Li and colleagues report the genomic landscape of over 100 patients with relapsed acute lymphoblastic leukemia. Analysis of diagnosis-relapse-remission trios suggest that whereas early relapse is mediated by retained subclones, late relapse is driven by mutations induced by and conferring resistance to chemotherapy.

Imaging chromatin dynamics is crucial to understand genome organization and its role in transcriptional regulation. Recently, the RNA-guidable feature of CRISPR-Cas9 has been utilized for imaging of chromatin within live cells. However, these methods are mostly applicable to highly repetitive regions, whereas imaging regions with low or no repeats remains as a challenge. To address this challenge, we design single-guide RNAs (sgRNAs) integrated with up to 16 MS2 binding motifs to enable robust fluorescent signal amplification. These engineered sgRNAs enable multicolour labelling of low-repeat-containing regions using a single sgRNA and of non-repetitive regions with as few as four unique sgRNAs. We achieve tracking of native chromatin loci throughout the cell cycle and determine differential positioning of transcriptionally active and inactive regions in the nucleus. These results demonstrate the feasibility of our approach to monitor the position and dynamics of both repetitive and non-repetitive genomic regions in live cells.

CRISPR-Cas9-induced DNA damage may have deleterious effects at high-copy-number genomic regions. Here, we use CRISPR base editors to knock out genes by changing single nucleotides to create stop codons. We show that the CRISPR-STOP method is an efficient and less deleterious alternative to wild-type Cas9 for gene-knockout studies. Early stop codons can be introduced in ∼17,000 human genes. CRISPR-STOP-mediated targeted screening demonstrates comparable efficiency to WT Cas9, which indicates the suitability of our approach for genome-wide functional screenings.

To discover driver fusions beyond canonical exon-to-exon chimeric transcripts, we develop CICERO, a local assembly-based algorithm that integrates RNA-seq read support with extensive annotation for candidate ranking. CICERO outperforms commonly used methods, achieving a 95% detection rate for 184 independently validated driver fusions including internal tandem duplications and other non-canonical events in 170 pediatric cancer transcriptomes. Re-analysis of TCGA glioblastoma RNA-seq unveils previously unreported kinase fusions (KLHL7-BRAF) and a 13% prevalence of EGFR C-terminal truncation. Accessible via standard or cloud-based implementation, CICERO enhances driver fusion detection for research and precision oncology. The CICERO source code is available at https://github.com/stjude/Cicero.

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