Detection of Tumor-specific DNA Methylation Markers in the Blood of Patients with Pituitary Neuroendocrine Tumors

Liquid Biopsy in Pituitary Neuroendocrine Tumors-Potential Biomarkers for Diagnosis, Prognosis, and Therapy

Pituitary neuroendocrine tumors (PitNETs) are slow-growing neoplasms with various clinical presentations, often leading to diagnostic challenges. While neuroimaging assessment and histopathological evaluation remain the gold standard for diagnosis, emerging research highlights the potential of liquid biopsy, mainly through the analysis of circulating non-coding RNAs (ncRNAs), as a promising diagnostic and prognostic tool. Recent studies have demonstrated distinct expression profiles in different types and subtypes of tumors, with implications in assessing tumor aggressiveness and predicting treatment response. The current article summarizes data about potential biofluid markers implicated in PitNET development, mainly circulating tumor DNA (ctDNA), cell-free RNAs (cfRNA), circulating tumor cells (CTCs), and exosomes. Many studies on genetic and molecular markers in PitNET tissue samples provide exciting information about tumor biology, but to date, specific studies on liquid biopsy biomarkers are still few. Over the past years, a certain understanding of the mechanisms involved in pituitary tumorigenesis has been gained, including the landscape of genomic alterations, changes in epigenetic profile, crucial molecules involved in specific signaling pathways, and non-coding RNA molecules with critical roles in malignant transformation. Genetic and molecular characterization of the PitNETs could help distinguish between functional and non-functional PitNETs, different types and subtypes of pituitary tumors, and invasive and non-invasive forms. Further studies are required to identify and validate innovative biomarkers or therapeutic targets for treating PitNET. Integrating liquid biopsy into clinical workflows could revolutionize the management of pituitary adenomas, enabling more personalized and less invasive diagnostic and therapeutic strategies.

A Nexus of Biomolecular Complexities in Pituitary Neuroendocrine Tumors: Insights into Key Molecular Drivers

Approximately 90% of the lesions of hypophyseal origins are represented by pituitary neuroendocrine tumors, which further account for up to 22.5% of the intracranial tumors in the adult population. Although the intricacy of this pathology is yet to be fully understood on a biomolecular level, it is well known that these lesions develop within a microenvironment that supports their evolution and existence. The role of the tumoral microenvironment in pituitary lesions is pivotal, mainly due to this gland's distinct anatomical, histological, and physiological structure and function. Each component of the tumoral microenvironment is specifically involved in tumorigenesis, angiogenesis, tumoral growth, progression, and dissemination. By recognizing and understanding how these elements are involved in such processes, targeted treatments can emerge, and better future management of pituitary lesions can be provided. This article aims to summarize the role of each component of the tumoral microenvironment in pituitary lesions while assessing their association with biomolecular mechanisms.

A Brief Overview of Molecular Biology in Pituitary Adenomas with a Focus on Aggressive Lesions

Arising from the anterior lobe of the hypophysis, pituitary neuroendocrine tumors (PitNETs), previously known as pituitary adenomas, constitute an intricate and heterogeneous entity. Although they are defined as benign pathology, these tumors can often invade neighboring structures and demonstrate aggressive behavior. The continuous advancement of molecular biology has begun to shed light on the genetic mutations behind the development and evolution of this pathology, providing a better understanding. Notwithstanding, the rising occurrence in recent decades can mainly be attributed to advanced diagnostic techniques; however, these tumors continue to increase in prevalence and incidence, creating a heavy burden on healthcare systems. Consequently, the need for further studies is dire, primarily due to a lack of tailored therapeutic approaches. Thus, this article aims to offer a brief overview of the molecular biology behind these complex tumors to contribute, even on a small scale, to more comprehensive care.

DNA Methylation in Pituitary Adenomas: A Scoping Review

Pituitary adenomas are a diverse group of neoplasms with variable clinical behavior. Despite advances in genetic analysis, understanding the role of epigenetic modifications, particularly DNA methylation, remains an area under investigation. This scoping review aimed to update and synthesize the current body of literature on DNA methylation in pituitary adenomas, focusing on methodological advancements and clinical correlations. A systematic search conducted across multiple databases, including Embase, Scopus, MEDLINE, and CENTRAL, identified 107 eligible studies. Early methods, such as methylation-restricted digestion and methylation-specific PCR (MSP), have evolved into more comprehensive approaches, such as chip-based DNA methylation analysis. Key findings suggest that genes like POMC, SOCS-1, and RASSF1A show a significant association between methylation and clinical behavior. However, methylation patterns alone are insufficient to fully explain tumorigenesis. Emerging data suggest that DNA methylation might serve as a prognostic marker for invasive growth and recurrence, but further longitudinal studies are needed. This review highlights the need for future research to explore the methylome more thoroughly and to better define the clinical impact of epigenetic modifications in pituitary adenomas.

A rare case of double pituitary prolactinomas: the diagnostic application of intraoperative ultrasonography and DNA methylation markers

The aim of this study is to describe the management and evolution of a patient with the rare condition of double lactotroph tumors and assess the role of intraoperative ultrasonography (IOUS) for their detection and methylation-based liquid biopsy for their diagnosis and monitoring. A 29-year-old woman diagnosed with double lactotroph tumors through hormonal and MRI workup underwent surgical resection due to cabergoline intolerance. To detect a tumor missing through visual inspection, IOUS was performed. Pituitary tumor (PT) and nontumor (NT) tissues and blood were collected for pathological and molecular assessments (genome-wide methylation level profiled using the EPIC array, at surgery and follow-up). Reference methylome data were obtained from publicly available repositories. Both tumors (T1 and T2) were detected via IOUS and confirmed as lactotroph tumors through immunohistochemistry. In tissue specimens, PT-specific markers distinguished T1 from NT tissue, while T2, primarily nontumor cells, clustered with NT specimens. In liquid biopsies, these markers differentiated between T and NT cohorts. During the 12-month follow-up, methylation profiling and prolactin blood assessments showed that methylation markers clustered with NT specimens, which coincided with prolactinemia normalization, indicating successful tumor control after surgery. This case illustrates the translational use of methylation-based liquid biopsy methodologies in detecting and monitoring PTs through the detection of tumor-specific markers in blood specimens. This approach can be useful to distinguish sellar masses mimicking PTs based on nonspecific imaging features and to monitor for early recurrence of PTs, particularly nonfunctioning PTs lacking specific biochemical markers. This case also illustrated the role of IOUS in identifying multiple PTs missed by visual inspection alone, leading to improved patient outcomes through complete tumor resection.

Liquid biopsy in brain tumors: moving on, slowly

PURPOSE OF REVIEW

Due to limited access to the tumor, there is an obvious clinical potential for liquid biopsy in patients with primary brain tumors. Here, we review current approaches, present limitations to be dealt with, and new promising data that may impact the field.

RECENT FINDINGS

The value of circulating tumor cell-free DNA (ctDNA) in the cerebrospinal fluid (CSF) for the noninvasive diagnosis of primary brain tumors has been confirmed in several reports. The detection of ctDNA in the peripheral blood is desirable for patient follow-up but requires ultrasensitive methods to identify low mutant allelic frequencies. Digital PCR approaches and targeted gene panels have been used to identify recurrent hotspot mutations and copy number variations (CNVs) from CSF or plasma. Tumor classification from circulating methylomes in plasma has been actively pursued, although the need of advanced bioinformatics currently hampers clinical application. The use of focused ultrasounds to open the blood-brain barrier may represent a way to enrich of ctDNA the peripheral blood and enhance plasma-based liquid biopsy.

SUMMARY

Monitoring CNVs and hotspot mutations by liquid biopsy is a promising tool to detect minimal residual disease and strengthen response assessment in patients with primary brain tumors. Novel methods to increase the relative and/or absolute amount of ctDNA can improve the clinical potential of plasma-based liquid biopsies.

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Exploring machine learning applications in Meningioma Research (2004-2023)

Objective

This study aims to examine the trends in machine learning application to meningiomas between 2004 and 2023.

Methods

Publication data were extracted from the Science Citation Index Expanded (SCI-E) within the Web of Science Core Collection (WOSCC). Using CiteSpace 6.2.R6, a comprehensive analysis of publications, authors, cited authors, countries, institutions, cited journals, references, and keywords was conducted on December 1, 2023.

Results

The analysis included a total of 342 articles. Prior to 2007, no publications existed in this field, and the number remained modest until 2017. A significant increase occurred in publications from 2018 onwards. The majority of the top 10 authors hailed from Germany and China, with the USA also exerting substantial international influence, particularly in academic institutions. Journals from the IEEE series contributed significantly to the publications. "Deep learning," "brain tumor," and "classification" emerged as the primary keywords of focus among researchers. The developmental pattern in this field primarily involved a combination of interdisciplinary integration and the refinement of major disciplinary branches.

Conclusion

Machine learning has demonstrated significant value in predicting early meningiomas and tailoring treatment plans. Key research focuses involve optimizing detection indicators and selecting superior machine learning algorithms. Future efforts should aim to develop high-performance algorithms to drive further innovation in this field.

Genome-Wide DNA Methylation Profiling as a Prognostic Marker in Pituitary Adenomas-A Pilot Study

BACKGROUND

The prediction of the regrowth potential of pituitary adenomas after surgery is challenging. The genome-wide DNA methylation profiling of pituitary adenomas may separate adenomas into distinct methylation classes corresponding to histology-based subtypes. Specific genes and differentially methylated probes involving regrowth have been proposed, but no study has linked this epigenetic variance with regrowth potential and the clinical heterogeneity of nonfunctioning pituitary adenomas. This study aimed to investigate whether DNA methylation profiling can be useful as a clinical prognostic marker.

METHODS

A DNA methylation analysis by Illumina's MethylationEPIC array was performed on 54 pituitary macroadenomas from patients who underwent transsphenoidal surgery during 2007-2017. Twelve patients were excluded due to an incomplete postoperative follow-up, degenerated biobank-stored tissue, or low DNA methylation quality. For the quantitative measurement of the tumor regrowth rate, we conducted a 3D volumetric analysis of tumor remnant volume via annual magnetic resonance imaging. A linear mixed effects model was used to examine whether different DNA methylation clusters had different regrowth patterns.

RESULTS

The DNA methylation profiling of 42 tissue samples showed robust DNA methylation clusters, comparable with previous findings. The subgroup of 33 nonfunctioning pituitary adenomas of an SF1-lineage showed five subclusters with an approximately unbiased score of 86%. There were no overall statistically significant differences when comparing hazard ratios for regrowth of 100%, 50%, or 0%. Despite this, plots of correlated survival estimates suggested higher regrowth rates for some clusters. The mixed effects model of accumulated regrowth similarly showed tendencies toward an association between specific DNA methylation clusters and regrowth potential.

CONCLUSION

The DNA methylation profiling of nonfunctioning pituitary adenomas may potentially identify adenomas with increased growth and recurrence potential. Larger validation studies are needed to confirm the findings from this explorative pilot study.

Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery

This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures. AI-powered technologies, such as liquid biopsy, machine learning, radiomic analysis, computer vision, and label-free optical imaging, aim to revolutionize skull base surgery. AI-driven advancements promise safer, more precise, and effective surgeries, improving patient outcomes and preoperative assessment.

Recent Developments in Blood Biomarkers in Neuro-oncology

PURPOSE OF REVIEW

Given the invasive and high-risk nature of brain surgery, the need for non-invasive biomarkers obtained from the peripheral blood is greatest in tumors of the central nervous system (CNS). In this comprehensive review, we highlight recent advances in blood biomarker development for adult and pediatric brain tumors.

RECENT FINDINGS

We summarize recent blood biomarker development for CNS tumors across multiple key analytes, including peripheral blood mononuclear cells, cell-free DNA, cell-free RNA, proteomics, circulating tumor cells, and tumor-educated platelets. We also discuss methods for enhancing blood biomarker detection through transient opening of the blood-brain barrier. Although blood-based biomarkers are not yet used in routine neuro-oncology practice, this field is advancing rapidly and holds great promise for improved and non-invasive management of patients with brain tumors. Prospective and adequately powered studies are needed to confirm the clinical utility of any blood biomarker prior to widespread clinical implementation.

Detection of diagnostic and prognostic methylation-based signatures in liquid biopsy specimens from patients with meningiomas

Recurrence of meningiomas is unpredictable by current invasive methods based on surgically removed specimens. Identification of patients likely to recur using noninvasive approaches could inform treatment strategy, whether intervention or monitoring. In this study, we analyze the DNA methylation levels in blood (serum and plasma) and tissue samples from 155 meningioma patients, compared to other central nervous system tumor and non-tumor entities. We discover DNA methylation markers unique to meningiomas and use artificial intelligence to create accurate and universal models for identifying and predicting meningioma recurrence, using either blood or tissue samples. Here we show that liquid biopsy is a potential noninvasive and reliable tool for diagnosing and predicting outcomes in meningioma patients. This approach can improve personalized management strategies for these patients.

An Overview of Circulating Biomarkers in Neuroendocrine Neoplasms: A Clinical Guide

Neuroendocrine neoplasms (NENs) are a heterogeneous group of diseases that are characterized by different behavior and clinical manifestations. The diagnosis and management of this group of tumors are challenging due to tumor complexity and lack of precise and widely validated biomarkers. Indeed, the current circulating mono-analyte biomarkers (such as chromogranin A) are ineffective in describing such complex tumors due to their poor sensitivity and specificity. In contrast, multi-analytical circulating biomarkers (including NETest) are emerging as more effective tools to determine the real-time profile of the disease, both in terms of accurate diagnosis and effective treatment. In this review, we will analyze the capabilities and limitations of different circulating biomarkers focusing on three relevant questions: (1) accurate and early diagnosis; (2) monitoring of disease progression and response to therapy; and (3) detection of early relapse.

Identification of biomarkers associated with the invasion of nonfunctional pituitary neuroendocrine tumors based on the immune microenvironment

Introduction

The invasive behavior of nonfunctioning pituitary neuroendocrine tumors (NF-PitNEts) affects complete resection and indicates a poor prognosis. Cancer immunotherapy has been experimentally used for the treatment of many tumors, including pituitary tumors. The current study aimed to screen the key immune-related genes in NF-PitNEts with invasion.

Methods

We used two cohorts to explore novel biomarkers in NF-PitNEts. The immune infiltration-associated differentially expressed genes (DEGs) were obtained based on high/low immune scores, which were calculated through the ESTIMATE algorithm. The abundance of immune cells was predicted using the ImmuCellAI database. WGCNA was used to construct a coexpression network of immune cell-related genes. Random forest analysis was used to select the candidate genes associated with invasion. The expression of key genes was verified in external validation set using quantitative real-time polymerase chain reaction (qRT‒PCR).

Results

The immune and invasion related DEGs was obtained based on the first dataset of NF-PitNEts (n=112). The immune cell-associated modules in NF-PitNEts were calculate by WGCNA. Random forest analysis was performed on 81 common genes intersected by immune-related genes, invasion-related genes, and module genes. Then, 20 of these genes with the highest RF score were selected to construct the invasion and immune-associated classification model. We found that this model had high prediction accuracy for tumor invasion, which had the largest area under the receiver operating characteristic curve (AUC) value in the training dataset from the first dataset (n=78), the self-test dataset from the first dataset (n=34), and the independent test dataset (n=73) (AUC=0.732/0.653/0.619). Functional enrichment analysis revealed that 8 out of the 20 genes were enriched in multiple signaling pathways. Subsequently, the 8-gene (BMP6, CIB2, FABP5, HOMER2, MAML3, NIN, PRKG2 and SIDT2) classification model was constructed and showed good efficiency in the first dataset (AUC=0.671). In addition, the expression levels of these 8 genes were verified by qRT‒PCR.

Conclusion

We identified eight key genes associated with invasion and immunity in NF-PitNEts that may play a fundamental role in invasive progression and may provide novel potential immunotherapy targets for NF-PitNEts.

Is There a Role for Machine Learning in Liquid Biopsy for Brain Tumors? A Systematic Review

The paucity of studies available in the literature on brain tumors demonstrates that liquid biopsy (LB) is not currently applied for central nervous system (CNS) cancers. The purpose of this systematic review focused on the application of machine learning (ML) to LB for brain tumors to provide practical guidance for neurosurgeons to understand the state-of-the-art practices and open challenges. The herein presented study was conducted in accordance with the PRISMA-P (preferred reporting items for systematic review and meta-analysis protocols) guidelines. An online literature search was launched on PubMed/Medline, Scopus, and Web of Science databases using the following query: "((Liquid biopsy) AND (Glioblastoma OR Brain tumor) AND (Machine learning OR Artificial Intelligence))". The last database search was conducted in April 2023. Upon the full-text review, 14 articles were included in the study. These were then divided into two subgroups: those dealing with applications of machine learning to liquid biopsy in the field of brain tumors, which is the main aim of this review (n = 8); and those dealing with applications of machine learning to liquid biopsy in the diagnosis of other tumors (n = 6). Although studies on the application of ML to LB in the field of brain tumors are still in their infancy, the rapid development of new techniques, as evidenced by the increase in publications on the subject in the past two years, may in the future allow for rapid, accurate, and noninvasive analysis of tumor data. Thus making it possible to identify key features in the LB samples that are associated with the presence of a brain tumor. These features could then be used by doctors for disease monitoring and treatment planning.

Circulating cell-free DNA methylation mirrors alterations in cerebral patterns in epilepsy

BACKGROUND

DNA methylation profiling of circulating cell-free DNA (cfDNA) has rapidly become a promising strategy for biomarker identification and development. The cell-type-specific nature of DNA methylation patterns and the direct relationship between cfDNA and apoptosis can potentially be used non-invasively to predict local alterations. In addition, direct detection of altered DNA methylation patterns performs well as a biomarker. In a previous study, we demonstrated marked DNA methylation alterations in brain tissue from patients with mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS).

RESULTS

We performed DNA methylation profiling in cfDNA isolated from the serum of MTLE patients and healthy controls using BeadChip arrays followed by systematic bioinformatic analysis including deconvolution analysis and integration with DNase accessibility data sets. Differential cfDNA methylation analysis showed an overrepresentation of gene ontology terms and transcription factors related to central nervous system function and regulation. Deconvolution analysis of the DNA methylation data sets ruled out the possibility that the observed differences were due to changes in the proportional contribution of cortical neurons in cfDNA. Moreover, we found no overrepresentation of neuron- or glia-specific patterns in the described cfDNA methylation patterns. However, the MTLE-HS cfDNA methylation patterns featured a significant overrepresentation of the epileptic DNA methylation alterations previously observed in the hippocampus.

CONCLUSIONS

Our results support the use of cfDNA methylation profiling as a rational approach to seeking non-invasive and reproducible epilepsy biomarkers.

A genome-wide cell-free DNA methylation analysis identifies an episignature associated with metastatic luminal B breast cancer

Breast cancers of the luminal B subtype are frequent tumors with high proliferation and poor prognosis. Epigenetic alterations have been found in breast tumors and in biological fluids. We aimed to profile the cell-free DNA (cfDNA) methylome of metastatic luminal B breast cancer (LBBC) patients using an epigenomic approach to discover potential noninvasive biomarkers. Plasma cfDNA was analyzed using the Infinium MethylationEpic array in a cohort of 14 women, including metastatic LBBC patients and nontumor controls. The methylation levels of cfDNA and tissue samples were validated with droplet digital PCR. The methylation and gene expression data of 582 primary luminal breast tumors and 79 nontumor tissues were obtained from The Cancer Genome Atlas (TCGA). We found an episignature of 1,467 differentially methylated CpGs that clearly identified patients with LBBC. Among the genes identified, the promoter hypermethylation of WNT1 was validated in cfDNA, showing an area under the ROC curve (AUC) of 0.86 for the noninvasive detection of metastatic LBBC. Both paired cfDNA and primary/metastatic breast tumor samples showed hypermethylation of WNT1. TCGA analysis revealed significant WNT1 hypermethylation in the primary tumors of luminal breast cancer patients, with a negative association between WNT1 methylation and gene expression. In this proof-of-principle study, we discovered an episignature associated with metastatic LBBC using a genome-wide cfDNA methylation approach. We also identified the promoter hypermethylation of WNT1 in cfDNA as a potential noninvasive biomarker for luminal breast cancer. Our results support the use of EPIC arrays to identify new epigenetic noninvasive biomarkers in breast cancer.