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Record W2984217605 · doi:10.1182/blood-2019-131243

Altered Genomic and Epigenetic Profiling of Myeloma Bone Marrow Stromal Cells Identifies Targets for Current and Future Immunotherapeutic Approaches

2019· article· en· W2984217605 on OpenAlexaff
Sophia Adamia, Daisuke Ogiya, Ivane Abiatari, Sigitas Verselis, Michael P. Chu, David M. Dorfman, Linda M. Pilarski, Teru Hideshima, Kenneth C. Anderson

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBone marrowEpigeneticsStromal cellMultiple myelomaBiologyCancer researchGeneImmunologyGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.032
GPT teacher head0.272
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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