Evaluation of MTAP and p16 immunohistochemical deficiency as surrogate marker for CDKN2A/B homozygous deletion in gliomas.

Homozygous deletion (HD) of the CDKN2A/B locus has emerged as an unfavourable prognostic marker in diffuse gliomas, both IDH-mutant and IDH-wild-type. Testing for CDKN2A/B deletions can be performed by a variety of approaches, including copy number variation (CNV) analysis based on gene array analysis, next generation sequencing (NGS) or fluorescence in situ hybridisation (FISH), but questions remain regarding the accuracy of testing modalities. In this study, we assessed: (1) the utility of S-methyl-5'-thioadenosine phosphorylase (MTAP) and cellular tumour suppressor protein pl61NK4a (p16) immunostainings as surrogate markers for CDKN2A/B HD in gliomas, and (2) the prognostic value of MTAP, across different histological tumour grades and IDH mutation status. One hundred consecutive cases of diffuse and circumscribed gliomas (Cohort 1) were collected, in order to correlate MTAP and p16 expression with the CDKN2A/B status in the CNV plot of each tumour. IDH1 R132H, ATRX and MTAP immunohistochemistry was performed on next generation tissue microarrays (ngTMAs) of 251 diffuse gliomas (Cohort 2) for implementing survival analysis. Complete loss of MTAP and p16 by immunohistochemistry was 100% and 90% sensitive as well as 97% and 89% specific for CDKN2A/B HD, respectively, as identified on CNV plot. Only two cases (2/100) with MTAP and p16 loss of expression did not demonstrate CDKN2A/B HD in CNV plot; however, FISH analysis confirmed the HD for CDKN2A/B. Moreover, MTAP deficiency was associated with shortened survival in IDH-mutant astrocytomas (n=75; median survival 61 vs 137 months; p<0.0001), IDH-mutant oligodendrogliomas (n=59; median survival 41 vs 147 months; p<0.0001) and IDH-wild-type gliomas (n=117; median survival 13 vs 16 months; p=0.011). In conclusion, MTAP immunostaining is an important complement for diagnostic work-up of gliomas, because of its excellent correlation with CDKN2A/B status, robustness, rapid turnaround time and low costs, and provides significant prognostic value in IDH-mutant astrocytomas and oligodendrogliomas, while p16 should be used cautiously.

Code-free machine learning for classification of central nervous system histopathology images.

Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomedical images including histopathology. The promises of this technology contrast, however, with its currently limited application in routine clinical practice. This discrepancy is in part due to the extent of informatics expertise typically required for implementation of ML. Therefore, we assessed the suitability of 2 publicly accessible code-free ML platforms (Microsoft Custom Vision and Google AutoML), for classification of histopathological images of diagnostic central nervous system tissue samples. When trained with typically 100 to more than 1000 images, both systems were able to perform nontrivial classifications (glioma vs brain metastasis; astrocytoma vs astrocytosis, prediction of 1p/19q co-deletion in IDH-mutant tumors) based on hematoxylin and eosin-stained images with high accuracy (from ∼80% to nearly 100%). External validation of the predicted accuracy and negative control experiments were found to be crucial for verification of the accuracy predicted by the algorithms. Furthermore, we propose a possible diagnostic workflow for pathologists to implement classification of histopathological images based on code-free machine platforms.