Abstract 6566: FTIR microscope imaging and neural network analysis reveal consistent spectral markers of cancer-associated fibroblasts in cells, plasma sEVs, and tissues

Background: Cancer-associated fibroblasts (CAFs) are pivotal contributors to tumor progression, influencing tumor biology through interactions within the tumor microenvironment. Distinguishing CAFs from normal fibroblasts is essential for understanding their molecular mechanisms and identifying relevant biomarkers. This study utilized Fourier-transform infrared (FTIR) microscope imaging spectra and neural networks (NNs) to classify CAFs and normal fibroblasts and explored the consistency of identified spectral markers across blood plasma small extracellular vesicles (sEVs) and tissue samples. Methods: FTIR spectra from CAFs and normal fibroblasts were preprocessed using normalization and baseline correction. A feed-forward NN model was developed, employing regularization techniques to reduce overfitting. Model performance was evaluated using 10-fold cross-validation, with metrics including accuracy, area under the curve (AUC), sensitivity, and specificity. NN-based sensitivity analysis identified key infrared (IR) spectral regions for classification. Comparative FTIR analysis of blood plasma sEVs and tissue samples was conducted to assess the reproducibility of identified spectral markers. Results: The NN model demonstrated high classification performance, achieving 92% accuracy, an AUC of 0.89, and robust sensitivity and specificity metrics. Sensitivity analysis highlighted significant IR spectral regions, including Amide I and Amide II (proteins): Indicative of changes in protein structure and extracellular matrix remodeling. Lipid Regions: Reflecting altered lipid metabolism, critical for membrane dynamics and signaling in CAFs. Nucleic Acid-Associated Regions: Suggesting transcriptional and epigenetic modifications linked to fibroblast activation. Notably, similar spectral markers were observed in FTIR analysis of blood plasma sEVs and tissue samples, revealing a consistent pattern associated with CAFs. This suggests systemic changes in the tumor microenvironment that are detectable across different biological matrices. Conclusion: This study highlights the potential of NN-based FTIR spectral analysis as a robust and versatile tool for identifying biomarkers of CAFs. The reproducibility of key spectral markers in blood plasma sEVs and tissue samples underscores the translational potential of this approach for cancer diagnostics and therapeutic targeting of the tumor microenvironment. Citation Format: Abhay Mishra, Akhilesh Mishra, Saroj Kumar. FTIR microscope imaging and neural network analysis reveal consistent spectral markers of cancer-associated fibroblasts in cells, plasma sEVs, and tissues [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 6566.