3+ mDC = migratory to lymph nodes; cDC1:cDC2 ratio predicts ICI response | Identify all DC subtypes; quantify cDC1/pDC frequency as IO response biomarker |
| Neutrophil / MDSC | S100A8, S100A9, S100A12, CXCR2, CEACAM8, FCGR3B, SELL, CXCL8, MPO, ELANE, LCN2, MMP8, VNN2, PROK2 (14) + 28 accessory | Tumor-associated neutrophils; MDSC immunosuppression | N1 (anti-tumor) vs N2 (pro-tumor) neutrophils; CXCL8/CXCR2 = neutrophil recruitment; MDSCs suppress T cells; S100A8/A9 = serum biomarkers in SJIA/solid tumors | Distinguish N1 vs N2 neutrophils; identify MDSC subpopulations per tumor sample |
| 2 · T & NK Lymphocyte States · 42 genes | ||||
| CD8 T Cell States | TCF7, CXCR5, PDCD1, TOX, HAVCR2, LAG3, TIGIT, CTLA4, ENTPD1, GZMB, GZMA, GZMK, PRF1, NKG7, IFNG, TBX21, CX3CR1, EOMES, TOX2, NR4A1, BATF, CD69, ITGAE (CD103), ZNF683 (23) | Progenitor/transitional/terminally exhausted/effector/Trm CD8 T cell states | Pan-cancer CD8 state spectrum; TCF7+ progenitor = ICI responder; TOX+ terminal = refractory; Trm = local surveillance; GZMK+ transitional = plastic state | Classify 5+ CD8 T cell states per single cell across any tumor type |
| CD4 T Helper / Treg | CD4, BCL6, FOXP3, RORC, TBX21, GATA3, IL17A, IL21, CXCR5, ICOS, IL2RA, IKZF2, ENTPD1, CTLA4, TNFRSF18, TGFB1, IL10, CCR6, CXCR3, CCR4 (20) | Th1/Th2/Th17/Tfh/Treg identity; B cell help; immunosuppression | FOXP3+ Tregs = immunosuppressive; BCL6+CXCR5+ Tfh = TLS GC reaction; Treg:CTL ratio = prognosis; RORC+IL17A = Th17 pathogenic in autoimmune | Resolve Th/Tfh/Treg states; link TLS B cell activation to Tfh frequency |
| NK / ILC | NCAM1, KLRD1, NKG7, NCR1, KLRB1, FCGR3A, KLRC1, KLRK1, PRF1, GZMB, XCL1, XCL2, EOMES, TBX21, RORC, IL22, IL13, HLA-E, MICA, MICB, ULBP1 (21) | NK cytotoxicity; ILC1/2/3 tissue innate responses; NKG2D-L stress sensing | NK dysfunction in immunosuppressed tumors; HLA-E/NKG2A axis = NK inhibition; NKG2D ligands (MICA/B) = tumor stress; ILC2 = type 2 inflammation (adverse in solid tumors) | Distinguish NK from ILC1/2/3; quantify NKG2D-L-expressing tumor cells |
| 3 · B Cell & Tertiary Lymphoid Structures · 25 genes | ||||
| TLS B Cell / Plasma Cell | MS4A1 (CD20), CD19, CD79A, CR2 (CD21), CXCR5, CXCL13, AICDA (AID), PRDM1 (BLIMP1), IRF4, XBP1, IGHG1, IGHM, CD27, CD38, PAX5, BCL6, SDC1 (CD138) (17) | TLS germinal center reaction; plasma cell differentiation; autoantibody production | TLS density = IO response predictor in melanoma, sarcoma, NSCLC; AID+ GC B cells produce high-affinity antibodies; TLS marker CXCL13 = favorable prognosis | Identify TLS clusters; quantify GC B cell vs plasma cell ratio; map TLS location within tumor tissue |
| Stromal-Immune Crosstalk at TLS | CXCL12, CXCL13, CCL19, CCL21, ICAM1, VCAM1, LTB, LTA, TNFSF14 (LIGHT), PDPN (podoplanin on HEV), PECAM1, MADCAM1 (8) - structural TLS genes | High endothelial venule (HEV) and TLS structural genes; lymphocyte homing into TLS | CCL19/CCL21 draw CCR7+ T cells into TLS; LTB/LTA drive stromal TLS organogenesis; HEV MADCAM1 = gut-tropism in IBD/GI cancers | Identify TLS structural cells; resolve TLS maturation state |
| 4 · Cancer-Associated Fibroblast (CAF) Subtypes · 32 genes | ||||
| myoCAF / iCAF / apCAF | ACTA2, FAP, PDGFRA, PDGFRB, POSTN, LRRC15, MYH11, CXCL12, IL6, CXCL14, LUM, DCN, COL1A1, COL3A1, FN1, SPARC, THY1, DKK3, CFD, MFAP5, SFRP2, SFRP4 (22) | myoCAF (contractile); iCAF (inflammatory/secretory); apCAF (antigen-presenting) identity | myoCAF = TGF-β-driven paclitaxel resistance; iCAF = IL-6/CXCL12 immunosuppression and T cell exclusion; apCAF = MHC-II+ CAF activates CD4 T cells | Classify CAF subtypes per single cell; identify CXCL12-high iCAF as immune exclusion driver |
| Pericyte / Endothelial | ACTA2 (pericyte), PDGFRB, MCAM (CD146), RGS5, CSPG4, NOTCH3, VEGFA, KDR, PECAM1, CDH5, ANGPT1, ANGPT2, TEK, NRP1, NRP2, CD34, ENG, THY1 (18) - 10 unique | Pericyte identity; endothelial cell subtypes; tumor vasculature | Pericyte loss = leaky vasculature; tip vs stalk endothelial cell fate (DLL4/NOTCH axis); VEGFA-driven angiogenesis in all solid tumors; anti-angiogenic therapy targets | Resolve tip vs stalk endothelial cells; identify pericyte-deficient leaky vessel regions |
| 5 · Tumor Cell States & Plasticity · 38 genes | ||||
| EMT & Invasion State | CDH1, CDH2, VIM, FN1, SNAI1, SNAI2, ZEB1, ZEB2, TWIST1, MMP2, MMP9, MMP14, PLAUR, S100A4, LAMC2, ITGB1, SPARC (17) | Epithelial-to-mesenchymal transition; invasive front cells | EMT-state tumor cells = immunosuppressed, therapy resistant, invasive; ZEB1/2 = EMT master regulators; S100A4 = metastasis marker | Identify EMT-state cells at tumor-stroma interface; link EMT program to immune exclusion |
| Hypoxia & Metabolic | HIF1A, EPAS1 (HIF2A), SLC2A1, LDHA, CA9, PDK1, BNIP3, LOX, VEGFA, ANGPTL4, P4HA1, NDRG1, DDIT4, HK2, FASN, ACACA (16) | Hypoxic adaptation; metabolic reprogramming; tumor core gene expression | Hypoxic tumor core = drug resistance; HIF1A = VEGFA induction; CA9 = hypoxia biomarker; LOX crosslinks ECM promoting metastasis | Map hypoxic cell states geographically within tumor; identify HIF-high resistant subpopulations |
| Stemness & Plasticity | ALDH1A1, CD44, PROM1 (CD133), SOX2, SOX9, MYC, NANOG, OCT4 (POU5F1), KLF4, LGR5, ASCL2, NOTCH1, HES1, WNT5A, FZD7, AXL (16) - pan-cancer CSC markers | Cancer stem cell (CSC) identity; phenotype switching; therapy resistance | CSC fraction = tumor-initiating cell reservoir; plasticity drives acquired resistance; AXL = ICI resistance state; MYC = CSC maintenance; WNT5A = mesenchymal plasticity | Identify CSC fraction; track phenotype switching under therapy; correlate CSC score with clinical outcomes |
| 6 · Complement & Innate Immune Sensing · 22 genes | ||||
| Complement / Innate Sensing | C1QA, C1QB, C1QC, C3, C3AR1, C5AR1, CFB, CFH, SERPING1, CGAS, STING1, TLR7, TLR8, TLR9, MYD88, IRAK4, DDX58 (RIG-I), IFIH1 (MDA5), IRF3, TBK1, NLRP3, PYCARD (22) | Complement activation; pattern recognition; innate sensing | C1Q+ macrophages = tissue-resident; C5AR1 = neutrophil/myeloid activation; cGAS/STING = innate tumor-immune crosstalk; NLRP3 = inflammasome; TLR signaling boosts innate anti-tumor | Identify complement-expressing macrophage subpopulations; map innate sensing activation state |
| 7 · Angiogenesis & Lymphangiogenesis · 25 genes | ||||
| Angiogenesis / Lymphangiogenesis | VEGFA, VEGFB, VEGFC, VEGFD (PGF), KDR, FLT1, FLT4, ANGPT1, ANGPT2, TEK, DLL4, NOTCH4, PDGFB, NRP1, NRP2, LYVE1, PROX1, PDPN (podoplanin), MRC1 (LYVE1-macro), PECAM1, CD34, ENG, THY1, FOXC2, COUP-TFII (NR2F2) (25) | Tumor vasculature; lymphangiogenesis; peritumoral lymphatics | VEGFA = bevacizumab; VEGFC/FLT4 = lymphangiogenesis and lymph node metastasis; DLL4/NOTCH tip-stalk fate; LYVE1/PROX1 = lymphatic endothelium; FOXC2 = lymphatic valve formation | Resolve blood vs lymphatic endothelial cells; identify tip/stalk fate per endothelial cell; map vascular density |
Total: 247 genesCat 1: 86 · Cat 2: 65 · Cat 3: 29 · Cat 4: 40 · Cat 5: 49 · Cat 6: 22 · Cat 7: 25
ⓘ 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.