Abstract 51: 10 second classification of spontaneous lymphoma in patient-derived tumor xenograft models

Abstract Patient-derived tumor xenografts (PDX) propagated in immunodeficient murine hosts have become effective preclinical models used in drug development and biological studies. PDX models have been established across tumor types with varying degrees of success. A main factor of engraftment failure is formation of spontaneous lymphocytic growths that may occur in murine hosts at the implant site of the patient tumor fragments or serial PDX fragments. Continuous monitoring of fidelity of PDXs to their parent patient tumors is by histological slide review of collected tumors. Processing for histological reviews could range from several hours to days, thus lacking in real-time decision-making support for utility of specimens. Method: Ambient Picosecond InfraRed Laser (PIRL) ablation soft ionization Mass Spectrometry (MS) was evaluated as a potential tool for rapid classification of spontaneous lymphocytic growth compared to PDX tumor growths. This method uses a 10-second sampling and analysis duration of 3 mm3 tumor fragments. Results: PIRL MS analyses across 5 common tumor types of pancreas, lung, colon, ovarian and head and neck with a total of 90 PDX specimens and 20 samples of H&E verified lymphocytic growths was used to train a Principal Component Analysis Linear Discriminate Analysis (PCA-LDA) model to discriminate lymphocytic growths from tumors. Cross-validation using a 20% leave-out test resulted in >94% accuracy for correctly classifying lymphocytic or tumor growths. To ensure the validity of this observation, a permutated version of this model with false annotation resulted in prediction accuracy of ∼50% in a 20% leave-out cross-validation. A test set of 90 specimens comprised of 15 lymphomas and 75 tumours was blindly sampled and subjected to the aforementioned model for prediction of specimen type. This blind assessment resulted in 99% sensitivity and 99% specificity values for either of the lymphoma or tumor classes. Conclusion: This proof-of-principle evaluation suggests that 10-second PIRL MS analysis is able to differentiate between lymphocytic and true tumor growths, further highlighting distinct mass to charge (m/z) values specific to each tissue type. PIRL-MS can therefore enable real-time decision-making with respect to the utility of PDX tissue collection for immediate downstream laboratory use, and to further minimize the cost associated with unintentional use of non-tumor outgrowths. The next steps involve determining the molecular identities of lymphoma classifying m/z values and to assess the PCA-LDA model’s sensitivity to engrafts that contain both lymphocytic and cancer growths. Citation Format: Nhu-An Pham, Lan Anna Ye, Julia Froment, Arash Zarrine-Afsar. 10 second classification of spontaneous lymphoma in patient-derived tumor xenograft models [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 51.