Evidence map›Paper›PMID 37218906›Full record

ArticleMethods and protocols2023

Automated Dashboards for the Identification of Pathogenic Circulating Tumor DNA Mutations in Longitudinal Blood Draws of Cancer Patients.

Aleksandr Udalov, Lexman Kumar, Anna N Gaudette, Ran Zhang, Joao Salomao, Sanjay Saigal, Mehdi Nosrati, Sean D McAllister, Pierre-Yves Desprez

Abstract read
In one paragraph

Article in Methods and protocols, 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

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

9 authors.

Aleksandr UdalovGraduate School of Management, UC Davis, 1 Shields Ave., Davis, CA 95616, USA.
Lexman KumarGraduate School of Management, UC Davis, 1 Shields Ave., Davis, CA 95616, USA.
Anna N GaudetteGraduate School of Management, UC Davis, 1 Shields Ave., Davis, CA 95616, USA.
Ran ZhangGraduate School of Management, UC Davis, 1 Shields Ave., Davis, CA 95616, USA.
Joao SalomaoGraduate School of Management, UC Davis, 1 Shields Ave., Davis, CA 95616, USA.
Sanjay SaigalGraduate School of Management, UC Davis, 1 Shields Ave., Davis, CA 95616, USA.
Mehdi NosratiCalifornia Pacific Medical Center, Research Institute, 475 Brannan St., San Francisco, CA 94107, USA.
Sean D McAllisterCalifornia Pacific Medical Center, Research Institute, 475 Brannan St., San Francisco, CA 94107, USA.
Pierre-Yves DesprezCalifornia Pacific Medical Center, Research Institute, 475 Brannan St., San Francisco, CA 94107, USA.ORCID 0000-0002-9880-4888

Funding

California Pacific Medical Center Research and Education Leadership Committee of the California Pacific Medical Center Foundation
6 · The paper itself

Abstract

The longitudinal monitoring of patient circulating tumor DNA (ctDNA) provides a powerful method for tracking the progression, remission, and recurrence of several types of cancer. Often, clinical and research approaches involve the manual review of individual liquid biopsy reports after sampling and genomic testing. Here, we describe a process developed to integrate techniques utilized in data science within a cancer research framework. Using data collection, an analysis that classifies genetic cancer mutations as pathogenic, and a patient matching methodology that identifies the same donor within all liquid biopsy reports, the manual work for research personnel is drastically reduced. Automated dashboards provide longitudinal views of patient data for research studies to investigate tumor progression and treatment efficacy via the identification of ctDNA variant allele frequencies over time.

Indexed as

liquid biopsymetastasisnext-generation sequencing

Identifiers

PMID37218906
PMCPMC10204543

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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.