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Supplementary Table S7 from A Functional Survey of the Regulatory Landscape of Estrogen Receptor–Positive Breast Cancer Evolution

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posted on 2024-09-04, 07:41 authored by Iros Barozzi, Neil Slaven, Eleonora Canale, Rui Lopes, Inês Amorim Monteiro Barbosa, Melusine Bleu, Diana Ivanoiu, Claudia Pacini, Emanuela Mensa’, Alfie Chambers, Sara Bravaccini, Sara Ravaioli, Balázs Győrffy, Maria Vittoria Dieci, Giancarlo Pruneri, Giorgio Giacomo Galli, Luca Magnani
<p>Supplementary Table S7: SIDP results (MCF7 & LTED) summary. S7.1: regions showing at least one overlapping sgRNA scoring in at least one of the different conditions assayed. For each region (hg38 genomic coordinates), the table indicates whether this was selected as a gene promoter, putative enhancer, or putative insulator. It also shows the symbol of the nearest gene, and the distance to its TSS in bp (positive or negative values indicate the region is either downstream or upstream the TSS, respectively). For each condition (MCF7 +E2, MCF7 -E2 or LTED) and direction of the change (DF vs IF), the table indicates whether the region overlaps one or more (columns labeled “single”) vs two or more (columns labeled “multiple”) sgRNAs. S7.2: summary of the overlaps between either scoring sgRNAs (“guides”), regions showing at least one scoring sgRNA (“regions_single”), or regions showing two or more consistently scoring sgRNAs (“regions_multiple”) between pairs of conditions (as indicated by columns assay_1 and assay_2). The nature of the change (either DF or IF), along with the total number of overlapping sgRNAs or regions, and the corresponding fraction, are also indicated. S7.3: results of gene set enrichment analysis using the indicated gene sets and the set of genes close to the regions showing scoring sgRNAs, according to the indicated pattern (SIDP_set). Statistics of the hyper-geometric test are shown, along with the total number of the overlapping genes (count), the observed and expected overlaps, and the odds ratio. S7.4: total number of regions showing multiple, consistent, scoring sgRNAs, and relative split-up based on the distance (bp) to the nearest TSS (0-0.5 kbp, 0.5-2.5 kbp, 2.5-5 kbp, 5-10 kbp, over 10 kbp), per condition (MCF7 +E2, MCF7 -E2 or LTED) and direction of the change (DF vs IF).</p>

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Cancer Research UK (CRUK)

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ARTICLE ABSTRACT

Only a handful of somatic alterations have been linked to endocrine therapy resistance in hormone-dependent breast cancer, potentially explaining ∼40% of relapses. If other mechanisms underlie the evolution of hormone-dependent breast cancer under adjuvant therapy is currently unknown. In this work, we employ functional genomics to dissect the contribution of cis-regulatory elements (CRE) to cancer evolution by focusing on 12 megabases of noncoding DNA, including clonal enhancers, gene promoters, and boundaries of topologically associating domains. Parallel epigenetic perturbation (CRISPRi) in vitro reveals context-dependent roles for many of these CREs, with a specific impact on dormancy entrance and endocrine therapy resistance. Profiling of CRE somatic alterations in a unique, longitudinal cohort of patients treated with endocrine therapies identifies a limited set of noncoding changes potentially involved in therapy resistance. Overall, our data uncover how endocrine therapies trigger the emergence of transient features which could ultimately be exploited to hinder the adaptive process.Significance: This study shows that cells adapting to endocrine therapies undergo changes in the usage or regulatory regions. Dormant cells are less vulnerable to regulatory perturbation but gain transient dependencies which can be exploited to decrease the formation of dormant persisters.