Data mining analyses for precision medicine in acromegaly

Context: Predicting
which acromegaly patients could benefit from somatostatin receptor ligand (SRL)
is crucial to avoid months of ineffective treatment for non-responding cases. Although
many biomarkers linked to SRL response have been identified, there is no consensus
criterion on how to assign pharmacologic treatment according to biomarker
levels.

Objective: Our aim
is to provide better predictive tools for a more accurate acromegaly patient
stratification regarding the ability to respond to SRL.

Design and patients:
Retrospective multicenter study of 71 acromegaly patients.

Methods: We used
advanced mathematical modelling and artificial intelligence to predict SRL
response combining molecular and clinical information.

Results: Different
models of patient stratification were obtained regarding SRL response, with a
much higher accuracy when the studied cohort is fragmented according to
relevant clinical characteristics. Considering all the models, a patient
stratification based on the extrasellar growth of the tumor, sex, age and the
expression of E-cadherin, GHRL, IN1-GHRL, DRD2, SSTR5 and PEBP1 is proposed, with accuracies that
stand between 71 to 95%. Furthermore, we show an association between
extrasellar growth and high BMI for SRL non-responding patients.

Conclusion.
The use of data mining is necessary for implementation of personalized medicine
in acromegaly and requires an interdisciplinary effort between computer
science, mathematics, biology and medicine. This new methodology opens a door
to more precise personalized medicine for acromegaly patients.