Uphyloplot2: visualizing phylogenetic trees from single-cell RNA-seq data

Background Recent advances in single cell sequencing technologies allow for greater resolution in assessing tumor clonality using chromosome copy number variations (CNVs). While single cell DNA sequencing technologies are ideal to identify tumor sub-clones, they remain expensive and in contrast to single cell RNA-seq (scRNA-seq) methods are more limited in the data they generate. However, CNV data can be inferred from scRNA-seq and bulk RNA-seq, for which several tools have been developed, including inferCNV, CaSpER, and HoneyBADGER. Inferences regarding tumor clonality from CNV data (and other sources) are frequently visualized using phylogenetic plots, which previously required time-consuming and error-prone, manual analysis. Results Here, we present Uphyloplot2, a python script that generates phylogenetic plots directly from inferred RNA-seq data, or any Newick formatted dendrogram file. The tool is publicly available at https://github.com/harbourlab/UPhyloplot2/. Conclusions Uphyloplot2 is an easy-to-use tool to generate phylogenetic plots to depict tumor clonality from scRNA-seq data and other sources.

UPhyloplot2: Visualizing Phylogenetic Trees from Single-Cell RNA-seq Data

Recent advances in single cell sequencing technologies allow for greater resolution in assessing tumor clonality using chromosome copy number variations (CNVs), which can be inferred from single cell RNA-seq (scRNA-seq) data using applications such as inferCNV. Inferences regarding tumor clonality are frequently visualized using phylogenetic plots, which previously required time-consuming and tedious manual analysis. Here, we present UPhyloplot2, a python script that generates phylogenetic plots directly from inferCNV output files. The tool is publicly available at https://github.com/harbourlab/UPhyloplot2/.

The Role of Transcription Factor and Signaling Gene Mutations in the Clonal Evolution of MDS to Secondary AML

Background: Expansion of one or more subclone occurs during progression from myelodysplastic syndrome (MDS) to secondary acute myeloid leukemia (sAML). Existing data suggest that acquired mutations in myeloid transcription factor (e.g., RUNX1, CEBPA, WT1) and signaling genes (e.g., receptor tyrosine kinases or RAS pathway genes) contribute to clonal evolution and the rising blast count that defines progression to sAML. While signaling gene (SG) mutations are typically acquired later in disease progression, our understanding of when transcription factor (TF) mutations occur, in what clone they occur (e.g. founding clone or subclone), and whether TF-mutated clones undergo further clonal evolution remains incomplete. This is largely due to the limited number of paired MDS and sAML samples analyzed, the limitation of current sequencing technology and the lack of serial samples, and incomplete characterization of tumor clonality. Methods: We banked paired MDS and sAML (plus skin) samples from 44 patients who progressed from MDS to sAML (median time to progression 306 days, range 21-3568). We sequenced sAML and skin samples for 285 recurrently mutated genes (RMGs) and genotyped the paired MDS sample in patients with TF and/or SG mutations. Twelve patients were selected for enhanced whole genome sequencing (eWGS) of MDS and sAML samples (plus skin) to characterize tumor clonality. Somatic mutations were validated using error-corrected sequencing and clones were identified in MDS and sAML samples using mutation variant allele frequencies (VAFs). We tracked clonal evolution by sequencing serial samples between MDS and sAML. Results: The frequency of both TF and SG mutations were elevated in the 44 sAML patients compared to our previously sequenced cohort of 150 independent de novo MDS patients (signaling: 36% vs. 15%, transcription factor: 30% vs. 11%, respectively, p<0.001). Next, we genotyped the 44 paired MDS samples to address whether TF mutations pre-existed at MDS. While SG mutations were rarely detected at MDS (4/22, 18%), TF mutations typically pre-existed at MDS (13/18, 72%; p<0.001). We next asked if SG mutations occur in the same or separate clone than TF mutations. In all 5 cases that had both a TF and SG mutation, and where we could address clonality, the SG mutations were acquired in a cell already containing a TF mutation. Collectively, the data suggest that TF mutations are usually detected before (median: 217 days, range: 21-1012) the blast count rises above 20%. We next addressed whether TF mutations were present in a founding clone or subclone at MDS. As the accuracy of discriminating founding clones and subclones increases with the number of detected mutations, we performed eWGS on 12 pairs of MDS and sAML samples to determine if TF and SG mutations were subclonal and how subclones evolved during progression. We validated a median of 596 (range: 305-1382) mutations per MDS sample and 582 (range 305-1373) mutations per sample at sAML. Mutation VAFs were used to cluster mutations and identify clones. A median of 4 clones (range: 2-6) were detected at MDS compared to 5 (range: 2-8) at sAML, with no patient showing a decrease in the number of clones at progression. When combined with cytogenetic/copy number alterations, 11/12 cases showed the expansion of a subclone during progression. Nine SG and 8 TF mutations (n=17 total) were detected in the 12 sAML samples. In total, 16/17 (94%) TF or SG mutations occurred in subclones, including 14 in subclones that expanded during progression to sAML. Only 1 TF mutation occurred in a founding clone, and 3/4 TF-mutated subclones gave rise to a new subclone, 2 containing a new RMG. Conclusions: Data from 44 patients shows that nearly half of sAML patients have transcription factor and/or signaling gene mutations. While both are enriched at sAML compared to MDS, TF and SG mutations show different timing of mutation acquisition. TF mutations are often present at MDS in subclones indicating that they may serve as a biomarker at MDS for later progression. In comparison, SG mutations are only rarely detected at MDS and are acquired between MDS and sAML. Additionally, for cells containing both TF and SG mutations, the TF mutation is typically acquired first. Despite these differences in acquisition, both TF and SG mutations occur in subclones that often expand and continue to clonally evolve during disease progression, suggesting that they may contribute to the rise in blast count. Disclosures No relevant conflicts of interest to declare.