Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster I
Hematology Disease Topics & Pathways:
Methods: Cytogenetics and somatic mutations (by targeted NGS) for 15 MM patients were input into the CBM model to predict responses to different therapeutic combinations. All patients were relapsed to prior treatment. CBM uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated disease pathways. We simulated the specific combinations of the drugs per patient and measured the quantitative drug effect on a composite MM disease inhibition score (i.e., cell proliferation, viability, apoptosis and paraproteins). The actual clinical outcome of the treatments was compared with predicted outcomes.
Results: Fifteen patients were analysed using CBM for prediction of treatment response after NGS was performed. 13/15 were clinically evaluable, of which 1 was a responder and 12 were non-responder. 6/13 patients were treated on clinical trial and 7/13 were on drug combinations per physician decision. CBM correctly predicted 1 responder and 11 non-responder with a PPV of 50%, NPV 100%, specificity 91.67%, sensitivity 100%. The accuracy of CBM prediction was 92.30%. CBM also predicted the response of prior drug therapies for its non-response at relapse. For prior drug treatment options, 14 patients were evaluable. All the 14 patients were clinically non-responders and CBM correctly predicted for 13 patients with NPV 100%, Specificity 92.85% and overall accuracy of 92.85%. The majority of patients did not respond to therapies recommended at relapse. As an example, the operative molecular pathways from 2 patients who did not respond to combination treatment, either pre-NGS or post-NGS profiling, are shown in Fig. 1 and Table 1. CBM identified amplification (AMP) of chromosome (chr) 1 (WNT3A, IL6R, CKS1B, MCL1, PIK3C2B, USF1), chr 3 (HES1, PIK3CA, CTNNB1, WNT7A, FANCD2), chr 5 (IL6ST, IRF1, GLRX, SKP2), chr 7 (CDK5, EZH2, IL6, CAV1, ABCB1), chr 9 (NOTCH1, HSPA5, FANCC, FANCG), chr 15 (DLL4, FANCI, ALDH1A2), chr 19 (ERCC1, ERCC2, USF2); deletion(DEL) of chr 13 (CUL4A) , chr 16 (AXIN1, CDH1) and TP53 mutation in different combinations, which confer resistance to therapies at relapse.
Conclusions: The CBM technology represents a potential means to identify therapeutic options for MM patients based on the patients individual tumor-genome profile and which can also be deployed for uncovering drug resistance mechanisms. This tool may aid clinicians in decision making for recommending the most appropriate therapy based on standard of care agents or clinical trials; thus improving patient outcomes and reducing unnecessary costs or drug-related toxicities.
Disclosures: Singh: Cellworks Research India Private Limited: Employment. Sauban: Cellworks Research India Private Limited: Employment. Husain: Cellworks Research India Private Limited: Employment. Kumar: Cellworks Research India Private Limited: Employment. Kumari: Cellworks Research India Private Limited: Employment. Tyagi: Cellworks Research India Private Limited: Employment. Abbasi: Cell Works Group Inc.: Employment. Vali: Cell Works Group Inc.: Employment. Ailawadhi: Pharmacyclics: Research Funding; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Janssen: Consultancy.
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