The multigenic nature of human tumours presents a fundamental challenge for cancer drug discovery. Here we use Drosophila to generate 32 multigenic models of colon cancer using patient data from The Cancer Genome Atlas. These models recapitulate key features of human cancer, often as emergent properties of multigenic combinations. Multigenic models such as ras p53 pten apc exhibit emergent resistance to a panel of cancer-relevant drugs. Exploring one drug in detail, we identify a mechanism of resistance for the PI3K pathway inhibitor BEZ235. We use this data to identify a combinatorial therapy that circumvents this resistance through a two-step process of emergent pathway dependence and sensitivity we term ‘induced dependence'. This approach is effective in cultured human tumour cells, xenografts and mouse models of colorectal cancer. These data demonstrate how multigenic animal models that reference cancer genomes can provide an effective approach for developing novel targeted therapies.
Colorectal cancer remains a leading source of cancer mortality worldwide. Initial response is often followed by emergent resistance that is poorly responsive to targeted therapies, reflecting currently undruggable cancer drivers such as KRAS and overall genomic complexity. Here, we report a novel approach to developing a personalized therapy for a patient with treatment-resistant metastatic KRAS-mutant colorectal cancer. An extensive genomic analysis of the tumor’s genomic landscape identified nine key drivers. A transgenic model that altered orthologs of these nine genes in the Drosophila hindgut was developed; a robotics-based screen using this platform identified trametinib plus zoledronate as a candidate treatment combination. Treating the patient led to a significant response: Target and nontarget lesions displayed a strong partial response and remained stable for 11 months. By addressing a disease’s genomic complexity, this personalized approach may provide an alternative treatment option for recalcitrant disease such as KRAS-mutant colorectal cancer.
Summary Adenoid cystic carcinoma (ACC) is a rare cancer type that originates in the salivary glands. Tumors commonly invade along nerve tracks in the head and neck, making surgery challenging. Follow-up treatments for recurrence or metastasis including chemotherapy and targeted therapies have shown limited efficacy, emphasizing the need for new therapies. Here, we report a Drosophila-based therapeutic approach for a patient with advanced ACC disease. A patient-specific Drosophila transgenic line was developed to model the five major variants associated with the patient's disease. Robotics-based screening identified a three-drug cocktail—vorinostat, pindolol, tofacitinib—that rescued transgene-mediated lethality in the Drosophila patient-specific line. Patient treatment led to a sustained stabilization and a partial metabolic response of 12 months. Subsequent resistance was associated with new genomic amplifications and deletions. Given the lack of options for patients with ACC, our data suggest that this approach may prove useful for identifying novel therapeutic candidates.
A fundamental difficulty in cancer research is the complex, multigenic and genetically diverse nature of human tumors, recently emphasized by cancer genome sequencing efforts. To capture this genetic complexity and diversity, we identified the most frequently observed double, triple and quadruple combinations of mutations in human colon cancer genomes and generated the corresponding combinations in Drosophila. We then investigated the tumorigenic and metastatic potential of 30 multigenic combinations we established by targeting them specifically to the adult Drosophila colon. These models recapitulate key features of human cancer, many of which arise as emergent properties of multigenic combinations. Importantly, we show that multigenic models are more resistant to compound effects than single-hit models and that combinatorial therapy is more effective against multigenic combinations. Specifically, we tested 16 clinically relevant targeted agents against our models using dissemination into the abdominal cavity as a quantitative read-out to monitor drug response. We found that 12 of these 16 compounds were effective against rasG12V alone while the four-hit model rasG12V p53RNAi ptenRNAi apcRNAi was resistant to all. We then uncovered a potential mechanism for resistance to PI3K pathway inhibitors as well as biomarkers for single agent response and resistance. We also developed a combination therapy that overcomes resistance of our four-hit model to single agent PI3K pathway inhibitors. We have validated our findings in human colorectal cancer cell lines, xenograft models as well as 3D colospheres and allografts derived from genetically engineered mouse models of colorectal cancer. We are currently investigating mechanisms mediating resistance of our four-hit model to the remaining compounds. We hope to use this information to devise additional rational combination therapies effective against genetically complex tumors. Overall, these models provide an excellent opportunity to study tumorigenesis and metastasis in the context of the whole animal and explore compound effects on a genetically diverse set of models. The emergent properties we identified in our multigenic combinations and interesting correlations we observed between tumor genotype and drug response also represent important steps towards personalized medicine. Citation Format: Erdem Bangi, Claudio Murgia, Alexander Teague, Owen Sansom, Ross Cagan. Identifying biomarkers of drug response and resistance using personalized Drosophila models of colorectal cancer. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr PR03.
Personalized cancer genomics is providing unprecedented access into the genetic complexity and diversity of human tumors. The next challenge is to utilize this information to establish effective therapeutics. Functional interrogation of cancer genomes using genetic model systems provides a powerful step towards realizing this goal. To capture the genetic complexity and diversity of human tumors, we identified the most frequently observed double, triple and quadruple combinations of mutations in human colon cancer genomes generated by TCGA. We used this information to generate the corresponding multigenic models in Drosophila and investigated the tumorigenic and metastatic potential of these 32 models by activating transgenes specifically in the adult Drosophila colon. These models recapitulate key features of human cancer, many of which arise as emergent properties of multigenic combinations. Importantly, we found that multigenic models were more resistant to targeted drugs and compounds: 12/16 of the targeted agents we tested were effective against rasG12V alone while 0/16 were effective against the four-hit model rasG12V p53RNAi ptenRNAi apcRNAi. We identified a potential mechanism for resistance to PI3K pathway inhibitors as well as biomarkers for single agent response and resistance. With this in hand, we developed a combination therapy that overcame resistance of our four-hit model to single agent PI3K pathway inhibitors. We validated our findings in human colorectal cancer cell lines including xenografts as well as 3D colospheres and allografts derived from genetically engineered mouse colorectal cancer models. Overall, our models provide an excellent opportunity to study tumorigenesis and metastasis in the context of the whole animal and explore compound effects on a genetically diverse set of models. Through these efforts we have developed a platform designed to screen large numbers of personalized fly models in a rapid and cost effective manner. We are now leveraging these technologies—using personalized fly models to identify personalized drug cocktails—to treat individual patients in a clinical study focusing on medullary thyroid cancer and colorectal cancer. Briefly, we first generate high quality genomic profiles for our patients and use this information to build a personalized fly model for each patient. These models are then screened against a large library of FDA approved drugs in an iterative manner to identify drug combinations specifically tailored to each patient. Our approach to personalized cancer therapeutics leverages sophisticated genetic tools and high throughput drug screening methods in Drosophila to address tumor and whole body complexities and provides a unique opportunity to identify novel treatment options for individual patients based on functional exploration of their tumor genomes. Citation Format: Erdem Bangi, Claudio Murgia, Alexander Teague, Peter Smibert, Jessica Esernio, Nelson Gruszczynski, Caitlyn Yeykal, Owen Sansom, Ross Cagan. A Drosophila approach to personalized cancer therapeutics. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; Jun 13-16, 2015; Salt Lake City, UT. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(1_Suppl):Abstract nr 34.
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