Cancer biology is complex
When you observe a drug target being overexpressed, do not jump to the conclusion that its inhibition will translate into clinical efficacy. Gleevec, Herceptin and Tagrisso succeed because they inhibit solo drivers.
Investment lessons · FGFR2
Solo driver vs a “team player”
Amgen paid $1.9B+ for an FGFR2 asset in gastric cancer, and later trials did not recapitulate the early efficacy signal. Patient data shows FGFR2 is not a solo driver but part of a “team”, so inhibiting it alone is necessary but insufficient. Ask whether your target is a solo driver before the trial.
FGFR2 case study · by Pawel Zawadzki, PhD
$1.9B+
Paid by Amgen for the FGFR2 asset
The case
A costly failure
Gastric cancer is a high unmet need. And FGFR2 showed strong preclinical data supported by an early clinical signal. What was there not to like?
Later phases of clinical development did not recapitulate the early efficacy signal. So what went wrong?
Patient data
What the patient data revealed
Patient data uncovered that FGFR2 is not a solo driver; instead, it is part of a “team” driving gastric cancer.
- Inhibiting sole drivers like HER2 or EGFR translates into high clinical response rates.
- FGFR2 is part of a “team” driving gastric cancer. Therefore, despite overexpression of FGFR2, its inhibition is necessary but insufficient to achieve high clinical response rates.
- Hence, the combination approach or a bispecific antibody targeting this “team” of drivers is required for clinical success.
Gastric cancer is indeed the strongest indication for FGFR2
The take-home message
Correlation is not causation
Patient data opportunity
Data from patients can reveal the complexity of tumor biology. Ask a good question: is my target a solo driver or part of a “team” and you will save years of clinical development and avoid painful surprises like FGFR2.
Recommendations
Consult patient data for reliable outcomes
01
Preclinical models are valuable but inherently limited.
The models are important, but remember, they are constructed to test a single dependency, not to assess the complexity of real tumors.
02
Solo vs team player.
Use patient data to determine whether your target is an independent driver or is supported by another player.
03
Patient data should be used before your trial begins.
There's a common misconception that clinical trials are required to assess response rates. Treatment-naive patient data can actually identify factors that limit clinical response before any patients are dosed.
Medicines intended to work in patients should be developed with patient data in mind.