Do chip mutations predict frailty and toxicity in transplant-eligible patients with multiple myeloma?
Bibliographic record
Abstract
Abstract Background We have previously reported that current frailty tools have limited ability to predict treatment-related toxicity in transplant-eligible (TE) patients with multiple myeloma (MM) (Devasia, ASH 2022). Although clonal hematopoiesis of indeterminate potential (CHIP) mutations has been associated with more frailty and treatment-related toxicities in patients with newly diagnosed MM (Gelli, Scientific Reports 2024), their clinical relevance specifically in TE patients has yet to be elucidated. Therefore, we aimed to evaluate whether the presence of CHIP mutations predicts frailty and toxicity in patients preparing for autologous stem cell transplant (ASCT). Method We have two ongoing prospective trials in TE-MM patients at our centre. The “Frailty” study evaluates the utility of various frailty assessments prior to ASCT in predicting transplant-related toxicity and outcomes (Shih, IMS 2025). The ARCH-001 study evaluates the prevalence and evolution of CHIP mutations at different time points relevant to their ASCT (Khan, ASH 2024). Patients enrolled in both studies were selected for this correlative analysis. The presence of CHIP mutations prior to ASCT (ARCH-001) served as the predictor variable. The following variables extracted from Frailty study served as the outcome variables: 1) subjective measures of function (Rockwood Frailty, Karnofsky Performance [KPS], ECOG), 2) objective measures of fitness [hand grip strength, 6-min walk test, Timed Up and Go, 3) comorbidity indices (Charlson and HCT-CI), 4) organ function (eGFR, serum albumin, BNP, PFT, ECHO), 5) Post-ASCT acute grade ≥3 organ toxicities and mortality to Day 100, ICU use, time to engraftment, transfusion requirements and length of stay (LOS). Chi2or Fisher’s exact test was used as appropriate for categorical variables. T-test was used for continuous variables. All P values were 2 sided and statistically significant at P < 0.05. Statistical analysis was performed using STATA version 19. Results We identified 101 participants who were enrolled in both studies and had evaluable data. Of these, 38 (37.6%) had at least one CHIP mutation and 5 (5.0%) had two CHIP mutations. DNMT3A, ASXL1 and TET2 mutations were most common, with 30 (29.7%) patients having at least one of these. Pre-ASCT There was a greater proportion of patients with CHIP mutation in those age ≥65 than those age <65 (76.3% vs 23.7% respectively, P=0.025), but no difference in other demographics (sex, BMI), or pre-transplant parameters (first vs salvage transplant, melphalan dose or stem cell dose). The presence of CHIP mutations was not associated with increased all-grade anemia, leukopenia or thrombocytopenia prior to ASCT (P = 0.443, P = 0.204 and P = 0.259, respectively), or any of the frailty parameters (data will be presented). Post-ASCT There was no correlation between the presence of CHIP mutations and any of the acute grade ≥3 transplant-related toxicities. There were only 2 ICU admissions and 0 deaths by day+100. There was a trend towards more cardiac-related adverse events in patients with CHIP mutations compared to those without (21.1% vs 9.5% respectively), but this was not statistically significant (P = 0.057). There was also a trend towards more cumulative toxicity (defined as ≥2 toxicity events) in those with CHIP mutations compared to those without (50.0% vs.31.8% respectively), but this was not statistically significant either (P = 0.068). The mean number of days to neutrophil and platelet engraftment in all patients was 12.6 and 17.6, respectively. The presence of CHIP mutations was not associated with prolonging these (P = 0.395 and P = 0.202, respectively). The mean number of bags of red cells and platelets required for transfusion during ASCT in all patients was 0.5 (0-8) and 1.0 (0-9), respectively. The presence of CHIP mutations did not increase these either (P = 0.109 and P = 0.230). The mean LOS in those with vs without CHIP mutations was 22.1 and 17.9 days, respectively, but the difference was not statistically significant (P = 0.197). Conclusion The presence of CHIP mutations in TE-MM patients was not associated with increased frailty, acute grade ≥3 transplant-related toxicity or any short-term transplant-related outcomes. However, this was a highly selected population that tends to be younger and fitter. Continued follow-up is required to evaluate whether these CHIP mutations affect longer-term treatment-related efficacy, toxicity or survival outcomes in TE-MM patients.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".