8d • General discussion
Biology Unlocked Article #8
A drug showing 15-fold downregulation of an oncogene sounds like a breakthrough.
It wasn't. And only a biologist caught it.
Jan was testing whether her drug could reduce expression of two oncogenes in a cancer cell line.
Her positive controls were showing roughly two-fold downregulation.
Her drug was showing fifteen-fold.
She sent the data to Jon, the bioinformatician in the collaborating lab. The numbers held up. They agreed: this drug had potential.
Except something was bothering Jan.
She wasn't seeing the protein-level changes you would expect from a 15-fold shift.
And the cells weren't dying the way they should be.
So she went back to the raw Ct values.
She found it.
One of her three reference genes had inconsistent expression across samples.
Reference genes are used in qPCR to normalise data across samples. The assumption is that they are stably expressed regardless of condition. But reference gene expression varies across tissues and cell types. A gene that works in one tissue may be completely unreliable in another.
When one reference gene is unstable, your normalisation is wrong.
When your normalisation is wrong, every fold-change built on it is wrong.
The 15-fold downregulation was not a drug effect.
It was a maths problem built on a biological assumption nobody had checked.
Jon never saw it. The analysis was clean because the input looked clean.
An AI would have called this a finding.
Jan caught it because she knew what raw Ct values meant. Because she knew that reference genes are not always reference genes.
If she hadn't, this would have been submitted. Possibly published. Resources spent chasing a result that was never real.
The difference between a finding and an artefact is not always in the statistics.
Sometimes it is in knowing which biological assumptions your numbers are resting on.
Would your pipeline have caught this?
#bioinformatics #datascience #molecularbiology #qPCR
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Biology Unlocked Article #8
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