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Record W3095670170 · doi:10.1182/blood-2020-142271

Monoclonal Protein Response Trajectory and Survival in Newly Diagnosed Multiple Myeloma: A Retrospective Cohort Study

2020· article· en· W3095670170 on OpenAlexaffabout
Jayce Bi, Pascal Lambert, Brett L. Houston, Leonard Minuk, Vi Dao, Rami Kotb, Donald S. Houston, Ryan Zarychanski, Emily Rimmer

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsCancerCare ManitobaResearch Institute in Oncology and HematologyUniversity of Manitoba
Fundersnot available
KeywordsMultiple myelomaRetrospective cohort studyMedicineCohortInternal medicineBortezomibOncologyTrajectorySurvival analysis

Abstract

fetched live from OpenAlex

Background: Multiple myeloma is a heterogenous plasma cell cancer characterized by expression of a monoclonal protein (M-protein) or free light chains (FLC). Although survival has improved over the past 15 years, outcomes are variable and not fully predicted by conventional prognostic markers. Survival is associated with depth and duration of response to treatment but it is not known if the trajectory of response is associated with survival. We hypothesized that the trajectory of M-protein over the first six months of treatment in patients with newly diagnosed multiple myeloma can identify distinct clinical groups and is an independent predictor of overall survival (OS). The objectives of our study are to: 1) Identify groups of patients with different M-protein trajectories using trajectory analysis; 2) Evaluate patient and illness factors associated with different M-protein trajectories; and 3) Determine whether M-protein trajectory is an independent predictor of OS in newly diagnosed multiple myeloma. Methods: We conducted a retrospective cohort review of all patients with newly diagnosed multiple myeloma in Manitoba between 2012 and 2017 who had a measurable M-protein at presentation and who were initially treated with bortezomib-based chemotherapy. Baseline characteristics and monthly response assessments up to six months were collected and analyzed using trajectory analysis. We used latent class mixed models (LCMM) to produce trajectory groups of M-protein response over time. We used the Bayesian Information Criterion and clinical validity characteristics to select the optimal trajectory model. We developed a multinomial logistic model to evaluate the association of patient and illness characteristics with M-protein trajectories. We constructed a multivariable Cox proportional hazard model adjusted for trajectory group, ISS stage, transplant status, LDH, renal failure and platelet count to evaluate the association of M-protein trajectory on OS in multiple myeloma. Results: 266 patients were included in the trajectory analysis. 154 (57.9%) were male. Mean age was 66.4 years (SD 10.1). 29 (10.9%) were classified as ISS stage 1, 80 (30.1%) as ISS stage 2, and 109 (41.0%) as ISS stage 3. The best fit trajectory model identified three trajectory groups representing distinct M-protein trajectories over time (Figure 1). We plotted the trajectory of the absolute value for M-protein as the percentage change from baseline did not separate the population into distinct groups. Baseline bone marrow plasma percentage (p<0.01), albumin (p<0.01), and hemoglobin (p<0.01) were significantly associated with trajectory group membership. The median follow-up was 2.74 years for the OS analysis. The median OS was not reached for the low group, 4.3 years for the moderate group, and 5.4 years for the high group. In a multivariable Cox proportional hazard model, we found that trajectory group was not significantly associated with OS (moderate group HR 1.18, 95%CI 0.48-2.91, high group HR 1.10, 95%Cl 0.62-1.88). Variables significantly associated with OS included ISS stage (p<0.025), LDH (p<0.022), platelet count (p<0.001), and receipt of a stem cell transplant (p<0.001). Discussion and Conclusions: In this retrospective cohort study of patients with newly diagnosed multiple myeloma, we identified three distinct groups based on their M-protein trajectory over time. The identified groups have different mean baseline M-protein values and follow different evolutionary patterns over time. While M-protein trajectory group was not associated with differences in OS, the small sample size and short follow-up may limit interpretation of the results. Our analyses only looked at the M-protein trajectory over the first six months after treatment start, which indicates an early response to treatment. It is possible this early response does not play a large role in the OS of patients with myeloma given the mutliple lines of chemotherapy they may receive. Further studies are planned to assess the achievement of a complete remission at one year or at renal recovery for patients affected with renal involvement. Alternative uses for M-protein trajectory analysis include the trajectory of M-protein prior to relapse or in patients with smoldering myeloma to identify who may benefit from earlier treatment. Disclosures Kotb: Takeda: Honoraria; Merck: Honoraria, Research Funding; Sanofi: Research Funding; Celgene: Honoraria; Janssen: Honoraria; Amgen: Honoraria; Karyopharm: Current equity holder in publicly-traded company.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.283
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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