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Abstract The Tumor MicroEnvironment (TME) plays a pivotal role in driving tumor progression, metastasis, and therapeutic resistance through diverse mechanisms. Notably, the TME influences the response to therapy by regulating immune surveillance evasion in tumor cells. Fibroblasts can play a significant role in resistance to therapy. In the context of cancer cancer-associated fibroblasts (CAFs) in the TME have been associated with resistance to various therapeutic approaches. CAFs can contribute to therapy resistance through multiple mechanisms, including extracellular matrix remodeling, immune modulation, angiogenesis and paracrine signaling. Understanding the intricate interactions between fibroblasts and cancer cells is crucial for developing more effective therapeutic strategies and overcoming resistance in cancer treatment. In the pre-clinical stages of therapy testing, the use of clinically and biologically relevant models is essential. However, current preclinical tumor models often lack a comprehensive characterization of the TME. To address this limitation, Champions Oncology utilized transcriptomic data from its exclusive TumorGraft models bank to develop a signature predicting TME composition. Validation involved confirming the predicted TME composition through histological analysis of Patient-Derived Xenograft (PDX) models grown in humanized mice. In an immune-competent host, the Tumor Microenvironment (TME) of the model more accurately replicates the conditions observed in cancer patients. PDX) models are recognized as accurate and clinically relevant for pre-clinical studies. Consequently, Champions Oncology built its pre-clinical research on a deeply characterized PDX bank, employing multi-omic datasets, including WES, RNAseq, proteomics, phospho-proteomics, kinase activity, and patient treatment history. To identify molecular signatures predictive of fibroblast presence, Champions analyzed the data with xCell3 computational method. Concurrently, mice were humanized, and high and low infiltration predicted PDXs models were implanted. Tissues were collected and stained for fibroblast populations. Immunohistochemistry analyses confirmed a strong correlation between the infiltration predicted by the molecular signature and positive staining. The application of this method to Champions' model bank provided a comprehensive understanding of the TME profile, culminating in the definition of a gene signature predictive of fibroblast infiltration. Histological analysis validated the computational findings, creating a TME atlas of the PDX bank. This data support the use of the pre-defined molecular signature, facilitating the pairing of ideal model systems with appropriate humanized hosts for future preclinical studies. Citation Format: Mara Gilardi, Gilad Silberberg, Hsiu-Wen Tsai, Stefano Cairo, Clare Killick-Cole, Michael Ritchie. A novel gene signature predicting fibroblast composition in the TME allows for an improved selection of patient-derived xenograft (PDX) models for preclinical studies 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 (6Suppl): Abstract nr 2941.
Gilardi et al. (Fri,) studied this question.