Research Use Only. KIRhub outputs are computational research artifacts. They are not validated for clinical decision-making, diagnosis, or treatment.
KIRhub 2.0· v2
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Subgroup landscape

ZFTA-RELA

ZFTA-RELA fusion-positive supratentorial ependymoma. The most common supratentorial subgroup; defined by the ZFTA-RELA gene fusion.

Cohort factsMeasuredDerived
Samples in atlas
117
fusion = ZFTA_RELA
Pediatric (<18y)
73%
85 of 117
Age median
8.0 y
range 0.6–40.0
Unknown age
17
of 117
5y OS (atlas, coarse)
n/a
literature ~70-80%
For a clinician with a patient in this subgroup

Standard of care, prognosis, and what's under investigation

Standard of care. Maximal safe surgery followed by focal radiation. Chemotherapy has limited established benefit. No targeted therapy is currently approved despite the recurrent driver fusion.

Prognosis. 5-year overall survival is approximately 70-80% in published series — better than PFA but recurrence still occurs and remains the major clinical problem. Most patients are older children rather than infants.

What's being investigated here. Two computational hypotheses target this subgroup: (1) multi-RTK polypharmacology (the fusion may drive co-expression of MERTK, AXL, MET — making broad RTK inhibitors like cabozantinib appealing), and (2) MERTK on tumor cells (vs macrophages in PFA). Both are currently parked because pathway-level scoring cannot resolve them.

Research Use Only — no entry below is clinical-grade evidence.

For a researcher exploring this subgroup

Active hypotheses, data status, and blockers

  • Leading hypotheses. Multi-RTK polypharmacology (zfta-rela) and MERTK dual-compartment (mertk).
  • Data status — strengths. ZFTA-RELA is well-represented in the atlas with a clear molecular definition (fusion call). Bulk RNA is sufficient to confirm fusion status and run drug ranking.
  • Data status — weaknesses. Both active hypotheses require resolution our current pipeline cannot provide. The multi-RTK story needs gene-level (not pathway-level) RNA scoring; MERTK compartments need bulk deconvolution or a scRNA reference.
  • Key blockers. Ingest raw per-gene RNA into the ETL pipeline so the ranker can score against gene-level RTK z-scores rather than the compressed 50-dim pathway space. For MERTK: xCell/CIBERSORTx deconvolution or scRNA reference (Frontiers 2022).
  • Drug-ranking observation. The actual top picks for ZFTA-RELA at pathway resolution are CDK4/6, CHK1, and JAK/BTK inhibitors — overlapping with PFA top picks and not the multi-RTK family the hypothesis predicted.

Insights for this subgroup

ModeledSpeculative

Tools for this subgroup