ArticleNature2019
Pan-cancer whole-genome analyses of metastatic solid tumours.
Article in Nature, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04981119 (An Observational Study Obtaining Solid Tumor Tissue From Participants and Apheresis for CAR T-Cell Therapy Manufacturing), which is not on this map. Cited by 688 papers, 1 of them a synthesis that pooled it.
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.
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.
An Observational Study Obtaining Solid Tumor Tissue From Participants and Apheresis for CAR T-Cell Therapy Manufacturing
Who cites it
688 citing papers in PubMed, 1 synthesis or guideline pooled it, 1,157 citations in OpenAlex.
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- Whole genome sequencing of 378 prostate cancer metastases reveals tissue selectivity for mismatch deficiency with potential therapeutic implications.Genome medicine · 2025Trial
- The pathway alteration load is a pan-cancer determinant of outcome of targeted therapies: results from the Drug Rediscovery Protocol (DRUP).ESMO open · 2025Trial
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- RHAMM drives formation of polyploid cancer cells and confers resistance to ER-targeted therapy in breast cancer.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- Systematic identification of genomic nonresponse biomarkers to cancer therapies.ESMO real world data and digital oncology · 2026Article
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- Expected costs and benefits of genetic counselling and germline genetic testing in metastatic prostate cancer.Human genetics · 2026Article
- Chronic interferon signaling at baseline is associated with acquired resistance to immunotherapy in metastatic melanoma.Cell reports. Medicine · 2026Article
628 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
21 authors at 9 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metastatic cancer is a major cause of death and is associated with poor treatment efficacy. A better understanding of the characteristics of late-stage cancer is required to help adapt personalized treatments, reduce overtreatment and improve outcomes. Here we describe the largest, to our knowledge, pan-cancer study of metastatic solid tumour genomes, including whole-genome sequencing data for 2,520 pairs of tumour and normal tissue, analysed at median depths of 106× and 38×, respectively, and surveying more than 70 million somatic variants. The characteristic mutations of metastatic lesions varied widely, with mutations that reflect those of the primary tumour types, and with high rates of whole-genome duplication events (56%). Individual metastatic lesions were relatively homogeneous, with the vast majority (96%) of driver mutations being clonal and up to 80% of tumour-suppressor genes being inactivated bi-allelically by different mutational mechanisms. Although metastatic tumour genomes showed similar mutational landscape and driver genes to primary tumours, we find characteristics that could contribute to responsiveness to therapy or resistance in individual patients. We implement an approach for the review of clinically relevant associations and their potential for actionability. For 62% of patients, we identify genetic variants that may be used to stratify patients towards therapies that either have been approved or are in clinical trials. This demonstrates the importance of comprehensive genomic tumour profiling for precision medicine in cancer.
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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.