ArticleCommunications biology2022
Data-driven design of targeted gene panels for estimating immunotherapy biomarkers.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Calcium ion-binding genes can predict tumor mutation burden and immune checkpoint blockade response in a pan-cancer model.Discover oncology · 2025Article
- Transcriptional and post-transcriptional regulation of checkpoint genes on the tumour side of the immunological synapse.Heredity · 2022Article
- Data-driven design of targeted gene panels for estimating immunotherapy biomarkers.Communications biology · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumour mutation burden and other exome-wide biomarkers are used to determine which patients will benefit from immunotherapy. However, the cost of whole exome sequencing limits the widespread use of such biomarkers. Here, we introduce a data-driven framework for the design of targeted gene panels for estimating a broad class of biomarkers including tumour mutation burden and tumour indel burden. Our first goal is to develop a generative model for the profile of mutation across the exome, which allows for gene- and variant type-dependent mutation rates. Based on this model, we then propose a procedure for constructing biomarker estimators. Our approach allows the practitioner to select a targeted gene panel of prespecified size and construct an estimator that only depends on the selected genes. Alternatively, our method may be applied to make predictions based on an existing gene panel, or to augment a gene panel to a given size. We demonstrate the excellent performance of our proposal using data from three non small-cell lung cancer studies, as well as data from six other cancer types.
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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.