Functional Drivers of Therapeutic Response in Diffuse Large B Cell Lymphoma
Diffuse large B cell lymphoma (DLBCL) is the most common form of non-Hodgkin’s lymphoma and a leading cause of death among B cell lymphomas. The disease displays a remarkable amount of genetic and clinical heterogeneity, hampering efforts at designing effective therapeutic agents. The last major change in frontline therapy for DLBCL came in 1997 with the addition of rituximab into widespread clinical use. Much effort has been put forth to identify new therapeutic agents in DLBCL, but these have been relatively unsuccessful due to the phenotypic heterogeneity rendering them useful in only small percentage of patients, and responding patients developing resistance to single agents. To find broadly effective, safe therapeutics, we must first perform deep genomic characterization of the disease to understand its true heterogeneity. We can then study those genes and pathways with genomic alteration and evaluate their effects on the disease. Using this information, we can develop relevant drug screening tools that leverage genomic technologies to better understand therapeutic interactions with the disease and use it to predict possible therapeutic synergy. In this dissertation, I utilize this three-pronged approach to identify novel functional variation in DLBCL and use it predict and verify synergistic therapeutic combinations for therapy.
First, I focus on the genomic variation across DLBCL patients. Since DLBCL displays vast genomic heterogeneity, we assemble the largest sequencing study of the disease to date, consisting of 1,001 cases. Here, we applied exome and transcriptome sequencing to these cases with paired clinical data. We identified 150 putative driver genes that were recurrently mutated with a mean of 7.75 per sample. Using genomic alteration types, we then classified these genes as oncogenes or tumor suppressors. We then used gene expression data to classify these tumors based on the cell of origin into activated B cell like (ABC) and germinal center B cell like (GCB) subtypes, which aligned well with standardized clinical methods. We found differential mutations in 20 genes based these subtypes. We also found significant overlaps of driver genes with both co-occurrence and mutual exclusion, suggesting subnetworks of mutation. To identify functional variation, we then use a CRISPR screen to identify putative oncogenes and tumor suppressors genome wide. Using this data in combination with matched clinical data, we then create the Genomic Risk Model for predicting single patient outcomes based his or her genomic profile. This model outperforms current models and successfully predicts outcomes in most patients.
I then focus on placing two focal adhesion genes identified as recurrently alerted in DLBCL into the functional context of the disease. The first gene is RHOA, a small GTPase that is found to be recurrently mutated in DLBCL in a hotspot specific manner. Overexpression of both wildtype RHOA and the enriched R5Q mutant form both significantly increase proliferation in DLBCL cell lines. We also find that RHOA loss causes a loss of fitness in DLBCL lines, causing the cells to arrest in the G2/M phase of the cell cycle and altering cellular morphology. I then develop a mouse model that knocks out Rhoa in either the full B cell lineage or germinal center B (GCB) cells specifically. We observe a massive loss of B cells across the B lineage driven by Rhoa deletion. In the GCB restricted knockout, we find that GCB cell numbers are reduced, the dark zone to light zone regulation in the germinal center is altered, as well as actin dysregulation in these cells. The next gene we modeled was focal adhesion kinase (FAK). Though FAK is not recurrently mutated in DLBCL, it is overexpressed in GCB cells and many cancers, and it is a master regulator of the focal adhesion pathway, which is overrepresented in mutation rates. Chemical inhibition and genetic knockdown of FAK in GBC cells causes cell death and a marked reduction in B cell receptor (BCR) signaling effectors. Further work in BCR signal transduction places FAK near the intracellular interface with the BCR, as the first effector molecules in the pathway have vastly reduced activity with FAK inhibition. Mouse models of Fak knockout also show a reduction in GCB cells, dysregulation of germinal centers in secondary lymph organs, and a reduction in serum levels of secreted immunoglobulins.
Lastly, we sought to understand the role of single agent therapeutics on gene expression to identify a method to use this data to inform combination therapy predictions. Using a panel of 152 FDA-approved drugs and 6 DLBCL cell lines, we screened all lines for drug efficacy. This revealed 3 classes of drugs: pan-effective, selective, and resistant. The selective drugs displayed pathway specific resistance as well as a subtype specific sensitivity for certain drugs. RNA sequencing to quantify gene expression was then performed for all drug-cell line pairs. Overall gene expression patterns show that drugs targeting similar primary targets or targets in the same pathway induced tightly correlated gene expression patterns. We also found that changes in expression of genes like MYC were correlated with sensitivity, giving possible proxies for sensitivity and mechanisms of resistance. We also found that baseline expression of the target gene was correlated with a higher dose requirement to achieve similar viability changes. Gene expression changes of the target gene were also found to be indicative of either sensitivity or resistance in some cases. We then developed a model for using gene expression to predict dual drug synergy that we are calling combination reversal of disease gene expression (cRDGE). This model accurately predicted both single agent effectiveness as well as combination synergy in previous datasets. We then validated this method across several drug combinations and found synergy between the tested combinations in vitro. We then tested one combination, panobinostat and ruxolitinib, and found it to be highly synergistic in both the cell lines tested within the study as well as a panel of cell lines representing a wide variety of B cell malignancies. We then show how ruxolitinib alone does not reduce STAT signaling in these cells at a lower dose, but sensitization of these cells with panobinostat greatly reduces STAT activity with the combination. Using xenograft models, we then tested this combination in vivo, finding synergy of the drug combination and low hematopoietic toxicity.
Broadly, this dissertation contributes novel findings to the fields of B cell biology, lymphoma genomics, and therapeutic screening. Cancer is an incredibly challenging entity that requires an approach that integrates interrogation of genomic alterations, exploration of functional alterations, and development of new tools to identify therapeutics. Leveraging these tools to understand the molecular basis of lymphoma, its functional variation, and therapeutic interactions, we can more accurately diagnose, give prognoses, and treat patients with the disease.
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