Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. However, while a global change in transcription is recognized as a defining feature of cancer, the contribution of alternative promoters still remains largely unexplored. Here, we infer active promoters using RNA-seq data from 18,468 cancer and normal samples, demonstrating that alternative promoters are a major contributor to context-specific regulation of transcription. We find that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates. For genes with independently regulated promoters, we demonstrate that promoter activity provides a more accurate predictor of patient survival than gene expression. Our study suggests that a dynamic landscape of active promoters shapes the cancer transcriptome, opening new diagnostic avenues and opportunities to further explore the interplay of regulatory mechanisms with transcriptional aberrations in cancer. 3 rd ≥4 th Rank of correlation (Spearman) Mean promoter activity (RNA-Seq) Mean log H3K4me3 (ChIP-Seq) read count Adrenal Gland Arterial Blood Vessel Blood Blood Vessel Brain

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. While the role of promoters as driver elements in cancer has been recognized, the contribution of alternative promoters to regulation of the cancer transcriptome remains largely unexplored. Here we show that active promoters can be identified using RNA-Seq data, enabling the analysis of promoter activity in more than 1,000 cancer samples with matched whole genome sequencing data. We find that alternative promoters are a major contributor to tissue-specific regulation of isoform expression and that alternative promoters are frequently deregulated in cancer, affecting known cancer-genes and novel candidates. Noncoding passenger mutations are enriched at promoters of genes with lower regulatory complexity, whereas noncoding driver mutations occur at genes with multiple promoters, often affecting the promoter that shows the highest level of activity. Together our study demonstrates that the landscape of active promoters shapes the cancer transcriptome, opening many opportunities to further explore the interplay of regulatory mechanism and noncoding somatic mutations with transcriptional aberrations in cancer.

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Background Blarcamesine (ANAVEX®2‐73), a novel oral selective sigma‐1 receptor (SIGMAR1) agonist was investigated in a clinical Phase 2a study in Alzheimer’s disease in which blarcamesine resulted in a lower rate of cognitive (MMSE) and functional (ADCS‐ADL) decline. Following the positive results of this study, a translational approach led to investigating blarcamesine in an international, double‐blind, multicenter, placebo‐controlled Phase 2 clinical study of 14‐week duration in 132 patients with Parkinson’s disease dementia (PDD). Whole blood transcriptomics analysis (RNAseq) was performed for the PDD study at two timepoints: baseline and Week 14. After quality control filtering, 14,150 genes were retained for analysis. Methods Weighted gene correlation network analysis (WGCNA) was used to identify clusters of genes that show a correlated expression change across patients and timepoints. Thus, a data‐driven gene network was generated. Associations between the identified clusters and treatment arms were explored in order to select clusters for which gene expression is significantly associated with the high blarcamesine dose. These associations were assessed using linear mixed effect models with three covariates: dose, patient and timepoint. Significance was assessed with Dunett’s test. Results Blarcamesine significantly restores functionality in key pathways of Alzheimer’s disease, Parkinson’s disease and Prion diseases. The analysis identified two clusters of genes that are significantly differentially expressed in treated patients and represent compensatory pathways to genes dysregulations induced by neurodegenerative diseases. In both clusters, majority of genes were confirmed to interact (STRING database). Amongst interacting genes, neurodegenerative pathways were identified as overrepresented (p < 0.01). Down‐regulation of genes involved with neurodegeneration was observed in the placebo arm, however, was compensated in the blarcamesine arm. Conclusions This analysis identified a gene network that is differentially expressed in patients treated with blarcamesine after 14 weeks of treatment, compared to placebo. The biological relevance of this gene network was assessed. Pathway analysis confirmed the impact of the treatment on pathways involved in neurodegenerative diseases The identification of a gene network as the blarcamesine response pathway lays the foundation to better understand the mechanism of action at the molecular level of blarcamesine, thus unlocking characterization of responders based on molecular profiling, as well as identification of new indications in the area of neurodegenerative and other disorders.