Does a Multiple Myeloma Polygenic Risk Score Predict Overall Survival of Patients with Myeloma?
Bibliographic record
Abstract
BACKGROUND: Genome-wide association studies (GWAS) of multiple myeloma in populations of European ancestry (EA) identified and confirmed 24 susceptibility loci. For other cancers (e.g., colorectum and melanoma), risk loci have also been associated with patient survival. METHODS: We explored the possible association of all the known risk variants and their polygenic risk score (PRS) with multiple myeloma overall survival (OS) in multiple populations of EA [the International Multiple Myeloma rESEarch (IMMEnSE) consortium, the International Lymphoma Epidemiology consortium, CoMMpass, and the German GWAS] for a total of 3,748 multiple myeloma cases. Cox proportional hazards regression was used to assess the association between each risk SNP with OS under the allelic and codominant models of inheritance. All analyses were adjusted for age, sex, country of origin (for IMMEnSE) or principal components (for the others) and disease stage (ISS). SNP associations were meta-analyzed. RESULTS: SNP associations were meta-analyzed. From the meta-analysis, two multiple myeloma risk SNPs were associated with OS (P < 0.05), specifically POT1-AS1-rs2170352 [HR = 1.37; 95% confidence interval (CI) = 1.09-1.73; P = 0.007] and TNFRSF13B-rs4273077 (HR = 1.19; 95% CI = 1.01-1.41; P = 0.04). The association between the combined 24 SNP MM-PRS and OS, however, was not significant. CONCLUSIONS: Overall, our results did not support an association between the majority of multiple myeloma risk SNPs and OS. IMPACT: This is the first study to investigate the association between multiple myeloma PRS and OS in multiple myeloma.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".