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Treatment Resistant Cancers

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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

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Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in

Yosui Nojima¹, Ryoji Yao², Takashi Suzuki¹

¹Center for Mathematical Modeling and Data Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan.

Biochimica Et Biophysica Acta. Molecular Basis of Disease

|January 27, 2025

Summary

Drug-tolerant persister (DTP) cells drive cancer drug resistance. Machine learning models identified DTP cells in familial adenomatous polyposis organoids, revealing potential targeted therapies for colorectal cancer treatment.

Keywords:

Colorectal cancer Drug-tolerant persister cell Familial adenomatous polyposis Machine learning Patient-derived organoid Single-cell RNA sequencing

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Area of Science:

Oncology
Genomics
Computational Biology

Background:

Drug resistance in cancer is often mediated by drug-tolerant persister (DTP) cells.
These cells originate from diverse lineages and display complex behaviors, complicating therapeutic strategies.
Targeting DTP cells is crucial for overcoming treatment resistance in various cancers.

Purpose of the Study:

To develop and apply machine learning (ML) models for identifying DTP cells using single-cell RNA sequencing (scRNA-Seq) data.
To computationally screen for potential drug candidates targeting DTP cells in familial adenomatous polyposis (FAP), a precursor to colorectal cancer.
To validate potential synergistic drug combinations against DTP cells in FAP organoids.

Main Methods:

scRNA-Seq was performed on three patient-derived organoids (PDOs): benign tumor, malignant tumor, and normal.
ML models were trained on public scRNA-Seq data to classify DTP versus non-DTP cells.
Candidate drugs targeting DTP cells were identified using public drug sensitivity data.
A specific TC1 cell cluster in FAP tumor organoids was analyzed for DTP cell proportion.
Viability assays were conducted to test synergistic drug effects with trametinib.

Main Results:

The ML model successfully identified DTP cells within the TC1 cell cluster of FAP tumor organoids, with up to 36% of these cells classified as DTP.
This proportion of DTP cells was higher compared to other identified cell clusters.
Viability assays confirmed that YM-155 and THZ2 exhibit synergistic effects when combined with trametinib in a malignant tumor organoid model.

Conclusions:

The developed ML model effectively identifies DTP cells from scRNA-Seq data in FAP models.
This approach provides a pipeline for identifying candidate treatments targeting DTP cells.
The findings suggest a promising strategy for enhancing DTP cell targeting in colorectal and other cancers.