Evidence map›Paper›PMID 34040000›Full record

ArticleNPJ breast cancer2021

Treatment scheduling effects on the evolution of drug resistance in heterogeneous cancer cell populations.

Gauri A Patwardhan, Michal Marczyk, Vikram B Wali, David F Stern, Lajos Pusztai, Christos Hatzis

Abstract read
In one paragraph

Article in NPJ breast cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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.

Gauri A PatwardhanBreast Medical Oncology, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA.
Michal MarczykBreast Medical Oncology, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA.ORCID http://orcid.org/0000-0003-2508-5736
Vikram B WaliBreast Medical Oncology, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA.
David F SternDepartment of Pathology, Yale School of Medicine, New Haven, CT, USA.
Lajos PusztaiBreast Medical Oncology, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA.ORCID http://orcid.org/0000-0001-9632-6686
Christos HatzisBreast Medical Oncology, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA. christos.hatzis@yale.edu.ORCID http://orcid.org/0000-0002-8120-2290

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

The effect of scheduling of targeted therapy combinations on drug resistance is underexplored in triple-negative breast cancer (TNBC). TNBC constitutes heterogeneous cancer cell populations the composition of which can change dynamically during treatment resulting in the selection of resistant clones with a fitness advantage. We evaluated crizotinib (ALK/MET inhibitor) and navitoclax (ABT-263; Bcl-2/Bcl-xL inhibitor) combinations in a large design consisting of 696 two-cycle sequential and concomitant treatment regimens with varying treatment dose, duration, and drug holiday length over a 26-day period in MDA-MB-231 TNBC cells and found that patterns of resistance depend on the schedule and sequence in which the drugs are given. Further, we tracked the clonal dynamics and mechanisms of resistance using DNA-integrated barcodes and single-cell RNA sequencing. Our study suggests that longer formats of treatment schedules in vitro screening assays are required to understand the effects of resistance and guide more realistically in vivo and clinical studies.

Identifiers

PMID34040000
PMCPMC8154902

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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.