The 3D- Telomere Profiling Assay Identifies High Risk Smoldering Multiple Myeloma Patients with High Precision
Bibliographic record
Abstract
Smoldering multiple myeloma (SMM) is an asymptomatic precursor stage of multiple myeloma (MM). According to the international Myeloma Working Group data (Blood Cancer J. 2020 Oct 16;10(10):102) the probability of progression to MM is 22% at 2 years, and 42% and 64% at 5 and 10 years, respectively. Thus, identification of patients with high risk of early progression to MM is crucial for optimal treatment management. Genomic instability is a sensitive indicator of disease progression in cancer. Telomere dysfunction is an early event in genomic instability. The 3-dimensional (3D) spatial profiling of telomeres using TeloView technology allows for quantification of telomere dysfunction and was shown to be instrumental in risk stratification of cancer patients generally, but particularly in selected hematological malignancies. Importantly, the utility of the 3D-telomere profiling was recently demonstrated in 2 clinical studies validating the utility of 3D telomere profiling as a structural biomarker to identify high risk SMM patients. The two studies achieved accuracy of >80% and specificity and sensitivity of over 80% & 76% respectively in a training dataset study followed by a blind validation (Kumar S.et al 2023. J Clin Oncol). To present the 3D telomere profiling assay as a reliable prognostic tool in the clinic, capable to precisely identify high risk SMM patients, it is important to demonstrate the repeatability and accuracy of the assay. In this study, we conducted a retrospective analytical validation including samples of 20 SMM patients with known progression outcome. The samples of each patient were blindly processed in triplets using the 3D-telomere assay, followed by the TeloView analysis. To eliminate any validation-bias the validation was conducted with uncontrolled variables. Samples were processed blindly by different operators on different dates using different instrumentation. We interrogated the repeatability and precision of the results of each patient analyzed on 2 levels. First, we calculated coefficient of variation (CV) of telomere predictors of each patient across the three runs; and secondly, we interrogated the repeatability of patient outcome using the scoring model developed and validated in (Kumar et al. 2023, J Clin Oncol) across the three runs of each patient. We set the acceptable concordance to 80% for CVs (CV <20%) and the acceptable repeatability of patient-outcome as predicted by the scoring model to 80% of the patients included in the study. We report CVs across all the telomere predictors, across the 3 runs of all patients ranging between 2.3 - 17.8. Furthermore, patient outcome as high/low risk of progression was consistent in 17 out of the 20 patients in all 3 runs (85%). The results of this study demonstrate the 3D-telomere profiling as an accurate and prognostic structural biomarker. The assay shows high level of precision, repeatability, and reliability, and presents a viable solution to accurately identify high risk SMM patients. This will potentially allow the treating physicians to make confident treatment decisions for high risk SMM patients based on the results of the 3D Telomere profiling assay.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".