Early Relapse After Single ASCT For MM Patients Is a Major Predictor Of Survival In The Era Of Novel Agents
Bibliographic record
Abstract
Abstract Introduction The role of high-dose therapy (HDT) followed by autologous stem cell transplantation (ASCT) in the treatment of multiple myeloma (MM) continues to evolve in with the availability of effective novel agents. It has been shown that exposure to novel agents (NA) can improve the post-relapse survival (Venner CP, et al, 2011). However, the impact of early relapse (ER) after ASCT in the era of NA has not been fully defined. In this study, we aimed to assess the impact of ER after ASCT on overall survival (OS) for MM patients (pts) undergoing single ASCT who had received NA induction regimens Patient and Methods All consecutive pts with documented MM undergoing single ASCT at Princess Margaret Cancer Center from 01/02 to 09/12 who had novel induction therapy were evaluated. ER was defined as progression of myeloma within 12 months from the ASCT. The Cox proportional hazard model was used to perform multivariate analyses of possible prognostic variables for overall survival. A p value of <0.05 was considered statistically significant. Results The clinical and laboratory characteristics are of the 184 pts given induction therapy with NAs are listed in Table 1. The median age for this cohort of patients was 58 years (range 31-72). At day-100 post-ASCT, CR was achieved in 15.3% and VGPR in 57.1%. At the time of this analysis, 81% of pts are alive and 75 (40.7%) have already progressed. The median OS of the whole group was 93 months (95% CI, 70-116) (Fig.1a) and medianPFS was 25.43 months. Median time to relapse was 17.23 months (C I95%; 12-22). Early relapse was seen in 27/75 pts (36% of relapsed cases). Median OS was significantly shorter for the group of patients with ER (20 months, CI 95%; 15.56-24.51 versus 93 months, CI 95%; 75-111) (p=0.001) (Fig. 1b) Multivariate analysis showed the presence of ER as the major independent prognostic factor for OS; age > 60, B2M > 460 µmol/L, LDH > 350 IU/L, CRP > 20mg/L, albumin < 35g/L, and creatinine>200 µmol/L did not correlate with OS. In addition, FISH was available in only 78/184 cases. Only 1/27 pts in the ER group exhibited high-risk cytogenetics versus 0/48 in the non-ER group. In conclusion, not only the quality of response but also maintatenance of a deep response after ASCT, appear to be major prognostic variables in MM. Patients with ER post-ASCT should be biologically characterized to better understand the mechanisms of resistance associated with this particular entity and to develop new predictive models that can identify prospectively this myeloma subset. Also, patients with ER should be included in the definition of “high-risk” disease and priority candidates for the development of individualized therapeutic measures. Disclosures: Reece: Otsuka: Honoraria, Research Funding; BMS: Research Funding; Merck: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Millennium Pharmaceuticals: Research Funding; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Onyx: Consultancy. Chen:Roche: Honoraria; Johnson & Johnson: Consultancy, Research Funding; Lundbeck: Consultancy; Celgene: Consultancy, Research Funding; GlaxoSmithKline: Research Funding. Tiedemann:Celgene: Honoraria; Janssen: Honoraria. Kukreti:Millennium Pharmaceuticals: Research Funding; Onyx: Research Funding.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".