Dasatinib (BMS-35482) potentiates the activity of gemcitabine and docetaxel in uterine leiomyosarcoma cell lines.

BACKGROUND: To explore the activity of dasatinib alone and in combination with gemcitabine and docetaxel in uterine leiomyosarcoma (uLMS) cell lines, and determine if dasatinib inhibits the SRC pathway. METHODS: SK-UT-1 and SK-UT-1B uLMS cells were treated with gemcitabine, docetaxel and dasatinib individually and in combination. SRC and paxcillin protein expression were determined pre- and post-dasatinib treatment using Meso Scale Discovery (MSD) multi-array immunogenicity assay. Dose-response curves were constructed and the coefficient of drug interaction (CDI) and combination index (CI) for drug interaction calculated. RESULTS: Activated phosphorylated levels of SRC and paxillin were decreased after treatment with dasatinib in both cell lines (p < 0.001). The addition of a minimally active concentration of dasatinib (IC25) decreased the IC50 of each cytotoxic agent by 2-4 fold. The combination of gemcitabine-docetaxel yielded a synergistic effect in SK-UT-1 (CI = 0.59) and an antagonistic effect in SK-UT-1B (CI = 1.36). Dasatinib combined with gemcitabine or docetaxel revealed a synergistic anti-tumor effect (CDI < 1) in both cell lines. The triple drug combination and sequencing revealed conflicting results with a synergistic effect in SK-UT-1B and antagonistic in SK-UT-1. CONCLUSION: Dasatinib inhibits the SRC pathway and yields a synergistic effect with the two-drug combination with either gemcitabine or docetaxel. The value of adding dasatinib to gemcitabine and docetaxel in a triple drug combination is uncertain, but may be beneficial in select uLMS cell lines. Based on our pre-clinical data and known activity of gemcitabine and docetaxel, further evaluation of dasatinib in combination with these agents for the treatment of uLMS is warranted.

Characterizing Genetic Drivers of Lymphoma through High-Throughput Sequencing

The advent of next-generation sequencing, now nearing a decade in age, has enabled, among other capabilities, measurement of genome-wide sequence features at unprecedented scale and resolution.

In this dissertation, I describe work to understand the genetic underpinnings of non-Hodgkin’s lymphoma through exploration of the epigenetics of its cell of origin, initial characterization and interpretation of driver mutations, and finally, a larger-scale, population-level study that incorporates mutation interpretation with clinical outcome.

In the first research chapter, I describe genomic characteristics of lymphomas through the lens of their cells of origin. Just as many other cancers, such as breast cancer or lung cancer, are categorized based on their cell of origin, lymphoma subtypes can be examined through the context of their normal B Cells of origin, Naïve, Germinal Center, and post-Germinal Center. By applying integrative analysis of the epigenetics of normal B Cells of origin through chromatin-immunoprecipitation sequencing, we find that differences in normal B Cell subtypes are reflected in the mutational landscapes of the cancers that arise from them, namely Mantle Cell, Burkitt, and Diffuse Large B-Cell Lymphoma.

In the next research chapter, I describe our first endeavor into understanding the genetic heterogeneity of Diffuse Large B Cell Lymphoma, the most common form of non-Hodgkin’s lymphoma, which affects 100,000 patients in the world. Through whole-genome sequencing of 1 case as well as whole-exome sequencing of 94 cases, we characterize the most recurrent genetic features of DLBCL and lay the groundwork for a larger study.

In the last research chapter, I describe work to characterize and interpret the whole exomes of 1001 cases of DLBCL in the largest single-cancer study to date. This highly-powered study enabled sub-gene, gene-level, and gene-network level understanding of driver mutations within DLBCL. Moreover, matched genomic and clinical data enabled the connection of these driver mutations to clinical features such as treatment response or overall survival. As sequencing costs continue to drop, whole-exome sequencing will become a routine clinical assay, and another diagnostic dimension in addition to existing methods such as histology. However, to unlock the full utility of sequencing data, we must be able to interpret it. This study undertakes a first step in developing the understanding necessary to uncover the genomic signals of DLBCL hidden within its exomes. However, beyond the scope of this one disease, the experimental and analytical methods can be readily applied to other cancer sequencing studies.

Thus, this dissertation leverages next-generation sequencing analysis to understand the genetic underpinnings of lymphoma, both by examining its normal cells of origin as well as through a large-scale study to sensitively identify recurrently mutated genes and their relationship to clinical outcome.