Abstract 7090: A scaled proteomic discovery study for prostate cancer diagnostic signatures using Proteograph workflow with trapped ion mobility mass spectrometry
Authors: Flory Mark , Chang Matthew , Cartier Jessie May , Lange Jane , Moore Travis , McGann James , Kopp Ryan , Liss Michael , Leach Robin , Albracht Brenna , Dai James , Krawitzky Michael , Elgierari Eltaher , Chu Jessica , Pease Paul , Mahoney Max , Ferdosi Shadi , Benz Ryan , Motamedchaboki Khatereh , Siddiqui Asim , Zamanighomi Mahdi , Alavi Amir , Guturu Harendra , Hornburg Daniel , Batzoglou Serafim 
Published: March 2024

Background and Aims: The low cancer specificity of serum Prostate-Specific Antigen (PSA), the principal biomarker for prostate cancer detection, leads to a high frequency of unwarranted prostate biopsies. To alleviate this deficit, we executed a proteomic discovery study seeking PSA reflex signatures using the Proteograph™ Product Suite, a multi-nanoparticle-based deep plasma/serum proteomics workflow (Seer, Inc.), with timsTOF Pro mass spectrometry (Bruker) to interrogate over 900 serum specimens from patients that underwent prostate biopsy due to elevated PSA and/or abnormal digital rectal exam.

Methods: Our study followed rigorous design principles including uniform collection, randomization and blinding of specimens, all of which were processed with the Proteograph Product Suite. Liquid chromatography-mass spectrometry (LC-MS) analyses leveraged the Bruker timsTOF Pro MS platform utilizing 30-minute reversed-phase chromatography and a label-free dia-PASEF (data independent acquisition - parallel accumulation serial fragmentation) data acquisition method. Peptides deriving from a chosen subset of specimens designed to maximize peptide diversity were pooled, fractionated and analyzed using DDA (data-dependent acquisition)-PASEF to build a spectral reference library.

Results: The DIA-NN algorithm was employed through the cloud-based Proteograph Analysis Suite (PAS) to search LC-MS data. Leveraging our study-specific spectral library proteomic depth achieved was approximately 3600 protein groups identified (median) per patient serum specimen and more than 4400 in at least 25% of samples across the study. Customized machine learning workflows and the SeerML pipeline were used to identify and assess diagnostic power of new proteomic signatures.

Conclusions: Our data demonstrate the remarkable proteomic depth achievable in a scaled patient serum specimen discovery study using a combination of Proteograph and timsTOF platforms. This significant effort revealed serum proteomic signatures with increased diagnostic performance over that provided by PSA blood measurement and traditional patient-associated metadata alone, providing a foundation for future clinical tests aimed to reduce the current high frequency of unnecessary prostate biopsy.

Citation Format: Mark Flory, Matthew Chang, Jessie May Cartier, Jane Lange, Travis Moore, James McGann, Ryan Kopp, Michael Liss, Robin Leach, Brenna Albracht, James Dai, Michael Krawitzky, Eltaher Elgierari, Jessica Chu, Paul Pease, Max Mahoney, Shadi Ferdosi, Ryan Benz, Khatereh Motamedchaboki, Asim Siddiqui, Mahdi Zamanighomi, Amir Alavi, Harendra Guturu, Daniel Hornburg, Serafim Batzoglou. A scaled proteomic discovery study for prostate cancer diagnostic signatures using Proteograph workflow with trapped ion mobility mass spectrometry [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 7090.