RNA TargetsHematologic OncologyResearch Use Only
Myeloproliferative Neoplasms (MPN)
Gene Expression Reference Targets
Gene Expression Reference Targets
A biologically curated RNA target reference for myeloproliferative neoplasms spanning JAK2/CALR/MPL driver mutations, inflammatory cytokine fibrosis pathways, myeloid lineage differentiation, and ruxolitinib/fedratinib resistance biology — enabling researchers to configure custom Tapestri assays for MPN disease mechanism, transformation risk, and precision therapy studies.
222
Total Genes
7
Functional Categories
4
MPN Subtypes Covered
6+
Curation Sources
1
Panel Power Scorecard & Functional Categories
● Panel Power Scorecard
Panel Score: 67 / 100
86%
Landmark
Biomarker
Coverage
Biomarker
Coverage
80%
COSMIC
Tier-1
Coverage
Tier-1
Coverage
7 genes
FDA
Biomarker
Genes
Biomarker
Genes
12 genes
Clinical Trial
Biomarkers
Biomarkers
7 states
Cell States
Resolvable
Resolvable
222 genes
Total Panel
Genes
Genes
Published precedent — targeted panels are sufficient
Psaila et al. 2020 Cancer Cell (MPN single-cell atlas)
Verstovsek et al. 2012 NEJM (COMFORT-I ruxolitinib MF)
45
JAK-STAT / Driver
82
Cytokine / Fibrosis
32
HSC / Myeloid
16
Immune / BM
34
Resistance
9
Cell Cycle
13
Epigenetic
2
Background & Curation Principles
Commercial Assays
- FoundationOne Heme
- IDT xGen MPN Panel
- QIAGEN QIAseq Myeloid
- Thermo Oncomine Myeloid
Public Databases
- COSMIC (MPN mutations)
- ELN MPN guidelines
- ClinVar (JAK2/CALR)
- GEO MPN scRNA datasets
- Human Cell Atlas (hematopoietic)
Peer-Reviewed Literature
- JAK2 V617F discovery (James 2005 Nature)
- CALR mutations in MPN (Klampfl 2013 NEJM)
- Myelofibrosis scRNA atlas (Psaila 2020 Cancer Cell)
- Ruxolitinib COMFORT trials (Verstovsek 2012 NEJM)
Why Single-Cell RNA for MPN?
MPN is a clonal HSC disorder where JAK2 V617F-positive and JAK2 wild-type cells coexist in the same patient — and their proportions predict transformation risk. Bulk RNA cannot resolve the transcriptional state of JAK2-mut vs JAK2-WT HSCs. Tapestri co-detects JAK2 V617F mutation alongside inflammatory gene expression per HSC, enabling direct linkage of driver mutation clone size to transcriptional output — the mechanistic underpinning of allele burden-directed therapy decisions.
CALR vs JAK2 — Different Mechanisms, Same Disease
CALR-mutant ET/MF activates MPL signaling via thrombopoietin receptor, while JAK2 V617F directly activates downstream JAK-STAT. These two oncogenic programs have distinct transcriptional signatures: CALR-mut = lower STAT1 activation; JAK2 V617F = higher STAT3/IFN response. Panel enables per-cell subclone identification + transcriptional program characterization that bulk methods cannot achieve.
3
Target Reference Structure — Gene Table
1 · JAK-STAT / Driver2 · Cytokine / Fibrosis3 · HSC / Myeloid Differentiation4 · Immune / Inflammation5 · Resistance / BCL-26 · Cell Cycle7 · Epigenetic / CHIP
| Category | Representative Genes (n) | Biological Function | Disease Relevance | scRNA Use Case |
|---|---|---|---|---|
| 1 · JAK-STAT / Driver Signaling · 45 genes | ||||
| Driver | JAK2, CALR, MPL, JAK1, JAK3, STAT1, STAT3, STAT5A, STAT5B, CBL, KRAS, NRAS, FLT3, KIT, CSF3R, EPOR, THPO, ASXL2, BRAF, CBLB, CCN2, COL1A2, COL3A1, CREBBP, DCN, DNMT3B, EP300, FGF2, JAK-STAT, KDM2B, LOXL2, LUM, MAP2K1, MAP2K2, MMP2, MMP8, MMP9, NF1, NF2, NRP1, PTPN11, RAF1, RRAS, SOS1, SPARC (45) | JAK-STAT pathway; TPO/EPO receptor signaling; RAS activation | JAK2 V617F = ruxolitinib/fedratinib/pacritinib target; CALR = luspatercept context; CSF3R = CNL (ruxolitinib); MPL = eltrombopag target | Co-detect JAK2 mutation + STAT3/5 activation state per HSC/progenitor |
| 2 · Cytokine / Fibrosis / BM Stroma · 82 genes | ||||
| Stromal | TGFB1, TGFB2, TGFBR1, TGFBR2, IL6, IL6R, TNF, CXCL8, IL1B, IL1A, CXCL4 (PF4), CXCL10, CCL2, CCL5, VEGFA, PDGFRA, PDGFRB, ACTA2, COL1A1, FN1, AIF1, ANGPT1, AZU1, CCL3, CCL4, CCND2, CCNE1, CCNE2, CD163, CD226, CDK1, CDK4, CDKN1A, CDKN2B, CLCF1, CNTF, CTSG, CXCL12, CXCL9, E2F2, E2F3, FCGR1A, FCGR2A, FCGR3A, GCSAML, GZMH, HOXA9, IKZF1, IL12A, IL15, IL3RA, ITGAX, KIF11, KIR2DL1, KIR2DL3, KIR3DL1, KLRB1, KLRC1, KLRD1, KLRK1, LIF, LIFR, LYZ, MCM4, MEIS1, MICA, MICB, MYC, NCR1, OSMR, PF4, PMAIP1, PRKCB, PROM1, PRTN3, S100A12, SIGLEC1, TFRC, TTK, VEGFB, XCL1, XIAP (82) | Inflammatory cytokine storm; TGFβ-driven fibrosis; megakaryocyte-derived profibrotic factors | TGFβ1 = MF fibrosis driver (released by megakaryocytes); PF4 = MF megakaryocyte; IL6 = constitutional symptoms; CXCL8 = BM niche retention | Identify TGFβ1-producing megakaryocyte subclones driving fibrosis |
| 3 · HSC / Myeloid Progenitor Differentiation · 32 genes | ||||
| Lineage | CD34, CD38, CD117 (KIT), CD133 (PROM1), GATA1, GATA2, KLF1, TAL1, FLI1, ETV6, RUNX1, SPI1, IRF8, GFI1, IKAROS (IKZF1), MPO, ELANE, CD71 (TFRC), GYPA, ITGA2B (GP2B), AURKA, BAD, BAK1, BCL2L2, BCLAF1, BID, BIRC3, CASP3, CASP8, CASP9, CCNA2, CCNB2 (32) | HSC/MPP identity; erythroid/megakaryocyte/myeloid lineage commitment | GATA1 = erythro-megakaryocyte fate; RUNX1 = HSC self-renewal; MPO = granulocyte commitment; ITGA2B = megakaryocyte identity | Reconstruct myeloid differentiation hierarchy; identify blocked maturation in blast-phase transformation |
| 4 · Immune / Inflammatory TME · 16 genes | ||||
| Immune | CD3E, CD8A, CD4, GZMB, PDCD1, LAG3, CD274, FOXP3, IL2RA, TREM2, SPP1, CSF1R, IL10, TNF, IFNG, NCAM1 (16) | Immune suppression in BM; NK exhaustion; inflammatory monocytes | T cell exhaustion in MF = impaired NK/T surveillance; IL10 = immunosuppressive; TNF = constitutionally symptomatic | Map BM immune cell states; identify exhausted NK subpopulations that fail to clear JAK2+ clones |
| 5 · Resistance / BCL-2 / Apoptosis · 34 genes | ||||
| Resistance | BCL2, MCL1, BCL2L1, BCL2L11, BAX, BIRC5, JAK2, STAT3, AURKB, CCND1, CDK6, RB1, MDM2, TP53, CDKN2A, CSF2RA, DDX17, DDX5, HNRNPA2B1, KDM5C, KITLG, KMT2A, NSD1, PDGFB, PDGFD, PRPF8, RBM10, RBM25, SETBP1, SETD2, SF1, SRSF1, TET1, TET3 (34) | JAK inhibitor resistance; apoptosis evasion; venetoclax combination | MCL1/BCL2 = ruxolitinib resistance (venetoclax combination rationale); AURKB = transformation marker; TP53 mut = blast-phase transformation | Identify anti-apoptotic subclones resistant to ruxolitinib; assess venetoclax eligibility |
| 6 · Cell Cycle / Proliferation · 9 genes | ||||
| Cell Cycle | MKI67, TOP2A, CCNB1, CDK2, E2F1, MCM2, PLK1, CDC20, BUB1 (9) | Proliferative index; transformation risk | MKI67 = blast-phase transformation risk; PLK1 = volasertib target in MPN-BP | Score proliferating progenitor fraction; track blast-phase transformation |
| 7 · Epigenetic / CHIP Mutations · 13 genes | ||||
| Epigenetic | DNMT3A, TET2, ASXL1, EZH2, IDH1, IDH2, SRSF2, SF3B1, U2AF1, ZRSR2, PHF6, BCOR, KDM6A (13) | CHIP-driver co-mutations; epigenetic progression; transformation | ASXL1/SRSF2 co-mutation = high transformation risk (MIPSS70); TET2/DNMT3A = early clonal events; IDH1/2 = blast-phase (enasidenib) | Identify co-mutation landscape per HSC clone; stratify transformation risk by epigenetic mutation profile |
Total: 222 genesCat 1: 45 · Cat 2: 82 · Cat 3: 32 · Cat 4: 16 · Cat 5: 34 · Cat 6: 9 · Cat 7: 13
ⓘ 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.