Deep Learning for Integrated Analysis of Breast Cancer Subtype Specific Multi-omics Data

Rakshit, Somnath; Saha, Indrajit; Chakraborty, Subha Shankar; Plewczyski, Dariusz

doi:10.1109/tencon.2018.8650144

Cited by 8 publications

(3 citation statements)

References 19 publications

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Section: Introductionmentioning

confidence: 99%

“…To understand the heterogeneity among breast cancer, quite a few works have been done to identify breast cancer molecular subtypes [7][8][9][10][11]. Multiple novel molecular subtypes are identified, ranging from 2 to 5, based on various data like histopathology and gene expression profiles [7][8][9][10][11][12]. However, most of these works explore the molecular subtypes without taking survival prognosis into account, making the identified subtypes less valuable in clinical practice [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Multiple novel molecular subtypes are identified, ranging from 2 to 5, based on various data like histopathology and gene expression profiles [7][8][9][10][11][12]. However, most of these works explore the molecular subtypes without taking survival prognosis into account, making the identified subtypes less valuable in clinical practice [7][8][9][10][11][12][13]. Instead, survival time is a success factor when classifying breast cancer patients into valuable subtypes with differential survival prognoses.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning based on multi-omics integration identifies potential therapeutic targets in breast cancer

Pan

Burgman

Sahni

et al. 2022

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Effective and precise classification of breast cancer patients for their disease risks is critical to improve early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data to subgroup cancer patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating breast cancer patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed and optimized a sophisticated deep learning-based model in breast cancer that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation, DNA methylation and protein expression. This framework achieved promising performance in distinguishing high-risk breast cancer patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in breast cancer.

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