Single-cell multi-gene identification of somatic mutations and gene rearrangements in cancer

Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer

Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Follicular lymphoma evolves with a surmountable dependency on acquired glycosylation motifs in the B-cell receptor

ABSTRACT

An early event in the genesis of follicular lymphoma (FL) is the acquisition of new glycosylation motifs in the B-cell receptor (BCR) due to gene rearrangement and/or somatic hypermutation. These N-linked glycosylation motifs (N-motifs) contain mannose-terminated glycans and can interact with lectins in the tumor microenvironment, activating the tumor BCR pathway. N-motifs are stable during FL evolution, suggesting that FL tumor cells are dependent on them for their survival. Here, we investigated the dynamics and potential impact of N-motif prevalence in FL at the single-cell level across distinct tumor sites and over time in 17 patients. Although most patients had acquired at least 1 N-motif as an early event, we also found (1) cases without N-motifs in the heavy or light chains at any tumor site or time point and (2) cases with discordant N-motif patterns across different tumor sites. Inferring phylogenetic trees of the patients with discordant patterns, we observed that both N-motif-positive and N-motif-negative tumor subclones could be selected and expanded during tumor evolution. Comparing N-motif-positive with N-motif-negative tumor cells within a patient revealed higher expression of genes involved in the BCR pathway and inflammatory response, whereas tumor cells without N-motifs had higher activity of pathways involved in energy metabolism. In conclusion, although acquired N-motifs likely support FL pathogenesis through antigen-independent BCR signaling in most patients with FL, N-motif-negative tumor cells can also be selected and expanded and may depend more heavily on altered metabolism for competitive survival.