Highlights d Systematic identification of colon cancer-associated proteins and phosphosites d Proteomics-supported neoantigens and cancer/testis antigens in 78% of the tumors d Rb phosphorylation is an oncogenic driver and a putative target in colon cancer d Glycolysis inhibition may render MSI tumors more sensitive to checkpoint blockade

Highlights d Comprehensive LUAD proteogenomics exposes multi-omic clusters and immune subtypes d Phosphoproteomics identifies candidate ALK-fusion diagnostic markers and targets d Candidate drug targets: PTPN11 (EGFR), SOS1 (KRAS), neutrophil degranulation (STK11) d Phospho and acetyl modifications denote tumor-specific markers and druggable proteins

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Highlights d Proteogenomic characterization reveals the functional impact of genomic alterations d Phosphoproteomics uncovers putative therapeutic targets downstream of KRAS d Multiomics links endothelial cell remodeling and glycolysis to immune exclusion d Proteomics and glycoproteomics reveal candidates for early detection or intervention

Highlights d A systematic inventory of HNSCC-associated proteins, phosphosites, and pathways d Three multi-omic subtypes linked to targeted treatment approaches and immunotherapy d Widespread deletion of immune modulatory genes accounts for loss of immunogenicity d Two modes of EGFR activation inform response to anti-EGFR monoclonal antibodies

Highlights d Unsupervised clustering revealed subtype with EMT and phosphoprotein signatures d Potential therapeutic vulnerabilities included survivin, NSD3, LSD1, and EZH2 d Rb phosphorylation nominated as a biomarker for trials with CDK4/6 inhibitors d Detailed immune landscape analysis highlighted targetable points of immuneregulation

Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.