Co-mapping Clonal and Transcriptional Heterogeneity in Somatic Evolution via GoT-Multi.

Somatic evolution leads to cancer progression, including therapy-resistant states, as in the progression of chronic lymphocytic leukemia (CLL) to large B-cell lymphoma (LBCL), called Richter Transformation (RT). To directly link clonal selection with phenotypic progression, we require a method that co-captures numerous genotypes and high-resolution cell state information. We developed Genotyping of Transcriptomes for Multiple Targets and Sample Types (GoT-Multi), a high-throughput, probe-based, and FFPE-compatible single-cell multi-omics method for simultaneous genotyping of multiple somatic mutations and whole transcriptomic profiling. We developed an analytical pipeline that integrates ensemble-based machine learning to the genotype calling algorithm. We applied GoT-Multi to nodal samples in which both CLL and LBCL components were observed simultaneously. We identified heterogeneous cancer cell states, with an enrichment of proliferating and germinal center B-cell states in LBCL, whereas inflammatory and immune response cell states predominated CLL. Integration with genotyping data of 27 mutations revealed the presence of LBCL-restricted mutations such as those in B2M, JUNB, POU2F2 and IRF8. However, subclones involved in therapy resistance, including those with BTK and PLCG2 mutations, were present in both CLL and LBCL, and displayed an inflammatory cell state and suppressed cell cycle entry. We further demonstrated the applicability of GoT-Multi to widely available pathology archived FFPE tissues. Together, the integration of clonal evolution with cancer cell state heterogeneity revealed that distinct therapy-resistant genotypes may converge on similar downstream cell states to enable cancer progression. Single-cell GoT-Multi (based on transcriptome and genotype profiling from 10x Genomics Fixed RNA Profiling) *************************************************************** Raw files for human/patient samples were not submitted to GEO due to concerns about submitting personally identifiable sequence data for open access. ***************************************************************