ArticleNature methods2017
A quantitative and multiplexed approach to uncover the fitness landscape of tumor suppression in vivo.
Article in Nature methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 1 of them a synthesis that pooled it.
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Who cites it
100 citing papers in PubMed, 1 synthesis or guideline pooled it, 163 citations in OpenAlex.
- A multiplexed in vivo approach to identify driver genes in small cell lung cancer.Cell reports · 2023Pooled it
- Antagonistic roles of histone H3 lysine methyltransferases dictate tumor immune surveillance.Nature communications · 2026Article
- Quantitative dissection of the metastatic cascade at single colony resolution.Research square · 2026Article
- Temporal control of sgRNA library activation unlocks large-scale in vivo CRISPR screens.Cell reports methods · 2026Article
- Tumor suppressor genotype influences the extent and mode of immunosurveillance in lung cancer.Nature communications · 2026Article
- Inducible CRISPR/Cas systems in precision oncology: Current applications and future perspectives.Clinical and translational medicine · 2026Review
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- Quantitative dissection of the metastatic cascade at single colony resolution.bioRxiv : the preprint server for biology · 2026Article
- Putting BASIL in a BLT: A Bayesian filtering method for estimating the fitness effects of nascent adaptive mutations.PLoS computational biology · 2026Article
- Scaling perturbations: beyond genome-scale CRISPR screens.bioRxiv : the preprint server for biology · 2026Article
- EML4-ALK Variant-Specific Genetic Interactions Shape Lung Tumorigenesis.Cancer discovery · 2026Article
- Integrated in vivo combinatorial functional genomics and spatial transcriptomics of tumours to decode genotype-to-phenotype relationships.Nature biomedical engineering · 2026Article
- Experimental evolution in an era of molecular manipulation.Nature reviews. Genetics · 2026Review
- Functional Mapping of Epigenomic Regulators Uncovers Coordinated Tumor Suppression by the HBO1 and MLL1 Complexes.Cancer discovery · 2025Article
- Integrative multiomic approaches reveal ZMAT3 and p21 as conserved hubs in the p53 tumor suppression network.Cell death and differentiation · 2025Article
- Aging represses oncogenic KRAS-driven lung tumorigenesis and alters tumor suppression.Nature aging · 2025Article
- Efficient and multiplexed somatic genome editing with Cas12a mice.Nature biomedical engineering · 2025Article
- Article
- Methods and applications of in vivo CRISPR screening.Nature reviews. Genetics · 2025Review
- RIT1 Drives Oncogenic Transformation and Is an Actionable Target in Lung Adenocarcinoma.Cancer research · 2025Article
40 more citing papers are in PubMed but not listed here.
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7 authors at 1 institution in 1 country.
Funding
Abstract
Cancer growth is a multistage, stochastic evolutionary process. While cancer genome sequencing has been instrumental in identifying the genomic alterations that occur in human tumors, the consequences of these alterations on tumor growth remain largely unexplored. Conventional genetically engineered mouse models enable the study of tumor growth in vivo, but they are neither readily scalable nor sufficiently quantitative to unravel the magnitude and mode of action of many tumor-suppressor genes. Here, we present a method that integrates tumor barcoding with ultradeep barcode sequencing (Tuba-seq) to interrogate tumor-suppressor function in mouse models of human cancer. Tuba-seq uncovers genotype-dependent distributions of tumor sizes. By combining Tuba-seq with multiplexed CRISPR-Cas9-mediated genome editing, we quantified the effects of 11 tumor-suppressor pathways that are frequently altered in human lung adenocarcinoma. Tuba-seq enables the broad quantification of the function of tumor-suppressor genes with unprecedented resolution, parallelization, and precision.
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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.