Abstract Formalin Fixed Paraffin Embedded (FFPE) tissues are a staple in clinical diagnostics associated with solid tumor. The preserved tumor tissue can be interrogated using classical histopathology and complemented with molecular approaches to elucidate cancerous SNPs, indels, structural variants, repeat expansion, copy number expansions, and mutational burden. Increasingly, approaches are being deployed to interrogate tissue not only through individual datasets but through integrated approaches that provide spatial information that defines the tumor microenvironment. Here, we illustrate using FFPE blocks from various tissues a multiomic workflow that allows for deep exploration of the molecular underpinnings of cancerous tissue. FFPE blocks were serially sliced into various FFPE slides with a single slide H&E stained. Individual slides were then utilized to explore the genome, epigenome, single cell RNA-seq, and digital spatial profiling. Genome information was captured using hybrid capture based approaches (whole exome sequencing and targeted cancer panel) followed by deep NGS on an Illumina platform and analyzed for a variety of variants and tumor mutational burden. DNA methylation was detailed using target capture probes targeting DNA methylation sites. Single cell approaches were applied to explore the transcriptome using 10X Genomics scRNAseq Flex kit. Digital spatial profiling was done using the NanoString for both transcriptomics and proteomics, with the GeoMx Whole Transcriptome Atlas and Human Core and Pan-Tumor protein panels. Aggregating these data illustrates the expanding landscape of information that can be extracted from FFPE derived tissue and the potential for novel discovery and diagnostic power integrating these complex data types holds. Citation Format: Andrea J. O'Hara, Yang Han, Ilaria DeVito, Laure Turner, Haythem Latif. Unraveling layers of proteogenomic complexity in cancer through multiomic exploration of archived FFPE tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 338.

Background & AimHarnessing the specificity and versatility of antibodies for cell and gene therapies as a delivery vehicle offers a transformative advance for precision medicines. From their ability for targeted delivery of therapeutic payloads to modulation of cellular interactions, antibodies hold immense potential in refining the precision, efficacy, and safety of future therapies. However, finding the right antibody candidate can be laborious and inefficient.Methods, Results & ConclusionHerein, we introduce an advanced and comprehensive solution to quickly identify and produce antibody leads for cell and gene therapy. This method involves the use of next-generation sequencing of outputs derived from both in vivo (i.e., B-cells and PBMCs) and in vitro (i.e., phage display) discovery campaigns. We pair this with an advanced bioinformatics platform that leverages machine learning to optimize the selection of potential antibody candidates. With this approach, it uncovered 5 – 50x more of the population diversity compared to traditional methods (e.g., random colony screening with Sanger). While next-generation sequencing expands the coverage of the underlying sequence diversity, top candidates are prioritizes using machine learning, selecting diverse leads from distinct clusters and prioritizing leads within clusters that have a reduced number of sequence-based liabilities. Once candidates are selected, they can be efficiently produced because we optimized expression vectors for high production rates. Thus, resulting in a quick and effective solution to generate candidates for vehicles in cell and gene therapy.In summary, we present our advanced end-to-end solution that uses next-generation sequencing, machine learning-based lead prioritization, and optimized antibody expression. Taken together, this platform consistently elevates the number of diverse leads and helps to uncover rare clones with favorable biophysical properties.