OnCo
ideasIdea

Monitor biomarker positivity rates across labs in real time to catch assay drift

If one lab suddenly starts finding twice as many 'positive' results as others, something has gone wrong with its test. Pooling positivity rates across labs would catch this automatically.

Statistical process control on population-level positivity rates is standard in clinical chemistry and screening programmes but not in predictive oncology biomarkers. A national feed of anonymised biomarker results by laboratory, assay and version, with automated outlier detection and case-mix adjustment, would detect reagent lot problems, protocol drift and algorithm changes within weeks rather than through occasional proficiency runs.

Hypothesis
Surveillance will identify at least two laboratory-level drift events per year in a national system, each confirmed on re-testing, that proficiency schemes had not detected.
Rationale
Screening programmes detect reader drift through recall-rate monitoring; the same logic applies to any test with a stable expected positivity rate.
What would test it
Pilot with PD-L1 and HER2 results from 30 laboratories over one year; investigate flagged outliers with sample re-testing.
Maturity
speculative
Who has to act
data
Cost to try
Small (under $1M)
Years to first evidence
2
Bottlenecks it attacks
  • Biomarkers are not validated or standardised · Tests that decide who gets a drug are often not validated prospectively and are measured differently in every lab.
  • Data silos · Records, scans, genomes and outcomes sit in separate systems that cannot talk. Every patient's experience is lost to the next.

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technologies

1
Histopathology & immunohistochemistry

ideas

2
Publish each laboratory's biomarker proficiency resultsVersion control and locked reference sets for AI algorithms used as companion diagnostics

bottlenecks

2
Biomarkers are not validated or standardisedData silos