Evidence map›Paper›PMID 35565358›Full record

ReviewCancers2022

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications.

Will Jiang, Jennifer C Jones, Uma Shankavaram, Mary Sproull, Kevin Camphausen, Andra V Krauze

Abstract readReview
In one paragraph

Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
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

6 authors.

Will JiangRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Jennifer C JonesTranslational Nanobiology Section, Laboratory of Pathology, NIH/NCI/CCR, Bethesda, MD 20892, USA.ORCID 0000-0002-9488-7719
Uma ShankavaramRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Mary SproullRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Kevin CamphausenRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Andra V KrauzeRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.

Funding

Radiation Oncology Branch - Radiation ClinicZIDBC010990 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2009 to 2025
$136.0M
Pre-clinical Development of Radiation Sensitizers for Patients with GlioblastomaZIASC010372 · NCI · DIVISION OF CLINICAL SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2009 to 2025
$12.0M
Novel Combinations of Radiation and ImmunotherapyZIABC011503 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI JONES, JENNIFER · 2013 to 2025
$5.3M
Radiation Oncology Branch - Radiation ClinicZ01BC010990 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2008 to 2008
$4.8M
Prognostic and predictive clinical and proteomic biomarker discovery in GBMZIABC012094 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI KRAUZE, ANDRA · 2022 to 2025
$847k
Generating a multi-channel data framework for AI analysis in Radiation therapyZIABC012095 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI KRAUZE, ANDRA · 2022 to 2025
$847k
Advancing clinically meaningful AI algorithms to improve oncologic outcomesZIABC012096 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI KRAUZE, ANDRA · 2022 to 2025
$847k
Proteogenomic characterization and biomarker discovery in glioblastomaZIABC012093 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI KRAUZE, ANDRA · 2022 to 2025
$847k
Using artificial intelligence and MRI to address limitations in glioblastomaZIABC012092 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI KRAUZE, ANDRA · 2022 to 2025
$847k
Intramural NIH HHS Z01 BC010990Intramural NIH HHS ZID BC010990NCI NIH HHS ZID BC 010990
6 · The paper itself

Abstract

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.

Indexed as

aptamersbioinformaticsbiomarkersproteomicstranslational

Identifiers

PMID35565358
PMCPMC9105298

What Socratic 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 Socratic graph.