Real World Outcomes of Newly Diagnosed High-Risk Multiple Myeloma Patients Treated at Princess Margaret Cancer Centre: A Retrospective Chart Review
Bibliographic record
Abstract
Abstract Background: In the era prior to the availability of novel agents, we have previously reported that the median progression-free survival (PFS) and overall survival (OS) of high-risk (HR) multiple myeloma (MM) patients (pts) undergoing a single autologous stem cell transplant (ASCT) was only 9.9 and 18.3 months, respectively (Chang H et al., Bone Marrow Transplant, 36 (2005), 793). The current study expands our experience in newly diagnosed HR pts and focuses on the outcomes in pts treated with single or tandem ASCT. Methods: A retrospective chart review of myeloma pts was conducted based on pt information retrieved from the Princess Margaret Myeloma Database. 245 pts with HR MM diagnosed and treated in our centre from Oct 1998 to Dec 2016 were identified. Survival rates were calculated using the Kaplan-Meier product-limit method and the log rank-statistic was used for comparison of survival curves. Results: Pts were considered HR based on the presence of any one of the cytogenetic features: t(4:14) (71%), t(14:16) (36%), and chromosome 17p deletion (42%). Characteristics at the time of MM diagnosis of the 245 pts identified as HR are summarized in Table 1. The median age was 60.9 years. First-line therapy included the following: tandem ASCT in 86 pts (35%), single ASCT in 84 pts (34%), and no ASCT in 75 pts (31%). Of the 170 pts who underwent single or tandem ASCT, 119 (70%) received maintenance therapy post-ASCT, including 84% of the tandem ASCT pts and 56% of the single ASCT pts. Median follow-up was 29.3 months (range 1.1-123.7). The median PFS from date of diagnosis of all HR pts was 33.1 months [95% CI 29.3-37.0 months], while the median OS was 79.2 months [95% CI 58.3-90.9 months]. Both PFS (p= 0.0003) and OS (p= 0.0011) were significantly affected by first-line therapy with a median PFS of 53.1 vs 32.9 vs 21.8 months and median OS of 88.5 vs 71.9 vs 55.0 months in pts treated with tandem ASCT, single ASCT and no ASCT, respectively. Tandem ASCT pts had improved PFS and OS survival at 1, 2, and 5-years compared to single and non-transplant pts (Fig. 1A & B). Age at diagnosis significantly affected the PFS (p= 0.0003) and marginally affected the OS (p= 0.0748), likely due in part to our policy for the preferential use of fixed-duration non-ASCT as first-line therapy in older individuals (typically ≥70 years of age). On the other hand, serum beta 2- microglobulin levels correlated significantly with OS (p= 0.0023) while the association with PFS was not significant. Among ASCT pts, those who received tandem ASCTs vs single ASCT demonstrated a significantly better PFS (p= 0.0039) and showed a trend for improved OS (p= 0.0707). Amongst pts who received single ASCT, those with maintenance vs no maintenance demonstrated significantly better PFS (p=0.0082). Finally, pts with both t(4:14) and 17p deletion (28 pts) had a significantly decreased PFS (p= 0.0414) and a trend for decreased OS (p= 0.0621) compared to those with only one abnormality (Fig. 1C & D). Conclusions: Although adverse factors such as older age and co-morbidities likely affected the ability to perform 1 or 2 autografts and likely introduced bias into our analysis, our data nevertheless suggest that tandem ASCT is an effective approach in extending PFS and OS among HR MM pts when compared to single transplants or none at all in the real-world setting. Multivariate analysis is underway to further delineate the factors contributing to PFS and OS in HR pts. As in other transplant studies, the PFS is improved in pts who receive maintenance therapy after single ASCT. Efforts should be made to offer newly diagnosed HR MM pts a program that includes tandem transplantation, if feasible, as well as maintenance therapy post-transplant. Pts with “ultra HR” MM, i.e., those with both t(4;14) and 17p deletion, require new innovative approaches. Download : Download high-res image (207KB) Download : Download full-size image Disclosures Reece: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Paid expert testimony, Research Funding, Speakers Bureau; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Other: Paid expert testimony, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Otsuka: Research Funding. Chen: Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Abbvie: Honoraria; Amgen: Honoraria. Kukreti: Celgene: Honoraria; Amgen: Honoraria. Prica: Janssen: Honoraria; Celgene: Honoraria. Tiedemann: BMS Canada: Honoraria; Celgene: Honoraria; Takeda Oncology: Honoraria; Amgen: Honoraria; Janssen: Honoraria; Novartis: Honoraria. Trudel: Janssen: Research Funding; Amgen: Consultancy, Honoraria; Astellas: Research Funding; Celgene: Consultancy, Honoraria; Takeda: Honoraria; GlaxoSmithKline: 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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".