Quantitative Integrative Prediction of Survival Probability in Multiple Myeloma Using Molecular and Clinical Prognostic Factors in 657 Patients Treated with Bortezomib-Based Induction, High-Dose Therapy and Autologous Stem Cell Transplantation

▪ Background Survival in multiple myeloma ranges from months to decades and the majority of patients remain incurable with current treatment approaches. Given this high variability, it would be clinically very useful to quantitatively predict survival on a continuous scale. Current risk prediction models attribute patients to 2-3 groups, i.e. high, intermediate, and low risk. Group size and survival rates largely vary between different systems. Rarely, molecular prognostic factors beyond iFISH are used. Widely accepted standard is the revised ISS score (rISS) including serum B2M, albumin, and adverse prognostic aberrations. Aim of our study was to develop quantitative prediction of individual myeloma patient's three and five year survival probability. We integrate prognostic factors into a comprehensive model, and evaluate its risk discrimination capabilities in relation to rISS. Patients and methods Symptomatic myeloma patients treated up-front with bortezomib-based induction regimen (PAD/PAd/VCD) and intention to undergo high-dose therapy and autologous stem cell transplantation with available GEP and iFISH-data (n=657) were split into training (TG, n=536) and validation group (VG, n=121). In TG and VG, 190 and 22 deaths were observed. Median f/u time was 5.4 and 3.5 years. Distribution of risk factors and 3-year overall survival (OS) were similar in both groups (80% vs 86%). Primary endpoint was OS. The following risk factors were considered for building the prognostic model: age (in years), ISS stage, elevated LDH level (>ULN), creatinine level >2 g/dL, heavy chain type IgA yes/no, del17p13 yes/no, t(4;14) yes/no, +1q21 no/3 copies/>3 copies, GEP-based GEP70-score and proliferation index (GPI). GEP-scores were analyzed as continuous variables. Due to low frequency, t(14;16) was excluded. A multivariable Cox regression model was fitted to estimate the individual prognostic index (PI). A non-stringent backward variable selection procedure with significance level for staying in the model of p=0.5 was applied to remove only surely non-informative predictors. Model selection, calibration, and validation were performed with the rms R package [Harrell 2017]. Harrell's c-index was used to assess the discrimination performance, and to compare the proposed prognostic model to the rISS [Kang 2015]. Results Quantitative Integrative Prediction of Survival Probability. The final Cox model was used to build a nomogram for estimating survival probabilities (Fig. 1). P