Abstract 4584: Advancing non small cell lung cancer detection and subtyping by MasSpec Pen technology
Authors: Yasmin Shanneik, Bryan Burt, Maheshwari Ramineni, Livia S. Eberlin
Journal: Cancer Research (2025)

Abstract Introduction: Non-small cell lung cancer (NSCLC) is the most aggressive and frequently diagnosed form of lung cancer. Histological and molecular subtyping are critical in lung cancer treatment-decision making. However, despite recent advances, the 5 year survival rate remains low. Within the field of mass spectrometry (MS), the MasSpec Pen (MSPen) technology stands out as a nondestructive technique employed intra-operatively for cancer detection. The MSPen deploys a single droplet of sterile water onto tissue, facilitating the extraction of molecules upon contact in real-time within seconds. Here, we employ the MSPen for NSCLC diagnosis based on molecular signatures, with a specific focus on classifying major histological subtypes and identifying key proteogenomic markers. Furthermore, we tested the reproducibility of the MSPen, using samples collected across multiple centers and analyzed by different personnel. Methods: 319 frozen human lung tissues (n=177 normal lung and n=142 NSCLC) were analyzed in negative ion mode using the MSPen coupled to a Q Exactive Orbitrap MS (Thermo Scientific). Following analysis, the analyzed region was marked with a surgical pen, frozen, and sectioned at 10µm. It was then histologically stained and evaluated by a pathologist to confirm diagnosis and subtype. Proteogenomic markers for lung cancer samples, such as EGFR and PD-L1, were obtained from clinical data or determined using biomarker and sequencing analysis. Mass spectra were processed and subjected to statistical classification using the least absolute shrinkage and selector operator (Lasso). Results: MSPen analyses enabled the detection of hundreds of biomolecules in seconds with high sensitivity, specificity, and accuracy. Negative ion mode mass spectra presented rich molecular profiles characterized by multiple cancer-specific markers. Normal lung tissue showed high relative abundances of phosphatidylglycerol (PG)(34:1) and PG(36:2). Mass spectra of NSCLC showed a different profile with high relative abundances of phosphatidylethanolamine (PE)(38:4) and phosphatidylinositol (PI)(38:4). Samples with high PD-L1 expression also presented a distinct lipid pattern with a high relative abundance of PG lipids, whereas samples with high EGFR expression were enriched with PE lipids. Histological staining confirmed no macro- or microscopic tissue damage, highlighting the potential of the MSPen in clinical settings. Lasso classification of MSPen data allowed for the predictive performance of the molecular evaluation and diagnosis of the NSCLC samples. Lasso classification yielded exceptional performance, with an AUC=0.9 in the training set and an overall accuracy of 93% across training and test sets of samples compared with the pathological evaluation. Conclusion: Our results highlight the robustness of MSPen technology and its utilization for lung cancer detection and subtyping in clinical procedures. Citation Format: Yasmin Shanneik, Bryan Burt, Maheshwari Ramineni, Livia S. Eberlin. Advancing non small cell lung cancer detection and subtyping by MasSpec Pen technology [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 4584.