ATLANTA-New research suggests relapsed myeloma patients may require highly individualized therapy to overcome the complexity of the disease during relapse. The data, which was presented at the 2017 American Society of Hematology Annual Meeting, found that single-cell RNA sequencing is an important tool that could improve characterization and treatment of these patients (Abstract 62).
"Genomic variation within a cancer in an individual patient (clonal heterogeneity) contributes to chemotherapy resistance in cancer," noted study author Samir Parekh, MBBS, Associate Professor of Oncological Sciences, Medicine, Hematology and Medical Oncology at The Tisch Cancer Institute at the Icahn School of Medicine at Mount Sinai. "We have been studying genomic heterogeneity in myeloma at Mount Sinai to improve our understanding and treatment of relapsed myeloma patients in our clinic."
Study Methodology
Given the complexities of multiple myeloma and the challenges often associated with treatment, researchers hypothesized that "the use of single-cell RNA sequencing can elucidate the complex clonal transcriptomic structure of individual patients' disease, and can be used in concert with other genomic techniques to better characterize and treat patients on a personalized level."
"Analysis of genomic changes in large numbers of single cells from patient tumors has been technically challenging. We collaborated with Drs. Dudley and Sebra from the Icahn Institute for Genomics and Multiscale Biology to isolate hundreds to thousands of tumor cells per patient from bone marrow aspirates from myeloma patients," Parekh explained. "Droplet sequencing technology was used, where individual cells are barcoded prior to next-generation sequencing, and reads are mapped back to each cells using bioinformatic techniques."
Individual CD138+ tumor cells were isolated and barcoded from bone marrow aspirates from eight relapsed myeloma patients, study authors noted. Researchers utilized a platform that "allows for large-scale simultaneous cellular sequencing, which facilitates a more robust analysis of cellular populations than previous technologies." Depending on sample cell suspension density, the number of cells analyzed ranged from 393 to 3,359 per sample.
Results, Practice Implications
Looking at the individual patient level, researchers processed "scRNA-seq data to visualize clusters of single cells with transcriptional similarity using t-distributed stochastic neighbor embedding plots."
"We identified pathway activation in individual scRNA-seq clones via differential expression analysis and Signaling Pathway Impact Analysis, in concordance with known somatic mutations," study authors wrote. "Genomic drivers and stages of differentiation in cell subsets (plasma cell vs. immature B cell-like plasma cells) within a given patient were also revealed by distinct expression profiles."
Open-source drug repurposing tools with the RNA signature of individual clones were utilized by researchers to interrogate transcriptional signatures of drugs in public databases. "The use of such drug repurposing methods will be effective in generating more targeted personalized therapies, utilizing cluster specific expression profiles," according to investigators. "This is especially useful for cellular populations which do not contain a known target based on DNA mutational analysis. In a pooled analysis of all samples, each patient sample remarkably formed its own spatially distinct cluster of cells."
"We could identify intra-patient as well as inter-patient clonal heterogeneity at an unprecedented single-cell resolution in hundreds to thousands of individual cells from myeloma patients. There was clone-specific variation in gene expression as well as clone-specific pathway activation in individual cancers," Parekh told Oncology Times. "This suggests that individual drugs may not work equally for all clones within a patient, and individualized multi-drug treatment approaches may need to be developed in a data-driven manner to effectively treat patients.
"Our results from single-cell analysis complement our group's results studying clonal heterogeneity using bulk sequencing (Abstract 325)," he continued. "The combination of the two technologies (single-cell and bulk sequencing) should be integrated for a complete picture of clonal heterogeneity. We are correlating these in sequential samples to determine efficacy of novel treatments in a clone-specific manner.
"These results indicate that studies need to incorporate single cells-sequencing assessment to better understand efficacy of new and existing drugs at a clone-specific level [and] to ultimately develop combination therapies to eradicate all clones for curing myeloma and other cancers," Parekh concluded.
Catlin Nalley is associate editor.