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.

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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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Colorectal cancer (CRC) is the third most common type of cancer in the United States. Although chemotherapy, radiation and targeted therapies can improve survival rates, recent studies have shown the potential benefit of immunotherapies to improve outcomes for patients with advanced CRC. Targeted therapies that use monoclonal antibodies (mAbs) to EGFR have been shown to benefit some CRC patients. Until recently, KRAS has been the only predictive biomarker for anti-EGFR therapy for metastatic CRC. However, 40% to 60% of patients with wild-type KRAS do not respond to anti-EGFR therapy. Therefore, to accurately predict patients’ response to treatments and improve clinical outcomes, additional prediction and treatment methods are imperative. One of the many efforts to improve prediction for CRC patient's response to the anti-EGFR therapy is the development of gene expression based RAS signature scores for identification of RAS activated tumors independent of mutations in the KRAS gene. Recently there have been major advances in immunotherapeutic approaches in a wide variety of cancers. In solid tumors such as melanoma and colon cancers, immune checkpoints have been shown to improve clinical outcomes. There is considerable effort being placed on combinations of targeted therapy and immunotherapies to improve responses for these cancers. Similarly, since no single treatment can apply to all CRC patients, we aim to stratify patients using a combination of three methods: 1. RAS signature score based on the expression profile of 18 genes. This RAS signature score enables measurements of mitogen-activated protein/extracellular signal-regulated kinase (MEK) pathway functional output independent of tumor genotype. 2. Expression profile of immune checkpoint inhibitor target genes, such as PD1 and PDL1, and 3. In-silico prediction of neo-antigens and peptide binding affinity between tumor antigens derived from mutations and human HLA alleles. 55 FFPE samples were selected from a cohort of 468 samples with matching FF samples. These 55 samples have about 1:1:1 ratio of high, medium and low RAS scores. Here we showed our ability to obtain RAS signature scores with concordant results using different platforms including RNA-seq, targeted RNA-seq, Nanostring and Affymetrix microarray. Samples that have RAS activating mutations such as KRAS and BRAF have significant higher RAS scores (p<0.001). Interestingly, expression of PD-L1 was significantly lower in tumor samples harboring mutations of genes such as MET, PTEN, NRAS, FBXW7, and GNAS. Kruskal-Wallis test showed that the expression of PD-L1 was significantly lower in samples with higher RAS signature scores (p<0.05). Combined with further prediction of tumor antigen derived from genes with missense mutations, we provide a combinatorial method for stratifying metastatic CRC patients. Citation Format: FangYin Lo, Sharon Austin, Kellie Howard, Mollie McWhorter, Heather Collins, Amanda Leonti, Lindsey Maassel, Christopher Subia, Tuuli Saloranta, Nicole Christopherson, Kathryn Shiji, Shradha Patil, Saman Tahir, Sally Dow, Evan Anderson, Jon Oblad, Kerry Deutsch, Timothy Yeatman, Steven Anderson, Anup Madan. Stratification of metastatic colorectal cancer patients using DNA and RNA sequencing and in-silico prediction of tumor antigens for consideration in immunotherapy. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3946.

Colorectal cancer (CRC) is one of the major causes of global cancer mortality. Until recently, KRAS has been the only predictive biomarker for anti-EGFR therapy for metastatic CRC, and yet predicting prognosis in clinical practice is still poor. Therefore, a more accurate method for prognosis of CRC patients is needed. Gene expression profiling has shown great promise in predicting prognosis of individual patients in diverse cancers. The development of RNA-sequencing has greatly facilitated identification of biomarkers that can be used to stratify patients for targeted therapies. Despite the decrease in cost of sequencing in last few years, the time and the resources needed for analysis limit its use in clinical trials for patient selection. Targeted gene expression technologies like qPCR and NanoString enable highly customizable assays that can be conveniently performed for patient recruitment. The aim of this study was to investigate potential alternatives for gene profiling using a novel NanoString Plex Set technology. The Plex Set system comes with prepackaged and custom code sets in identifying genetic markers. Up to 8 samples can be pooled to each nCounter cartridge lane, enabling a total of 96 samples per run, thus making the total cost relatively affordable. For this study, gene expression signature was developed using RNA-Seq data where we have profiled 74 CRC samples, 20 of which have matching normal samples. A RAS signature score based on expression profile was calculated for each sample. In order to look for potential gene signatures, differential gene expression analysis was performed between the following groups: (a) samples with high versus those with low RAS signature scores in the 54 CRC, (b) KRAS mutant versus wild-type samples, and (c) tumor versus normal samples in the clinical study. We hypothesized that our genes of interest are most likely significantly differentially expressed in one of these groups. The counts of significantly expressed gene for the groups (a-c) are 1560 and 34, respectively, and we are working on the third case. Therefore, significantly deferentially expressed genes between groups were selected and ranked based on frequency of occurrence. These genes of interest are being analyzed using NanoString Plex Set and qPCR to evaluate the potential of using NanoString Plex Set system for targeted gene expression profiling. Results of these analyses will be presented. Citation Format: Raghavee Venkatramanan, Tuuli Saloranta, Inah Golez, Elliot Swanson, Kimberly Kruse, Vickie Satele, Saman Tahir, Sally Dow, Evan Anderson, Briana Hudson, Spencer Chee, Kerry Deutsch, Steve Anderson, Fang Yin Lo, Anup Madan. Cross-comparison of targeted gene expression technologies for patient stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3418.