Poster Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

CRCprot: a novel and practical protein-based classification system for CRC prognosis using FFPE biopsy tissues (#837)

Yingkuan Shao 1 2 , Kailun Xu 1 2 , Xi Zheng 1 2 , Fangfei Zhang 1 , Qi Yuan 1 , Biting Zhou 2 , Ziqi Li 1 , Xue Cai 1 , Yi Zhu 1 , Shu Zhen 2 , Tiannan Guo 1
  1. Westlake University, Hangzhou
  2. Cancer institution, Zhejiang University, Hangzhou, ZHEJIANG, China

Background Colorectal cancer (CRC) is the third most common cancer worldwide and the fourth leading cause of cancer-related deaths. Genomic and transcriptomic classification system has been developed for heterogeneous CRC tumors, but their applications in clinical tissues are limited due to the degradability of mRNAs. No protein-based classification system has been reported.

Methods We analyzed the proteome of FFPE biopsy samples from 217 CRC patients with up to ~9 years survival using pressure cycling technology (PCT) and data-independent acquisition (DIA) mass spectrometry. Then we trained a model using deep neural network. An independent CRC cohort of 117 patients was further procured to validate the protein-based classifier.

Results We quantified > 8000 proteins from 490 FFPE samples including 88 biological replicates (r = 0.77) and 66 technical replicates (r = 0.95). Using machine learning technology, we established a novel and practical protein-based classification system, containing the expression of about 10 ten proteins, for CRC prognosis which was further verified in an independent validation cohort.

Conclusion We demonstrated the practicality of PCT-DIA for analyzing large number of FFPE biopsy samples from multiple cohorts and established a novel and practical protein-based classification system CRCprot for CRC prognosis.