Life diversity can now be clearly explored with the next-generation DNA sequencing technology, allowing the discovery of genetic variants among individuals, patients and tumors. However, beyond causal mutations catalog completion, systems medicine is essential to link genotype to phenotypic cancer diversity towards personalized medicine. Despite advances with traditional single genes molecular research, including rare mutations in BRCA1/2 and CDH1 for primary prevention and trastuzumab for treating HER2-overexpressing breast and gastric tumors, overall, treatment failure and death rates are still alarmingly high. Revolution in sequencing reveals that, now both a huge number and widespread variability of driver mutations, including single-nucleotide polymorphisms, genomic rearrangements and copy-number changes involved in breast cancer development. All these genetic alterations result in a heterogeneous deregulation of signaling pathways, including EGFR, HER2, VEGF, Wnt/Notch, TGF and others.Cancer initiation, progression and metastases are driven by complex molecular networks rather than linear genotype-phenotype relationship. Therefore, clinical expectations by traditional molecular research strategies targeting single genes and single signaling pathways are likely minimal. This review discusses the necessity of molecular networks modeling to understand complex gene-gene, protein-protein and gene-environment interactions. Moreover, the potential of systems clinico-biological approaches to predict intracellular signaling pathways components networks and cancer heterogeneous cells within an individual tumor is described. A flowchart specific for three steps in cancer evolution separately tumorigenesis, early-stage and advanced-stage breast cancer is presented. Using reverse engineering starting with the integration of available established clinical, environmental, treatment and oncological outcomes (survival and death) data and then the still incomplete but progressively accumulating genotypic data into computational networks modeling may lead to bionetworks-based discovery of robust biomarkers and highly effective cancer drugs targets.