Citations

Abstract POEMS syndrome is a rare paraneoplastic syndrome and therapies are directed against plasma cells that produce the proteins that cause this syndrome. Novel therapies are widely used in multiple myeloma aiming for plasma cell eradication. However, data on their use in POEMS syndrome are lacking. In this case series, we provide the Mayo Clinic experience in treating 16 patients with relapsed POEMS syndrome with novel agents (daratumumab, carfilzomib, pomalidomide, and elotuzumab). The patients were treated with a doublet including dexamethasone (N = 5) (31%) or in various combinations with other agents: DRd (N = 6), DC(V)d (N = 3), KRd (N = 3), KPd (N = 1), DP(V)d (N = 5), and EloRd (N = 1). The outcomes with novel agent therapies were favorable. Overall, twelve patients treated with daratumumab-based therapies (86%) and five patients treated with carfilzomib-based therapies (83%) responded to therapy. Among patients treated with daratumumab based therapies (N = 14), 9 patients achieved CR/VGPRH, 7 patients achieved CRV, and 5 patients achieved CRP. Among patients treated with carfilzomib-based therapies (N = 6), 3 patients achieved CR/VGPRH, and one achieved PRH. At a median follow-up of 38 months since starting of the novel agent (IQR 24–57), 15 of the patients (93%) are still alive, and the median TTNT was not reached. None of the patients discontinued therapy due to adverse events and no deaths occurred on therapy. Novel therapies were safe with 7 events of hospitalization due to pneumonia (4 in daratumumab-based therapies and 3 on carfilzomib-based therapies), and 4 patients were hospitalized due to volume overload. Three patients experienced infusion-related reactions (IRR) to the first dose of IV daratumumab. The response rate to novel agents was high, and the responses were deep. Novel agent therapies were safe, and no death case occurred on therapy. Future studies are needed to clarify the optimal sequence of novel agents and the best combination.

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.