Abstract B034: A transcriptional atlas provides a universal diagnostic platform for mesenchymal tumors and validation of preclinical models
Authors: Joshua O. Nash, Pedro L. Ballestar, Scott Davidson, Astra Schwertschkow, Yael Babichev, Jodi Lees, Noa Alon, Nalan Gokgoz, Stephen M. Yu, Kyoko Yuki, Miranda Lorenti, Zhanqin Liu, Alaina McGoey, Famida Spatare, Bernard Castro, Kim Tsoi, Hagit Peretz-Soroka, Jack Brzezinski, Anita Villani, Albiruni Razaq, Abha Gupta, Elizabeth Demicco, Joanna Przybyl, Matt van de Rijn, Livia Garzia, Jay Wunder, Irene L. Andrulis, David Malkin, Rose Chami, Brendan C. Dickson, Rebecca A. Gladdy, Adam Shlien
Journal: Cancer Research (2024)

Abstract Objectives: Mesenchymal neoplasms, or sarcomas, are a diverse and diagnostically challenging group including &amp;gt;150 histotypes, and are biased to pediatric patients, comprising 20% of pediatric solid tumor diagnoses compared to 1% in adults. A universal molecular taxonomy and classification system for sarcoma would be an invaluable tool for patient diagnosis and subtype discovery. We previously demonstrated a transcriptional pan-cancer taxonomy and classifier; however, we neither captured the full diversity of mesenchymal neoplasms nor classified preclinical models. We therefore created a second-generation, mesenchymal-specific atlas with improved diagnostic coverage, refining our ability to describe the molecular landscape of sarcoma. Methods: Fresh frozen, poly-A enriched RNA-seq datasets including mesenchymal tumors (n=24) were combined with the UCSC Treehouse Childhood Cancer Compendium. Transcriptomic data (n=14,315) were uniformly processed and all diagnostic labels were harmonized to ICD-O v3.2. To create a taxonomy, processed expression matrices were clustered one level deep and mesenchymal classes were selected and clustered to completion using established tools. To determine cluster identities, each cluster was annotated using gene expression profiles and available clinical and molecular data. We then trained a companion classifier to classify preclinical models. RNA-seq data for cell lines were obtained from the Cancer Cell Line Atlas and patient derived xenografts (PDX) from the UCSC Treehouse database. Results: All mesenchymal neoplasms (n=2,153, 82 ICD-O codes) form one cluster at the pan-cancer level, an increase of 190% in sample size and 200% in histotype diversity over our previous effort[YB1](n=1,125, 43 codes). The resulting taxonomy comprises 153 clusters over seven hierarchical levels. Out of 109 terminal nodes, 60% represent a consensus diagnosis (defined as &amp;gt;50% of cases classified under a single code). Of these, 50% represent novel subtypes. The remaining 40% of terminal nodes are diagnostically heterogeneous, with 25% of these containing a unifying genetic lesion, suggesting current diagnostic conventions do not capture the diversity of existing molecular entities. Sarcoma cell lines (n=54) show variable assignment to their disease of origin: several histotypes lose disease specificity, while fusion-driven histotypes conserve parental identity. Fusion-plasmid-generated models fail to recapitulate their in vivo profile. PDX’s (n=33) broadly maintain expected classification. Conclusion: RNA-seq continues to facilitate subtype discovery and disease classification in ongoing patients and preclinical models. Our results suggest a large portion of mesenchymal entities require molecular, rather than histotypic definitions. Classification of preclinical models resolves their suitability for disease modeling. The expression profiles outputted by our methods can be leveraged for future therapeutic nominations. We are expanding this effort to reach 3,000 samples this year and are open to the community to contribute. Citation Format: Joshua O. Nash, Pedro L. Ballestar, Scott Davidson, Astra Schwertschkow, Yael Babichev, Jodi Lees, Noa Alon, Nalan Gokgoz, Stephen M. Yu, Kyoko Yuki, Miranda Lorenti, Zhanqin Liu, Alaina McGoey, Famida Spatare, Bernard Castro, Kim Tsoi, Hagit Peretz-Soroka, Jack Brzezinski, Anita Villani, Albiruni Razaq, Abha Gupta, Elizabeth Demicco, Joanna Przybyl, Matt van de Rijn, Livia Garzia, Jay Wunder, Irene L. Andrulis, David Malkin, Rose Chami, Brendan C. Dickson, Rebecca A. Gladdy, Adam Shlien. A transcriptional atlas provides a universal diagnostic platform for mesenchymal tumors and validation of preclinical models [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B034.