Luc de Waal

Mapping the Hallmarks of Lung Adenocarcinoma with Massively Parallel Sequencing

Lung adenocarcinoma, the most common subtype of non-small cell lung cancer, is responsible for more than 500,000 deaths per year worldwide. Here, we report exome and genome sequences of 183 lung adenocarcinoma tumor/normal DNA pairs. These analyses revealed a mean exonic somatic mutation rate of 12.0 events/megabase and identified the majority of genes previously reported as significantly mutated in lung adenocarcinoma. In addition, we identified statistically recurrent somatic mutations in the splicing factor gene U2AF1 and truncating mutations affecting RBM10 and ARID1A. Analysis of nucleotide context-specific mutation signatures grouped the sample set into distinct clusters that correlated with smoking history and alterations of reported lung adenocarcinoma genes. Whole-genome sequence analysis revealed frequent structural rearrangements, including in-frame exonic alterations within EGFR and SIK2 kinases. The candidate genes identified in this study are attractive targets for biological characterization and therapeutic targeting of lung adenocarcinoma.

A Pan-Cancer Analysis of Transcriptome Changes Associated with Somatic Mutations in U2AF1 Reveals Commonly Altered Splicing Events

Although recurrent somatic mutations in the splicing factor U2AF1 (also known as U2AF35) have been identified in multiple cancer types, the effects of these mutations on the cancer transcriptome have yet to be fully elucidated. Here, we identified splicing alterations associated with U2AF1 mutations across distinct cancers using DNA and RNA sequencing data from The Cancer Genome Atlas (TCGA). Using RNA-Seq data from 182 lung adenocarcinomas and 167 acute myeloid leukemias (AML), in which U2AF1 is somatically mutated in 3–4% of cases, we identified 131 and 369 splicing alterations, respectively, that were significantly associated with U2AF1 mutation. Of these, 30 splicing alterations were statistically significant in both lung adenocarcinoma and AML, including three genes in the Cancer Gene Census, CTNNB1, CHCHD7, and PICALM. Cell line experiments expressing U2AF1 S34F in HeLa cells and in 293T cells provide further support that these altered splicing events are caused by U2AF1 mutation. Consistent with the function of U2AF1 in 3′ splice site recognition, we found that S34F/Y mutations cause preferences for CAG over UAG 3′ splice site sequences. This report demonstrates consistent effects of U2AF1 mutation on splicing in distinct cancer cell types.

Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics

High cancer death rates indicate the need for new anti-cancer therapeutic agents. Approaches to discover new cancer drugs include target-based drug discovery and phenotypic screening. Here, we identified phosphodiesterase 3A modulators as cell-selective cancer cytotoxic compounds by phenotypic compound library screening and target deconvolution by predictive chemogenomics. We found that sensitivity to 6-(4-(diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one, or DNMDP, across 766 cancer cell lines correlates with expression of the phosphodiesterase 3A gene, PDE3A. Like DNMDP, a subset of known PDE3A inhibitors kill selected cancer cells while others do not. Furthermore, PDE3A depletion leads to DNMDP resistance. We demonstrated that DNMDP binding to PDE3A promotes an interaction between PDE3A and Schlafen 12 (SLFN12), suggesting a neomorphic activity. Co-expression of SLFN12 with PDE3A correlates with DNMDP sensitivity, while depletion of SLFN12 results in decreased DNMDP sensitivity. Our results implicate PDE3A modulators as candidate cancer therapeutic agents and demonstrate the power of predictive chemogenomics in small-molecule discovery.

Abstract 4867: Comparative analysis of RNA sequencing methods for characterization of cancer transcriptomics

RNA sequencing (RNASeq) provides the ability to comprehensively assay the transcriptome in a high-throughput manner. Current there are a variety of library preparation methodologies for measuring and sequencing the transcriptome depending on (a) the sample source and (b) outcomes of interest. Beyond protocol selection, the requisite computational tools and resources are significant considerations in processing, analyzing and reporting the experimental results. While there are many resources readily available to effectively perform RNA-seq experiments, optimal protocols and analysis tools for the cancer domain remain to be developed. We have developed and characterized a set of protocols and analysis procedures that comprise an RNA-seq pipeline that can effectively be used in a cancer research setting. The analysis pipeline consists of a sequence of functions and tools to process and clean the raw data, generate quality control and summary metrics, and perform secondary analyses that include expression quantification, fusion detection and somatic mutation calling. We applied this pipeline to three different RNAseq strategies (whole-transcriptome, exome, and targeted RNA-seq) and performed an in-depth comparative analysis to investigate the implications of the choice of strategy on the downstream analysis and results. More specifically, we investigated the impact of library preparation methods on the dynamic range and expression profiles, variant calling and fusion detection. While the data indicated that capture-based protocols provided efficient methods for sampling transcripts as compared to whole-transcriptome RNA-seq, there are considerations in its use, particularly for duplicate reads and uncaptured transcripts. We illustrate the implications of these issues on downstream analysis, such as somatic mutation and fusion calling and differential expression. In summary, we have described a RNA-seq analysis platform that provides a varied set of library preparations and analytical components for large-scale clinical or research transcriptomics. Our analysis has characterized the technical features of the different library preparations, providing a necessary understanding of the costs and benefits of each method and the potential effects on the downstream analyses. Citation Format: Ryan P. Abo, Ling Lin, Samuel S. Hunter, Deniz N. Dolcen, Rachel R. Paquette, Angelica Laing, Luc de Waal, Aaron R. Thorner, Matthew D. Ducar, Liuda Ziaugra, William C. Hahn, Matthew L. Meyerson, Laura E. MacConaill, Paul Van Hummelen. Comparative analysis of RNA sequencing methods for characterization of cancer transcriptomics. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4867. doi:10.1158/1538-7445.AM2015-4867

Abstract 1115: Targeted RNA sequencing improves transcript analysis in cancer samples

RNA sequencing (RNA-seq) is a transcriptome profiling technology that provides multiple levels of insight into the genome. In addition to expression levels (transcript abundance), it generates endpoints such as alternative splicing, somatic mutations and rearrangements, which may have functional consequences in cancer. Although somatic mutations are generally identified by DNA sequencing, RNA-seq has the advantage of detecting allele-specific expression affecting a variant allele, as well as functional chimeric transcripts that result from structural rearrangements. Compared to microarray technologies, RNA-seq can provide additional information about novel transcripts. Due to the complexity of the human transcriptome and the variability of gene abundance, the cost of whole transcriptome sequencing to achieve sufficient coverage to detect these types of alterations remains high. To explore the feasibility of a more cost-effective method, we compared the performance of three different RNA-seq methods: whole-transcriptome-, exome-, and targeted RNA-seq, using RNA derived from cancer cell lines and Formaldehyde Fixed-Paraffin Embedded (FFPE) samples. For whole-transcriptome preparation, we used the Illumina TruSeq Stranded mRNA and total RNA kits for cell line and FFPE samples, respectively. Exome-RNAseq was performed using the Illumina Access kit. The libraries from whole-transcriptome RNAseq were subjected to hybridization capture using OncoPanel-an Agilent SureSelect baitset of 500 cancer-related genes. Compared to whole-transcriptome, exome- and targeted-RNA-seq demonstrated (1) higher coding exon coverage and multiplexing capability; (2) reduced rRNA composition to 1%; (3) comparable gene abundance information and (4) over 90% of reads aligned to coding exon regions in FFPE samples, compare to ∼30% when using whole transcriptome method. In conclusion, we demonstrated that exome- and targeted RNA-seq provide a cost-effective way to analyze a subset of the transcriptome. Furthermore, targeted RNA-seq can be highly multiplexed and is therefore amenable for large-scale tumor profiling in clinical or research settings. Citation Format: Ling Lin, Ryan Abo, Deniz Dolcen, Rachel Paquette, Angelica Laing, Luc de Waal, Aaron Thorner, Matthew Ducar, Liuda Ziaugra, Bruce Wollison, Marc Breneiser, William Hahn, Matthew Meyerson, Paul Van Hummelen, Laura MacConaill. Targeted RNA sequencing improves transcript analysis in cancer samples. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1115. doi:10.1158/1538-7445.AM2015-1115

Abstract 4596: An integrated genomic characterization of the target of a small molecule identifies a novel cancer dependency

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Recent large sequencing and cancer dependency studies have accelerated the identification of candidate targets for precision medicine. However, the current drug development paradigm starting with target identification and validation can be slow and has thus far yielded a limited variety of successful targets. We sought to return to an empirical approach to drug discovery and performed a high throughput screen to identify small molecules that were both potent and selective. In a primary screen of 2000 compounds in two cell-lines: A549 and H1734, three compounds only affected H1734 viability. One of which validated in a dose-response experiment with great potency and specificity, we called this small molecule ‘Compound 1B’. In an effort to identify the target of Compound 1B, we profiled 766 genomically-characterized cancer cell lines and found that approximately 4% were sensitive to our compound. Sensitivity was not restricted to a particular tissue of origin. Interestingly, expression of Phosphodiesterase 3A (PDE3A) correlated with cytotoxicity. We further showed that Compound 1B specifically inhibited the enzymatic activity of PDE3A and PDE3B in a panel of 11 different phosphodiesterase family members. However, only a subset of other PDE3 inhibitors shared the same cytotoxic phenotype of Compound 1B. In a rescue screen of 1600 bioactive compounds, we identified the non-lethal PDE3 inhibitors as compounds that were able to rescue cell death induced by Compound 1B. Biochemical assays showed that both Compound 1B, cytotoxic and non-cytotoxic PDE3 inhibitors compete for binding to PDE3A. Knockdown of PDE3A did not affect cell viability and inhibited response of sensitive cell lines to Compound 1B. Thus we have identified a potent and selective small molecule that likely acts through PDE3A to induce cancer cell-line cytotoxicity. Our data suggest a hyper- or neomorphic function of PDE3A induced upon binding of Compound 1B. By cross-referencing integrative datasets with compound-sensitivity data, we show that reversal of the current drug-development paradigm can elucidate novel cancer targets, which are not yet identifiable by analysis of large next-generation sequencing datasets. Citation Format: Luc M. de Waal, Tim A. Lewis, Lara Gechijian, Aviad Tsherniak, Willmen Youngsaye, Matthew Rees, Oliver Mikse, Mark Hickey, Patrick Faloon, Nicola Tolliday, Angela Koehler, Monica Schenone, Kwok Wong, Alykhan Shamji, Benito Munoz, Stuart L. Schreiber, Heidi Greulich, Matthew L. Meyerson. An integrated genomic characterization of the target of a small molecule identifies a novel cancer dependency. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4596. doi:10.1158/1538-7445.AM2014-4596