Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology.

AIMS: Resource-strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings. METHODS: We used simple information theory calculations on a brain cancer simulation model and real-world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H&E and Olig2 stained images obtained from digital slides. An auto-adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH-mutant tumors. RESULTS: Information theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53, and ATRX status have significant information for the diagnosis of IDH-mutant tumors. The predictive models may facilitate the reduction of false-positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing. CONCLUSIONS: We posit that this approach provides an improvement on the cIMPACT-NOW workflow recommendations for IDH-mutant tumors and a framework for future resource and testing allocation.

Astrocyte regional heterogeneity revealed through machine learning-based glial neuroanatomical assays.

Evaluation of reactive astrogliosis by neuroanatomical assays represents a common experimental outcome for neuroanatomists. The literature demonstrates several conflicting results as to the accuracy of such measures. We posited that the diverging results within the neuroanatomy literature were due to suboptimal analytical workflows in addition to astrocyte regional heterogeneity. We therefore generated an automated segmentation workflow to extract features of glial fibrillary acidic protein (GFAP) and aldehyde dehydrogenase family 1, member L1 (ALDH1L1) labeled astrocytes with and without neuroinflammation. We achieved this by capturing multiplexed immunofluorescent confocal images of mouse brains treated with either vehicle or lipopolysaccharide (LPS) followed by implementation of our workflows. Using classical image analysis techniques focused on pixel intensity only, we were unable to identify differences between vehicle-treated and LPS-treated animals. However, when utilizing machine learning-based algorithms, we were able to (1) accurately predict which objects were derived from GFAP or ALDH1L1-stained images indicating that GFAP and ALDH1L1 highlight distinct morphological aspects of astrocytes, (2) we could predict which neuroanatomical region the segmented GFAP or ALDH1L1 object had been derived from, indicating that morphological features of astrocytes change as a function of neuroanatomical location. (3) We discovered a statistically significant, albeit not highly accurate, prediction of which objects had come from LPS versus vehicle-treated animals, indicating that although features exist capable of distinguishing LPS-treated versus vehicle-treated GFAP and ALDH1L1-segmented objects, that significant overlap between morphologies exists. We further determined that for most classification scenarios, nonlinear models were required for improved treatment class designations. We propose that unbiased automated image analysis techniques coupled with well-validated machine learning tools represent highly useful models capable of providing insights into neuroanatomical assays.

Neonatal apneic phenotype in a murine congenital central hypoventilation syndrome model is induced through non-cell autonomous developmental mechanisms.

Congenital central hypoventilation syndrome (CCHS) represents a rare genetic disorder usually caused by mutations in the homeodomain transcription factor PHOX2B. Some CCHS patients suffer mainly from deficiencies in CO2 and/or O2 respiratory chemoreflex, whereas other patients present with full apnea shortly after birth. Our goal was to identify the neuropathological mechanisms of apneic presentations in CCHS. In the developing murine neuroepithelium, Phox2b is expressed in three discrete progenitor domains across the dorsal-ventral axis, with different domains responsible for producing unique autonomic or visceral motor neurons. Restricting the expression of mutant Phox2b to the ventral visceral motor neuron domain induces marked newborn apnea together with a significant loss of visceral motor neurons, RTN ablation, and preBötzinger complex dysfunction. This finding suggests that the observed apnea develops through non-cell autonomous developmental mechanisms. Mutant Phox2b expression in dorsal rhombencephalic neurons did not generate significant respiratory dysfunction, but did result in subtle metabolic thermoregulatory deficiencies. We confirm the expression of a novel murine Phox2b splice variant which shares exons 1 and 2 with the more widely studied Phox2b splice variant, but which differs in exon 3 where most CCHS mutations occur. We also show that mutant Phox2b expression in the visceral motor neuron progenitor domain increases cell proliferation at the expense of visceral motor neuron development. We propose that visceral motor neurons may function as organizers of brainstem respiratory neuron development, and that disruptions in their development result in secondary/non-cell autonomous maldevelopment of key brainstem respiratory neurons.