Abstract New oncology therapies are driven by multiple approaches, with a heavy emphasis on small-molecule therapeutics to target cancer cells and provide a therapeutic benefit to cancer patients. These small-molecule compounds can be used as a stand-alone treatment approach or may be combined with other compounds to bring enhanced effects. Compound therapeutic discovery relies heavily on high-throughput screening assays for measurement of phenotypic responses following compound treatment of oncology cell cultures which can range from very targeted assays to wide-ranging unbiased approaches. Standard whole transcriptome RNA-seq is a preferred method for unbiased measurement of phenotypic responses but is often overlooked due to limited scalability and high sample screening costs. To address these challenges, we have developed a high-throughput gene expression (HT-GEx) assay that combines a variety of strategies to enable a low-cost and high-throughput alternative to standard RNA-seq. First, a simplified workflow removes upstream RNA isolation steps and tags transcripts directly from cell lysate. This is achieved by incorporating both a sample barcode and a unique molecular index (UMI) during the reverse transcription reaction. Next, we leverage a 3’ end counting approach to enable a reduction in sequencing depth coverage (i.e. sample cost) without compromising gene detection sensitivity. With these key advances, a high-plex and high-throughput screening assay is achievable at a reduced cost by removing the need to purify RNA, tagging transcripts early in the workflow to allow pooling of samples and reducing sequencing depth. We have combined these high-throughput and cost-saving strategies and present results which confirm HT-GEx offers results on par with standard RNA-seq. Gene detection sensitivity based on number of genes detected per millions of reads is highly similar, with gene detection sensitivity saturated at approximately 2 million reads. Additionally, the number of genes detected between replicates for RNA samples and lysate samples demonstrate strong linear correlation, implying highly reproducible results. Thus, high-throughput gene expression is an ideal method for phenotypic screening in oncology cell lines following stand-alone or combined compound treatments. Citation Format: Andrea O'Hara, Michael Stephens, Ilaria DeVito, Laure Turner, Christopher Mozdzierz, Haythem Latif, Ginger Zhou. High-throughput RNA sequencing directly from cell lysates enables reproducible phenotypic profiling for assessing novel compounds and treatments in cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB130.