Deciphering Clonal Evolution and Dissemination of Multiple Myeloma Cells In Vivo

Introduction: Multiple Myeloma (MM) is a genetically complex and evolutionary process with well defined precursor states, which offer a unique opportunity to study the sequential evolution of the disease. A small number of detectable pre-malignant clones are present in early stage and continue to acquire more genomic abnormalities leading to overt disease. The interaction between cancer cells and their environment is reciprocal, multiple components in the tissue environment can influence cancer clonal evolution and cancer cells in turn can also remodel the microenvironment and further disseminate to spatially separated areas of BM. To accurately predict the course of disease with the presence of BM environment, we require methods to estimate clone-specific growth rates and define clones that have the propensity of dissemination.

Methods: We developed a novel 'bone chip' MM metastatic xenograft model using fluorescent protein tagged 'rainbow' system which enables both molecular profiling and functional tracking of clonal dissemination of tumor cells by performing tumor-bearing bone chip implantation subcutaneously to SCID-beige mice (SCID-murine model). Rainbow MM cells with equal proportion of all 15 colors were injected into donor femurs and implanted into recipient mice. After paralysis, the mice were sacrificed and tumor cells were analyzed using flow cytometry and confocal microscopy. Tumor clones in the implanted bone chip (primary sites) and distant host BM (metastatic sites) were purified by sorting and underwent RNA sequencing. By intersecting differentially expressed genes, we identified a set of genes, the expression of which were altered during disease dissemination and designated this set of genes as 'metastatic signature'. In addition, we also performed genome-wide CRISPR/Cas9-mediated loss-of-function screen in a subcutaneous xenograft mouse model to investigate the essential drivers of tumor growth and metastasis in MM. The cell library infected with human sgRNA library was injected subcutaneously into SCID-Beige mice on both flanks. When metastasis was established, the fractions of each sgRNA of the primary and metastatic tumors were calculated 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. 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 SCID-murine model demonstrated a distinct gene expression profile of the metastatic tumors. Gene Set Enrichment Analysis of the metastatic signature in publicly available MM patient datasets (GSE6477 and GSE2658) demonstrated that this signature is significantly correlated with overall survival and with clinical progression from MGUS/smoldering MM to overt myeloma and relapsed disease. Through genome-wide CRISPR screening in vivo, we found that the gene targets of the most enriched sgRNAs in the BM samples were preferentially involved in important cellular processes, such as cell cycle regulation and several oncogenic signaling pathways. Additionally, many sgRNAs that remained the implanted sites until late stage were depleted during dissemination, indicating their targeted genes were important for progression. These depleted sgRNAs mainly targeted genes involved in mTORC1 and DNA repair pathways, many of which are regulated by MYC and cell cycle related targets of E2F transcription factors. By using a network-based inference of protein activity method, we chose 4 genes (HMGA1, KLF6, TRIM28 and PA2G4) and validated in SCID-murine model using CRISPR mediated loss-of-function screen which prioritized HMGA1 as the key regulator in MM dissemination.

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. We then established a platform for future invivo CRISPR screens to investigate essential genes of response to targeted therapies and/or immunotherapies. Furthermore, a metastatic gene signature was identified and among these, HMGA1 was validated as potential regulator of MM metastasis.