Long-read single-cell RNA sequencing enables the study of cancer subclone-specific genotype and phenotype in chronic lymphocytic leukemia

Abstract

Bruton's tyrosine kinase (BTK) inhibitors are effective for the treatment of chronic lymphocytic leukemia (CLL) due to BTK's role in B cell survival and proliferation. Treatment resistance is most commonly caused by the emergence of the hallmark BTK^C481S mutation that inhibits drug binding. In this study, we aimed to investigate whether the presence of additional CLL driver mutations in cancer subclones harboring a BTK^C481S mutation accelerates subclone expansion. In addition, we sought to determine whether BTK-mutated subclones exhibit distinct transcriptomic behavior when compared to other cancer subclones. To achieve these goals, we employ our recently published method (Qiao et al. 2024) that combines bulk DNA sequencing and single-cell RNA sequencing (scRNA-seq) data to genotype individual cells for the presence or absence of subclone-defining mutations. While the most common approach for scRNA-seq includes short-read sequencing, transcript coverage is limited due to the vast majority of the reads being concentrated at the priming end of the transcript. Here, we utilized MAS-seq, a long-read scRNAseq technology, to substantially increase transcript coverage across the entire length of the transcripts and expand the set of informative mutations to link cells to cancer subclones in six CLL patients who acquired BTK^C481S mutations during BTK inhibitor treatment. We found that BTK-mutated subclones often acquire additional mutations in CLL driver genes, leading to faster subclone proliferation. When examining subclone-specific gene expression, we found that in one patient, BTK-mutated subclones are transcriptionally distinct from the rest of the malignant B cell population with an overexpression of CLL-relevant genes.

Figure 1.. Long-read scRNA sequencing metrics.

A) Comparison of the total number of HiFi reads, the total number of segmented reads, and the mean reads per cell for each sample, colored by the sequencer used. B) The canonical transcript coverage for each read aligning to a given gene is calculated for all protein-coding genes with at least one read aligned to it. The percentage of reads covering X% of the given transcript is plotted for each sample, colored by the sequencer used for the sample. Eight short-read samples are included in black for comparison.

Figure 2.. Variant coverage provided by each scRNA-seq technology.

A) The overall variant coverage provided by the Sequel II and Revio compared to Illumina short-reads. The percentage of cells covering each heterozygous germline variant in the patient’s WES data is used to determine the percent of variants covered by at least X% of cells. B) The variant coverage binned by the variant’s distance from the priming site, as indicated above each plot (bp = base pairs).

Figure 3.. Overview of workflow to identify and use cell genotypes.

A) Pre-determined subclone structures with accompanying somatic variants are used to genotype individual cells in scRNA-seq data. Cells are assigned to a pre-determined subclone based on the presence or absence of subclone-defining mutations. Subclone assignments are then used to group cells of the same subclone to identify subclone-specific gene expression. B) Genotype matrix plots visualize the genotypes of all cells at each variant of interest, showing green for reference allele, red for alternate allele, and white for no coverage.

Figure 4.. Visualization of single-cell genotypes to identify subclone structures.

A) The subclone structure of Patient 1 identified in the bulk DNA sequencing data. Subclones are depicted by the colored circles, with representative variant clusters inside each circle. B) The cell genotypes at subclone-defining variants in Patient 1, with green markers representing only reference alleles present in the scRNA-seq reads at the given variant location within the cell and red markers indicating at least one scRNA-seq read in the cell contains the somatic variant allele. Darker marker coloring indicates an increased number of reads supporting that genotype. Variants and cells are grouped by their subclone assignment.

Figure 5.. Refining the subclone structure of Patient 3.

A) The subclone structure identified in the bulk DNA sequencing data. CLL-relevant gene mutations are annotated under the subclone they are found in. B) The genotype matrix plot from the relapse sample of Patient 3 enables refinement of the original subclone structure. Green markers indicate that only reference alleles were present in the scRNA-seq reads at the given variant location within the cell, and red markers indicate that at least one scRNA-seq read in the cell contains the somatic variant allele. Darker coloring indicates an increased number of reads supporting that genotype. Only the CLL-relevant mutations are included for increased resolution to differentiate subclones. C) The refined subclone structure that depicts the subclone containing the BTK c.1543T>A mutation is independent of the subclone containing the BTK c.1544G>C and DICER1 mutations.

Figure 6.. Using cell assignments to identify subclone-specific gene expression patterns.

A) Mapping subclone assignment to clustered cells enables the identification of phenotypically distinct subclones. B) Differential gene expression analysis between subclones illuminates over- and under-expressed genes within the BTK-mutated subclone. (***) adjusted p-value < 0.001.