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Comprehensive profiling of hormone-dependent breast cancer (HDBC) has identified hundreds of protein-coding alterations contributing to cancer initiation1, 2, but only a handful have been linked to endocrine therapy resistance, potentially contributing to 40% of relapses1, 3–9. If other mechanisms underlie the evolution of HDBC under adjuvant therapy is currently unknown. In this work, we employ integrative functional genomics to dissect the contribution of cis-regulatory elements (CREs) to cancer evolution by focusing on 12 megabases of non-coding DNA, including clonal enhancers10, gene promoters, and boundaries of topologically associating domains11. Massive parallel perturbation in vitro reveals context-dependent roles for many of these CREs, with a specific impact on dormancy entrance12, 13 and endocrine therapy resistance9. Profiling of CRE somatic alterations in a unique, longitudinal cohort of patients treated with endocrine therapies identifies non-coding changes involved in therapy resistance. Overall, our data uncover actionable transient transcriptional programs critical for dormant persister cells and unveil new regulatory nodes driving evolutionary trajectories towards disease progression.

Droplet-based single-cell omics, including single-cell RNA sequencing (scRNAseq), single-cell CRISPR perturbations (e.g., CROP-seq), and single-cell protein and transcriptomic profiling (CITE-seq) hold great promise for comprehensive cell profiling and genetic screening at the single-cell resolution. However, these technologies suffer from substantial noise, among which ambient signals present in the cell suspension may be the predominant source. Current models to address this issue are highly technology-specific and relatively scRNAseq-centric. while a universal model to describe the noise across these technologies may reveal this common source, improving the denoising accuracy. To this end, we explicitly examined these unexpected signals in multiple datasets across droplet-based technologies, summarised a predictable pattern, and developed single-cell Ambient Remover (scAR) – a hypothesis-driven machine learning model to predict and remove ambient signals (including mRNA counts, protein counts, and sgRNA counts) at the molecular level. We benchmarked scAR on three technologies – single-cell CRISPR screens, CITE-seq, and scRNAseq along with the state-of-the-art single-technology-specific approaches. scAR showed high denoising accuracy for each type of dataset.