Patient, Disease and Treatment Characteristics of Long-Term Survivors of Multiple Myeloma
Bibliographic record
Abstract
Abstract Introduction With routine use of autologous stem cell transplantation (ASCT) and novel agents, survival of patients with multiple myeloma (MM) has improved in recent years. Yet, MM remains incurable and long-term survivors (LTS) of ≥10 years from diagnosis remain uncommon. This study aims to identify patient, disease and treatment characteristics of MM LTS, with particular interest in the effect of novel therapies. Methods A retrospective analysis was conducted of MM patients diagnosed between 1998 and 2002 and treated at Princess Margaret Cancer Centre, a tertiary care institution. LTS were identified by survival of ≥10 years from diagnosis and were compared with patients diagnosed and followed contemporaneously at our institution with survival <10 years from diagnosis. Candidate predictor variables were identified using univariate and multivariate logistic regression analysis; a p value <0.05 was considered statistically significant. Results Seventy-five patients were identified as LTS, with a control group of 119 patients with survival <10 years. The median survival for all patients was 7.3 years (range 0.6-14.5 years). Comparison of patient, disease and treatment characteristics between groups are detailed in Table 1. Patient and disease characteristics: At diagnosis, LTS were younger (p = 0.0005) and at earlier ISS stage (p = 0.02) than non-LTS. At diagnosis, LTS had a higher baseline mean hemoglobin level (p = 0.02) and platelet count (p = 0.003), and less frequently had lytic bone lesions (p = 0.03), consistent with earlier stage at diagnosis. There were no significant differences in baseline mean leukocyte count, serum calcium and creatinine. Cytogenetics were not routinely performed during this time period. Treatment characteristics: Of the LTS, 95% received an ASCT, as compared to 86% of non-LTS (p = 0.77). Median age at transplant was younger in the LTS (p = 0.003). LTS experienced a longer time from transplant to disease progression (TTP) than non-LTS (p < 0.0001) despite achieving similar rates of complete response (CR) and very good partial response (VGPR). Exposure to novel agents was common in both the LTS and control groups (73% vs. 82%, p = 0.24). Length of exposure to thalidomide (p = 0.01) and lenalidomide (p = 0.002) was greater in LTS, leading to higher quality responses and longer TTP with both agents (p < 0.0001 and p = 0.002, respectively). Similarly, bortezomib exposure was longer in the LTS (p = 0.02) with a longer TTP over that achieved in non-LTS (p = 0.008), although the quality of response was not significantly different. In a multivariate analysis, a longer TTP after ASCT (OR = 1.004; 95% CI 1.002-1.006, p = 0.0008), thalidomide (OR = 34; 95% CI 1.7-690.6; p = 0.023) and bortezomib (OR = 28.2; 95% CI 3.5-228; p = 0.002) treatment, though not after lenalidomide, were independently predictive of LTS. Table 1. Comparison of characteristics between LTS and non-LTS Disease characteristics LTS (n=75) Non-LTS (n=119) p -value Age (y) 53.2 59.1 0.0005 ISS stage I 66 43 0.02 II 20 33 III 14 24 Hemoglobin (g/L) 109 102 0.03 Leukocytes (x 109/L) 5.97 6.29 0.45 Platelets (x 109/L) 255 218 0.003 Calcium (mmol/L) 2.41 2.46 0.39 Creatinine (umol/L) 107.5 148.6 0.16 Presence of lytic lesions (%) 55 70 0.03 Treatment characteristics Autologous stem cell transplant Age (median, years) 53.3 59.4 0.003 Response (CR or VGPR, %) 47 44 0.62 TTP (median, months) 59 19.9 0.001 Thalidomide Age (median, years) 11.4 8.2 0.01 Response (CR or VGPR, %) 40 23 0.02 TTP (median, months) 32.4 9.6 <0.0001 Lenalidomide Age (median, years) 23.2 8.1 0.002 Response (CR or VGPR, %) 56 35 0.04 TTP (median, months) 24 10.8 0.002 Bortezomib Age (median, years) 6.8 3.3 0.02 Response (CR or VGPR, %) 40 28 0.24 TTP (median, months) 18 6 0.008 Conclusion LTS with MM received prolonged therapy and achieved higher quality responses to both transplant and novel agents. Our analysis suggests that LTS have baseline characteristics (age, early-stage disease, greater marrow reserve) that may enable them to tolerate more intensive or prolonged therapy. However, it is possible that LTS have disease more indolent or sensitive to therapeutic interventions. The retrospective nature of the study limits our ability to further characterize this. Regardless, these data suggest that the practice of continued exposure to novel agents may contribute to long-term survival in MM. Disclosures Reece: Otsuka: Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Millennium: Honoraria, Research Funding; Merck: Research Funding; BMS: Research Funding; Novartis: Honoraria, Research Funding; Amgen: Honoraria. Trudel:Celgene: Honoraria; Novartis: Honoraria; Glaxo Smith Kline: Honoraria, Research Funding; Oncoethix: Research Funding. Kukreti:Celgene: Consultancy, Honoraria. Tiedemann:Janssen: Honoraria. Chen:Celgene: Honoraria; Janssen: Honoraria.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".