Data mining analyses for precision medicine in acromegaly: a proof of concept

Predicting which acromegaly patients could benefit from somatostatin receptor ligands (SRL) is a must for personalized medicine. Although many biomarkers linked to SRL response have been identified, there is no consensus criterion on how to assign this pharmacologic treatment according to biomarker levels. Our aim is to provide better predictive tools for an accurate acromegaly patient stratification regarding the ability to respond to SRL. We took advantage of a multicenter study of 71 acromegaly patients and we used advanced mathematical modelling to predict SRL response combining molecular and clinical information. Different models of patient stratification were obtained, 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%. In conclusion, the use of data mining could be very useful for implementation of personalized medicine in acromegaly through an interdisciplinary work between computer science, mathematics, biology and medicine. This new methodology opens a door to more precise and personalized medicine for acromegaly patients.

Molecular determinants of enhanced response to somatostatin receptor ligands after debulking in large GH-producing adenomas

OBJECTIVE

Large somatotrophic adenomas depict poor response to somatostatin receptor ligands (SRLs). Debulking has shown to enhance SRLs effect in some but not all cases and tumour volume reduction has been proposed as the main predictor of response. No biological studies have been performed so far in this matter. We aimed to identify molecular markers of response to SRLs after surgical debulking in GH-secreting adenomas.

DESIGN

We performed a multicenter retrospective study.

PATIENTS

24 patients bearing large GH-producing tumours.

MEASUREMENTS

Clinical data and SRLs response both before and after surgical debulking were collected, and 21 molecular biomarkers of SRLs response were studied in tumour samples by gene expression.

RESULTS

From the 21 molecular markers studied, only two of them predicted enhanced SRLs response after surgery. Tumours with improved response to SRLs after surgical debulking showed lower levels of Ki-67 (MKI67, FC = 0.17 and P = .008) and higher levels of RAR-related orphan receptor C (RORC) (FC = 3.1 and P ˂ .001). When a cut-off of no detectable expression was used for Ki-67, the model provided a sensitivity of 100% and a specificity of 52.6% with an area under the curve of 65.8%. Using a cut-off of 2 units of relative expression of RORC, the prediction model showed 100% of sensitivity and specificity.

CONCLUSIONS

High levels of RORC and low levels of Ki-67 identify improved SRLs response after surgical debulking in large somatotropic adenomas. To determine their expression would facilitate medical treatment decision-making after surgery.