Evidence map›Paper›PMID 35697702›Full record

ArticleScientific reports2022

A digital assay for programmed death-ligand 1 (22C3) quantification combined with immune cell recognition algorithms in non-small cell lung cancer.

Will Paces, Elliott Ergon, Elizabeth Bueche, G Dave Young, Vitria Adisetiyo, Cris Luengo, Meredith James, Charles Caldwell, Dannah Miller, Morgan Wambaugh and 2 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. 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
0.1field-weighted citation impact, top 55% of its field
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 citations in OpenAlex.

  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

12 authors at 1 institution in 1 country.

Will PacesFlagship Biosciences, Inc., Broomfield, CO, USA.
Elliott ErgonFlagship Biosciences, Inc., Broomfield, CO, USA.
Elizabeth BuecheFlagship Biosciences, Inc., Broomfield, CO, USA.
G Dave YoungFlagship Biosciences, Inc., Broomfield, CO, USA.
Vitria AdisetiyoFlagship Biosciences, Inc., Broomfield, CO, USA.
Cris LuengoFlagship Biosciences, Inc., Broomfield, CO, USA.
Meredith JamesFlagship Biosciences, Inc., Broomfield, CO, USA.
Charles CaldwellFlagship Biosciences, Inc., Broomfield, CO, USA.
Dannah MillerFlagship Biosciences, Inc., Broomfield, CO, USA.
Morgan WambaughFlagship Biosciences, Inc., Broomfield, CO, USA.
Geoffrey MetcalfFlagship Biosciences, Inc., Broomfield, CO, USA.
Roberto GiananiFlagship Biosciences, Inc., Broomfield, CO, USA. rgianani@flagshipbio.com.
Flag Therapeutics (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PD-L1 (22C3) checkpoint inhibitor therapy represents a mainstay of modern cancer immunotherapy for non-small cell lung cancer (NSCLC). In vitro diagnostic (IVD) PD-L1 antibody staining is widely used to predict clinical intervention efficacy. However, pathologist interpretation of this assay is cumbersome and variable, resulting in poor positive predictive value concerning patient therapy response. To address this, we developed a digital assay (DA) termed Tissue Insight (TI) 22C3 NSCLC, for the quantification of PD-L1 in NSCLC tissues, including digital recognition of macrophages and lymphocytes. We completed clinical validation of this digital image analysis solution in 66 NSCLC patient samples, followed by concordance studies (comparison of PD-L1 manual and digital scores) in an additional 99 patient samples. We then combined this DA with three distinct immune cell recognition algorithms for detecting tissue macrophages, alveolar macrophages, and lymphocytes to aid in sample interpretation. Our PD-L1 (22C3) DA was successfully validated and had a scoring agreement (digital to manual) higher than the inter-pathologist scoring. Furthermore, the number of algorithm-identified immune cells showed significant correlation when compared with those identified by immunohistochemistry in serial sections stained by double immunofluorescence. Here, we demonstrated that TI 22C3 NSCLC DA yields comparable results to pathologist interpretation while eliminating the intra- and inter-pathologist variability associated with manual scoring while providing characterization of the immune microenvironment, which can aid in clinical treatment decisions.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsAlgorithmsB7-H1 AntigenBiomarkers, TumorHumansTumor MicroenvironmentB7-H1 AntigenBiomarkers, TumorCD274 protein, human

Identifiers

PMID35697702
PMCPMC9192755
OpenAlexW4282591082

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.