ArticleProceedings of the National Academy of Sciences of the United States of America2015
Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- Transcriptome-Based Synthetic Lethality Predictions to Improve Targeted Therapy/Immunotherapy Treatment Prioritization in Nonmetastatic Breast Cancer: BC-SELECT.Cancer research communications · 2026Article
- NERINE reveals rare variant associations in gene networks across phenotypes and implicates an SNCA-PRL-LRRK2 subnetwork in Parkinson's disease.Cell genomics · 2026Article
- Optimizing transcriptome-based synthetic lethality predictions to improve targeted therapy / immunotherapy treatment prioritization in non-metastatic breast cancer: BC-SELECT.bioRxiv : the preprint server for biology · 2026Article
- Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.Bioinformatics (Oxford, England) · 2026Article
- Beyond synthetic lethality in large-scale metabolic and regulatory network models via genetic minimal intervention set.Bioinformatics advances · 2026Article
- Epigenetic synthetic lethality as a cancer therapeutic strategy: synergy of experimental and computational approaches.Epigenomics · 2025Review
- Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers.Briefings in bioinformatics · 2025Article
- Machine Learning Approaches for the Identification of Genetic Interactions.Methods in molecular biology (Clifton, N.J.) · 2025Review
- MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.PLoS computational biology · 2024Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments.Nature communications · 2022Article
- Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer.Biology · 2021Article
- Alternative glycosylation controls endoplasmic reticulum dynamics and tubular extension in mammalian cells.Science advances · 2021Article
- Serine Biosynthesis Is a Metabolic Vulnerability in IDH2-Driven Breast Cancer Progression.Cancer research · 2021Article
- Development of synthetic lethality in cancer: molecular and cellular classification.Signal transduction and targeted therapy · 2020Review
- PARP inhibitors in pancreatic cancer: molecular mechanisms and clinical applications.Molecular cancer · 2020Review
- Adapt and conquer: Metabolic flexibility in cancer growth, invasion and evasion.Molecular metabolism · 2020Review
- Human artificial chromosome (HAC) for measuring chromosome instability (CIN) and identification of genes required for proper chromosome transmission.Experimental cell research · 2020Review
- Identification of Cancer-associated metabolic vulnerabilities by modeling multi-objective optimality in metabolism.Cell communication and signaling : CCS · 2019Article
- Beyond Synthetic Lethality: Charting the Landscape of Pairwise Gene Expression States Associated with Survival in Cancer.Cell reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.
Indexed as
Identifiers
What OpenQuestion holds
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.