Genomic instability in multiple myeloma: mechanisms and therapeutic implications
Bibliographic record
Abstract
INTRODUCTION: Clonal plasma cells in multiple myeloma (MM) are typified by their nearly universal aneuploidy and the presence of recurrent chromosomal aberrations reflecting their chromosomal instability. Multiple myeloma is also recognized to be heterogeneous with distinct molecular subgroups. Deep genome sequencing studies have recently revealed an even wider heterogeneity and genomic instability with the identification of a complex mutational landscape and a branching pattern of clonal evolution. AREAS COVERED: Despite the lack of full understanding of the exact mechanisms driving the genomic instability in MM, recent observations have correlated these abnormalities with impairments in the DNA damage repair machinery as well as epigenetic changes. These mechanisms and the resulting therapeutic implications will be the subject of this review. EXPERT OPINION: By providing growth advantage of the fittest clone and promoting the acquisition of drug resistance, genomic instability is unarguably beneficial to MM cells, however, it may also well be its Achilles heal by creating exploitable vulnerabilities. As such, targeting presumptive DNA repair defects and other oncogenic addictions represent a promising area of clinical investigation. In particular, by inducing gene or pathway dependencies not present in normal cells, genomic instability can generate targets of contextual synthetic lethality in MM cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".