ArticleNPJ precision oncology2026
Finding the most promising indications for novel treatments in oncology.
Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tackling cancer with effective therapies is a major challenge our society faces with significant costs and complexities of designing and validating treatments. In this article we show that representation learning based on real-world data can suggest indications for which a chosen mechanism of action is likely to be effective. We trained a machine learning model to rank histology-based malignant indications against expected biological relevance of anti-PD-1 treatment, leveraging reference indications and an embedding generating approach for features based on events in the patient's medical journey. We call our approach INSPIRE. When restricting the model to data before broad establishment of PD-1 inhibitors in the clinic, the method successfully prioritizes 70% of subsequent approvals. This indicates that INSPIRE could accelerate the process of selecting, testing and eventually treating patients with effective drugs, therefore reducing costs and improving care for patients.
Identifiers
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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.