Age, GH/IGF-1 levels, tumor volume, T2 hypointensity, and tumor subtype rather than proliferation and invasion are all reliable predictors of biochemical response to somatostatin analogue therapy in patients with acromegaly: A clinicopathological study

Personalized Medicine in Acromegaly: The ACROFAST Study

CONTEXT

Medical treatment of acromegaly is currently performed through a trial-and-error approach using first-generation somatostatin receptor ligands (fgSRLs) as first-line drugs, with an effectiveness of about 50%, and subsequent drugs are indicated through clinical judgment. Some biomarkers can predict fgSRLs response.

OBJECTIVE

Here we report the results of the ACROFAST study, a clinical trial in which a protocol based on predictive biomarkers of fgSRLs was evaluated.

METHODS

This was a prospective trial (21 university hospitals) comparing the effectiveness and time-to-control of 2 treatment protocols during 12 months: (A) a personalized protocol in which the first options were fgSRLs as monotherapy or in combination with pegvisomant, or pegvisomant as monotherapy depending on the short acute octreotide test (sAOT) results, tumor T2 magnetic resonance (MRI) signal or immunostaining for E-cadherin; and (B) a control group with treatment always started by fgSRLs and the other drugs included after demonstrating inadequate control.

RESULTS

Eighty-five patients participated; 45 in the personalized and 40 in the control group. More patients in the personalized protocol achieved hormonal control compared to those in the control group (78% vs 53%, P < .05). Survival analysis revealed a hazard ratio for achieving hormonal control adjusted by age and sex of 2.53 (CI, 1.30-4.80). Patients from the personalized arm were controlled in a shorter period of time (P = .01).

CONCLUSION

Personalized medicine is feasible using a relatively simple protocol, and it allows a higher number of patients to achieve control in a shorter period of time.

New molecular tools for precision medicine in pituitary neuroendocrine tumors

Precision, personalized, or individualized medicine in pituitary neuroendocrine tumors (PitNETs) has become a major topic in the last few years. It is based on the use of biomarkers that predictively segregate patients and give answers to clinically relevant questions that help us in the individualization of their management. It allows us to make early diagnosis, predict response to medical treatments, predict surgical outcomes and investigate new targets for therapeutic molecules. So far, substantial progress has been made in this field, although there are still not enough precise tools that can be implemented in clinical practice. One of the main reasons is the excess overlap among clustered patients, with an error probability that is not currently acceptable for clinical practice. This overlap is due to the high heterogeneity of PitNETs, which is too complex to be overcome by the classical biomarker investigation approach. A systems biology approach based on artificial intelligence techniques seems to be able to give answers to each patient individually by building mathematical models through the interaction of multiple factors, including those of omics sciences. Integrated studies of different molecular omics techniques, as well as radiomics and clinical data are necessary to understand the whole system and to finally achieve the key to obtain precise biomarkers and implement personalized medicine. In this review we have focused on describing the current advances in the area of PitNETs based on the omics sciences, that are clearly going to be the new tool for precision medicine.

Clinical implications of the 2022 WHO classification on the multidisciplinary management of PitNETS patients

The review explores the 2022 update to the World Health Organization (WHO) classification of pituitary adenomas, now referred to as pituitary neuroendocrine tumors (PitNETs), and his possible impact on the clinical management of PitNET patients. The review highlights the differences and the evolution from the 2017 to 2022 version, with the current classification considering the lineage of the tumor cells, cell type, hormones produced, and other auxiliary characteristics for a comprehensive histological classification. The revision in terminology reflects a broader perspective on neuroendocrine neoplasia. The new approach based on transcription factors, hormone expression and other biomarkers has allowed a major revision of the nomenclature and a more accurate classification of pituitary adenomas. Furthermore, in some cases this approach is also assuming a prognostic value, useful in clinical practice. However, despite this elaborate classification and stratification, the review points out the lack of a robust grading or staging system and suggests the need for further research and validation of diagnostic methods. Despite these limitations, the revised classification presents a significant step towards understanding and managing PitNETs patients.

Immunological signatures and predictive biomarkers for first-generation somatostatin receptor ligand resistance in Acromegaly

PURPOSE

Predicting resistance to first-generation Somatostatin Receptor Ligands (fg-SRL) in Acromegaly patients remains an ongong challenge. Tumor-associated immune components participate in various pathological processes, including drug-resistance. We aimed to identify the immune components involved in resistance of fg-SRL, and to investigate biomarkers that can be targeted to treat those drug-resistant Acromegaly.

METHODS

We conducted a retrospective study involving 35 Acromegaly patients with somatotropinomas treated postoperatively with fg-SRL. Gathering clinicopathological data, SSTR2 expression, and immunological profiles, we utilized univariate, binary logistic regression, and ROC analyses to assess their predictive roles in fg-SRL resistance. Spearman correlation analysis further examined interactions among interested characteristics.

RESULTS

19 patients (54.29%) exhibited resistance to postoperative fg-SRL. GH level at diagnosis, preoperative tumor volume, T2WI-MRI intensity, granularity, PD-L1, SSTR2, and CD8 + T cell infiltration showed association with clinical outcomes of fg-SRL. Notably, T2WI-MRI hyperintensity, PD-L1-IRS > 7, CD8 + T cell infiltration < 14.8/HPF, and SSTR2-IRS < 5.4 emerged as reliable predictors for fg-SRL resistance. Correlation analysis highlighted a negative relationship between PD-L1 expression and CD8 + T cell infiltration, while showcasing a positive correlation with preoperative tumor volume of somatotropinomas. Additionally, 5 patients with fg-SRL resistance underwent re-operation were involved. Following fg-SRL treatment, significant increases in PD-L1 and SSTR5 expression were observed, while SSTR2 expression decreased in somatotropinoma.

CONCLUSION

PD-L1 expression and CD8 + T cell infiltration, either independently or combined with SSTR2 expression and T2WI-MRI intensity, could form a predictive model guiding clinical decisions on fg-SRL employment. Furthermore, targeting PD-L1 through immunotherapy and embracing second-generations of SRL with higher affinity to SSTR5 represent promising strategies to tackle fg-SRL resistance in somatotropinomas.

Biomarkers of response to treatment in acromegaly

INTRODUCTION

Medical treatment of acromegaly is based in a `trial and error` approach. First-generation somatostatin receptor ligands (fg-SRL) are prescribed as first-line medical therapy to the vast majority of patients, despite lack of disease control in approximately 60% of patients. However, other drugs used in acromegaly treatment are available (cabergoline, pasireotide and pegvisomant).

AREAS COVERED

In this article, we review and discuss the biomarkers of response to medical treatment in acromegaly.

EXPERT OPINION

Biomarkers for fg-SRL that can already be applied in clinical practice are: gender, age, pretreatment GH and IGF-I levels, cytokeratin granulation pattern, and the expression of somatostatin receptor type 2. Using biomarkers of response could guide treatment towards precision medicine with greater efficacy and lower costs.

Clinical and prognostic significance of granulation patterns in somatotroph adenomas/tumors of the pituitary: a meta-analysis

INTRODUCTION

Sparsely granulated somatotroph adenoma/tumor (SGST) is thought to be more clinically aggressive than densely granulated somatotroph adenoma/tumor (DGST). However, the literature is not entirely consistent as to the disparate demographic and behavioral features of these subtypes. In this study, we conducted a meta-analysis to further clarify the demographic, clinicopathological, prognostic, and molecular characteristics of SGST versus DGST.

METHODS

We accessed two electronic databases to search for potential data. Pooled estimates of odds ratio (OR), mean difference (MD), and corresponding 95% confidence interval (CI) were calculated using the random-effect model.

RESULTS

SGST was associated with younger patient age and lower male-to-female ratio (p < 0.001) compared to DGST. Clinically, SGST had larger tumor size and high rate of cavernous sinus and suprasellar extension (p < 0.001) than DGST. During postoperative follow-up, SGST was associated with a lower endocrinological remission rate (OR 0.60; 95% CI 0.40 to 0.90; p = 0.01) and a poorer response rate to SRL (OR 0.16; 95% CI 0.08-0.35; p < 0.001) in comparison to DGST. The prevalence of GSP mutations was significantly lower in SGST (OR 0.36; 95% CI 0.17 to 0.79; p = 0.01).

CONCLUSION

SGST and DGST were demographically, clinicopathologically, and molecularly different from each other with the former associated with adverse treatment outcomes and poor response to medical therapy. There are still gaps in translational studies that could help us better understand the behavior of these tumors and identify potential targets in the treatment of sparsely granulated tumors.

Predictors of Response to Treatment with First-Generation Somatostatin Receptor Ligands in Patients with Acromegaly

BACKGROUND AND AIMS

Predictors of first-generation somatostatin receptor ligands (fgSRLs) response in acromegaly have been studied for over 30 years, but they are still not recommended in clinical guidelines. Is there not enough evidence to support their use? This systematic review aims to describe the current knowledge of the main predictors of fgSRLs response and discuss their current usefulness, as well as future research directions.

METHODS

A systematic search was performed in the Scopus and PubMed databases for functional, imaging, and molecular predictive factors.

RESULTS

A total of 282 articles were detected, of which 64 were included. Most of them are retrospective studies performed between 1990 and 2023 focused on the predictive response to fgSRLs in acromegaly. The usefulness of the predictive factors is confirmed, with good response identified by the most replicated factors, specifically low GH nadir in the acute octreotide test, T2 MRI hypointensity, high Somatostatin receptor 2 (SSTR2) and E-cadherin expression, and a densely granulated pattern. Even if these biomarkers are interrelated, the association is quite heterogeneous. With classical statistical methods, it is complex to define reliable and generalizable cut-off values worth recommending in clinical guidelines. Machine-learning models involving omics are a promising approach to achieve the highest accuracy values to date.

CONCLUSIONS

This survey confirms a sufficiently robust level of evidence to apply knowledge of predictive factors for greater efficiency in the treatment decision process. The irruption of artificial intelligence in this field is providing definitive answers to such long-standing questions that may change clinical guidelines and make personalized medicine a reality.

Revisiting the usefulness of the short acute octreotide test to predict treatment outcomes in acromegaly

Introduction

We previously described that a short version of the acute octreotide test (sAOT) can predict the response to first-generation somatostatin receptor ligands (SRLs) in patients with acromegaly. We have prospectively reassessed the sAOT in patients from the ACROFAST study using current ultra-sensitive GH assays. We also studied the correlation of sAOT with tumor expression of E-cadherin and somatostatin receptor 2 (SSTR2) .

Methods

A total of 47 patients treated with SRLs for 6 months were evaluated with the sAOT at diagnosis and correlated with SRLs' response. Those patients whose IGF1 decreased to <3SDS from normal value were considered responders and those whose IGF1 was ≥3SDS, were considered non-responders. The 2 hours GH value (GH2h) after s.c. administration of 100 mcg of octreotide was used to define predictive cutoffs. E-cadherin and SSTR2 immunostaining in somatotropinoma tissue were investigated in 24/47 and 18/47 patients, respectively.

Results

In all, 30 patients were responders and 17 were non-responders. GH2h was 0.68 (0.25-1.98) ng/mL in responders vs 2.35 (1.59-9.37) ng/mL in non-responders (p<0.001). GH2h = 1.4ng/mL showed the highest ability to identify responders (accuracy of 81%, sensitivity of 73.3%, and specificity of 94.1%). GH2h = 4.3ng/mL was the best cutoff for non-response prediction (accuracy of 74%, sensitivity of 35.3%, and specificity of 96.7%). Patients with E-cadherin-positive tumors showed a lower GH2h than those with E-cadherin-negative tumors [0.9 (0.3-2.1) vs 3.3 (1.5-12.1) ng/mL; p<0.01], and patients with positive E-cadherin presented a higher score of SSTR2 (7.5 ± 4.2 vs 3.3 ± 2.1; p=0.01).

Conclusion

The sAOT is a good predictor tool for assessing response to SRLs and correlates with tumor E-cadherin and SSTR2 expression. Thus, it can be useful in clinical practice for therapeutic decision-making in patients with acromegaly.