posted on 2024-01-08, 22:13authored byMichael Woolman, Taira Kiyota, Siham A. Belgadi, Naohide Fujita, Alexa Fiorante, Vijay Ramaswamy, Craig Daniels, James T. Rutka, Chris McIntosh, David G. Munoz, Howard J. Ginsberg, Ahmed Aman, Arash Zarrine-Afsar
Picosecond infrared
laser mass spectrometry (PIRL-MS) is shown,
through a retrospective patient tissue study, to differentiate medulloblastoma
cancers from pilocytic astrocytoma and two molecular subtypes of ependymoma
(PF-EPN-A, ST-EPN-RELA) using laser-extracted lipids profiled with
PIRL-MS in 10 s of sampling and analysis time. The average sensitivity
and specificity values for this classification, taking genomic profiling
data as standard, were 96.41 and 99.54%, and this classification used
many molecular features resolvable in 10 s PIRL-MS spectra. Data analysis
and liquid chromatography coupled with tandem high-resolution mass
spectrometry (LC-MS/MS) further allowed us to reduce the molecular
feature list to only 18 metabolic lipid markers most strongly involved
in this classification. The identified ‘metabolite array’
was comprised of a variety of phosphatidic and fatty acids, ceramides,
and phosphatidylcholine/ethanolamine and could mediate the above-mentioned
classification with average sensitivity and specificity values of
94.39 and 98.78%, respectively, at a 95% confidence in prediction
probability threshold. Therefore, a rapid and accurate pathology classification
of select pediatric brain cancer types from 10 s PIRL-MS analysis
using known metabolic biomarkers can now be available to the neurosurgeon.
Based on retrospective mining of ‘survival’ versus ‘extent-of-resection’
data, we further identified pediatric cancer types that may benefit
from actionable 10 s PIRL-MS pathology feedback. In such cases, aggressiveness
of the surgical resection can be optimized in a manner that is expected
to benefit the patient’s overall or progression-free survival.
PIRL-MS is a promising tool to drive such personalized decision-making
in the operating theater.