Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

SIMPLE SUMMARY: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome, particularly for the intermediate domains of adenocarcinomas and large-cell neuroendocrine carcinomas. Moreover, subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. The aim of this study was to design and evaluate an objective and reproducible approach to the grading of lung NENs, potentially extendable to other NENs, by exploring a completely new perspective of interpreting the well-recognised proliferation marker Ki-67. We designed an automated pipeline to harvest quantitative information from the spatial distribution of Ki-67-positive cells, analysing its heterogeneity in the entire extent of tumour tissue—which currently represents the main weakness of Ki-67—and employed machine learning techniques to predict prognosis based on this information. Demonstrating the efficacy of the proposed framework would hint at a possible path for the future of grading and classification of NENs. ABSTRACT: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs

Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs

Coexpression of \u394Np63/p40 and TTF1 Within Most of the Same Individual Cells Identifies Life-Threatening NSCLC Featuring Squamous and Glandular Biphenotypic Differentiation: Clinicopathologic Correlations

Introduction: Double occurrence of TTF1 and \u394Np63/p40 (henceforth, p40) within the same individual cells is exceedingly rare in lung cancer. Little is known on their biological and clinical implications. Methods: Two index cases immunoreactive for both p40 and TTF1 and nine tumors selected from The Cancer Genome Atlas (TCGA) according to the mRNA levels of the two relevant genes entered the study. Results: The two index cases were peripherally located, poorly differentiated, and behaviorally unfavorable carcinomas, which shared widespread p40 and TTF1 decoration within the same individual tumor cells. They also retained SMARCA2 and SMARCA4 expression, while variably stained for p53, cytokeratin 5, and programmed death-ligand 1. A subset of basal cells p40+/TTF1+ could be found in normal distal airways. Biphenotypic glandular and squamous differentiation was unveiled by electron microscopy, along with EGFR, RAD51B, CCND3, or NF1 mutations and IGF1R, MYC, CCND1, or CDK2 copy number variations on next-generation sequencing analysis. The nine tumors from TCGA (0.88% of 1018 tumors) shared the same poor prognosis, clinical presentation, and challenging histology and had activated pathways of enhanced angiogenesis and epithelial-mesenchymal transition. Mutation and copy number variation profiles did not differ from the other TCGA tumors. Conclusions: Double p40+/TTF1+ lung carcinomas are aggressive and likely underrecognized non-small cell carcinomas, whose origin could reside in double-positive distal airway stem-like basal cells through either de novo-basal-like or differentiating cell mechanisms according to a model of epithelial renewal