Multiple myeloma is affected by multiple and heterogeneous somatic mutations in adhesion- and receptor tyrosine kinase signaling molecules
Bibliographic record
Abstract
Multiple myeloma (MM) is a largely incurable plasma cell malignancy with a poorly understood and heterogeneous clinical course. To identify potential, functionally relevant somatic mutations in MM, we performed whole-exome sequencing of five primary MM, corresponding germline DNA and six MM cell lines, and developed a bioinformatics strategy that also integrated published mutational data of 38 MM patients. Our analysis confirms that identical, recurrent mutations of single genes are infrequent in MM, but highlights that mutations cluster in important cellular pathways. Specifically, we show enrichment of mutations in adhesion molecules of MM cells, emphasizing the important role for the interaction of the MM cells with their microenvironment. We describe an increased rate of mutations in receptor tyrosine kinases (RTKs) and associated signaling effectors, for example, in EGFR, ERBB3, KRAS and MAP2K2, pointing to a role of aberrant RTK signaling in the development or progression of MM. The diversity of mutations affecting different nodes of a particular signaling network appears to be an intrinsic feature of individual MM samples, and the elucidation of intra- as well as interindividual redundancy in mutations that affect survival pathways will help to better tailor targeted therapeutic strategies to the specific needs of the MM patient.
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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 | 0.001 | 0.001 |
| 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".