**Title: Therapeutic Insights from Multi-Omics in TNBC Subtypes**

Multi-omics Analysis Identifies Therapeutic Vulnerabilities in Triple-Negative Breast Cancer Subtypes

Overview

This image presents a multi-omics analysis to identify therapeutic vulnerabilities in Triple-Negative Breast Cancer (TNBC) subtypes.

Key Points

• TNBC Subtypes Identification:

  • Non-Mesenchymal TNBC
  • Mesenchymal TNBC
  • Mesenchymal TNBC + EZH2 Inhibitor Treatment

• Methods and Data Types (a) :

  • Breast Cancer Datasets:
    • TCGA
    • CPTAC
    • METABRIC
    • MET500
  • Data Types:
    • Transcriptomic
    • Proteomic
    • Clinical
    • Metabolic
    • Mutation
    • RPPA
    • Phosphoproteomic
    • DNA Methylation
  • This multi-omics approach allows comprehensive analysis spanning various biological layers, helping in identifying subtype-specific targets.

• In silico Validation:

  • Genetic Dependency
  • Pharmacological Vulnerability
  • PDXE (Patient-Derived Xenograft) Drug Sensitivity

Genetic Dependency Data (b)

Extracted Highlights:

• Genetic dependencies are visualized across various TNBC subtypes, aiding in spotting potential therapeutic targets for specific TNBC subtypes.

Pharmacologic Dependency Data (c)

Analyzed drugs and their efficacy:

• This helps in understanding which drugs are more effective for different TNBC subtypes, guiding personalized treatment strategies.

PDXT Drug Dependency Data (d)

Analyzed drugs and their TNBC subtype-specific dependency:

• Provides insights into drug efficacy for patient-derived TNBC subtypes, promoting a personalized medicine approach based on individual tumor characteristics.

Tumor Microenvironments (c)

• Tumour Samples:

  • Fully Inflamed (FI)
  • Stroma-Restricted (SR)
  • Margin-Restricted (MR)
  • Immune Desert (ID)

• Depicts various histopathological appearances and immune contexts, crucial for understanding the tumor-immune microenvironment interactions, which can influence treatment responses.

Fraction of Tumor Types (d)

Bar graph showing distribution across TNBC subtypes (BL1, BL2, LAR, M):

• BL1: Most tumors fall in this subtype.

• BL2: Moderate number of samples.

• LAR and M: Fewer samples.

• Distribution helps visualize and quantify the prevalence of each subtype in the total sample studied, beneficial for identifying statistically significant patterns.

Conclusion

The multi-omics approach combined with in silico validation provides a comprehensive understanding of the TNBC landscape, guiding precise therapeutic interventions based on distinct molecular vulnerabilities across subtypes.

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