Evidence map›Paper›PMID 40447752›Full record

ArticleNPJ precision oncology2025

Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer.

Anna Dirner, Dóra Kormos, Dóra Lakatos, Márton Bolyácz, Mária Kocsis-Steinbach, Gábor György Kalmár, Dóra Tihanyi, Ákos Takács, Ákos Boldizsár, Viktor Kardos and 15 more

Abstract read
In one paragraph

Article in NPJ precision oncology, 2025. 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Anna Dirner *Genomate Health Inc, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-8032-7104
Dóra Kormos *Department of Pulmonology, Mátraháza University and Teaching Hospital, Mátraháza, Hungary.
Dóra LakatosGenomate Health Inc, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-6461-5800
Márton BolyáczGenomate Health Inc, Cambridge, MA, USA.
Mária Kocsis-SteinbachGenomate Health Inc, Cambridge, MA, USA.
Gábor György KalmárGenomate Health Inc, Cambridge, MA, USA.
Dóra TihanyiGenomate Health Inc, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-0213-7273
Ákos TakácsGenomate Health Inc, Cambridge, MA, USA.
Ákos BoldizsárOncompass Medicine Hungary Ltd, Budapest, Hungary.ORCID http://orcid.org/0000-0002-0729-9944
Viktor KardosDepartment of Pulmonology, Mátraháza University and Teaching Hospital, Mátraháza, Hungary.
Réka Szalkai-DénesGenomate Health Inc, Cambridge, MA, USA.
Barbara VodicskaGenomate Health Inc, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-1191-234X
Edit VárkondiOncompass Medicine Hungary Ltd, Budapest, Hungary.
Júlia DériOncompass Medicine Hungary Ltd, Budapest, Hungary.
Gábor PajkosOncompass Medicine Hungary Ltd, Budapest, Hungary.
Dóra MathiászGenomate Health Inc, Cambridge, MA, USA.
István Vályi-NagyNational Hematology and Infectology Institute, Centrum Hospital of Southern Pest, 1097, Budapest, Hungary.
Richárd SchwábMIND Clinic, Budapest, Hungary.
Maud KamalDepartment of Drug Development and Innovation (D3i), Institute Curie, Paris, France.
Christian RolfoDivision of Medical Oncology, The James Comprehensive Cancer Center, Ohio State University, School of Medicine, Columbus, Ohio, USA.ORCID http://orcid.org/0000-0002-7860-8417
Arkadiusz Z DudekDivision of Oncology, Mayo Clinic, Rochester, USA.ORCID http://orcid.org/0000-0002-9114-8945
Christophe Le TourneauDepartment of Drug Development and Innovation (D3i), Institute Curie, Paris, France.ORCID http://orcid.org/0000-0001-9772-4686
Róbert DócziGenomate Health Inc, Cambridge, MA, USA. robert.doczi@genomate.health.ORCID http://orcid.org/0000-0001-5305-8036
László UrbánDepartment of Pulmonology, Mátraháza University and Teaching Hospital, Mátraháza, Hungary. drurban.laszlo@magy.eu.
István PetákGenomate Health Inc, Cambridge, MA, USA. istvan.petak@genomate.health.ORCID http://orcid.org/0000-0003-0422-9286

Funding

Nemzeti Kutatási Fejlesztési és Innovációs Hivatal 2019-1.1.1- PIACI-KFI-2019-00367
6 · The paper itself

Abstract

Tumors harbor multiple genetic alterations, yet treatment decisions are commonly based on single biomarkers, leading to underutilization of genomic information by comprehensive molecular tests, uncertainty in clinical practice, and frequent treatment failures. Although molecular tumor boards can assist personalized treatments, this process is not scalable or standardized, resulting in highly discordant recommendations. Validated digital solutions for personalized decision support are highly needed. The Digital Drug Assignment (DDA) system is a computational reasoning model that scores treatment options based on the full tumor genomic data. We retrospectively analyzed data of 111 lung cancer patients and found that high-score MTAs (1000≦DDA score) provided significant clinical benefit over other treatments, in terms of ORR, PFS, and OS. These results demonstrate that the DDA system is predictive of relative benefit of the various agents used in lung cancer care. Digital drug assignment can potentially address challenges with complex molecular profiles in routine clinical settings.

Identifiers

PMID40447752
PMCPMC12125307

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.