European Myeloma Network Group Consensus Statement on the use of next‐generation sequencing for prognostic stratification of newly diagnosed multiple myeloma
Bibliographic record
Abstract
Given the evolving understanding of genetic risk factors in multiple myeloma (MM), this paper assesses whether next-generation sequencing (NGS) could complement or even replace fluorescence in situ hybridization (FISH) at diagnosis. A structured consensus process within European Myeloma Network (EMN) clinical and laboratory groups was conducted to establish recommendations on routine clinical deployment of NGS in MM risk assessment. Four key questions were addressed: (1) should NGS be used in addition to, or alternatively to FISH in identifying prognostic genetic markers, (2) which prognostic markers are most relevant for analysis by NGS, (3) which patients should be offered NGS testing, and (4) what is the optimal timing for performing NGS. The panel reviewed current literature, evaluated available NGS technologies, and compared their performance with that of FISH-based methodologies. The paper reviews current standard NGS protocols, quality control measures, and provides practical points for the implementation of an NGS diagnosis in MM. While NGS shows promise in improving risk stratification, challenges such as cost, accessibility, and clinical workflow integration must be addressed. The consensus supports the initial incorporation of NGS as a complementary tool to FISH. Recommendations emphasize that: a broader list of genetic events should be incorporated into such a test than what currently requested by risk scores; the test should be offered at least to the fit patients who could be candidates for modern triplet or quadruplet treatments; the test should be repeated at the time relapse, especially in the future when targeted treatments may mandate the use of predictive markers of response. This consensus provides a foundation for future research and policy development, guiding the adoption of NGS in MM risk assessment.
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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 | 0.001 |
| 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) | 0.000 | 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".