Evidence map›Paper›PMID 35510362›Full record

ArticleBlood cancer discovery2022

Efficient Algorithms Unlock Understanding of Clonal Evolution in Cancer.

Christopher A Miller

Open access · bronzeAbstract readEditorialComment
In one paragraph

Article in Blood cancer discovery, 2022. 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, top 95% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Christopher A MillerDepartment of Medicine, Washington University in St. Louis, St. Louis, Missouri.ORCID 0000-0003-4266-6700
Washington University in St. Louis · US

Funding

COMPREHENSIVE INFORMATIC ANALYSES OF AML GENOMES AND EPIGENOMESR50CA211782 · NCI · WASHINGTON UNIVERSITY · PI Christopher A Miller · 2017 to 2026
$1.5M
NCI NIH HHS R50 CA211782
6 · The paper itself

Abstract

In this issue of Blood Cancer Discovery, Wintersinger and colleagues present a new algorithm for quickly and accurately inferring clonal phylogenies from heterogeneous tumors sampled at many timepoints and/or many sites. When coupled with serial sequencing of tumors, this advance promises to increase our understanding of the clonal dynamics that shape tumor evolution and response to therapy. See related article by Wintersinger et al., p. 208 (9).

Indexed as

Clonal EvolutionNeoplasmsAlgorithmsDNAHumansSequence AnalysisDNA

Identifiers

PMID35510362
PMCPMC9245365
OpenAlexW4226328876

What OpenQuestion holds

Textmetadata
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.