ReviewCurrent opinion in genetics & development2019
Mapping the protein-protein and genetic interactions of cancer to guide precision medicine.
Review in Current opinion in genetics & development, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Mining cancer genomes for copy number alterations identifies glycosylation enzymes as oncogenic drivers.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- When neighbors play a role: A systems-level analysis of protein interactions conditioning cancer driver mutation effects.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Functional Analysis and Experimental Validation of the Prognostic and Immune Effects of the Oncogenic Protein CDC45 in Breast Cancer.Breast cancer (Dove Medical Press) · 2025Article
- Death-associated protein kinase 3 modulates migration and invasion of triple-negative breast cancer cells.PNAS nexus · 2024Article
- In Silico Analysis of Protein-Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 inCurrent issues in molecular biology · 2024Article
- Targeting receptor tyrosine kinases in ovarian cancer: Genomic dysregulation, clinical evaluation of inhibitors, and potential for combinatorial therapies.Molecular therapy oncolytics · 2023Review
- Targeting stressor-induced dysfunctions in protein-protein interaction networks via epichaperomes.Trends in pharmacological sciences · 2023Review
- CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.Science advances · 2022Article
- Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease.The FEBS journal · 2022Review
- Artificial intelligence, molecular subtyping, biomarkers, and precision oncology.Emerging topics in life sciences · 2021Review
- Caveolin-1 and Sox-2 are predictive biomarkers of cetuximab response in head and neck cancer.JCI insight · 2021Article
- A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity.Science (New York, N.Y.) · 2021Article
- A protein interaction landscape of breast cancer.Science (New York, N.Y.) · 2021Article
- My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype.Genome biology · 2021Article
- An integrative model of cardiometabolic traits identifies two types of metabolic syndrome.eLife · 2021Article
- e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks.Nucleic acids research · 2021Article
- Mass spectrometry-based protein-protein interaction networks for the study of human diseases.Molecular systems biology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Massive efforts to sequence cancer genomes have compiled an impressive catalogue of cancer mutations, revealing the recurrent exploitation of a handful of 'hallmark cancer pathways'. However, unraveling how sets of mutated proteins in these and other pathways hijack pro-proliferative signaling networks and dictate therapeutic responsiveness remains challenging. Here, we show that cancer driver protein-protein interactions are enriched for additional cancer drivers, highlighting the power of physical interaction maps to explain known, as well as uncover new, disease-promoting pathway interrelationships. We hypothesize that by systematically mapping the protein-protein and genetic interactions in cancer-thereby creating Cancer Cell Maps-we will create resources against which to contextualize a patient's mutations into perturbed pathways/complexes and thereby specify a matching targeted therapeutic cocktail.
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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.