Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis.

BACKGROUND: Cellular senescence can have positive and negative effects on the body, including aiding in damage repair and facilitating tumor growth. Adamantinomatous craniopharyngioma (ACP), the most common pediatric sellar/suprasellar brain tumor, poses significant treatment challenges. Recent studies suggest that senescent cells in ACP tumors may contribute to tumor growth and invasion by releasing a senesecence-associated secretory phenotype. However, a detailed analysis of these characteristics has yet to be completed. METHODS: We analyzed primary tissue samples from ACP patients using single-cell, single-nuclei, and spatial RNA sequencing. We performed various analyses, including gene expression clustering, inferred senescence cells from gene expression, and conducted cytokine signaling inference. We utilized LASSO to select essential gene expression pathways associated with senescence. Finally, we validated our findings through immunostaining. RESULTS: We observed significant diversity in gene expression and tissue structure. Key factors such as NFKB, RELA, and SP1 are essential in regulating gene expression, while senescence markers are present throughout the tissue. SPP1 is the most significant cytokine signaling network among ACP cells, while the Wnt signaling pathway predominantly occurs between epithelial and glial cells. Our research has identified links between senescence-associated features and pathways, such as PI3K/Akt/mTOR, MYC, FZD, and Hedgehog, with increased P53 expression associated with senescence in these cells. CONCLUSIONS: A complex interplay between cellular senescence, cytokine signaling, and gene expression pathways underlies ACP development. Further research is crucial to understand how these elements interact to create novel therapeutic approaches for patients with ACP.

Recurrent adamantinomatous craniopharyngiomas show MAPK pathway activation, clonal evolution and rare TP53-loss-mediated malignant progression.

The two types of craniopharyngioma, adamantinomatous (ACP) and papillary (PCP), are clinically relevant tumours in children and adults. Although the biology of primary craniopharyngioma is starting to be unravelled, little is known about the biology of recurrence. To fill this gap in knowledge, we have analysed through methylation array, RNA sequencing and pERK1/2 immunohistochemistry a cohort of paired primary and recurrent samples (32 samples from 14 cases of ACP and 4 cases of PCP). We show the presence of copy number alterations and clonal evolution across recurrence in 6 cases of ACP, and analysis of additional whole genome sequencing data from the Children's Brain Tumour Network confirms chromosomal arm copy number changes in at least 7/67 ACP cases. The activation of the MAPK/ERK pathway, a feature previously shown in primary ACP, is observed in all but one recurrent cases of ACP. The only ACP without MAPK activation is an aggressive case of recurrent malignant human craniopharyngioma harbouring a CTNNB1 mutation and loss of TP53. Providing support for a functional role of this TP53 mutation, we show that Trp53 loss in a murine model of ACP results in aggressive tumours and reduced mouse survival. Finally, we characterise the tumour immune infiltrate showing differences in the cellular composition and spatial distribution between ACP and PCP. Together, these analyses have revealed novel insights into recurrent craniopharyngioma and provided preclinical evidence supporting the evaluation of MAPK pathway inhibitors and immunomodulatory approaches in clinical trials in against recurrent ACP.

EASL: A Framework for Designing, Implementing, and Evaluating ML Solutions in Clinical Healthcare Settings.

We introduce the Explainable Analytical Systems Lab (EASL) framework, an end-to-end solution designed to facilitate the development, implementation, and evaluation of clinical machine learning (ML) tools. EASL is highly versatile and applicable to a variety of contexts and includes resources for data management, ML model development, visualization and user interface development, service hosting, and usage analytics. To demonstrate its practical applications, we present the EASL framework in the context of a case study: designing and evaluating a deep learning classifier to predict diagnoses from medical imaging. The framework is composed of three modules, each with their own set of resources. The Workbench module stores data and develops initial ML models, the Canvas module contains a medical imaging viewer and web development framework, and the Studio module hosts the ML model and provides web analytics and support for conducting user studies. EASL encourages model developers to take a holistic view by integrating the model development, implementation, and evaluation into one framework, and thus ensures that models are both effective and reliable when used in a clinical setting. EASL contributes to our understanding of machine learning applied to healthcare by providing a comprehensive framework that makes it easier to develop and evaluate ML tools within a clinical setting.

The Iterative Design Process of an Explainable AI Application for Non-Invasive Diagnosis of CNS Tumors: A User-Centered Approach.

Artificial Intelligence (AI) is well-suited to help support complex decision-making tasks within clinical medicine, including clinical imaging applications like radiographic differential diagnosis of central nervous system (CNS) tumors. So far, there have been numerous examples of theoretical AI solutions for this space, for example, large-scale corporate efforts like IBM's Watson AI. However, clinical implementation remains limited due to factors related to the alignment of this technology in the clinical setting. User-Centered Design (UCD) is a design philosophy that focuses on developing tailored solutions for specific users or user groups. In this study, we applied UCD to develop an explainable AI tool to support clinicians in our use case. Through four design iterations, starting from basic functionality and visualizations, we progressed to functional prototypes in a realistic testing environment. We discuss our motivation and approach for each iteration, along with key insights gained. This UCD process has advanced our conceptual idea from feasibility testing to interactive functional AI interfaces designed for specific clinical and cognitive tasks. It has also provided us with directions to develop further an AI system for the non-invasive diagnosis of CNS tumors.