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1893 Clinical Validation of Treatment Response Predictions Using a Genomics Driven Computational Biology Modelling Multiple Myeloma Algorithm

Program: Oral and Poster Abstracts
Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster I
Hematology Disease Topics & Pathways:
Biological, multiple myeloma, Adult, apoptosis, Diseases, Therapies, chemical interactions, Biological Processes, Technology and Procedures, Study Population, Plasma Cell Disorders, Clinically relevant, Lymphoid Malignancies, genetic profiling, NGS
Saturday, December 1, 2018, 6:15 PM-8:15 PM
Hall GH (San Diego Convention Center)

Ravi Vij, MBBS1, Justin King1*, Mark A. Fiala, BS2*, Neeraj Kumar Singh, B.Tech3*, Mohammed Sauban, B.E3*, Zakir Husain, M.Tech3*, Anjanasree V Lakshminarayana, B.E3*, Anay Ashok Talawdekar, M.Tech3*, Upasana Mitra, M.Sc.3*, Taher Abbasi, MS, MBA4* and Shireen Vali, PhD4*

1Section of Stem Cell Transplant and Leukemia, Division of Oncology, Washington University School of Medicine, Saint Louis, MO
2Washington University School of Medicine, Saint Louis, MO
3Cellworks Research India Private Limited, Bangalore, India
4Cell Works Group Inc., San Jose, CA

Background: Multiple myeloma (MM) is an incurable and heterogeneous haematological malignancy in which immune suppression and complex biology affect the disease and its response to treatment. Several new treatments have been approved for MM in recent years providing numerous options for patients with relapsed/refractory disease. However, there is no validated method for selecting the best treatment combination for each patient, making patient management difficult. The ability to predict treatment response based on disease characteristics could improve clinically outcomes.

Aim: This was a validation of a genomics-informed response prediction using computational biology modelling (CBM) in patients with relapsed/refractory MM.

Methods: Input data from fluorescence in-situ hybridization (FISH), karyotype, and a MM specific next generation sequencing capture array were analysed using CBM. This was a retrospective review of patients which were treated with different combinations based on patient/physician choice. The CBM uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated pathways. The specific drug combination for each patient was simulated and the quantitative drug effect was measured on a composite MM disease inhibition score (i.e., cell proliferation, viability, apoptosis and paraproteins). The predicted outcomes were then compared to the clinical response (≥PR or < PR per IMWG) to assess the accuracy of this CBM predictive approach.

Results: 27 patients were selected for the study; 3 failed CBM due to missing inputs and in 3 clinical response was not able to be assessed, leaving 21 eligible for the analysis. The median age at presentation was 57 years (range 37-76) and 52% were male. The median prior lines of MM therapy was 5 (range 1-15). 38% were refractory to bortezomib, 62% to lenalidomide, 52% to carfilzomib, 57% to pomalidomide, and 43% to daratumumab. 81% had a prior autologous stem cell transplant.

The treatments modelled included IMiD-based regimens (n = 9), PI-based regimens (n = 6), chemo-based regimens (n = 3), selinexor (n = 2), PI/IMiD combination regimens (n = 1). Sixteen were clinical responders and 5 were non-responders. CBM predictions matched for 17 of 21 treatments overall, 15 of 16 clinical responders and 2 of 5 non-responders. The statistics of prediction accuracy against clinical outcome are presented in Table 1.

Interestingly, the CBM identified drugs within the combination regimens which may not have impacted efficacy. For example, the CBM predicted that one patient treated with bortezomib, venetoclax, and dexamethasone would have had similar response if venetoclax had been omitted from the regimen.

Conclusion: We have demonstrated that a CBM approach, which incorporates genomics, can help predict response in patients with relapsed or refractory MM. Prospective studies using the CBM as part of treatment decision-making will help determine its application into clinical settings.

Disclosures: Vij: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees. Singh: Cellworks Research India Private Limited: Employment. Sauban: Cellworks Research India Private Limited: Employment. Husain: Cellworks Research India Private Limited: Employment. Lakshminarayana: Cellworks Research India Private Limited: Employment. Talawdekar: Cellworks Research India Private Limited: Employment. Mitra: Cellworks Research India Private Limited: Employment. Abbasi: Cell Works Group Inc.: Employment. Vali: Cell Works Group Inc.: Employment.

*signifies non-member of ASH