Yale researchers uncover novel implications of arachnoid cysts
Using an integrated multiomics approach, a recent Yale-led study found that arachnoid cysts may serve as radiographic biomarkers for neuropathologies.
Courtesy of Yale School of Medicine
Could something as innocuous as a common brain cyst hold the key to unlocking a deeper understanding of brain development and pathology?
A recent study published in “Nature Medicine” — led by Adam Kundishora, a resident at Yale-New Haven Hospital, Garrett Allington GRD ’23 and co-authors from Yale and several other institutions — found that arachnoid cysts may serve as presage of neurodevelopmental disorders, paving the way for earlier diagnosis and clinical follow-up.
“Our motivation to study arachnoid cysts came from from that relationship of them to the neurosurgical community — it sometimes becomes very difficult to figure out if the cyst is really at the center or is the etiology of the symptoms or if the cyst is just an innocent bystander,” Kundishora said.
Arachnoid cysts are cerebrospinal fluid-filled arachnoid cysts that develop between two membrane layers of the brain — the arachnoid membrane and pia mater. According to Kundishora and Allington, arachnoid cysts are typically seen as incidental and not a particularly motivating factor in clinical decision-making. However, in some instances, the presence of arachnoid cysts may suggest early signs of an underlying neurological condition.
“Usually, arachnoid cysts are considered to be nothing — an anatomical variant — but in some situations, if you look further into patients, they can be associated with genetic malformation,” Mariam Aboian, an assistant professor at the Yale School of Medicine, said.
According to Allington, there is an association between arachnoid cysts and neurodevelopmental phenotypes such as autism and seizures. Further exploring the nature of this association was part of the driving interest behind the study.
The study evaluates whether any de novo variants — types of genetic variation that are not inherited from either parent — are involved in the causes of complex inheritance for neurodevelopmental disorders.
Specifically, the research team examined the exome, which, according to Allington, is the entire protein coding region of the genome and is a “high yield place to look for potential variants that cause disease.” The team’s findings revealed seven genes that are implicated in similar biological processes, such as chromatin modification and gene transcription regulation, potentially involved in mechanistic pathways for arachnoid cysts formation.
“This strengthened our hypothesis that those genes were not just simply mutated and correlated … but were really involved in the biological process leading to arachnoid cysts formation as they all seem to take part in the same regulatory transcription and translation processes,” Kundishora said.
These de novo variants offer valuable insights into not only arachnoid cysts formation but also arachnoid cysts mediated pathogenesis. To investigate this, the authors spearheaded a novel technique involving an artificially intelligent natural language processor to comb through electronic medical data to identify distinct phenotypic profiles across patients.
They also performed cluster analysis to evaluate whether these neurodevelopmental phenotypic groups were significantly related to the presence of de novo variants. Their findings revealed four distinct clinically-significant clusters related to hypotonia, like decreased muscle tone and seizures, further supporting the idea that neurological conditions may be related to epigenetic dysregulation from de novo variants in arachnoid cysts pathogenesis.
Distinct arachnoid cysts phenotypic subtypes may be associated with varying prognoses and guiding courses of treatment, according to Kundishora. The nature of the phenotypic subtype may be able to serve as an accurate predictor of whether or not certain drug treatments or surgeries will be effective, although further research is needed to evaluate this.
These prognostic and clinical implications are applicable to a wide variety of neurological conditions, according to Kundishora and Allington. Allington discussed specifically how arachnoid cysts could have compelling benefits for flagging early neurodevelopmental processes, which could be essential for better clinical outcomes.
“If you have a pre-verbal child, it might be very difficult to diagnose a neurodevelopmental disease because you can’t speak to them yet,” Allington said. “However, if you were to see a radiographic harbinger, it may serve as a way to flag a potential risk … and might help to make a clinical decision on whether that patient is indicated for additional genetic or neurocognitive testing.”
Aboian suggested that this research may have implications for brain tumors as well. She cautioned that more research was warranted before these types of diagnostic decisions can be made since arachnoid cysts alter standard anatomic features of the brain and can skew features such as volumetric measurements of different portions of the brain such as the hippocampus.
“When we make a diagnosis using imaging, there’s a lot of information in the MRI of the brain that we can’t see with our eyes … so in the future we can use AI algorithms that analyze radiomic features of images to assist in establishing the diagnosis,” Aboian said. “If you’re trying to find the etiology of behavioral problems, maybe in a patient who has an arachnid cyst, it may not be a bad idea to do radiomic features analysis with assistance of AI algorithms to help make a diagnosis.”
Kundishora and Aboian both emphasize the importance of such artificially intelligent methodologies in the fields of radiology and neurology. This has the potential to recognize not only different types of brain tumors, but also different molecular subtypes, according to Aboian.
“If you’re looking at a high grade glioma, for example, is it a glioblastoma or a grade four astrocytoma — they look very similar, right?” Aboian explained. “On imaging, it’s hard to tell a difference … but there are radiomics features that can differentiate these, so we can actually use an algorithm to help a radiologist make a diagnosis before surgical intervention.”
These artificially intelligent, integrated, multiomics approaches may be the future to identifying more key biomarkers that help scientists and clinicians understand disease progression.
The Yale Department of Neurosurgery leads cutting-edge research in the fields of neuro-oncology, neurovascular surgery, spinal surgery, brain trauma and more.