Session: 652. Multiple Myeloma and Plasma Cell Dyscrasias: Clinical and Epidemiological: Prognostic Markers in Multiple Myeloma
Hematology Disease Topics & Pathways:
Research, artificial intelligence (AI), Translational Research, Clinical Research, Combination therapy, genomics, Plasma Cell Disorders, bioinformatics, patient-reported outcomes, Diseases, Therapies, Lymphoid Malignancies, computational biology, Biological Processes, Technology and Procedures, machine learning, omics technologies
Methods. We included 1840 patients with available clinical and genomic data from the following cohorts: MMRF CoMMpass (n=1062), MGP (n=492), Moffit AVATAR (n=177), and MSKCC (n=109). The median follow up was 42 months. To build a treatment-adjusted predictive model of individualized risk for patients with NDMM, we considered 160 variables across clinical (e.g., age, ECOG, sex, ISS), therapeutics, genomics, and time-dependent treatments such as autologous stem cell transplant (ASCT) and continuous treatment (Palumbo et al. JCO 2015). A multi-state model was designed across two phases: induction (phase 1), and post-induction (phase 2). Phase 1 included patients that: 1) completed the induction without progression (PD); 2) PD of failed to respond during induction remaining alive after; 3) PD during induction and subsequently died. Phase 2 have patients that: 4) PD after induction and were alive; 5) PD after induction and died; 6) reached remission after induction and were alive; 7) responded after induction and died due to other causes. We leveraged and compared survival methods based on deep neural networks (Neural Cox Non-proportional-hazards; NCNPH), Random Survival Forest (RSF), and Cox proportional-hazard (CPH).
Results. NCNPH showed the best cross-validated prognostic performance for OS with median Uno’s concordance (C=0.67), followed by RSF (C=0.65) and CPH (C=0.64; Fig. 1a-b). Overall, the model significantly outperformed R2-ISS (C=0.6), ISS (C=0.59) and R-ISS (C=0.57; Fig. 1b). Additionally, the model found 28 genomic features to increase concordance accuracy for PFS by (3% in phase 1 and 1.5% in phase 2), and with greater effect on OS (10% in phase 1 and 5% in phase 2). The 14% of patients who did not respond in phase 1 were enriched for ISS3, age >75 y, 1q amp, NSD2 translocation, TP53 mutations, and deletions on 17p13 and 1p.
Varying therapies emerged as a key determinant of risk, in particular, in phase 2, supporting the idea that effective combinations can have a different impact in individual patients and have the potential to significantly change the clinical outcome despite poor prognostication. The model not only predicts patient risk but also the impact of various treatment strategies (i.e., treatment variance). We identified 9 distinct clusters (C#1-9) based on predicted risk and treatment variance. C#8 and C#9 show favorable outcome, especially with ASCT and continuous treatment. C#4 and C#5 included patients with favorable outcome but low treatment variance where ASCT had marginal impact. C#6 had a substantial presence of high-risk patients associated with low variance, age >75y, 1q gain, and NSD2 translocations. Interestingly, NSD2 translocated patients not included in C#6 had an intermediate favorable outcome (p<0.0001). Early PD was observed in C#1 and C#7, both enriched for low treatment variance, and high risk genomic and clinical features. Finally, we identified a group of high-risk patients (C#2 and C#3) with high treatment variance whose poor outcome was mostly driven by a suboptimal treatment (i.e., use of doublet, no ASCT, no continuous treatment) rather than presence of distinct clinical and genomic features, highlighting the need for treatment to be considered to build more robust and accurate prognostic models.
Conclusion: This work shows the first comprehensive model integrating new and historical features to train a neural network for individualized risk prediction in NDMM. Utilizing data from 1840 patients that received a variety of therapies, the model captures the interaction of genomic, clinical and therapy factors, enabling personally-tailored therapeutic decisions in NDMM.
Disclosures: Davies: Takeda, Abbvie, Amgen, BMS/Celgene, Sanofi, GSK, Janssen: Membership on an entity's Board of Directors or advisory committees. Walker: Genentech: Research Funding; Bristol Myers Squibb: Research Funding. Hultcrantz: Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Amgen, Daichii Sankyo, Cosette, GSK: Research Funding; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Intellisphere LLC: Consultancy; Curio Science LLC: Consultancy. Hampton: M2GEN: Current Employment. Jackson: BMS: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; J and |J: Consultancy, Honoraria, Speakers Bureau; Pfizer: Consultancy, Honoraria; GSK: Consultancy, Honoraria, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Oncopeptides: Consultancy. Kaiser: Janssen: Honoraria, Research Funding; BMS/Celgene: Honoraria, Research Funding; Karyopharm: Consultancy; Takeda: Honoraria; Pfizer: Consultancy; AbbVie: Consultancy; GSK: Consultancy; Seattle Genetics: Consultancy. Pawlyn: Janssen: Consultancy, Honoraria, Other: Travel support; Sanofi: Consultancy, Honoraria; Celgene/BMS: Consultancy, Honoraria; Abbvie: Consultancy. Cook: Takeda, BMS, Amgen, Roche, Janssen, Sanofi, Karyopharm, Pfizer: Consultancy; Takeda, BMS: Research Funding; Takeda, BMS, Amgen, Janssen, Sanofi: Speakers Bureau. Kazandjian: Plexus Communications: Honoraria; Curio Science: Honoraria; Sanofi: Membership on an entity's Board of Directors or advisory committees; Aptitude Health: Honoraria; MMRF: Honoraria; Arcellx: Membership on an entity's Board of Directors or advisory committees; SINTOMA: Honoraria; BMS: Membership on an entity's Board of Directors or advisory committees; CURE: Honoraria. van Rhee: Janssen Pharmaceuticals: Research Funding; Karyopharm: Consultancy; GlaxoSmithKline: Consultancy; Takeda: Consultancy; Bristol Myers Squibb: Research Funding; EUSA Pharma: Consultancy. Usmani: AbbVie, Amgen, BMS, Celgene, EdoPharma, Genentech, Gilead, GSK, Janssen Pharmaceuticals, Oncopeptides, Sanofi, Seagen Inc., formerly Seattle Genetics, Inc., Secura Bio, Inc., SkylineDX, Takeda, TeneoBio , Amgen, Array Biopharma, BMS, Celgene, GSK, Janssen: Research Funding; Amgen, Array Biopharma, BMS, Celgene, GSK, Janssen Pharmaceuticals, Merck, Pharmacyclics, Sanofi, Seagen Inc., formerly Seattle Genetics, Inc., SkylineDX, Takeda: Consultancy; Amgen, BMS, Janssen Pharmaceuticals, Sanofi: Speakers Bureau. Shain: Bristol Myers Squibb (BMS), Janssen, GlaxoSmithKline (GSK), Adaptive, Sanofi, and Takeda, and Amgen: Honoraria; GSK, Janssen and BMS: Membership on an entity's Board of Directors or advisory committees; GSK, BMS, Sanofi, Karyopharm, Takeda, Janssen, Adaptive and Amgen: Speakers Bureau; AbbVie and Karyopharm: Research Funding; Janssen and BMS: Other: PI of clinical trials. Raab: Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Heidelberg Pharma: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Landgren: Pfizer: Consultancy; Amgen: Research Funding; Merck: Consultancy, Other: Independent Data Monitoring Committee (IDMC) member for clinical trials; Janssen: Consultancy, Other: Independent Data Monitoring Committee (IDMC) member for clinical trials, Research Funding.
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