Altered Genomic and Epigenetic Profiling of Myeloma Bone Marrow Stromal Cells Identifies Targets for Current and Future Immunotherapeutic Approaches
Bibliographic record
Abstract
Background: Novel drug discoveries have shifted the treatment paradigms of most hematological malignancies including multiple myeloma (MM), but minimal residual disease and drug resistance underlie relapses in MM. Although many genetic and epigenetic alterations regulate MM progression, MM cells are not autonomous. Dynamic interactions between MM cells and cells of the bone marrow (BM) microenvironment have been reported by our group and others. MM plasma cells (PCs) depend on interactions with bone marrow stromal cells (BMSCs) for their survival and growth, but little is known about the specific genetic events taking place in the MM BM microenvironment. Methods: Here we report a detailed analysis of the genetic and epigenetic events that are characteristic of MM BMSC as compared to HD-BMSC interacting with BM PCs. To evaluate genetic and epigenetic landscapes, RNA was extracted from bulk sorted populations of 16 MM-BMSC, 3 HD-BMSC, and 10 autologous MM cells. We prepared libraries for 32 samples using the NEBNext Ultra II Stranded Poly A kit, and then sequenced on the NextSeq 500, PE150. Sequencing data were analyzed using a custom computational and statistical pipeline at the Department of Biostatistics, School of Public Health and Partek software. Results: Unsupervised clustering showed that MM-BMSC samples clustered as a distinct and completely separate cluster from HD-BMSC and autologous MM cells. Gene level analyses of these three groups identified 990 genes differentially expressed (upregulated or downregulated, P< 0.005). Sequential filtration analyses of the differentially expressed genes in MM-BMSC identified significant deregulation of : transcripts in the Jak-STAT signaling pathway (JAK3, PIM1, IL6, CSF2R, AKT1/2, BCL2L1, CDKN1A and range of IL transcripts); genes encoding extracellular matrix interacting proteins (CD36, CD49, LAMA3, CD44, CD47); and various plasma membrane proteins that define different subpopulations of hematopoietic cells. These genes were deregulated in >24% of MM-BMSC samples analyzed as compared to HD-BMSC samples. These transcripts were downregulated in autologous tumor cells. Next, we interrogated the epigenomic landscape and identified the splicing signature of MM-BMSC as compared to HD-BMSC, and autologous MM cells. Comparison of the splicing patterns (exon skipping, intron retention, novel splice acceptor and/or donor activation) of these three distinct groups showed that a total of 2,100 genes were differentially expressed and 566 were alternatively spliced among the three groups (P < 0.01). These analyses identified a limited number of the transcripts with ~3% significantly spliced in MM-BMSC compared to HD-BMSC. However, comparing MM-BMSC splicing events to MM cells splicing events, we identified >30% of genes which were alternatively spliced in MM cells but not in MM-BMSC. Further, gene enrichment and pathway analyses identified a selective set of transcripts that were alternatively and differentially spliced in MM-BMSC including genes involved in MAPK and Ras signaling pathways, homologous recombination, mismatch repair, and adherens junction. Conclusions: Taken together, our studies identified marked differences between important stromal elements in MM- and HD-BM. We identified genes that were specifically upregulated/suppressed in MM-BMSC compared to MM-cells and HD-BMSC. Within MM BMSCs, we identified several splicing events on genes of signaling pathways implicated in development and progression of MM. Furthermore, altered splicing events identified on these transcripts represent potential new immunotherapeutic targets. Disclosures Chu: Gilead: Honoraria; Celgene: Honoraria; Teva: Consultancy; Amgen Inc.: Honoraria; AstraZeneca: Honoraria. Anderson:C4 Therapeutics: Other: Scientific founder ; Gilead Sciences: Other: Advisory Board; OncoPep: Other: Scientific founder ; Sanofi-Aventis: Other: Advisory Board; Janssen: Other: Advisory Board.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".