Evidence map›Paper›PMID 36711769›Full record

ArticlebioRxiv : the preprint server for biology2023

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource.

Teemu D Laajala, Varsha Sreekanth, Alex Soupir, Jordan Creed, Federico Cf Calboli, Kalaimathy Singaravelu, Michael Orman, Christelle Colin-Leitzinger, Travis Gerke, Brooke L Fidley and 2 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Teemu D LaajalaDepartment of Mathematics and Statistics, University of Turku, Turku, Finland.
Varsha SreekanthDepartment of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Alex SoupirDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
Jordan CreedDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
Federico Cf CalboliDepartment of Mathematics and Statistics, University of Turku, Turku, Finland.
Kalaimathy SingaraveluDepartment of Mathematics and Statistics, University of Turku, Turku, Finland.
Michael OrmanDepartment of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Christelle Colin-LeitzingerDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
Travis GerkeDepartment of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA.
Brooke L FidleyDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
Svitlana TyekuchevaDepartment of Data Science, Dana-Farber Cancer Institute; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
James C CostelloDepartment of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Funding

University of Colorado Cancer Center Support Grant - Lung Cancer Patient-Derived Xenografts with Autologous Human Immune SystemsP30CA046934 · NCI · UNIVERSITY OF COLORADO DENVER · PI James V Degregori · 1988 to 2026
$117.0M
TRANSLATIONAL RESEARCHP30CA076292 · NCI · UNIVERSITY OF SOUTH FLORIDA · PI Christine O'Connell · 1998 to 2026
$93.5M
Systems analysis of aggressive prostate cancer pathologyU01CA231978 · NCI · UNIVERSITY OF COLORADO DENVER · PI COSTELLO, JAMES CHRISTOPHER, CRAMER, SCOTT D · 2019 to 2023
$3.4M
A prediction model to simultaneously estimate personal risk of breast cancer and death from other causes in women aged 55 and olderR01CA242747 · NCI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI SCHONBERG, MARA A · 2020 to 2023
$2.3M
NCI NIH HHS P30 CA046934NCI NIH HHS P30 CA076292NCI NIH HHS R01 CA242747NCI NIH HHS U01 CA231978
6 · The paper itself

Abstract

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the

Identifiers

PMID36711769
PMCPMC9882125

What OpenQuestion holds

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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.