Machine learning analysis identifies genes differentiating triple negative breast cancers

Abstract

Triple negative breast cancer (TNBC) is one of the most aggressive form of breast cancer (BC) with the highest mortality due to high rate of relapse, resistance, and lack of an effective treatment. Various molecular approaches have been used to target TNBC but with little success. Here, using machine learning algorithms, we analyzed the available BC data from the Cancer Genome Atlas Network (TCGA) and have identified two potential genes, TBC1D9 (TBC1 domain family member 9) and MFGE8 (Milk Fat Globule-EGF Factor 8 Protein), that could successfully differentiate TNBC from non-TNBC, irrespective of their heterogeneity. TBC1D9 is under-expressed in TNBC as compared to non-TNBC patients, while MFGE8 is over-expressed. Overexpression of TBC1D9 has a better prognosis whereas overexpression of MFGE8 correlates with a poor prognosis. Protein-protein interaction analysis by affinity purification mass spectrometry (AP-MS) and proximity biotinylation (BioID) experiments identified a role for TBC1D9 in maintaining cellular integrity, whereas MFGE8 would be involved in various tumor survival processes. These promising genes could serve as biomarkers for TNBC and deserve further investigation as they have the potential to be developed as therapeutic targets for TNBC. Triple negative breast cancer (TNBC) accounts for 10-20% of all breast cancers (BC). They are characterized by lack of the hormonal receptors estrogen (ER) and progesterone (PR), and the overexpression of human epidermal growth factor receptor 2 (HER2) 1. It is the most aggressive form of BC and is very heterogeneous 2. The complexity of TNBC increases due to its high risk of relapse, and poor progression-free survival (PFS) and overall survival (OS) 3. The PFS for metastatic TNBC patients is 3-4 months after treatment failure 4. The 5-year mortality rate for early stage TNBC after surgery is 37%, whereas half of them relapse 5. According to gene expression pattern, TNBC has been classified in 6 different molecular subtypes namely Basal like (BL)1, BL2, Luminal androgen receptor (LAR), Immunomodulatory (IM), Mesenchymal (M) and Mesenchymal stem like (MSL), with some that cannot be classified 6. Lehmann et al., 2011, have shown that each of these subgroups can be further divided into intrinsic subtypes of BC (Luminal A, Luminal B, HER2, normal breast like, Basal like and unclassified) based on their gene expression 6. This stipulates why TNBC has different clinicopathological outcomes for different patients, rendering treatment arduous. On March 8, 2019, FDA approved immunotherapy Atezolizumab (targeting PD-L1) in combination with chemotherapy (nab-paclitaxel) for initial treatment of women with advanced TNBC positive for PD-L1 protein expression 7,8. Nevertheless, there is no FDA approved target therapy for TNBC patients as a whole so far 9. TNBC heterogeneity and aggressiveness call for an unmet need to identify genes that could serve as biomarkers to differentiate TNBC from other BCs, as well as serve as potential targets therapy irrespective of their heterogene...

Transcriptome Analysis Reveals MFGE8-HAPLN3 Fusion as a Novel Biomarker in Triple-Negative Breast Cancer

“…Recent studies have clarified the effect of MFGE8 on cell survival, adhesion, and migration in a wide spectrum of tumor types, such as ovarian cancer ( 36 ) and hepatocellular carcinoma ( 37 ). Consistent with these results, MFGE8 have been found to play a critical role in breast cancer pathobiology and clinical prognosis ( 38 , 39 ). Furthermore, MFGE8 knockdown significantly inhibited both migration and proliferation of tumor cells, attenuating their tumorigenic properties ( 40 ).…”

Identification of MFGE8 and KLK5/7 as mediators of breast tumorigenesis and resistance to COX-2 inhibition

“…Our results also suggest that combination treatments aiming at disabling both COX-2 and MFGE8 could represent a therapeutic strategy for the treatment of TNBC. Although MFGE8 has been shown to be overexpressed in TNBC compared with non-TNBC patients [ 45 ], it is also an essential gene for the breast involution process [ 46 ]. Thus, precise examination and attentive care will be required when targeting MFGE8 in clinical settings to avoid any potential side effects related to abnormal mammary gland remodeling.…”

Investigation of the SPAG5 gene expression and amplification related to the NuMA mRNA levels in breast ductal carcinoma

“…They reported TBC1D9 as a candidate gene that its expression was linked to improved patient’s survival. Further analysis revealed different roles for TBC1D9 in organelle biogenesis and localization as well as recruitment of NuMA to the mitotic centrosome [ 22 ]. It has been shown that BRCA deficiency causes cortical asymmetry of NuMA-dynein complex in dividing cells which provoke genomic instability and aneuploidy [ 23 ].…”