Abstract 2205: Isoform-level transcriptome analysis for enriched cancer stem cells from patient-derived ovarian cancer cell lines.

Abstract Background: In high-grade serous ovarian cancer (HGSOC), the abilities of tumor cells to resist cytotoxic treatment and relapse are largely driven by ovarian cancer stem cells (CSCs). CSCs are a population of cells within a tumor that maintain stem-like qualities, such as self-renewal and asymmetric division, but also have the capacity to produce metastasis and drive drug resistance​. Despite extensive research over the past two decades, no specific CSC target has been identified in ovarian cancer, and no drug has proven effective in neutralizing the CSC population. Therefore, identifying the molecular mechanisms underlying CSC development in HGSOC is urgently needed, as eradicating CSCs represents one of the most formidable challenges in oncology, yet also offers one of the most transformative opportunities to alter the course of cancer therapies. Based on the emerging evidence of linking differential isoform usage to the regulatory mechanisms of developmental process, we hypothesize that CSCs exploit isoform diversity to sustain stemness, drive therapy resistance, and shape prognosis while gene-level programs shape general differentiation status. Methods: We employed CSC-enriched sphere culture system to emphasize the features of CSC population from 3 patient derived high grade serous ovarian carcinomas (PD-HGSOC) with different metastatic abilities and tumor aggressiveness. For these cell lines, we generated RNA-seq data for genome-wide transcriptome profiling to investigate stemness and differentiation status comparing standard culture condition and CSC-enriched spheres. In this study, we analyzed RNA-seq data in gene-level as well as in isoform-level with the most up-to-date transcript annotation to identify patient-specific CSC features and define the mechanism of cancer related stemness and plasticity. Results: We analyzed gene-level transcriptome profiles focusing on stemness signatures in PD-HGSOC and CSC-enriched spheres. We observed that CSCs exhibited markedly higher stemness characteristics and identified differentially expressed gene sets across PD-HGSOCs. We noted that each cell line had a distinct subset of genes regulating stemness which suggested multiple mechanisms of CSC development and maintenance. In result of isoform-level analysis, we found that a large proportion of gene-level expression changes were driven by the differential expression of a single predominant isoform. Importantly, our isoform-level analysis enabled the identification of differential isoform usage that would not be detected at the gene level expression analysis, as changes in isoform composition can occur while total gene expression remain unchanged. These results are instrumental for foundational understanding of CSC plasticity and for next generation drug design, increasing target specificity and reducing adverse effects. Citation Format: Rachel S. Perkins, Jisun Kang, Matthew S. Jung, Sue Chin Lee, Wenjing Zhang, Won-Young Choi. Isoform-level transcriptome analysis for enriched cancer stem cells from patient-derived ovarian cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2205.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-2205
Primary Topic
Cancer Cells and Metastasis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 2205: Isoform-level transcriptome analysis for enriched cancer stem cells from patient-derived ovarian cancer cell lines.

Rachel S. Perkins, Sue Çhin Lee, Matthew S. Jung, Wenjing Zhang et al.
Cancer Research
Cancer Cells and Metastasis
article

Abstract 2205: Isoform-level transcriptome analysis for enriched cancer stem cells from patient-derived ovarian cancer cell lines.

Rachel S. Perkins, Sue Çhin Lee, Matthew S. Jung, Wenjing Zhang, Jisun Kang, Won-Young Choi
article en

Abstract

Abstract Background: In high-grade serous ovarian cancer (HGSOC), the abilities of tumor cells to resist cytotoxic treatment and relapse are largely driven by ovarian cancer stem cells (CSCs). CSCs are a population of cells within a tumor that maintain stem-like qualities, such as self-renewal and asymmetric division, but also have the capacity to produce metastasis and drive drug resistance​. Despite extensive research over the past two decades, no specific CSC target has been identified in ovarian cancer, and no drug has proven effective in neutralizing the CSC population. Therefore, identifying the molecular mechanisms underlying CSC development in HGSOC is urgently needed, as eradicating CSCs represents one of the most formidable challenges in oncology, yet also offers one of the most transformative opportunities to alter the course of cancer therapies. Based on the emerging evidence of linking differential isoform usage to the regulatory mechanisms of developmental process, we hypothesize that CSCs exploit isoform diversity to sustain stemness, drive therapy resistance, and shape prognosis while gene-level programs shape general differentiation status. Methods: We employed CSC-enriched sphere culture system to emphasize the features of CSC population from 3 patient derived high grade serous ovarian carcinomas (PD-HGSOC) with different metastatic abilities and tumor aggressiveness. For these cell lines, we generated RNA-seq data for genome-wide transcriptome profiling to investigate stemness and differentiation status comparing standard culture condition and CSC-enriched spheres. In this study, we analyzed RNA-seq data in gene-level as well as in isoform-level with the most up-to-date transcript annotation to identify patient-specific CSC features and define the mechanism of cancer related stemness and plasticity. Results: We analyzed gene-level transcriptome profiles focusing on stemness signatures in PD-HGSOC and CSC-enriched spheres. We observed that CSCs exhibited markedly higher stemness characteristics and identified differentially expressed gene sets across PD-HGSOCs. We noted that each cell line had a distinct subset of genes regulating stemness which suggested multiple mechanisms of CSC development and maintenance. In result of isoform-level analysis, we found that a large proportion of gene-level expression changes were driven by the differential expression of a single predominant isoform. Importantly, our isoform-level analysis enabled the identification of differential isoform usage that would not be detected at the gene level expression analysis, as changes in isoform composition can occur while total gene expression remain unchanged. These results are instrumental for foundational understanding of CSC plasticity and for next generation drug design, increasing target specificity and reducing adverse effects. Citation Format: Rachel S. Perkins, Jisun Kang, Matthew S. Jung, Sue Chin Lee, Wenjing Zhang, Won-Young Choi. Isoform-level transcriptome analysis for enriched cancer stem cells from patient-derived ovarian cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2205.

Cancer ResearchVol. 86(7_Supplement)
University of Tennessee Health Science Center (US)
Openalex Percentile: Top 29%
Cancer Cells and Metastasis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.