Abstract
Breast cancer (BC) is a heterogeneous and complex disease as witnessed by the existence of different subtypes and clinical characteristics that poses significant challenges in disease management. The complexity of this tumor may rely on the highly interconnected nature of the various biological processes as stated by the new paradigm of Network Medicine. We explored The Cancer Genome Atlas (TCGA)-BRCA data set, by applying the network-based algorithm named SWItch Miner, and mapping the findings on the human interactome to capture the molecular interconnections associated with the disease modules. To characterize BC phenotypes, we constructed protein–protein interaction modules based on “hub genes”, called switch genes, both common and specific to the four tumor subtypes. Transcriptomic profiles of patients were stratified according to both clinical (immunohistochemistry) and genetic (PAM50) classifications. 266 and 372 switch genes were identified from immunohistochemistry and PAM50 classifications, respectively. Moreover, the identified switch genes were functionally characterized to select an interconnected pathway of disease genes. By intersecting the common switch genes of the two classifications, we selected a unique signature of 28 disease genes that were BC subtype-independent and classification subtype-independent. Data were validated both in vitro (10 BC cell lines) and ex vivo (66 BC tissues) experiments. Results showed that four of these hub proteins (AURKA, CDC45, ESPL1, and RAD54L) were over-expressed in all tumor subtypes. Moreover, the inhibition of one of the identified switch genes (AURKA) similarly affected all BC subtypes. In conclusion, using a network-based approach, we identified a common BC disease module which might reflect its pathological signature, suggesting a new vision to face with the disease heterogeneity.
Highlights
Breast cancer (BC) impacts 2.1 million women each year, representing the most frequent cancer among females, such that in 2018 approximately 15% of all cancer deaths among women were for BC (627,000 women) [1]
We explored The Cancer Genome Atlas (TCGA)-BRCA data set, by applying the network-based algorithm named SWItch Miner, and mapping the findings on the human interactome to capture the molecular interconnections associated with the disease modules
These results indicated that the increased mRNA amount of these molecules in BC cell lines and tissues was independent of tumor subtype, so it was not influenced by the heterogeneity of this pathology
Summary
Breast cancer (BC) impacts 2.1 million women each year, representing the most frequent cancer among females, such that in 2018 approximately 15% of all cancer deaths among women were for BC (627,000 women) [1]. Despite the remarkable increase in the depth of understanding of BC, the disease is still a major public health problem worldwide and poses significant open challenges This failure may be attributed to continuing adherence to the classical hypothesis (one gene, one drug, one disease) of the reductionist paradigm that has driven medical diagnosis in the modern era. Another important factor limiting the development of effective therapeutic strategies is the canonical disease classification that is largely based on clinicopathological evidence and often categorized according to the organ that the disease primarily affects, neglecting the interconnected nature of many diseases. Network Medicine research promises to provide a more global understanding of how the specific interactome neighborhood is perturbed in a certain disease, identify pathways and key components to be targeted in clinical interventions and reveal common molecular mechanisms between seemingly unrelated diseases [6,7]
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