Secondary Cancers Among Patients with Multiple Myeloma: A 15-Year Analysis of a Population-Based Cohort in Ontario, Canada
Bibliographic record
Abstract
Introduction: With improving overall survival among MM patients, there is an increasing need to both understand the absolute risk and identify risk factors for developing a second primary malignancy (SPM). Understanding these risk and potential modulation factors may help guide decisions regarding treatment options, monitoring surveillance strategies, and patient counselling. Furthermore, understanding this baseline SPM risk will help contextualize the future SPM risk associated with newer therapeutics, including chimeric antigen receptor therapies. Thus, we conducted a population-based study with following objectives: 1) to evaluate the rate and distribution of SPMs, and 2) to identify risk factors including a history of prior malignancy associated with an increased risk of SPMs in patients with MM. Methods: We conducted a retrospective population-based study using data from Institute for Clinical Evaluative Sciences (ICES), an administrative database that captures all health records in the publicly funded health care system in Ontario, Canada. Adult patients treated for newly diagnosed MM between 2007-2022 were identified using ICD-O-3 code 9732/3 (MM). The list of secondary cancers (excluding non-melanoma skin cancers) were identified from pathology codes maintained in the Cancer Care Ontario Registry. Additional risk factors were also collected including a history and type of any pre-existing cancer (at least 1 year prior to MM diagnosis), treatment details including history of autologous stem cell transplantation (ASCT) and lenalidomide usage. The Kaplan-Meier method was used to estimate the median overall survival (OS). We estimated the cumulative incidence of secondary cancer accounting for death as a competing risk. To identify the association of prognostic factors on the risk of developing a SPM, we performed competing risks regression and estimated the subdistribution hazard ratios (sHR) accounting for death as a competing risk. Results: A total of 12123 patients . The median age at diagnosis was 70 (IQR 61-77) years, 6860 (57%) of our cohort had a male sex. In our 4511 (37%) received at least one ASCT, 8062 (67%) were lenalidomide exposed and 6917 (57%) were cyclophosphamide exposed at any time post MM diagnosis, and 3017 (16%) received oral melphalan based induction regimens. The median follow up for cohort was 4.53 (95% CI 4.4-4.7) years. A total of 2079 patients (17%) had a prior cancer diagnosis at least 1 year prior to their MM diagnosis. Of these patients, 665 (32%) had received chemotherapy and 553 (27%) had received radiation to treat their prior malignancy. The three most common prior cancers were: prostate cancer (25%), breast cancer (13%) and heme malignancies (4%).The median time from diagnosis of a prior malignancy to MM diagnosis was 6.4 (IQR 2.04-13.26) years. A total of 1070 (8.8%) of the cohort developed a SPM. The cumulative incidence of SPM post MM diagnosis, accounting for death as a competing risk, was 3.7% at 2 years, 7.6% at 5 years, and 11.5% at 10 years. The three most common SPMs were hematologic (21%), prostate (7%), and lung (7%) cancer. The three most common hematological SPMs were acute myeloid leukemia (37%), B-acute lymphoblastic leukemia (16%), and myelodysplastic syndrome (19%). The median OS following after any SPM diagnosis was 1.64 (95% CI 1.36-1.92) years, was 1.85 (95% CI 1.54-2.28) years for patients that developed a non-hematologic SPM, and was 1.05 (95% CI 0.88-1.40) years for patients that developed a hematologic SPM. Conclusion: This study represents one of the largest real-world cohort studies examining the rates, risk factor and outcomes of MM patients developing secondary cancers. We show that a prior malignancy diagnosis increased the risk of SPM after adjusting for ASCT and lenalidomide exposure, sex, and age at MM diagnosis. These data are important for patients and physicians to understand the risks associated with treatment of MM.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".