Evidence map›Paper›PMID 41279090›Full record

ArticlebioRxiv : the preprint server for biology2025

Integrated single-cell whole genome sequencing and spatial transcriptomics reveal latent intra-tumoral heterogeneity in ovarian cancer.

Rania Bassiouni, Yuxin Jin, Lee D Gibbs, Jing Qian, Solomon O Rotimi, Heather Miller, Michelle G Webb, Seeta Rajpara, Javier Arias-Stella, David W Craig and 2 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

5 · Who and what money

Authors and funding

12 authors.

Rania BassiouniDepartment of Integrative Translational Sciences, Beckman Research Institute, City of Hope; Duarte, CA.ORCID 0000-0002-4754-9311
Yuxin JinDepartment of Integrative Translational Sciences, Beckman Research Institute, City of Hope; Duarte, CA.ORCID 0000-0002-6124-9681
Lee D GibbsDepartment of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA.
Jing QianDepartment of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA.ORCID 0009-0008-7026-9386
Solomon O RotimiDepartment of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA.
Heather MillerDepartment of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA.
Michelle G WebbDepartment of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA.
Seeta RajparaDepartment of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA.
Javier Arias-StellaDepartment of Pathology, City of Hope Comprehensive Cancer Center; Duarte, CA.
David W CraigDepartment of Integrative Translational Sciences, Beckman Research Institute, City of Hope; Duarte, CA.
Lynda RomanDepartment of Obstetrics and Gynecology, Keck School of Medicine, University of Southern California; Los Angeles, CA.
John D CarptenDepartment of Integrative Translational Sciences, Beckman Research Institute, City of Hope; Duarte, CA.

Funding

USC/NORRIS COMPREHENSIVE CANCER CENTER (CORE) SUPPORTP30CA014089 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Fumito Ito · 1985 to 2026
$181.4M
NCI NIH HHS P30 CA014089
6 · The paper itself

Abstract

The mortality rate of ovarian cancer remains disproportionately high compared to its incidence. This is partly due to a high level of intra-tumoral heterogeneity that promotes disease recurrence and treatment failure. In this study, we describe degrees of heterogeneity revealed by single-cell whole genome sequencing and spatial transcriptomics of five epithelial ovarian carcinomas. At the cellular level, we describe pseudo-diploid cells that match the malignant cell population in both somatic variant and copy number patterns. At the clonal and subclonal levels, we describe diversification associated with copy number gains and whole genome doubling. In multi-clonal samples, we infer evolutionary relationships from single cell copy number, loss of heterozygosity analysis, and somatic variant detection, and correlate these with tissue histology and gene expression programs. In one sample, we identify functionally consequential copy number alterations that contribute to molecular diversity, cell proliferation, and inflammation in a minor clone that persisted without major expansion alongside a more complex major clone. In another, we describe a complex evolutionary history including a spontaneous reversion of a driver mutation in a secondary clone, which correlated with a switch in oncogenic expression programs.

Identifiers

PMID41279090
PMCPMC12632484

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Registered trials

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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.