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.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".