Abstract 3759: First clinical evidence of the tissue nanomechanical signature as a novel diagnostic and prognostic tool in patients with non-small-cell lung cancer
Authors: Alcaraz Jordi , Sanz-Fraile Héctor , Marin Elba , Busquets Paula Gausa , Neal Kate , Arshakyan Marselina , Rico Marc , Boada Marc , Sanchez David , Nunez Reinier Oropesa , Srivastava Gitika , Jizawi Ahmed , Nizzero Sara , Appenzeller Tobias , Gachechiladze Mariam , Loparic Marko , Plodinec Marija , Reguart Noemi 
Published: March 2024

Abstract
Introduction: Complex structural remodelling of cells and extracellular matrix during cancer initiation and progression elicit substantial biomechanical alterations, which can be measured by Atomic Force Microscopy (AFM) at the nanoscale level. Clinical and translational evidence indicates that tissue nanomechanical signature (NS) can be a useful tool for rapid cancer diagnosis, and assessment of cancer aggressiveness. We demonstrated that the AFM-based Automated and Reliable Tissue Diagnostics (ARTIDIS) platform represents a valid method for rapid diagnosis, prognosis, and treatment response assessment in patients with breast cancer. Here we aimed to use this platform to examine tumour biopsies from non-small cell lung cancer (NSCLC) patients for the first time.
Methods: This is an ongoing, single center study at the Hospital Clinic of Barcelona and University of Barcelona (UHB) Spain, aiming to enroll 75 patients by January 2024 in the first phase, followed by 200 patients in the expansion phase. To date, 54 matching tumor and non-tumor fresh tissue biopsies were taken by experienced pathologists from fresh surgical resection specimens from 27 patients with previously diagnosed NSCLC. Tissue biopsies were measured with the ARTIDIS device in a blinded manner, without knowing the malignancy status of the biopsy. Routine clinico-pathologic parameters have been collected. The multiparametric nanomechanical signature has been analyzed in an automated manner using the proprietary AI-based ARTIDISNET software platform together with available clinico-pathologic parameters.
Results: In total, the NSCLC tissue samples included 16 squamous cell carcinoma (SCC), 6 Adenocarcinoma (ADC), 1 large cell carcinoma (LCC) and 4 other histological subtypes. Non-tumor tissues exhibited significantly varied composition, including normal lung parenchyma, bronchial tissues, fibrosis and inflammatory changes. Among the 27 patients, 5 patients developed distant progression within 12 months after ARTIDIS measurements. Results analysis showed that the ARTIDIS NS can reliably differentiate malignant NSCLC lesions from non-tumor lung tissue, with a sensitivity of 0.84, specificity of 0.81, positive predictive value (PPV) of 0.79, negative predictive value (NPV) of 0.86 and accuracy of 0.82. In addition, the ARTIDIS NS can differentiate patients with and without distant progression with a sensitivity of 0.62, specificity of 0.81, PPV of 0.42, NPV of 0.90 and accuracy of 0.77.
Conclusions: Here we demonstrated first clinical evidence of the ARTIDIS tissue NS as a novel diagnostic and prognostic tool in patients with NSCLC. This study aims to establish the diagnostic, prognostic, and predictive NS in patients NSCLC. In addition to local expansion at the UHB, study results will be further validated in ARTIDIS clinical program at MD Anderson Cancer Centre, USA.
Citation Format: Jordi Alcaraz, Héctor Sanz-Fraile, Elba Marin, Paula Gausa Busquets, Kate Neal, Marselina Arshakyan, Marc Rico, Marc Boada, David Sanchez, Reinier Oropesa Nunez, Gitika Srivastava, Ahmed Jizawi, Sara Nizzero, Tobias Appenzeller, Mariam Gachechiladze, Marko Loparic, Marija Plodinec, Noemi Reguart. First clinical evidence of the tissue nanomechanical signature as a novel diagnostic and prognostic tool in patients with non-small-cell lung cancer [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(6_Suppl):Abstract nr 3759.