Evidence map›Paper›PMID 41916275›Full record

ArticleCell genomics2026

Pre-existing cell states predict resistance to multiple treatments.

Dylan L Schaff, Phoebe E White, Christopher J Cote, Grace E Watterson, Kevin Z Lin, Aria J Fasse, Nancy R Zhang, Sydney M Shaffer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell genomics, 2026. 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

8 authors.

Dylan L SchaffDepartment of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19146, USA.
Phoebe E WhiteDepartment of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19146, USA; Department of Biochemistry, Cell and Molecular Biology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Christopher J CoteDepartment of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19146, USA.
Grace E WattersonCellular and Molecular Biology Graduate Group, University of Pennsylvania, Philadelphia, PA 19146, USA.
Kevin Z LinDepartment of Biostatistics, School of Public Health, University of Washington, Seattle, WA 98195, USA.
Aria J FasseDepartment of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19146, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
Nancy R ZhangDepartment of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA 19146, USA.
Sydney M ShafferDepartment of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19146, USA; Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19146, USA. Electronic address: sydshaffer@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pre-existing differences between individual cancer cells can predict which cells will become resistant to treatment. DNA barcoding methods that track clones and their cell states during treatment have furthered this understanding, previously focusing on resistance to single treatments. Here, we performed multi-treatment, high-throughput clonal tracking and single-cell RNA sequencing to trace rare clones through resistance development across many treatments in parallel, identifying cell states associated with multi-treatment resistance. We found that clones resistant to one treatment had an increased chance of separately developing resistance to other treatments. We identified high CD44 expression in treatment-naive cells as a predictor of future multi-treatment resistance. Additionally, we found that differences in pre-treatment gene expression states can lead cells within the same treatment condition to follow divergent paths toward their ultimate resistance fate. This work provides a framework for extracting targetable gene expression states from complex resistance dynamics to eliminate multi-treatment resistance.

Indexed as

Drug Resistance, NeoplasmCell Line, TumorGene Expression Regulation, NeoplasticHumansHyaluronan ReceptorsSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisHyaluronan Receptorsclonal tracingmelanomascRNA-seqtreatment resistance

Identifiers

PMID41916275
PMCPMC13261651

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.