ArticleCell2024
Pan-cancer proteogenomics expands the landscape of therapeutic targets.
Article in Cell, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
78 citing papers in PubMed.
- DNA polymerase theta (Polθ): a novel candidate for targeted cancer therapy.Cancer biology & therapy · 2026Review
- Pan-Cancer Quantification of Driver Alteration Transmission Across Molecular Layers Reveals Limited Propagation to Protein Abundance.International journal of cancer · 2026Article
- Article
- A modular framework for antibody-drug conjugate biomarkers.Nature reviews. Clinical oncology · 2026Review
- Deep Learning-Based Multimodal Prediction of Neoadjuvant Chemotherapy Response in Locally Advanced Rectal Cancer by Integrating MRI and Proteomics.Cancer research and treatment · 2026Article
- Multi-Omics and Computational Pharmacology Approach With Experimental Validation Reveals the Antiproliferative Activity of Sophoricoside Against Pancreatic Cancer.Chemistry & biodiversity · 2026Article
- Article
- Pan-cancer multi-omics analysis identifies TOMM22 as an oncogenic driver and therapeutic target in LIHC via ferroptosis regulation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Nano-biosensors for circulating tumor markers: advancing liquid biopsy toward precision cancer diagnostics.Journal of cancer research and clinical oncology · 2026Review
- The role of the WD40-repeat protein family in cancer.Molecular cancer · 2026Review
- Proteomic health archetypes identified in disease-free adults enable risk assessment for diverse chronic diseases.Genome medicine · 2026Article
- Pan-Cancer Analysis of NOP2 Reveals Its Prognostic Relevance and Association With the Tumor Immune Microenvironment.World journal of oncology · 2026Article
- Integrated proteogenomic and metabolomic profiling of acute myeloid leukemias to identify molecular subtypes and associated therapy targets.Nature cancer · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- Integrative interpretable learning reveals shared patterns of epitranscriptomic regulation across multiple cancer types.BMC biology · 2026Article
- A 15-layer multi-omics analysis of gastric cancer ecotypes provides therapeutic insights.Cell reports. Medicine · 2026Article
- Differential phosphorylation of PHIP phosphopeptides with implications in insulin signaling.BMC molecular and cell biology · 2026Article
- Discovering proteo-transcriptomic networks via biologically informed heterogeneous graph learning.Nucleic acids research · 2026Article
- A multi-omics framework for survival mediation analysis of high-dimensional proteogenomic data.PLoS computational biology · 2026Article
- Proteogenomics: decoding cancer in multiple dimensions.MedScience · 2026Review
18 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors.
Funding
Abstract
Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.