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

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

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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:

PDCX GenesDependency
SF3B1High
AClyModerate
PLK1Moderate
CCDN2Low
  • 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:

Drug NamePathway Targeted
BicalutamideAR
NilotinibABL/PDGFR
BX-795PDK1
BortezomibProteasome
VinblastineMicrotubules
  • 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:

Drug NamePathway Targeted
BicalutamideAR
NilotinibABL/PDGFR
BX-795PDK1
BortezomibProteasome
VinblastineMicrotubules
SN38TOP1
GemcitabineDNA Replication
CyclophosphamideDNA Alkylation
  • 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.

Reference:

pubmed.ncbi.nlm.nih.gov
Multi-omics analysis identifies therapeutic vulnerabilities in triple ...
digitalcommons.fiu.edu
"Multi-omics analysis identifies therapeutic vulnerabilities in triple ...
www.researchgate.net
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