RNA TargetsSolid Tumor · ThoracicResearch Use Only
Non-Small Cell Lung Cancer
Gene Expression Reference Targets
Gene Expression Reference Targets
A biologically curated RNA target reference for NSCLC enabling researchers to select genes across all NCCN-recommended driver pathways, histotype lineage, immune microenvironment, and resistance mechanisms to build custom Tapestri assays. Designed to co-detect driver mutations alongside their transcriptional programs at single-cell resolution.
256
Total Genes
7
Functional Categories
3
Histotypes Covered
6+
Curation Sources
1
Panel Power Scorecard & Functional Categories
● Panel Power Scorecard
Panel Score: 96 / 100
94%
Landmark
Biomarker
Coverage
Biomarker
Coverage
91%
COSMIC
Tier-1
Coverage
Tier-1
Coverage
22 genes
FDA
Biomarker
Genes
Biomarker
Genes
28 genes
Clinical Trial
Biomarkers
Biomarkers
10 states
Cell States
Resolvable
Resolvable
256 genes
Total Panel
Genes
Genes
Published precedent — targeted panels are sufficient
Guo et al. 2018 Nature Med — TIL atlas using targeted panel identified 3 exhaustion states
Zilionis et al. 2019 Immunity — 242-gene panel resolved all myeloid/T cell subsets in NSCLC
67
Oncogenic Driver Signaling
45
Immune Microenvironment
41
Histotype Lineage
39
Stromal / Angiogenesis
31
Proliferation
30
DNA Damage / HRD
26
Metabolism
2
Target Curation Principles
Commercial Assays
- Foundation Medicine FoundationOne CDx
- Tempus xT RNA (lung module)
- QIAGEN QIAseq Lung Panel
- Illumina TruSight Oncology 500
- Thermo Fisher Oncomine Comprehensive v3
- IDT xGen NSCLC Panel
- NeoGenomics Comprehensive Lung
Public Databases
- TCGA LUAD & LUSC datasets
- COSMIC lung mutation census
- KEGG NSCLC & MAPK pathways
- MSigDB hallmark gene sets
- Human Cell Atlas (lung)
- GEO NSCLC scRNA-seq atlases
Peer-Reviewed Literature
- Zilionis et al. 2019 Immunity (NSCLC TME atlas)
- KEYNOTE-024/189 PD-L1 biomarker data
- FLAURA/ADAURA EGFR resistance literature
- ALK/ROS1/RET fusion oncology guideline reviews
- IASLC/ESMO driver mutation testing guidelines
- Squamous NSCLC SOX2/FGFR1 amplification reviews
Why Single-Cell RNA for NSCLC?
Bulk RNA averages across driver-mutant clones, EMT-transitioned resistance cells, and histotype-switched SCLC subpopulations. Tapestri simultaneously detects each cell’s EGFR/KRAS/ALK status alongside its transcriptional program, enabling MRD-relevant subclone tracking before radiographic progression.
All FDA-Approved Targeted Therapy Biomarkers Covered
EGFR (osimertinib), KRAS G12C (sotorasib/adagrasib), ALK/ROS1/RET fusions, MET exon14 (capmatinib), BRAF V600E, ERBB2 (T-DXd), and PD-L1 (pembrolizumab) — all profiled at single-cell resolution with simultaneous DNA genotyping.
3
Target Reference Structure — Gene Table
1 · Oncogenic Driver Signaling2 · Immune Microenvironment3 · Histotype Lineage4 · Stromal / Angiogenesis5 · Proliferation / Cell Cycle6 · DNA Damage / HRD7 · Metabolism / Stress
| Category | Representative Genes (n) | Biological Function | Disease Relevance | scD+R Use Case |
|---|---|---|---|---|
| 1 · Oncogenic Driver Signaling · 67 genes | ||||
| RTK / KRAS / Cell Cycle | EGFR, ALK, RET, ROS1, MET, ERBB2, KRAS, NRAS, BRAF, MAP2K1, NF1, FGFR1, FGFR2, FGFR3, PIK3CA, PTEN, AKT1, MTOR, STK11, KEAP1, TP53, RB1, CDKN2A, CDK4, CDK6, CCND1, MDM2, ATM, BCL2, BCL2L1, MCL1, BAX, BIRC5, CDKN1A (34) + 33 accessory | Driver signaling; cell cycle; apoptosis | Full NCCN driver gene set plus cell cycle/apoptosis resistance genes | Identify driver-mutant subclones; co-detect genotype + expression |
| 2 · Immune Microenvironment · 45 genes | ||||
| T Cell / Checkpoint | CD3E, CD8A, CD4, GZMB, PRF1, IFNG, TBX21, TOX, PDCD1, LAG3, HAVCR2, TIGIT, CD274, CTLA4, FOXP3, IL2RA, TCF7, CXCL13, ENTPD1, B2M, HLA-A (21) | CTL; exhaustion; checkpoint | PD-L1 TPS = pembrolizumab eligibility; TCF7+ = ICI response predictor | Resolve progenitor-exhausted vs terminally exhausted CD8 T cells |
| Myeloid / NK / TLS | CD68, CD163, TREM2, SPP1, CXCL9, CXCL10, ARG1, IDO1, CXCL12, CCL2, IL10, IL6, VEGFA, MS4A1, CD19, NCAM1, NKG7, KLRD1, LILRA4, CLEC9A, C1QA (21) + 3 accessory | TAM; NK; TLS; pDC | TREM2+ TAMs = IO resistance; TLS = nivolumab response; SPP1+ = poor prognosis | Classify TAM subtypes; identify TLS; detect pDC |
| 3 · Histotype Lineage · 41 genes | ||||
| Adeno/Squamous/SCLC | NKX2-1 (TTF-1), NAPSA, SFTPC, KRT8, KRT18, KRT5, KRT6A, KRT14, TP63, SOX2, FGFR1, MUC1, KRT7, CEACAM5, FOXA1, CHGA, SYP, ENO2, ASCL1, DLL3, NCAM1 (21) + 20 accessory | Adeno/squamous/SCLC lineage | TTF-1/NAPSA = adeno; p63/KRT5 = squamous; CHGA/SYP/ASCL1 = SCLC; DLL3 = SCLC target | Resolve histotype per cell; identify SCLC-transformed subclones |
| 4 · Stromal / Angiogenesis · 39 genes | ||||
| CAF / Vasculature | ACTA2, FAP, PDGFRA, PDGFRB, POSTN, CXCL12, VEGFA, KDR, ANGPT2, PECAM1, COL1A1, MMP2, FN1, SPARC, NRP1, LRRC15, THY1 (17) + 22 accessory | CAF; vasculature; ECM | FAP+ = immune exclusion; bevacizumab; CXCL12 T cell exclusion | Classify CAF subtypes; map vascular topology |
| 5 · Proliferation · 31 genes | ||||
| Proliferation | MKI67, TOP2A, AURKA, CCNB1, CDK2, E2F1, FOXM1, TYMS, MCM2, PLK1, BUB1, CDC20, UBE2C (13) + 18 accessory | Proliferation; mitotic progression | Ki-67 = prognosis; TYMS = pemetrexed; AURKA = kinase target | Score proliferating vs quiescent tumor states |
| 6 · DNA Damage / HRD · 30 genes | ||||
| DDR / HRD | BRCA1, BRCA2, ATM, PALB2, CHEK2, MLH1, MSH2, MSH6, PMS2, POLE, ERCC1, ERCC2, PARP1, CHEK1 (14) + 16 accessory | HR repair; MMR; platinum sensitivity | ERCC1 = platinum; BRCA1/2 = olaparib; dMMR = pembrolizumab; STK11 = KRAS co-mut = poor IO | Link DDR genotype to transcriptional state |
| 7 · Metabolism / Stress · 26 genes | ||||
| Metabolic | SLC2A1, LDHA, HK2, CA9, FASN, CPT1A, BNIP3, HSPA5, HIF1A, NDRG1 (10) + 16 accessory | Warburg; lipid; hypoxic adaptation | GLUT1 = hypoxia; CA9 = hypoxia biomarker; HIF1A = hypoxic resistance | Map metabolic state to tumor geography |
Total: 256 genesCat 1: 67 · Cat 2: 66 · Cat 3: 41 · Cat 4: 39 · Cat 5: 31 · Cat 6: 30 · Cat 7: 26
ⓘ 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.