Stem Cell Mobilization Capacity Is Not a Predictor of Survival in Multiple Myeloma (MM) After Autologous Stem Cell Transplantation (ASCT).
Bibliographic record
Abstract
Abstract Abstract 2154 Poster Board II-131 Introduction: High dose therapy followed by ASCT is standard therapy for younger patients (pts) with newly diagnosed MM. Using a typical stem cell (SC) mobilization regimen of chemotherapy and growth factors, sufficient SCs to support ASCT (≥2×106 CD34+ cells/kg) can be achieved in the majority of MM cases. In addition, it is not uncommon to observe pts who mobilize much larger numbers of SCs (≥8×106 CD34+ cells/kg), or ‘supermobilizers', sufficient to support several transplants. In lymphoid malignancies, these supermobilizers have improved survival outcomes after ASCT (Bolwell et al, 2007). To investigate whether mobilization capacity was a predictor of survival outcomes in MM, we conducted a retrospective review of pts who underwent SC mobilization, collection, and transplant over a 3 year period. Methods: From January 2004 to December 2006, 298 pts with MM undergoing their first transplant (MEL200mg/m2) at our institution were reviewed. SC mobilization was performed with cyclophosphamide 2.5g/m2 IV and GCSF 10ug/kg/day followed by leukapheresis (up to 5 days), aiming for a target CD34+ cell count of ≥5×106/kg (minimum ≥2×106/kg). In this review, pts were classified as ‘supermobilizers' if total number of CD34+ cells collected was >15×106/kg, with those who collected ≤15×106/kg in the control group. Baseline patient and disease characteristics, SC collection details, complications and transplant outcomes, including overall survival (OS) and progression free survival (PFS), were compared between the 2 groups. Results: Of the total 298 pts, 121 (40.6%) were identified as ‘supermobilizers'. Patient and disease characteristics: For the supermobilizer group, median age was 57 yrs (range 32-74), 60.3% male. MM subtypes included: IgG 61.1%, IgA 16.5%, light chain only 20.7%. Median time from diagnosis to transplant was 8.5 mos (range 5.4-48.8). Baseline labs: median Hb 111g/L (range 59-160), platelets 243 (range 48-634), serum creatinine 93 umol/L (range 45-1240), ISS 2-3 (50%). The supermobilizers were younger (median age 57 vs 61 yrs, p=0.002) and had higher baseline platelet counts (243 vs 219, p=0.006) than the control group. SC collection: The median number CD34+ cells collected was 19.9×106/kg (range 15.1-61.8) for the supermobilizer group and 11.1×106/kg (range 1.9-15.0) for the control group. The median number of days on apheresis was shorter for the supermobilizers (1 vs 2 days, p<0.0001), with more supermobilizers able to collect sufficient SCs in a single day on apheresis (74 vs 42%, p<0.0001). Transplant outcomes: At our institution, SC products with ≥5×106/kg CD34+ cells were typically split, reserving half for future salvage transplant. Therefore, regardless of number of SCs mobilized, median number of SCs reinfused for 1st transplant was higher in the supermobilizer group (9.1×106/kg vs 5.5×106/kg, p<0.0001). Not surprisingly, the supermobilizers achieved neutrophil engraftment at a median of 1 day earlier than the control group (11 vs 12 days, p=0.002). There was no significant difference between the 2 groups in grade 3-4 toxicity during the peri-transplant period. At a median followup of 32 mos, the median PFS for all pts was 22.8 months (95% CI: 19.8-25.0) and OS has not yet been reached (no differences between groups). Using total SC number collected as a continuous variable, we were unable to further identify a cut-off level that predicted for improved PFS or OS. Conclusions: From our study, we confirm that younger age and higher baseline platelet count are predictors of improved SC mobilization capacity in MM, consistent with what has been previously described. Supermobilizers achieved neutrophil engraftment sooner than normal or poor mobilizers but in contrast to data in lymphoid malignancies, this did not translate into a survival benefit. SC mobilization in lymphoma is a far more challenging task than in MM, with higher rates of SC mobilization failure due to extensive chemotherapy exposure or marrow infiltration. Thus, differences in transplant outcomes in lymphoma, based on a single parameter such as mobilization capacity, may be more easily detected than in MM where mobilization failures are uncommon. With over 40% of MM patients achieving ‘supermobilizer' status, future efforts to improve SC mobilization may better target approaches to decrease the cost or time involved in the process, rather than identification of novel mobilization agents. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 | 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".