Search bioRxiv⌕ Search

Biology subjects

Ledoux, L.

Publications and source records attributed to Ledoux, L..

2 recordsLinked to original sources

Transforming Esogastric Cancer Surgery Integrating SpiderMass Mass Spectrometry with Clinical and Microbiome Data for Margin Delineation and Prognosis

Esophageal-gastric cancers (EC) represent a significant global health concern, with esophageal cancer ranking seventh in terms of incidence and mortality worldwide. Gastric cancer is especially concerning, with an estimated one million new cases and 800,000 deaths annually. Late diagnoses often lead to poor outcomes, requiring critical interventions such as radical surgical resection with clear margins, in conjunction with chemotherapy, or radiotherapy to prevent recurrences and enhance survival. Thus, EC represents a significant clinical challenge, especially given the difficulty in achieving precise surgical margins in aggressive subtypes like poorly cohesive carcinoma (PCC). Moreover, pathological intraoperative margin assessment encounters significant issues, especially for PCCs, due to lacks of sensitivity for microscopic infiltration, potentially leading to recurrence and poorer patient outcomes. We address these critical limitations by integrating SpiderMass, an ambient mass spectrometry (MS) technology, with clinical metadata and microbiome profiling couple along with Machine learning. We demonstrate SpiderMass capability in real-time molecular margin delineation and identify distinct lipidomic and microbiome signatures correlating with tissue type and prognosis. Our integrative approach provides a more precise and biologically informative intraoperative diagnostic tool, significantly enhancing surgical decision-makin, to improve patient outcomes and extend survival.

cancer biology↗

Development of Molecular Digital Twins Based on Ambient Ionization Mass Spectrometry Imaging for Real-Time Application in Oncological Surgery

Cancer surgery is a fundamental component of oncology treatment, its quality significantly impacts patient outcomes, influencing both relapse rates and survival. However, achieving this customization is contingent upon early collection of robust molecular data during surgery, providing accurate information for diagnosis, prognosis, and delineating surgical margins. The introduction of digital twin (DT) technology has recently opened a new era of precision and effectiveness in cancer surgery. Expanding from its successful implementations in the industrial sector, DT concept has evolved into a highly promising breakthrough in healthcare. Therefore, our study goal is on creating DT by using accurate and high-throughput molecular data obtained through mass spectrometry imaging. We developed a machine-learning-based pipeline that allow to depict infiltration of cancer cells into normal tissue that offer precise delineation of tumor margins thanks to SpiderMass. This process also enables the prediction of relative presence of bacterial strains in tumoral and healthy mammary glands.

bioengineering↗