Myers, Chad L.

Abstract Acute myeloid leukemia (AML) with TP53 mutations (TP53Mut) has poor clinical outcomes. We investigated whether this AML subtype harbors a distinct gene expression profile (GEP) and whether this GEP is prognostic in TP53 wild type (TP53WT) AML. We applied a supervised machine-learning approach to detect the TP53Mut GEP. We divided the samples in the Beat-AML dataset into training and testing datasets. The TCGA LAML dataset was used as a validation dataset. We trained a ridge regression machine learning model to classify TP53Mut and TP53WT cases. This model was highly accurate in distinguishing TP53Mut versus TP53WT cases in both the test and validation datasets. Additionally, we identified TP53WT samples with high ridge regression scores. These high-scoring TP53WT samples also have poor overall survival, suggesting they share clinical and GEP features with TP53Mut AML. We defined these TP53WT samples as TP53Mut-like. Using drug sensitivity data in the Beat AML dataset, we found TP53Mut-like AMLs have distinct drug sensitivity patterns that phenocopy TP53Mut cases. Finally, we identified a 25-gene signature that can identify TP53Mut-like AML. This signature could be used clinically to identify this novel subset of poor-prognosis AML.

Abstract Synthetic lethal interactions enable a novel approach for discovering specific genetic vulnerabilities in cancer cells that can be exploited for the development of therapeutics. This concept has recently been exploited in the development of PARP inhibitors as novel chemotherapeutics for breast cancer. While PARP is not essential in normal cells, BRCA mutant cells are dependent on it for their survival. This led to the successful treatment of BRCA mutant tumors of the breast, ovary, and prostate with the oral PARP inhibitor, olaparib. Therapeutic strategies based on such synthetic-lethal (SL) interactions can enable drug targeting of cancer-specific alterations for otherwise undruggable tumor suppressors. Large-scale analyses of genetic interaction networks in model organisms like yeast suggest that disease-specific networks should not only give rise to destructive phenotypes (e.g. uncontrolled cell growth), but will also present unique vulnerabilities that can be exploited for novel therapies. Despite successes in model organisms such as yeast, discovering synthetic lethal interactions on a large scale in human cells remains a significant challenge. We have used a combination of computational and experimental approach whereby yeast interactions between human orthologs are filtered by cancer association and prioritized by genomic features, and candidates from the prioritized list are then validated in human cell lines using an RNA interference approach. As a proof of principle, we utilized this approach to discover two previously unknown synthetic lethal/sick interactions, one between SMARCB1 (yeast SNF5) and PSMA4 (yeast PRE9), and another between ASPSCR1 (yeast UBX4) and PSMC2 (yeast RPT1) based on shRNA double knock-down in normal human fibroblast cells. These interactions suggest potentially new therapeutic targets for SMARCB1 and ASPSCR1 mutated cancers, and more broadly, illustrate the potential of this cross-species approach. Citation Format: Michael K. Asiedu, Raamesh Deshpande, Mitchell Klebig, Shari Sutor, Elena Kuzmin, Jeff Piotrowski, Seung Ho Shin, Michael Costanzo, Charles Boone, Dennis A. Wigle, Chad L Myers. A comparative genomic approach for identifying synthetic lethal interactions in human cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-234. doi:10.1158/1538-7445.AM2013-LB-234

Abstract Synthetic lethal interactions enable a novel approach for discovering specific genetic vulnerabilities in cancer cells that can be exploited for the development of therapeutics. Despite successes in model organisms such as yeast, discovering synthetic lethal interactions on a large scale in human cells remains a significant challenge. We describe a comparative genomic strategy for identifying cancer-relevant synthetic lethal interactions whereby candidate interactions are prioritized on the basis of genetic interaction data available in yeast, followed by targeted testing of candidate interactions in human cell lines. As a proof of principle, we describe two novel synthetic lethal interactions in human cells discovered by this approach, one between the tumor suppressor gene SMARCB1 and PSMA4, and another between alveolar soft-part sarcoma-associated ASPSCR1 and PSMC2. These results suggest therapeutic targets for cancers harboring mutations in SMARCB1 or ASPSCR1 and highlight the potential of a targeted, cross-species strategy for identifying synthetic lethal interactions relevant to human cancer. Cancer Res; 73(20); 6128–36. ©2013 AACR.