Background: Cancer reversion, converting the phenotypes of a cancer cell into those of a normal cell, has been sporadically observed throughout history. However, no systematic analysis has been attempted so far. Results: To investigate this from a systems biological perspective, we have constructed a logical network model of colorectal tumorigenesis by integrating key regulatory molecules and their interactions from previous experimental data. We identified molecular targets that can reverse cancerous cellular states to a normal state by systematically perturbing each molecular activity in the network and evaluating the resulting changes of the attractor landscape with respect to uncontrolled proliferation, EMT, and stemness. Intriguingly, many of the identified targets were well in accord with previous studies. We further revealed that the identified targets constitute stable network motifs that contribute to enhancing the robustness of attractors in cancerous cellular states against diverse regulatory signals.
Basal-like breast cancer is the most aggressive breast cancer subtype with the worst prognosis. Despite its high recurrence rate, chemotherapy is the only treatment for basal-like breast cancer, which lacks expression of hormone receptors. In contrast, luminal A tumors express ERα and can undergo endocrine therapy for treatment. Previous studies have tried to develop effective treatments for basal-like patients using various therapeutics but failed due to the complex and dynamic nature of the disease. In this study, we performed a transcriptomic analysis of patients with breast cancer to construct a simplified but essential molecular regulatory network model. Network control analysis identified potential targets and elucidated the underlying mechanisms of reprogramming basal-like cancer cells into luminal A cells. Inhibition of BCL11A and HDAC1/2 effectively drove basal-like cells to transition to luminal A cells and increased ERα expression, leading to increased tamoxifen sensitivity. High expression of BCL11A and HDAC1/2 correlated with poor prognosis in patients with breast cancer. These findings identify mechanisms regulating breast cancer phenotypes and suggest the potential to reprogram basal-like breast cancer cells to enhance their targetability.
Significance:
A network model enables investigation of mechanisms regulating the basal-to-luminal transition in breast cancer, identifying BCL11A and HDAC1/2 as optimal targets that can induce basal-like breast cancer reprogramming and endocrine therapy sensitivity.
Many clinical trials for cancer precision medicine have yielded unsatisfactory results due to challenges such as drug resistance and low efficacy. Drug resistance is often caused by the complex compensatory regulation within the biomolecular network in a cancer cell. Recently, systems biological studies have modeled and simulated such complex networks to unravel the hidden mechanisms of drug resistance and identify promising new drug targets or combinatorial or sequential treatments for overcoming resistance to anticancer drugs. However, many of the identified targets or treatments present major difficulties for drug development and clinical application. Nanocarriers represent a path forward for developing therapies with these “undruggable” targets or those that require precise combinatorial or sequential application, for which conventional drug delivery mechanisms are unsuitable. Conversely, a challenge in nanomedicine has been low efficacy due to heterogeneity of cancers in patients. This problem can also be resolved through systems biological approaches by identifying personalized targets for individual patients or promoting the drug responses. Therefore, integration of systems biology and nanomaterial engineering will enable the clinical application of cancer precision medicine to overcome both drug resistance of conventional treatments and low efficacy of nanomedicine due to patient heterogeneity.
Recently, FGFR inhibitors have been highlighted as promising targeted drugs due to the high prevalence of FGFR1 amplification in cancer patients. Although various potential biomarkers for FGFR inhibitors have been suggested, their functional effects have been shown to be limited due to the complexity of the cancer signaling network and the heterogenous genomic conditions of patients. To overcome such limitations, we have reconstructed a lung cancer network model by integrating a cell line genomic database and analyzing the model in order to understand the underlying mechanism of heterogeneous drug responses. Here, we identify novel genomic context-specific candidates that can increase the efficacy of FGFR inhibitors. Furthermore, we suggest optimal targets that can induce more effective therapeutic responses than that of FGFR inhibitors in each of the FGFR-resistant lung cancer cells through computational simulations at a system level. Our findings provide new insights into the regulatory mechanism of differential responses to FGFR inhibitors for optimal therapeutic strategies in lung cancer.
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