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Abstract Immunotherapy is the first-line therapy for esophageal squamous cell carcinoma (ESCC), yet many patients do not respond due to drug resistance and the lack of reliable predictive markers. We collected 73 ESCC patients (including discovery cohort and validation cohort) without immune thrombocytopenia and undergoing anti-PD1 immunotherapy. Proteomic and phosphoproteomic analysis of 73 ESCC treatment-naive samples by mass spectrometry-based label-free quantification were applied to explore the potential resistant and sensitive mechanisms, and identify predictive markers of ESCC immunotherapy. Comparative analysis found the pathways related to immune and mitochondrial functions were associated with ESCC immunotherapy sensitivity; while platelet activation bioprocess showed negative correlation with CD8+ T cells and related to ESCC immunotherapy non-sensitivity. Finally, we identified 10 ESCC immunotherapy predictive biomarkers with high accuracy (≥ 0.90) to predict the immunotherapeutic response, which was validated in the independent cohort.

Abstract Cetuximab therapy is the major treatment for colorectal cancer (CRC), but drug resistance limits its effectiveness. Here, we perform longitudinal and deep proteomic profiling of 641 plasma samples originated from 147 CRC patients (CRCs) undergoing cetuximab therapy with multi-course treatment, and 90 healthy controls (HCs). COL12A1, THBS2, S100A8, and S100A9 are screened as potential proteins to distinguish CRCs from HCs both in plasma and tissue validation cohorts. We identify the potential biomarkers (RRAS2, MMP8, FBLN1, RPTOR, and IMPDH2) for the initial response prediction. In a longitudinal setting, we identify two clusters with distinct fluctuations and construct the model with high accuracy to predict the longitudinal response, further validated in the independent cohort. This study reveals the heterogeneity of different biomarkers for tumor diagnosis, the initial and longitudinal response prediction respectively in the first course and multi-course cetuximab treatment, may ultimately be useful in monitoring and intervention strategies for CRC.

Abstract The progression of urothelial bladder cancer (UC) is a complicated multi-step process. We perform a comprehensive multi-omics analysis of 448 samples from 190 UC patients, covering the whole spectrum of disease stages and grades. Proteogenomic integration analysis indicates the mutations of HRAS regulated mTOR signaling to form urothelial papilloma rather than papillary urothelial cancer (PUC). DNA damage is a key signaling pathway in the progression of carcinoma in situ (CIS) and related to APOBEC signature. Glucolipid metabolism increase and lower immune cell infiltration are associated with PUC compared to CIS. Proteomic analysis distinguishes the origins of invasive tumors (PUC-derived and CIS-derived), related to distinct clinical prognosis and molecular features. Additionally, loss of RBPMS, associated with CIS-derived tumors, is validated to increase the activity of AP-1 and promote metastasis. This study reveals the characteristics of two distinct branches (PUC and CIS) of UC progression and may eventually benefit clinical practice.

The mechanisms of resistance to therapy in gastric cancer remain to be explored. Here, proteomic profiling of 206 tumour tissues from patients treated with chemotherapy and anti-HER2-based therapy results in the identification of four molecular subtypes and the development of prognostic models.

Background/Aim: Body fluids are considered to be a rich source of disease biomarkers. Proteins in many body fluids have potential clinical applications for disease diagnostic and prognostic purposes. The aim of this study was to establish an in-depth multi-body fluid proteome. Materials and Methods: Ten body fluids associated with 8 types of cancers collected from 23 patients involved in 19 common diseases underwent liquid chromatography tandem mass spectrometry (MS) analysis after gel-based protein separation (SDS-PAGE) or peptide-based fractionations. Bioinformatic analysis, including principal component analysis (PCA), consensus clustering, and hierarchical clustering analysis were also performed. The biological function was determined using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Results: We profiled the proteome of ten body fluids, including ascites, bile, cerebrospinal fluid (CSF), hydrothorax, knee joint fluid (KJF), plasma, saliva, serum, tears, and urine. A total of 3,396 nonredundant proteins were identified, of which 304 were shared among ten body fluids, with common functions in focal adhesion and complement/coagulation cascades. A total of 41.5% (1,409) of the proteins were detected in only one body fluid and were closely related to their adjacent tissues by function. The functional analysis of the remaining 1,683 proteins showed that similar functions might be shared among different body fluids, which further highlighted the close connection of body fluids in the human body. Conclusion: A deep proteome of multi-body fluids originated from patients diagnosed with 19 common diseases provides a valuable data resource, and might indicate the potential application of body fluids for biomarker discovery. [ABSTRACT FROM AUTHOR]