Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer

Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations

Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQO1. This method offers researchers a valuable tool in understanding how and why dysregulation occurs, and how perturbations to the network (such as targeted therapies) effect the network itself, and ultimately patient outcomes.

Potential of the postoperative lymphocyte-to-monocyte and monocyte-to-red blood cell ratio in predicting locoregional and distant metastases after breast cancer resection - Retrospective study

PURPOSE

Breast cancer is the most common malignancy with high recurrence and mortality rates in women. There are still insufficient biomarkers to predict disease prognosis. Therefore, the present study aimed to investigate the clinical significance of postoperative hematologic parameters and their derivatives in patients with breast cancer who underwent tumor resection.

PATIENTS AND METHODS

The clinicopathological and laboratory data of 90 female breast cancer patients who underwent surgical treatment in the Greater Poland Cancer Center in Poznan from December 2015 to November 2017 were retrospectively analyzed. Postoperative hematologic parameters, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), monocyte-to-red blood cell ratio (MRR), lymphocyte-to-red blood cell ratio (LRR), platelet-to-red blood cell ratio (PRR) were evaluated in recurrence and non-recurrence group. Receiver-operating characteristic (ROC) curve analysis was used to assess the optimal cutoff value of postoperative hematologic parameters for tumor recurrence. The association of postoperative hematologic parameters with disease-free survival (DFS) was investigated by the Kaplan-Meier method and Cox regression analysis.

RESULTS

Patients with local, regional, or distant metastases accounted for 14% of the total. The postoperative monocyte count and MRR were significantly elevated, whereas postoperative LMR was statistically decreased in the recurrence group. Univariate and multivariate Cox analysis revealed that postoperative LMR ≤3.044 and postoperative MRR >0.1398 were associated with significantly shorter DFS.

CONCLUSION

Our results revealed that both postoperative LMR and MRR are independent predictors of DFS in breast cancer patients. Large-scale prospective investigations are needed to validate our findings.