Evaluation of the TruSight Tumor 170 (TST170) assay and its value in clinical research

Heydt, Carina; Pappesch, Roberto; Stecker, Katrin; Neumann, Jens; Buettner, Reinhard; Merkelbach‐Bruse, Sabine

doi:10.1093/annonc/mdy318.003

Cited by 3 publications

(3 citation statements)

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“…The three next-generation sequencing panels that we consider are chosen for their relevance to TMB. These are TST-170 35 , Foundation One 36 and MSK-IMPACT 37 . Further, the panel selected by the approach in Lyu et al 27 consists of the genes that are mutated more than 10% of the time, that are less than 0.015 Mb in length and for which the presence of a mutation in the gene is significantly associated with higher TMB values.…”

Section: Resultsmentioning

confidence: 99%

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

Bradley

Cannings

2022

Commun Biol

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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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Section: Resultsmentioning

confidence: 99%

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

Bradley

Cannings

2022

Commun Biol

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“…The three next-generation sequencing panels that we consider are chosen for their relevance to TMB. These are TST-170 (Heydt et al, 2018), Foundation One (Frampton et al, 2013) and MSK-IMPACT (Cheng et al, 2015). For each panel P ⊆ G, we use four different methods to predict TMB: (i) Our refitted estimator applied to the panel P : we estimate TMB using T (ŵ P ), where 332ŵ P ∈ arg min w∈W P {f (w)}, and W P is defined in (7).…”

Section: Predicting Tumour Mutation Burdenmentioning

confidence: 99%

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

Bradley

Cannings

2021

Preprint

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We introduce a novel data-driven framework for the design of targeted gene panels for estimating exome-wide biomarkers in cancer immunotherapy. 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 new procedure for estimating biomarkers such as Tumour Mutation Burden and Tumour Indel Burden. Our approach allows the practitioner to select a targeted gene panel of a prespecified size, and then construct an estimator that only depends on the selected genes. Alternatively, the practitioner may apply our method 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 an annotated mutation dataset from 1144 Non-Small Cell Lung Cancer patients.

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Section: Predicting Tumour Mutation Burdenmentioning

confidence: 99%

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

Bradley¹,

Cannings²

2021

Preprint

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We introduce a novel data-driven framework for the design of targeted gene panels for estimating exome-wide biomarkers in cancer immunotherapy. 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 new procedure for estimating biomarkers such as Tumour Mutation Burden and Tumour Indel Burden. Our approach allows the practitioner to select a targeted gene panel of a prespecified size, and then construct an estimator that only depends on the selected genes. Alternatively, the practitioner may apply our method 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 an annotated mutation dataset from 1144 Non-Small Cell Lung Cancer patients.

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Evaluation of the TruSight Tumor 170 (TST170) assay and its value in clinical research

Cited by 3 publications

References 0 publications

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

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