Integrative subspace clustering by common and specific decomposition for applications on cancer subtype identification

摘要

With the advancements of biological technologies, there are many kinds of data available such as genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays and so on. By analyzing the multiple data generated by cancer patients, it is now possible to classify cancer patients to different subgroups, and thus improve the diagnostic and treatment. For example, Breast cancer is one of the most common cancers worldwide, and it is clinically categorized into four basic therapeutic subgroups: (1). Luminal A with oestrogen receptor (ER) positive group; (2). Luminal B with oestrogen receptor (ER) positive group; (3) HER2 amplified group; (4) triple-negative breast cancers (TNBCs, also called basal-like, lacking expression of ER, progesterone receptor (PR) and HER2). The ER positive (including Luminal A and B) is the most common and diverse, and several genomic tests can be used to predict outcomes for ER+ patients receiving endocrine therapy. The treatment for the HER2 amplified subtype has a great success due to the effective therapeutic targeting of HER2. The basal-like breast cancers, often with BRCA1 mutations or of African ancestry have only option of chemotherapy. Therefore, subtype identification for breast cancers surely can assist the treatment for the patients.