In Vivo Modeling of Clonal Competition Using CRISPR-Based Gene Editing Reveals Novel Fitness Variables in Multiple Myeloma
Authors: Shen Yujia , Manier Salomon , Tahri Sabrin , Berrios Brianna , Zavidij Oksana , Park Jihye , Pistofidis Romanos , Ghobrial Irene M. 
Published: October 2019

Introduction: Multiple Myeloma (MM) is an incurable malignancy characterized by the proliferation of clonal plasma cells in the bone marrow (BM). MM almost always progresses from the precursor states of monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), which indicates the presence of a gradual clonal evolution underlying progression from the original stages of tumor development to the time of clinical presentation. Clonal heterogeneity adds another layer of complexity to that, by introducing interclonal competition in the context of disease progression or therapeutic bottlenecks. Here we developed a mouse model to investigate the impact of multiple clonal mutations on tumor development, as well as the competitive expansion of individual clones.

Methods: Primary mouse MM Vk*Myc cells stably expressing Cas9 were infected with validated sgRNAs to knockout (KO) genes of interest (P53, Cyld, Rb1, Dis3, Prdm1, Traf3 and Fam46c) that are significantly mutated in human MM. KO cells were mixed at a 1:1 ratio with control cells infected with control sgRNA and injected intravenously into 8-week-old RAG2 KO mice. Vk*Myc cells were then isolated from bone marrow and spleen through CD138 positive selection, followed by genomic DNA extraction and NGS sequencing to understand the dynamic changes in abundance of mutants from injection to early and late timepoints.

Results: In vitro, most knockout Vk*Myc cells had a similar proliferation rate to control cells with the exception of P53 and Rb1 knockout cells, which grew faster as expected; both P53 and RB1 are known cell cycle regulators. However, when co-injected into RAG2 KO mice (Vk*Myc cells constructed with Cas9 do not engraft in C57BL/6 mice), although P53 and Rb1 knockout cells remained the strongest competitors, occupying the majority of the tumor, most KO cells exhibited significantly enhanced proliferation over control cells. These results indicate that certain mutations only become advantageous in the context of the tumor microenvironment, while mutations that directly affect the tumor cell's proliferation rate give rise to more flexible, potent clones. To better understand these differences, we took advantage of the CRISPR-induced heterogeneous pool of genomic edits per gene, and looked at clonal abundancy rates within each knockout population separately. Interestingly, we found mutants with certain insertions/deletions grew faster than others and were overrepresented at the late stage of disease, even when they were generated from the same double-stranded break. Although it is well established that mutations in different regions of the same gene might have different effects, these results indicate that different mutations in the exact same spot can give rise to clones of variable potency and beg the question of whether mutation sequence is as important as mutation hotspot.

Conclusion: We established a mouse model to study clonal competition in vivo, utilizing the CRISPR-Cas9 genome editing toolset. Through our model, we were able to witness a range of competitive potential among genes that are significantly mutated in multiple myeloma, with P53- and RB1-mutants as the strongest competitors. Furthermore, we observed that competitive potential can be conditional, with certain mutants conferring fitness advantage only in the context of tumor microenvironment. Adding another layer of complexity to differential fitness, we found that different mutations in the same spot of the same gene give rise to clones of varied potency, implicating mutation sequence as a novel fitness variable. In this study, we thus demonstrate that mutational candidates can be prioritized based on competitive potential, a process of the utmost importance given multiple myeloma's marked genetic heterogeneity.

Disclosures

Ghobrial: Celgene: Consultancy; Takeda: Consultancy; Janssen: Consultancy; BMS: Consultancy.