Cystatin C-Based Equations Detect Hidden Kidney Disease and Poor Prognosis in Newly Diagnosed Patients with Multiple Myeloma.

Objectives: The aim of this study was to compare the creatinine equations with cystatin C (CysC) equations to define renal impairment (RI) in newly diagnosed multiple myeloma (MM) patients and to analyse the equation that allows for identifying patients with more and worse prognostic factors. Methods: Renal function was evaluated prospectively in 61 patients with newly diagnosed untreated MM employing CKD-EPI and CAPA equations. The comparison was conducted using Bland-Altman graphics and Cohen's Kappa statistic. Mann-Whitney T and Chi-square tests were used, and univariate and multivariate analyses were carried out. Results: According to the IMWG criteria, 26% of patients showed RI (3 women/13 men) whilst the use of CysC equations allowed us to identify up to 39% of patients (7 women/17 men). The CAPA equation was less biased and dispersed and more sensitive than CKD-EPI-creatinine. Furthermore, univariate analysis unveiled an association between decreased CKD-EPI-CysC and poor prognosis based on R-ISS-3. Conclusions: The IMWG criteria may underestimate kidney disease, mostly in women, which could affect the dose received as well as its toxicity. Altogether, our data suggest that equations that include CysC are more accurate to detect hidden kidney disease, as well as patients with more and worse prognostic factors, in newly diagnosed MM.

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data.

Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools.

Subcutaneous bortezomib in newly diagnosed patients with multiple myeloma nontransplant eligible: Retrospective evaluation.

Bortezomib-melphalan-prednisone combination is one of the standards of care for nontransplant eligible patients with newly diagnosed multiple myeloma. However, bortezomib intravenous (twice weekly for 4 cycles then weekly for 5 cycles) results in ~13% of patients with grade 3-4 peripheral neuropathy. Bortezomib subcutaneous (SQ) and weekly delivery, improves tolerability without impairment of efficacy. The aim of this study was to evaluate the safety and effectiveness of SQ bortezomib-based combinations in nontransplant eligible patients with newly diagnosed myeloma in a real-world setting. A total of 135 patients (median age [range] = 76 [58-89], International Staging System-III = 54%, median follow-up = 14.8 months [1-40], Intensive group [twice weekly bortezomib] = 65%, Optimized group [weekly bortezomib] = 35%) were included and evaluable for safety, whereas 121 were evaluable for effectiveness. Overall response rate (95% CI) was 61% (53%, 71%) (complete response = 27%, very good partial response = 13%, and partial response = 21%) and median progression-free survival was 22.2 months (95% CI: 16.1-not reached). The 3-year overall survival was 75%. The most frequent grade 3-4 adverse events were thrombocytopenia (18%), neutropenia (17%), and anemia (11%). Peripheral neuropathy of any grade was observed in 44% of patients (2% with grade 3). Comparison between regimens (Intensive vs Optimized) showed similar overall response rate (57% vs 70%) and PFS (25 vs 19 months). A similar safety profile was observed between regimens. Thus, SQ bortezomib showed similar effectiveness and better tolerability as compared with results from intravenous bortezomib studies, and showing no differences either in effectiveness or safety in different bortezomib-based combinations.