A dependency map enhanced with next-generation 3D cancer models

Area of Science:

• Cancer Research
• Genomics
• Precision Oncology

Background:

• Precision oncology aims for personalized cancer treatments, but effective therapies remain elusive for many patients.
• The Cancer Dependency Map (DepMap) uses preclinical models to identify cancer vulnerabilities, but traditional cell lines have limitations in subtype representation and culture condition effects.
• There is a need for advanced cancer models to improve the discovery of targeted therapies.

Purpose of the Study:

• To expand the Cancer Dependency Map (DepMap) using next-generation cancer models (organoids and spheroids).
• To identify new genomic and molecular subtypes and associated cancer vulnerabilities.
• To compare gene essentiality between traditional cell lines and next-generation models.

Main Methods:

• Performed 147 genome-scale CRISPR screens on next-generation cancer models (organoids and spheroids).
• Conducted multi-omic characterizations across 10 cancer types.
• Integrated data from both traditional and next-generation cancer models.

Main Results:

• Successfully expanded DepMap coverage to new genomic and molecular subtypes.
• Identified novel biomarker-associated cancer vulnerabilities.
• Discovered distinct effects of growth format and culture medium on gene essentiality, with next-generation models preserving crucial transcriptional programs.
• Facilitated the discovery of gene dependencies linked to preserved transcriptional programs.

Conclusions:

• Next-generation cancer models offer a more comprehensive resource for identifying cancer vulnerabilities compared to traditional cell lines.
• The integrated dataset provides a valuable resource for exploring cancer dependencies and developing personalized therapeutic strategies.
• This work advances the field of precision oncology by enabling the discovery of new targeted treatments.