NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders.

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.

Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions. SIGNIFICANCE: The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Reduced Cerebral Blood Flow in Mild Cognitive Impairment Assessed Using Phase-Contrast MRI.

There is increasing evidence of a vascular contribution to Alzheimer's disease (AD). In some cases, prior work suggests that chronic brain hypoperfusion could play a prime pathogenic role contributing to the accumulation of amyloid-ß,while other studies favor the hypothesis that vascular dysfunction and amyloid pathology are independent, although synergistic, mechanisms contributing to cognitive impairment. Vascular dysfunction can be evaluated by assessing cerebral blood flow impairment. Phase contrast velocity mapping by MRI offers a non-invasive means of quantifying the total inflow of blood to the brain. This quantitative parameter could be a sensitive indicator of vascular disease at early stages of AD. In this work, phase contrast MRI was used to evaluate cerebral hemodynamics in patients with subjective memory complaints, amnestic mild cognitive impairment, and mild to moderate AD, and compare them with control subjects. Results showed that blood flow and velocity were decreased in the patients with cognitive dysfunction and the decrease correlated with the degree of cognitive impairment as assessed by means of neuropsychological tests. Total cerebral blood flow measurements were clearly reduced in AD patients, but more importantly appeared to be sensitive enough to distinguish between healthy subjects and those with mild cognitive impairment. A quantitative measurement of total brain blood flow could potentially predict vascular dysfunction and compromised brain perfusion in early stages of AD.