Computational Modelling and Clinical Validation of an Alzheimer’s-Related Network in Brain Cancer: The SKM034 Model

TL;DR

This paper explores the connection between Alzheimer's disease and cancer using a computational model focused on SORL1 and its network, revealing new insights into their molecular interactions.

Contribution

The paper introduces a novel computational model (SKM034) that identifies 27 potentially novel predictions about SORL1's role in cancer and Alzheimer's.

Findings

The SKM034 model predicted 29 molecular changes following the loss of SORL1 or ERBB2, with 27 being potentially novel.

Signal flow analysis showed a 60% correct prediction rate using RNA-seq data from cancer patients and cell lines.

The model highlights the clinical relevance of Alzheimer’s-related networks in cancer contexts.

Abstract

Cancer and Alzheimer’s disease (AD) display an inverse relationship, and there is a need to further explore this interplay. One key genetic contributor to AD is SORL1, the loss of which is thought to be causally related to AD development. SORL1 also appears to be implicated in cancer. To examine SORL1 and its network, this article simulated SORL1 and its interactions via signal-flow Boolean modelling, including in silico knockouts (mirroring in vivo loss-of-function mutations). This model (SKM034) predicted a total of 29 key changes in molecular relationships following the loss of SORL1 or another highly connected protein (ERBB2). Literature validation demonstrated that 2 of these predictions were at least partially validated experimentally, whilst 27 were Potentially Novel Predictions (PNPs). Complementing the in-depth relationship analyses was signal flow analysis through the network’s structure, validated using cell line and cancer patient RNA-seq data. Correct prediction rates for these analyses reached 60% (statistically significant relative to a random model). This article demonstrates the clinical relevance of this Alzheimer’s-related network in a cancer context and, through the PNPs, provides a strong starting point for in vitro experimental validation. As with previously published models using similar methods, the model may be reanalysed in different contexts for further discoveries.

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