Founding Precision Therapy in 1q-Amplified Multiple Myeloma

Introduction Multiple myeloma is an incurable plasma cell malignancy with a strikingly heterogeneous genomic landscape. Other than IgH translocations and hyperdiploidy, only a few alterations are observed in large enough numbers. Amplification of the long arm of chromosome 1 (1q) is among the most common copy number alterations encountered, with a confirmed adverse effect on survival. Gene expression profiling has identified a minimal common amplified region between 1q21 and 1q23 as a probable target of the amplification event, however the actionable gene dependencies in that region have not been explored. In this study, we employ a large number of in-house and publicly available CRISPR, shRNA and drug screens in an effort to characterize the genetic dependencies of 1q-amplified myeloma and discover drugs that target them. Ultimately, we hope to propose a tailored therapeutic strategy for patients with 1q-amplified multiple myeloma. Methods To assess the genetic dependencies of 1q-amplified myeloma, we performed an shRNA screen in multiple myeloma cell lines, targeting genes in the 1q21-1q23 region. Corresponding C911 hairpins were designed for every target shRNA, and DEMETER2 was used to infer on-target effect. To that same end, we analyzed publicly available dependency data from Project Achilles (Whole-genome CRISPR screen, Avana library, 18Q4 release) and Dependency Map (combined RNAi dataset, accessed on 6/20/2018) and looked for differential dependencies in 1q-amplified multiple myeloma cell lines. Different sets of 1q-amplified and non-amplified cell lines were included in each dataset to avoid cell line-specific effects. Genes that both constituted differential dependencies and were differentially expressed were considered as hits. GSEA was used for pathway analysis. To assess differential sensitivity of 1q-amplified myeloma to drugs, we performed a drug screen utilizing the Broad Institute's Drug Repurposing Library-a library of over 5,000 drugs that have cleared varied stages of clinical testing, and compared normalized viability values between 1q-amplified and non-amplified myeloma cell lines. Utilizing publicly available patient data, we also built a 1q-amplification gene expression signature and used it to query the Connectivity Map (CMap) database. Drugs that were predicted to reverse our signature were then used in a new drug screen of myeloma cell lines. Results Through multiple dependency screens, we identified a total of 206 differential d