In Vivo Analysis of Clonal Evolution of Multiple Myeloma

Introduction According to the clonal evolution model, tumor progression proceeds in a branching rather than in a linear manner, leading to substantial clonal diversity and coexistence of genetically heterogeneous sets of subclones. Unlike many cancers, in which the evolutionary history can only be inferred from the established disease, Multiple Myeloma (MM) has well defined precursor states, which offer a unique opportunity to study the sequential evolution of the disease. In MM, multiple subclones can co-exist because they are of similar fitness, potentially interact with each other or with the surrounding microenvironment and further disseminate to spatially separate areas of the bone marrow (BM). Therefore, in order to accurately predict the course of the disease, we require methods to estimate clone-specific growth rates within the BM and define clones that have the propensity of dissemination.

Methods We developed a MM metastatic xenograft model by performing tumor-bearing bone chip implantation to SCID-beige mice (SCID-murine model) and examining tumor clones that are present in the implanted bone chips (primary sites) compared to those present in the distant BM sites (metastatic sites). To obtain a perspective of clonal heterogeneity in vivo, we used the "rainbow" system by which fluorescent proteins were infected into cells using lentiviruses to label the cells with 15 distinctive fluorescence profiles (rainbow MM cells). Rainbow MM cells with equal proportion of all 15 colors were injected into donorfemurs and implanted into recipient mice. After paralysis, the mice were sacrificed and tumor cells were analyzed using flow cytometry and confocal microscopy. To further investigate the dynamics of heterogeneity at the single cell level, similar experiments were performed using a DNA-barcode library. For genomic and transcriptomic characterization, primary and metastatic tumor clones were purified by sorting and underwent whole exome and RNA sequencing. To identify key regulators of the metastatic process, we conducted in vivo CRISPR library screening of the most critical targets identified. Briefly, the MM cell library was prepared by transduction of sgRNAs targeted for 20 genes and control sgRNAs to MM.1S cells stably expressing Cas9. The cell library was used in SCID-murine model and the fractions of each sgRNA were calculated in the primary and metastatic sites to identify genes that facilitate tumor metastasis.

Results We found that the 15 rainbow subpopulations were present with equal distribution in the primary sites but not at the metastatic sites. Specific subclones (winner clones) had a greater advantage of growing in the metastatic site. Interestingly, the winner clones were similar between the bilateral femurs of most of the mice, suggesting the existence of potential metastatic subclones. Experiments using DNA-barcoding further demonstrated that single clones could become disproportionately present in the metastatic sites, even though they account for a smaller fraction of the primary tumors. Confocal imaging showed the difference in cluster structures between primary and metastatic tumors. Most of the clusters in the metastatic sites consisted of cells of single colors. RNA sequencing analysis of two human MM cell lines derived from these mouse models demonstrated a distinct gene expression profile of the metastatic tumors compared to the primary sites. By intersecting differentially expressed genes, we identified 110 shared up-regulated genes and 238 shared down-regulated genes, which we designated as the "metastatic signature". Gene Set enrichment analysis of the metastatic signaturein publicly available MM patient datasets (GSE6477 and GSE2658) demonstrated that this signature significantly correlated with overall survival and with clinical progression from MGUS/smoldering MM to overt myeloma and relapsed disease. Finally, the CRISPR in vivo screening prioritized two transcription factors as the key regulatory molecules, namely EGR3 and ATF3.

Conclusions Here, we demonstrate that in vivo clonal evolution can be characterized using an in vivo model of MM. The data defines specific subclones that have a higher metastatic potential and are likely driver clones for tumor metastasis in MM. On the molecular level, a metastatic gene signature was found and two genes were identified as potential regulator of MM metastasis.