Kourelis, Taxiarchis

BackgroundSarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning–based segmentation approach, clinical computed tomography (CT) images of the abdomen of patients with newly diagnosed multiple myeloma (NDMM) were reviewed to determine whether the presence of sarcopenia had any prognostic value.MethodsSarcopenia was detected by accurate segmentation and measurement of the skeletal muscle components present at the level of the L3 vertebrae. These skeletal muscle measurements were further normalized by the height of the patient to obtain the skeletal muscle index for each patient to classify them as sarcopenic or not.ResultsThe study cohort consisted of 322 patients of which 67 (28%) were categorized as having high risk (HR) fluorescence in situ hybridization (FISH) cytogenetics. A total of 171 (53%) patients were sarcopenic based on their peri‐diagnosis standard‐dose CT scan. The median overall survival (OS) and 2‐year mortality rate for sarcopenic patients was 44 months and 40% compared to 90 months and 18% for those not sarcopenic, respectively (p < .0001 for both comparisons). In a multivariable model, the adverse prognostic impact of sarcopenia was independent of International Staging System stage, age, and HR FISH cytogenetics.ConclusionsSarcopenia identified by a machine learning–based convolutional neural network algorithm significantly affects OS in patients with NDMM. Future studies using this machine learning–based methodology of assessing sarcopenia in larger prospective clinical trials are required to validate these findings.

Abstract Overall Survival estimates from diagnosis are valuable for guiding treatment, but do not consider the years already survived. Conditional survival (CS) provides dynamic survival predictions over time. This study was conducted to estimate CS at 1–8 years from diagnosis and the impact of baseline prognostic factors on CS in multiple myeloma (MM) patients. This is a retrospective study including 2556 MM patients diagnosed between 2004–2019. CS (t|s) was defined as the probability of surviving t years given survival of s years. Median age was 64 years. Median follow-up was 6.2 years and median overall survival from diagnosis was 7.5 years. The 5-year CS estimates at s = 0, 1, 2, 3, and 5 years were 0.64, 0.61, 0.61, 0.61, and 0.58, respectively. On multivariate analysis, age ≥ 65 and proteasome inhibitor + immunomodulatory-based induction were associated with decreased survival and increased survival, respectively, retained at 5 years. The adverse impact of 1q gain/amplification, high-risk IgH translocation, and ISS-3 was significant at 1 and 3 years but not 5 years. Chromosome 17 abnormality was associated with decreased survival only at 1 year. Among MM patients, 5-year CS was stable at 1–5 years from diagnosis. The prognostic impact of high-risk cytogenetic factors decreased with additional years survived.

AbstractPatients with multiple myeloma (MM) have a lower efficacy from COVID‐19 vaccination and a high rate of mortality from COVID‐19 in hospitalized patients. However, the overall rate and severity of COVID‐19 infection in all settings (including non‐hospitalized patients) and the independent impact of plasma cell‐directed therapies on outcomes needs further study. We reviewed the medical records of 9225 patients with MM or AL amyloidosis (AL) seen at Mayo Clinic Rochester, Arizona, and Florida between 12/01/2019 and 8/31/2021 and identified 187 patients with a COVID‐19 infection (n = 174 MM, n = 13 AL). The infection rate in our cohort was relatively low at 2% but one‐fourth of the COVID‐19 infections were severe. Nineteen (10%) patients required intensive care unit (ICU) admission and 5 (3%) patients required mechanical ventilation. The mortality rate among hospitalized patients with COVID‐19 was 22% (16/72 patients). Among patients that were fully vaccinated at the time of infection (n = 12), two (17%) developed severe COVID‐19 infection, without any COVID‐related death. On multivariable analysis, treatment with CD38 antibody within 6 months of COVID‐19 infection [Risk ratio (RR) 3.6 (95% CI: 1.2, 10.5), p = .02], cardiac [RR 4.1 (95% CI: 1.3, 12.4), p = .014] or pulmonary comorbidities [RR 3.6 (95% CI 1.1, 11.6); p = .029] were independent predictors for ICU admission. Cardiac comorbidity [RR 2.6 (95% CI: 1.1, 6.5), p = .038] was an independent predictor of mortality whereas MM/AL in remission was associated with lower mortality [RR 0.4 (95% CI: 0.2–0.8); p = .008].

Two Letters to Blood address the risks of COVID-19 in populations with precursors of hematological disease. In the first article, Miller and colleagues report on whether clonal hematopoiesis of intermediate potential (CHIP) is associated with adverse outcomes with COVID-19, finding no association between CHIP and 28-day mortality while providing data indirectly linking IL-6 signaling and patient outcomes. In the second article, Ho and colleagues investigate the outcomes of patients with monoclonal gammopathy of undetermined significance (MGUS) with COVID-19, reporting that one-fourth had a severe infection and that on multivariable analysis, adverse outcomes are more likely if immunoparesis is present.

Abstract The treatment landscape for relapsed multiple myeloma (MM) has increased. In this study we aimed to characterize 2nd (n = 1439) and 3rd (n = 1104) line regimens and compare the results between subgroups based on the year of treatment initiation (2nd line: 2003–2008, 2009–2015, 2016–2021; 3rd line: 2004–2009, 2010–2015, and 2016–2021). In both 2nd and 3rd line we observed increasing use of novel agents (from 78–95% and from 77–95%, respectively) and triplet regimens (from 15–69% and from 21–71%, respectively). Most frequently used regimens in the last studied periods included lenalidomide-dexamethasone (RD; 14%), carfilzomib-RD (12%) and daratumumab-RD (10%) for 2nd line, and daratumumab-pomalidomide-dexamethasone (11%) and daratumumab-RD (10%) for 3rd line. Median time to next treatment from 2nd line therapy has improved from 10.4 months (95% CI 8.4–12.4) to 16.6 months (95% CI 13.3–20.3; p < 0.001). The median overall survival from 1st relapse increased from 30.9 months (95% CI 26.8–183.0) to 65.8 months (95% CI 50.7–72.8; p < 0.001). Over the last two decades more patients were treated with newer agents and triplets for relapsed MM. The landscape of regimens has become more diverse, and the survival after 1st relapse is continually improving.

As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4+ T/CD8+ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project. Significance: scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.