Evidence map›Paper›PMID 42538373›Full record

ArticleNature genetics2026

Gene mutant dosage is associated with prognosis and metastatic tropism in 60,000 clinical cancer samples.

Nicola Calonaci, Eriseld Krasniqi, Daniel Colic, Stefano Scalera, Giorgia Gandolfi, Salvatore Milite, Konstantin Bräutigam, Andrea Sottoriva, Trevor A Graham, Leonardo Egidi and 3 more

Abstract read
In one paragraph

Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Nicola Calonaci *Department of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy.ORCID http://orcid.org/0000-0003-2721-4888
Eriseld Krasniqi *Phase IV Clinical Studies Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy.ORCID http://orcid.org/0000-0002-6702-0087
Daniel ColicInstitute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology (BIST), Barcelona, Spain.
Stefano ScaleraClinical Trial Center, Biostatistics and Bioinformatics Division, IRCCS Regina Elena National Cancer Institute, Rome, Italy.
Giorgia GandolfiDepartment of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy.
Salvatore MiliteComputational Biology Research Centre, Human Technopole, Milan, Italy.ORCID http://orcid.org/0000-0002-0156-3636
Konstantin BräutigamCentre for Evolution and Cancer, Institute of Cancer Research, London, UK.ORCID http://orcid.org/0000-0002-3966-3579
Andrea SottorivaComputational Biology Research Centre, Human Technopole, Milan, Italy.ORCID http://orcid.org/0000-0001-6709-9533
Trevor A GrahamCentre for Evolution and Cancer, Institute of Cancer Research, London, UK.ORCID http://orcid.org/0000-0001-9582-1597
Leonardo EgidiDepartment of Economics, Business, Mathematics, and Statistics 'Bruno de Finetti', University of Trieste, Trieste, Italy.
Biagio RicciutiLowe Center for Thoracic Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Marcello Maugeri-SaccàClinical Trial Center, Biostatistics and Bioinformatics Division, IRCCS Regina Elena National Cancer Institute, Rome, Italy.
Giulio CaravagnaDepartment of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy. gcaravagna@units.it.ORCID http://orcid.org/0000-0003-4240-3265

Funding

Associazione Italiana per la Ricerca sul Cancro (Italian Association for Cancer Research) 32107Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) P500PM_217647/1
6 · The paper itself

Abstract

The interplay between somatic mutations and copy number alterations influences tumor evolution and prognosis. These alterations are often treated independently, overlooking gene mutant dosage (GMD)-a key property of their interaction. Here we develop a computational framework that infers mutation copy number and multiplicity from targeted sequencing panels without requiring matched normal samples. We derive GMD for over 500,000 mutations across 60,000 pan-cancer samples. By stratifying more than 20,000 patients according to GMD across multiple genes, we identify 46 tumor-type-specific biomarkers predictive of survival, 13 of which were undetectable using binary mutant/wild-type models, 26 were associated with metastatic spread and 20 predicted metastatic tropism. Our method reveals GMD patterns as independent predictors of disease prognosis, metastatic potential and site-specific dissemination across diverse tumor types. This augmented insight into genomic drivers enhances our understanding of cancer progression and metastasis and holds the potential to substantially enhance biomarker discovery.

Indexed as

Gene DosageMutationNeoplasm MetastasisNeoplasmsBiomarkers, TumorDNA Copy Number VariationsHumansPrognosisBiomarkers, Tumor

Identifiers

PMID42538373
PMCPMC13447112

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Registered trials

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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.