Identification of Molecular Vulnerabilities in Human Multiple Myeloma Cells by RNA Interference Lethality Screening of the Druggable Genome
Bibliographic record
Abstract
Despite recent advances in targeted treatments for multiple myeloma, optimal molecular therapeutic targets have yet to be identified. To functionally identify critical molecular targets, we conducted a genome-scale lethality study in multiple myeloma cells using siRNAs. We validated the top 160 lethal hits with four siRNAs per gene in three multiple myeloma cell lines and two non-myeloma cell lines, cataloging a total of 57 potent multiple myeloma survival genes. We identified the Bcl2 family member MCL1 and several 26S proteasome subunits among the most important and selective multiple myeloma survival genes. These results provided biologic validation of our screening strategy. Other essential targets included genes involved in RNA splicing, ubiquitination, transcription, translation, and mitosis. Several of the multiple myeloma survival genes, especially MCL1, TNK2, CDK11, and WBSCR22, exhibited differential expression in primary plasma cells compared with other human primary somatic tissues. Overall, the most striking differential functional vulnerabilities between multiple myeloma and non-multiple myeloma cells were found to occur within the 20S proteasome subunits, MCL1, RRM1, USP8, and CKAP5. We propose that these genes should be investigated further as potential therapeutic targets in multiple myeloma.
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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) | 0.001 | 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".