Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

The functions of many open reading frames (ORFs) identified in genome-sequencing projects are unknown. New, whole-genome approaches are required to systematically determine their function. A total of 6925 Saccharomyces cerevisiae strains were constructed, by a high-throughput strategy, each with a precise deletion of one of 2026 ORFs (more than one-third of the ORFs in the genome). Of the deleted ORFs, 17 percent were essential for viability in rich medium. The phenotypes of more than 500 deletion strains were assayed in parallel. Of the deletion strains, 40 percent showed quantitative growth defects in either rich or minimal medium.

Expression profiling using DNA microarrays holds great promise for a variety of research applications, including the systematic characterization of genes discovered by sequencing projects. To demonstrate the general usefulness of this approach, we recently obtained expression profiles for nearly 300 Saccharomyces cerevisiae deletion mutants. Approximately 8% of the mutants profiled exhibited chromosome-wide expression biases, leading to spurious correlations among profiles. Competitive hybridization of genomic DNA from the mutant strains and their isogenic parental wild-type strains showed they were aneuploid for whole chromosomes or chromosomal segments. Expression profile data published by several other laboratories also suggest the use of aneuploid strains. In five separate cases, the extra chromosome harboured a close homologue of the deleted gene; in two cases, a clear growth advantage for cells acquiring the extra chromosome was demonstrated. Our results have implications for interpreting whole-genome expression data, particularly from cells known to suffer genomic instability, such as malignant or immortalized cells.

The success of immunotherapy for the treatment of metastatic cancers relies on the prediction and identification of potential neo-antigens. In recent years expression levels of these neo-antigens along with other immune system related genes have been evaluated in an effort to better understand response rates for immunotherapy in various cancers. Gene expression levels can be assessed by numerous techniques including hybridization-based or direct sequencing technologies. Two platforms-HTG Molecular and NanoString nCounter have been utilized to profile changes in gene expression and offer unique advantages for analyzing challenging specimens such as formalin-fixed paraffin embedded (FFPE) tissues. The NanoString nCounter platform utilizes hybridized fluorescent probes targeted against genes of interest for a non-amplified measurement of gene expression. Several studies have been shown that the NanoString platform has good sensitivity, specificity, and reproducibility for the assessment of gene expression levels from FFPE samples. The HTG platform is relatively new and also uses a hybridization based method to enrich genes of interest without first isolating RNA. To determine the robustness of the HTG platform, we profiled a set of 30 metastatic prostate cancer samples using the HTG Molecular EdgeSeq Immuno-Oncology Assay. In these experiments, we found that expression data obtained by using both extracted RNA and lysate from FFPE slides was highly reproducible (Spearman coefficient > 0.85). In addition, the expression profile of targeted genes obtained by using different slides from the same blocks was also highly correlated (Spearman coefficient > 0.90). Our experiments also showed a high correlation between gene expressions profiles obtained by HTG, the NanoString PanCancer Immune Profiling panel and RNA-Seq from the same set of 30 metastatic prostate cancer samples. Further analysis to evaluate and compare the sensitivity of different platforms is being performed and results of these will be presented. Citation Format: Kimberly Kruse, Fang Yin Lo, Ryan Fleming, Douglas Chung, Vickie Satele, Lindsey Maassel, Tuuli Saloranta, Inah Golez, Saman Tahir, Sally Dow, Evan Anderson, Spencer Chee, Raghavee Venkatramanan, Steve Anderson, Peter S. Nelson, Colm Morrissey, Anup Madan, Sharon Austin, Kellie Howard. A cross comparison of technologies for the detection of immune system related gene expression signatures in clinical FFPE samples of metastatic prostate cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3983. doi:10.1158/1538-7445.AM2017-3983

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