Multimodal data integration in early-stage breast cancer

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Biomarkers have advanced breast cancer treatment with targeted therapies.
• Current knowledge gaps exist, particularly for triple-negative breast cancer (TNBC).
• Multi-omics and multimodal data integration hold potential for deeper biological understanding.

Purpose of the Study:

• To review state-of-the-art multimodal data integration algorithms for breast cancer.
• To assess applicability in patient stratification, prognosis, and biomarker identification.
• To highlight clinical relevance of these advanced computational models.

Main Methods:

• Comprehensive literature review of multimodal data integration algorithms.
• Focus on algorithms combining molecular (multi-omics) and imaging data.
• Analysis of algorithm advantages, limitations, and clinical applicability.

Main Results:

• Multimodal data integration can reveal novel biological insights in breast cancer.
• Improved patient stratification and prediction of treatment response are achievable.
• Identification of new biomarkers for diverse breast cancer subtypes is facilitated.

Conclusions:

• Multimodal data integration is crucial for advancing breast cancer research and clinical practice.
• Algorithms reviewed show promise for preclinical and clinical applications.
• Further research is needed to optimize and validate these integration approaches for patient benefit.