Multiple Myeloma (MM) is a heterogeneous malignancy characterized by abnormal clonal plasma cell infiltration in the bone marrow. It is the second most common hematologic malignancy, after non-Hodgkin’s Lymphoma. This deadly disease globally affects 1 to 5 people in every 100,000 people each year. After initial diagnosis, the 5-year survival rate is 44% because there is no curative therapy, and most patients will eventually experience relapse, and some become refractory. Large inter-patient and intra-patient genetic heterogeneity limit the identification of universal drivers of MM. However, several oncogenic dependencies such as primary events related to driver gene mutations and primary translocations can be targeted for better treatment strategies for R/R multiple myeloma. To establish a robust drug sensitivity screening platform, both MM cell lines and cryopreserved primary MM patient samples were used. MM cell lines screen with H929, MM1R and RPMI-8226 was performed to narrow down optimal concentrations of investigative test agents. During assay development, a panel of FDA approved agents for relapsed MM including panobinostat, bortezomib, melphalan, omacetaxine, and selinexor were tested. The cells were seeded at 50,000 cells per well in triplicates in a 96-well plate on day 0. The test agents were added in 9 concentrations with the top concentration being 25 uM with 2-fold dilutions. After 6- days of incubation, a luminescent cell viability assay was performed to calculate relative viability with drug treatment and IC50 was computed by fitting data using a standard four-parameter logistic model. For primary multiple myeloma samples, bone marrow aspirates were collected from Multiple Myeloma patients procured from different providers. BM mononuclear (BMMNC) cells were separated by Ficoll-Hypaque density sedimentation. Unfractionated BMMNC cells were used in these assays to preserve intra-tumor heterogeneity. Primary cells were seeded in triplicates per dose point on day 0 in Champions’ proprietary media. The test agents were also added on day 0 and were incubated for 6 days. On Day 6, the relative cell viability was measured based on Cell Titer Glo readout. The cryopreserved primary MM patient samples were viable in culture for the entire assay duration validating assay feasibility. A panel of FDA approved, standard of care drugs were tested in numerous clinically well-annotated and diverse MM patient models. Primary model selection was based on disease burden including plasma cell count from Core Biopsy and BM aspirates. Multiparametric flow cytometric immunophenotyping was also performed in these primary samples using monoclonal antibodies against CD56, CD19, CD117, CD27, CD138, and CD38. For the first time, we demonstrate the feasibility of a novel, ex-vivo drug sensitivity screening platform with cryopreserved, primary multiple myeloma cells from diverse, clinically well-annotated patient samples. Citation Format: Vaishnavi Sambandam, Sharvari Inamdar, Haoting Hsu, Brandon Walling, Paolo Schiviani, Abhay Andar, Marianna Zipeto, Michael Ritchie, Karin Abarca Heidemann, Maria Mancini. Ex vivo modeling of multiple myeloma: A novel drug sensitivity screening platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 207.

Despite considerable progress made in improving therapeutic strategies, the overall survival for patients diagnosed with various cancer types remains low. Further, patients often cycle through multiple therapeutic options before finding an effective regimen for the specific malignancy being treated. A focus on building enhanced computational models, which prioritize therapeutic regimens based on a tumors complete molecular profile, will improve the patient experience and augment initial outcomes. In this study, we present an integrative analysis of multiple omic datasets coupled with phenotypic and therapeutic response profiles of Cytarabine from a cohort of primary AML tumors, and Olaparib from a cohort of Patient-Derived Xenograft (PDX) models of ovarian cancer. These analyses, termed Pharmaco-Pheno-Multiomic (PPMO) Integration, established novel complex biomarker profiles that were used to accurately predict prospective therapeutic response profiles in cohorts of newly profiled AML and ovarian tumors. Results from the computational analyses also provide new insights into disease etiology and the mechanisms of therapeutic resistance. Collectively, this study provides proof-of-concept in the use of PPMO to establish highly accurate predictive models of therapeutic response, and the power of leveraging this method to unveil cancer disease mechanisms.

The overall survival of patients diagnosed with Acute Myeloid Leukemia (AML) remains low. While initial responses to therapy are favorable, the duration of response is short and overcoming therapeutic resistance has proven difficult. A better understanding of the tumor cell biology and resistance mechanisms may shed light onto novel therapeutic targets that improve long-term outcome. In this study, we performed an exhaustive analysis to include deep tumor phenotyping, drug sensitivity profiling and comprehensive omic characterization. These datasets were included in integrative pharmaco-phenotypic-multiomic analyses to identify targets and biomarkers associated with cellular phenotype and drug response. Our results reveal that the major cellular discriminant within the cellular phenotype is CD34 expression, which associates with a high PDK-mediated metabolic profile and cytarabine sensitivity. Tumors exhibiting cytarabine resistance associate with a CD34-negative cellular phenotype and molecular characteristics such as MYC copy number gain, and increased expression of SAMDH1, FBP1 and TYMP proteins. Citation Format: Gilad Silberberg, Bandana Vishwakarama, Brandon Walling, Chelsea Riveley, Alessandra Audia, Marianna Zipeto, Ido Sloma, Amy Wesa, Michael Ritchie. A pheno-multiomic integration analysis of primary samples of acute myeloid leukemia reveals biomarkers of cytarabine resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3907.