Evidence map›Paper›PMID 38147400›Full record

ArticleThe Journal of urology2024

Development and Optimization of a Subtraction-Normalized Immunocyte Profiling Signature for Prostate Cancer Active Surveillance Risk Stratification.

Leander Van Neste, Ricardo Henao, Kirk J Wojno, Jorge Signes, Jessica DeHart, Angela Busta, Elyse Marriott, Marian Willing, Andrea Argentini, Patrick M Hurley and 8 more

Abstract read
In one paragraph

Article in The Journal of urology, 2024. 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

18 authors.

Leander Van NesteImmunis.AI, Royal Oak, Michigan.
Ricardo HenaoDuke University, Durham, North Carolina.
Kirk J WojnoImmunis.AI, Royal Oak, Michigan.
Jorge SignesDavid H. Murdock Research Institute, Kannapolis, North Carolina.
Jessica DeHartImmunis.AI, Royal Oak, Michigan.
Angela BustaImmunis.AI, Royal Oak, Michigan.
Elyse MarriottImmunis.AI, Royal Oak, Michigan.
Marian WillingImmunis.AI, Royal Oak, Michigan.
Andrea ArgentiniBioLizard, Ghent, Belgium.
Patrick M HurleyComprehensive Urology, Royal Oak, Michigan.
Howard KormanComprehensive Urology, Royal Oak, Michigan.
Jason HafronMichigan Institute of Urology, Troy, Michigan.
Mathew PutziAustin Urology, Austin, Texas.
Chris M PieczonkaAssociated Medical Professionals, Syracuse, New York.
Lawrence I KarshThe Urology Center of Colorado, Denver, Colorado.
David S MorrisUrology Nashville, Nashville, Tennessee.
Amin I KassisImmunis.AI, Royal Oak, Michigan.
Philip W KantoffConvergent Therapeutics, Boston, Massachusetts.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeLess invasive decision support tools are desperately needed to identify occult high-risk disease in men with prostate cancer (PCa) on active surveillance (AS). For a variety of reasons, many men on AS with low- or intermediate-risk disease forgo the necessary repeat surveillance biopsies needed to identify potentially higher-risk PCa. Here, we describe the development of a blood-based immunocyte transcriptomic signature to identify men harboring occult aggressive PCa. We then validate it on a biopsy-positive population with the goal of identifying men who should not be on AS and confirm those men with indolent disease who can safely remain on AS. This model uses subtraction-normalized immunocyte transcriptomic profiles to risk-stratify men with PCa who could be candidates for AS. MATERIALS AND

methodsMen were eligible for enrollment in the study if they were determined by their physician to have a risk profile that warranted prostate biopsy. Both training (n = 1017) and validation cohort (n = 1198) populations had blood samples drawn coincident to their prostate biopsy. Purified CD2

resultsThe best final model for the AS setting was obtained by combining an immunocyte transcriptomic profile based on 2 cell types with PSA density and age, reaching an AUC of 0.73 (95% CI: 0.69-0.77). The model significantly outperforms (

conclusionsWhile further validation in an intended-use cohort is needed, the immunocyte transcriptomic model offers a promising tool for risk stratification of individual patients who are being considered for AS.

Indexed as

Prostate-Specific AntigenProstatic NeoplasmsBiopsyHumansLeukocytes, MononuclearMaleRisk AssessmentWatchful WaitingProstate-Specific Antigenactive surveillanceclinically significant prostate cancerpatient managementprostate cancerrisk score

Identifiers

PMID38147400
PMCPMC12721612

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LicenceCC BY-NC-ND
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Registered trials

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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.