Abstract 5042: The Paipu pipeline for pan-mammalian cancer genomics

Abstract The emerging field of comparative oncology may provide a novel solution to rare human cancer studies, as genomic resources, reference genomes, and non-human model cancer sequencing data are proliferating rapidly. Studying cancer across a diverse array of species provides a unique opportunity to interrogate factors underpinning cancer, facilitating disease modeling in the setting of spontaneous tumors complicated by comorbidities and metastases. Unfortunately, spontaneously developed tumors in non-human mammalian models have been largely overlooked, leaving a foundational resource underutilized. Several key barriers prevent the adoption of these critical data. Extensive reporting of the frequency and diversity of animal tumors has not been systematically linked to the plethora of genomic resources available. Additionally, complications in integrating data from hundreds of genomes have prevented large-scale analysis. Fortunately, recent advances in comparative genomics allowing for joint alignments of hundreds of species have bridged this gap, enabling us to integrate the rapidly expanding genetic resources for non-traditional model species and presenting an enormous potential resource for oncology. To facilitate the transition of comparative oncology studies from human plus one or two other species to pan-mammalian analyses, we developed the Paipu computational pipeline to systematically identify and process data from hundreds of mammalian species, creating a pan-mammalian tumor compendium. Paipu consists of three phases: reference genome preparation, SRA metadata retrieval and harmonization, and FREYA for RNA-seq data processing and harmonization. Paipu identified and harmonized data from over 100, 000 non-human mammalian cancer RNA-seq samples from NCBI, spanning 52 of the 241 mammalian species in the Zoonomia multi-species genome alignment. We demonstrate Paipu’s potential via a comparative analysis of human cancers and a rapidly emerging model system, the dog. Using the Paipu cancer data compendium, we analyzed ten cancers in human and dog. We identified a subset of the cancers as strong models of human cancer, which we validated through literature analysis. Additionally, our comparative cancer analysis results parallel the original TCGA pan-cancer analysis findings. Paipu’s harmonization of data from numerous mammalian species facilitates downstream comparative genomics analysis. Increasing the diversity of genomes used in comparative studies can reveal shared and lineage-specific variation across species, leading to the identification of novel mechanisms of cancer risks and resistance. Our approach provides a unique opportunity to harness significant advances in the rapidly developing fields of machine learning and genomics for human cancer research by identifying reciprocal links between evolution and cancer resistance. Citation Format: Bria S. Smith, Geesa V. Daluwatumulle, Leslie A. Smith, James A. Cahill, Kiley Graim. The Paipu pipeline for pan-mammalian cancer genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5042.