Assessment of the current and emerging criteria for the histopathological classification of lung neuroendocrine tumours in the lungNENomics project.

BACKGROUND: Six thoracic pathologists reviewed 259 lung neuroendocrine tumours (LNETs) from the lungNENomics project, with 171 of them having associated survival data. This cohort presents a unique opportunity to assess the strengths and limitations of current World Health Organization (WHO) classification criteria and to evaluate the utility of emerging markers. PATIENTS AND METHODS: Patients were diagnosed based on the 2021 WHO criteria, with atypical carcinoids (ACs) defined by the presence of focal necrosis and/or 2-10 mitoses per 2 mm2. We investigated two markers of tumour proliferation: the Ki-67 index and phospho-histone H3 (PHH3) protein expression, quantified by pathologists and automatically via deep learning. Additionally, an unsupervised deep learning algorithm was trained to uncover previously unnoticed morphological features with diagnostic value. RESULTS: The accuracy in distinguishing typical from ACs is hampered by interobserver variability in mitotic counting and the limitations of morphological criteria in identifying aggressive cases. Our study reveals that different Ki-67 cut-offs can categorise LNETs similarly to current WHO criteria. Counting mitoses in PHH3+ areas does not improve diagnosis, while providing a similar prognostic value to the current criteria. With the advantage of being time efficient, automated assessment of these markers leads to similar conclusions. Lastly, state-of-the-art deep learning modelling does not uncover undisclosed morphological features with diagnostic value. CONCLUSIONS: This study suggests that the mitotic criteria can be complemented by manual or automated assessment of Ki-67 or PHH3 protein expression, but these markers do not significantly improve the prognostic value of the current classification, as the AC group remains highly unspecific for aggressive cases. Therefore, we may have exhausted the potential of morphological features in classifying and prognosticating LNETs. Our study suggests that it might be time to shift the research focus towards investigating molecular markers that could contribute to a more clinically relevant morpho-molecular classification.

The potential for epigenetic analysis of paediatric CNS tumours to improve diagnosis, treatment and prognosis.

Tumours of central nervous system (CNS) origin are the second most prevalent group of cancers in children, yet account for the majority of childhood cancer-related deaths. Such tumours show diverse location, cell type of origin, disease course and long-term outcome, both across and within tumour types, making treatment problematic and contributing to the relatively modest progress in reducing mortality over recent decades. As technological advances begin to reveal the genetic landscape of all cancers, it is becoming increasingly clear that genetic disruption represents only one 'layer' of molecular disruption associated with disease aetiology. Obtaining a full understanding of tumour behaviour requires an understanding of the cellular and molecular pathways disrupted during tumourigenesis, particularly in relation to gene expression. The utility of such an approach has allowed stratification of cancers such as medulloblastoma into subgroups based on molecular features, with potential to refine risk prediction. Given that epigenetic disruption is a universal feature of all human cancers, it is logical to speculate that interrogating epigenetic marks may help to further define the molecular profile, and therefore the clinical trajectory, of tumours. An integrated approach to build a molecular 'signature' of individual tumours that incorporates traditional morphological and demographic information, genetic and transcriptome analysis, in addition to epigenomics (DNA methylation and non-coding RNA analysis), offers tremendous promise to (i) inform treatment approach, (ii) facilitate accurate early identification (preferably at diagnosis) of variable risk groups (both good and poor prognosis groups), and (iii) track disease progression in childhood CNS tumours.